diff --git a/.gitattributes b/.gitattributes index a6344aac8c09253b3b630fb776ae94478aa0275b..9e64b16b720bce98764b06bef9db5b4074b460c6 100644 --- a/.gitattributes +++ b/.gitattributes @@ -33,3 +33,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text *.zip filter=lfs diff=lfs merge=lfs -text *.zst filter=lfs diff=lfs merge=lfs -text *tfevents* filter=lfs diff=lfs merge=lfs -text +.github/workflows/logo.gif filter=lfs diff=lfs merge=lfs -text +diffsynth/tokenizer_configs/hunyuan_video/tokenizer_2/tokenizer.json filter=lfs diff=lfs merge=lfs -text +diffsynth/tokenizer_configs/kolors/tokenizer/vocab.txt filter=lfs diff=lfs merge=lfs -text diff --git a/.github/workflows/logo.gif b/.github/workflows/logo.gif new file mode 100644 index 0000000000000000000000000000000000000000..ef5717efc17bbb2018a37a530a9d7a09e86277d9 --- /dev/null +++ b/.github/workflows/logo.gif @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:36a7627b7f0f0a508ec64aba72e5d95d38dfe7958bd8cf42d2a63f6ac2641529 +size 149067 diff --git a/.github/workflows/publish.yaml b/.github/workflows/publish.yaml new file mode 100644 index 0000000000000000000000000000000000000000..f31e6bb7bf1a10f9394f9a395c093aa37eba4855 --- /dev/null +++ b/.github/workflows/publish.yaml @@ -0,0 +1,29 @@ +name: release + +on: + push: + tags: + - 'v**' + +concurrency: + group: ${{ github.workflow }}-${{ github.ref }}-publish + cancel-in-progress: true + +jobs: + build-n-publish: + runs-on: ubuntu-20.04 + #if: startsWith(github.event.ref, 'refs/tags') + steps: + - uses: actions/checkout@v2 + - name: Set up Python 3.10 + uses: actions/setup-python@v2 + with: + python-version: '3.10' + - name: Install wheel + run: pip install wheel==0.44.0 && pip install -r requirements.txt + - name: Build DiffSynth + run: python setup.py sdist bdist_wheel + - name: Publish package to PyPI + run: | + pip install twine + twine upload dist/* --skip-existing -u __token__ -p ${{ secrets.PYPI_API_TOKEN }} diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..d484f7458c0c5addfadb614125a4abc7e3041d72 --- /dev/null +++ b/.gitignore @@ -0,0 +1,8 @@ +*.png +*pycache* +*.safetensors +*.ckpt +models/ +*.log +*.html +*.jpg diff --git a/.vscode/launch.json b/.vscode/launch.json new file mode 100644 index 0000000000000000000000000000000000000000..11f7607fead5f64761524f75899365ac37478e0b --- /dev/null +++ b/.vscode/launch.json @@ -0,0 +1,36 @@ +{ + "version": "0.2.0", + "configurations": [ + { + "name": "KontextT2I 1201 debug", + "type": "python", + "request": "launch", + "program": "examples/flux/model_training/train.py", // 关键：指定 accelerate 可执行文件路径 + "args": [ + "--dataset_base_path", "/fi-lib/workspace/sjx/DiffSynth-Studio/dataset/multi_frame", + "--dataset_metadata_path", "/fi-lib/workspace/sjx/DiffSynth-Studio/dataset/multi_frame/pairs.txt", + "--data_file_keys", "image,prompt", + "--max_pixels", "1048576", + "--dataset_repeat", "400", + "--model_id_with_origin_paths", "black-forest-labs/FLUX.1-Kontext-dev:flux1-kontext-dev.safetensors,black-forest-labs/FLUX.1-Kontext-dev:text_encoder/model.safetensors,black-forest-labs/FLUX.1-Kontext-dev:text_encoder_2/,black-forest-labs/FLUX.1-Kontext-dev:ae.safetensors", + "--learning_rate", "1e-5", + "--num_epochs", "5", + "--remove_prefix_in_ckpt", "pipe.dit.", + "--output_path", "./models/train/FLUX.1_lora_1127_mbti", + "--lora_base_model", "dit", + "--lora_target_modules", "a_to_qkv,b_to_qkv,ff_a.0,ff_a.2,ff_b.0,ff_b.2,a_to_out,b_to_out,proj_out,norm.linear,norm1_a.linear,norm1_b.linear,to_qkv_mlp", + "--lora_rank", "32", + "--align_to_opensource_format", + "--use_gradient_checkpointing", + "--default_caption", "Convert this real photo into a mbti style." + ], + "console": "integratedTerminal", // 输出日志到 VS Code 内置终端（方便查看） + "justMyCode": false, // 允许调试第三方库（如 accelerate、transformers） + "cwd": "${workspaceFolder}", // 工作目录设为项目根目录（确保路径正确） + "env": { + "PYTHONUNBUFFERED": "1", // 禁用输出缓冲，实时查看日志 + "CUDA_VISIBLE_DEVICES": "7" + } + } + ] +} \ No newline at end of file diff --git a/FLUX.1-Kontext-dev.py b/FLUX.1-Kontext-dev.py new file mode 100644 index 0000000000000000000000000000000000000000..3b5f00e011ae59a8731957a5c69aa46b0f03782a --- /dev/null +++ b/FLUX.1-Kontext-dev.py @@ -0,0 +1,68 @@ +import torch +from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig +from PIL import Image +import os +import json + + + +# for i in range(2): +# pipe.load_lora(pipe.dit, f"models/train/FLUX.1_lora_1126/epoch-{i}.safetensors", alpha=1) +# step = 25 +# input_path = "dataset/multi_frame" +# base_path = f"validate_result/multi_frame{step}" +# save_path = f"{base_path}/epoch{i}" +# save_path_GT = f"{base_path}/GT" +# os.makedirs(save_path, exist_ok=True) +# os.makedirs(save_path_GT, exist_ok=True) +# for img in os.listdir(input_path): +# image = Image.open(os.path.join(input_path,img)) +# image.save(os.path.join(save_path_GT,img)) +# prompt="Convert this image into a line art style: retain the original scenes and characters unchanged, present it as a black-and-white sketch effect, and make it suitable for storyboard design. Requirements: use bold and powerful lines, highlight structures and textures with concise strokes, adopt a style close to comic sketching, roughly outline the scenes and character movements with simple lines, prohibit the depiction of details, and represent the characters' facial features with the simplest lines.", +# # prompt = "Convert this image into a mbti style" +# for fig in os.listdir(input_path): +# if not fig.endswith(".png"): +# continue +# image = pipe( +# prompt = prompt, +# kontext_images=Image.open(os.path.join(input_path,fig)).resize((768, 768)), +# height=768, width=768, +# seed=0, +# num_inference_steps=step +# ) +# image.save(os.path.join(save_path,fig)) + +for i in range(2): + pipe = FluxImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="black-forest-labs/FLUX.1-Kontext-dev", origin_file_pattern="flux1-kontext-dev.safetensors"), + ModelConfig(model_id="black-forest-labs/FLUX.1-Kontext-dev", origin_file_pattern="text_encoder/model.safetensors"), + ModelConfig(model_id="black-forest-labs/FLUX.1-Kontext-dev", origin_file_pattern="text_encoder_2/"), + ModelConfig(model_id="black-forest-labs/FLUX.1-Kontext-dev", origin_file_pattern="ae.safetensors"), + ], + ) + pipe.load_lora(pipe.dit, f"models/train/FLUX.1_lora_1126/epoch-{i}.safetensors", alpha=1) + step = 25 + base_path = "/fi-lib/workspace/sjx/DiffSynth-Studio/validate_result/t2i_1201{step}" + save_path = f"{base_path}/epoch{i}" + os.makedirs(save_path, exist_ok=True) + with open("nano_comprehension_1201.txt", "r") as f: + prompts = f.readlines() + for prompt in prompts: + prompt = prompt.strip() + if prompt == "": + continue + prompt_dict = json.loads(prompt) + fig = f"{prompt_dict["Image_Name"]}.png" + del prompt_dict["Image_Name"] + prompt = json.dumps(prompt_dict, ensure_ascii=False) + image = pipe( + prompt = prompt, + height=768, width=768, + seed=0, + num_inference_steps=step + ) + image.save(os.path.join(save_path,fig)) + \ No newline at end of file diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..0e5a49f5f70af9e9d37278f72315d1b1afd34895 --- /dev/null +++ b/LICENSE @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [2023] [Zhongjie Duan] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..a04fabbf7a4c4abc2694cde80253168762a07f5a --- /dev/null +++ b/README.md @@ -0,0 +1,522 @@ +# DiffSynth-Studio + + modelscope%2FDiffSynth-Studio | Trendshift

+ +[![PyPI](https://img.shields.io/pypi/v/DiffSynth)](https://pypi.org/project/DiffSynth/) +[![license](https://img.shields.io/github/license/modelscope/DiffSynth-Studio.svg)](https://github.com/modelscope/DiffSynth-Studio/blob/master/LICENSE) +[![open issues](https://isitmaintained.com/badge/open/modelscope/DiffSynth-Studio.svg)](https://github.com/modelscope/DiffSynth-Studio/issues) +[![GitHub pull-requests](https://img.shields.io/github/issues-pr/modelscope/DiffSynth-Studio.svg)](https://GitHub.com/modelscope/DiffSynth-Studio/pull/) +[![GitHub latest commit](https://badgen.net/github/last-commit/modelscope/DiffSynth-Studio)](https://GitHub.com/modelscope/DiffSynth-Studio/commit/) + +[切换到中文](./README_zh.md) + +## Introduction + +Welcome to the magic world of Diffusion models! DiffSynth-Studio is an open-source Diffusion model engine developed and maintained by [ModelScope](https://www.modelscope.cn/) team. We aim to foster technical innovation through framework development, bring together the power of the open-source community, and explore the limits of generative models! + +DiffSynth currently includes two open-source projects: +* [DiffSynth-Studio](https://github.com/modelscope/DiffSynth-Studio): Focused on aggressive technical exploration, for academia, providing support for more cutting-edge model capabilities. +* [DiffSynth-Engine](https://github.com/modelscope/DiffSynth-Engine): Focused on stable model deployment, for industry, offering higher computing performance and more stable features. + +[DiffSynth-Studio](https://github.com/modelscope/DiffSynth-Studio) and [DiffSynth-Engine](https://github.com/modelscope/DiffSynth-Engine) are the core projects behind ModelScope [AIGC zone](https://modelscope.cn/aigc/home), offering powerful AI content generation abilities. Come and try our carefully designed features and start your AI creation journey! + +## Installation + +Install from source (recommended): + +``` +git clone https://github.com/modelscope/DiffSynth-Studio.git +cd DiffSynth-Studio +pip install -e . +``` + +
+Other installation methods + +Install from PyPI (version updates may be delayed; for latest features, install from source) + +``` +pip install diffsynth +``` + +If you meet problems during installation, they might be caused by upstream dependencies. Please check the docs of these packages: + +* [torch](https://pytorch.org/get-started/locally/) +* [sentencepiece](https://github.com/google/sentencepiece) +* [cmake](https://cmake.org) +* [cupy](https://docs.cupy.dev/en/stable/install.html) + +
+ +## Basic Framework + +DiffSynth-Studio redesigns the inference and training pipelines for mainstream Diffusion models (including FLUX, Wan, etc.), enabling efficient memory management and flexible model training. + +### Qwen-Image Series (🔥New Model) + +Details: [./examples/qwen_image/](./examples/qwen_image/) + +![Image](https://github.com/user-attachments/assets/738078d8-8749-4a53-a046-571861541924) + +
+ +Quick Start + +```python +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig +from PIL import Image +import torch + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), +) +prompt = "A detailed portrait of a girl underwater, wearing a blue flowing dress, hair gently floating, clear light and shadow, surrounded by bubbles, calm expression, fine details, dreamy and beautiful." +image = pipe( + prompt, seed=0, num_inference_steps=40, + # edit_image=Image.open("xxx.jpg").resize((1328, 1328)) # For Qwen-Image-Edit +) +image.save("image.jpg") +``` + +
+ +
+ +Model Overview + +|Model ID|Inference|Low VRAM Inference|Full Training|Validation after Full Training|LoRA Training|Validation after LoRA Training| +|-|-|-|-|-|-|-| +|[Qwen/Qwen-Image](https://www.modelscope.cn/models/Qwen/Qwen-Image)|[code](./examples/qwen_image/model_inference/Qwen-Image.py)|[code](./examples/qwen_image/model_inference_low_vram/Qwen-Image.py)|[code](./examples/qwen_image/model_training/full/Qwen-Image.sh)|[code](./examples/qwen_image/model_training/validate_full/Qwen-Image.py)|[code](./examples/qwen_image/model_training/lora/Qwen-Image.sh)|[code](./examples/qwen_image/model_training/validate_lora/Qwen-Image.py)| +|[Qwen/Qwen-Image-Edit](https://www.modelscope.cn/models/Qwen/Qwen-Image-Edit)|[code](./examples/qwen_image/model_inference/Qwen-Image-Edit.py)|[code](./examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit.py)|[code](./examples/qwen_image/model_training/full/Qwen-Image-Edit.sh)|[code](./examples/qwen_image/model_training/validate_full/Qwen-Image-Edit.py)|[code](./examples/qwen_image/model_training/lora/Qwen-Image-Edit.sh)|[code](./examples/qwen_image/model_training/validate_lora/Qwen-Image-Edit.py)| +|[Qwen/Qwen-Image-Edit-2509](https://www.modelscope.cn/models/Qwen/Qwen-Image-Edit-2509)|[code](./examples/qwen_image/model_inference/Qwen-Image-Edit-2509.py)|[code](./examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit-2509.py)|[code](./examples/qwen_image/model_training/full/Qwen-Image-Edit-2509.sh)|[code](./examples/qwen_image/model_training/validate_full/Qwen-Image-Edit-2509.py)|[code](./examples/qwen_image/model_training/lora/Qwen-Image-Edit-2509.sh)|[code](./examples/qwen_image/model_training/validate_lora/Qwen-Image-Edit-2509.py)| +|[DiffSynth-Studio/Qwen-Image-EliGen-V2](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-EliGen-V2)|[code](./examples/qwen_image/model_inference/Qwen-Image-EliGen-V2.py)|[code](./examples/qwen_image/model_inference_low_vram/Qwen-Image-EliGen-V2.py)|-|-|[code](./examples/qwen_image/model_training/lora/Qwen-Image-EliGen.sh)|[code](./examples/qwen_image/model_training/validate_lora/Qwen-Image-EliGen.py)| +|[DiffSynth-Studio/Qwen-Image-EliGen-Poster](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-EliGen-Poster)|[code](./examples/qwen_image/model_inference/Qwen-Image-EliGen-Poster.py)|[code](./examples/qwen_image/model_inference_low_vram/Qwen-Image-EliGen-Poster.py)|-|-|[code](./examples/qwen_image/model_training/lora/Qwen-Image-EliGen-Poster.sh)|[code](./examples/qwen_image/model_training/validate_lora/Qwen-Image-EliGen-Poster.py)| +|[DiffSynth-Studio/Qwen-Image-Distill-Full](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Distill-Full)|[code](./examples/qwen_image/model_inference/Qwen-Image-Distill-Full.py)|[code](./examples/qwen_image/model_inference_low_vram/Qwen-Image-Distill-Full.py)|[code](./examples/qwen_image/model_training/full/Qwen-Image-Distill-Full.sh)|[code](./examples/qwen_image/model_training/validate_full/Qwen-Image-Distill-Full.py)|[code](./examples/qwen_image/model_training/lora/Qwen-Image-Distill-Full.sh)|[code](./examples/qwen_image/model_training/validate_lora/Qwen-Image-Distill-Full.py)| +|[DiffSynth-Studio/Qwen-Image-Distill-LoRA](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Distill-LoRA)|[code](./examples/qwen_image/model_inference/Qwen-Image-Distill-LoRA.py)|[code](./examples/qwen_image/model_inference_low_vram/Qwen-Image-Distill-LoRA.py)|-|-|[code](./examples/qwen_image/model_training/lora/Qwen-Image-Distill-LoRA.sh)|[code](./examples/qwen_image/model_training/validate_lora/Qwen-Image-Distill-LoRA.py)| +|[DiffSynth-Studio/Qwen-Image-EliGen](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-EliGen)|[code](./examples/qwen_image/model_inference/Qwen-Image-EliGen.py)|[code](./examples/qwen_image/model_inference_low_vram/Qwen-Image-EliGen.py)|-|-|[code](./examples/qwen_image/model_training/lora/Qwen-Image-EliGen.sh)|[code](./examples/qwen_image/model_training/validate_lora/Qwen-Image-EliGen.py)| +|[DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Canny](https://modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Canny)|[code](./examples/qwen_image/model_inference/Qwen-Image-Blockwise-ControlNet-Canny.py)|[code](./examples/qwen_image/model_inference_low_vram/Qwen-Image-Blockwise-ControlNet-Canny.py)|[code](./examples/qwen_image/model_training/full/Qwen-Image-Blockwise-ControlNet-Canny.sh)|[code](./examples/qwen_image/model_training/validate_full/Qwen-Image-Blockwise-ControlNet-Canny.py)|[code](./examples/qwen_image/model_training/lora/Qwen-Image-Blockwise-ControlNet-Canny.sh)|[code](./examples/qwen_image/model_training/validate_lora/Qwen-Image-Blockwise-ControlNet-Canny.py)| +|[DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Depth](https://modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Depth)|[code](./examples/qwen_image/model_inference/Qwen-Image-Blockwise-ControlNet-Depth.py)|[code](./examples/qwen_image/model_inference_low_vram/Qwen-Image-Blockwise-ControlNet-Depth.py)|[code](./examples/qwen_image/model_training/full/Qwen-Image-Blockwise-ControlNet-Depth.sh)|[code](./examples/qwen_image/model_training/validate_full/Qwen-Image-Blockwise-ControlNet-Depth.py)|[code](./examples/qwen_image/model_training/lora/Qwen-Image-Blockwise-ControlNet-Depth.sh)|[code](./examples/qwen_image/model_training/validate_lora/Qwen-Image-Blockwise-ControlNet-Depth.py)| +|[DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Inpaint](https://modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Inpaint)|[code](./examples/qwen_image/model_inference/Qwen-Image-Blockwise-ControlNet-Inpaint.py)|[code](./examples/qwen_image/model_inference_low_vram/Qwen-Image-Blockwise-ControlNet-Inpaint.py)|[code](./examples/qwen_image/model_training/full/Qwen-Image-Blockwise-ControlNet-Inpaint.sh)|[code](./examples/qwen_image/model_training/validate_full/Qwen-Image-Blockwise-ControlNet-Inpaint.py)|[code](./examples/qwen_image/model_training/lora/Qwen-Image-Blockwise-ControlNet-Inpaint.sh)|[code](./examples/qwen_image/model_training/validate_lora/Qwen-Image-Blockwise-ControlNet-Inpaint.py)| +|[DiffSynth-Studio/Qwen-Image-In-Context-Control-Union](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-In-Context-Control-Union)|[code](./examples/qwen_image/model_inference/Qwen-Image-In-Context-Control-Union.py)|[code](./examples/qwen_image/model_inference_low_vram/Qwen-Image-In-Context-Control-Union.py)|-|-|[code](./examples/qwen_image/model_training/lora/Qwen-Image-In-Context-Control-Union.sh)|[code](./examples/qwen_image/model_training/validate_lora/Qwen-Image-In-Context-Control-Union.py)| +|[DiffSynth-Studio/Qwen-Image-Edit-Lowres-Fix](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Edit-Lowres-Fix)|[code](./examples/qwen_image/model_inference/Qwen-Image-Edit-Lowres-Fix.py)|[code](./examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit-Lowres-Fix.py)|-|-|-|-| + +
+ +### FLUX Series + +Detail page: [./examples/flux/](./examples/flux/) + +![Image](https://github.com/user-attachments/assets/c01258e2-f251-441a-aa1e-ebb22f02594d) + +
+ +Quick Start + +```python +import torch +from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig + +pipe = FluxImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-dev.safetensors"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors"), + ], +) + +image = pipe(prompt="a cat", seed=0) +image.save("image.jpg") +``` + +
+ +
+ +Model Overview + +| Model ID | Extra Parameters | Inference | Low VRAM Inference | Full Training | Validate After Full Training | LoRA Training | Validate After LoRA Training | +|-|-|-|-|-|-|-|-| +|[FLUX.1-dev](https://www.modelscope.cn/models/black-forest-labs/FLUX.1-dev)||[code](./examples/flux/model_inference/FLUX.1-dev.py)|[code](./examples/flux/model_inference_low_vram/FLUX.1-dev.py)|[code](./examples/flux/model_training/full/FLUX.1-dev.sh)|[code](./examples/flux/model_training/validate_full/FLUX.1-dev.py)|[code](./examples/flux/model_training/lora/FLUX.1-dev.sh)|[code](./examples/flux/model_training/validate_lora/FLUX.1-dev.py)| +|[FLUX.1-Krea-dev](https://www.modelscope.cn/models/black-forest-labs/FLUX.1-Krea-dev)||[code](./examples/flux/model_inference/FLUX.1-Krea-dev.py)|[code](./examples/flux/model_inference_low_vram/FLUX.1-Krea-dev.py)|[code](./examples/flux/model_training/full/FLUX.1-Krea-dev.sh)|[code](./examples/flux/model_training/validate_full/FLUX.1-Krea-dev.py)|[code](./examples/flux/model_training/lora/FLUX.1-Krea-dev.sh)|[code](./examples/flux/model_training/validate_lora/FLUX.1-Krea-dev.py)| +|[FLUX.1-Kontext-dev](https://www.modelscope.cn/models/black-forest-labs/FLUX.1-Kontext-dev)|`kontext_images`|[code](./examples/flux/model_inference/FLUX.1-Kontext-dev.py)|[code](./examples/flux/model_inference_low_vram/FLUX.1-Kontext-dev.py)|[code](./examples/flux/model_training/full/FLUX.1-Kontext-dev.sh)|[code](./examples/flux/model_training/validate_full/FLUX.1-Kontext-dev.py)|[code](./examples/flux/model_training/lora/FLUX.1-Kontext-dev.sh)|[code](./examples/flux/model_training/validate_lora/FLUX.1-Kontext-dev.py)| +|[FLUX.1-dev-Controlnet-Inpainting-Beta](https://www.modelscope.cn/models/alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta)|`controlnet_inputs`|[code](./examples/flux/model_inference/FLUX.1-dev-Controlnet-Inpainting-Beta.py)|[code](./examples/flux/model_inference_low_vram/FLUX.1-dev-Controlnet-Inpainting-Beta.py)|[code](./examples/flux/model_training/full/FLUX.1-dev-Controlnet-Inpainting-Beta.sh)|[code](./examples/flux/model_training/validate_full/FLUX.1-dev-Controlnet-Inpainting-Beta.py)|[code](./examples/flux/model_training/lora/FLUX.1-dev-Controlnet-Inpainting-Beta.sh)|[code](./examples/flux/model_training/validate_lora/FLUX.1-dev-Controlnet-Inpainting-Beta.py)| +|[FLUX.1-dev-Controlnet-Union-alpha](https://www.modelscope.cn/models/InstantX/FLUX.1-dev-Controlnet-Union-alpha)|`controlnet_inputs`|[code](./examples/flux/model_inference/FLUX.1-dev-Controlnet-Union-alpha.py)|[code](./examples/flux/model_inference_low_vram/FLUX.1-dev-Controlnet-Union-alpha.py)|[code](./examples/flux/model_training/full/FLUX.1-dev-Controlnet-Union-alpha.sh)|[code](./examples/flux/model_training/validate_full/FLUX.1-dev-Controlnet-Union-alpha.py)|[code](./examples/flux/model_training/lora/FLUX.1-dev-Controlnet-Union-alpha.sh)|[code](./examples/flux/model_training/validate_lora/FLUX.1-dev-Controlnet-Union-alpha.py)| +|[FLUX.1-dev-Controlnet-Upscaler](https://www.modelscope.cn/models/jasperai/Flux.1-dev-Controlnet-Upscaler)|`controlnet_inputs`|[code](./examples/flux/model_inference/FLUX.1-dev-Controlnet-Upscaler.py)|[code](./examples/flux/model_inference_low_vram/FLUX.1-dev-Controlnet-Upscaler.py)|[code](./examples/flux/model_training/full/FLUX.1-dev-Controlnet-Upscaler.sh)|[code](./examples/flux/model_training/validate_full/FLUX.1-dev-Controlnet-Upscaler.py)|[code](./examples/flux/model_training/lora/FLUX.1-dev-Controlnet-Upscaler.sh)|[code](./examples/flux/model_training/validate_lora/FLUX.1-dev-Controlnet-Upscaler.py)| +|[FLUX.1-dev-IP-Adapter](https://www.modelscope.cn/models/InstantX/FLUX.1-dev-IP-Adapter)|`ipadapter_images`, `ipadapter_scale`|[code](./examples/flux/model_inference/FLUX.1-dev-IP-Adapter.py)|[code](./examples/flux/model_inference_low_vram/FLUX.1-dev-IP-Adapter.py)|[code](./examples/flux/model_training/full/FLUX.1-dev-IP-Adapter.sh)|[code](./examples/flux/model_training/validate_full/FLUX.1-dev-IP-Adapter.py)|[code](./examples/flux/model_training/lora/FLUX.1-dev-IP-Adapter.sh)|[code](./examples/flux/model_training/validate_lora/FLUX.1-dev-IP-Adapter.py)| +|[FLUX.1-dev-InfiniteYou](https://www.modelscope.cn/models/ByteDance/InfiniteYou)|`infinityou_id_image`, `infinityou_guidance`, `controlnet_inputs`|[code](./examples/flux/model_inference/FLUX.1-dev-InfiniteYou.py)|[code](./examples/flux/model_inference_low_vram/FLUX.1-dev-InfiniteYou.py)|[code](./examples/flux/model_training/full/FLUX.1-dev-InfiniteYou.sh)|[code](./examples/flux/model_training/validate_full/FLUX.1-dev-InfiniteYou.py)|[code](./examples/flux/model_training/lora/FLUX.1-dev-InfiniteYou.sh)|[code](./examples/flux/model_training/validate_lora/FLUX.1-dev-InfiniteYou.py)| +|[FLUX.1-dev-EliGen](https://www.modelscope.cn/models/DiffSynth-Studio/Eligen)|`eligen_entity_prompts`, `eligen_entity_masks`, `eligen_enable_on_negative`, `eligen_enable_inpaint`|[code](./examples/flux/model_inference/FLUX.1-dev-EliGen.py)|[code](./examples/flux/model_inference_low_vram/FLUX.1-dev-EliGen.py)|-|-|[code](./examples/flux/model_training/lora/FLUX.1-dev-EliGen.sh)|[code](./examples/flux/model_training/validate_lora/FLUX.1-dev-EliGen.py)| +|[FLUX.1-dev-LoRA-Encoder](https://www.modelscope.cn/models/DiffSynth-Studio/LoRA-Encoder-FLUX.1-Dev)|`lora_encoder_inputs`, `lora_encoder_scale`|[code](./examples/flux/model_inference/FLUX.1-dev-LoRA-Encoder.py)|[code](./examples/flux/model_inference_low_vram/FLUX.1-dev-LoRA-Encoder.py)|[code](./examples/flux/model_training/full/FLUX.1-dev-LoRA-Encoder.sh)|[code](./examples/flux/model_training/validate_full/FLUX.1-dev-LoRA-Encoder.py)|-|-| +|[FLUX.1-dev-LoRA-Fusion-Preview](https://modelscope.cn/models/DiffSynth-Studio/LoRAFusion-preview-FLUX.1-dev)||[code](./examples/flux/model_inference/FLUX.1-dev-LoRA-Fusion.py)|-|-|-|-|-| +|[Step1X-Edit](https://www.modelscope.cn/models/stepfun-ai/Step1X-Edit)|`step1x_reference_image`|[code](./examples/flux/model_inference/Step1X-Edit.py)|[code](./examples/flux/model_inference_low_vram/Step1X-Edit.py)|[code](./examples/flux/model_training/full/Step1X-Edit.sh)|[code](./examples/flux/model_training/validate_full/Step1X-Edit.py)|[code](./examples/flux/model_training/lora/Step1X-Edit.sh)|[code](./examples/flux/model_training/validate_lora/Step1X-Edit.py)| +|[FLEX.2-preview](https://www.modelscope.cn/models/ostris/Flex.2-preview)|`flex_inpaint_image`, `flex_inpaint_mask`, `flex_control_image`, `flex_control_strength`, `flex_control_stop`|[code](./examples/flux/model_inference/FLEX.2-preview.py)|[code](./examples/flux/model_inference_low_vram/FLEX.2-preview.py)|[code](./examples/flux/model_training/full/FLEX.2-preview.sh)|[code](./examples/flux/model_training/validate_full/FLEX.2-preview.py)|[code](./examples/flux/model_training/lora/FLEX.2-preview.sh)|[code](./examples/flux/model_training/validate_lora/FLEX.2-preview.py)| +|[Nexus-Gen](https://www.modelscope.cn/models/DiffSynth-Studio/Nexus-GenV2)|`nexus_gen_reference_image`|[code](./examples/flux/model_inference/Nexus-Gen-Editing.py)|[code](./examples/flux/model_inference_low_vram/Nexus-Gen-Editing.py)|[code](./examples/flux/model_training/full/Nexus-Gen.sh)|[code](./examples/flux/model_training/validate_full/Nexus-Gen.py)|[code](./examples/flux/model_training/lora/Nexus-Gen.sh)|[code](./examples/flux/model_training/validate_lora/Nexus-Gen.py)| + +
+ + + +### Wan Series + +Detail page: [./examples/wanvideo/](./examples/wanvideo/) + +https://github.com/user-attachments/assets/1d66ae74-3b02-40a9-acc3-ea95fc039314 + +
+ +Quick Start + +```python +import torch +from diffsynth import save_video +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"), + ], +) +pipe.enable_vram_management() + +video = pipe( + prompt="A documentary photography style scene: a lively puppy rapidly running on green grass. The puppy has brown-yellow fur, upright ears, and looks focused and joyful. Sunlight shines on its body, making the fur appear soft and shiny. The background is an open field with occasional wildflowers, and faint blue sky and clouds in the distance. Strong sense of perspective captures the motion of the puppy and the vitality of the surrounding grass. Mid-shot side-moving view.", + negative_prompt="Bright colors, overexposed, static, blurry details, subtitles, style, artwork, image, still, overall gray, worst quality, low quality, JPEG compression artifacts, ugly, deformed, extra fingers, poorly drawn hands, poorly drawn face, malformed limbs, fused fingers, still frame, messy background, three legs, crowded background people, walking backwards", + seed=0, tiled=True, +) +save_video(video, "video1.mp4", fps=15, quality=5) +``` + +
+ +
+ +Model Overview + +| Model ID | Extra Parameters | Inference | Full Training | Validate After Full Training | LoRA Training | Validate After LoRA Training | +|-|-|-|-|-|-|-| +|[Wan-AI/Wan2.2-Animate-14B](https://www.modelscope.cn/models/Wan-AI/Wan2.2-Animate-14B)|`input_image`, `animate_pose_video`, `animate_face_video`, `animate_inpaint_video`, `animate_mask_video`|[code](./examples/wanvideo/model_inference/Wan2.2-Animate-14B.py)|[code](./examples/wanvideo/model_training/full/Wan2.2-Animate-14B.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.2-Animate-14B.py)|[code](./examples/wanvideo/model_training/lora/Wan2.2-Animate-14B.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.2-Animate-14B.py)| +|[Wan-AI/Wan2.2-S2V-14B](https://www.modelscope.cn/models/Wan-AI/Wan2.2-S2V-14B)|`input_image`, `input_audio`, `audio_sample_rate`, `s2v_pose_video`|[code](./examples/wanvideo/model_inference/Wan2.2-S2V-14B_multi_clips.py)|[code](./examples/wanvideo/model_training/full/Wan2.2-S2V-14B.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.2-S2V-14B.py)|[code](./examples/wanvideo/model_training/lora/Wan2.2-S2V-14B.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.2-S2V-14B.py)| +|[Wan-AI/Wan2.2-I2V-A14B](https://modelscope.cn/models/Wan-AI/Wan2.2-I2V-A14B)|`input_image`|[code](./examples/wanvideo/model_inference/Wan2.2-I2V-A14B.py)|[code](./examples/wanvideo/model_training/full/Wan2.2-I2V-A14B.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.2-I2V-A14B.py)|[code](./examples/wanvideo/model_training/lora/Wan2.2-I2V-A14B.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.2-I2V-A14B.py)| +|[Wan-AI/Wan2.2-T2V-A14B](https://modelscope.cn/models/Wan-AI/Wan2.2-T2V-A14B)||[code](./examples/wanvideo/model_inference/Wan2.2-T2V-A14B.py)|[code](./examples/wanvideo/model_training/full/Wan2.2-T2V-A14B.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.2-T2V-A14B.py)|[code](./examples/wanvideo/model_training/lora/Wan2.2-T2V-A14B.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.2-T2V-A14B.py)| +|[Wan-AI/Wan2.2-TI2V-5B](https://modelscope.cn/models/Wan-AI/Wan2.2-TI2V-5B)|`input_image`|[code](./examples/wanvideo/model_inference/Wan2.2-TI2V-5B.py)|[code](./examples/wanvideo/model_training/full/Wan2.2-TI2V-5B.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.2-TI2V-5B.py)|[code](./examples/wanvideo/model_training/lora/Wan2.2-TI2V-5B.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.2-TI2V-5B.py)| +|[Wan-AI/Wan2.2-VACE-Fun-A14B](https://www.modelscope.cn/models/PAI/Wan2.2-VACE-Fun-A14B)|`vace_control_video`, `vace_reference_image`|[code](./examples/wanvideo/model_inference/Wan2.2-VACE-Fun-A14B.py)|[code](./examples/wanvideo/model_training/full/Wan2.2-VACE-Fun-A14B.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.2-VACE-Fun-A14B.py)|[code](./examples/wanvideo/model_training/lora/Wan2.2-VACE-Fun-A14B.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.2-VACE-Fun-A14B.py)| +|[PAI/Wan2.2-Fun-A14B-InP](https://modelscope.cn/models/PAI/Wan2.2-Fun-A14B-InP)|`input_image`, `end_image`|[code](./examples/wanvideo/model_inference/Wan2.2-Fun-A14B-InP.py)|[code](./examples/wanvideo/model_training/full/Wan2.2-Fun-A14B-InP.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.2-Fun-A14B-InP.py)|[code](./examples/wanvideo/model_training/lora/Wan2.2-Fun-A14B-InP.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.2-Fun-A14B-InP.py)| +|[PAI/Wan2.2-Fun-A14B-Control](https://modelscope.cn/models/PAI/Wan2.2-Fun-A14B-Control)|`control_video`, `reference_image`|[code](./examples/wanvideo/model_inference/Wan2.2-Fun-A14B-Control.py)|[code](./examples/wanvideo/model_training/full/Wan2.2-Fun-A14B-Control.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.2-Fun-A14B-Control.py)|[code](./examples/wanvideo/model_training/lora/Wan2.2-Fun-A14B-Control.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.2-Fun-A14B-Control.py)| +|[PAI/Wan2.2-Fun-A14B-Control-Camera](https://modelscope.cn/models/PAI/Wan2.2-Fun-A14B-Control-Camera)|`control_camera_video`, `input_image`|[code](./examples/wanvideo/model_inference/Wan2.2-Fun-A14B-Control-Camera.py)|[code](./examples/wanvideo/model_training/full/Wan2.2-Fun-A14B-Control-Camera.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.2-Fun-A14B-Control-Camera.py)|[code](./examples/wanvideo/model_training/lora/Wan2.2-Fun-A14B-Control-Camera.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.2-Fun-A14B-Control-Camera.py)| +|[Wan-AI/Wan2.1-T2V-1.3B](https://modelscope.cn/models/Wan-AI/Wan2.1-T2V-1.3B)||[code](./examples/wanvideo/model_inference/Wan2.1-T2V-1.3B.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-T2V-1.3B.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-T2V-1.3B.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-T2V-1.3B.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-T2V-1.3B.py)| +|[Wan-AI/Wan2.1-T2V-14B](https://modelscope.cn/models/Wan-AI/Wan2.1-T2V-14B)||[code](./examples/wanvideo/model_inference/Wan2.1-T2V-14B.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-T2V-14B.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-T2V-14B.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-T2V-14B.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-T2V-14B.py)| +|[Wan-AI/Wan2.1-I2V-14B-480P](https://modelscope.cn/models/Wan-AI/Wan2.1-I2V-14B-480P)|`input_image`|[code](./examples/wanvideo/model_inference/Wan2.1-I2V-14B-480P.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-I2V-14B-480P.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-I2V-14B-480P.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-I2V-14B-480P.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-I2V-14B-480P.py)| +|[Wan-AI/Wan2.1-I2V-14B-720P](https://modelscope.cn/models/Wan-AI/Wan2.1-I2V-14B-720P)|`input_image`|[code](./examples/wanvideo/model_inference/Wan2.1-I2V-14B-720P.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-I2V-14B-720P.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-I2V-14B-720P.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-I2V-14B-720P.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-I2V-14B-720P.py)| +|[Wan-AI/Wan2.1-FLF2V-14B-720P](https://modelscope.cn/models/Wan-AI/Wan2.1-FLF2V-14B-720P)|`input_image`, `end_image`|[code](./examples/wanvideo/model_inference/Wan2.1-FLF2V-14B-720P.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-FLF2V-14B-720P.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-FLF2V-14B-720P.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-FLF2V-14B-720P.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-FLF2V-14B-720P.py)| +|[PAI/Wan2.1-Fun-1.3B-InP](https://modelscope.cn/models/PAI/Wan2.1-Fun-1.3B-InP)|`input_image`, `end_image`|[code](./examples/wanvideo/model_inference/Wan2.1-Fun-1.3B-InP.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-Fun-1.3B-InP.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-Fun-1.3B-InP.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-Fun-1.3B-InP.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-1.3B-InP.py)| +|[PAI/Wan2.1-Fun-1.3B-Control](https://modelscope.cn/models/PAI/Wan2.1-Fun-1.3B-Control)|`control_video`|[code](./examples/wanvideo/model_inference/Wan2.1-Fun-1.3B-Control.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-Fun-1.3B-Control.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-Fun-1.3B-Control.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-Fun-1.3B-Control.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-1.3B-Control.py)| +|[PAI/Wan2.1-Fun-14B-InP](https://modelscope.cn/models/PAI/Wan2.1-Fun-14B-InP)|`input_image`, `end_image`|[code](./examples/wanvideo/model_inference/Wan2.1-Fun-14B-InP.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-Fun-14B-InP.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-Fun-14B-InP.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-Fun-14B-InP.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-14B-InP.py)| +|[PAI/Wan2.1-Fun-14B-Control](https://modelscope.cn/models/PAI/Wan2.1-Fun-14B-Control)|`control_video`|[code](./examples/wanvideo/model_inference/Wan2.1-Fun-14B-Control.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-Fun-14B-Control.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-Fun-14B-Control.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-Fun-14B-Control.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-14B-Control.py)| +|[PAI/Wan2.1-Fun-V1.1-1.3B-Control](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-1.3B-Control)|`control_video`, `reference_image`|[code](./examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-1.3B-Control.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-1.3B-Control.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-1.3B-Control.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-1.3B-Control.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-1.3B-Control.py)| +|[PAI/Wan2.1-Fun-V1.1-14B-Control](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-14B-Control)|`control_video`, `reference_image`|[code](./examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-14B-Control.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-14B-Control.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-14B-Control.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-14B-Control.sh)|[code](./examples/wanvideo/examples/wanmodel_training/validate_lora/Wan2.1-Fun-V1.1-14B-Control.py)| +|[PAI/Wan2.1-Fun-V1.1-1.3B-InP](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-1.3B-InP)|`input_image`, `end_image`|[code](./examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-1.3B-InP.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-1.3B-InP.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-1.3B-InP.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-1.3B-InP.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-1.3B-InP.py)| +|[PAI/Wan2.1-Fun-V1.1-14B-InP](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-14B-InP)|`input_image`, `end_image`|[code](./examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-14B-InP.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-14B-InP.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-14B-InP.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-14B-InP.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-14B-InP.py)| +|[PAI/Wan2.1-Fun-V1.1-1.3B-Control-Camera](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-1.3B-Control-Camera)|`control_camera_video`, `input_image`|[code](./examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-1.3B-Control-Camera.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-1.3B-Control-Camera.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-1.3B-Control-Camera.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-1.3B-Control-Camera.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-1.3B-Control-Camera.py)| +|[PAI/Wan2.1-Fun-V1.1-14B-Control-Camera](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-14B-Control-Camera)|`control_camera_video`, `input_image`|[code](./examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-14B-Control-Camera.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-14B-Control-Camera.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-14B-Control-Camera.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-14B-Control-Camera.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-14B-Control-Camera.py)| +|[iic/VACE-Wan2.1-1.3B-Preview](https://modelscope.cn/models/iic/VACE-Wan2.1-1.3B-Preview)|`vace_control_video`, `vace_reference_image`|[code](./examples/wanvideo/model_inference/Wan2.1-VACE-1.3B-Preview.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-VACE-1.3B-Preview.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-VACE-1.3B-Preview.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-VACE-1.3B-Preview.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-VACE-1.3B-Preview.py)| +|[Wan-AI/Wan2.1-VACE-1.3B](https://modelscope.cn/models/Wan-AI/Wan2.1-VACE-1.3B)|`vace_control_video`, `vace_reference_image`|[code](./examples/wanvideo/model_inference/Wan2.1-VACE-1.3B.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-VACE-1.3B.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-VACE-1.3B.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-VACE-1.3B.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-VACE-1.3B.py)| +|[Wan-AI/Wan2.1-VACE-14B](https://modelscope.cn/models/Wan-AI/Wan2.1-VACE-14B)|`vace_control_video`, `vace_reference_image`|[code](./examples/wanvideo/model_inference/Wan2.1-VACE-14B.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-VACE-14B.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-VACE-14B.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-VACE-14B.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-VACE-14B.py)| +|[DiffSynth-Studio/Wan2.1-1.3b-speedcontrol-v1](https://modelscope.cn/models/DiffSynth-Studio/Wan2.1-1.3b-speedcontrol-v1)|`motion_bucket_id`|[code](./examples/wanvideo/model_inference/Wan2.1-1.3b-speedcontrol-v1.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-1.3b-speedcontrol-v1.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-1.3b-speedcontrol-v1.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-1.3b-speedcontrol-v1.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-1.3b-speedcontrol-v1.py)| +|[krea/krea-realtime-video](https://www.modelscope.cn/models/krea/krea-realtime-video)||[code](./examples/wanvideo/model_inference/krea-realtime-video.py)|[code](./examples/wanvideo/model_training/full/krea-realtime-video.sh)|[code](./examples/wanvideo/model_training/validate_full/krea-realtime-video.py)|[code](./examples/wanvideo/model_training/lora/krea-realtime-video.sh)|[code](./examples/wanvideo/model_training/validate_lora/krea-realtime-video.py)| +|[meituan-longcat/LongCat-Video](https://www.modelscope.cn/models/meituan-longcat/LongCat-Video)|`longcat_video`|[code](./examples/wanvideo/model_inference/LongCat-Video.py)|[code](./examples/wanvideo/model_training/full/LongCat-Video.sh)|[code](./examples/wanvideo/model_training/validate_full/LongCat-Video.py)|[code](./examples/wanvideo/model_training/lora/LongCat-Video.sh)|[code](./examples/wanvideo/model_training/validate_lora/LongCat-Video.py)| +|[ByteDance/Video-As-Prompt-Wan2.1-14B](https://modelscope.cn/models/ByteDance/Video-As-Prompt-Wan2.1-14B)|`vap_video`, `vap_prompt`|[code](./examples/wanvideo/model_inference/Video-As-Prompt-Wan2.1-14B.py)|[code](./examples/wanvideo/model_training/full/Video-As-Prompt-Wan2.1-14B.sh)|[code](./examples/wanvideo/model_training/validate_full/Video-As-Prompt-Wan2.1-14B.py)|[code](./examples/wanvideo/model_training/lora/Video-As-Prompt-Wan2.1-14B.sh)|[code](./examples/wanvideo/model_training/validate_lora/Video-As-Prompt-Wan2.1-14B.py)| + +
+ +### More Models + + + +
+Image Generation Models + +Detail page: [./examples/image_synthesis/](./examples/image_synthesis/) + +|FLUX|Stable Diffusion 3| +|-|-| +|![image_1024_cfg](https://github.com/user-attachments/assets/984561e9-553d-4952-9443-79ce144f379f)|![image_1024](https://github.com/modelscope/DiffSynth-Studio/assets/35051019/4df346db-6f91-420a-b4c1-26e205376098)| + +|Kolors|Hunyuan-DiT| +|-|-| +|![image_1024](https://github.com/modelscope/DiffSynth-Studio/assets/35051019/53ef6f41-da11-4701-8665-9f64392607bf)|![image_1024](https://github.com/modelscope/DiffSynth-Studio/assets/35051019/60b022c8-df3f-4541-95ab-bf39f2fa8bb5)| + +|Stable Diffusion|Stable Diffusion XL| +|-|-| +|![1024](https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/6fc84611-8da6-4a1f-8fee-9a34eba3b4a5)|![1024](https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/67687748-e738-438c-aee5-96096f09ac90)| + +
+ + + +
+Video Generation Models + +- HunyuanVideo: [./examples/HunyuanVideo/](./examples/HunyuanVideo/) + +https://github.com/user-attachments/assets/48dd24bb-0cc6-40d2-88c3-10feed3267e9 + +- StepVideo: [./examples/stepvideo/](./examples/stepvideo/) + +https://github.com/user-attachments/assets/5954fdaa-a3cf-45a3-bd35-886e3cc4581b + +- CogVideoX: [./examples/CogVideoX/](./examples/CogVideoX/) + +https://github.com/user-attachments/assets/26b044c1-4a60-44a4-842f-627ff289d006 + +
+ + + +
+Image Quality Assessment Models + +We have integrated a series of image quality assessment models. These models can be used for evaluating image generation models, alignment training, and similar tasks. + +Detail page: [./examples/image_quality_metric/](./examples/image_quality_metric/) + +* [ImageReward](https://github.com/THUDM/ImageReward) +* [Aesthetic](https://github.com/christophschuhmann/improved-aesthetic-predictor) +* [PickScore](https://github.com/yuvalkirstain/pickscore) +* [CLIP](https://github.com/openai/CLIP) +* [HPSv2](https://github.com/tgxs002/HPSv2) +* [HPSv2.1](https://github.com/tgxs002/HPSv2) +* [MPS](https://github.com/Kwai-Kolors/MPS) + +
+ + + +## Innovative Achievements + +DiffSynth-Studio is not just an engineering model framework, but also a platform for incubating innovative results. + +
+Nexus-Gen: Unified Architecture for Image Understanding, Generation, and Editing + +- Detail page: https://github.com/modelscope/Nexus-Gen +- Paper: [Nexus-Gen: Unified Image Understanding, Generation, and Editing via Prefilled Autoregression in Shared Embedding Space](https://arxiv.org/pdf/2504.21356) +- Model: [ModelScope](https://www.modelscope.cn/models/DiffSynth-Studio/Nexus-GenV2), [HuggingFace](https://huggingface.co/modelscope/Nexus-GenV2) +- Dataset: [ModelScope Dataset](https://www.modelscope.cn/datasets/DiffSynth-Studio/Nexus-Gen-Training-Dataset) +- Online Demo: [ModelScope Nexus-Gen Studio](https://www.modelscope.cn/studios/DiffSynth-Studio/Nexus-Gen) + +![](https://github.com/modelscope/Nexus-Gen/raw/main/assets/illustrations/gen_edit.jpg) + +
+ +
+ArtAug: Aesthetic Enhancement for Image Generation Models + +- Detail page: [./examples/ArtAug/](./examples/ArtAug/) +- Paper: [ArtAug: Enhancing Text-to-Image Generation through Synthesis-Understanding Interaction](https://arxiv.org/abs/2412.12888) +- Model: [ModelScope](https://www.modelscope.cn/models/DiffSynth-Studio/ArtAug-lora-FLUX.1dev-v1), [HuggingFace](https://huggingface.co/ECNU-CILab/ArtAug-lora-FLUX.1dev-v1) +- Online Demo: [ModelScope AIGC Tab](https://www.modelscope.cn/aigc/imageGeneration?tab=advanced&versionId=7228&modelType=LoRA&sdVersion=FLUX_1&modelUrl=modelscope%3A%2F%2FDiffSynth-Studio%2FArtAug-lora-FLUX.1dev-v1%3Frevision%3Dv1.0) + +|FLUX.1-dev|FLUX.1-dev + ArtAug LoRA| +|-|-| +|![image_1_base](https://github.com/user-attachments/assets/e1d5c505-b423-45fe-be01-25c2758f5417)|![image_1_enhance](https://github.com/user-attachments/assets/335908e3-d0bd-41c2-9d99-d10528a2d719)| + +
+ +
+EliGen: Precise Image Region Control + +- Detail page: [./examples/EntityControl/](./examples/EntityControl/) +- Paper: [EliGen: Entity-Level Controlled Image Generation with Regional Attention](https://arxiv.org/abs/2501.01097) +- Model: [ModelScope](https://www.modelscope.cn/models/DiffSynth-Studio/Eligen), [HuggingFace](https://huggingface.co/modelscope/EliGen) +- Online Demo: [ModelScope EliGen Studio](https://www.modelscope.cn/studios/DiffSynth-Studio/EliGen) +- Dataset: [EliGen Train Set](https://www.modelscope.cn/datasets/DiffSynth-Studio/EliGenTrainSet) + +|Entity Control Mask|Generated Image| +|-|-| +|![eligen_example_2_mask_0](https://github.com/user-attachments/assets/1c6d9445-5022-4d91-ad2e-dc05321883d1)|![eligen_example_2_0](https://github.com/user-attachments/assets/86739945-cb07-4a49-b3b3-3bb65c90d14f)| + +
+ +
+ExVideo: Extended Training for Video Generation Models + +- Project Page: [Project Page](https://ecnu-cilab.github.io/ExVideoProjectPage/) +- Paper: [ExVideo: Extending Video Diffusion Models via Parameter-Efficient Post-Tuning](https://arxiv.org/abs/2406.14130) +- Code Example: [./examples/ExVideo/](./examples/ExVideo/) +- Model: [ModelScope](https://modelscope.cn/models/ECNU-CILab/ExVideo-SVD-128f-v1), [HuggingFace](https://huggingface.co/ECNU-CILab/ExVideo-SVD-128f-v1) + +https://github.com/modelscope/DiffSynth-Studio/assets/35051019/d97f6aa9-8064-4b5b-9d49-ed6001bb9acc + +
+ +
+Diffutoon: High-Resolution Anime-Style Video Rendering + +- Project Page: [Project Page](https://ecnu-cilab.github.io/DiffutoonProjectPage/) +- Paper: [Diffutoon: High-Resolution Editable Toon Shading via Diffusion Models](https://arxiv.org/abs/2401.16224) +- Code Example: [./examples/Diffutoon/](./examples/Diffutoon/) + +https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/b54c05c5-d747-4709-be5e-b39af82404dd + +
+ +
+DiffSynth: The Initial Version of This Project + +- Project Page: [Project Page](https://ecnu-cilab.github.io/DiffSynth.github.io/) +- Paper: [DiffSynth: Latent In-Iteration Deflickering for Realistic Video Synthesis](https://arxiv.org/abs/2308.03463) +- Code Example: [./examples/diffsynth/](./examples/diffsynth/) + +https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/59fb2f7b-8de0-4481-b79f-0c3a7361a1ea + +
+ + + +## Update History + +- **November 4, 2025**: We support [ByteDance/Video-As-Prompt-Wan2.1-14B](https://modelscope.cn/models/ByteDance/Video-As-Prompt-Wan2.1-14B) model, which is trained on Wan 2.1 and enables motion generation conditioned on reference videos. + +- **October 30, 2025**: We support [meituan-longcat/LongCat-Video](https://www.modelscope.cn/models/meituan-longcat/LongCat-Video) model, which enables text-to-video, image-to-video, and video continuation capabilities. This model adopts Wan's framework for both inference and training in this project. + +- **October 27, 2025**: We support [krea/krea-realtime-video](https://www.modelscope.cn/models/krea/krea-realtime-video) model, further expanding Wan's ecosystem. + +- **September 23, 2025** [DiffSynth-Studio/Qwen-Image-EliGen-Poster](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-EliGen-Poster) is released! This model is jointly developed and open-sourced by us and the Taobao Design Team. The model is built upon Qwen-Image, specifically designed for e-commerce poster scenarios, and supports precise partition layout control. Please refer to [our example code](./examples/qwen_image/model_inference/Qwen-Image-EliGen-Poster.py). + +- **September 9, 2025**: Our training framework now supports multiple training modes and has been adapted for Qwen-Image. In addition to the standard SFT training mode, Direct Distill is now also supported; please refer to [our example code](./examples/qwen_image/model_training/lora/Qwen-Image-Distill-LoRA.sh). This feature is experimental, and we will continue to improve it to support comprehensive model training capabilities. + +- **August 28, 2025** We support Wan2.2-S2V, an audio-driven cinematic video generation model open-sourced by Alibaba. See [./examples/wanvideo/](./examples/wanvideo/). + +- **August 21, 2025**: [DiffSynth-Studio/Qwen-Image-EliGen-V2](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-EliGen-V2) is released! Compared to the V1 version, the training dataset has been updated to the [Qwen-Image-Self-Generated-Dataset](https://www.modelscope.cn/datasets/DiffSynth-Studio/Qwen-Image-Self-Generated-Dataset), enabling generated images to better align with the inherent image distribution and style of Qwen-Image. Please refer to [our sample code](./examples/qwen_image/model_inference_low_vram/Qwen-Image-EliGen-V2.py). + +- **August 21, 2025**: We open-sourced the [DiffSynth-Studio/Qwen-Image-In-Context-Control-Union](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-In-Context-Control-Union) structure control LoRA model. Following "In Context" routine, it supports various types of structural control conditions, including canny, depth, lineart, softedge, normal, and openpose. Please refer to [our sample code](./examples/qwen_image/model_inference/Qwen-Image-In-Context-Control-Union.py). + +- **August 20, 2025** We open-sourced [DiffSynth-Studio/Qwen-Image-Edit-Lowres-Fix](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Edit-Lowres-Fix), which improves the editing performance of Qwen-Image-Edit on low-resolution image inputs. Please refer to [our example code](./examples/qwen_image/model_inference/Qwen-Image-Edit-Lowres-Fix.py). + +- **August 19, 2025** 🔥 Qwen-Image-Edit is now open source. Welcome the new member to the image editing model family! + +- **August 18, 2025** We trained and open-sourced the Inpaint ControlNet model for Qwen-Image, [DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Inpaint](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Inpaint), which adopts a lightweight architectural design. Please refer to [our sample code](./examples/qwen_image/model_inference/Qwen-Image-Blockwise-ControlNet-Inpaint.py). + +- **August 15, 2025** We open-sourced the [Qwen-Image-Self-Generated-Dataset](https://www.modelscope.cn/datasets/DiffSynth-Studio/Qwen-Image-Self-Generated-Dataset). This is an image dataset generated using the Qwen-Image model, with a total of 160,000 `1024 x 1024` images. It includes the general, English text rendering, and Chinese text rendering subsets. We provide caption, entity and control images annotations for each image. Developers can use this dataset to train models such as ControlNet and EliGen for the Qwen-Image model. We aim to promote technological development through open-source contributions! + +- **August 13, 2025** We trained and open-sourced the ControlNet model for Qwen-Image, [DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Depth](https://modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Depth), which adopts a lightweight architectural design. Please refer to [our sample code](./examples/qwen_image/model_inference/Qwen-Image-Blockwise-ControlNet-Depth.py). + +- **August 12, 2025** We trained and open-sourced the ControlNet model for Qwen-Image, [DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Canny](https://modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Canny), which adopts a lightweight architectural design. Please refer to [our sample code](./examples/qwen_image/model_inference/Qwen-Image-Blockwise-ControlNet-Canny.py). + +- **August 11, 2025** We released another distilled acceleration model for Qwen-Image, [DiffSynth-Studio/Qwen-Image-Distill-LoRA](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Distill-LoRA). It uses the same training process as [DiffSynth-Studio/Qwen-Image-Distill-Full](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Distill-Full), but the model structure is changed to LoRA. This makes it work better with other open-source models. + +- **August 7, 2025** We open-sourced the entity control LoRA of Qwen-Image, [DiffSynth-Studio/Qwen-Image-EliGen](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-EliGen). Qwen-Image-EliGen is able to achieve entity-level controlled text-to-image generation. See the [paper](https://arxiv.org/abs/2501.01097) for technical details. Training dataset: [EliGenTrainSet](https://www.modelscope.cn/datasets/DiffSynth-Studio/EliGenTrainSet). + +- **August 5, 2025** We open-sourced the distilled acceleration model of Qwen-Image, [DiffSynth-Studio/Qwen-Image-Distill-Full](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Distill-Full), achieving approximately 5x speedup. + +- **August 4, 2025** 🔥 Qwen-Image is now open source. Welcome the new member to the image generation model family! + +- **August 1, 2025** [FLUX.1-Krea-dev](https://www.modelscope.cn/models/black-forest-labs/FLUX.1-Krea-dev) with a focus on aesthetic photography is comprehensively supported, including low-GPU-memory layer-by-layer offload, LoRA training and full training. See [./examples/flux/](./examples/flux/). + +- **July 28, 2025** With the open-sourcing of Wan 2.2, we immediately provided comprehensive support, including low-GPU-memory layer-by-layer offload, FP8 quantization, sequence parallelism, LoRA training, full training. See [./examples/wanvideo/](./examples/wanvideo/). + +- **July 11, 2025** We propose Nexus-Gen, a unified model that synergizes the language reasoning capabilities of LLMs with the image synthesis power of diffusion models. This framework enables seamless image understanding, generation, and editing tasks. + - Paper: [Nexus-Gen: Unified Image Understanding, Generation, and Editing via Prefilled Autoregression in Shared Embedding Space](https://arxiv.org/pdf/2504.21356) + - Github Repo: https://github.com/modelscope/Nexus-Gen + - Model: [ModelScope](https://www.modelscope.cn/models/DiffSynth-Studio/Nexus-GenV2), [HuggingFace](https://huggingface.co/modelscope/Nexus-GenV2) + - Training Dataset: [ModelScope Dataset](https://www.modelscope.cn/datasets/DiffSynth-Studio/Nexus-Gen-Training-Dataset) + - Online Demo: [ModelScope Nexus-Gen Studio](https://www.modelscope.cn/studios/DiffSynth-Studio/Nexus-Gen) + +
+More + +- **June 15, 2025** ModelScope's official evaluation framework, [EvalScope](https://github.com/modelscope/evalscope), now supports text-to-image generation evaluation. Try it with the [Best Practices](https://evalscope.readthedocs.io/zh-cn/latest/best_practice/t2i_eval.html) guide. + +- **March 25, 2025** Our new open-source project, [DiffSynth-Engine](https://github.com/modelscope/DiffSynth-Engine), is now open-sourced! Focused on stable model deployment. Geared towards industry. Offers better engineering support, higher computational performance, and more stable functionality. + +- **March 31, 2025** We support InfiniteYou, an identity preserving method for FLUX. Please refer to [./examples/InfiniteYou/](./examples/InfiniteYou/) for more details. + +- **March 13, 2025** We support HunyuanVideo-I2V, the image-to-video generation version of HunyuanVideo open-sourced by Tencent. Please refer to [./examples/HunyuanVideo/](./examples/HunyuanVideo/) for more details. + +- **February 25, 2025** We support Wan-Video, a collection of SOTA video synthesis models open-sourced by Alibaba. See [./examples/wanvideo/](./examples/wanvideo/). + +- **February 17, 2025** We support [StepVideo](https://modelscope.cn/models/stepfun-ai/stepvideo-t2v/summary)! State-of-the-art video synthesis model! See [./examples/stepvideo](./examples/stepvideo/). + +- **December 31, 2024** We propose EliGen, a novel framework for precise entity-level controlled text-to-image generation, complemented by an inpainting fusion pipeline to extend its capabilities to image inpainting tasks. EliGen seamlessly integrates with existing community models, such as IP-Adapter and In-Context LoRA, enhancing its versatility. For more details, see [./examples/EntityControl](./examples/EntityControl/). + - Paper: [EliGen: Entity-Level Controlled Image Generation with Regional Attention](https://arxiv.org/abs/2501.01097) + - Model: [ModelScope](https://www.modelscope.cn/models/DiffSynth-Studio/Eligen), [HuggingFace](https://huggingface.co/modelscope/EliGen) + - Online Demo: [ModelScope EliGen Studio](https://www.modelscope.cn/studios/DiffSynth-Studio/EliGen) + - Training Dataset: [EliGen Train Set](https://www.modelscope.cn/datasets/DiffSynth-Studio/EliGenTrainSet) + +- **December 19, 2024** We implement advanced VRAM management for HunyuanVideo, making it possible to generate videos at a resolution of 129x720x1280 using 24GB of VRAM, or at 129x512x384 resolution with just 6GB of VRAM. Please refer to [./examples/HunyuanVideo/](./examples/HunyuanVideo/) for more details. + +- **December 18, 2024** We propose ArtAug, an approach designed to improve text-to-image synthesis models through synthesis-understanding interactions. We have trained an ArtAug enhancement module for FLUX.1-dev in the format of LoRA. This model integrates the aesthetic understanding of Qwen2-VL-72B into FLUX.1-dev, leading to an improvement in the quality of generated images. + - Paper: https://arxiv.org/abs/2412.12888 + - Examples: https://github.com/modelscope/DiffSynth-Studio/tree/main/examples/ArtAug + - Model: [ModelScope](https://www.modelscope.cn/models/DiffSynth-Studio/ArtAug-lora-FLUX.1dev-v1), [HuggingFace](https://huggingface.co/ECNU-CILab/ArtAug-lora-FLUX.1dev-v1) + - Demo: [ModelScope](https://modelscope.cn/aigc/imageGeneration?tab=advanced&versionId=7228&modelType=LoRA&sdVersion=FLUX_1&modelUrl=modelscope%3A%2F%2FDiffSynth-Studio%2FArtAug-lora-FLUX.1dev-v1%3Frevision%3Dv1.0), HuggingFace (Coming soon) + +- **October 25, 2024** We provide extensive FLUX ControlNet support. This project supports many different ControlNet models that can be freely combined, even if their structures differ. Additionally, ControlNet models are compatible with high-resolution refinement and partition control techniques, enabling very powerful controllable image generation. See [`./examples/ControlNet/`](./examples/ControlNet/). + +- **October 8, 2024.** We release the extended LoRA based on CogVideoX-5B and ExVideo. You can download this model from [ModelScope](https://modelscope.cn/models/ECNU-CILab/ExVideo-CogVideoX-LoRA-129f-v1) or [HuggingFace](https://huggingface.co/ECNU-CILab/ExVideo-CogVideoX-LoRA-129f-v1). + +- **August 22, 2024.** CogVideoX-5B is supported in this project. See [here](/examples/video_synthesis/). We provide several interesting features for this text-to-video model, including + - Text to video + - Video editing + - Self-upscaling + - Video interpolation + +- **August 22, 2024.** We have implemented an interesting painter that supports all text-to-image models. Now you can create stunning images using the painter, with assistance from AI! + - Use it in our [WebUI](#usage-in-webui). + +- **August 21, 2024.** FLUX is supported in DiffSynth-Studio. + - Enable CFG and highres-fix to improve visual quality. See [here](/examples/image_synthesis/README.md) + - LoRA, ControlNet, and additional models will be available soon. + +- **June 21, 2024.** We propose ExVideo, a post-tuning technique aimed at enhancing the capability of video generation models. We have extended Stable Video Diffusion to achieve the generation of long videos up to 128 frames. + - [Project Page](https://ecnu-cilab.github.io/ExVideoProjectPage/) + - Source code is released in this repo. See [`examples/ExVideo`](./examples/ExVideo/). + - Models are released on [HuggingFace](https://huggingface.co/ECNU-CILab/ExVideo-SVD-128f-v1) and [ModelScope](https://modelscope.cn/models/ECNU-CILab/ExVideo-SVD-128f-v1). + - Technical report is released on [arXiv](https://arxiv.org/abs/2406.14130). + - You can try ExVideo in this [Demo](https://huggingface.co/spaces/modelscope/ExVideo-SVD-128f-v1)! + +- **June 13, 2024.** DiffSynth Studio is transferred to ModelScope. The developers have transitioned from "I" to "we". Of course, I will still participate in development and maintenance. + +- **Jan 29, 2024.** We propose Diffutoon, a fantastic solution for toon shading. + - [Project Page](https://ecnu-cilab.github.io/DiffutoonProjectPage/) + - The source codes are released in this project. + - The technical report (IJCAI 2024) is released on [arXiv](https://arxiv.org/abs/2401.16224). + +- **Dec 8, 2023.** We decide to develop a new Project, aiming to release the potential of diffusion models, especially in video synthesis. The development of this project is started. + +- **Nov 15, 2023.** We propose FastBlend, a powerful video deflickering algorithm. + - The sd-webui extension is released on [GitHub](https://github.com/Artiprocher/sd-webui-fastblend). + - Demo videos are shown on Bilibili, including three tasks. + - [Video deflickering](https://www.bilibili.com/video/BV1d94y1W7PE) + - [Video interpolation](https://www.bilibili.com/video/BV1Lw411m71p) + - [Image-driven video rendering](https://www.bilibili.com/video/BV1RB4y1Z7LF) + - The technical report is released on [arXiv](https://arxiv.org/abs/2311.09265). + - An unofficial ComfyUI extension developed by other users is released on [GitHub](https://github.com/AInseven/ComfyUI-fastblend). + +- **Oct 1, 2023.** We release an early version of this project, namely FastSDXL. A try for building a diffusion engine. + - The source codes are released on [GitHub](https://github.com/Artiprocher/FastSDXL). + - FastSDXL includes a trainable OLSS scheduler for efficiency improvement. + - The original repo of OLSS is [here](https://github.com/alibaba/EasyNLP/tree/master/diffusion/olss_scheduler). + - The technical report (CIKM 2023) is released on [arXiv](https://arxiv.org/abs/2305.14677). + - A demo video is shown on [Bilibili](https://www.bilibili.com/video/BV1w8411y7uj). + - Since OLSS requires additional training, we don't implement it in this project. + +- **Aug 29, 2023.** We propose DiffSynth, a video synthesis framework. + - [Project Page](https://ecnu-cilab.github.io/DiffSynth.github.io/). + - The source codes are released in [EasyNLP](https://github.com/alibaba/EasyNLP/tree/master/diffusion/DiffSynth). + - The technical report (ECML PKDD 2024) is released on [arXiv](https://arxiv.org/abs/2308.03463). + +
\ No newline at end of file diff --git a/README_zh.md b/README_zh.md new file mode 100644 index 0000000000000000000000000000000000000000..22a879d647741e42ffe2a883f0a682cda99da6c7 --- /dev/null +++ b/README_zh.md @@ -0,0 +1,538 @@ +# DiffSynth-Studio + + modelscope%2FDiffSynth-Studio | Trendshift

+ +[![PyPI](https://img.shields.io/pypi/v/DiffSynth)](https://pypi.org/project/DiffSynth/) +[![license](https://img.shields.io/github/license/modelscope/DiffSynth-Studio.svg)](https://github.com/modelscope/DiffSynth-Studio/blob/master/LICENSE) +[![open issues](https://isitmaintained.com/badge/open/modelscope/DiffSynth-Studio.svg)](https://github.com/modelscope/DiffSynth-Studio/issues) +[![GitHub pull-requests](https://img.shields.io/github/issues-pr/modelscope/DiffSynth-Studio.svg)](https://GitHub.com/modelscope/DiffSynth-Studio/pull/) +[![GitHub latest commit](https://badgen.net/github/last-commit/modelscope/DiffSynth-Studio)](https://GitHub.com/modelscope/DiffSynth-Studio/commit/) + +[Switch to English](./README.md) + +## 简介 + +欢迎来到 Diffusion 模型的魔法世界！DiffSynth-Studio 是由[魔搭社区](https://www.modelscope.cn/)团队开发和维护的开源 Diffusion 模型引擎。我们期望以框架建设孵化技术创新，凝聚开源社区的力量，探索生成式模型技术的边界！ + +DiffSynth 目前包括两个开源项目： +* [DiffSynth-Studio](https://github.com/modelscope/DiffSynth-Studio): 聚焦于激进的技术探索，面向学术界，提供更前沿的模型能力支持。 +* [DiffSynth-Engine](https://github.com/modelscope/DiffSynth-Engine): 聚焦于稳定的模型部署，面向工业界，提供更高的计算性能与更稳定的功能。 + +[DiffSynth-Studio](https://github.com/modelscope/DiffSynth-Studio) 与 [DiffSynth-Engine](https://github.com/modelscope/DiffSynth-Engine) 作为魔搭社区 [AIGC 专区](https://modelscope.cn/aigc/home) 的核心技术支撑，提供了强大的AI生成内容能力。欢迎体验我们精心打造的产品化功能，开启您的AI创作之旅！ + +## 安装 + +从源码安装（推荐）： + +``` +git clone https://github.com/modelscope/DiffSynth-Studio.git +cd DiffSynth-Studio +pip install -e . +``` + +
+其他安装方式 + +从 pypi 安装（存在版本更新延迟，如需使用最新功能，请从源码安装） + +``` +pip install diffsynth +``` + +如果在安装过程中遇到问题，可能是由上游依赖包导致的，请参考这些包的文档： + +* [torch](https://pytorch.org/get-started/locally/) +* [sentencepiece](https://github.com/google/sentencepiece) +* [cmake](https://cmake.org) +* [cupy](https://docs.cupy.dev/en/stable/install.html) + +
+ + + +## 基础框架 + +DiffSynth-Studio 为主流 Diffusion 模型（包括 FLUX、Wan 等）重新设计了推理和训练流水线，能够实现高效的显存管理、灵活的模型训练。 + +### Qwen-Image 系列 (🔥新模型) + +详细页面：[./examples/qwen_image/](./examples/qwen_image/) + +![Image](https://github.com/user-attachments/assets/738078d8-8749-4a53-a046-571861541924) + +
+ +快速开始 + +```python +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig +from PIL import Image +import torch + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), +) +prompt = "精致肖像，水下少女，蓝裙飘逸，发丝轻扬，光影透澈，气泡环绕，面容恬静，细节精致，梦幻唯美。" +image = pipe( + prompt, seed=0, num_inference_steps=40, + # edit_image=Image.open("xxx.jpg").resize((1328, 1328)) # For Qwen-Image-Edit +) +image.save("image.jpg") +``` + +
+ +
+ +模型总览 + +|模型 ID|推理|低显存推理|全量训练|全量训练后验证|LoRA 训练|LoRA 训练后验证| +|-|-|-|-|-|-|-| +|[Qwen/Qwen-Image](https://www.modelscope.cn/models/Qwen/Qwen-Image)|[code](./examples/qwen_image/model_inference/Qwen-Image.py)|[code](./examples/qwen_image/model_inference_low_vram/Qwen-Image.py)|[code](./examples/qwen_image/model_training/full/Qwen-Image.sh)|[code](./examples/qwen_image/model_training/validate_full/Qwen-Image.py)|[code](./examples/qwen_image/model_training/lora/Qwen-Image.sh)|[code](./examples/qwen_image/model_training/validate_lora/Qwen-Image.py)| +|[Qwen/Qwen-Image-Edit](https://www.modelscope.cn/models/Qwen/Qwen-Image-Edit)|[code](./examples/qwen_image/model_inference/Qwen-Image-Edit.py)|[code](./examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit.py)|[code](./examples/qwen_image/model_training/full/Qwen-Image-Edit.sh)|[code](./examples/qwen_image/model_training/validate_full/Qwen-Image-Edit.py)|[code](./examples/qwen_image/model_training/lora/Qwen-Image-Edit.sh)|[code](./examples/qwen_image/model_training/validate_lora/Qwen-Image-Edit.py)| +|[Qwen/Qwen-Image-Edit-2509](https://www.modelscope.cn/models/Qwen/Qwen-Image-Edit-2509)|[code](./examples/qwen_image/model_inference/Qwen-Image-Edit-2509.py)|[code](./examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit-2509.py)|[code](./examples/qwen_image/model_training/full/Qwen-Image-Edit-2509.sh)|[code](./examples/qwen_image/model_training/validate_full/Qwen-Image-Edit-2509.py)|[code](./examples/qwen_image/model_training/lora/Qwen-Image-Edit-2509.sh)|[code](./examples/qwen_image/model_training/validate_lora/Qwen-Image-Edit-2509.py)| +|[DiffSynth-Studio/Qwen-Image-EliGen-V2](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-EliGen-V2)|[code](./examples/qwen_image/model_inference/Qwen-Image-EliGen-V2.py)|[code](./examples/qwen_image/model_inference_low_vram/Qwen-Image-EliGen-V2.py)|-|-|[code](./examples/qwen_image/model_training/lora/Qwen-Image-EliGen.sh)|[code](./examples/qwen_image/model_training/validate_lora/Qwen-Image-EliGen.py)| +|[DiffSynth-Studio/Qwen-Image-EliGen-Poster](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-EliGen-Poster)|[code](./examples/qwen_image/model_inference/Qwen-Image-EliGen-Poster.py)|[code](./examples/qwen_image/model_inference_low_vram/Qwen-Image-EliGen-Poster.py)|-|-|[code](./examples/qwen_image/model_training/lora/Qwen-Image-EliGen-Poster.sh)|[code](./examples/qwen_image/model_training/validate_lora/Qwen-Image-EliGen-Poster.py)| +|[DiffSynth-Studio/Qwen-Image-Distill-Full](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Distill-Full)|[code](./examples/qwen_image/model_inference/Qwen-Image-Distill-Full.py)|[code](./examples/qwen_image/model_inference_low_vram/Qwen-Image-Distill-Full.py)|[code](./examples/qwen_image/model_training/full/Qwen-Image-Distill-Full.sh)|[code](./examples/qwen_image/model_training/validate_full/Qwen-Image-Distill-Full.py)|[code](./examples/qwen_image/model_training/lora/Qwen-Image-Distill-Full.sh)|[code](./examples/qwen_image/model_training/validate_lora/Qwen-Image-Distill-Full.py)| +|[DiffSynth-Studio/Qwen-Image-Distill-LoRA](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Distill-LoRA)|[code](./examples/qwen_image/model_inference/Qwen-Image-Distill-LoRA.py)|[code](./examples/qwen_image/model_inference_low_vram/Qwen-Image-Distill-LoRA.py)|-|-|[code](./examples/qwen_image/model_training/lora/Qwen-Image-Distill-LoRA.sh)|[code](./examples/qwen_image/model_training/validate_lora/Qwen-Image-Distill-LoRA.py)| +|[DiffSynth-Studio/Qwen-Image-EliGen](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-EliGen)|[code](./examples/qwen_image/model_inference/Qwen-Image-EliGen.py)|[code](./examples/qwen_image/model_inference_low_vram/Qwen-Image-EliGen.py)|-|-|[code](./examples/qwen_image/model_training/lora/Qwen-Image-EliGen.sh)|[code](./examples/qwen_image/model_training/validate_lora/Qwen-Image-EliGen.py)| +|[DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Canny](https://modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Canny)|[code](./examples/qwen_image/model_inference/Qwen-Image-Blockwise-ControlNet-Canny.py)|[code](./examples/qwen_image/model_inference_low_vram/Qwen-Image-Blockwise-ControlNet-Canny.py)|[code](./examples/qwen_image/model_training/full/Qwen-Image-Blockwise-ControlNet-Canny.sh)|[code](./examples/qwen_image/model_training/validate_full/Qwen-Image-Blockwise-ControlNet-Canny.py)|[code](./examples/qwen_image/model_training/lora/Qwen-Image-Blockwise-ControlNet-Canny.sh)|[code](./examples/qwen_image/model_training/validate_lora/Qwen-Image-Blockwise-ControlNet-Canny.py)| +|[DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Depth](https://modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Depth)|[code](./examples/qwen_image/model_inference/Qwen-Image-Blockwise-ControlNet-Depth.py)|[code](./examples/qwen_image/model_inference_low_vram/Qwen-Image-Blockwise-ControlNet-Depth.py)|[code](./examples/qwen_image/model_training/full/Qwen-Image-Blockwise-ControlNet-Depth.sh)|[code](./examples/qwen_image/model_training/validate_full/Qwen-Image-Blockwise-ControlNet-Depth.py)|[code](./examples/qwen_image/model_training/lora/Qwen-Image-Blockwise-ControlNet-Depth.sh)|[code](./examples/qwen_image/model_training/validate_lora/Qwen-Image-Blockwise-ControlNet-Depth.py)| +|[DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Inpaint](https://modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Inpaint)|[code](./examples/qwen_image/model_inference/Qwen-Image-Blockwise-ControlNet-Inpaint.py)|[code](./examples/qwen_image/model_inference_low_vram/Qwen-Image-Blockwise-ControlNet-Inpaint.py)|[code](./examples/qwen_image/model_training/full/Qwen-Image-Blockwise-ControlNet-Inpaint.sh)|[code](./examples/qwen_image/model_training/validate_full/Qwen-Image-Blockwise-ControlNet-Inpaint.py)|[code](./examples/qwen_image/model_training/lora/Qwen-Image-Blockwise-ControlNet-Inpaint.sh)|[code](./examples/qwen_image/model_training/validate_lora/Qwen-Image-Blockwise-ControlNet-Inpaint.py)| +|[DiffSynth-Studio/Qwen-Image-In-Context-Control-Union](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-In-Context-Control-Union)|[code](./examples/qwen_image/model_inference/Qwen-Image-In-Context-Control-Union.py)|[code](./examples/qwen_image/model_inference_low_vram/Qwen-Image-In-Context-Control-Union.py)|-|-|[code](./examples/qwen_image/model_training/lora/Qwen-Image-In-Context-Control-Union.sh)|[code](./examples/qwen_image/model_training/validate_lora/Qwen-Image-In-Context-Control-Union.py)| +|[DiffSynth-Studio/Qwen-Image-Edit-Lowres-Fix](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Edit-Lowres-Fix)|[code](./examples/qwen_image/model_inference/Qwen-Image-Edit-Lowres-Fix.py)|[code](./examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit-Lowres-Fix.py)|-|-|-|-| + +
+ +### FLUX 系列 + +详细页面：[./examples/flux/](./examples/flux/) + +![Image](https://github.com/user-attachments/assets/c01258e2-f251-441a-aa1e-ebb22f02594d) + +
+ +快速开始 + +```python +import torch +from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig + +pipe = FluxImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-dev.safetensors"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors"), + ], +) + +image = pipe(prompt="a cat", seed=0) +image.save("image.jpg") +``` + +
+ +
+ +模型总览 + +|模型 ID|额外参数|推理|低显存推理|全量训练|全量训练后验证|LoRA 训练|LoRA 训练后验证| +|-|-|-|-|-|-|-|-| +|[FLUX.1-dev](https://www.modelscope.cn/models/black-forest-labs/FLUX.1-dev)||[code](./examples/flux/model_inference/FLUX.1-dev.py)|[code](./examples/flux/model_inference_low_vram/FLUX.1-dev.py)|[code](./examples/flux/model_training/full/FLUX.1-dev.sh)|[code](./examples/flux/model_training/validate_full/FLUX.1-dev.py)|[code](./examples/flux/model_training/lora/FLUX.1-dev.sh)|[code](./examples/flux/model_training/validate_lora/FLUX.1-dev.py)| +|[FLUX.1-Krea-dev](https://www.modelscope.cn/models/black-forest-labs/FLUX.1-Krea-dev)||[code](./examples/flux/model_inference/FLUX.1-Krea-dev.py)|[code](./examples/flux/model_inference_low_vram/FLUX.1-Krea-dev.py)|[code](./examples/flux/model_training/full/FLUX.1-Krea-dev.sh)|[code](./examples/flux/model_training/validate_full/FLUX.1-Krea-dev.py)|[code](./examples/flux/model_training/lora/FLUX.1-Krea-dev.sh)|[code](./examples/flux/model_training/validate_lora/FLUX.1-Krea-dev.py)| +|[FLUX.1-Kontext-dev](https://www.modelscope.cn/models/black-forest-labs/FLUX.1-Kontext-dev)|`kontext_images`|[code](./examples/flux/model_inference/FLUX.1-Kontext-dev.py)|[code](./examples/flux/model_inference_low_vram/FLUX.1-Kontext-dev.py)|[code](./examples/flux/model_training/full/FLUX.1-Kontext-dev.sh)|[code](./examples/flux/model_training/validate_full/FLUX.1-Kontext-dev.py)|[code](./examples/flux/model_training/lora/FLUX.1-Kontext-dev.sh)|[code](./examples/flux/model_training/validate_lora/FLUX.1-Kontext-dev.py)| +|[FLUX.1-dev-Controlnet-Inpainting-Beta](https://www.modelscope.cn/models/alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta)|`controlnet_inputs`|[code](./examples/flux/model_inference/FLUX.1-dev-Controlnet-Inpainting-Beta.py)|[code](./examples/flux/model_inference_low_vram/FLUX.1-dev-Controlnet-Inpainting-Beta.py)|[code](./examples/flux/model_training/full/FLUX.1-dev-Controlnet-Inpainting-Beta.sh)|[code](./examples/flux/model_training/validate_full/FLUX.1-dev-Controlnet-Inpainting-Beta.py)|[code](./examples/flux/model_training/lora/FLUX.1-dev-Controlnet-Inpainting-Beta.sh)|[code](./examples/flux/model_training/validate_lora/FLUX.1-dev-Controlnet-Inpainting-Beta.py)| +|[FLUX.1-dev-Controlnet-Union-alpha](https://www.modelscope.cn/models/InstantX/FLUX.1-dev-Controlnet-Union-alpha)|`controlnet_inputs`|[code](./examples/flux/model_inference/FLUX.1-dev-Controlnet-Union-alpha.py)|[code](./examples/flux/model_inference_low_vram/FLUX.1-dev-Controlnet-Union-alpha.py)|[code](./examples/flux/model_training/full/FLUX.1-dev-Controlnet-Union-alpha.sh)|[code](./examples/flux/model_training/validate_full/FLUX.1-dev-Controlnet-Union-alpha.py)|[code](./examples/flux/model_training/lora/FLUX.1-dev-Controlnet-Union-alpha.sh)|[code](./examples/flux/model_training/validate_lora/FLUX.1-dev-Controlnet-Union-alpha.py)| +|[FLUX.1-dev-Controlnet-Upscaler](https://www.modelscope.cn/models/jasperai/Flux.1-dev-Controlnet-Upscaler)|`controlnet_inputs`|[code](./examples/flux/model_inference/FLUX.1-dev-Controlnet-Upscaler.py)|[code](./examples/flux/model_inference_low_vram/FLUX.1-dev-Controlnet-Upscaler.py)|[code](./examples/flux/model_training/full/FLUX.1-dev-Controlnet-Upscaler.sh)|[code](./examples/flux/model_training/validate_full/FLUX.1-dev-Controlnet-Upscaler.py)|[code](./examples/flux/model_training/lora/FLUX.1-dev-Controlnet-Upscaler.sh)|[code](./examples/flux/model_training/validate_lora/FLUX.1-dev-Controlnet-Upscaler.py)| +|[FLUX.1-dev-IP-Adapter](https://www.modelscope.cn/models/InstantX/FLUX.1-dev-IP-Adapter)|`ipadapter_images`, `ipadapter_scale`|[code](./examples/flux/model_inference/FLUX.1-dev-IP-Adapter.py)|[code](./examples/flux/model_inference_low_vram/FLUX.1-dev-IP-Adapter.py)|[code](./examples/flux/model_training/full/FLUX.1-dev-IP-Adapter.sh)|[code](./examples/flux/model_training/validate_full/FLUX.1-dev-IP-Adapter.py)|[code](./examples/flux/model_training/lora/FLUX.1-dev-IP-Adapter.sh)|[code](./examples/flux/model_training/validate_lora/FLUX.1-dev-IP-Adapter.py)| +|[FLUX.1-dev-InfiniteYou](https://www.modelscope.cn/models/ByteDance/InfiniteYou)|`infinityou_id_image`, `infinityou_guidance`, `controlnet_inputs`|[code](./examples/flux/model_inference/FLUX.1-dev-InfiniteYou.py)|[code](./examples/flux/model_inference_low_vram/FLUX.1-dev-InfiniteYou.py)|[code](./examples/flux/model_training/full/FLUX.1-dev-InfiniteYou.sh)|[code](./examples/flux/model_training/validate_full/FLUX.1-dev-InfiniteYou.py)|[code](./examples/flux/model_training/lora/FLUX.1-dev-InfiniteYou.sh)|[code](./examples/flux/model_training/validate_lora/FLUX.1-dev-InfiniteYou.py)| +|[FLUX.1-dev-EliGen](https://www.modelscope.cn/models/DiffSynth-Studio/Eligen)|`eligen_entity_prompts`, `eligen_entity_masks`, `eligen_enable_on_negative`, `eligen_enable_inpaint`|[code](./examples/flux/model_inference/FLUX.1-dev-EliGen.py)|[code](./examples/flux/model_inference_low_vram/FLUX.1-dev-EliGen.py)|-|-|[code](./examples/flux/model_training/lora/FLUX.1-dev-EliGen.sh)|[code](./examples/flux/model_training/validate_lora/FLUX.1-dev-EliGen.py)| +|[FLUX.1-dev-LoRA-Encoder](https://www.modelscope.cn/models/DiffSynth-Studio/LoRA-Encoder-FLUX.1-Dev)|`lora_encoder_inputs`, `lora_encoder_scale`|[code](./examples/flux/model_inference/FLUX.1-dev-LoRA-Encoder.py)|[code](./examples/flux/model_inference_low_vram/FLUX.1-dev-LoRA-Encoder.py)|[code](./examples/flux/model_training/full/FLUX.1-dev-LoRA-Encoder.sh)|[code](./examples/flux/model_training/validate_full/FLUX.1-dev-LoRA-Encoder.py)|-|-| +|[FLUX.1-dev-LoRA-Fusion-Preview](https://modelscope.cn/models/DiffSynth-Studio/LoRAFusion-preview-FLUX.1-dev)||[code](./examples/flux/model_inference/FLUX.1-dev-LoRA-Fusion.py)|-|-|-|-|-| +|[Step1X-Edit](https://www.modelscope.cn/models/stepfun-ai/Step1X-Edit)|`step1x_reference_image`|[code](./examples/flux/model_inference/Step1X-Edit.py)|[code](./examples/flux/model_inference_low_vram/Step1X-Edit.py)|[code](./examples/flux/model_training/full/Step1X-Edit.sh)|[code](./examples/flux/model_training/validate_full/Step1X-Edit.py)|[code](./examples/flux/model_training/lora/Step1X-Edit.sh)|[code](./examples/flux/model_training/validate_lora/Step1X-Edit.py)| +|[FLEX.2-preview](https://www.modelscope.cn/models/ostris/Flex.2-preview)|`flex_inpaint_image`, `flex_inpaint_mask`, `flex_control_image`, `flex_control_strength`, `flex_control_stop`|[code](./examples/flux/model_inference/FLEX.2-preview.py)|[code](./examples/flux/model_inference_low_vram/FLEX.2-preview.py)|[code](./examples/flux/model_training/full/FLEX.2-preview.sh)|[code](./examples/flux/model_training/validate_full/FLEX.2-preview.py)|[code](./examples/flux/model_training/lora/FLEX.2-preview.sh)|[code](./examples/flux/model_training/validate_lora/FLEX.2-preview.py)| +|[Nexus-Gen](https://www.modelscope.cn/models/DiffSynth-Studio/Nexus-GenV2)|`nexus_gen_reference_image`|[code](./examples/flux/model_inference/Nexus-Gen-Editing.py)|[code](./examples/flux/model_inference_low_vram/Nexus-Gen-Editing.py)|[code](./examples/flux/model_training/full/Nexus-Gen.sh)|[code](./examples/flux/model_training/validate_full/Nexus-Gen.py)|[code](./examples/flux/model_training/lora/Nexus-Gen.sh)|[code](./examples/flux/model_training/validate_lora/Nexus-Gen.py)| + +
+ +### Wan 系列 + +详细页面：[./examples/wanvideo/](./examples/wanvideo/) + +https://github.com/user-attachments/assets/1d66ae74-3b02-40a9-acc3-ea95fc039314 + +
+ +快速开始 + +```python +import torch +from diffsynth import save_video +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"), + ], +) +pipe.enable_vram_management() + +video = pipe( + prompt="纪实摄影风格画面，一只活泼的小狗在绿茵茵的草地上迅速奔跑。小狗毛色棕黄，两只耳朵立起，神情专注而欢快。阳光洒在它身上，使得毛发看上去格外柔软而闪亮。背景是一片开阔的草地，偶尔点缀着几朵野花，远处隐约可见蓝天和几片白云。透视感鲜明，捕捉小狗奔跑时的动感和四周草地的生机。中景侧面移动视角。", + negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + seed=0, tiled=True, +) +save_video(video, "video1.mp4", fps=15, quality=5) +``` + +
+ +
+ +模型总览 + +|模型 ID|额外参数|推理|全量训练|全量训练后验证|LoRA 训练|LoRA 训练后验证| +|-|-|-|-|-|-|-| +|[Wan-AI/Wan2.2-Animate-14B](https://www.modelscope.cn/models/Wan-AI/Wan2.2-Animate-14B)|`input_image`, `animate_pose_video`, `animate_face_video`, `animate_inpaint_video`, `animate_mask_video`|[code](./examples/wanvideo/model_inference/Wan2.2-Animate-14B.py)|[code](./examples/wanvideo/model_training/full/Wan2.2-Animate-14B.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.2-Animate-14B.py)|[code](./examples/wanvideo/model_training/lora/Wan2.2-Animate-14B.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.2-Animate-14B.py)| +|[Wan-AI/Wan2.2-S2V-14B](https://www.modelscope.cn/models/Wan-AI/Wan2.2-S2V-14B)|`input_image`, `input_audio`, `audio_sample_rate`, `s2v_pose_video`|[code](./examples/wanvideo/model_inference/Wan2.2-S2V-14B_multi_clips.py)|[code](./examples/wanvideo/model_training/full/Wan2.2-S2V-14B.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.2-S2V-14B.py)|[code](./examples/wanvideo/model_training/lora/Wan2.2-S2V-14B.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.2-S2V-14B.py)| +|[Wan-AI/Wan2.2-I2V-A14B](https://modelscope.cn/models/Wan-AI/Wan2.2-I2V-A14B)|`input_image`|[code](./examples/wanvideo/model_inference/Wan2.2-I2V-A14B.py)|[code](./examples/wanvideo/model_training/full/Wan2.2-I2V-A14B.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.2-I2V-A14B.py)|[code](./examples/wanvideo/model_training/lora/Wan2.2-I2V-A14B.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.2-I2V-A14B.py)| +|[Wan-AI/Wan2.2-T2V-A14B](https://modelscope.cn/models/Wan-AI/Wan2.2-T2V-A14B)||[code](./examples/wanvideo/model_inference/Wan2.2-T2V-A14B.py)|[code](./examples/wanvideo/model_training/full/Wan2.2-T2V-A14B.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.2-T2V-A14B.py)|[code](./examples/wanvideo/model_training/lora/Wan2.2-T2V-A14B.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.2-T2V-A14B.py)| +|[Wan-AI/Wan2.2-TI2V-5B](https://modelscope.cn/models/Wan-AI/Wan2.2-TI2V-5B)|`input_image`|[code](./examples/wanvideo/model_inference/Wan2.2-TI2V-5B.py)|[code](./examples/wanvideo/model_training/full/Wan2.2-TI2V-5B.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.2-TI2V-5B.py)|[code](./examples/wanvideo/model_training/lora/Wan2.2-TI2V-5B.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.2-TI2V-5B.py)| +|[Wan-AI/Wan2.2-VACE-Fun-A14B](https://www.modelscope.cn/models/PAI/Wan2.2-VACE-Fun-A14B)|`vace_control_video`, `vace_reference_image`|[code](./examples/wanvideo/model_inference/Wan2.2-VACE-Fun-A14B.py)|[code](./examples/wanvideo/model_training/full/Wan2.2-VACE-Fun-A14B.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.2-VACE-Fun-A14B.py)|[code](./examples/wanvideo/model_training/lora/Wan2.2-VACE-Fun-A14B.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.2-VACE-Fun-A14B.py)| +|[PAI/Wan2.2-Fun-A14B-InP](https://modelscope.cn/models/PAI/Wan2.2-Fun-A14B-InP)|`input_image`, `end_image`|[code](./examples/wanvideo/model_inference/Wan2.2-Fun-A14B-InP.py)|[code](./examples/wanvideo/model_training/full/Wan2.2-Fun-A14B-InP.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.2-Fun-A14B-InP.py)|[code](./examples/wanvideo/model_training/lora/Wan2.2-Fun-A14B-InP.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.2-Fun-A14B-InP.py)| +|[PAI/Wan2.2-Fun-A14B-Control](https://modelscope.cn/models/PAI/Wan2.2-Fun-A14B-Control)|`control_video`, `reference_image`|[code](./examples/wanvideo/model_inference/Wan2.2-Fun-A14B-Control.py)|[code](./examples/wanvideo/model_training/full/Wan2.2-Fun-A14B-Control.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.2-Fun-A14B-Control.py)|[code](./examples/wanvideo/model_training/lora/Wan2.2-Fun-A14B-Control.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.2-Fun-A14B-Control.py)| +|[PAI/Wan2.2-Fun-A14B-Control-Camera](https://modelscope.cn/models/PAI/Wan2.2-Fun-A14B-Control-Camera)|`control_camera_video`, `input_image`|[code](./examples/wanvideo/model_inference/Wan2.2-Fun-A14B-Control-Camera.py)|[code](./examples/wanvideo/model_training/full/Wan2.2-Fun-A14B-Control-Camera.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.2-Fun-A14B-Control-Camera.py)|[code](./examples/wanvideo/model_training/lora/Wan2.2-Fun-A14B-Control-Camera.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.2-Fun-A14B-Control-Camera.py)| +|[Wan-AI/Wan2.1-T2V-1.3B](https://modelscope.cn/models/Wan-AI/Wan2.1-T2V-1.3B)||[code](./examples/wanvideo/model_inference/Wan2.1-T2V-1.3B.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-T2V-1.3B.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-T2V-1.3B.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-T2V-1.3B.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-T2V-1.3B.py)| +|[Wan-AI/Wan2.1-T2V-14B](https://modelscope.cn/models/Wan-AI/Wan2.1-T2V-14B)||[code](./examples/wanvideo/model_inference/Wan2.1-T2V-14B.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-T2V-14B.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-T2V-14B.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-T2V-14B.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-T2V-14B.py)| +|[Wan-AI/Wan2.1-I2V-14B-480P](https://modelscope.cn/models/Wan-AI/Wan2.1-I2V-14B-480P)|`input_image`|[code](./examples/wanvideo/model_inference/Wan2.1-I2V-14B-480P.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-I2V-14B-480P.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-I2V-14B-480P.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-I2V-14B-480P.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-I2V-14B-480P.py)| +|[Wan-AI/Wan2.1-I2V-14B-720P](https://modelscope.cn/models/Wan-AI/Wan2.1-I2V-14B-720P)|`input_image`|[code](./examples/wanvideo/model_inference/Wan2.1-I2V-14B-720P.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-I2V-14B-720P.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-I2V-14B-720P.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-I2V-14B-720P.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-I2V-14B-720P.py)| +|[Wan-AI/Wan2.1-FLF2V-14B-720P](https://modelscope.cn/models/Wan-AI/Wan2.1-FLF2V-14B-720P)|`input_image`, `end_image`|[code](./examples/wanvideo/model_inference/Wan2.1-FLF2V-14B-720P.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-FLF2V-14B-720P.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-FLF2V-14B-720P.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-FLF2V-14B-720P.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-FLF2V-14B-720P.py)| +|[PAI/Wan2.1-Fun-1.3B-InP](https://modelscope.cn/models/PAI/Wan2.1-Fun-1.3B-InP)|`input_image`, `end_image`|[code](./examples/wanvideo/model_inference/Wan2.1-Fun-1.3B-InP.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-Fun-1.3B-InP.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-Fun-1.3B-InP.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-Fun-1.3B-InP.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-1.3B-InP.py)| +|[PAI/Wan2.1-Fun-1.3B-Control](https://modelscope.cn/models/PAI/Wan2.1-Fun-1.3B-Control)|`control_video`|[code](./examples/wanvideo/model_inference/Wan2.1-Fun-1.3B-Control.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-Fun-1.3B-Control.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-Fun-1.3B-Control.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-Fun-1.3B-Control.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-1.3B-Control.py)| +|[PAI/Wan2.1-Fun-14B-InP](https://modelscope.cn/models/PAI/Wan2.1-Fun-14B-InP)|`input_image`, `end_image`|[code](./examples/wanvideo/model_inference/Wan2.1-Fun-14B-InP.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-Fun-14B-InP.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-Fun-14B-InP.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-Fun-14B-InP.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-14B-InP.py)| +|[PAI/Wan2.1-Fun-14B-Control](https://modelscope.cn/models/PAI/Wan2.1-Fun-14B-Control)|`control_video`|[code](./examples/wanvideo/model_inference/Wan2.1-Fun-14B-Control.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-Fun-14B-Control.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-Fun-14B-Control.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-Fun-14B-Control.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-14B-Control.py)| +|[PAI/Wan2.1-Fun-V1.1-1.3B-Control](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-1.3B-Control)|`control_video`, `reference_image`|[code](./examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-1.3B-Control.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-1.3B-Control.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-1.3B-Control.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-1.3B-Control.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-1.3B-Control.py)| +|[PAI/Wan2.1-Fun-V1.1-14B-Control](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-14B-Control)|`control_video`, `reference_image`|[code](./examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-14B-Control.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-14B-Control.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-14B-Control.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-14B-Control.sh)|[code](./examples/wanvideo/examples/wanmodel_training/validate_lora/Wan2.1-Fun-V1.1-14B-Control.py)| +|[PAI/Wan2.1-Fun-V1.1-1.3B-InP](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-1.3B-InP)|`input_image`, `end_image`|[code](./examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-1.3B-InP.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-1.3B-InP.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-1.3B-InP.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-1.3B-InP.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-1.3B-InP.py)| +|[PAI/Wan2.1-Fun-V1.1-14B-InP](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-14B-InP)|`input_image`, `end_image`|[code](./examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-14B-InP.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-14B-InP.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-14B-InP.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-14B-InP.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-14B-InP.py)| +|[PAI/Wan2.1-Fun-V1.1-1.3B-Control-Camera](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-1.3B-Control-Camera)|`control_camera_video`, `input_image`|[code](./examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-1.3B-Control-Camera.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-1.3B-Control-Camera.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-1.3B-Control-Camera.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-1.3B-Control-Camera.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-1.3B-Control-Camera.py)| +|[PAI/Wan2.1-Fun-V1.1-14B-Control-Camera](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-14B-Control-Camera)|`control_camera_video`, `input_image`|[code](./examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-14B-Control-Camera.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-14B-Control-Camera.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-14B-Control-Camera.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-14B-Control-Camera.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-14B-Control-Camera.py)| +|[iic/VACE-Wan2.1-1.3B-Preview](https://modelscope.cn/models/iic/VACE-Wan2.1-1.3B-Preview)|`vace_control_video`, `vace_reference_image`|[code](./examples/wanvideo/model_inference/Wan2.1-VACE-1.3B-Preview.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-VACE-1.3B-Preview.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-VACE-1.3B-Preview.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-VACE-1.3B-Preview.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-VACE-1.3B-Preview.py)| +|[Wan-AI/Wan2.1-VACE-1.3B](https://modelscope.cn/models/Wan-AI/Wan2.1-VACE-1.3B)|`vace_control_video`, `vace_reference_image`|[code](./examples/wanvideo/model_inference/Wan2.1-VACE-1.3B.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-VACE-1.3B.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-VACE-1.3B.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-VACE-1.3B.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-VACE-1.3B.py)| +|[Wan-AI/Wan2.1-VACE-14B](https://modelscope.cn/models/Wan-AI/Wan2.1-VACE-14B)|`vace_control_video`, `vace_reference_image`|[code](./examples/wanvideo/model_inference/Wan2.1-VACE-14B.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-VACE-14B.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-VACE-14B.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-VACE-14B.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-VACE-14B.py)| +|[DiffSynth-Studio/Wan2.1-1.3b-speedcontrol-v1](https://modelscope.cn/models/DiffSynth-Studio/Wan2.1-1.3b-speedcontrol-v1)|`motion_bucket_id`|[code](./examples/wanvideo/model_inference/Wan2.1-1.3b-speedcontrol-v1.py)|[code](./examples/wanvideo/model_training/full/Wan2.1-1.3b-speedcontrol-v1.sh)|[code](./examples/wanvideo/model_training/validate_full/Wan2.1-1.3b-speedcontrol-v1.py)|[code](./examples/wanvideo/model_training/lora/Wan2.1-1.3b-speedcontrol-v1.sh)|[code](./examples/wanvideo/model_training/validate_lora/Wan2.1-1.3b-speedcontrol-v1.py)| +|[krea/krea-realtime-video](https://www.modelscope.cn/models/krea/krea-realtime-video)||[code](./examples/wanvideo/model_inference/krea-realtime-video.py)|[code](./examples/wanvideo/model_training/full/krea-realtime-video.sh)|[code](./examples/wanvideo/model_training/validate_full/krea-realtime-video.py)|[code](./examples/wanvideo/model_training/lora/krea-realtime-video.sh)|[code](./examples/wanvideo/model_training/validate_lora/krea-realtime-video.py)| +|[meituan-longcat/LongCat-Video](https://www.modelscope.cn/models/meituan-longcat/LongCat-Video)|`longcat_video`|[code](./examples/wanvideo/model_inference/LongCat-Video.py)|[code](./examples/wanvideo/model_training/full/LongCat-Video.sh)|[code](./examples/wanvideo/model_training/validate_full/LongCat-Video.py)|[code](./examples/wanvideo/model_training/lora/LongCat-Video.sh)|[code](./examples/wanvideo/model_training/validate_lora/LongCat-Video.py)| +|[ByteDance/Video-As-Prompt-Wan2.1-14B](https://modelscope.cn/models/ByteDance/Video-As-Prompt-Wan2.1-14B)|`vap_video`, `vap_prompt`|[code](./examples/wanvideo/model_inference/Video-As-Prompt-Wan2.1-14B.py)|[code](./examples/wanvideo/model_training/full/Video-As-Prompt-Wan2.1-14B.sh)|[code](./examples/wanvideo/model_training/validate_full/Video-As-Prompt-Wan2.1-14B.py)|[code](./examples/wanvideo/model_training/lora/Video-As-Prompt-Wan2.1-14B.sh)|[code](./examples/wanvideo/model_training/validate_lora/Video-As-Prompt-Wan2.1-14B.py)| + +
+ + + +### 更多模型 + + + +
+图像生成模型 + +详细页面：[./examples/image_synthesis/](./examples/image_synthesis/) + +|FLUX|Stable Diffusion 3| +|-|-| +|![image_1024_cfg](https://github.com/user-attachments/assets/984561e9-553d-4952-9443-79ce144f379f)|![image_1024](https://github.com/modelscope/DiffSynth-Studio/assets/35051019/4df346db-6f91-420a-b4c1-26e205376098)| + +|Kolors|Hunyuan-DiT| +|-|-| +|![image_1024](https://github.com/modelscope/DiffSynth-Studio/assets/35051019/53ef6f41-da11-4701-8665-9f64392607bf)|![image_1024](https://github.com/modelscope/DiffSynth-Studio/assets/35051019/60b022c8-df3f-4541-95ab-bf39f2fa8bb5)| + +|Stable Diffusion|Stable Diffusion XL| +|-|-| +|![1024](https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/6fc84611-8da6-4a1f-8fee-9a34eba3b4a5)|![1024](https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/67687748-e738-438c-aee5-96096f09ac90)| + +
+ + + +
+视频生成模型 + +- HunyuanVideo：[./examples/HunyuanVideo/](./examples/HunyuanVideo/) + +https://github.com/user-attachments/assets/48dd24bb-0cc6-40d2-88c3-10feed3267e9 + +- StepVideo：[./examples/stepvideo/](./examples/stepvideo/) + +https://github.com/user-attachments/assets/5954fdaa-a3cf-45a3-bd35-886e3cc4581b + +- CogVideoX：[./examples/CogVideoX/](./examples/CogVideoX/) + +https://github.com/user-attachments/assets/26b044c1-4a60-44a4-842f-627ff289d006 + +
+ + + +
+图像质量评估模型 + +我们集成了一系列图像质量评估模型，这些模型可以用于图像生成模型的评测、对齐训练等场景中。 + +详细页面：[./examples/image_quality_metric/](./examples/image_quality_metric/) + +* [ImageReward](https://github.com/THUDM/ImageReward) +* [Aesthetic](https://github.com/christophschuhmann/improved-aesthetic-predictor) +* [PickScore](https://github.com/yuvalkirstain/pickscore) +* [CLIP](https://github.com/openai/CLIP) +* [HPSv2](https://github.com/tgxs002/HPSv2) +* [HPSv2.1](https://github.com/tgxs002/HPSv2) +* [MPS](https://github.com/Kwai-Kolors/MPS) + +
+ + + +## 创新成果 + +DiffSynth-Studio 不仅仅是一个工程化的模型框架，更是创新成果的孵化器。 + +
+Nexus-Gen: 统一架构的图像理解、生成、编辑 + +- 详细页面：https://github.com/modelscope/Nexus-Gen +- 论文：[Nexus-Gen: Unified Image Understanding, Generation, and Editing via Prefilled Autoregression in Shared Embedding Space](https://arxiv.org/pdf/2504.21356) +- 模型：[ModelScope](https://www.modelscope.cn/models/DiffSynth-Studio/Nexus-GenV2), [HuggingFace](https://huggingface.co/modelscope/Nexus-GenV2) +- 数据集：[ModelScope Dataset](https://www.modelscope.cn/datasets/DiffSynth-Studio/Nexus-Gen-Training-Dataset) +- 在线体验：[ModelScope Nexus-Gen Studio](https://www.modelscope.cn/studios/DiffSynth-Studio/Nexus-Gen) + +![](https://github.com/modelscope/Nexus-Gen/raw/main/assets/illustrations/gen_edit.jpg) + +
+ + + +
+ArtAug: 图像生成模型的美学提升 + +- 详细页面：[./examples/ArtAug/](./examples/ArtAug/) +- 论文：[ArtAug: Enhancing Text-to-Image Generation through Synthesis-Understanding Interaction](https://arxiv.org/abs/2412.12888) +- 模型：[ModelScope](https://www.modelscope.cn/models/DiffSynth-Studio/ArtAug-lora-FLUX.1dev-v1), [HuggingFace](https://huggingface.co/ECNU-CILab/ArtAug-lora-FLUX.1dev-v1) +- 在线体验：[ModelScope AIGC Tab](https://www.modelscope.cn/aigc/imageGeneration?tab=advanced&versionId=7228&modelType=LoRA&sdVersion=FLUX_1&modelUrl=modelscope%3A%2F%2FDiffSynth-Studio%2FArtAug-lora-FLUX.1dev-v1%3Frevision%3Dv1.0) + +|FLUX.1-dev|FLUX.1-dev + ArtAug LoRA| +|-|-| +|![image_1_base](https://github.com/user-attachments/assets/e1d5c505-b423-45fe-be01-25c2758f5417)|![image_1_enhance](https://github.com/user-attachments/assets/335908e3-d0bd-41c2-9d99-d10528a2d719)| + +
+ + + +
+ +EliGen: 精准的图像分区控制 + +- 详细页面：[./examples/EntityControl/](./examples/EntityControl/) +- 论文：[EliGen: Entity-Level Controlled Image Generation with Regional Attention](https://arxiv.org/abs/2501.01097) +- 模型：[ModelScope](https://www.modelscope.cn/models/DiffSynth-Studio/Eligen), [HuggingFace](https://huggingface.co/modelscope/EliGen) +- 在线体验：[ModelScope EliGen Studio](https://www.modelscope.cn/studios/DiffSynth-Studio/EliGen) +- 数据集：[EliGen Train Set](https://www.modelscope.cn/datasets/DiffSynth-Studio/EliGenTrainSet) + +|实体控制区域|生成图像| +|-|-| +|![eligen_example_2_mask_0](https://github.com/user-attachments/assets/1c6d9445-5022-4d91-ad2e-dc05321883d1)|![eligen_example_2_0](https://github.com/user-attachments/assets/86739945-cb07-4a49-b3b3-3bb65c90d14f)| + +
+ + + +
+ +ExVideo: 视频生成模型的扩展训练 + +- 项目页面：[Project Page](https://ecnu-cilab.github.io/ExVideoProjectPage/) +- 论文：[ExVideo: Extending Video Diffusion Models via Parameter-Efficient Post-Tuning](https://arxiv.org/abs/2406.14130) +- 代码样例：[./examples/ExVideo/](./examples/ExVideo/) +- 模型：[ModelScope](https://modelscope.cn/models/ECNU-CILab/ExVideo-SVD-128f-v1), [HuggingFace](https://huggingface.co/ECNU-CILab/ExVideo-SVD-128f-v1) + +https://github.com/modelscope/DiffSynth-Studio/assets/35051019/d97f6aa9-8064-4b5b-9d49-ed6001bb9acc + +
+ + + +
+ +Diffutoon: 高分辨率动漫风格视频渲染 + +- 项目页面：[Project Page](https://ecnu-cilab.github.io/DiffutoonProjectPage/) +- 论文：[Diffutoon: High-Resolution Editable Toon Shading via Diffusion Models](https://arxiv.org/abs/2401.16224) +- 代码样例：[./examples/Diffutoon/](./examples/Diffutoon/) + +https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/b54c05c5-d747-4709-be5e-b39af82404dd + +
+ + + +
+ +DiffSynth: 本项目的初代版本 + +- 项目页面：[Project Page](https://ecnu-cilab.github.io/DiffSynth.github.io/) +- 论文：[DiffSynth: Latent In-Iteration Deflickering for Realistic Video Synthesis](https://arxiv.org/abs/2308.03463) +- 代码样例：[./examples/diffsynth/](./examples/diffsynth/) + +https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/59fb2f7b-8de0-4481-b79f-0c3a7361a1ea + +
+ + + +## 更新历史 + +- **2025年11月4日** 支持了 [ByteDance/Video-As-Prompt-Wan2.1-14B](https://modelscope.cn/models/ByteDance/Video-As-Prompt-Wan2.1-14B) 模型，该模型基于 Wan 2.1 训练，支持根据参考视频生成相应的动作。 + +- **2025年10月30日** 支持了 [meituan-longcat/LongCat-Video](https://www.modelscope.cn/models/meituan-longcat/LongCat-Video) 模型，该模型支持文生视频、图生视频、视频续写。这个模型在本项目中沿用 Wan 的框架进行推理和训练。 + +- **2025年10月27日** 支持了 [krea/krea-realtime-video](https://www.modelscope.cn/models/krea/krea-realtime-video) 模型，Wan 模型生态再添一员。 + +- **2025年9月23日** [DiffSynth-Studio/Qwen-Image-EliGen-Poster](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-EliGen-Poster) 发布！本模型由我们与淘天体验设计团队联合研发并开源。模型基于 Qwen-Image 构建，专为电商海报场景设计，支持精确的分区布局控制。 请参考[我们的示例代码](./examples/qwen_image/model_inference/Qwen-Image-EliGen-Poster.py)。 + +- **2025年9月9日** 我们的训练框架支持了多种训练模式，目前已适配 Qwen-Image，除标准 SFT 训练模式外，已支持 Direct Distill，请参考[我们的示例代码](./examples/qwen_image/model_training/lora/Qwen-Image-Distill-LoRA.sh)。这项功能是实验性的，我们将会继续完善已支持更全面的模型训练功能。 + +- **2025年8月28日** 我们支持了Wan2.2-S2V，一个音频驱动的电影级视频生成模型。请参见[./examples/wanvideo/](./examples/wanvideo/)。 + +- **2025年8月21日** [DiffSynth-Studio/Qwen-Image-EliGen-V2](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-EliGen-V2) 发布！相比于 V1 版本，训练数据集变为 [Qwen-Image-Self-Generated-Dataset](https://www.modelscope.cn/datasets/DiffSynth-Studio/Qwen-Image-Self-Generated-Dataset)，因此，生成的图像更符合 Qwen-Image 本身的图像分布和风格。 请参考[我们的示例代码](./examples/qwen_image/model_inference_low_vram/Qwen-Image-EliGen-V2.py)。 + +- **2025年8月21日** 我们开源了 [DiffSynth-Studio/Qwen-Image-In-Context-Control-Union](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-In-Context-Control-Union) 结构控制 LoRA 模型，采用 In Context 的技术路线，支持多种类别的结构控制条件，包括 canny, depth, lineart, softedge, normal, openpose。 请参考[我们的示例代码](./examples/qwen_image/model_inference/Qwen-Image-In-Context-Control-Union.py)。 + +- **2025年8月20日** 我们开源了 [DiffSynth-Studio/Qwen-Image-Edit-Lowres-Fix](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Edit-Lowres-Fix) 模型，提升了 Qwen-Image-Edit 对低分辨率图像输入的编辑效果。请参考[我们的示例代码](./examples/qwen_image/model_inference/Qwen-Image-Edit-Lowres-Fix.py) + +- **2025年8月19日** 🔥 Qwen-Image-Edit 开源，欢迎图像编辑模型新成员！ + +- **2025年8月18日** 我们训练并开源了 Qwen-Image 的图像重绘 ControlNet 模型 [DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Inpaint](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Inpaint)，模型结构采用了轻量化的设计，请参考[我们的示例代码](./examples/qwen_image/model_inference/Qwen-Image-Blockwise-ControlNet-Inpaint.py)。 + +- **2025年8月15日** 我们开源了 [Qwen-Image-Self-Generated-Dataset](https://www.modelscope.cn/datasets/DiffSynth-Studio/Qwen-Image-Self-Generated-Dataset) 数据集。这是一个使用 Qwen-Image 模型生成的图像数据集，共包含 160,000 张`1024 x 1024`图像。它包括通用、英文文本渲染和中文文本渲染子集。我们为每张图像提供了图像描述、实体和结构控制图像的标注。开发者可以使用这个数据集来训练 Qwen-Image 模型的 ControlNet 和 EliGen 等模型，我们旨在通过开源推动技术发展！ + +- **2025年8月13日** 我们训练并开源了 Qwen-Image 的 ControlNet 模型 [DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Depth](https://modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Depth)，模型结构采用了轻量化的设计，请参考[我们的示例代码](./examples/qwen_image/model_inference/Qwen-Image-Blockwise-ControlNet-Depth.py)。 + +- **2025年8月12日** 我们训练并开源了 Qwen-Image 的 ControlNet 模型 [DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Canny](https://modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Canny)，模型结构采用了轻量化的设计，请参考[我们的示例代码](./examples/qwen_image/model_inference/Qwen-Image-Blockwise-ControlNet-Canny.py)。 + +- **2025年8月11日** 我们开源了 Qwen-Image 的蒸馏加速模型 [DiffSynth-Studio/Qwen-Image-Distill-LoRA](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Distill-LoRA)，沿用了与 [DiffSynth-Studio/Qwen-Image-Distill-Full](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Distill-Full) 相同的训练流程，但模型结构修改为了 LoRA，因此能够更好地与其他开源生态模型兼容。 + +- **2025年8月7日** 我们开源了 Qwen-Image 的实体控制 LoRA 模型 [DiffSynth-Studio/Qwen-Image-EliGen](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-EliGen)。Qwen-Image-EliGen 能够实现实体级可控的文生图。技术细节请参见[论文](https://arxiv.org/abs/2501.01097)。训练数据集：[EliGenTrainSet](https://www.modelscope.cn/datasets/DiffSynth-Studio/EliGenTrainSet)。 + +- **2025年8月5日** 我们开源了 Qwen-Image 的蒸馏加速模型 [DiffSynth-Studio/Qwen-Image-Distill-Full](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Distill-Full)，实现了约 5 倍加速。 + +- **2025年8月4日** 🔥 Qwen-Image 开源，欢迎图像生成模型家族新成员！ + +- **2025年8月1日** [FLUX.1-Krea-dev](https://www.modelscope.cn/models/black-forest-labs/FLUX.1-Krea-dev) 开源，这是一个专注于美学摄影的文生图模型。我们第一时间提供了全方位支持，包括低显存逐层 offload、LoRA 训练、全量训练。详细信息请参考 [./examples/flux/](./examples/flux/)。 + +- **2025年7月28日** Wan 2.2 开源，我们第一时间提供了全方位支持，包括低显存逐层 offload、FP8 量化、序列并行、LoRA 训练、全量训练。详细信息请参考 [./examples/wanvideo/](./examples/wanvideo/)。 + +- **2025年7月11日** 我们提出 Nexus-Gen，一个将大语言模型（LLM）的语言推理能力与扩散模型的图像生成能力相结合的统一框架。该框架支持无缝的图像理解、生成和编辑任务。 + - 论文: [Nexus-Gen: Unified Image Understanding, Generation, and Editing via Prefilled Autoregression in Shared Embedding Space](https://arxiv.org/pdf/2504.21356) + - Github 仓库: https://github.com/modelscope/Nexus-Gen + - 模型: [ModelScope](https://www.modelscope.cn/models/DiffSynth-Studio/Nexus-GenV2), [HuggingFace](https://huggingface.co/modelscope/Nexus-GenV2) + - 训练数据集: [ModelScope Dataset](https://www.modelscope.cn/datasets/DiffSynth-Studio/Nexus-Gen-Training-Dataset) + - 在线体验: [ModelScope Nexus-Gen Studio](https://www.modelscope.cn/studios/DiffSynth-Studio/Nexus-Gen) + +
+更多 + +- **2025年6月15日** ModelScope 官方评测框架 [EvalScope](https://github.com/modelscope/evalscope) 现已支持文生图生成评测。请参考[最佳实践](https://evalscope.readthedocs.io/zh-cn/latest/best_practice/t2i_eval.html)指南进行尝试。 + +- **2025年3月25日** 我们的新开源项目 [DiffSynth-Engine](https://github.com/modelscope/DiffSynth-Engine) 现已开源！专注于稳定的模型部署，面向工业界，提供更好的工程支持、更高的计算性能和更稳定的功能。 + +- **2025年3月31日** 我们支持 InfiniteYou，一种用于 FLUX 的人脸特征保留方法。更多细节请参考 [./examples/InfiniteYou/](./examples/InfiniteYou/)。 + +- **2025年3月13日** 我们支持 HunyuanVideo-I2V，即腾讯开源的 HunyuanVideo 的图像到视频生成版本。更多细节请参考 [./examples/HunyuanVideo/](./examples/HunyuanVideo/)。 + +- **2025年2月25日** 我们支持 Wan-Video，这是阿里巴巴开源的一系列最先进的视频合成模型。详见 [./examples/wanvideo/](./examples/wanvideo/)。 + +- **2025年2月17日** 我们支持 [StepVideo](https://modelscope.cn/models/stepfun-ai/stepvideo-t2v/summary)！先进的视频合成模型！详见 [./examples/stepvideo](./examples/stepvideo/)。 + +- **2024年12月31日** 我们提出 EliGen，一种用于精确实体级别控制的文本到图像生成的新框架，并辅以修复融合管道，将其能力扩展到图像修复任务。EliGen 可以无缝集成现有的社区模型，如 IP-Adapter 和 In-Context LoRA，提升其通用性。更多详情，请见 [./examples/EntityControl](./examples/EntityControl/)。 + - 论文: [EliGen: Entity-Level Controlled Image Generation with Regional Attention](https://arxiv.org/abs/2501.01097) + - 模型: [ModelScope](https://www.modelscope.cn/models/DiffSynth-Studio/Eligen), [HuggingFace](https://huggingface.co/modelscope/EliGen) + - 在线体验: [ModelScope EliGen Studio](https://www.modelscope.cn/studios/DiffSynth-Studio/EliGen) + - 训练数据集: [EliGen Train Set](https://www.modelscope.cn/datasets/DiffSynth-Studio/EliGenTrainSet) + +- **2024年12月19日** 我们为 HunyuanVideo 实现了高级显存管理，使得在 24GB 显存下可以生成分辨率为 129x720x1280 的视频，或在仅 6GB 显存下生成分辨率为 129x512x384 的视频。更多细节请参考 [./examples/HunyuanVideo/](./examples/HunyuanVideo/)。 + +- **2024年12月18日** 我们提出 ArtAug，一种通过合成-理解交互来改进文生图模型的方法。我们以 LoRA 格式为 FLUX.1-dev 训练了一个 ArtAug 增强模块。该模型将 Qwen2-VL-72B 的美学理解融入 FLUX.1-dev，从而提升了生成图像的质量。 + - 论文: https://arxiv.org/abs/2412.12888 + - 示例: https://github.com/modelscope/DiffSynth-Studio/tree/main/examples/ArtAug + - 模型: [ModelScope](https://www.modelscope.cn/models/DiffSynth-Studio/ArtAug-lora-FLUX.1dev-v1), [HuggingFace](https://huggingface.co/ECNU-CILab/ArtAug-lora-FLUX.1dev-v1) + - 演示: [ModelScope](https://modelscope.cn/aigc/imageGeneration?tab=advanced&versionId=7228&modelType=LoRA&sdVersion=FLUX_1&modelUrl=modelscope%3A%2F%2FDiffSynth-Studio%2FArtAug-lora-FLUX.1dev-v1%3Frevision%3Dv1.0), HuggingFace (即将上线) + +- **2024年10月25日** 我们提供了广泛的 FLUX ControlNet 支持。该项目支持许多不同的 ControlNet 模型，并且可以自由组合，即使它们的结构不同。此外，ControlNet 模型兼容高分辨率优化和分区控制技术，能够实现非常强大的可控图像生成。详见 [`./examples/ControlNet/`](./examples/ControlNet/)。 + +- **2024年10月8日** 我们发布了基于 CogVideoX-5B 和 ExVideo 的扩展 LoRA。您可以从 [ModelScope](https://modelscope.cn/models/ECNU-CILab/ExVideo-CogVideoX-LoRA-129f-v1) 或 [HuggingFace](https://huggingface.co/ECNU-CILab/ExVideo-CogVideoX-LoRA-129f-v1) 下载此模型。 + +- **2024年8月22日** 本项目现已支持 CogVideoX-5B。详见 [此处](/examples/video_synthesis/)。我们为这个文生视频模型提供了几个有趣的功能，包括： + - 文本到视频 + - 视频编辑 + - 自我超分 + - 视频插帧 + +- **2024年8月22日** 我们实现了一个有趣的画笔功能，支持所有文生图模型。现在，您可以在 AI 的辅助下使用画笔创作惊艳的图像了！ + - 在我们的 [WebUI](#usage-in-webui) 中使用它。 + +- **2024年8月21日** DiffSynth-Studio 现已支持 FLUX。 + - 启用 CFG 和高分辨率修复以提升视觉质量。详见 [此处](/examples/image_synthesis/README.md) + - LoRA、ControlNet 和其他附加模型将很快推出。 + +- **2024年6月21日** 我们提出 ExVideo，一种旨在增强视频生成模型能力的后训练微调技术。我们将 Stable Video Diffusion 进行了扩展，实现了长达 128 帧的长视频生成。 + - [项目页面](https://ecnu-cilab.github.io/ExVideoProjectPage/) + - 源代码已在此仓库中发布。详见 [`examples/ExVideo`](./examples/ExVideo/)。 + - 模型已发布于 [HuggingFace](https://huggingface.co/ECNU-CILab/ExVideo-SVD-128f-v1) 和 [ModelScope](https://modelscope.cn/models/ECNU-CILab/ExVideo-SVD-128f-v1)。 + - 技术报告已发布于 [arXiv](https://arxiv.org/abs/2406.14130)。 + - 您可以在此 [演示](https://huggingface.co/spaces/modelscope/ExVideo-SVD-128f-v1) 中试用 ExVideo！ + +- **2024年6月13日** DiffSynth Studio 已迁移至 ModelScope。开发团队也从“我”转变为“我们”。当然，我仍会参与后续的开发和维护工作。 + +- **2024年1月29日** 我们提出 Diffutoon，这是一个出色的卡通着色解决方案。 + - [项目页面](https://ecnu-cilab.github.io/DiffutoonProjectPage/) + - 源代码已在此项目中发布。 + - 技术报告（IJCAI 2024）已发布于 [arXiv](https://arxiv.org/abs/2401.16224)。 + +- **2023年12月8日** 我们决定启动一个新项目，旨在释放扩散模型的潜力，尤其是在视频合成方面。该项目的开发工作正式开始。 + +- **2023年11月15日** 我们提出 FastBlend，一种强大的视频去闪烁算法。 + - sd-webui 扩展已发布于 [GitHub](https://github.com/Artiprocher/sd-webui-fastblend)。 + - 演示视频已在 Bilibili 上展示，包含三个任务： + - [视频去闪烁](https://www.bilibili.com/video/BV1d94y1W7PE) + - [视频插帧](https://www.bilibili.com/video/BV1Lw411m71p) + - [图像驱动的视频渲染](https://www.bilibili.com/video/BV1RB4y1Z7LF) + - 技术报告已发布于 [arXiv](https://arxiv.org/abs/2311.09265)。 + - 其他用户开发的非官方 ComfyUI 扩展已发布于 [GitHub](https://github.com/AInseven/ComfyUI-fastblend)。 + +- **2023年10月1日** 我们发布了该项目的早期版本，名为 FastSDXL。这是构建一个扩散引擎的初步尝试。 + - 源代码已发布于 [GitHub](https://github.com/Artiprocher/FastSDXL)。 + - FastSDXL 包含一个可训练的 OLSS 调度器，以提高效率。 + - OLSS 的原始仓库位于 [此处](https://github.com/alibaba/EasyNLP/tree/master/diffusion/olss_scheduler)。 + - 技术报告（CIKM 2023）已发布于 [arXiv](https://arxiv.org/abs/2305.14677)。 + - 演示视频已发布于 [Bilibili](https://www.bilibili.com/video/BV1w8411y7uj)。 + - 由于 OLSS 需要额外训练，我们未在本项目中实现它。 + +- **2023年8月29日** 我们提出 DiffSynth，一个视频合成框架。 + - [项目页面](https://ecnu-cilab.github.io/DiffSynth.github.io/)。 + - 源代码已发布在 [EasyNLP](https://github.com/alibaba/EasyNLP/tree/master/diffusion/DiffSynth)。 + - 技术报告（ECML PKDD 2024）已发布于 [arXiv](https://arxiv.org/abs/2308.03463)。 + +
diff --git a/_temp_.py b/_temp_.py new file mode 100644 index 0000000000000000000000000000000000000000..5a010f7ba80d9ea4dffd57315b62a4200d771bed --- /dev/null +++ b/_temp_.py @@ -0,0 +1,184 @@ + +# import os +# render2real_path = "/fi-lib/workspace/sjx/DiffSynth-Studio/dataset/spotlight_sketch/epoch0" +# sketch_enhance_body_path = "/fi-lib/workspace/sjx/DiffSynth-Studio/dataset/spotlight_sketch/GT" +# render2real_files = set(os.listdir(render2real_path)) +# sketch_enhance_body_files = set(os.listdir(sketch_enhance_body_path)) +# for file in render2real_files: +# if file not in sketch_enhance_body_files: +# print(f"Removing {file} from render2real_path") +# os.remove(os.path.join(render2real_path, file)) +# for file in sketch_enhance_body_files: +# if file not in render2real_files: +# print(f"Removing {file} from sketch_enhance_body_path") +# os.remove(os.path.join(sketch_enhance_body_path, file)) + +# import os +# input_path = "/fi-lib/workspace/sjx/DiffSynth-Studio/dataset/mbti/Realistic" +# for file in os.listdir(input_path): +# # 把文件名称最后的_Realistic去掉 +# new_name = file.replace("_Realistic", "") +# os.rename(os.path.join(input_path, file), os.path.join(input_path, new_name)) + +# import os +# for file in os.listdir("dataset/spotlight_sketch_cat/GT"): +# with open("dataset/spotlight_sketch_cat/pairs_t2t.txt", "a") as f: +# # 目标图 原图 +# f.write(f"GT/{file}\tepoch0/{file}\n") + + +# import os +# import json +# from tqdm import tqdm +# input_txt = "/fi-lib/workspace/sjx/DiffSynth-Studio/dataset/spotlight_sketch_cat/spotlight_nano_comprehension_1203.txt" +# with open(input_txt, "r") as f: +# lines = f.readlines() +# for i in tqdm(range(len(lines))): +# data = json.loads(lines[i]) +# fig_id = f"{data['Image_Name']}.png" +# del data["Image_Name"] +# input_dir = "dataset/spotlight_sketch_cat/epoch0" +# GT_dir = "dataset/spotlight_sketch_cat/GT" +# for file in os.listdir(input_dir): +# if fig_id in file: +# with open("dataset/spotlight_sketch_cat/pairs_i2i.txt", "a") as f: +# # 目标 原图 prompt +# f.write(f"{GT_dir}/{file}\t{input_dir}/{file}\t{data}\n") + +# 把文件夹中的图片每六张拼成一个3行两列的大图，保存到另一个文件夹中，原图拼接不要截图 +import os +from PIL import Image +from tqdm import tqdm +import numpy as np +base_dirs = ["/fi-lib/workspace/sjx/DiffSynth-Studio/dataset/the roses","/fi-lib/workspace/sjx/DiffSynth-Studio/dataset/nouvelle","/fi-lib/workspace/sjx/DiffSynth-Studio/dataset/legs","/fi-lib/workspace/sjx/DiffSynth-Studio/dataset/frankenstein"] +# 核心配置参数（按需求定义） +crop_size = (1920, 800) # 目标CenterCrop尺寸 (宽, 高) +resize_size = (477, 188) # 下采样后的单张尺寸 (宽, 高) +line_width = 6 # 黑线宽度（6像素） +target_merge_size = (960, 576) # 最终拼接目标尺寸 (宽, 高) + + +def center_crop_to_size(img, target_size): + """ + 对图片进行CenterCrop到指定尺寸，不足部分用黑色像素填充 + :param img: PIL Image对象 + :param target_size: (target_w, target_h) 目标裁剪尺寸 + :return: crop+补黑后的PIL Image + """ + target_w, target_h = target_size + img_w, img_h = img.size + + # Step1: 计算CenterCrop的区域（中心对齐） + # 水平方向裁剪 + if img_w >= target_w: + left = (img_w - target_w) // 2 + right = left + target_w + else: + left = 0 + right = img_w + # 垂直方向裁剪 + if img_h >= target_h: + top = (img_h - target_h) // 2 + bottom = top + target_h + else: + top = 0 + bottom = img_h + + # Step2: 执行CenterCrop + cropped = img.crop((left, top, right, bottom)) + + # Step3: 不足目标尺寸的部分用黑色填充 + if cropped.size != (target_w, target_h): + new_img = Image.new("RGB", (target_w, target_h), (0, 0, 0)) # 黑色背景 + new_img.paste(cropped, ((target_w - cropped.width) // 2, (target_h - cropped.height) // 2)) + cropped = new_img + + return cropped +for k in range(len(base_dirs)): + save_path = f"{base_dirs[k]}_dedup_cat" + os.makedirs(save_path, exist_ok=True) + input_path = f"{base_dirs[k]}_dedup" +# 获取并排序文件列表 + files = [f for f in os.listdir(input_path) if f.lower().endswith(('.png', '.jpg', '.jpeg'))] + files.sort() + + # 遍历文件，每6张拼接一次 + for i in tqdm(range(0, len(files), 6), desc="拼接图片"): + # 初始化最终拼接画布（目标尺寸960×576，黑色背景） + merged_image = np.zeros((target_merge_size[1], target_merge_size[0], 3), dtype=np.uint8) + + # 逐个处理6张图片 + valid_imgs = [] # 存储处理后的有效图片 + for j in range(6): + if i + j >= len(files): + # 不足6张时，break + img_np = np.zeros((resize_size[1], resize_size[0], 3), dtype=np.uint8) + valid_imgs.append(img_np) + continue + + img_path = os.path.join(input_path, files[i + j]) + try: + # 读取图片（确保RGB格式） + img = Image.open(img_path).convert("RGB") + img_w, img_h = img.size + + # 过滤条件：原始宽度不足1800则跳过并打印 + if img_w < 1800: + print(f"跳过文件 {files[i+j]}: 原始宽度 {img_w} < 1800") + # 用黑色图片填充该位置 + img_np = np.zeros((resize_size[1], resize_size[0], 3), dtype=np.uint8) + valid_imgs.append(img_np) + continue + + # Step1: CenterCrop到1920×800，不足补黑 + cropped_img = center_crop_to_size(img, crop_size) + + # Step2: 下采样到477×188（LANCZOS插值，保持画质） + resized_img = cropped_img.resize(resize_size, resample=Image.LANCZOS) + + # 转为numpy数组 + img_np = np.array(resized_img) + valid_imgs.append(img_np) + + except Exception as e: + print(f"处理文件 {files[i+j]} 出错: {str(e)}") + # 出错时用黑色图片填充 + img_np = np.zeros((resize_size[1], resize_size[0], 3), dtype=np.uint8) + valid_imgs.append(img_np) + + # Step3: 计算每张图在拼接画布中的位置（3行2列 + 6像素黑线） + # 验证拼接尺寸兼容性（防止配置错误） + assert len(valid_imgs) == 6, "有效图片数量必须为6张" + # 计算图片+黑线的总占位，确保适配960×576 + total_col = 2 * resize_size[0] + 1 * line_width # 2列+1条竖线 + total_row = 3 * resize_size[1] + 2 * line_width # 3行+2条横线 + # 计算画布中的偏移（居中放置，保证最终尺寸960×576） + offset_x = (target_merge_size[0] - total_col) // 2 + offset_y = (target_merge_size[1] - total_row) // 2 + + # 逐个放置图片到拼接画布 + for idx, img_np in enumerate(valid_imgs): + row = idx // 2 # 0/1/2行 + col = idx % 2 # 0/1列 + + # 计算当前图片的起始位置（含黑线+整体偏移） + x_start = offset_x + col * (resize_size[0] + line_width) + y_start = offset_y + row * (resize_size[1] + line_width) + x_end = x_start + resize_size[0] + y_end = y_start + resize_size[1] + + # 确保不超出画布边界 + x_end = min(x_end, target_merge_size[0]) + y_end = min(y_end, target_merge_size[1]) + x_start = max(x_start, 0) + y_start = max(y_start, 0) + + # 放置图片到画布 + merged_image[y_start:y_end, x_start:x_end, :] = img_np[:y_end-y_start, :x_end-x_start, :] + + # Step4: 保存最终拼接图片 + save_name = f'merged_{i//6}.png' + save_full_path = os.path.join(save_path, save_name) + Image.fromarray(merged_image).save(save_full_path) + + print(f"所有图片处理完成！结果保存至: {save_path}") \ No newline at end of file diff --git a/apps/gradio/DiffSynth_Studio.py b/apps/gradio/DiffSynth_Studio.py new file mode 100644 index 0000000000000000000000000000000000000000..d26549202db2540a3b411cc7ba21d4d643c9d21b --- /dev/null +++ b/apps/gradio/DiffSynth_Studio.py @@ -0,0 +1,252 @@ +import gradio as gr +from diffsynth import ModelManager, SDImagePipeline, SDXLImagePipeline, SD3ImagePipeline, HunyuanDiTImagePipeline, FluxImagePipeline +import os, torch +from PIL import Image +import numpy as np + + +config = { + "model_config": { + "Stable Diffusion": { + "model_folder": "models/stable_diffusion", + "pipeline_class": SDImagePipeline, + "default_parameters": { + "cfg_scale": 7.0, + "height": 512, + "width": 512, + } + }, + "Stable Diffusion XL": { + "model_folder": "models/stable_diffusion_xl", + "pipeline_class": SDXLImagePipeline, + "default_parameters": { + "cfg_scale": 7.0, + } + }, + "Stable Diffusion 3": { + "model_folder": "models/stable_diffusion_3", + "pipeline_class": SD3ImagePipeline, + "default_parameters": { + "cfg_scale": 7.0, + } + }, + "Stable Diffusion XL Turbo": { + "model_folder": "models/stable_diffusion_xl_turbo", + "pipeline_class": SDXLImagePipeline, + "default_parameters": { + "negative_prompt": "", + "cfg_scale": 1.0, + "num_inference_steps": 1, + "height": 512, + "width": 512, + } + }, + "Kolors": { + "model_folder": "models/kolors", + "pipeline_class": SDXLImagePipeline, + "default_parameters": { + "cfg_scale": 7.0, + } + }, + "HunyuanDiT": { + "model_folder": "models/HunyuanDiT", + "pipeline_class": HunyuanDiTImagePipeline, + "default_parameters": { + "cfg_scale": 7.0, + } + }, + "FLUX": { + "model_folder": "models/FLUX", + "pipeline_class": FluxImagePipeline, + "default_parameters": { + "cfg_scale": 1.0, + } + } + }, + "max_num_painter_layers": 8, + "max_num_model_cache": 1, +} + + +def load_model_list(model_type): + if model_type is None: + return [] + folder = config["model_config"][model_type]["model_folder"] + file_list = [i for i in os.listdir(folder) if i.endswith(".safetensors")] + if model_type in ["HunyuanDiT", "Kolors", "FLUX"]: + file_list += [i for i in os.listdir(folder) if os.path.isdir(os.path.join(folder, i))] + file_list = sorted(file_list) + return file_list + + +def load_model(model_type, model_path): + global model_dict + model_key = f"{model_type}:{model_path}" + if model_key in model_dict: + return model_dict[model_key] + model_path = os.path.join(config["model_config"][model_type]["model_folder"], model_path) + model_manager = ModelManager() + if model_type == "HunyuanDiT": + model_manager.load_models([ + os.path.join(model_path, "clip_text_encoder/pytorch_model.bin"), + os.path.join(model_path, "mt5/pytorch_model.bin"), + os.path.join(model_path, "model/pytorch_model_ema.pt"), + os.path.join(model_path, "sdxl-vae-fp16-fix/diffusion_pytorch_model.bin"), + ]) + elif model_type == "Kolors": + model_manager.load_models([ + os.path.join(model_path, "text_encoder"), + os.path.join(model_path, "unet/diffusion_pytorch_model.safetensors"), + os.path.join(model_path, "vae/diffusion_pytorch_model.safetensors"), + ]) + elif model_type == "FLUX": + model_manager.torch_dtype = torch.bfloat16 + file_list = [ + os.path.join(model_path, "text_encoder/model.safetensors"), + os.path.join(model_path, "text_encoder_2"), + ] + for file_name in os.listdir(model_path): + if file_name.endswith(".safetensors"): + file_list.append(os.path.join(model_path, file_name)) + model_manager.load_models(file_list) + else: + model_manager.load_model(model_path) + pipe = config["model_config"][model_type]["pipeline_class"].from_model_manager(model_manager) + while len(model_dict) + 1 > config["max_num_model_cache"]: + key = next(iter(model_dict.keys())) + model_manager_to_release, _ = model_dict[key] + model_manager_to_release.to("cpu") + del model_dict[key] + torch.cuda.empty_cache() + model_dict[model_key] = model_manager, pipe + return model_manager, pipe + + +model_dict = {} + +with gr.Blocks() as app: + gr.Markdown("# DiffSynth-Studio Painter") + with gr.Row(): + with gr.Column(scale=382, min_width=100): + + with gr.Accordion(label="Model"): + model_type = gr.Dropdown(choices=[i for i in config["model_config"]], label="Model type") + model_path = gr.Dropdown(choices=[], interactive=True, label="Model path") + + @gr.on(inputs=model_type, outputs=model_path, triggers=model_type.change) + def model_type_to_model_path(model_type): + return gr.Dropdown(choices=load_model_list(model_type)) + + with gr.Accordion(label="Prompt"): + prompt = gr.Textbox(label="Prompt", lines=3) + negative_prompt = gr.Textbox(label="Negative prompt", lines=1) + cfg_scale = gr.Slider(minimum=1.0, maximum=10.0, value=7.0, step=0.1, interactive=True, label="Classifier-free guidance scale") + embedded_guidance = gr.Slider(minimum=0.0, maximum=10.0, value=0.0, step=0.1, interactive=True, label="Embedded guidance scale (only for FLUX)") + + with gr.Accordion(label="Image"): + num_inference_steps = gr.Slider(minimum=1, maximum=100, value=20, step=1, interactive=True, label="Inference steps") + height = gr.Slider(minimum=64, maximum=2048, value=1024, step=64, interactive=True, label="Height") + width = gr.Slider(minimum=64, maximum=2048, value=1024, step=64, interactive=True, label="Width") + with gr.Column(): + use_fixed_seed = gr.Checkbox(value=True, interactive=False, label="Use fixed seed") + seed = gr.Number(minimum=0, maximum=10**9, value=0, interactive=True, label="Random seed", show_label=False) + + @gr.on( + inputs=[model_type, model_path, prompt, negative_prompt, cfg_scale, embedded_guidance, num_inference_steps, height, width], + outputs=[prompt, negative_prompt, cfg_scale, embedded_guidance, num_inference_steps, height, width], + triggers=model_path.change + ) + def model_path_to_default_params(model_type, model_path, prompt, negative_prompt, cfg_scale, embedded_guidance, num_inference_steps, height, width): + load_model(model_type, model_path) + cfg_scale = config["model_config"][model_type]["default_parameters"].get("cfg_scale", cfg_scale) + embedded_guidance = config["model_config"][model_type]["default_parameters"].get("embedded_guidance", embedded_guidance) + num_inference_steps = config["model_config"][model_type]["default_parameters"].get("num_inference_steps", num_inference_steps) + height = config["model_config"][model_type]["default_parameters"].get("height", height) + width = config["model_config"][model_type]["default_parameters"].get("width", width) + return prompt, negative_prompt, cfg_scale, embedded_guidance, num_inference_steps, height, width + + + with gr.Column(scale=618, min_width=100): + with gr.Accordion(label="Painter"): + enable_local_prompt_list = [] + local_prompt_list = [] + mask_scale_list = [] + canvas_list = [] + for painter_layer_id in range(config["max_num_painter_layers"]): + with gr.Tab(label=f"Layer {painter_layer_id}"): + enable_local_prompt = gr.Checkbox(label="Enable", value=False, key=f"enable_local_prompt_{painter_layer_id}") + local_prompt = gr.Textbox(label="Local prompt", key=f"local_prompt_{painter_layer_id}") + mask_scale = gr.Slider(minimum=0.0, maximum=5.0, value=1.0, step=0.1, interactive=True, label="Mask scale", key=f"mask_scale_{painter_layer_id}") + canvas = gr.ImageEditor(canvas_size=(512, 1), sources=None, layers=False, interactive=True, image_mode="RGBA", + brush=gr.Brush(default_size=100, default_color="#000000", colors=["#000000"]), + label="Painter", key=f"canvas_{painter_layer_id}") + @gr.on(inputs=[height, width, canvas], outputs=canvas, triggers=[height.change, width.change, canvas.clear, enable_local_prompt.change], show_progress="hidden") + def resize_canvas(height, width, canvas): + h, w = canvas["background"].shape[:2] + if h != height or width != w: + return np.ones((height, width, 3), dtype=np.uint8) * 255 + else: + return canvas + + enable_local_prompt_list.append(enable_local_prompt) + local_prompt_list.append(local_prompt) + mask_scale_list.append(mask_scale) + canvas_list.append(canvas) + with gr.Accordion(label="Results"): + run_button = gr.Button(value="Generate", variant="primary") + output_image = gr.Image(sources=None, show_label=False, interactive=False, type="pil") + with gr.Row(): + with gr.Column(): + output_to_painter_button = gr.Button(value="Set as painter's background") + with gr.Column(): + output_to_input_button = gr.Button(value="Set as input image") + painter_background = gr.State(None) + input_background = gr.State(None) + @gr.on( + inputs=[model_type, model_path, prompt, negative_prompt, cfg_scale, embedded_guidance, num_inference_steps, height, width, seed] + enable_local_prompt_list + local_prompt_list + mask_scale_list + canvas_list, + outputs=[output_image], + triggers=run_button.click + ) + def generate_image(model_type, model_path, prompt, negative_prompt, cfg_scale, embedded_guidance, num_inference_steps, height, width, seed, *args, progress=gr.Progress()): + _, pipe = load_model(model_type, model_path) + input_params = { + "prompt": prompt, + "negative_prompt": negative_prompt, + "cfg_scale": cfg_scale, + "num_inference_steps": num_inference_steps, + "height": height, + "width": width, + "progress_bar_cmd": progress.tqdm, + } + if isinstance(pipe, FluxImagePipeline): + input_params["embedded_guidance"] = embedded_guidance + enable_local_prompt_list, local_prompt_list, mask_scale_list, canvas_list = ( + args[0 * config["max_num_painter_layers"]: 1 * config["max_num_painter_layers"]], + args[1 * config["max_num_painter_layers"]: 2 * config["max_num_painter_layers"]], + args[2 * config["max_num_painter_layers"]: 3 * config["max_num_painter_layers"]], + args[3 * config["max_num_painter_layers"]: 4 * config["max_num_painter_layers"]] + ) + local_prompts, masks, mask_scales = [], [], [] + for enable_local_prompt, local_prompt, mask_scale, canvas in zip( + enable_local_prompt_list, local_prompt_list, mask_scale_list, canvas_list + ): + if enable_local_prompt: + local_prompts.append(local_prompt) + masks.append(Image.fromarray(canvas["layers"][0][:, :, -1]).convert("RGB")) + mask_scales.append(mask_scale) + input_params.update({ + "local_prompts": local_prompts, + "masks": masks, + "mask_scales": mask_scales, + }) + torch.manual_seed(seed) + image = pipe(**input_params) + return image + + @gr.on(inputs=[output_image] + canvas_list, outputs=canvas_list, triggers=output_to_painter_button.click) + def send_output_to_painter_background(output_image, *canvas_list): + for canvas in canvas_list: + h, w = canvas["background"].shape[:2] + canvas["background"] = output_image.resize((w, h)) + return tuple(canvas_list) +app.launch() diff --git a/apps/gradio/entity_level_control.py b/apps/gradio/entity_level_control.py new file mode 100644 index 0000000000000000000000000000000000000000..58f4722d5a1b36b1746610bfb231727fc8b0a986 --- /dev/null +++ b/apps/gradio/entity_level_control.py @@ -0,0 +1,390 @@ +import os +import torch +import numpy as np +from PIL import Image, ImageDraw, ImageFont +import random +import json +import gradio as gr +from diffsynth import ModelManager, FluxImagePipeline, download_customized_models +from modelscope import dataset_snapshot_download + + +dataset_snapshot_download(dataset_id="DiffSynth-Studio/examples_in_diffsynth", local_dir="./", allow_file_pattern=f"data/examples/eligen/entity_control/*") +example_json = 'data/examples/eligen/entity_control/ui_examples.json' +with open(example_json, 'r') as f: + examples = json.load(f)['examples'] + +for idx in range(len(examples)): + example_id = examples[idx]['example_id'] + entity_prompts = examples[idx]['local_prompt_list'] + examples[idx]['mask_lists'] = [Image.open(f"data/examples/eligen/entity_control/example_{example_id}/{i}.png").convert('RGB') for i in range(len(entity_prompts))] + +def create_canvas_data(background, masks): + if background.shape[-1] == 3: + background = np.dstack([background, np.full(background.shape[:2], 255, dtype=np.uint8)]) + layers = [] + for mask in masks: + if mask is not None: + mask_single_channel = mask if mask.ndim == 2 else mask[..., 0] + layer = np.zeros((mask_single_channel.shape[0], mask_single_channel.shape[1], 4), dtype=np.uint8) + layer[..., -1] = mask_single_channel + layers.append(layer) + else: + layers.append(np.zeros_like(background)) + + composite = background.copy() + for layer in layers: + if layer.size > 0: + composite = np.where(layer[..., -1:] > 0, layer, composite) + return { + "background": background, + "layers": layers, + "composite": composite, + } + +def load_example(load_example_button): + example_idx = int(load_example_button.split()[-1]) - 1 + example = examples[example_idx] + result = [ + 50, + example["global_prompt"], + example["negative_prompt"], + example["seed"], + *example["local_prompt_list"], + ] + num_entities = len(example["local_prompt_list"]) + result += [""] * (config["max_num_painter_layers"] - num_entities) + masks = [] + for mask in example["mask_lists"]: + mask_single_channel = np.array(mask.convert("L")) + masks.append(mask_single_channel) + for _ in range(config["max_num_painter_layers"] - len(masks)): + blank_mask = np.zeros_like(masks[0]) if masks else np.zeros((512, 512), dtype=np.uint8) + masks.append(blank_mask) + background = np.ones((masks[0].shape[0], masks[0].shape[1], 4), dtype=np.uint8) * 255 + canvas_data_list = [] + for mask in masks: + canvas_data = create_canvas_data(background, [mask]) + canvas_data_list.append(canvas_data) + result.extend(canvas_data_list) + return result + +def save_mask_prompts(masks, mask_prompts, global_prompt, seed=0, random_dir='0000000'): + save_dir = os.path.join('workdirs/tmp_mask', random_dir) + print(f'save to {save_dir}') + os.makedirs(save_dir, exist_ok=True) + for i, mask in enumerate(masks): + save_path = os.path.join(save_dir, f'{i}.png') + mask.save(save_path) + sample = { + "global_prompt": global_prompt, + "mask_prompts": mask_prompts, + "seed": seed, + } + with open(os.path.join(save_dir, f"prompts.json"), 'w') as f: + json.dump(sample, f, indent=4) + +def visualize_masks(image, masks, mask_prompts, font_size=35, use_random_colors=False): + # Create a blank image for overlays + overlay = Image.new('RGBA', image.size, (0, 0, 0, 0)) + colors = [ + (165, 238, 173, 80), + (76, 102, 221, 80), + (221, 160, 77, 80), + (204, 93, 71, 80), + (145, 187, 149, 80), + (134, 141, 172, 80), + (157, 137, 109, 80), + (153, 104, 95, 80), + (165, 238, 173, 80), + (76, 102, 221, 80), + (221, 160, 77, 80), + (204, 93, 71, 80), + (145, 187, 149, 80), + (134, 141, 172, 80), + (157, 137, 109, 80), + (153, 104, 95, 80), + ] + # Generate random colors for each mask + if use_random_colors: + colors = [(random.randint(0, 255), random.randint(0, 255), random.randint(0, 255), 80) for _ in range(len(masks))] + # Font settings + try: + font = ImageFont.truetype("arial", font_size) # Adjust as needed + except IOError: + font = ImageFont.load_default(font_size) + # Overlay each mask onto the overlay image + for mask, mask_prompt, color in zip(masks, mask_prompts, colors): + if mask is None: + continue + # Convert mask to RGBA mode + mask_rgba = mask.convert('RGBA') + mask_data = mask_rgba.getdata() + new_data = [(color if item[:3] == (255, 255, 255) else (0, 0, 0, 0)) for item in mask_data] + mask_rgba.putdata(new_data) + # Draw the mask prompt text on the mask + draw = ImageDraw.Draw(mask_rgba) + mask_bbox = mask.getbbox() # Get the bounding box of the mask + if mask_bbox is None: + continue + text_position = (mask_bbox[0] + 10, mask_bbox[1] + 10) # Adjust text position based on mask position + draw.text(text_position, mask_prompt, fill=(255, 255, 255, 255), font=font) + # Alpha composite the overlay with this mask + overlay = Image.alpha_composite(overlay, mask_rgba) + # Composite the overlay onto the original image + result = Image.alpha_composite(image.convert('RGBA'), overlay) + return result + +config = { + "model_config": { + "FLUX": { + "model_folder": "models/FLUX", + "pipeline_class": FluxImagePipeline, + "default_parameters": { + "cfg_scale": 3.0, + "embedded_guidance": 3.5, + "num_inference_steps": 30, + } + }, + }, + "max_num_painter_layers": 8, + "max_num_model_cache": 1, +} + +model_dict = {} + +def load_model(model_type='FLUX', model_path='FLUX.1-dev'): + global model_dict + model_key = f"{model_type}:{model_path}" + if model_key in model_dict: + return model_dict[model_key] + model_path = os.path.join(config["model_config"][model_type]["model_folder"], model_path) + model_manager = ModelManager(torch_dtype=torch.bfloat16, device="cuda", model_id_list=["FLUX.1-dev"]) + model_manager.load_lora( + download_customized_models( + model_id="DiffSynth-Studio/Eligen", + origin_file_path="model_bf16.safetensors", + local_dir="models/lora/entity_control", + ), + lora_alpha=1, + ) + pipe = config["model_config"][model_type]["pipeline_class"].from_model_manager(model_manager) + model_dict[model_key] = model_manager, pipe + return model_manager, pipe + + +with gr.Blocks() as app: + gr.Markdown( + """## EliGen: Entity-Level Controllable Text-to-Image Model + 1. On the left, input the **global prompt** for the overall image, such as "a person stands by the river." + 2. On the right, input the **local prompt** for each entity, such as "person," and draw the corresponding mask in the **Entity Mask Painter**. Generally, solid rectangular masks yield better results. + 3. Click the **Generate** button to create the image. By selecting different **random seeds**, you can generate diverse images. + 4. **You can directly click the "Load Example" button on any sample at the bottom to load example inputs.** + """ + ) + + loading_status = gr.Textbox(label="Loading Model...", value="Loading model... Please wait...", visible=True) + main_interface = gr.Column(visible=False) + + def initialize_model(): + try: + load_model() + return { + loading_status: gr.update(value="Model loaded successfully!", visible=False), + main_interface: gr.update(visible=True), + } + except Exception as e: + print(f'Failed to load model with error: {e}') + return { + loading_status: gr.update(value=f"Failed to load model: {str(e)}", visible=True), + main_interface: gr.update(visible=True), + } + + app.load(initialize_model, inputs=None, outputs=[loading_status, main_interface]) + + with main_interface: + with gr.Row(): + local_prompt_list = [] + canvas_list = [] + random_mask_dir = gr.State(f'{random.randint(0, 1000000):08d}') + with gr.Column(scale=382, min_width=100): + model_type = gr.State('FLUX') + model_path = gr.State('FLUX.1-dev') + with gr.Accordion(label="Global prompt"): + prompt = gr.Textbox(label="Global Prompt", lines=3) + negative_prompt = gr.Textbox(label="Negative prompt", value="worst quality, low quality, monochrome, zombie, interlocked fingers, Aissist, cleavage, nsfw, blur,", lines=3) + with gr.Accordion(label="Inference Options", open=True): + seed = gr.Number(minimum=0, maximum=10**9, value=42, interactive=True, label="Random seed", show_label=True) + num_inference_steps = gr.Slider(minimum=1, maximum=100, value=30, step=1, interactive=True, label="Inference steps") + cfg_scale = gr.Slider(minimum=2.0, maximum=10.0, value=3.0, step=0.1, interactive=True, label="Classifier-free guidance scale") + embedded_guidance = gr.Slider(minimum=0.0, maximum=10.0, value=3.5, step=0.1, interactive=True, label="Embedded guidance scale") + height = gr.Slider(minimum=64, maximum=2048, value=1024, step=64, interactive=True, label="Height") + width = gr.Slider(minimum=64, maximum=2048, value=1024, step=64, interactive=True, label="Width") + with gr.Accordion(label="Inpaint Input Image", open=False): + input_image = gr.Image(sources=None, show_label=False, interactive=True, type="pil") + background_weight = gr.Slider(minimum=0.0, maximum=1000., value=0., step=1, interactive=False, label="background_weight", visible=False) + + with gr.Column(): + reset_input_button = gr.Button(value="Reset Inpaint Input") + send_input_to_painter = gr.Button(value="Set as painter's background") + @gr.on(inputs=[input_image], outputs=[input_image], triggers=reset_input_button.click) + def reset_input_image(input_image): + return None + + with gr.Column(scale=618, min_width=100): + with gr.Accordion(label="Entity Painter"): + for painter_layer_id in range(config["max_num_painter_layers"]): + with gr.Tab(label=f"Entity {painter_layer_id}"): + local_prompt = gr.Textbox(label="Local prompt", key=f"local_prompt_{painter_layer_id}") + canvas = gr.ImageEditor( + canvas_size=(512, 512), + sources=None, + layers=False, + interactive=True, + image_mode="RGBA", + brush=gr.Brush( + default_size=50, + default_color="#000000", + colors=["#000000"], + ), + label="Entity Mask Painter", + key=f"canvas_{painter_layer_id}", + width=width, + height=height, + ) + @gr.on(inputs=[height, width, canvas], outputs=canvas, triggers=[height.change, width.change, canvas.clear], show_progress="hidden") + def resize_canvas(height, width, canvas): + h, w = canvas["background"].shape[:2] + if h != height or width != w: + return np.ones((height, width, 3), dtype=np.uint8) * 255 + else: + return canvas + local_prompt_list.append(local_prompt) + canvas_list.append(canvas) + with gr.Accordion(label="Results"): + run_button = gr.Button(value="Generate", variant="primary") + output_image = gr.Image(sources=None, show_label=False, interactive=False, type="pil") + with gr.Row(): + with gr.Column(): + output_to_painter_button = gr.Button(value="Set as painter's background") + with gr.Column(): + return_with_mask = gr.Checkbox(value=False, interactive=True, label="show result with mask painting") + output_to_input_button = gr.Button(value="Set as input image", visible=False, interactive=False) + real_output = gr.State(None) + mask_out = gr.State(None) + + @gr.on( + inputs=[model_type, model_path, prompt, negative_prompt, cfg_scale, embedded_guidance, num_inference_steps, height, width, return_with_mask, seed, input_image, background_weight, random_mask_dir] + local_prompt_list + canvas_list, + outputs=[output_image, real_output, mask_out], + triggers=run_button.click + ) + def generate_image(model_type, model_path, prompt, negative_prompt, cfg_scale, embedded_guidance, num_inference_steps, height, width, return_with_mask, seed, input_image, background_weight, random_mask_dir, *args, progress=gr.Progress()): + _, pipe = load_model(model_type, model_path) + input_params = { + "prompt": prompt, + "negative_prompt": negative_prompt, + "cfg_scale": cfg_scale, + "num_inference_steps": num_inference_steps, + "height": height, + "width": width, + "progress_bar_cmd": progress.tqdm, + } + if isinstance(pipe, FluxImagePipeline): + input_params["embedded_guidance"] = embedded_guidance + if input_image is not None: + input_params["input_image"] = input_image.resize((width, height)).convert("RGB") + input_params["enable_eligen_inpaint"] = True + + local_prompt_list, canvas_list = ( + args[0 * config["max_num_painter_layers"]: 1 * config["max_num_painter_layers"]], + args[1 * config["max_num_painter_layers"]: 2 * config["max_num_painter_layers"]], + ) + local_prompts, masks = [], [] + for local_prompt, canvas in zip(local_prompt_list, canvas_list): + if isinstance(local_prompt, str) and len(local_prompt) > 0: + local_prompts.append(local_prompt) + masks.append(Image.fromarray(canvas["layers"][0][:, :, -1]).convert("RGB")) + entity_masks = None if len(masks) == 0 else masks + entity_prompts = None if len(local_prompts) == 0 else local_prompts + input_params.update({ + "eligen_entity_prompts": entity_prompts, + "eligen_entity_masks": entity_masks, + }) + torch.manual_seed(seed) + # save_mask_prompts(masks, local_prompts, prompt, seed, random_mask_dir) + image = pipe(**input_params) + masks = [mask.resize(image.size) for mask in masks] + image_with_mask = visualize_masks(image, masks, local_prompts) + + real_output = gr.State(image) + mask_out = gr.State(image_with_mask) + + if return_with_mask: + return image_with_mask, real_output, mask_out + return image, real_output, mask_out + + @gr.on(inputs=[input_image] + canvas_list, outputs=canvas_list, triggers=send_input_to_painter.click) + def send_input_to_painter_background(input_image, *canvas_list): + if input_image is None: + return tuple(canvas_list) + for canvas in canvas_list: + h, w = canvas["background"].shape[:2] + canvas["background"] = input_image.resize((w, h)) + return tuple(canvas_list) + @gr.on(inputs=[real_output] + canvas_list, outputs=canvas_list, triggers=output_to_painter_button.click) + def send_output_to_painter_background(real_output, *canvas_list): + if real_output is None: + return tuple(canvas_list) + for canvas in canvas_list: + h, w = canvas["background"].shape[:2] + canvas["background"] = real_output.value.resize((w, h)) + return tuple(canvas_list) + @gr.on(inputs=[return_with_mask, real_output, mask_out], outputs=[output_image], triggers=[return_with_mask.change], show_progress="hidden") + def show_output(return_with_mask, real_output, mask_out): + if return_with_mask: + return mask_out.value + else: + return real_output.value + @gr.on(inputs=[real_output], outputs=[input_image], triggers=output_to_input_button.click) + def send_output_to_pipe_input(real_output): + return real_output.value + + with gr.Column(): + gr.Markdown("## Examples") + for i in range(0, len(examples), 2): + with gr.Row(): + if i < len(examples): + example = examples[i] + with gr.Column(): + example_image = gr.Image( + value=f"data/examples/eligen/entity_control/example_{example['example_id']}/example_image.png", + label=example["description"], + interactive=False, + width=1024, + height=512 + ) + load_example_button = gr.Button(value=f"Load Example {example['example_id']}") + load_example_button.click( + load_example, + inputs=[load_example_button], + outputs=[num_inference_steps, prompt, negative_prompt, seed] + local_prompt_list + canvas_list + ) + + if i + 1 < len(examples): + example = examples[i + 1] + with gr.Column(): + example_image = gr.Image( + value=f"data/examples/eligen/entity_control/example_{example['example_id']}/example_image.png", + label=example["description"], + interactive=False, + width=1024, + height=512 + ) + load_example_button = gr.Button(value=f"Load Example {example['example_id']}") + load_example_button.click( + load_example, + inputs=[load_example_button], + outputs=[num_inference_steps, prompt, negative_prompt, seed] + local_prompt_list + canvas_list + ) +app.config["show_progress"] = "hidden" +app.launch() diff --git a/apps/gradio/qwen_image_eligen.py b/apps/gradio/qwen_image_eligen.py new file mode 100644 index 0000000000000000000000000000000000000000..c224f01cad5e7fd41bb8102728a899a8ecb3b90f --- /dev/null +++ b/apps/gradio/qwen_image_eligen.py @@ -0,0 +1,382 @@ +import os +import torch +import numpy as np +from PIL import Image, ImageDraw, ImageFont +import random +import json +import gradio as gr +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig +from modelscope import dataset_snapshot_download, snapshot_download + +# pip install pydantic==2.10.6 +# pip install gradio==5.4.0 + +snapshot_download("DiffSynth-Studio/Qwen-Image-EliGen", local_dir="models/DiffSynth-Studio/Qwen-Image-EliGen", allow_file_pattern="model.safetensors") + +dataset_snapshot_download(dataset_id="DiffSynth-Studio/examples_in_diffsynth", local_dir="./", allow_file_pattern=f"data/examples/eligen/qwen-image/*") +example_json = 'data/examples/eligen/qwen-image/ui_examples.json' +with open(example_json, 'r') as f: + examples = json.load(f)['examples'] + +for idx in range(len(examples)): + example_id = examples[idx]['example_id'] + entity_prompts = examples[idx]['local_prompt_list'] + examples[idx]['mask_lists'] = [Image.open(f"data/examples/eligen/qwen-image/example_{example_id}/{i}.png").convert('RGB') for i in range(len(entity_prompts))] + +def create_canvas_data(background, masks): + if background.shape[-1] == 3: + background = np.dstack([background, np.full(background.shape[:2], 255, dtype=np.uint8)]) + layers = [] + for mask in masks: + if mask is not None: + mask_single_channel = mask if mask.ndim == 2 else mask[..., 0] + layer = np.zeros((mask_single_channel.shape[0], mask_single_channel.shape[1], 4), dtype=np.uint8) + layer[..., -1] = mask_single_channel + layers.append(layer) + else: + layers.append(np.zeros_like(background)) + + composite = background.copy() + for layer in layers: + if layer.size > 0: + composite = np.where(layer[..., -1:] > 0, layer, composite) + return { + "background": background, + "layers": layers, + "composite": composite, + } + +def load_example(load_example_button): + example_idx = int(load_example_button.split()[-1]) - 1 + example = examples[example_idx] + result = [ + 50, + example["global_prompt"], + example["negative_prompt"], + example["seed"], + *example["local_prompt_list"], + ] + num_entities = len(example["local_prompt_list"]) + result += [""] * (config["max_num_painter_layers"] - num_entities) + masks = [] + for mask in example["mask_lists"]: + mask_single_channel = np.array(mask.convert("L")) + masks.append(mask_single_channel) + for _ in range(config["max_num_painter_layers"] - len(masks)): + blank_mask = np.zeros_like(masks[0]) if masks else np.zeros((512, 512), dtype=np.uint8) + masks.append(blank_mask) + background = np.ones((masks[0].shape[0], masks[0].shape[1], 4), dtype=np.uint8) * 255 + canvas_data_list = [] + for mask in masks: + canvas_data = create_canvas_data(background, [mask]) + canvas_data_list.append(canvas_data) + result.extend(canvas_data_list) + return result + +def save_mask_prompts(masks, mask_prompts, global_prompt, seed=0, random_dir='0000000'): + save_dir = os.path.join('workdirs/tmp_mask', random_dir) + print(f'save to {save_dir}') + os.makedirs(save_dir, exist_ok=True) + for i, mask in enumerate(masks): + save_path = os.path.join(save_dir, f'{i}.png') + mask.save(save_path) + sample = { + "global_prompt": global_prompt, + "mask_prompts": mask_prompts, + "seed": seed, + } + with open(os.path.join(save_dir, f"prompts.json"), 'w', encoding='utf-8') as f: + json.dump(sample, f, ensure_ascii=False, indent=4) + +def visualize_masks(image, masks, mask_prompts, font_size=35, use_random_colors=False): + # Create a blank image for overlays + overlay = Image.new('RGBA', image.size, (0, 0, 0, 0)) + colors = [ + (165, 238, 173, 80), + (76, 102, 221, 80), + (221, 160, 77, 80), + (204, 93, 71, 80), + (145, 187, 149, 80), + (134, 141, 172, 80), + (157, 137, 109, 80), + (153, 104, 95, 80), + (165, 238, 173, 80), + (76, 102, 221, 80), + (221, 160, 77, 80), + (204, 93, 71, 80), + (145, 187, 149, 80), + (134, 141, 172, 80), + (157, 137, 109, 80), + (153, 104, 95, 80), + ] + # Generate random colors for each mask + if use_random_colors: + colors = [(random.randint(0, 255), random.randint(0, 255), random.randint(0, 255), 80) for _ in range(len(masks))] + # Font settings + try: + font = ImageFont.truetype("wqy-zenhei.ttc", font_size) # Adjust as needed + except IOError: + font = ImageFont.load_default(font_size) + # Overlay each mask onto the overlay image + for mask, mask_prompt, color in zip(masks, mask_prompts, colors): + if mask is None: + continue + # Convert mask to RGBA mode + mask_rgba = mask.convert('RGBA') + mask_data = mask_rgba.getdata() + new_data = [(color if item[:3] == (255, 255, 255) else (0, 0, 0, 0)) for item in mask_data] + mask_rgba.putdata(new_data) + # Draw the mask prompt text on the mask + draw = ImageDraw.Draw(mask_rgba) + mask_bbox = mask.getbbox() # Get the bounding box of the mask + if mask_bbox is None: + continue + text_position = (mask_bbox[0] + 10, mask_bbox[1] + 10) # Adjust text position based on mask position + draw.text(text_position, mask_prompt, fill=(255, 255, 255, 255), font=font) + # Alpha composite the overlay with this mask + overlay = Image.alpha_composite(overlay, mask_rgba) + # Composite the overlay onto the original image + result = Image.alpha_composite(image.convert('RGBA'), overlay) + return result + +config = { + "max_num_painter_layers": 8, + "max_num_model_cache": 1, +} + +model_dict = {} + +def load_model(model_type='qwen-image'): + global model_dict + model_key = f"{model_type}" + if model_key in model_dict: + return model_dict[model_key] + pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), + ) + pipe.load_lora(pipe.dit, "models/DiffSynth-Studio/Qwen-Image-EliGen/model.safetensors") + model_dict[model_key] = pipe + return pipe + +load_model('qwen-image') + +with gr.Blocks() as app: + gr.Markdown( + """## EliGen: Entity-Level Controllable Text-to-Image Model + 1. On the left, input the **global prompt** for the overall image, such as "a person stands by the river." + 2. On the right, input the **local prompt** for each entity, such as "person," and draw the corresponding mask in the **Entity Mask Painter**. Generally, solid rectangular masks yield better results. + 3. Click the **Generate** button to create the image. By selecting different **random seeds**, you can generate diverse images. + 4. **You can directly click the "Load Example" button on any sample at the bottom to load example inputs.** + """ + ) + + loading_status = gr.Textbox(label="Loading Model...", value="Loading model... Please wait...", visible=True) + main_interface = gr.Column(visible=False) + + def initialize_model(): + try: + load_model('qwen-image') + return { + loading_status: gr.update(value="Model loaded successfully!", visible=False), + main_interface: gr.update(visible=True), + } + except Exception as e: + print(f'Failed to load model with error: {e}') + return { + loading_status: gr.update(value=f"Failed to load model: {str(e)}", visible=True), + main_interface: gr.update(visible=True), + } + + app.load(initialize_model, inputs=None, outputs=[loading_status, main_interface]) + + with main_interface: + with gr.Row(): + local_prompt_list = [] + canvas_list = [] + random_mask_dir = gr.State(f'{random.randint(0, 1000000):08d}') + with gr.Column(scale=382, min_width=100): + model_type = gr.State('qwen-image') + with gr.Accordion(label="Global prompt"): + prompt = gr.Textbox(label="Global Prompt", lines=3) + negative_prompt = gr.Textbox(label="Negative prompt", value="", lines=3) + with gr.Accordion(label="Inference Options", open=True): + seed = gr.Number(minimum=0, maximum=10**9, value=42, interactive=True, label="Random seed", show_label=True) + num_inference_steps = gr.Slider(minimum=1, maximum=100, value=30, step=1, interactive=True, label="Inference steps") + cfg_scale = gr.Slider(minimum=2.0, maximum=10.0, value=4.0, step=0.1, interactive=True, label="Classifier-free guidance scale") + height = gr.Slider(minimum=64, maximum=2048, value=1024, step=64, interactive=True, label="Height") + width = gr.Slider(minimum=64, maximum=2048, value=1024, step=64, interactive=True, label="Width") + with gr.Accordion(label="Inpaint Input Image", open=False, visible=False): + input_image = gr.Image(sources=None, show_label=False, interactive=True, type="pil") + background_weight = gr.Slider(minimum=0.0, maximum=1000., value=0., step=1, interactive=False, label="background_weight", visible=False) + + with gr.Column(): + reset_input_button = gr.Button(value="Reset Inpaint Input") + send_input_to_painter = gr.Button(value="Set as painter's background") + @gr.on(inputs=[input_image], outputs=[input_image], triggers=reset_input_button.click) + def reset_input_image(input_image): + return None + + with gr.Column(scale=618, min_width=100): + with gr.Accordion(label="Entity Painter"): + for painter_layer_id in range(config["max_num_painter_layers"]): + with gr.Tab(label=f"Entity {painter_layer_id}"): + local_prompt = gr.Textbox(label="Local prompt", key=f"local_prompt_{painter_layer_id}") + canvas = gr.ImageEditor( + canvas_size=(1024, 1024), + sources=None, + layers=False, + interactive=True, + image_mode="RGBA", + brush=gr.Brush( + default_size=50, + default_color="#000000", + colors=["#000000"], + ), + label="Entity Mask Painter", + key=f"canvas_{painter_layer_id}", + width=width, + height=height, + ) + @gr.on(inputs=[height, width, canvas], outputs=canvas, triggers=[height.change, width.change, canvas.clear], show_progress="hidden") + def resize_canvas(height, width, canvas): + if canvas is None or canvas["background"] is None: + return np.ones((height, width, 3), dtype=np.uint8) * 255 + h, w = canvas["background"].shape[:2] + if h != height or width != w: + return np.ones((height, width, 3), dtype=np.uint8) * 255 + else: + return canvas + local_prompt_list.append(local_prompt) + canvas_list.append(canvas) + with gr.Accordion(label="Results"): + run_button = gr.Button(value="Generate", variant="primary") + output_image = gr.Image(sources=None, show_label=False, interactive=False, type="pil") + with gr.Row(): + with gr.Column(): + output_to_painter_button = gr.Button(value="Set as painter's background") + with gr.Column(): + return_with_mask = gr.Checkbox(value=False, interactive=True, label="show result with mask painting") + output_to_input_button = gr.Button(value="Set as input image", visible=False, interactive=False) + real_output = gr.State(None) + mask_out = gr.State(None) + + @gr.on( + inputs=[model_type, prompt, negative_prompt, cfg_scale, num_inference_steps, height, width, return_with_mask, seed, input_image, background_weight, random_mask_dir] + local_prompt_list + canvas_list, + outputs=[output_image, real_output, mask_out], + triggers=run_button.click + ) + def generate_image(model_type, prompt, negative_prompt, cfg_scale, num_inference_steps, height, width, return_with_mask, seed, input_image, background_weight, random_mask_dir, *args, progress=gr.Progress()): + pipe = load_model(model_type) + input_params = { + "prompt": prompt, + "negative_prompt": negative_prompt, + "cfg_scale": cfg_scale, + "num_inference_steps": num_inference_steps, + "height": height, + "width": width, + "progress_bar_cmd": progress.tqdm, + } + # if input_image is not None: + # input_params["input_image"] = input_image.resize((width, height)).convert("RGB") + # input_params["enable_eligen_inpaint"] = True + + local_prompt_list, canvas_list = ( + args[0 * config["max_num_painter_layers"]: 1 * config["max_num_painter_layers"]], + args[1 * config["max_num_painter_layers"]: 2 * config["max_num_painter_layers"]], + ) + local_prompts, masks = [], [] + for local_prompt, canvas in zip(local_prompt_list, canvas_list): + if isinstance(local_prompt, str) and len(local_prompt) > 0: + local_prompts.append(local_prompt) + masks.append(Image.fromarray(canvas["layers"][0][:, :, -1]).convert("RGB")) + entity_prompts = None if len(local_prompts) == 0 else local_prompts + entity_masks = None if len(masks) == 0 or entity_prompts is None else masks + input_params.update({ + "eligen_entity_prompts": entity_prompts, + "eligen_entity_masks": entity_masks, + }) + torch.manual_seed(seed) + save_mask_prompts(masks, local_prompts, prompt, seed, random_mask_dir) + image = pipe(**input_params) + masks = [mask.resize(image.size) for mask in masks] + image_with_mask = visualize_masks(image, masks, local_prompts) + + real_output = gr.State(image) + mask_out = gr.State(image_with_mask) + + if return_with_mask: + return image_with_mask, real_output, mask_out + return image, real_output, mask_out + + @gr.on(inputs=[input_image] + canvas_list, outputs=canvas_list, triggers=send_input_to_painter.click) + def send_input_to_painter_background(input_image, *canvas_list): + if input_image is None: + return tuple(canvas_list) + for canvas in canvas_list: + h, w = canvas["background"].shape[:2] + canvas["background"] = input_image.resize((w, h)) + return tuple(canvas_list) + @gr.on(inputs=[real_output] + canvas_list, outputs=canvas_list, triggers=output_to_painter_button.click) + def send_output_to_painter_background(real_output, *canvas_list): + if real_output is None: + return tuple(canvas_list) + for canvas in canvas_list: + h, w = canvas["background"].shape[:2] + canvas["background"] = real_output.value.resize((w, h)) + return tuple(canvas_list) + @gr.on(inputs=[return_with_mask, real_output, mask_out], outputs=[output_image], triggers=[return_with_mask.change], show_progress="hidden") + def show_output(return_with_mask, real_output, mask_out): + if return_with_mask: + return mask_out.value + else: + return real_output.value + @gr.on(inputs=[real_output], outputs=[input_image], triggers=output_to_input_button.click) + def send_output_to_pipe_input(real_output): + return real_output.value + + with gr.Column(): + gr.Markdown("## Examples") + for i in range(0, len(examples), 2): + with gr.Row(): + if i < len(examples): + example = examples[i] + with gr.Column(): + example_image = gr.Image( + value=f"data/examples/eligen/qwen-image/example_{example['example_id']}/example_image.png", + label=example["description"], + interactive=False, + width=1024, + height=512 + ) + load_example_button = gr.Button(value=f"Load Example {example['example_id']}") + load_example_button.click( + load_example, + inputs=[load_example_button], + outputs=[num_inference_steps, prompt, negative_prompt, seed] + local_prompt_list + canvas_list + ) + + if i + 1 < len(examples): + example = examples[i + 1] + with gr.Column(): + example_image = gr.Image( + value=f"data/examples/eligen/qwen-image/example_{example['example_id']}/example_image.png", + label=example["description"], + interactive=False, + width=1024, + height=512 + ) + load_example_button = gr.Button(value=f"Load Example {example['example_id']}") + load_example_button.click( + load_example, + inputs=[load_example_button], + outputs=[num_inference_steps, prompt, negative_prompt, seed] + local_prompt_list + canvas_list + ) +app.config["show_progress"] = "hidden" +app.launch(share=False) diff --git a/apps/streamlit/DiffSynth_Studio.py b/apps/streamlit/DiffSynth_Studio.py new file mode 100644 index 0000000000000000000000000000000000000000..cfd38565da745df53111a94b77d4354a2d475042 --- /dev/null +++ b/apps/streamlit/DiffSynth_Studio.py @@ -0,0 +1,15 @@ +# Set web page format +import streamlit as st +st.set_page_config(layout="wide") +# Disable virtual VRAM on windows system +import torch +torch.cuda.set_per_process_memory_fraction(0.999, 0) + + +st.markdown(""" +# DiffSynth Studio + +[Source Code](https://github.com/Artiprocher/DiffSynth-Studio) + +Welcome to DiffSynth Studio. +""") diff --git a/apps/streamlit/pages/1_Image_Creator.py b/apps/streamlit/pages/1_Image_Creator.py new file mode 100644 index 0000000000000000000000000000000000000000..732d2195ce45061739c8cbe46623a181f6a30c08 --- /dev/null +++ b/apps/streamlit/pages/1_Image_Creator.py @@ -0,0 +1,362 @@ +import torch, os, io, json, time +import numpy as np +from PIL import Image +import streamlit as st +st.set_page_config(layout="wide") +from streamlit_drawable_canvas import st_canvas +from diffsynth.models import ModelManager +from diffsynth.pipelines import SDImagePipeline, SDXLImagePipeline, SD3ImagePipeline, HunyuanDiTImagePipeline, FluxImagePipeline +from diffsynth.data.video import crop_and_resize + + +config = { + "Stable Diffusion": { + "model_folder": "models/stable_diffusion", + "pipeline_class": SDImagePipeline, + "fixed_parameters": {} + }, + "Stable Diffusion XL": { + "model_folder": "models/stable_diffusion_xl", + "pipeline_class": SDXLImagePipeline, + "fixed_parameters": {} + }, + "Stable Diffusion 3": { + "model_folder": "models/stable_diffusion_3", + "pipeline_class": SD3ImagePipeline, + "fixed_parameters": {} + }, + "Stable Diffusion XL Turbo": { + "model_folder": "models/stable_diffusion_xl_turbo", + "pipeline_class": SDXLImagePipeline, + "fixed_parameters": { + "negative_prompt": "", + "cfg_scale": 1.0, + "num_inference_steps": 1, + "height": 512, + "width": 512, + } + }, + "Kolors": { + "model_folder": "models/kolors", + "pipeline_class": SDXLImagePipeline, + "fixed_parameters": {} + }, + "HunyuanDiT": { + "model_folder": "models/HunyuanDiT", + "pipeline_class": HunyuanDiTImagePipeline, + "fixed_parameters": { + "height": 1024, + "width": 1024, + } + }, + "FLUX": { + "model_folder": "models/FLUX", + "pipeline_class": FluxImagePipeline, + "fixed_parameters": { + "cfg_scale": 1.0, + } + } +} + + +def load_model_list(model_type): + folder = config[model_type]["model_folder"] + file_list = [i for i in os.listdir(folder) if i.endswith(".safetensors")] + if model_type in ["HunyuanDiT", "Kolors", "FLUX"]: + file_list += [i for i in os.listdir(folder) if os.path.isdir(os.path.join(folder, i))] + file_list = sorted(file_list) + return file_list + + +def release_model(): + if "model_manager" in st.session_state: + st.session_state["model_manager"].to("cpu") + del st.session_state["loaded_model_path"] + del st.session_state["model_manager"] + del st.session_state["pipeline"] + torch.cuda.empty_cache() + + +def load_model(model_type, model_path): + model_manager = ModelManager() + if model_type == "HunyuanDiT": + model_manager.load_models([ + os.path.join(model_path, "clip_text_encoder/pytorch_model.bin"), + os.path.join(model_path, "mt5/pytorch_model.bin"), + os.path.join(model_path, "model/pytorch_model_ema.pt"), + os.path.join(model_path, "sdxl-vae-fp16-fix/diffusion_pytorch_model.bin"), + ]) + elif model_type == "Kolors": + model_manager.load_models([ + os.path.join(model_path, "text_encoder"), + os.path.join(model_path, "unet/diffusion_pytorch_model.safetensors"), + os.path.join(model_path, "vae/diffusion_pytorch_model.safetensors"), + ]) + elif model_type == "FLUX": + model_manager.torch_dtype = torch.bfloat16 + file_list = [ + os.path.join(model_path, "text_encoder/model.safetensors"), + os.path.join(model_path, "text_encoder_2"), + ] + for file_name in os.listdir(model_path): + if file_name.endswith(".safetensors"): + file_list.append(os.path.join(model_path, file_name)) + model_manager.load_models(file_list) + else: + model_manager.load_model(model_path) + pipeline = config[model_type]["pipeline_class"].from_model_manager(model_manager) + st.session_state.loaded_model_path = model_path + st.session_state.model_manager = model_manager + st.session_state.pipeline = pipeline + return model_manager, pipeline + + +def use_output_image_as_input(update=True): + # Search for input image + output_image_id = 0 + selected_output_image = None + while True: + if f"use_output_as_input_{output_image_id}" not in st.session_state: + break + if st.session_state[f"use_output_as_input_{output_image_id}"]: + selected_output_image = st.session_state["output_images"][output_image_id] + break + output_image_id += 1 + if update and selected_output_image is not None: + st.session_state["input_image"] = selected_output_image + return selected_output_image is not None + + +def apply_stroke_to_image(stroke_image, image): + image = np.array(image.convert("RGB")).astype(np.float32) + height, width, _ = image.shape + + stroke_image = np.array(Image.fromarray(stroke_image).resize((width, height))).astype(np.float32) + weight = stroke_image[:, :, -1:] / 255 + stroke_image = stroke_image[:, :, :-1] + + image = stroke_image * weight + image * (1 - weight) + image = np.clip(image, 0, 255).astype(np.uint8) + image = Image.fromarray(image) + return image + + +@st.cache_data +def image2bits(image): + image_byte = io.BytesIO() + image.save(image_byte, format="PNG") + image_byte = image_byte.getvalue() + return image_byte + + +def show_output_image(image): + st.image(image, use_column_width="always") + st.button("Use it as input image", key=f"use_output_as_input_{image_id}") + st.download_button("Download", data=image2bits(image), file_name="image.png", mime="image/png", key=f"download_output_{image_id}") + + +column_input, column_output = st.columns(2) +with st.sidebar: + # Select a model + with st.expander("Model", expanded=True): + model_type = st.selectbox("Model type", [model_type_ for model_type_ in config]) + fixed_parameters = config[model_type]["fixed_parameters"] + model_path_list = ["None"] + load_model_list(model_type) + model_path = st.selectbox("Model path", model_path_list) + + # Load the model + if model_path == "None": + # No models are selected. Release VRAM. + st.markdown("No models are selected.") + release_model() + else: + # A model is selected. + model_path = os.path.join(config[model_type]["model_folder"], model_path) + if st.session_state.get("loaded_model_path", "") != model_path: + # The loaded model is not the selected model. Reload it. + st.markdown(f"Loading model at {model_path}.") + st.markdown("Please wait a moment...") + release_model() + model_manager, pipeline = load_model(model_type, model_path) + st.markdown("Done.") + else: + # The loaded model is not the selected model. Fetch it from `st.session_state`. + st.markdown(f"Loading model at {model_path}.") + st.markdown("Please wait a moment...") + model_manager, pipeline = st.session_state.model_manager, st.session_state.pipeline + st.markdown("Done.") + + # Show parameters + with st.expander("Prompt", expanded=True): + prompt = st.text_area("Positive prompt") + if "negative_prompt" in fixed_parameters: + negative_prompt = fixed_parameters["negative_prompt"] + else: + negative_prompt = st.text_area("Negative prompt") + if "cfg_scale" in fixed_parameters: + cfg_scale = fixed_parameters["cfg_scale"] + else: + cfg_scale = st.slider("Classifier-free guidance scale", min_value=1.0, max_value=10.0, value=7.5) + with st.expander("Image", expanded=True): + if "num_inference_steps" in fixed_parameters: + num_inference_steps = fixed_parameters["num_inference_steps"] + else: + num_inference_steps = st.slider("Inference steps", min_value=1, max_value=100, value=20) + if "height" in fixed_parameters: + height = fixed_parameters["height"] + else: + height = st.select_slider("Height", options=[256, 512, 768, 1024, 2048], value=512) + if "width" in fixed_parameters: + width = fixed_parameters["width"] + else: + width = st.select_slider("Width", options=[256, 512, 768, 1024, 2048], value=512) + num_images = st.number_input("Number of images", value=2) + use_fixed_seed = st.checkbox("Use fixed seed", value=False) + if use_fixed_seed: + seed = st.number_input("Random seed", min_value=0, max_value=10**9, step=1, value=0) + + # Other fixed parameters + denoising_strength = 1.0 + repetition = 1 + + +# Show input image +with column_input: + with st.expander("Input image (Optional)", expanded=True): + with st.container(border=True): + column_white_board, column_upload_image = st.columns([1, 2]) + with column_white_board: + create_white_board = st.button("Create white board") + delete_input_image = st.button("Delete input image") + with column_upload_image: + upload_image = st.file_uploader("Upload image", type=["png", "jpg"], key="upload_image") + + if upload_image is not None: + st.session_state["input_image"] = crop_and_resize(Image.open(upload_image), height, width) + elif create_white_board: + st.session_state["input_image"] = Image.fromarray(np.ones((height, width, 3), dtype=np.uint8) * 255) + else: + use_output_image_as_input() + + if delete_input_image and "input_image" in st.session_state: + del st.session_state.input_image + if delete_input_image and "upload_image" in st.session_state: + del st.session_state.upload_image + + input_image = st.session_state.get("input_image", None) + if input_image is not None: + with st.container(border=True): + column_drawing_mode, column_color_1, column_color_2 = st.columns([4, 1, 1]) + with column_drawing_mode: + drawing_mode = st.radio("Drawing tool", ["transform", "freedraw", "line", "rect"], horizontal=True, index=1) + with column_color_1: + stroke_color = st.color_picker("Stroke color") + with column_color_2: + fill_color = st.color_picker("Fill color") + stroke_width = st.slider("Stroke width", min_value=1, max_value=50, value=10) + with st.container(border=True): + denoising_strength = st.slider("Denoising strength", min_value=0.0, max_value=1.0, value=0.7) + repetition = st.slider("Repetition", min_value=1, max_value=8, value=1) + with st.container(border=True): + input_width, input_height = input_image.size + canvas_result = st_canvas( + fill_color=fill_color, + stroke_width=stroke_width, + stroke_color=stroke_color, + background_color="rgba(255, 255, 255, 0)", + background_image=input_image, + update_streamlit=True, + height=int(512 / input_width * input_height), + width=512, + drawing_mode=drawing_mode, + key="canvas" + ) + + num_painter_layer = st.number_input("Number of painter layers", min_value=0, max_value=10, step=1, value=0) + local_prompts, masks, mask_scales = [], [], [] + white_board = Image.fromarray(np.ones((512, 512, 3), dtype=np.uint8) * 255) + painter_layers_json_data = [] + for painter_tab_id in range(num_painter_layer): + with st.expander(f"Painter layer {painter_tab_id}", expanded=True): + enable_local_prompt = st.checkbox(f"Enable prompt {painter_tab_id}", value=True) + local_prompt = st.text_area(f"Prompt {painter_tab_id}") + mask_scale = st.slider(f"Mask scale {painter_tab_id}", min_value=0.0, max_value=3.0, value=1.0) + stroke_width = st.slider(f"Stroke width {painter_tab_id}", min_value=1, max_value=300, value=100) + canvas_result_local = st_canvas( + fill_color="#000000", + stroke_width=stroke_width, + stroke_color="#000000", + background_color="rgba(255, 255, 255, 0)", + background_image=white_board, + update_streamlit=True, + height=512, + width=512, + drawing_mode="freedraw", + key=f"canvas_{painter_tab_id}" + ) + if canvas_result_local.json_data is not None: + painter_layers_json_data.append(canvas_result_local.json_data.copy()) + painter_layers_json_data[-1]["prompt"] = local_prompt + if enable_local_prompt: + local_prompts.append(local_prompt) + if canvas_result_local.image_data is not None: + mask = apply_stroke_to_image(canvas_result_local.image_data, white_board) + else: + mask = white_board + mask = Image.fromarray(255 - np.array(mask)) + masks.append(mask) + mask_scales.append(mask_scale) + save_painter_layers = st.button("Save painter layers") + if save_painter_layers: + os.makedirs("data/painter_layers", exist_ok=True) + json_file_path = f"data/painter_layers/{time.time_ns()}.json" + with open(json_file_path, "w") as f: + json.dump(painter_layers_json_data, f, indent=4) + st.markdown(f"Painter layers are saved in {json_file_path}.") + + +with column_output: + run_button = st.button("Generate image", type="primary") + auto_update = st.checkbox("Auto update", value=False) + num_image_columns = st.slider("Columns", min_value=1, max_value=8, value=2) + image_columns = st.columns(num_image_columns) + + # Run + if (run_button or auto_update) and model_path != "None": + + if input_image is not None: + input_image = input_image.resize((width, height)) + if canvas_result.image_data is not None: + input_image = apply_stroke_to_image(canvas_result.image_data, input_image) + + output_images = [] + for image_id in range(num_images * repetition): + if use_fixed_seed: + torch.manual_seed(seed + image_id) + else: + torch.manual_seed(np.random.randint(0, 10**9)) + if image_id >= num_images: + input_image = output_images[image_id - num_images] + with image_columns[image_id % num_image_columns]: + progress_bar_st = st.progress(0.0) + image = pipeline( + prompt, negative_prompt=negative_prompt, + local_prompts=local_prompts, masks=masks, mask_scales=mask_scales, + cfg_scale=cfg_scale, num_inference_steps=num_inference_steps, + height=height, width=width, + input_image=input_image, denoising_strength=denoising_strength, + progress_bar_st=progress_bar_st + ) + output_images.append(image) + progress_bar_st.progress(1.0) + show_output_image(image) + st.session_state["output_images"] = output_images + + elif "output_images" in st.session_state: + for image_id in range(len(st.session_state.output_images)): + with image_columns[image_id % num_image_columns]: + image = st.session_state.output_images[image_id] + progress_bar = st.progress(1.0) + show_output_image(image) + if "upload_image" in st.session_state and use_output_image_as_input(update=False): + st.markdown("If you want to use an output image as input image, please delete the uploaded image manually.") diff --git a/apps/streamlit/pages/2_Video_Creator.py b/apps/streamlit/pages/2_Video_Creator.py new file mode 100644 index 0000000000000000000000000000000000000000..87480726d3d32f48a2648266ce84d14e81e42315 --- /dev/null +++ b/apps/streamlit/pages/2_Video_Creator.py @@ -0,0 +1,197 @@ +import streamlit as st +st.set_page_config(layout="wide") +from diffsynth import SDVideoPipelineRunner +import os +import numpy as np + + +def load_model_list(folder): + file_list = os.listdir(folder) + file_list = [i for i in file_list if i.endswith(".safetensors") or i.endswith(".pth") or i.endswith(".ckpt")] + file_list = sorted(file_list) + return file_list + + +def match_processor_id(model_name, supported_processor_id_list): + sorted_processor_id = [i[1] for i in sorted([(-len(i), i) for i in supported_processor_id_list])] + for processor_id in sorted_processor_id: + if processor_id in model_name: + return supported_processor_id_list.index(processor_id) + 1 + return 0 + + +config = { + "models": { + "model_list": [], + "textual_inversion_folder": "models/textual_inversion", + "device": "cuda", + "lora_alphas": [], + "controlnet_units": [] + }, + "data": { + "input_frames": None, + "controlnet_frames": [], + "output_folder": "output", + "fps": 60 + }, + "pipeline": { + "seed": 0, + "pipeline_inputs": {} + } +} + + +with st.expander("Model", expanded=True): + stable_diffusion_ckpt = st.selectbox("Stable Diffusion", ["None"] + load_model_list("models/stable_diffusion")) + if stable_diffusion_ckpt != "None": + config["models"]["model_list"].append(os.path.join("models/stable_diffusion", stable_diffusion_ckpt)) + animatediff_ckpt = st.selectbox("AnimateDiff", ["None"] + load_model_list("models/AnimateDiff")) + if animatediff_ckpt != "None": + config["models"]["model_list"].append(os.path.join("models/AnimateDiff", animatediff_ckpt)) + column_lora, column_lora_alpha = st.columns([2, 1]) + with column_lora: + sd_lora_ckpt = st.selectbox("LoRA", ["None"] + load_model_list("models/lora")) + with column_lora_alpha: + lora_alpha = st.slider("LoRA Alpha", min_value=-4.0, max_value=4.0, value=1.0, step=0.1) + if sd_lora_ckpt != "None": + config["models"]["model_list"].append(os.path.join("models/lora", sd_lora_ckpt)) + config["models"]["lora_alphas"].append(lora_alpha) + + +with st.expander("Data", expanded=True): + with st.container(border=True): + input_video = st.text_input("Input Video File Path (e.g., data/your_video.mp4)", value="") + column_height, column_width, column_start_frame_index, column_end_frame_index = st.columns([2, 2, 1, 1]) + with column_height: + height = st.select_slider("Height", options=[256, 512, 768, 1024, 1536, 2048], value=1024) + with column_width: + width = st.select_slider("Width", options=[256, 512, 768, 1024, 1536, 2048], value=1024) + with column_start_frame_index: + start_frame_id = st.number_input("Start Frame id", value=0) + with column_end_frame_index: + end_frame_id = st.number_input("End Frame id", value=16) + if input_video != "": + config["data"]["input_frames"] = { + "video_file": input_video, + "image_folder": None, + "height": height, + "width": width, + "start_frame_id": start_frame_id, + "end_frame_id": end_frame_id + } + with st.container(border=True): + output_video = st.text_input("Output Video File Path (e.g., data/a_folder_to_save_something)", value="output") + fps = st.number_input("FPS", value=60) + config["data"]["output_folder"] = output_video + config["data"]["fps"] = fps + + +with st.expander("ControlNet Units", expanded=True): + supported_processor_id_list = ["canny", "depth", "softedge", "lineart", "lineart_anime", "openpose", "tile"] + controlnet_units = st.tabs(["ControlNet Unit 0", "ControlNet Unit 1", "ControlNet Unit 2"]) + for controlnet_id in range(len(controlnet_units)): + with controlnet_units[controlnet_id]: + controlnet_ckpt = st.selectbox("ControlNet", ["None"] + load_model_list("models/ControlNet"), + key=f"controlnet_ckpt_{controlnet_id}") + processor_id = st.selectbox("Processor", ["None"] + supported_processor_id_list, + index=match_processor_id(controlnet_ckpt, supported_processor_id_list), + disabled=controlnet_ckpt == "None", key=f"processor_id_{controlnet_id}") + controlnet_scale = st.slider("Scale", min_value=0.0, max_value=1.0, step=0.01, value=0.5, + disabled=controlnet_ckpt == "None", key=f"controlnet_scale_{controlnet_id}") + use_input_video_as_controlnet_input = st.checkbox("Use input video as ControlNet input", value=True, + disabled=controlnet_ckpt == "None", + key=f"use_input_video_as_controlnet_input_{controlnet_id}") + if not use_input_video_as_controlnet_input: + controlnet_input_video = st.text_input("ControlNet Input Video File Path", value="", + disabled=controlnet_ckpt == "None", key=f"controlnet_input_video_{controlnet_id}") + column_height, column_width, column_start_frame_index, column_end_frame_index = st.columns([2, 2, 1, 1]) + with column_height: + height = st.select_slider("Height", options=[256, 512, 768, 1024, 1536, 2048], value=1024, + disabled=controlnet_ckpt == "None", key=f"controlnet_height_{controlnet_id}") + with column_width: + width = st.select_slider("Width", options=[256, 512, 768, 1024, 1536, 2048], value=1024, + disabled=controlnet_ckpt == "None", key=f"controlnet_width_{controlnet_id}") + with column_start_frame_index: + start_frame_id = st.number_input("Start Frame id", value=0, + disabled=controlnet_ckpt == "None", key=f"controlnet_start_frame_id_{controlnet_id}") + with column_end_frame_index: + end_frame_id = st.number_input("End Frame id", value=16, + disabled=controlnet_ckpt == "None", key=f"controlnet_end_frame_id_{controlnet_id}") + if input_video != "": + config["data"]["input_video"] = { + "video_file": input_video, + "image_folder": None, + "height": height, + "width": width, + "start_frame_id": start_frame_id, + "end_frame_id": end_frame_id + } + if controlnet_ckpt != "None": + config["models"]["model_list"].append(os.path.join("models/ControlNet", controlnet_ckpt)) + config["models"]["controlnet_units"].append({ + "processor_id": processor_id, + "model_path": os.path.join("models/ControlNet", controlnet_ckpt), + "scale": controlnet_scale, + }) + if use_input_video_as_controlnet_input: + config["data"]["controlnet_frames"].append(config["data"]["input_frames"]) + else: + config["data"]["controlnet_frames"].append({ + "video_file": input_video, + "image_folder": None, + "height": height, + "width": width, + "start_frame_id": start_frame_id, + "end_frame_id": end_frame_id + }) + + +with st.container(border=True): + with st.expander("Seed", expanded=True): + use_fixed_seed = st.checkbox("Use fixed seed", value=False) + if use_fixed_seed: + seed = st.number_input("Random seed", min_value=0, max_value=10**9, step=1, value=0) + else: + seed = np.random.randint(0, 10**9) + with st.expander("Textual Guidance", expanded=True): + prompt = st.text_area("Positive prompt") + negative_prompt = st.text_area("Negative prompt") + column_cfg_scale, column_clip_skip = st.columns(2) + with column_cfg_scale: + cfg_scale = st.slider("Classifier-free guidance scale", min_value=1.0, max_value=10.0, value=7.0) + with column_clip_skip: + clip_skip = st.slider("Clip Skip", min_value=1, max_value=4, value=1) + with st.expander("Denoising", expanded=True): + column_num_inference_steps, column_denoising_strength = st.columns(2) + with column_num_inference_steps: + num_inference_steps = st.slider("Inference steps", min_value=1, max_value=100, value=10) + with column_denoising_strength: + denoising_strength = st.slider("Denoising strength", min_value=0.0, max_value=1.0, value=1.0) + with st.expander("Efficiency", expanded=False): + animatediff_batch_size = st.slider("Animatediff batch size (sliding window size)", min_value=1, max_value=32, value=16, step=1) + animatediff_stride = st.slider("Animatediff stride", + min_value=1, + max_value=max(2, animatediff_batch_size), + value=max(1, animatediff_batch_size // 2), + step=1) + unet_batch_size = st.slider("UNet batch size", min_value=1, max_value=32, value=1, step=1) + controlnet_batch_size = st.slider("ControlNet batch size", min_value=1, max_value=32, value=1, step=1) + cross_frame_attention = st.checkbox("Enable Cross-Frame Attention", value=False) + config["pipeline"]["seed"] = seed + config["pipeline"]["pipeline_inputs"] = { + "prompt": prompt, + "negative_prompt": negative_prompt, + "cfg_scale": cfg_scale, + "clip_skip": clip_skip, + "denoising_strength": denoising_strength, + "num_inference_steps": num_inference_steps, + "animatediff_batch_size": animatediff_batch_size, + "animatediff_stride": animatediff_stride, + "unet_batch_size": unet_batch_size, + "controlnet_batch_size": controlnet_batch_size, + "cross_frame_attention": cross_frame_attention, + } + +run_button = st.button("☢️Run☢️", type="primary") +if run_button: + SDVideoPipelineRunner(in_streamlit=True).run(config) diff --git a/deal1.py b/deal1.py new file mode 100644 index 0000000000000000000000000000000000000000..6891c20ca74a428d31af5fad77bf0f6fc083f556 --- /dev/null +++ b/deal1.py @@ -0,0 +1,82 @@ +import cv2 +import os +import sys +from datetime import timedelta + +def extract_frames_per_second(video_path, output_dir, interval_seconds=3): + """ + 从视频中每隔 interval_seconds 提取一帧并保存到指定目录（按时间定位，保证时间戳单调递增）。 + :param video_path: 视频文件的路径 + :param output_dir: 帧图片的保存目录 + :param interval_seconds: 每隔多少秒保存一帧（默认3秒） + """ + os.makedirs(output_dir, exist_ok=True) + # 1. 如果输出目录已存在且非空则跳过，避免重复处理 + if os.path.exists(output_dir) and os.listdir(output_dir): + print(f"输出目录已存在且非空，跳过提取：{os.path.abspath(output_dir)}") + return + os.makedirs(output_dir, exist_ok=True) + print(f"帧保存目录：{os.path.abspath(output_dir)}") + + # 2. 打开视频文件 + cap = cv2.VideoCapture(video_path) + if not cap.isOpened(): + raise ValueError(f"无法打开视频文件：{video_path}") + + # 3. 获取视频基本信息 + fps = cap.get(cv2.CAP_PROP_FPS) # 视频帧率（每秒帧数） + total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) # 视频总帧数 + duration = total_frames / fps if fps > 0 else 0 # 视频总时长（秒） + print(f"视频信息：帧率={fps:.2f} FPS | 总帧数={total_frames} | 总时长={timedelta(seconds=duration)}") + + if fps <= 0: + raise ValueError("无法获取视频帧率，视频文件可能损坏或格式不支持") + + saved_count = 0 # 已保存的帧序号 + + try: + t = 0.0 # 当前时间点（秒） + # 使用按时间定位的方式读取帧，避免帧计数舍入或读取跳帧导致时间戳混乱 + while t <= duration: + # 定位到指定毫秒位置（更可靠地获取指定时间的帧） + cap.set(cv2.CAP_PROP_POS_MSEC, t * 1000) + ret, frame = cap.read() + if not ret: + # 定位或读取失败，跳出循环 + break + + # 使用时间戳作为文件名的一部分，保证按时间顺序保存 + frame_filename = f"{saved_count:06d}_{t:.2f}s.jpg" + frame_path = os.path.join(output_dir, frame_filename) + + # 保存帧图片 + cv2.imwrite(frame_path, frame) + saved_count += 1 + + # 打印进度（每10帧打印一次，避免刷屏） + if saved_count % 10 == 0: + progress = (t / duration) * 100 if duration > 0 else 0 + print(f"进度：{progress:.1f}% | 已保存 {saved_count} 帧 | 时间：{t:.2f}s") + + t += interval_seconds + + except Exception as e: + raise RuntimeError(f"提取帧时发生错误：{str(e)}") + finally: + # 释放视频资源 + cap.release() + cv2.destroyAllWindows() + + # 打印最终结果 + print(f"\n提取完成！共保存 {saved_count} 帧，保存路径：{os.path.abspath(output_dir)}") + +output_dirs = ["/fi-lib/workspace/sjx/DiffSynth-Studio/dataset/no other choice","/fi-lib/workspace/sjx/DiffSynth-Studio/dataset/the roses","/fi-lib/workspace/sjx/DiffSynth-Studio/dataset/nouvelle","/fi-lib/workspace/sjx/DiffSynth-Studio/dataset/legs","/fi-lib/workspace/sjx/DiffSynth-Studio/dataset/frankenstein"] +input_dirs = ["/fi-lib/workspace/sjx/DiffSynth-Studio/dataset/films/어쩔수가없다 NO OTHER CHOICE, 2025.1080p.WEB-DL.H264.AAC.mp4","/fi-lib/workspace/sjx/DiffSynth-Studio/dataset/films/The.Roses.2025.2160p.WEB-DL.DDP5.1.Atmos.SDR.H265-AOC/The.Roses.2025.2160p.WEB-DL.DDP5.1.Atmos.SDR.H265-AOC.mkv","/fi-lib/workspace/sjx/DiffSynth-Studio/dataset/films/NOUVELLE.VAGUE.2025.2160p.NF.WEB-DL.DDP.5.1.H.265-CHDWEB[PianYuan]/NOUVELLE.VAGUE.2025.2160p.NF.WEB-DL.DDP.5.1.H.265-CHDWEB.mkv","/fi-lib/workspace/sjx/DiffSynth-Studio/dataset/films/If.I.Had.Legs.Id.Kick.You.2025.1080p.iT.WEB-DL.DDP5.1.Atmos.H264-BTM/If.I.Had.Legs.Id.Kick.You.2025.1080p.iT.WEB-DL[Ben The Men].mkv","/fi-lib/workspace/sjx/DiffSynth-Studio/dataset/films/Frankenstein.2025.1080p.NF.WEB-DL.DDP5.1.Atmos.H.264-FLUX/Frankenstein.2025.1080p.NF.WEB-DL.DDP5.1.Atmos.H.264-FLUX.mkv"] +if __name__ == "__main__": + # 执行帧提取（每3秒保存一帧） + for i in range(len(output_dirs)): + try: + extract_frames_per_second(video_path=input_dirs[i],output_dir=output_dirs[i],interval_seconds=3) + except Exception as e: + print(f"程序异常：{str(e)}", file=sys.stderr) + sys.exit(1) diff --git a/deal2.py b/deal2.py new file mode 100644 index 0000000000000000000000000000000000000000..9111184f5cfbcd1f646cdbeb300327e452560ad1 --- /dev/null +++ b/deal2.py @@ -0,0 +1,127 @@ +import cv2 +import numpy as np +import os +from pathlib import Path +import imagehash +from PIL import Image + +def calculate_phash(image_path): + """ + 计算图片的感知哈希值（pHash） + :param image_path: 图片路径 + :return: 感知哈希值（imagehash.ImageHash对象） + """ + try: + # 用PIL读取图片（兼容更多格式），转为灰度图 + img = Image.open(image_path).convert("L") + # 计算pHash，hash_size越小，哈希值越短，计算越快（默认8，生成64位哈希） + phash = imagehash.phash(img, hash_size=8) + return phash + except Exception as e: + print(f"计算哈希失败：{image_path}，错误：{e}") + return None + +def calculate_clarity(image_path): + """ + 拉普拉斯方差法计算图片清晰度评分 + :param image_path: 图片路径 + :return: 清晰度评分（方差值），若读取失败返回0 + """ + img = cv2.imread(image_path) + if img is None: + return 0 + gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) + laplacian = cv2.Laplacian(gray, cv2.CV_64F) + clarity_score = np.var(laplacian) + return clarity_score + +def calculate_hamming_distance(hash1, hash2): + """ + 计算两个哈希值的汉明距离 + :param hash1, hash2: 感知哈希值（imagehash.ImageHash对象） + :return: 汉明距离（越小越相似） + """ + if hash1 is None or hash2 is None: + return float("inf") # 哈希计算失败，视为不相似 + return hash1 - hash2 + +def process_duplicate_frames(input_dir, output_dir, similarity_threshold=10): + """ + 处理视频截帧，去除相似帧并保留更清晰的图片 + :param input_dir: 原始图片文件夹路径 + :param output_dir: 去重后保存的文件夹路径 + :param similarity_threshold: 相似度阈值（汉明距离≤该值为相似帧） + """ + # 创建输出目录 + Path(output_dir).mkdir(parents=True, exist_ok=True) + + # 1. 获取文件夹内所有图片，按文件名排序（保证视频截帧的顺序） + img_extensions = [".jpg", ".jpeg", ".png", ".bmp", ".tiff", ".webp"] + img_paths = [ + os.path.join(input_dir, f) for f in os.listdir(input_dir) + if Path(f).suffix.lower() in img_extensions + ] + # 按文件名排序（关键：保证视频截帧的时间顺序） + img_paths.sort(key=lambda x: os.path.basename(x)) + + if len(img_paths) == 0: + print("文件夹中未找到图片！") + return + + # 2. 初始化：保留第一张图片作为基准，遍历后续图片 + saved_img_path = img_paths[0] # 已保存的基准图片路径 + saved_phash = calculate_phash(saved_img_path) # 基准图片的哈希值 + saved_clarity = calculate_clarity(saved_img_path) # 基准图片的清晰度 + + # 保存第一张图片 + save_name = os.path.basename(saved_img_path) + cv2.imwrite(os.path.join(output_dir, save_name), cv2.imread(saved_img_path)) + print(f"初始保存：{save_name}，清晰度：{saved_clarity:.2f}") + + # 3. 从第二张开始遍历，逐张对比 + for current_img_path in img_paths[1:]: + current_name = os.path.basename(current_img_path) + current_phash = calculate_phash(current_img_path) + current_clarity = calculate_clarity(current_img_path) + + # 计算与基准图片的汉明距离 + hamming_dist = calculate_hamming_distance(saved_phash, current_phash) + print(f"\n对比：{saved_img_path.split('/')[-1]} vs {current_name}") + print(f"汉明距离：{hamming_dist}，当前图片清晰度：{current_clarity:.2f}") + + if hamming_dist <= similarity_threshold: + # 相似帧：保留清晰度更高的图片 + if current_clarity > saved_clarity: + # 当前图片更清晰：删除原基准图片，保存当前图片作为新基准 + os.remove(os.path.join(output_dir, os.path.basename(saved_img_path))) + cv2.imwrite(os.path.join(output_dir, current_name), cv2.imread(current_img_path)) + print(f"替换：{current_name} 更清晰，已替换原基准图片") + # 更新基准信息 + saved_img_path = current_img_path + saved_phash = current_phash + saved_clarity = current_clarity + else: + # 当前图片更模糊：跳过，保留原基准 + print(f"跳过：{current_name} 模糊，保留原基准图片") + else: + # 非相似帧：保存当前图片，作为新的基准 + cv2.imwrite(os.path.join(output_dir, current_name), cv2.imread(current_img_path)) + print(f"保存：{current_name} 为新基准，与原基准非相似帧") + # 更新基准信息 + saved_img_path = current_img_path + saved_phash = current_phash + saved_clarity = current_clarity + + print(f"\n处理完成！去重后图片保存在：{output_dir}") + print(f"原始图片数量：{len(img_paths)}，去重后数量：{len(os.listdir(output_dir))}") + +# 主函数调用 +if __name__ == "__main__": + input_dirs = ["/fi-lib/workspace/sjx/DiffSynth-Studio/dataset/no other choice","/fi-lib/workspace/sjx/DiffSynth-Studio/dataset/the roses","/fi-lib/workspace/sjx/DiffSynth-Studio/dataset/nouvelle","/fi-lib/workspace/sjx/DiffSynth-Studio/dataset/legs","/fi-lib/workspace/sjx/DiffSynth-Studio/dataset/frankenstein"] + # 配置参数（根据实际情况修改） + for i in range(len(input_dirs)): + + SIMILARITY_THRESHOLD = 10 # 相似性阈值（汉明距离），可调整 + + # 执行去重处理 + process_duplicate_frames(input_dirs[i], f"{input_dirs[i]}_dedup", SIMILARITY_THRESHOLD) \ No newline at end of file diff --git a/diffsynth.egg-info/PKG-INFO b/diffsynth.egg-info/PKG-INFO new file mode 100644 index 0000000000000000000000000000000000000000..622ef73a6d298d3ec050718d3db8d0013e551fdd --- /dev/null +++ b/diffsynth.egg-info/PKG-INFO @@ -0,0 +1,31 @@ +Metadata-Version: 2.4 +Name: diffsynth +Version: 1.1.9 +Summary: Enjoy the magic of Diffusion models! +Author: Artiprocher +Classifier: Programming Language :: Python :: 3 +Classifier: License :: OSI Approved :: Apache Software License +Classifier: Operating System :: OS Independent +Requires-Python: >=3.6 +License-File: LICENSE +Requires-Dist: torch>=2.0.0 +Requires-Dist: torchvision +Requires-Dist: transformers +Requires-Dist: imageio +Requires-Dist: imageio[ffmpeg] +Requires-Dist: safetensors +Requires-Dist: einops +Requires-Dist: sentencepiece +Requires-Dist: protobuf +Requires-Dist: modelscope +Requires-Dist: ftfy +Requires-Dist: pynvml +Requires-Dist: pandas +Requires-Dist: accelerate +Requires-Dist: peft +Dynamic: author +Dynamic: classifier +Dynamic: license-file +Dynamic: requires-dist +Dynamic: requires-python +Dynamic: summary diff --git a/diffsynth.egg-info/SOURCES.txt b/diffsynth.egg-info/SOURCES.txt new file mode 100644 index 0000000000000000000000000000000000000000..9325f9079aaa0a7b212732c58fc83791a089e7d1 --- /dev/null +++ b/diffsynth.egg-info/SOURCES.txt @@ -0,0 +1,247 @@ +LICENSE +README.md +setup.py +diffsynth/__init__.py +diffsynth.egg-info/PKG-INFO +diffsynth.egg-info/SOURCES.txt +diffsynth.egg-info/dependency_links.txt +diffsynth.egg-info/requires.txt +diffsynth.egg-info/top_level.txt +diffsynth/configs/__init__.py +diffsynth/configs/model_config.py +diffsynth/controlnets/__init__.py +diffsynth/controlnets/controlnet_unit.py +diffsynth/controlnets/processors.py +diffsynth/data/__init__.py +diffsynth/data/simple_text_image.py +diffsynth/data/video.py +diffsynth/distributed/__init__.py +diffsynth/distributed/xdit_context_parallel.py +diffsynth/extensions/__init__.py +diffsynth/extensions/ESRGAN/__init__.py +diffsynth/extensions/FastBlend/__init__.py +diffsynth/extensions/FastBlend/api.py +diffsynth/extensions/FastBlend/cupy_kernels.py +diffsynth/extensions/FastBlend/data.py +diffsynth/extensions/FastBlend/patch_match.py +diffsynth/extensions/FastBlend/runners/__init__.py +diffsynth/extensions/FastBlend/runners/accurate.py +diffsynth/extensions/FastBlend/runners/balanced.py +diffsynth/extensions/FastBlend/runners/fast.py +diffsynth/extensions/FastBlend/runners/interpolation.py +diffsynth/extensions/ImageQualityMetric/__init__.py +diffsynth/extensions/ImageQualityMetric/aesthetic.py +diffsynth/extensions/ImageQualityMetric/clip.py +diffsynth/extensions/ImageQualityMetric/config.py +diffsynth/extensions/ImageQualityMetric/hps.py +diffsynth/extensions/ImageQualityMetric/imagereward.py +diffsynth/extensions/ImageQualityMetric/mps.py +diffsynth/extensions/ImageQualityMetric/pickscore.py +diffsynth/extensions/ImageQualityMetric/BLIP/__init__.py +diffsynth/extensions/ImageQualityMetric/BLIP/blip.py +diffsynth/extensions/ImageQualityMetric/BLIP/blip_pretrain.py +diffsynth/extensions/ImageQualityMetric/BLIP/med.py +diffsynth/extensions/ImageQualityMetric/BLIP/vit.py +diffsynth/extensions/ImageQualityMetric/open_clip/__init__.py +diffsynth/extensions/ImageQualityMetric/open_clip/coca_model.py +diffsynth/extensions/ImageQualityMetric/open_clip/constants.py +diffsynth/extensions/ImageQualityMetric/open_clip/factory.py +diffsynth/extensions/ImageQualityMetric/open_clip/generation_utils.py +diffsynth/extensions/ImageQualityMetric/open_clip/hf_configs.py +diffsynth/extensions/ImageQualityMetric/open_clip/hf_model.py +diffsynth/extensions/ImageQualityMetric/open_clip/loss.py +diffsynth/extensions/ImageQualityMetric/open_clip/model.py +diffsynth/extensions/ImageQualityMetric/open_clip/modified_resnet.py +diffsynth/extensions/ImageQualityMetric/open_clip/openai.py +diffsynth/extensions/ImageQualityMetric/open_clip/pretrained.py +diffsynth/extensions/ImageQualityMetric/open_clip/push_to_hf_hub.py +diffsynth/extensions/ImageQualityMetric/open_clip/timm_model.py +diffsynth/extensions/ImageQualityMetric/open_clip/tokenizer.py +diffsynth/extensions/ImageQualityMetric/open_clip/transform.py +diffsynth/extensions/ImageQualityMetric/open_clip/transformer.py +diffsynth/extensions/ImageQualityMetric/open_clip/utils.py +diffsynth/extensions/ImageQualityMetric/open_clip/version.py +diffsynth/extensions/ImageQualityMetric/trainer/__init__.py 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+diffsynth/tokenizer_configs/stable_diffusion_3/tokenizer_3/tokenizer.json +diffsynth/tokenizer_configs/stable_diffusion_3/tokenizer_3/tokenizer_config.json +diffsynth/tokenizer_configs/stable_diffusion_xl/tokenizer_2/merges.txt +diffsynth/tokenizer_configs/stable_diffusion_xl/tokenizer_2/special_tokens_map.json +diffsynth/tokenizer_configs/stable_diffusion_xl/tokenizer_2/tokenizer_config.json +diffsynth/tokenizer_configs/stable_diffusion_xl/tokenizer_2/vocab.json +diffsynth/trainers/__init__.py +diffsynth/trainers/text_to_image.py +diffsynth/trainers/unified_dataset.py +diffsynth/trainers/utils.py +diffsynth/utils/__init__.py +diffsynth/vram_management/__init__.py +diffsynth/vram_management/gradient_checkpointing.py +diffsynth/vram_management/layers.py \ No newline at end of file diff --git a/diffsynth.egg-info/dependency_links.txt b/diffsynth.egg-info/dependency_links.txt new file mode 100644 index 0000000000000000000000000000000000000000..8b137891791fe96927ad78e64b0aad7bded08bdc --- /dev/null +++ b/diffsynth.egg-info/dependency_links.txt @@ -0,0 +1 @@ + diff --git a/diffsynth.egg-info/requires.txt b/diffsynth.egg-info/requires.txt new file mode 100644 index 0000000000000000000000000000000000000000..dd8aab031586615077ccf65d0036adeddfd23833 --- /dev/null +++ b/diffsynth.egg-info/requires.txt @@ -0,0 +1,15 @@ +torch>=2.0.0 +torchvision +transformers +imageio +imageio[ffmpeg] +safetensors +einops +sentencepiece +protobuf +modelscope +ftfy +pynvml +pandas +accelerate +peft diff --git a/diffsynth.egg-info/top_level.txt b/diffsynth.egg-info/top_level.txt new file mode 100644 index 0000000000000000000000000000000000000000..a4c845e23f3cd1bc9c56115f1c9a0b1a0c6b68bc --- /dev/null +++ b/diffsynth.egg-info/top_level.txt @@ -0,0 +1 @@ +diffsynth diff --git a/diffsynth/__init__.py b/diffsynth/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..ae0a45c2e2dc61f8f16354feb1b0c481776b523f --- /dev/null +++ b/diffsynth/__init__.py @@ -0,0 +1,6 @@ +from .data import * +from .models import * +from .prompters import * +from .schedulers import * +from .pipelines import * +from .controlnets import * diff --git a/diffsynth/configs/__init__.py b/diffsynth/configs/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/diffsynth/configs/model_config.py b/diffsynth/configs/model_config.py new file mode 100644 index 0000000000000000000000000000000000000000..743ffba5b37ee3a812f1253c88e34eb531129601 --- /dev/null +++ b/diffsynth/configs/model_config.py @@ -0,0 +1,859 @@ +from typing_extensions import Literal, TypeAlias + +from ..models.sd_text_encoder import SDTextEncoder +from ..models.sd_unet import SDUNet +from ..models.sd_vae_encoder import SDVAEEncoder +from ..models.sd_vae_decoder import SDVAEDecoder + +from ..models.sdxl_text_encoder import SDXLTextEncoder, SDXLTextEncoder2 +from ..models.sdxl_unet import SDXLUNet +from ..models.sdxl_vae_decoder import SDXLVAEDecoder +from ..models.sdxl_vae_encoder import SDXLVAEEncoder + +from ..models.sd3_text_encoder import SD3TextEncoder1, SD3TextEncoder2, SD3TextEncoder3 +from ..models.sd3_dit import SD3DiT +from ..models.sd3_vae_decoder import SD3VAEDecoder +from ..models.sd3_vae_encoder import SD3VAEEncoder + +from ..models.sd_controlnet import SDControlNet +from ..models.sdxl_controlnet import SDXLControlNetUnion + +from ..models.sd_motion import SDMotionModel +from ..models.sdxl_motion import SDXLMotionModel + +from ..models.svd_image_encoder import SVDImageEncoder +from ..models.svd_unet import SVDUNet +from ..models.svd_vae_decoder import SVDVAEDecoder +from ..models.svd_vae_encoder import SVDVAEEncoder + +from ..models.sd_ipadapter import SDIpAdapter, IpAdapterCLIPImageEmbedder +from ..models.sdxl_ipadapter import SDXLIpAdapter, IpAdapterXLCLIPImageEmbedder + +from ..models.hunyuan_dit_text_encoder import HunyuanDiTCLIPTextEncoder, HunyuanDiTT5TextEncoder +from ..models.hunyuan_dit import HunyuanDiT + +from ..models.flux_dit import FluxDiT +from ..models.flux_text_encoder import FluxTextEncoder2 +from ..models.flux_vae import FluxVAEEncoder, FluxVAEDecoder +from ..models.flux_controlnet import FluxControlNet +from ..models.flux_ipadapter import FluxIpAdapter +from ..models.flux_infiniteyou import InfiniteYouImageProjector + +from ..models.cog_vae import CogVAEEncoder, CogVAEDecoder +from ..models.cog_dit import CogDiT + +from ..models.omnigen import OmniGenTransformer + +from ..models.hunyuan_video_vae_decoder import HunyuanVideoVAEDecoder +from ..models.hunyuan_video_vae_encoder import HunyuanVideoVAEEncoder + +from ..extensions.RIFE import IFNet +from ..extensions.ESRGAN import RRDBNet + +from ..models.hunyuan_video_dit import HunyuanVideoDiT + +from ..models.stepvideo_vae import StepVideoVAE +from ..models.stepvideo_dit import StepVideoModel + +from ..models.wan_video_dit import WanModel +from ..models.wan_video_dit_s2v import WanS2VModel +from ..models.wan_video_text_encoder import WanTextEncoder +from ..models.wan_video_image_encoder import WanImageEncoder +from ..models.wan_video_vae import WanVideoVAE, WanVideoVAE38 +from ..models.wan_video_motion_controller import WanMotionControllerModel +from ..models.wan_video_vace import VaceWanModel +from ..models.wav2vec import WanS2VAudioEncoder +from ..models.wan_video_animate_adapter import WanAnimateAdapter +from ..models.wan_video_mot import MotWanModel + +from ..models.step1x_connector import Qwen2Connector + +from ..models.flux_value_control import SingleValueEncoder + +from ..lora.flux_lora import FluxLoraPatcher +from ..models.flux_lora_encoder import FluxLoRAEncoder + +from ..models.nexus_gen_projector import NexusGenAdapter, NexusGenImageEmbeddingMerger +from ..models.nexus_gen import NexusGenAutoregressiveModel + +from ..models.qwen_image_dit import QwenImageDiT +from ..models.qwen_image_text_encoder import QwenImageTextEncoder +from ..models.qwen_image_vae import QwenImageVAE +from ..models.qwen_image_controlnet import QwenImageBlockWiseControlNet + +from ..models.longcat_video_dit import LongCatVideoTransformer3DModel + +model_loader_configs = [ + # These configs are provided for detecting model type automatically. + # The format is (state_dict_keys_hash, state_dict_keys_hash_with_shape, model_names, model_classes, model_resource) + (None, "091b0e30e77c76626b3ba62acdf95343", ["sd_controlnet"], [SDControlNet], "civitai"), + (None, "4a6c8306a27d916dea81263c8c88f450", ["hunyuan_dit_clip_text_encoder"], [HunyuanDiTCLIPTextEncoder], "civitai"), + (None, "f4aec400fe394297961218c768004521", ["hunyuan_dit"], [HunyuanDiT], "civitai"), + (None, "9e6e58043a5a2e332803ed42f6ee7181", ["hunyuan_dit_t5_text_encoder"], [HunyuanDiTT5TextEncoder], "civitai"), + (None, "13115dd45a6e1c39860f91ab073b8a78", ["sdxl_vae_encoder", "sdxl_vae_decoder"], [SDXLVAEEncoder, SDXLVAEDecoder], "diffusers"), + (None, "d78aa6797382a6d455362358a3295ea9", ["sd_ipadapter_clip_image_encoder"], [IpAdapterCLIPImageEmbedder], "diffusers"), + (None, "e291636cc15e803186b47404262ef812", ["sd_ipadapter"], [SDIpAdapter], "civitai"), + (None, "399c81f2f8de8d1843d0127a00f3c224", ["sdxl_ipadapter_clip_image_encoder"], [IpAdapterXLCLIPImageEmbedder], "diffusers"), + (None, "a64eac9aa0db4b9602213bc0131281c7", ["sdxl_ipadapter"], [SDXLIpAdapter], "civitai"), + (None, "52817e4fdd89df154f02749ca6f692ac", ["sdxl_unet"], [SDXLUNet], "diffusers"), + (None, "03343c606f16d834d6411d0902b53636", ["sd_text_encoder", "sd_unet", "sd_vae_decoder", "sd_vae_encoder"], [SDTextEncoder, SDUNet, SDVAEDecoder, SDVAEEncoder], "civitai"), + (None, "d4ba77a7ece070679b4a987f58f201e9", ["sd_text_encoder"], [SDTextEncoder], "civitai"), + (None, "d0c89e55c5a57cf3981def0cb1c9e65a", ["sd_vae_decoder", "sd_vae_encoder"], [SDVAEDecoder, SDVAEEncoder], "civitai"), + (None, "3926bf373b39a67eeafd7901478a47a7", ["sd_unet"], [SDUNet], "civitai"), + (None, "1e0c39ec176b9007c05f76d52b554a4d", ["sd3_text_encoder_1", "sd3_text_encoder_2", "sd3_dit", "sd3_vae_encoder", "sd3_vae_decoder"], [SD3TextEncoder1, SD3TextEncoder2, SD3DiT, SD3VAEEncoder, SD3VAEDecoder], "civitai"), + (None, "d9e0290829ba8d98e28e1a2b1407db4a", ["sd3_text_encoder_1", "sd3_text_encoder_2", "sd3_text_encoder_3", "sd3_dit", "sd3_vae_encoder", "sd3_vae_decoder"], [SD3TextEncoder1, SD3TextEncoder2, SD3TextEncoder3, SD3DiT, SD3VAEEncoder, SD3VAEDecoder], "civitai"), + (None, "5072d0b24e406b49507abe861cf97691", ["sd3_text_encoder_3"], [SD3TextEncoder3], "civitai"), + (None, "4cf64a799d04260df438c6f33c9a047e", ["sdxl_text_encoder", "sdxl_text_encoder_2", "sdxl_unet", "sdxl_vae_decoder", "sdxl_vae_encoder"], [SDXLTextEncoder, SDXLTextEncoder2, SDXLUNet, SDXLVAEDecoder, SDXLVAEEncoder], "civitai"), + (None, "d9b008a867c498ab12ad24042eff8e3f", ["sdxl_text_encoder", "sdxl_text_encoder_2", "sdxl_unet", "sdxl_vae_decoder", "sdxl_vae_encoder"], [SDXLTextEncoder, SDXLTextEncoder2, SDXLUNet, SDXLVAEDecoder, SDXLVAEEncoder], "civitai"), # SDXL-Turbo + (None, "025bb7452e531a3853d951d77c63f032", ["sdxl_text_encoder", "sdxl_text_encoder_2"], [SDXLTextEncoder, SDXLTextEncoder2], "civitai"), + (None, "298997b403a4245c04102c9f36aac348", ["sdxl_unet"], [SDXLUNet], "civitai"), + (None, "2a07abce74b4bdc696b76254ab474da6", ["svd_image_encoder", "svd_unet", "svd_vae_decoder", "svd_vae_encoder"], [SVDImageEncoder, SVDUNet, SVDVAEDecoder, SVDVAEEncoder], "civitai"), + (None, "c96a285a6888465f87de22a984d049fb", ["sd_motion_modules"], [SDMotionModel], "civitai"), + (None, "72907b92caed19bdb2adb89aa4063fe2", ["sdxl_motion_modules"], [SDXLMotionModel], "civitai"), + (None, "31d2d9614fba60511fc9bf2604aa01f7", ["sdxl_controlnet"], [SDXLControlNetUnion], "diffusers"), + (None, "94eefa3dac9cec93cb1ebaf1747d7b78", ["sd3_text_encoder_1"], [SD3TextEncoder1], "diffusers"), + (None, "1aafa3cc91716fb6b300cc1cd51b85a3", ["flux_vae_encoder", "flux_vae_decoder"], [FluxVAEEncoder, FluxVAEDecoder], "diffusers"), + (None, "21ea55f476dfc4fd135587abb59dfe5d", ["flux_vae_encoder", "flux_vae_decoder"], [FluxVAEEncoder, FluxVAEDecoder], "civitai"), + (None, "a29710fea6dddb0314663ee823598e50", ["flux_dit"], [FluxDiT], "civitai"), + (None, "57b02550baab820169365b3ee3afa2c9", ["flux_dit"], [FluxDiT], "civitai"), + (None, "3394f306c4cbf04334b712bf5aaed95f", ["flux_dit"], [FluxDiT], "civitai"), + (None, "023f054d918a84ccf503481fd1e3379e", ["flux_dit"], [FluxDiT], "civitai"), + (None, "d02f41c13549fa5093d3521f62a5570a", ["flux_dit"], [FluxDiT], "civitai"), + (None, "605c56eab23e9e2af863ad8f0813a25d", ["flux_dit"], [FluxDiT], "diffusers"), + (None, "0629116fce1472503a66992f96f3eb1a", ["flux_value_controller"], [SingleValueEncoder], "civitai"), + (None, "280189ee084bca10f70907bf6ce1649d", ["cog_vae_encoder", "cog_vae_decoder"], [CogVAEEncoder, CogVAEDecoder], "diffusers"), + (None, "9b9313d104ac4df27991352fec013fd4", ["rife"], [IFNet], "civitai"), + (None, "6b7116078c4170bfbeaedc8fe71f6649", ["esrgan"], [RRDBNet], "civitai"), + (None, "61cbcbc7ac11f169c5949223efa960d1", ["omnigen_transformer"], [OmniGenTransformer], "diffusers"), + (None, "78d18b9101345ff695f312e7e62538c0", ["flux_controlnet"], [FluxControlNet], "diffusers"), + (None, "b001c89139b5f053c715fe772362dd2a", ["flux_controlnet"], [FluxControlNet], "diffusers"), + (None, "52357cb26250681367488a8954c271e8", ["flux_controlnet"], [FluxControlNet], "diffusers"), + (None, "0cfd1740758423a2a854d67c136d1e8c", ["flux_controlnet"], [FluxControlNet], "diffusers"), + (None, "7f9583eb8ba86642abb9a21a4b2c9e16", ["flux_controlnet"], [FluxControlNet], "diffusers"), + (None, "43ad5aaa27dd4ee01b832ed16773fa52", ["flux_controlnet"], [FluxControlNet], "diffusers"), + (None, "c07c0f04f5ff55e86b4e937c7a40d481", ["infiniteyou_image_projector"], [InfiniteYouImageProjector], "diffusers"), + (None, "4daaa66cc656a8fe369908693dad0a35", ["flux_ipadapter"], [FluxIpAdapter], "diffusers"), + (None, "51aed3d27d482fceb5e0739b03060e8f", ["sd3_dit", "sd3_vae_encoder", "sd3_vae_decoder"], [SD3DiT, SD3VAEEncoder, SD3VAEDecoder], "civitai"), + (None, "98cc34ccc5b54ae0e56bdea8688dcd5a", ["sd3_text_encoder_2"], [SD3TextEncoder2], "civitai"), + (None, "77ff18050dbc23f50382e45d51a779fe", ["sd3_dit", "sd3_vae_encoder", "sd3_vae_decoder"], [SD3DiT, SD3VAEEncoder, SD3VAEDecoder], "civitai"), + (None, "5da81baee73198a7c19e6d2fe8b5148e", ["sd3_text_encoder_1"], [SD3TextEncoder1], "diffusers"), + (None, "aeb82dce778a03dcb4d726cb03f3c43f", ["hunyuan_video_vae_decoder", "hunyuan_video_vae_encoder"], [HunyuanVideoVAEDecoder, HunyuanVideoVAEEncoder], "diffusers"), + (None, "b9588f02e78f5ccafc9d7c0294e46308", ["hunyuan_video_dit"], [HunyuanVideoDiT], "civitai"), + (None, "84ef4bd4757f60e906b54aa6a7815dc6", ["hunyuan_video_dit"], [HunyuanVideoDiT], "civitai"), + (None, "68beaf8429b7c11aa8ca05b1bd0058bd", ["stepvideo_vae"], [StepVideoVAE], "civitai"), + (None, "5c0216a2132b082c10cb7a0e0377e681", ["stepvideo_dit"], [StepVideoModel], "civitai"), + (None, "9269f8db9040a9d860eaca435be61814", ["wan_video_dit"], [WanModel], "civitai"), + (None, "aafcfd9672c3a2456dc46e1cb6e52c70", ["wan_video_dit"], [WanModel], "civitai"), + (None, "6bfcfb3b342cb286ce886889d519a77e", ["wan_video_dit"], [WanModel], "civitai"), + (None, "6d6ccde6845b95ad9114ab993d917893", ["wan_video_dit"], [WanModel], "civitai"), + (None, "349723183fc063b2bfc10bb2835cf677", ["wan_video_dit"], [WanModel], "civitai"), + (None, "efa44cddf936c70abd0ea28b6cbe946c", ["wan_video_dit"], [WanModel], "civitai"), + (None, "3ef3b1f8e1dab83d5b71fd7b617f859f", ["wan_video_dit"], [WanModel], "civitai"), + (None, "70ddad9d3a133785da5ea371aae09504", ["wan_video_dit"], [WanModel], "civitai"), + (None, "26bde73488a92e64cc20b0a7485b9e5b", ["wan_video_dit"], [WanModel], "civitai"), + (None, "ac6a5aa74f4a0aab6f64eb9a72f19901", ["wan_video_dit"], [WanModel], "civitai"), + (None, "b61c605c2adbd23124d152ed28e049ae", ["wan_video_dit"], [WanModel], "civitai"), + (None, "1f5ab7703c6fc803fdded85ff040c316", ["wan_video_dit"], [WanModel], "civitai"), + (None, "5b013604280dd715f8457c6ed6d6a626", ["wan_video_dit"], [WanModel], "civitai"), + (None, "2267d489f0ceb9f21836532952852ee5", ["wan_video_dit"], [WanModel], "civitai"), + (None, "5ec04e02b42d2580483ad69f4e76346a", ["wan_video_dit"], [WanModel], "civitai"), + (None, "47dbeab5e560db3180adf51dc0232fb1", ["wan_video_dit"], [WanModel], "civitai"), + (None, "5f90e66a0672219f12d9a626c8c21f61", ["wan_video_dit", "wan_video_vap"], [WanModel,MotWanModel], "diffusers"), + (None, "a61453409b67cd3246cf0c3bebad47ba", ["wan_video_dit", "wan_video_vace"], [WanModel, VaceWanModel], "civitai"), + (None, "7a513e1f257a861512b1afd387a8ecd9", ["wan_video_dit", "wan_video_vace"], [WanModel, VaceWanModel], "civitai"), + (None, "cb104773c6c2cb6df4f9529ad5c60d0b", ["wan_video_dit"], [WanModel], "diffusers"), + (None, "966cffdcc52f9c46c391768b27637614", ["wan_video_dit"], [WanS2VModel], "civitai"), + (None, "8b27900f680d7251ce44e2dc8ae1ffef", ["wan_video_dit"], [LongCatVideoTransformer3DModel], "civitai"), + (None, "9c8818c2cbea55eca56c7b447df170da", ["wan_video_text_encoder"], [WanTextEncoder], "civitai"), + (None, "5941c53e207d62f20f9025686193c40b", ["wan_video_image_encoder"], [WanImageEncoder], "civitai"), + (None, "1378ea763357eea97acdef78e65d6d96", ["wan_video_vae"], [WanVideoVAE], "civitai"), + (None, "ccc42284ea13e1ad04693284c7a09be6", ["wan_video_vae"], [WanVideoVAE], "civitai"), + (None, "e1de6c02cdac79f8b739f4d3698cd216", ["wan_video_vae"], [WanVideoVAE38], "civitai"), + (None, "dbd5ec76bbf977983f972c151d545389", ["wan_video_motion_controller"], [WanMotionControllerModel], "civitai"), + (None, "d30fb9e02b1dbf4e509142f05cf7dd50", ["flux_dit", "step1x_connector"], [FluxDiT, Qwen2Connector], "civitai"), + (None, "30143afb2dea73d1ac580e0787628f8c", ["flux_lora_patcher"], [FluxLoraPatcher], "civitai"), + (None, "77c2e4dd2440269eb33bfaa0d004f6ab", ["flux_lora_encoder"], [FluxLoRAEncoder], "civitai"), + (None, "3e6c61b0f9471135fc9c6d6a98e98b6d", ["flux_dit", "nexus_gen_generation_adapter"], [FluxDiT, NexusGenAdapter], "civitai"), + (None, "63c969fd37cce769a90aa781fbff5f81", ["flux_dit", "nexus_gen_editing_adapter"], [FluxDiT, NexusGenImageEmbeddingMerger], "civitai"), + (None, "2bd19e845116e4f875a0a048e27fc219", ["nexus_gen_llm"], [NexusGenAutoregressiveModel], "civitai"), + (None, "0319a1cb19835fb510907dd3367c95ff", ["qwen_image_dit"], [QwenImageDiT], "civitai"), + (None, "8004730443f55db63092006dd9f7110e", ["qwen_image_text_encoder"], [QwenImageTextEncoder], "diffusers"), + (None, "ed4ea5824d55ec3107b09815e318123a", ["qwen_image_vae"], [QwenImageVAE], "diffusers"), + (None, "073bce9cf969e317e5662cd570c3e79c", ["qwen_image_blockwise_controlnet"], [QwenImageBlockWiseControlNet], "civitai"), + (None, "a9e54e480a628f0b956a688a81c33bab", ["qwen_image_blockwise_controlnet"], [QwenImageBlockWiseControlNet], "civitai"), + (None, "06be60f3a4526586d8431cd038a71486", ["wans2v_audio_encoder"], [WanS2VAudioEncoder], "civitai"), + (None, "31fa352acb8a1b1d33cd8764273d80a2", ["wan_video_dit", "wan_video_animate_adapter"], [WanModel, WanAnimateAdapter], "civitai"), +] +huggingface_model_loader_configs = [ + # These configs are provided for detecting model type automatically. + # The format is (architecture_in_huggingface_config, huggingface_lib, model_name, redirected_architecture) + ("ChatGLMModel", "diffsynth.models.kolors_text_encoder", "kolors_text_encoder", None), + ("MarianMTModel", "transformers.models.marian.modeling_marian", "translator", None), + ("BloomForCausalLM", "transformers.models.bloom.modeling_bloom", "beautiful_prompt", None), + ("Qwen2ForCausalLM", "transformers.models.qwen2.modeling_qwen2", "qwen_prompt", None), + # ("LlamaForCausalLM", "transformers.models.llama.modeling_llama", "omost_prompt", None), + ("T5EncoderModel", "diffsynth.models.flux_text_encoder", "flux_text_encoder_2", "FluxTextEncoder2"), + ("CogVideoXTransformer3DModel", "diffsynth.models.cog_dit", "cog_dit", "CogDiT"), + ("SiglipModel", "transformers.models.siglip.modeling_siglip", "siglip_vision_model", "SiglipVisionModel"), + ("LlamaForCausalLM", "diffsynth.models.hunyuan_video_text_encoder", "hunyuan_video_text_encoder_2", "HunyuanVideoLLMEncoder"), + ("LlavaForConditionalGeneration", "diffsynth.models.hunyuan_video_text_encoder", "hunyuan_video_text_encoder_2", "HunyuanVideoMLLMEncoder"), + ("Step1Model", "diffsynth.models.stepvideo_text_encoder", "stepvideo_text_encoder_2", "STEP1TextEncoder"), + ("Qwen2_5_VLForConditionalGeneration", "diffsynth.models.qwenvl", "qwenvl", "Qwen25VL_7b_Embedder"), +] +patch_model_loader_configs = [ + # These configs are provided for detecting model type automatically. + # The format is (state_dict_keys_hash_with_shape, model_name, model_class, extra_kwargs) + ("9a4ab6869ac9b7d6e31f9854e397c867", ["svd_unet"], [SVDUNet], {"add_positional_conv": 128}), +] + +preset_models_on_huggingface = { + "HunyuanDiT": [ + ("Tencent-Hunyuan/HunyuanDiT", "t2i/clip_text_encoder/pytorch_model.bin", "models/HunyuanDiT/t2i/clip_text_encoder"), + ("Tencent-Hunyuan/HunyuanDiT", "t2i/mt5/pytorch_model.bin", "models/HunyuanDiT/t2i/mt5"), + ("Tencent-Hunyuan/HunyuanDiT", "t2i/model/pytorch_model_ema.pt", "models/HunyuanDiT/t2i/model"), + ("Tencent-Hunyuan/HunyuanDiT", "t2i/sdxl-vae-fp16-fix/diffusion_pytorch_model.bin", "models/HunyuanDiT/t2i/sdxl-vae-fp16-fix"), + ], + "stable-video-diffusion-img2vid-xt": [ + ("stabilityai/stable-video-diffusion-img2vid-xt", "svd_xt.safetensors", "models/stable_video_diffusion"), + ], + "ExVideo-SVD-128f-v1": [ + ("ECNU-CILab/ExVideo-SVD-128f-v1", "model.fp16.safetensors", "models/stable_video_diffusion"), + ], + # Stable Diffusion + "StableDiffusion_v15": [ + ("benjamin-paine/stable-diffusion-v1-5", "v1-5-pruned-emaonly.safetensors", "models/stable_diffusion"), + ], + "DreamShaper_8": [ + ("Yntec/Dreamshaper8", "dreamshaper_8.safetensors", "models/stable_diffusion"), + ], + # Textual Inversion + "TextualInversion_VeryBadImageNegative_v1.3": [ + ("gemasai/verybadimagenegative_v1.3", "verybadimagenegative_v1.3.pt", "models/textual_inversion"), + ], + # Stable Diffusion XL + "StableDiffusionXL_v1": [ + ("stabilityai/stable-diffusion-xl-base-1.0", "sd_xl_base_1.0.safetensors", "models/stable_diffusion_xl"), + ], + "BluePencilXL_v200": [ + ("frankjoshua/bluePencilXL_v200", "bluePencilXL_v200.safetensors", "models/stable_diffusion_xl"), + ], + "StableDiffusionXL_Turbo": [ + ("stabilityai/sdxl-turbo", "sd_xl_turbo_1.0_fp16.safetensors", "models/stable_diffusion_xl_turbo"), + ], + # Stable Diffusion 3 + "StableDiffusion3": [ + ("stabilityai/stable-diffusion-3-medium", "sd3_medium_incl_clips_t5xxlfp16.safetensors", "models/stable_diffusion_3"), + ], + "StableDiffusion3_without_T5": [ + ("stabilityai/stable-diffusion-3-medium", "sd3_medium_incl_clips.safetensors", "models/stable_diffusion_3"), + ], + # ControlNet + "ControlNet_v11f1p_sd15_depth": [ + ("lllyasviel/ControlNet-v1-1", "control_v11f1p_sd15_depth.pth", "models/ControlNet"), + ("lllyasviel/Annotators", "dpt_hybrid-midas-501f0c75.pt", "models/Annotators") + ], + "ControlNet_v11p_sd15_softedge": [ + ("lllyasviel/ControlNet-v1-1", "control_v11p_sd15_softedge.pth", "models/ControlNet"), + ("lllyasviel/Annotators", "ControlNetHED.pth", "models/Annotators") + ], + "ControlNet_v11f1e_sd15_tile": [ + ("lllyasviel/ControlNet-v1-1", "control_v11f1e_sd15_tile.pth", "models/ControlNet") + ], + "ControlNet_v11p_sd15_lineart": [ + ("lllyasviel/ControlNet-v1-1", "control_v11p_sd15_lineart.pth", "models/ControlNet"), + ("lllyasviel/Annotators", "sk_model.pth", "models/Annotators"), + ("lllyasviel/Annotators", "sk_model2.pth", "models/Annotators") + ], + "ControlNet_union_sdxl_promax": [ + ("xinsir/controlnet-union-sdxl-1.0", "diffusion_pytorch_model_promax.safetensors", "models/ControlNet/controlnet_union"), + ("lllyasviel/Annotators", "dpt_hybrid-midas-501f0c75.pt", "models/Annotators") + ], + # AnimateDiff + "AnimateDiff_v2": [ + ("guoyww/animatediff", "mm_sd_v15_v2.ckpt", "models/AnimateDiff"), + ], + "AnimateDiff_xl_beta": [ + ("guoyww/animatediff", "mm_sdxl_v10_beta.ckpt", "models/AnimateDiff"), + ], + + # Qwen Prompt + "QwenPrompt": [ + ("Qwen/Qwen2-1.5B-Instruct", "config.json", "models/QwenPrompt/qwen2-1.5b-instruct"), + ("Qwen/Qwen2-1.5B-Instruct", "generation_config.json", "models/QwenPrompt/qwen2-1.5b-instruct"), + ("Qwen/Qwen2-1.5B-Instruct", "model.safetensors", "models/QwenPrompt/qwen2-1.5b-instruct"), + ("Qwen/Qwen2-1.5B-Instruct", "special_tokens_map.json", "models/QwenPrompt/qwen2-1.5b-instruct"), + ("Qwen/Qwen2-1.5B-Instruct", "tokenizer.json", "models/QwenPrompt/qwen2-1.5b-instruct"), + ("Qwen/Qwen2-1.5B-Instruct", "tokenizer_config.json", "models/QwenPrompt/qwen2-1.5b-instruct"), + ("Qwen/Qwen2-1.5B-Instruct", "merges.txt", "models/QwenPrompt/qwen2-1.5b-instruct"), + ("Qwen/Qwen2-1.5B-Instruct", "vocab.json", "models/QwenPrompt/qwen2-1.5b-instruct"), + ], + # Beautiful Prompt + "BeautifulPrompt": [ + ("alibaba-pai/pai-bloom-1b1-text2prompt-sd", "config.json", "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd"), + ("alibaba-pai/pai-bloom-1b1-text2prompt-sd", "generation_config.json", "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd"), + ("alibaba-pai/pai-bloom-1b1-text2prompt-sd", "model.safetensors", "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd"), + ("alibaba-pai/pai-bloom-1b1-text2prompt-sd", "special_tokens_map.json", "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd"), + ("alibaba-pai/pai-bloom-1b1-text2prompt-sd", "tokenizer.json", "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd"), + ("alibaba-pai/pai-bloom-1b1-text2prompt-sd", "tokenizer_config.json", "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd"), + ], + # Omost prompt + "OmostPrompt":[ + ("lllyasviel/omost-llama-3-8b-4bits", "model-00001-of-00002.safetensors", "models/OmostPrompt/omost-llama-3-8b-4bits"), + ("lllyasviel/omost-llama-3-8b-4bits", "model-00002-of-00002.safetensors", "models/OmostPrompt/omost-llama-3-8b-4bits"), + ("lllyasviel/omost-llama-3-8b-4bits", "tokenizer.json", "models/OmostPrompt/omost-llama-3-8b-4bits"), + ("lllyasviel/omost-llama-3-8b-4bits", "tokenizer_config.json", "models/OmostPrompt/omost-llama-3-8b-4bits"), + ("lllyasviel/omost-llama-3-8b-4bits", "config.json", "models/OmostPrompt/omost-llama-3-8b-4bits"), + ("lllyasviel/omost-llama-3-8b-4bits", "generation_config.json", "models/OmostPrompt/omost-llama-3-8b-4bits"), + ("lllyasviel/omost-llama-3-8b-4bits", "model.safetensors.index.json", "models/OmostPrompt/omost-llama-3-8b-4bits"), + ("lllyasviel/omost-llama-3-8b-4bits", "special_tokens_map.json", "models/OmostPrompt/omost-llama-3-8b-4bits"), + ], + # Translator + "opus-mt-zh-en": [ + ("Helsinki-NLP/opus-mt-zh-en", "config.json", "models/translator/opus-mt-zh-en"), + ("Helsinki-NLP/opus-mt-zh-en", "generation_config.json", "models/translator/opus-mt-zh-en"), + ("Helsinki-NLP/opus-mt-zh-en", "metadata.json", "models/translator/opus-mt-zh-en"), + ("Helsinki-NLP/opus-mt-zh-en", "pytorch_model.bin", "models/translator/opus-mt-zh-en"), + ("Helsinki-NLP/opus-mt-zh-en", "source.spm", "models/translator/opus-mt-zh-en"), + ("Helsinki-NLP/opus-mt-zh-en", "target.spm", "models/translator/opus-mt-zh-en"), + ("Helsinki-NLP/opus-mt-zh-en", "tokenizer_config.json", "models/translator/opus-mt-zh-en"), + ("Helsinki-NLP/opus-mt-zh-en", "vocab.json", "models/translator/opus-mt-zh-en"), + ], + # IP-Adapter + "IP-Adapter-SD": [ + ("h94/IP-Adapter", "models/image_encoder/model.safetensors", "models/IpAdapter/stable_diffusion/image_encoder"), + ("h94/IP-Adapter", "models/ip-adapter_sd15.bin", "models/IpAdapter/stable_diffusion"), + ], + "IP-Adapter-SDXL": [ + ("h94/IP-Adapter", "sdxl_models/image_encoder/model.safetensors", "models/IpAdapter/stable_diffusion_xl/image_encoder"), + ("h94/IP-Adapter", "sdxl_models/ip-adapter_sdxl.bin", "models/IpAdapter/stable_diffusion_xl"), + ], + "SDXL-vae-fp16-fix": [ + ("madebyollin/sdxl-vae-fp16-fix", "diffusion_pytorch_model.safetensors", "models/sdxl-vae-fp16-fix") + ], + # Kolors + "Kolors": [ + ("Kwai-Kolors/Kolors", "text_encoder/config.json", "models/kolors/Kolors/text_encoder"), + ("Kwai-Kolors/Kolors", "text_encoder/pytorch_model.bin.index.json", "models/kolors/Kolors/text_encoder"), + ("Kwai-Kolors/Kolors", "text_encoder/pytorch_model-00001-of-00007.bin", "models/kolors/Kolors/text_encoder"), + ("Kwai-Kolors/Kolors", "text_encoder/pytorch_model-00002-of-00007.bin", "models/kolors/Kolors/text_encoder"), + ("Kwai-Kolors/Kolors", "text_encoder/pytorch_model-00003-of-00007.bin", "models/kolors/Kolors/text_encoder"), + ("Kwai-Kolors/Kolors", "text_encoder/pytorch_model-00004-of-00007.bin", "models/kolors/Kolors/text_encoder"), + ("Kwai-Kolors/Kolors", "text_encoder/pytorch_model-00005-of-00007.bin", "models/kolors/Kolors/text_encoder"), + ("Kwai-Kolors/Kolors", "text_encoder/pytorch_model-00006-of-00007.bin", "models/kolors/Kolors/text_encoder"), + ("Kwai-Kolors/Kolors", "text_encoder/pytorch_model-00007-of-00007.bin", "models/kolors/Kolors/text_encoder"), + ("Kwai-Kolors/Kolors", "unet/diffusion_pytorch_model.safetensors", "models/kolors/Kolors/unet"), + ("Kwai-Kolors/Kolors", "vae/diffusion_pytorch_model.safetensors", "models/kolors/Kolors/vae"), + ], + # FLUX + "FLUX.1-dev": [ + ("black-forest-labs/FLUX.1-dev", "text_encoder/model.safetensors", "models/FLUX/FLUX.1-dev/text_encoder"), + ("black-forest-labs/FLUX.1-dev", "text_encoder_2/config.json", "models/FLUX/FLUX.1-dev/text_encoder_2"), + ("black-forest-labs/FLUX.1-dev", "text_encoder_2/model-00001-of-00002.safetensors", "models/FLUX/FLUX.1-dev/text_encoder_2"), + ("black-forest-labs/FLUX.1-dev", "text_encoder_2/model-00002-of-00002.safetensors", "models/FLUX/FLUX.1-dev/text_encoder_2"), + ("black-forest-labs/FLUX.1-dev", "text_encoder_2/model.safetensors.index.json", "models/FLUX/FLUX.1-dev/text_encoder_2"), + ("black-forest-labs/FLUX.1-dev", "ae.safetensors", "models/FLUX/FLUX.1-dev"), + ("black-forest-labs/FLUX.1-dev", "flux1-dev.safetensors", "models/FLUX/FLUX.1-dev"), + ], + "InstantX/FLUX.1-dev-IP-Adapter": { + "file_list": [ + ("InstantX/FLUX.1-dev-IP-Adapter", "ip-adapter.bin", "models/IpAdapter/InstantX/FLUX.1-dev-IP-Adapter"), + ("google/siglip-so400m-patch14-384", "model.safetensors", "models/IpAdapter/InstantX/FLUX.1-dev-IP-Adapter/image_encoder"), + ("google/siglip-so400m-patch14-384", "config.json", "models/IpAdapter/InstantX/FLUX.1-dev-IP-Adapter/image_encoder"), + ], + "load_path": [ + "models/IpAdapter/InstantX/FLUX.1-dev-IP-Adapter/ip-adapter.bin", + "models/IpAdapter/InstantX/FLUX.1-dev-IP-Adapter/image_encoder", + ], + }, + # RIFE + "RIFE": [ + ("AlexWortega/RIFE", "flownet.pkl", "models/RIFE"), + ], + # CogVideo + "CogVideoX-5B": [ + ("THUDM/CogVideoX-5b", "text_encoder/config.json", "models/CogVideo/CogVideoX-5b/text_encoder"), + ("THUDM/CogVideoX-5b", "text_encoder/model.safetensors.index.json", "models/CogVideo/CogVideoX-5b/text_encoder"), + ("THUDM/CogVideoX-5b", "text_encoder/model-00001-of-00002.safetensors", "models/CogVideo/CogVideoX-5b/text_encoder"), + ("THUDM/CogVideoX-5b", "text_encoder/model-00002-of-00002.safetensors", "models/CogVideo/CogVideoX-5b/text_encoder"), + ("THUDM/CogVideoX-5b", "transformer/config.json", "models/CogVideo/CogVideoX-5b/transformer"), + ("THUDM/CogVideoX-5b", "transformer/diffusion_pytorch_model.safetensors.index.json", "models/CogVideo/CogVideoX-5b/transformer"), + ("THUDM/CogVideoX-5b", "transformer/diffusion_pytorch_model-00001-of-00002.safetensors", "models/CogVideo/CogVideoX-5b/transformer"), + ("THUDM/CogVideoX-5b", "transformer/diffusion_pytorch_model-00002-of-00002.safetensors", "models/CogVideo/CogVideoX-5b/transformer"), + ("THUDM/CogVideoX-5b", "vae/diffusion_pytorch_model.safetensors", "models/CogVideo/CogVideoX-5b/vae"), + ], + # Stable Diffusion 3.5 + "StableDiffusion3.5-large": [ + ("stabilityai/stable-diffusion-3.5-large", "sd3.5_large.safetensors", "models/stable_diffusion_3"), + ("stabilityai/stable-diffusion-3.5-large", "text_encoders/clip_l.safetensors", "models/stable_diffusion_3/text_encoders"), + ("stabilityai/stable-diffusion-3.5-large", "text_encoders/clip_g.safetensors", "models/stable_diffusion_3/text_encoders"), + ("stabilityai/stable-diffusion-3.5-large", "text_encoders/t5xxl_fp16.safetensors", "models/stable_diffusion_3/text_encoders"), + ], +} +preset_models_on_modelscope = { + # Hunyuan DiT + "HunyuanDiT": [ + ("modelscope/HunyuanDiT", "t2i/clip_text_encoder/pytorch_model.bin", "models/HunyuanDiT/t2i/clip_text_encoder"), + ("modelscope/HunyuanDiT", "t2i/mt5/pytorch_model.bin", "models/HunyuanDiT/t2i/mt5"), + ("modelscope/HunyuanDiT", "t2i/model/pytorch_model_ema.pt", "models/HunyuanDiT/t2i/model"), + ("modelscope/HunyuanDiT", "t2i/sdxl-vae-fp16-fix/diffusion_pytorch_model.bin", "models/HunyuanDiT/t2i/sdxl-vae-fp16-fix"), + ], + # Stable Video Diffusion + "stable-video-diffusion-img2vid-xt": [ + ("AI-ModelScope/stable-video-diffusion-img2vid-xt", "svd_xt.safetensors", "models/stable_video_diffusion"), + ], + # ExVideo + "ExVideo-SVD-128f-v1": [ + ("ECNU-CILab/ExVideo-SVD-128f-v1", "model.fp16.safetensors", "models/stable_video_diffusion"), + ], + "ExVideo-CogVideoX-LoRA-129f-v1": [ + ("ECNU-CILab/ExVideo-CogVideoX-LoRA-129f-v1", "ExVideo-CogVideoX-LoRA-129f-v1.safetensors", "models/lora"), + ], + # Stable Diffusion + "StableDiffusion_v15": [ + ("AI-ModelScope/stable-diffusion-v1-5", "v1-5-pruned-emaonly.safetensors", "models/stable_diffusion"), + ], + "DreamShaper_8": [ + ("sd_lora/dreamshaper_8", "dreamshaper_8.safetensors", "models/stable_diffusion"), + ], + "AingDiffusion_v12": [ + ("sd_lora/aingdiffusion_v12", "aingdiffusion_v12.safetensors", "models/stable_diffusion"), + ], + "Flat2DAnimerge_v45Sharp": [ + ("sd_lora/Flat-2D-Animerge", "flat2DAnimerge_v45Sharp.safetensors", "models/stable_diffusion"), + ], + # Textual Inversion + "TextualInversion_VeryBadImageNegative_v1.3": [ + ("sd_lora/verybadimagenegative_v1.3", "verybadimagenegative_v1.3.pt", "models/textual_inversion"), + ], + # Stable Diffusion XL + "StableDiffusionXL_v1": [ + ("AI-ModelScope/stable-diffusion-xl-base-1.0", "sd_xl_base_1.0.safetensors", "models/stable_diffusion_xl"), + ], + "BluePencilXL_v200": [ + ("sd_lora/bluePencilXL_v200", "bluePencilXL_v200.safetensors", "models/stable_diffusion_xl"), + ], + "StableDiffusionXL_Turbo": [ + ("AI-ModelScope/sdxl-turbo", "sd_xl_turbo_1.0_fp16.safetensors", "models/stable_diffusion_xl_turbo"), + ], + "SDXL_lora_zyd232_ChineseInkStyle_SDXL_v1_0": [ + ("sd_lora/zyd232_ChineseInkStyle_SDXL_v1_0", "zyd232_ChineseInkStyle_SDXL_v1_0.safetensors", "models/lora"), + ], + # Stable Diffusion 3 + "StableDiffusion3": [ + ("AI-ModelScope/stable-diffusion-3-medium", "sd3_medium_incl_clips_t5xxlfp16.safetensors", "models/stable_diffusion_3"), + ], + "StableDiffusion3_without_T5": [ + ("AI-ModelScope/stable-diffusion-3-medium", "sd3_medium_incl_clips.safetensors", "models/stable_diffusion_3"), + ], + # ControlNet + "ControlNet_v11f1p_sd15_depth": [ + ("AI-ModelScope/ControlNet-v1-1", "control_v11f1p_sd15_depth.pth", "models/ControlNet"), + ("sd_lora/Annotators", "dpt_hybrid-midas-501f0c75.pt", "models/Annotators") + ], + "ControlNet_v11p_sd15_softedge": [ + ("AI-ModelScope/ControlNet-v1-1", "control_v11p_sd15_softedge.pth", "models/ControlNet"), + ("sd_lora/Annotators", "ControlNetHED.pth", "models/Annotators") + ], + "ControlNet_v11f1e_sd15_tile": [ + ("AI-ModelScope/ControlNet-v1-1", "control_v11f1e_sd15_tile.pth", "models/ControlNet") + ], + "ControlNet_v11p_sd15_lineart": [ + ("AI-ModelScope/ControlNet-v1-1", "control_v11p_sd15_lineart.pth", "models/ControlNet"), + ("sd_lora/Annotators", "sk_model.pth", "models/Annotators"), + ("sd_lora/Annotators", "sk_model2.pth", "models/Annotators") + ], + "ControlNet_union_sdxl_promax": [ + ("AI-ModelScope/controlnet-union-sdxl-1.0", "diffusion_pytorch_model_promax.safetensors", "models/ControlNet/controlnet_union"), + ("sd_lora/Annotators", "dpt_hybrid-midas-501f0c75.pt", "models/Annotators") + ], + "Annotators:Depth": [ + ("sd_lora/Annotators", "dpt_hybrid-midas-501f0c75.pt", "models/Annotators"), + ], + "Annotators:Softedge": [ + ("sd_lora/Annotators", "ControlNetHED.pth", "models/Annotators"), + ], + "Annotators:Lineart": [ + ("sd_lora/Annotators", "sk_model.pth", "models/Annotators"), + ("sd_lora/Annotators", "sk_model2.pth", "models/Annotators"), + ], + "Annotators:Normal": [ + ("sd_lora/Annotators", "scannet.pt", "models/Annotators"), + ], + "Annotators:Openpose": [ + ("sd_lora/Annotators", "body_pose_model.pth", "models/Annotators"), + ("sd_lora/Annotators", "facenet.pth", "models/Annotators"), + ("sd_lora/Annotators", "hand_pose_model.pth", "models/Annotators"), + ], + # AnimateDiff + "AnimateDiff_v2": [ + ("Shanghai_AI_Laboratory/animatediff", "mm_sd_v15_v2.ckpt", "models/AnimateDiff"), + ], + "AnimateDiff_xl_beta": [ + ("Shanghai_AI_Laboratory/animatediff", "mm_sdxl_v10_beta.ckpt", "models/AnimateDiff"), + ], + # RIFE + "RIFE": [ + ("Damo_XR_Lab/cv_rife_video-frame-interpolation", "flownet.pkl", "models/RIFE"), + ], + # Qwen Prompt + "QwenPrompt": { + "file_list": [ + ("qwen/Qwen2-1.5B-Instruct", "config.json", "models/QwenPrompt/qwen2-1.5b-instruct"), + ("qwen/Qwen2-1.5B-Instruct", "generation_config.json", "models/QwenPrompt/qwen2-1.5b-instruct"), + ("qwen/Qwen2-1.5B-Instruct", "model.safetensors", "models/QwenPrompt/qwen2-1.5b-instruct"), + ("qwen/Qwen2-1.5B-Instruct", "special_tokens_map.json", "models/QwenPrompt/qwen2-1.5b-instruct"), + ("qwen/Qwen2-1.5B-Instruct", "tokenizer.json", "models/QwenPrompt/qwen2-1.5b-instruct"), + ("qwen/Qwen2-1.5B-Instruct", "tokenizer_config.json", "models/QwenPrompt/qwen2-1.5b-instruct"), + ("qwen/Qwen2-1.5B-Instruct", "merges.txt", "models/QwenPrompt/qwen2-1.5b-instruct"), + ("qwen/Qwen2-1.5B-Instruct", "vocab.json", "models/QwenPrompt/qwen2-1.5b-instruct"), + ], + "load_path": [ + "models/QwenPrompt/qwen2-1.5b-instruct", + ], + }, + # Beautiful Prompt + "BeautifulPrompt": { + "file_list": [ + ("AI-ModelScope/pai-bloom-1b1-text2prompt-sd", "config.json", "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd"), + ("AI-ModelScope/pai-bloom-1b1-text2prompt-sd", "generation_config.json", "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd"), + ("AI-ModelScope/pai-bloom-1b1-text2prompt-sd", "model.safetensors", "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd"), + ("AI-ModelScope/pai-bloom-1b1-text2prompt-sd", "special_tokens_map.json", "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd"), + ("AI-ModelScope/pai-bloom-1b1-text2prompt-sd", "tokenizer.json", "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd"), + ("AI-ModelScope/pai-bloom-1b1-text2prompt-sd", "tokenizer_config.json", "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd"), + ], + "load_path": [ + "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd", + ], + }, + # Omost prompt + "OmostPrompt": { + "file_list": [ + ("Omost/omost-llama-3-8b-4bits", "model-00001-of-00002.safetensors", "models/OmostPrompt/omost-llama-3-8b-4bits"), + ("Omost/omost-llama-3-8b-4bits", "model-00002-of-00002.safetensors", "models/OmostPrompt/omost-llama-3-8b-4bits"), + ("Omost/omost-llama-3-8b-4bits", "tokenizer.json", "models/OmostPrompt/omost-llama-3-8b-4bits"), + ("Omost/omost-llama-3-8b-4bits", "tokenizer_config.json", "models/OmostPrompt/omost-llama-3-8b-4bits"), + ("Omost/omost-llama-3-8b-4bits", "config.json", "models/OmostPrompt/omost-llama-3-8b-4bits"), + ("Omost/omost-llama-3-8b-4bits", "generation_config.json", "models/OmostPrompt/omost-llama-3-8b-4bits"), + ("Omost/omost-llama-3-8b-4bits", "model.safetensors.index.json", "models/OmostPrompt/omost-llama-3-8b-4bits"), + ("Omost/omost-llama-3-8b-4bits", "special_tokens_map.json", "models/OmostPrompt/omost-llama-3-8b-4bits"), + ], + "load_path": [ + "models/OmostPrompt/omost-llama-3-8b-4bits", + ], + }, + # Translator + "opus-mt-zh-en": { + "file_list": [ + ("moxying/opus-mt-zh-en", "config.json", "models/translator/opus-mt-zh-en"), + ("moxying/opus-mt-zh-en", "generation_config.json", "models/translator/opus-mt-zh-en"), + ("moxying/opus-mt-zh-en", "metadata.json", "models/translator/opus-mt-zh-en"), + ("moxying/opus-mt-zh-en", "pytorch_model.bin", "models/translator/opus-mt-zh-en"), + ("moxying/opus-mt-zh-en", "source.spm", "models/translator/opus-mt-zh-en"), + ("moxying/opus-mt-zh-en", "target.spm", "models/translator/opus-mt-zh-en"), + ("moxying/opus-mt-zh-en", "tokenizer_config.json", "models/translator/opus-mt-zh-en"), + ("moxying/opus-mt-zh-en", "vocab.json", "models/translator/opus-mt-zh-en"), + ], + "load_path": [ + "models/translator/opus-mt-zh-en", + ], + }, + # IP-Adapter + "IP-Adapter-SD": [ + ("AI-ModelScope/IP-Adapter", "models/image_encoder/model.safetensors", "models/IpAdapter/stable_diffusion/image_encoder"), + ("AI-ModelScope/IP-Adapter", "models/ip-adapter_sd15.bin", "models/IpAdapter/stable_diffusion"), + ], + "IP-Adapter-SDXL": [ + ("AI-ModelScope/IP-Adapter", "sdxl_models/image_encoder/model.safetensors", "models/IpAdapter/stable_diffusion_xl/image_encoder"), + ("AI-ModelScope/IP-Adapter", "sdxl_models/ip-adapter_sdxl.bin", "models/IpAdapter/stable_diffusion_xl"), + ], + # Kolors + "Kolors": { + "file_list": [ + ("Kwai-Kolors/Kolors", "text_encoder/config.json", "models/kolors/Kolors/text_encoder"), + ("Kwai-Kolors/Kolors", "text_encoder/pytorch_model.bin.index.json", "models/kolors/Kolors/text_encoder"), + ("Kwai-Kolors/Kolors", "text_encoder/pytorch_model-00001-of-00007.bin", "models/kolors/Kolors/text_encoder"), + ("Kwai-Kolors/Kolors", "text_encoder/pytorch_model-00002-of-00007.bin", "models/kolors/Kolors/text_encoder"), + ("Kwai-Kolors/Kolors", "text_encoder/pytorch_model-00003-of-00007.bin", "models/kolors/Kolors/text_encoder"), + ("Kwai-Kolors/Kolors", "text_encoder/pytorch_model-00004-of-00007.bin", "models/kolors/Kolors/text_encoder"), + ("Kwai-Kolors/Kolors", "text_encoder/pytorch_model-00005-of-00007.bin", "models/kolors/Kolors/text_encoder"), + ("Kwai-Kolors/Kolors", "text_encoder/pytorch_model-00006-of-00007.bin", "models/kolors/Kolors/text_encoder"), + ("Kwai-Kolors/Kolors", "text_encoder/pytorch_model-00007-of-00007.bin", "models/kolors/Kolors/text_encoder"), + ("Kwai-Kolors/Kolors", "unet/diffusion_pytorch_model.safetensors", "models/kolors/Kolors/unet"), + ("Kwai-Kolors/Kolors", "vae/diffusion_pytorch_model.safetensors", "models/kolors/Kolors/vae"), + ], + "load_path": [ + "models/kolors/Kolors/text_encoder", + "models/kolors/Kolors/unet/diffusion_pytorch_model.safetensors", + "models/kolors/Kolors/vae/diffusion_pytorch_model.safetensors", + ], + }, + "SDXL-vae-fp16-fix": [ + ("AI-ModelScope/sdxl-vae-fp16-fix", "diffusion_pytorch_model.safetensors", "models/sdxl-vae-fp16-fix") + ], + # FLUX + "FLUX.1-dev": { + "file_list": [ + ("AI-ModelScope/FLUX.1-dev", "text_encoder/model.safetensors", "models/FLUX/FLUX.1-dev/text_encoder"), + ("AI-ModelScope/FLUX.1-dev", "text_encoder_2/config.json", "models/FLUX/FLUX.1-dev/text_encoder_2"), + ("AI-ModelScope/FLUX.1-dev", "text_encoder_2/model-00001-of-00002.safetensors", "models/FLUX/FLUX.1-dev/text_encoder_2"), + ("AI-ModelScope/FLUX.1-dev", "text_encoder_2/model-00002-of-00002.safetensors", "models/FLUX/FLUX.1-dev/text_encoder_2"), + ("AI-ModelScope/FLUX.1-dev", "text_encoder_2/model.safetensors.index.json", "models/FLUX/FLUX.1-dev/text_encoder_2"), + ("AI-ModelScope/FLUX.1-dev", "ae.safetensors", "models/FLUX/FLUX.1-dev"), + ("AI-ModelScope/FLUX.1-dev", "flux1-dev.safetensors", "models/FLUX/FLUX.1-dev"), + ], + "load_path": [ + "models/FLUX/FLUX.1-dev/text_encoder/model.safetensors", + "models/FLUX/FLUX.1-dev/text_encoder_2", + "models/FLUX/FLUX.1-dev/ae.safetensors", + "models/FLUX/FLUX.1-dev/flux1-dev.safetensors" + ], + }, + "FLUX.1-schnell": { + "file_list": [ + ("AI-ModelScope/FLUX.1-dev", "text_encoder/model.safetensors", "models/FLUX/FLUX.1-dev/text_encoder"), + ("AI-ModelScope/FLUX.1-dev", "text_encoder_2/config.json", "models/FLUX/FLUX.1-dev/text_encoder_2"), + ("AI-ModelScope/FLUX.1-dev", "text_encoder_2/model-00001-of-00002.safetensors", "models/FLUX/FLUX.1-dev/text_encoder_2"), + ("AI-ModelScope/FLUX.1-dev", "text_encoder_2/model-00002-of-00002.safetensors", "models/FLUX/FLUX.1-dev/text_encoder_2"), + ("AI-ModelScope/FLUX.1-dev", "text_encoder_2/model.safetensors.index.json", "models/FLUX/FLUX.1-dev/text_encoder_2"), + ("AI-ModelScope/FLUX.1-dev", "ae.safetensors", "models/FLUX/FLUX.1-dev"), + ("AI-ModelScope/FLUX.1-schnell", "flux1-schnell.safetensors", "models/FLUX/FLUX.1-schnell"), + ], + "load_path": [ + "models/FLUX/FLUX.1-dev/text_encoder/model.safetensors", + "models/FLUX/FLUX.1-dev/text_encoder_2", + "models/FLUX/FLUX.1-dev/ae.safetensors", + "models/FLUX/FLUX.1-schnell/flux1-schnell.safetensors" + ], + }, + "InstantX/FLUX.1-dev-Controlnet-Union-alpha": [ + ("InstantX/FLUX.1-dev-Controlnet-Union-alpha", "diffusion_pytorch_model.safetensors", "models/ControlNet/InstantX/FLUX.1-dev-Controlnet-Union-alpha"), + ], + "jasperai/Flux.1-dev-Controlnet-Depth": [ + ("jasperai/Flux.1-dev-Controlnet-Depth", "diffusion_pytorch_model.safetensors", "models/ControlNet/jasperai/Flux.1-dev-Controlnet-Depth"), + ], + "jasperai/Flux.1-dev-Controlnet-Surface-Normals": [ + ("jasperai/Flux.1-dev-Controlnet-Surface-Normals", "diffusion_pytorch_model.safetensors", "models/ControlNet/jasperai/Flux.1-dev-Controlnet-Surface-Normals"), + ], + "jasperai/Flux.1-dev-Controlnet-Upscaler": [ + ("jasperai/Flux.1-dev-Controlnet-Upscaler", "diffusion_pytorch_model.safetensors", "models/ControlNet/jasperai/Flux.1-dev-Controlnet-Upscaler"), + ], + "alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Alpha": [ + ("alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Alpha", "diffusion_pytorch_model.safetensors", "models/ControlNet/alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Alpha"), + ], + "alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta": [ + ("alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta", "diffusion_pytorch_model.safetensors", "models/ControlNet/alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta"), + ], + "Shakker-Labs/FLUX.1-dev-ControlNet-Depth": [ + ("Shakker-Labs/FLUX.1-dev-ControlNet-Depth", "diffusion_pytorch_model.safetensors", "models/ControlNet/Shakker-Labs/FLUX.1-dev-ControlNet-Depth"), + ], + "Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro": [ + ("Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro", "diffusion_pytorch_model.safetensors", "models/ControlNet/Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro"), + ], + "InstantX/FLUX.1-dev-IP-Adapter": { + "file_list": [ + ("InstantX/FLUX.1-dev-IP-Adapter", "ip-adapter.bin", "models/IpAdapter/InstantX/FLUX.1-dev-IP-Adapter"), + ("AI-ModelScope/siglip-so400m-patch14-384", "model.safetensors", "models/IpAdapter/InstantX/FLUX.1-dev-IP-Adapter/image_encoder"), + ("AI-ModelScope/siglip-so400m-patch14-384", "config.json", "models/IpAdapter/InstantX/FLUX.1-dev-IP-Adapter/image_encoder"), + ], + "load_path": [ + "models/IpAdapter/InstantX/FLUX.1-dev-IP-Adapter/ip-adapter.bin", + "models/IpAdapter/InstantX/FLUX.1-dev-IP-Adapter/image_encoder", + ], + }, + "InfiniteYou":{ + "file_list":[ + ("ByteDance/InfiniteYou", "infu_flux_v1.0/aes_stage2/InfuseNetModel/diffusion_pytorch_model-00001-of-00002.safetensors", "models/InfiniteYou/InfuseNetModel"), + ("ByteDance/InfiniteYou", "infu_flux_v1.0/aes_stage2/InfuseNetModel/diffusion_pytorch_model-00002-of-00002.safetensors", "models/InfiniteYou/InfuseNetModel"), + ("ByteDance/InfiniteYou", "infu_flux_v1.0/aes_stage2/image_proj_model.bin", "models/InfiniteYou"), + ("ByteDance/InfiniteYou", "supports/insightface/models/antelopev2/1k3d68.onnx", "models/InfiniteYou/insightface/models/antelopev2"), + ("ByteDance/InfiniteYou", "supports/insightface/models/antelopev2/2d106det.onnx", "models/InfiniteYou/insightface/models/antelopev2"), + ("ByteDance/InfiniteYou", "supports/insightface/models/antelopev2/genderage.onnx", "models/InfiniteYou/insightface/models/antelopev2"), + ("ByteDance/InfiniteYou", "supports/insightface/models/antelopev2/glintr100.onnx", "models/InfiniteYou/insightface/models/antelopev2"), + ("ByteDance/InfiniteYou", "supports/insightface/models/antelopev2/scrfd_10g_bnkps.onnx", "models/InfiniteYou/insightface/models/antelopev2"), + ], + "load_path":[ + [ + "models/InfiniteYou/InfuseNetModel/diffusion_pytorch_model-00001-of-00002.safetensors", + "models/InfiniteYou/InfuseNetModel/diffusion_pytorch_model-00002-of-00002.safetensors" + ], + "models/InfiniteYou/image_proj_model.bin", + ], + }, + # ESRGAN + "ESRGAN_x4": [ + ("AI-ModelScope/Real-ESRGAN", "RealESRGAN_x4.pth", "models/ESRGAN"), + ], + # RIFE + "RIFE": [ + ("AI-ModelScope/RIFE", "flownet.pkl", "models/RIFE"), + ], + # Omnigen + "OmniGen-v1": { + "file_list": [ + ("BAAI/OmniGen-v1", "vae/diffusion_pytorch_model.safetensors", "models/OmniGen/OmniGen-v1/vae"), + ("BAAI/OmniGen-v1", "model.safetensors", "models/OmniGen/OmniGen-v1"), + ("BAAI/OmniGen-v1", "config.json", "models/OmniGen/OmniGen-v1"), + ("BAAI/OmniGen-v1", "special_tokens_map.json", "models/OmniGen/OmniGen-v1"), + ("BAAI/OmniGen-v1", "tokenizer_config.json", "models/OmniGen/OmniGen-v1"), + ("BAAI/OmniGen-v1", "tokenizer.json", "models/OmniGen/OmniGen-v1"), + ], + "load_path": [ + "models/OmniGen/OmniGen-v1/vae/diffusion_pytorch_model.safetensors", + "models/OmniGen/OmniGen-v1/model.safetensors", + ] + }, + # CogVideo + "CogVideoX-5B": { + "file_list": [ + ("ZhipuAI/CogVideoX-5b", "text_encoder/config.json", "models/CogVideo/CogVideoX-5b/text_encoder"), + ("ZhipuAI/CogVideoX-5b", "text_encoder/model.safetensors.index.json", "models/CogVideo/CogVideoX-5b/text_encoder"), + ("ZhipuAI/CogVideoX-5b", "text_encoder/model-00001-of-00002.safetensors", "models/CogVideo/CogVideoX-5b/text_encoder"), + ("ZhipuAI/CogVideoX-5b", "text_encoder/model-00002-of-00002.safetensors", "models/CogVideo/CogVideoX-5b/text_encoder"), + ("ZhipuAI/CogVideoX-5b", "transformer/config.json", "models/CogVideo/CogVideoX-5b/transformer"), + ("ZhipuAI/CogVideoX-5b", "transformer/diffusion_pytorch_model.safetensors.index.json", "models/CogVideo/CogVideoX-5b/transformer"), + ("ZhipuAI/CogVideoX-5b", "transformer/diffusion_pytorch_model-00001-of-00002.safetensors", "models/CogVideo/CogVideoX-5b/transformer"), + ("ZhipuAI/CogVideoX-5b", "transformer/diffusion_pytorch_model-00002-of-00002.safetensors", "models/CogVideo/CogVideoX-5b/transformer"), + ("ZhipuAI/CogVideoX-5b", "vae/diffusion_pytorch_model.safetensors", "models/CogVideo/CogVideoX-5b/vae"), + ], + "load_path": [ + "models/CogVideo/CogVideoX-5b/text_encoder", + "models/CogVideo/CogVideoX-5b/transformer", + "models/CogVideo/CogVideoX-5b/vae/diffusion_pytorch_model.safetensors", + ], + }, + # Stable Diffusion 3.5 + "StableDiffusion3.5-large": [ + ("AI-ModelScope/stable-diffusion-3.5-large", "sd3.5_large.safetensors", "models/stable_diffusion_3"), + ("AI-ModelScope/stable-diffusion-3.5-large", "text_encoders/clip_l.safetensors", "models/stable_diffusion_3/text_encoders"), + ("AI-ModelScope/stable-diffusion-3.5-large", "text_encoders/clip_g.safetensors", "models/stable_diffusion_3/text_encoders"), + ("AI-ModelScope/stable-diffusion-3.5-large", "text_encoders/t5xxl_fp16.safetensors", "models/stable_diffusion_3/text_encoders"), + ], + "StableDiffusion3.5-medium": [ + ("AI-ModelScope/stable-diffusion-3.5-medium", "sd3.5_medium.safetensors", "models/stable_diffusion_3"), + ("AI-ModelScope/stable-diffusion-3.5-large", "text_encoders/clip_l.safetensors", "models/stable_diffusion_3/text_encoders"), + ("AI-ModelScope/stable-diffusion-3.5-large", "text_encoders/clip_g.safetensors", "models/stable_diffusion_3/text_encoders"), + ("AI-ModelScope/stable-diffusion-3.5-large", "text_encoders/t5xxl_fp16.safetensors", "models/stable_diffusion_3/text_encoders"), + ], + "StableDiffusion3.5-large-turbo": [ + ("AI-ModelScope/stable-diffusion-3.5-large-turbo", "sd3.5_large_turbo.safetensors", "models/stable_diffusion_3"), + ("AI-ModelScope/stable-diffusion-3.5-large", "text_encoders/clip_l.safetensors", "models/stable_diffusion_3/text_encoders"), + ("AI-ModelScope/stable-diffusion-3.5-large", "text_encoders/clip_g.safetensors", "models/stable_diffusion_3/text_encoders"), + ("AI-ModelScope/stable-diffusion-3.5-large", "text_encoders/t5xxl_fp16.safetensors", "models/stable_diffusion_3/text_encoders"), + ], + "HunyuanVideo":{ + "file_list": [ + ("AI-ModelScope/clip-vit-large-patch14", "model.safetensors", "models/HunyuanVideo/text_encoder"), + ("DiffSynth-Studio/HunyuanVideo_MLLM_text_encoder", "model-00001-of-00004.safetensors", "models/HunyuanVideo/text_encoder_2"), + ("DiffSynth-Studio/HunyuanVideo_MLLM_text_encoder", "model-00002-of-00004.safetensors", "models/HunyuanVideo/text_encoder_2"), + ("DiffSynth-Studio/HunyuanVideo_MLLM_text_encoder", "model-00003-of-00004.safetensors", "models/HunyuanVideo/text_encoder_2"), + ("DiffSynth-Studio/HunyuanVideo_MLLM_text_encoder", "model-00004-of-00004.safetensors", "models/HunyuanVideo/text_encoder_2"), + ("DiffSynth-Studio/HunyuanVideo_MLLM_text_encoder", "config.json", "models/HunyuanVideo/text_encoder_2"), + ("DiffSynth-Studio/HunyuanVideo_MLLM_text_encoder", "model.safetensors.index.json", "models/HunyuanVideo/text_encoder_2"), + ("AI-ModelScope/HunyuanVideo", "hunyuan-video-t2v-720p/vae/pytorch_model.pt", "models/HunyuanVideo/vae"), + ("AI-ModelScope/HunyuanVideo", "hunyuan-video-t2v-720p/transformers/mp_rank_00_model_states.pt", "models/HunyuanVideo/transformers") + ], + "load_path": [ + "models/HunyuanVideo/text_encoder/model.safetensors", + "models/HunyuanVideo/text_encoder_2", + "models/HunyuanVideo/vae/pytorch_model.pt", + "models/HunyuanVideo/transformers/mp_rank_00_model_states.pt" + ], + }, + "HunyuanVideoI2V":{ + "file_list": [ + ("AI-ModelScope/clip-vit-large-patch14", "model.safetensors", "models/HunyuanVideoI2V/text_encoder"), + ("AI-ModelScope/llava-llama-3-8b-v1_1-transformers", "model-00001-of-00004.safetensors", "models/HunyuanVideoI2V/text_encoder_2"), + ("AI-ModelScope/llava-llama-3-8b-v1_1-transformers", "model-00002-of-00004.safetensors", "models/HunyuanVideoI2V/text_encoder_2"), + ("AI-ModelScope/llava-llama-3-8b-v1_1-transformers", "model-00003-of-00004.safetensors", "models/HunyuanVideoI2V/text_encoder_2"), + ("AI-ModelScope/llava-llama-3-8b-v1_1-transformers", "model-00004-of-00004.safetensors", "models/HunyuanVideoI2V/text_encoder_2"), + ("AI-ModelScope/llava-llama-3-8b-v1_1-transformers", "config.json", "models/HunyuanVideoI2V/text_encoder_2"), + ("AI-ModelScope/llava-llama-3-8b-v1_1-transformers", "model.safetensors.index.json", "models/HunyuanVideoI2V/text_encoder_2"), + ("AI-ModelScope/HunyuanVideo-I2V", "hunyuan-video-i2v-720p/vae/pytorch_model.pt", "models/HunyuanVideoI2V/vae"), + ("AI-ModelScope/HunyuanVideo-I2V", "hunyuan-video-i2v-720p/transformers/mp_rank_00_model_states.pt", "models/HunyuanVideoI2V/transformers") + ], + "load_path": [ + "models/HunyuanVideoI2V/text_encoder/model.safetensors", + "models/HunyuanVideoI2V/text_encoder_2", + "models/HunyuanVideoI2V/vae/pytorch_model.pt", + "models/HunyuanVideoI2V/transformers/mp_rank_00_model_states.pt" + ], + }, + "HunyuanVideo-fp8":{ + "file_list": [ + ("AI-ModelScope/clip-vit-large-patch14", "model.safetensors", "models/HunyuanVideo/text_encoder"), + ("DiffSynth-Studio/HunyuanVideo_MLLM_text_encoder", "model-00001-of-00004.safetensors", "models/HunyuanVideo/text_encoder_2"), + ("DiffSynth-Studio/HunyuanVideo_MLLM_text_encoder", "model-00002-of-00004.safetensors", "models/HunyuanVideo/text_encoder_2"), + ("DiffSynth-Studio/HunyuanVideo_MLLM_text_encoder", "model-00003-of-00004.safetensors", "models/HunyuanVideo/text_encoder_2"), + ("DiffSynth-Studio/HunyuanVideo_MLLM_text_encoder", "model-00004-of-00004.safetensors", "models/HunyuanVideo/text_encoder_2"), + ("DiffSynth-Studio/HunyuanVideo_MLLM_text_encoder", "config.json", "models/HunyuanVideo/text_encoder_2"), + ("DiffSynth-Studio/HunyuanVideo_MLLM_text_encoder", "model.safetensors.index.json", "models/HunyuanVideo/text_encoder_2"), + ("AI-ModelScope/HunyuanVideo", "hunyuan-video-t2v-720p/vae/pytorch_model.pt", "models/HunyuanVideo/vae"), + ("DiffSynth-Studio/HunyuanVideo-safetensors", "model.fp8.safetensors", "models/HunyuanVideo/transformers") + ], + "load_path": [ + "models/HunyuanVideo/text_encoder/model.safetensors", + "models/HunyuanVideo/text_encoder_2", + "models/HunyuanVideo/vae/pytorch_model.pt", + "models/HunyuanVideo/transformers/model.fp8.safetensors" + ], + }, +} +Preset_model_id: TypeAlias = Literal[ + "HunyuanDiT", + "stable-video-diffusion-img2vid-xt", + "ExVideo-SVD-128f-v1", + "ExVideo-CogVideoX-LoRA-129f-v1", + "StableDiffusion_v15", + "DreamShaper_8", + "AingDiffusion_v12", + "Flat2DAnimerge_v45Sharp", + "TextualInversion_VeryBadImageNegative_v1.3", + "StableDiffusionXL_v1", + "BluePencilXL_v200", + "StableDiffusionXL_Turbo", + "ControlNet_v11f1p_sd15_depth", + "ControlNet_v11p_sd15_softedge", + "ControlNet_v11f1e_sd15_tile", + "ControlNet_v11p_sd15_lineart", + "AnimateDiff_v2", + "AnimateDiff_xl_beta", + "RIFE", + "BeautifulPrompt", + "opus-mt-zh-en", + "IP-Adapter-SD", + "IP-Adapter-SDXL", + "StableDiffusion3", + "StableDiffusion3_without_T5", + "Kolors", + "SDXL-vae-fp16-fix", + "ControlNet_union_sdxl_promax", + "FLUX.1-dev", + "FLUX.1-schnell", + "InstantX/FLUX.1-dev-Controlnet-Union-alpha", + "jasperai/Flux.1-dev-Controlnet-Depth", + "jasperai/Flux.1-dev-Controlnet-Surface-Normals", + "jasperai/Flux.1-dev-Controlnet-Upscaler", + "alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Alpha", + "alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta", + "Shakker-Labs/FLUX.1-dev-ControlNet-Depth", + "Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro", + "InstantX/FLUX.1-dev-IP-Adapter", + "InfiniteYou", + "SDXL_lora_zyd232_ChineseInkStyle_SDXL_v1_0", + "QwenPrompt", + "OmostPrompt", + "ESRGAN_x4", + "RIFE", + "OmniGen-v1", + "CogVideoX-5B", + "Annotators:Depth", + "Annotators:Softedge", + "Annotators:Lineart", + "Annotators:Normal", + "Annotators:Openpose", + "StableDiffusion3.5-large", + "StableDiffusion3.5-medium", + "HunyuanVideo", + "HunyuanVideo-fp8", + "HunyuanVideoI2V", +] diff --git a/diffsynth/controlnets/__init__.py b/diffsynth/controlnets/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..a3e15add6ab116bf261804b8c83c86ff4d61c41b --- /dev/null +++ b/diffsynth/controlnets/__init__.py @@ -0,0 +1,2 @@ +from .controlnet_unit import ControlNetConfigUnit, ControlNetUnit, MultiControlNetManager, FluxMultiControlNetManager +from .processors import Annotator diff --git a/diffsynth/controlnets/controlnet_unit.py b/diffsynth/controlnets/controlnet_unit.py new file mode 100644 index 0000000000000000000000000000000000000000..fdb4829483d208ec0295d1b5a8f82681b4251ea4 --- /dev/null +++ b/diffsynth/controlnets/controlnet_unit.py @@ -0,0 +1,91 @@ +import torch +import numpy as np +from .processors import Processor_id + + +class ControlNetConfigUnit: + def __init__(self, processor_id: Processor_id, model_path, scale=1.0, skip_processor=False): + self.processor_id = processor_id + self.model_path = model_path + self.scale = scale + self.skip_processor = skip_processor + + +class ControlNetUnit: + def __init__(self, processor, model, scale=1.0): + self.processor = processor + self.model = model + self.scale = scale + + +class MultiControlNetManager: + def __init__(self, controlnet_units=[]): + self.processors = [unit.processor for unit in controlnet_units] + self.models = [unit.model for unit in controlnet_units] + self.scales = [unit.scale for unit in controlnet_units] + + def cpu(self): + for model in self.models: + model.cpu() + + def to(self, device): + for model in self.models: + model.to(device) + for processor in self.processors: + processor.to(device) + + def process_image(self, image, processor_id=None): + if processor_id is None: + processed_image = [processor(image) for processor in self.processors] + else: + processed_image = [self.processors[processor_id](image)] + processed_image = torch.concat([ + torch.Tensor(np.array(image_, dtype=np.float32) / 255).permute(2, 0, 1).unsqueeze(0) + for image_ in processed_image + ], dim=0) + return processed_image + + def __call__( + self, + sample, timestep, encoder_hidden_states, conditionings, + tiled=False, tile_size=64, tile_stride=32, **kwargs + ): + res_stack = None + for processor, conditioning, model, scale in zip(self.processors, conditionings, self.models, self.scales): + res_stack_ = model( + sample, timestep, encoder_hidden_states, conditioning, **kwargs, + tiled=tiled, tile_size=tile_size, tile_stride=tile_stride, + processor_id=processor.processor_id + ) + res_stack_ = [res * scale for res in res_stack_] + if res_stack is None: + res_stack = res_stack_ + else: + res_stack = [i + j for i, j in zip(res_stack, res_stack_)] + return res_stack + + +class FluxMultiControlNetManager(MultiControlNetManager): + def __init__(self, controlnet_units=[]): + super().__init__(controlnet_units=controlnet_units) + + def process_image(self, image, processor_id=None): + if processor_id is None: + processed_image = [processor(image) for processor in self.processors] + else: + processed_image = [self.processors[processor_id](image)] + return processed_image + + def __call__(self, conditionings, **kwargs): + res_stack, single_res_stack = None, None + for processor, conditioning, model, scale in zip(self.processors, conditionings, self.models, self.scales): + res_stack_, single_res_stack_ = model(controlnet_conditioning=conditioning, processor_id=processor.processor_id, **kwargs) + res_stack_ = [res * scale for res in res_stack_] + single_res_stack_ = [res * scale for res in single_res_stack_] + if res_stack is None: + res_stack = res_stack_ + single_res_stack = single_res_stack_ + else: + res_stack = [i + j for i, j in zip(res_stack, res_stack_)] + single_res_stack = [i + j for i, j in zip(single_res_stack, single_res_stack_)] + return res_stack, single_res_stack diff --git a/diffsynth/controlnets/processors.py b/diffsynth/controlnets/processors.py new file mode 100644 index 0000000000000000000000000000000000000000..06553e06d1c6d09f5a3deecfd4ea5604c5dd4352 --- /dev/null +++ b/diffsynth/controlnets/processors.py @@ -0,0 +1,62 @@ +from typing_extensions import Literal, TypeAlias + + +Processor_id: TypeAlias = Literal[ + "canny", "depth", "softedge", "lineart", "lineart_anime", "openpose", "normal", "tile", "none", "inpaint" +] + +class Annotator: + def __init__(self, processor_id: Processor_id, model_path="models/Annotators", detect_resolution=None, device='cuda', skip_processor=False): + if not skip_processor: + if processor_id == "canny": + from controlnet_aux.processor import CannyDetector + self.processor = CannyDetector() + elif processor_id == "depth": + from controlnet_aux.processor import MidasDetector + self.processor = MidasDetector.from_pretrained(model_path).to(device) + elif processor_id == "softedge": + from controlnet_aux.processor import HEDdetector + self.processor = HEDdetector.from_pretrained(model_path).to(device) + elif processor_id == "lineart": + from controlnet_aux.processor import LineartDetector + self.processor = LineartDetector.from_pretrained(model_path).to(device) + elif processor_id == "lineart_anime": + from controlnet_aux.processor import LineartAnimeDetector + self.processor = LineartAnimeDetector.from_pretrained(model_path).to(device) + elif processor_id == "openpose": + from controlnet_aux.processor import OpenposeDetector + self.processor = OpenposeDetector.from_pretrained(model_path).to(device) + elif processor_id == "normal": + from controlnet_aux.processor import NormalBaeDetector + self.processor = NormalBaeDetector.from_pretrained(model_path).to(device) + elif processor_id == "tile" or processor_id == "none" or processor_id == "inpaint": + self.processor = None + else: + raise ValueError(f"Unsupported processor_id: {processor_id}") + else: + self.processor = None + + self.processor_id = processor_id + self.detect_resolution = detect_resolution + + def to(self,device): + if hasattr(self.processor,"model") and hasattr(self.processor.model,"to"): + + self.processor.model.to(device) + + def __call__(self, image, mask=None): + width, height = image.size + if self.processor_id == "openpose": + kwargs = { + "include_body": True, + "include_hand": True, + "include_face": True + } + else: + kwargs = {} + if self.processor is not None: + detect_resolution = self.detect_resolution if self.detect_resolution is not None else min(width, height) + image = self.processor(image, detect_resolution=detect_resolution, image_resolution=min(width, height), **kwargs) + image = image.resize((width, height)) + return image + diff --git a/diffsynth/data/__init__.py b/diffsynth/data/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..749c03fbc421e30b5190703587e57af8412aab73 --- /dev/null +++ b/diffsynth/data/__init__.py @@ -0,0 +1 @@ +from .video import VideoData, save_video, save_frames, merge_video_audio, save_video_with_audio diff --git a/diffsynth/data/simple_text_image.py b/diffsynth/data/simple_text_image.py new file mode 100644 index 0000000000000000000000000000000000000000..7a9525e3c8a4d21418c1464fe11fc621450fd0d8 --- /dev/null +++ b/diffsynth/data/simple_text_image.py @@ -0,0 +1,41 @@ +import torch, os, torchvision +from torchvision import transforms +import pandas as pd +from PIL import Image + + + +class TextImageDataset(torch.utils.data.Dataset): + def __init__(self, dataset_path, steps_per_epoch=10000, height=1024, width=1024, center_crop=True, random_flip=False): + self.steps_per_epoch = steps_per_epoch + metadata = pd.read_csv(os.path.join(dataset_path, "train/metadata.csv")) + self.path = [os.path.join(dataset_path, "train", file_name) for file_name in metadata["file_name"]] + self.text = metadata["text"].to_list() + self.height = height + self.width = width + self.image_processor = transforms.Compose( + [ + transforms.CenterCrop((height, width)) if center_crop else transforms.RandomCrop((height, width)), + transforms.RandomHorizontalFlip() if random_flip else transforms.Lambda(lambda x: x), + transforms.ToTensor(), + transforms.Normalize([0.5], [0.5]), + ] + ) + + + def __getitem__(self, index): + data_id = torch.randint(0, len(self.path), (1,))[0] + data_id = (data_id + index) % len(self.path) # For fixed seed. + text = self.text[data_id] + image = Image.open(self.path[data_id]).convert("RGB") + target_height, target_width = self.height, self.width + width, height = image.size + scale = max(target_width / width, target_height / height) + shape = [round(height*scale),round(width*scale)] + image = torchvision.transforms.functional.resize(image,shape,interpolation=transforms.InterpolationMode.BILINEAR) + image = self.image_processor(image) + return {"text": text, "image": image} + + + def __len__(self): + return self.steps_per_epoch diff --git a/diffsynth/data/video.py b/diffsynth/data/video.py new file mode 100644 index 0000000000000000000000000000000000000000..c6b9daa41bea9d36e012d52a1d280d1cf8d92850 --- /dev/null +++ b/diffsynth/data/video.py @@ -0,0 +1,217 @@ +import imageio, os +import numpy as np +from PIL import Image +from tqdm import tqdm +import subprocess +import shutil + + +class LowMemoryVideo: + def __init__(self, file_name): + self.reader = imageio.get_reader(file_name) + + def __len__(self): + return self.reader.count_frames() + + def __getitem__(self, item): + return Image.fromarray(np.array(self.reader.get_data(item))).convert("RGB") + + def __del__(self): + self.reader.close() + + +def split_file_name(file_name): + result = [] + number = -1 + for i in file_name: + if ord(i)>=ord("0") and ord(i)<=ord("9"): + if number == -1: + number = 0 + number = number*10 + ord(i) - ord("0") + else: + if number != -1: + result.append(number) + number = -1 + result.append(i) + if number != -1: + result.append(number) + result = tuple(result) + return result + + +def search_for_images(folder): + file_list = [i for i in os.listdir(folder) if i.endswith(".jpg") or i.endswith(".png")] + file_list = [(split_file_name(file_name), file_name) for file_name in file_list] + file_list = [i[1] for i in sorted(file_list)] + file_list = [os.path.join(folder, i) for i in file_list] + return file_list + + +class LowMemoryImageFolder: + def __init__(self, folder, file_list=None): + if file_list is None: + self.file_list = search_for_images(folder) + else: + self.file_list = [os.path.join(folder, file_name) for file_name in file_list] + + def __len__(self): + return len(self.file_list) + + def __getitem__(self, item): + return Image.open(self.file_list[item]).convert("RGB") + + def __del__(self): + pass + + +def crop_and_resize(image, height, width): + image = np.array(image) + image_height, image_width, _ = image.shape + if image_height / image_width < height / width: + croped_width = int(image_height / height * width) + left = (image_width - croped_width) // 2 + image = image[:, left: left+croped_width] + image = Image.fromarray(image).resize((width, height)) + else: + croped_height = int(image_width / width * height) + left = (image_height - croped_height) // 2 + image = image[left: left+croped_height, :] + image = Image.fromarray(image).resize((width, height)) + return image + + +class VideoData: + def __init__(self, video_file=None, image_folder=None, height=None, width=None, **kwargs): + if video_file is not None: + self.data_type = "video" + self.data = LowMemoryVideo(video_file, **kwargs) + elif image_folder is not None: + self.data_type = "images" + self.data = LowMemoryImageFolder(image_folder, **kwargs) + else: + raise ValueError("Cannot open video or image folder") + self.length = None + self.set_shape(height, width) + + def raw_data(self): + frames = [] + for i in range(self.__len__()): + frames.append(self.__getitem__(i)) + return frames + + def set_length(self, length): + self.length = length + + def set_shape(self, height, width): + self.height = height + self.width = width + + def __len__(self): + if self.length is None: + return len(self.data) + else: + return self.length + + def shape(self): + if self.height is not None and self.width is not None: + return self.height, self.width + else: + height, width, _ = self.__getitem__(0).shape + return height, width + + def __getitem__(self, item): + frame = self.data.__getitem__(item) + width, height = frame.size + if self.height is not None and self.width is not None: + if self.height != height or self.width != width: + frame = crop_and_resize(frame, self.height, self.width) + return frame + + def __del__(self): + pass + + def save_images(self, folder): + os.makedirs(folder, exist_ok=True) + for i in tqdm(range(self.__len__()), desc="Saving images"): + frame = self.__getitem__(i) + frame.save(os.path.join(folder, f"{i}.png")) + + +def save_video(frames, save_path, fps, quality=9, ffmpeg_params=None): + writer = imageio.get_writer(save_path, fps=fps, quality=quality, ffmpeg_params=ffmpeg_params) + for frame in tqdm(frames, desc="Saving video"): + frame = np.array(frame) + writer.append_data(frame) + writer.close() + +def save_frames(frames, save_path): + os.makedirs(save_path, exist_ok=True) + for i, frame in enumerate(tqdm(frames, desc="Saving images")): + frame.save(os.path.join(save_path, f"{i}.png")) + + +def merge_video_audio(video_path: str, audio_path: str): + # TODO: may need a in-python implementation to avoid subprocess dependency + """ + Merge the video and audio into a new video, with the duration set to the shorter of the two, + and overwrite the original video file. + + Parameters: + video_path (str): Path to the original video file + audio_path (str): Path to the audio file + """ + + # check + if not os.path.exists(video_path): + raise FileNotFoundError(f"video file {video_path} does not exist") + if not os.path.exists(audio_path): + raise FileNotFoundError(f"audio file {audio_path} does not exist") + + base, ext = os.path.splitext(video_path) + temp_output = f"{base}_temp{ext}" + + try: + # create ffmpeg command + command = [ + 'ffmpeg', + '-y', # overwrite + '-i', + video_path, + '-i', + audio_path, + '-c:v', + 'copy', # copy video stream + '-c:a', + 'aac', # use AAC audio encoder + '-b:a', + '192k', # set audio bitrate (optional) + '-map', + '0:v:0', # select the first video stream + '-map', + '1:a:0', # select the first audio stream + '-shortest', # choose the shortest duration + temp_output + ] + + # execute the command + result = subprocess.run( + command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True) + + # check result + if result.returncode != 0: + error_msg = f"FFmpeg execute failed: {result.stderr}" + print(error_msg) + raise RuntimeError(error_msg) + + shutil.move(temp_output, video_path) + print(f"Merge completed, saved to {video_path}") + + except Exception as e: + if os.path.exists(temp_output): + os.remove(temp_output) + print(f"merge_video_audio failed with error: {e}") + + +def save_video_with_audio(frames, save_path, audio_path, fps=16, quality=9, ffmpeg_params=None): + save_video(frames, save_path, fps, quality, ffmpeg_params) + merge_video_audio(save_path, audio_path) diff --git a/diffsynth/distributed/__init__.py b/diffsynth/distributed/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/diffsynth/distributed/xdit_context_parallel.py b/diffsynth/distributed/xdit_context_parallel.py new file mode 100644 index 0000000000000000000000000000000000000000..4887e2f16a87fa3a28e8057de41c6537be83f8ed --- /dev/null +++ b/diffsynth/distributed/xdit_context_parallel.py @@ -0,0 +1,131 @@ +import torch +from typing import Optional +from einops import rearrange +from xfuser.core.distributed import (get_sequence_parallel_rank, + get_sequence_parallel_world_size, + get_sp_group) +from xfuser.core.long_ctx_attention import xFuserLongContextAttention + +def sinusoidal_embedding_1d(dim, position): + sinusoid = torch.outer(position.type(torch.float64), torch.pow( + 10000, -torch.arange(dim//2, dtype=torch.float64, device=position.device).div(dim//2))) + x = torch.cat([torch.cos(sinusoid), torch.sin(sinusoid)], dim=1) + return x.to(position.dtype) + +def pad_freqs(original_tensor, target_len): + seq_len, s1, s2 = original_tensor.shape + pad_size = target_len - seq_len + padding_tensor = torch.ones( + pad_size, + s1, + s2, + dtype=original_tensor.dtype, + device=original_tensor.device) + padded_tensor = torch.cat([original_tensor, padding_tensor], dim=0) + return padded_tensor + +def rope_apply(x, freqs, num_heads): + x = rearrange(x, "b s (n d) -> b s n d", n=num_heads) + s_per_rank = x.shape[1] + + x_out = torch.view_as_complex(x.to(torch.float64).reshape( + x.shape[0], x.shape[1], x.shape[2], -1, 2)) + + sp_size = get_sequence_parallel_world_size() + sp_rank = get_sequence_parallel_rank() + freqs = pad_freqs(freqs, s_per_rank * sp_size) + freqs_rank = freqs[(sp_rank * s_per_rank):((sp_rank + 1) * s_per_rank), :, :] + + x_out = torch.view_as_real(x_out * freqs_rank).flatten(2) + return x_out.to(x.dtype) + +def usp_dit_forward(self, + x: torch.Tensor, + timestep: torch.Tensor, + context: torch.Tensor, + clip_feature: Optional[torch.Tensor] = None, + y: Optional[torch.Tensor] = None, + use_gradient_checkpointing: bool = False, + use_gradient_checkpointing_offload: bool = False, + **kwargs, + ): + t = self.time_embedding( + sinusoidal_embedding_1d(self.freq_dim, timestep)) + t_mod = self.time_projection(t).unflatten(1, (6, self.dim)) + context = self.text_embedding(context) + + if self.has_image_input: + x = torch.cat([x, y], dim=1) # (b, c_x + c_y, f, h, w) + clip_embdding = self.img_emb(clip_feature) + context = torch.cat([clip_embdding, context], dim=1) + + x, (f, h, w) = self.patchify(x) + + freqs = torch.cat([ + self.freqs[0][:f].view(f, 1, 1, -1).expand(f, h, w, -1), + self.freqs[1][:h].view(1, h, 1, -1).expand(f, h, w, -1), + self.freqs[2][:w].view(1, 1, w, -1).expand(f, h, w, -1) + ], dim=-1).reshape(f * h * w, 1, -1).to(x.device) + + def create_custom_forward(module): + def custom_forward(*inputs): + return module(*inputs) + return custom_forward + + # Context Parallel + chunks = torch.chunk(x, get_sequence_parallel_world_size(), dim=1) + pad_shape = chunks[0].shape[1] - chunks[-1].shape[1] + chunks = [torch.nn.functional.pad(chunk, (0, 0, 0, chunks[0].shape[1]-chunk.shape[1]), value=0) for chunk in chunks] + x = chunks[get_sequence_parallel_rank()] + + for block in self.blocks: + if self.training and use_gradient_checkpointing: + if use_gradient_checkpointing_offload: + with torch.autograd.graph.save_on_cpu(): + x = torch.utils.checkpoint.checkpoint( + create_custom_forward(block), + x, context, t_mod, freqs, + use_reentrant=False, + ) + else: + x = torch.utils.checkpoint.checkpoint( + create_custom_forward(block), + x, context, t_mod, freqs, + use_reentrant=False, + ) + else: + x = block(x, context, t_mod, freqs) + + x = self.head(x, t) + + # Context Parallel + x = get_sp_group().all_gather(x, dim=1) + x = x[:, :-pad_shape] if pad_shape > 0 else x + + # unpatchify + x = self.unpatchify(x, (f, h, w)) + return x + + +def usp_attn_forward(self, x, freqs): + q = self.norm_q(self.q(x)) + k = self.norm_k(self.k(x)) + v = self.v(x) + + q = rope_apply(q, freqs, self.num_heads) + k = rope_apply(k, freqs, self.num_heads) + q = rearrange(q, "b s (n d) -> b s n d", n=self.num_heads) + k = rearrange(k, "b s (n d) -> b s n d", n=self.num_heads) + v = rearrange(v, "b s (n d) -> b s n d", n=self.num_heads) + + x = xFuserLongContextAttention()( + None, + query=q, + key=k, + value=v, + ) + x = x.flatten(2) + + del q, k, v + torch.cuda.empty_cache() + return self.o(x) \ No newline at end of file diff --git a/diffsynth/extensions/ESRGAN/__init__.py b/diffsynth/extensions/ESRGAN/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..94aff4c6fe8d75ff65e30d672dbe3e38a0d919c3 --- /dev/null +++ b/diffsynth/extensions/ESRGAN/__init__.py @@ -0,0 +1,137 @@ +import torch +from einops import repeat +from PIL import Image +import numpy as np + + +class ResidualDenseBlock(torch.nn.Module): + + def __init__(self, num_feat=64, num_grow_ch=32): + super(ResidualDenseBlock, self).__init__() + self.conv1 = torch.nn.Conv2d(num_feat, num_grow_ch, 3, 1, 1) + self.conv2 = torch.nn.Conv2d(num_feat + num_grow_ch, num_grow_ch, 3, 1, 1) + self.conv3 = torch.nn.Conv2d(num_feat + 2 * num_grow_ch, num_grow_ch, 3, 1, 1) + self.conv4 = torch.nn.Conv2d(num_feat + 3 * num_grow_ch, num_grow_ch, 3, 1, 1) + self.conv5 = torch.nn.Conv2d(num_feat + 4 * num_grow_ch, num_feat, 3, 1, 1) + self.lrelu = torch.nn.LeakyReLU(negative_slope=0.2, inplace=True) + + def forward(self, x): + x1 = self.lrelu(self.conv1(x)) + x2 = self.lrelu(self.conv2(torch.cat((x, x1), 1))) + x3 = self.lrelu(self.conv3(torch.cat((x, x1, x2), 1))) + x4 = self.lrelu(self.conv4(torch.cat((x, x1, x2, x3), 1))) + x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1)) + return x5 * 0.2 + x + + +class RRDB(torch.nn.Module): + + def __init__(self, num_feat, num_grow_ch=32): + super(RRDB, self).__init__() + self.rdb1 = ResidualDenseBlock(num_feat, num_grow_ch) + self.rdb2 = ResidualDenseBlock(num_feat, num_grow_ch) + self.rdb3 = ResidualDenseBlock(num_feat, num_grow_ch) + + def forward(self, x): + out = self.rdb1(x) + out = self.rdb2(out) + out = self.rdb3(out) + return out * 0.2 + x + + +class RRDBNet(torch.nn.Module): + + def __init__(self, num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, **kwargs): + super(RRDBNet, self).__init__() + self.conv_first = torch.nn.Conv2d(num_in_ch, num_feat, 3, 1, 1) + self.body = torch.torch.nn.Sequential(*[RRDB(num_feat=num_feat, num_grow_ch=num_grow_ch) for _ in range(num_block)]) + self.conv_body = torch.nn.Conv2d(num_feat, num_feat, 3, 1, 1) + # upsample + self.conv_up1 = torch.nn.Conv2d(num_feat, num_feat, 3, 1, 1) + self.conv_up2 = torch.nn.Conv2d(num_feat, num_feat, 3, 1, 1) + self.conv_hr = torch.nn.Conv2d(num_feat, num_feat, 3, 1, 1) + self.conv_last = torch.nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) + self.lrelu = torch.nn.LeakyReLU(negative_slope=0.2, inplace=True) + + def forward(self, x): + feat = x + feat = self.conv_first(feat) + body_feat = self.conv_body(self.body(feat)) + feat = feat + body_feat + # upsample + feat = repeat(feat, "B C H W -> B C (H 2) (W 2)") + feat = self.lrelu(self.conv_up1(feat)) + feat = repeat(feat, "B C H W -> B C (H 2) (W 2)") + feat = self.lrelu(self.conv_up2(feat)) + out = self.conv_last(self.lrelu(self.conv_hr(feat))) + return out + + @staticmethod + def state_dict_converter(): + return RRDBNetStateDictConverter() + + +class RRDBNetStateDictConverter: + def __init__(self): + pass + + def from_diffusers(self, state_dict): + return state_dict, {"upcast_to_float32": True} + + def from_civitai(self, state_dict): + return state_dict, {"upcast_to_float32": True} + + +class ESRGAN(torch.nn.Module): + def __init__(self, model): + super().__init__() + self.model = model + + @staticmethod + def from_model_manager(model_manager): + return ESRGAN(model_manager.fetch_model("esrgan")) + + def process_image(self, image): + image = torch.Tensor(np.array(image, dtype=np.float32) / 255).permute(2, 0, 1) + return image + + def process_images(self, images): + images = [self.process_image(image) for image in images] + images = torch.stack(images) + return images + + def decode_images(self, images): + images = (images.permute(0, 2, 3, 1) * 255).clip(0, 255).numpy().astype(np.uint8) + images = [Image.fromarray(image) for image in images] + return images + + @torch.no_grad() + def upscale(self, images, batch_size=4, progress_bar=lambda x:x): + if not isinstance(images, list): + images = [images] + is_single_image = True + else: + is_single_image = False + + # Preprocess + input_tensor = self.process_images(images) + + # Interpolate + output_tensor = [] + for batch_id in progress_bar(range(0, input_tensor.shape[0], batch_size)): + batch_id_ = min(batch_id + batch_size, input_tensor.shape[0]) + batch_input_tensor = input_tensor[batch_id: batch_id_] + batch_input_tensor = batch_input_tensor.to( + device=self.model.conv_first.weight.device, + dtype=self.model.conv_first.weight.dtype) + batch_output_tensor = self.model(batch_input_tensor) + output_tensor.append(batch_output_tensor.cpu()) + + # Output + output_tensor = torch.concat(output_tensor, dim=0) + + # To images + output_images = self.decode_images(output_tensor) + if is_single_image: + output_images = output_images[0] + return output_images diff --git a/diffsynth/extensions/FastBlend/__init__.py b/diffsynth/extensions/FastBlend/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..2bf812c2085082bfa82658dd249ebca89e9fb465 --- /dev/null +++ b/diffsynth/extensions/FastBlend/__init__.py @@ -0,0 +1,63 @@ +from .runners.fast import TableManager, PyramidPatchMatcher +from PIL import Image +import numpy as np +import cupy as cp + + +class FastBlendSmoother: + def __init__(self): + self.batch_size = 8 + self.window_size = 64 + self.ebsynth_config = { + "minimum_patch_size": 5, + "threads_per_block": 8, + "num_iter": 5, + "gpu_id": 0, + "guide_weight": 10.0, + "initialize": "identity", + "tracking_window_size": 0, + } + + @staticmethod + def from_model_manager(model_manager): + # TODO: fetch GPU ID from model_manager + return FastBlendSmoother() + + def run(self, frames_guide, frames_style, batch_size, window_size, ebsynth_config): + frames_guide = [np.array(frame) for frame in frames_guide] + frames_style = [np.array(frame) for frame in frames_style] + table_manager = TableManager() + patch_match_engine = PyramidPatchMatcher( + image_height=frames_style[0].shape[0], + image_width=frames_style[0].shape[1], + channel=3, + **ebsynth_config + ) + # left part + table_l = table_manager.build_remapping_table(frames_guide, frames_style, patch_match_engine, batch_size, desc="FastBlend Step 1/4") + table_l = table_manager.remapping_table_to_blending_table(table_l) + table_l = table_manager.process_window_sum(frames_guide, table_l, patch_match_engine, window_size, batch_size, desc="FastBlend Step 2/4") + # right part + table_r = table_manager.build_remapping_table(frames_guide[::-1], frames_style[::-1], patch_match_engine, batch_size, desc="FastBlend Step 3/4") + table_r = table_manager.remapping_table_to_blending_table(table_r) + table_r = table_manager.process_window_sum(frames_guide[::-1], table_r, patch_match_engine, window_size, batch_size, desc="FastBlend Step 4/4")[::-1] + # merge + frames = [] + for (frame_l, weight_l), frame_m, (frame_r, weight_r) in zip(table_l, frames_style, table_r): + weight_m = -1 + weight = weight_l + weight_m + weight_r + frame = frame_l * (weight_l / weight) + frame_m * (weight_m / weight) + frame_r * (weight_r / weight) + frames.append(frame) + frames = [Image.fromarray(frame.clip(0, 255).astype("uint8")) for frame in frames] + return frames + + def __call__(self, rendered_frames, original_frames=None, **kwargs): + frames = self.run( + original_frames, rendered_frames, + self.batch_size, self.window_size, self.ebsynth_config + ) + mempool = cp.get_default_memory_pool() + pinned_mempool = cp.get_default_pinned_memory_pool() + mempool.free_all_blocks() + pinned_mempool.free_all_blocks() + return frames \ No newline at end of file diff --git a/diffsynth/extensions/FastBlend/api.py b/diffsynth/extensions/FastBlend/api.py new file mode 100644 index 0000000000000000000000000000000000000000..2db24330e375ed62065af54613b6ab956c9c64cf --- /dev/null +++ b/diffsynth/extensions/FastBlend/api.py @@ -0,0 +1,397 @@ +from .runners import AccurateModeRunner, FastModeRunner, BalancedModeRunner, InterpolationModeRunner, InterpolationModeSingleFrameRunner +from .data import VideoData, get_video_fps, save_video, search_for_images +import os +import gradio as gr + + +def check_input_for_blending(video_guide, video_guide_folder, video_style, video_style_folder): + frames_guide = VideoData(video_guide, video_guide_folder) + frames_style = VideoData(video_style, video_style_folder) + message = "" + if len(frames_guide) < len(frames_style): + message += f"The number of frames mismatches. Only the first {len(frames_guide)} frames of style video will be used.\n" + frames_style.set_length(len(frames_guide)) + elif len(frames_guide) > len(frames_style): + message += f"The number of frames mismatches. Only the first {len(frames_style)} frames of guide video will be used.\n" + frames_guide.set_length(len(frames_style)) + height_guide, width_guide = frames_guide.shape() + height_style, width_style = frames_style.shape() + if height_guide != height_style or width_guide != width_style: + message += f"The shape of frames mismatches. The frames in style video will be resized to (height: {height_guide}, width: {width_guide})\n" + frames_style.set_shape(height_guide, width_guide) + return frames_guide, frames_style, message + + +def smooth_video( + video_guide, + video_guide_folder, + video_style, + video_style_folder, + mode, + window_size, + batch_size, + tracking_window_size, + output_path, + fps, + minimum_patch_size, + num_iter, + guide_weight, + initialize, + progress = None, +): + # input + frames_guide, frames_style, message = check_input_for_blending(video_guide, video_guide_folder, video_style, video_style_folder) + if len(message) > 0: + print(message) + # output + if output_path == "": + if video_style is None: + output_path = os.path.join(video_style_folder, "output") + else: + output_path = os.path.join(os.path.split(video_style)[0], "output") + os.makedirs(output_path, exist_ok=True) + print("No valid output_path. Your video will be saved here:", output_path) + elif not os.path.exists(output_path): + os.makedirs(output_path, exist_ok=True) + print("Your video will be saved here:", output_path) + frames_path = os.path.join(output_path, "frames") + video_path = os.path.join(output_path, "video.mp4") + os.makedirs(frames_path, exist_ok=True) + # process + if mode == "Fast" or mode == "Balanced": + tracking_window_size = 0 + ebsynth_config = { + "minimum_patch_size": minimum_patch_size, + "threads_per_block": 8, + "num_iter": num_iter, + "gpu_id": 0, + "guide_weight": guide_weight, + "initialize": initialize, + "tracking_window_size": tracking_window_size, + } + if mode == "Fast": + FastModeRunner().run(frames_guide, frames_style, batch_size=batch_size, window_size=window_size, ebsynth_config=ebsynth_config, save_path=frames_path) + elif mode == "Balanced": + BalancedModeRunner().run(frames_guide, frames_style, batch_size=batch_size, window_size=window_size, ebsynth_config=ebsynth_config, save_path=frames_path) + elif mode == "Accurate": + AccurateModeRunner().run(frames_guide, frames_style, batch_size=batch_size, window_size=window_size, ebsynth_config=ebsynth_config, save_path=frames_path) + # output + try: + fps = int(fps) + except: + fps = get_video_fps(video_style) if video_style is not None else 30 + print("Fps:", fps) + print("Saving video...") + video_path = save_video(frames_path, video_path, num_frames=len(frames_style), fps=fps) + print("Success!") + print("Your frames are here:", frames_path) + print("Your video is here:", video_path) + return output_path, fps, video_path + + +class KeyFrameMatcher: + def __init__(self): + pass + + def extract_number_from_filename(self, file_name): + result = [] + number = -1 + for i in file_name: + if ord(i)>=ord("0") and ord(i)<=ord("9"): + if number == -1: + number = 0 + number = number*10 + ord(i) - ord("0") + else: + if number != -1: + result.append(number) + number = -1 + if number != -1: + result.append(number) + result = tuple(result) + return result + + def extract_number_from_filenames(self, file_names): + numbers = [self.extract_number_from_filename(file_name) for file_name in file_names] + min_length = min(len(i) for i in numbers) + for i in range(min_length-1, -1, -1): + if len(set(number[i] for number in numbers))==len(file_names): + return [number[i] for number in numbers] + return list(range(len(file_names))) + + def match_using_filename(self, file_names_a, file_names_b): + file_names_b_set = set(file_names_b) + matched_file_name = [] + for file_name in file_names_a: + if file_name not in file_names_b_set: + matched_file_name.append(None) + else: + matched_file_name.append(file_name) + return matched_file_name + + def match_using_numbers(self, file_names_a, file_names_b): + numbers_a = self.extract_number_from_filenames(file_names_a) + numbers_b = self.extract_number_from_filenames(file_names_b) + numbers_b_dict = {number: file_name for number, file_name in zip(numbers_b, file_names_b)} + matched_file_name = [] + for number in numbers_a: + if number in numbers_b_dict: + matched_file_name.append(numbers_b_dict[number]) + else: + matched_file_name.append(None) + return matched_file_name + + def match_filenames(self, file_names_a, file_names_b): + matched_file_name = self.match_using_filename(file_names_a, file_names_b) + if sum([i is not None for i in matched_file_name]) > 0: + return matched_file_name + matched_file_name = self.match_using_numbers(file_names_a, file_names_b) + return matched_file_name + + +def detect_frames(frames_path, keyframes_path): + if not os.path.exists(frames_path) and not os.path.exists(keyframes_path): + return "Please input the directory of guide video and rendered frames" + elif not os.path.exists(frames_path): + return "Please input the directory of guide video" + elif not os.path.exists(keyframes_path): + return "Please input the directory of rendered frames" + frames = [os.path.split(i)[-1] for i in search_for_images(frames_path)] + keyframes = [os.path.split(i)[-1] for i in search_for_images(keyframes_path)] + if len(frames)==0: + return f"No images detected in {frames_path}" + if len(keyframes)==0: + return f"No images detected in {keyframes_path}" + matched_keyframes = KeyFrameMatcher().match_filenames(frames, keyframes) + max_filename_length = max([len(i) for i in frames]) + if sum([i is not None for i in matched_keyframes])==0: + message = "" + for frame, matched_keyframe in zip(frames, matched_keyframes): + message += frame + " " * (max_filename_length - len(frame) + 1) + message += "--> No matched keyframes\n" + else: + message = "" + for frame, matched_keyframe in zip(frames, matched_keyframes): + message += frame + " " * (max_filename_length - len(frame) + 1) + if matched_keyframe is None: + message += "--> [to be rendered]\n" + else: + message += f"--> {matched_keyframe}\n" + return message + + +def check_input_for_interpolating(frames_path, keyframes_path): + # search for images + frames = [os.path.split(i)[-1] for i in search_for_images(frames_path)] + keyframes = [os.path.split(i)[-1] for i in search_for_images(keyframes_path)] + # match frames + matched_keyframes = KeyFrameMatcher().match_filenames(frames, keyframes) + file_list = [file_name for file_name in matched_keyframes if file_name is not None] + index_style = [i for i, file_name in enumerate(matched_keyframes) if file_name is not None] + frames_guide = VideoData(None, frames_path) + frames_style = VideoData(None, keyframes_path, file_list=file_list) + # match shape + message = "" + height_guide, width_guide = frames_guide.shape() + height_style, width_style = frames_style.shape() + if height_guide != height_style or width_guide != width_style: + message += f"The shape of frames mismatches. The rendered keyframes will be resized to (height: {height_guide}, width: {width_guide})\n" + frames_style.set_shape(height_guide, width_guide) + return frames_guide, frames_style, index_style, message + + +def interpolate_video( + frames_path, + keyframes_path, + output_path, + fps, + batch_size, + tracking_window_size, + minimum_patch_size, + num_iter, + guide_weight, + initialize, + progress = None, +): + # input + frames_guide, frames_style, index_style, message = check_input_for_interpolating(frames_path, keyframes_path) + if len(message) > 0: + print(message) + # output + if output_path == "": + output_path = os.path.join(keyframes_path, "output") + os.makedirs(output_path, exist_ok=True) + print("No valid output_path. Your video will be saved here:", output_path) + elif not os.path.exists(output_path): + os.makedirs(output_path, exist_ok=True) + print("Your video will be saved here:", output_path) + output_frames_path = os.path.join(output_path, "frames") + output_video_path = os.path.join(output_path, "video.mp4") + os.makedirs(output_frames_path, exist_ok=True) + # process + ebsynth_config = { + "minimum_patch_size": minimum_patch_size, + "threads_per_block": 8, + "num_iter": num_iter, + "gpu_id": 0, + "guide_weight": guide_weight, + "initialize": initialize, + "tracking_window_size": tracking_window_size + } + if len(index_style)==1: + InterpolationModeSingleFrameRunner().run(frames_guide, frames_style, index_style, batch_size=batch_size, ebsynth_config=ebsynth_config, save_path=output_frames_path) + else: + InterpolationModeRunner().run(frames_guide, frames_style, index_style, batch_size=batch_size, ebsynth_config=ebsynth_config, save_path=output_frames_path) + try: + fps = int(fps) + except: + fps = 30 + print("Fps:", fps) + print("Saving video...") + video_path = save_video(output_frames_path, output_video_path, num_frames=len(frames_guide), fps=fps) + print("Success!") + print("Your frames are here:", output_frames_path) + print("Your video is here:", video_path) + return output_path, fps, video_path + + +def on_ui_tabs(): + with gr.Blocks(analytics_enabled=False) as ui_component: + with gr.Tab("Blend"): + gr.Markdown(""" +# Blend + +Given a guide video and a style video, this algorithm will make the style video fluent according to the motion features of the guide video. Click [here](https://github.com/Artiprocher/sd-webui-fastblend/assets/35051019/208d902d-6aba-48d7-b7d5-cd120ebd306d) to see the example. Note that this extension doesn't support long videos. Please use short videos (e.g., several seconds). The algorithm is mainly designed for 512*512 resolution. Please use a larger `Minimum patch size` for higher resolution. + """) + with gr.Row(): + with gr.Column(): + with gr.Tab("Guide video"): + video_guide = gr.Video(label="Guide video") + with gr.Tab("Guide video (images format)"): + video_guide_folder = gr.Textbox(label="Guide video (images format)", value="") + with gr.Column(): + with gr.Tab("Style video"): + video_style = gr.Video(label="Style video") + with gr.Tab("Style video (images format)"): + video_style_folder = gr.Textbox(label="Style video (images format)", value="") + with gr.Column(): + output_path = gr.Textbox(label="Output directory", value="", placeholder="Leave empty to use the directory of style video") + fps = gr.Textbox(label="Fps", value="", placeholder="Leave empty to use the default fps") + video_output = gr.Video(label="Output video", interactive=False, show_share_button=True) + btn = gr.Button(value="Blend") + with gr.Row(): + with gr.Column(): + gr.Markdown("# Settings") + mode = gr.Radio(["Fast", "Balanced", "Accurate"], label="Inference mode", value="Fast", interactive=True) + window_size = gr.Slider(label="Sliding window size", value=15, minimum=1, maximum=1000, step=1, interactive=True) + batch_size = gr.Slider(label="Batch size", value=8, minimum=1, maximum=128, step=1, interactive=True) + tracking_window_size = gr.Slider(label="Tracking window size (only for accurate mode)", value=0, minimum=0, maximum=10, step=1, interactive=True) + gr.Markdown("## Advanced Settings") + minimum_patch_size = gr.Slider(label="Minimum patch size (odd number)", value=5, minimum=5, maximum=99, step=2, interactive=True) + num_iter = gr.Slider(label="Number of iterations", value=5, minimum=1, maximum=10, step=1, interactive=True) + guide_weight = gr.Slider(label="Guide weight", value=10.0, minimum=0.0, maximum=100.0, step=0.1, interactive=True) + initialize = gr.Radio(["identity", "random"], label="NNF initialization", value="identity", interactive=True) + with gr.Column(): + gr.Markdown(""" +# Reference + +* Output directory: the directory to save the video. +* Inference mode + +|Mode|Time|Memory|Quality|Frame by frame output|Description| +|-|-|-|-|-|-| +|Fast|■|■■■|■■|No|Blend the frames using a tree-like data structure, which requires much RAM but is fast.| +|Balanced|■■|■|■■|Yes|Blend the frames naively.| +|Accurate|■■■|■|■■■|Yes|Blend the frames and align them together for higher video quality. When [batch size] >= [sliding window size] * 2 + 1, the performance is the best.| + +* Sliding window size: our algorithm will blend the frames in a sliding windows. If the size is n, each frame will be blended with the last n frames and the next n frames. A large sliding window can make the video fluent but sometimes smoggy. +* Batch size: a larger batch size makes the program faster but requires more VRAM. +* Tracking window size (only for accurate mode): The size of window in which our algorithm tracks moving objects. Empirically, 1 is enough. +* Advanced settings + * Minimum patch size (odd number): the minimum patch size used for patch matching. (Default: 5) + * Number of iterations: the number of iterations of patch matching. (Default: 5) + * Guide weight: a parameter that determines how much motion feature applied to the style video. (Default: 10) + * NNF initialization: how to initialize the NNF (Nearest Neighbor Field). (Default: identity) + """) + btn.click( + smooth_video, + inputs=[ + video_guide, + video_guide_folder, + video_style, + video_style_folder, + mode, + window_size, + batch_size, + tracking_window_size, + output_path, + fps, + minimum_patch_size, + num_iter, + guide_weight, + initialize + ], + outputs=[output_path, fps, video_output] + ) + with gr.Tab("Interpolate"): + gr.Markdown(""" +# Interpolate + +Given a guide video and some rendered keyframes, this algorithm will render the remaining frames. Click [here](https://github.com/Artiprocher/sd-webui-fastblend/assets/35051019/3490c5b4-8f67-478f-86de-f9adc2ace16a) to see the example. The algorithm is experimental and is only tested for 512*512 resolution. + """) + with gr.Row(): + with gr.Column(): + with gr.Row(): + with gr.Column(): + video_guide_folder_ = gr.Textbox(label="Guide video (images format)", value="") + with gr.Column(): + rendered_keyframes_ = gr.Textbox(label="Rendered keyframes (images format)", value="") + with gr.Row(): + detected_frames = gr.Textbox(label="Detected frames", value="Please input the directory of guide video and rendered frames", lines=9, max_lines=9, interactive=False) + video_guide_folder_.change(detect_frames, inputs=[video_guide_folder_, rendered_keyframes_], outputs=detected_frames) + rendered_keyframes_.change(detect_frames, inputs=[video_guide_folder_, rendered_keyframes_], outputs=detected_frames) + with gr.Column(): + output_path_ = gr.Textbox(label="Output directory", value="", placeholder="Leave empty to use the directory of rendered keyframes") + fps_ = gr.Textbox(label="Fps", value="", placeholder="Leave empty to use the default fps") + video_output_ = gr.Video(label="Output video", interactive=False, show_share_button=True) + btn_ = gr.Button(value="Interpolate") + with gr.Row(): + with gr.Column(): + gr.Markdown("# Settings") + batch_size_ = gr.Slider(label="Batch size", value=8, minimum=1, maximum=128, step=1, interactive=True) + tracking_window_size_ = gr.Slider(label="Tracking window size", value=0, minimum=0, maximum=10, step=1, interactive=True) + gr.Markdown("## Advanced Settings") + minimum_patch_size_ = gr.Slider(label="Minimum patch size (odd number, larger is better)", value=15, minimum=5, maximum=99, step=2, interactive=True) + num_iter_ = gr.Slider(label="Number of iterations", value=5, minimum=1, maximum=10, step=1, interactive=True) + guide_weight_ = gr.Slider(label="Guide weight", value=10.0, minimum=0.0, maximum=100.0, step=0.1, interactive=True) + initialize_ = gr.Radio(["identity", "random"], label="NNF initialization", value="identity", interactive=True) + with gr.Column(): + gr.Markdown(""" +# Reference + +* Output directory: the directory to save the video. +* Batch size: a larger batch size makes the program faster but requires more VRAM. +* Tracking window size (only for accurate mode): The size of window in which our algorithm tracks moving objects. Empirically, 1 is enough. +* Advanced settings + * Minimum patch size (odd number): the minimum patch size used for patch matching. **This parameter should be larger than that in blending. (Default: 15)** + * Number of iterations: the number of iterations of patch matching. (Default: 5) + * Guide weight: a parameter that determines how much motion feature applied to the style video. (Default: 10) + * NNF initialization: how to initialize the NNF (Nearest Neighbor Field). (Default: identity) + """) + btn_.click( + interpolate_video, + inputs=[ + video_guide_folder_, + rendered_keyframes_, + output_path_, + fps_, + batch_size_, + tracking_window_size_, + minimum_patch_size_, + num_iter_, + guide_weight_, + initialize_, + ], + outputs=[output_path_, fps_, video_output_] + ) + + return [(ui_component, "FastBlend", "FastBlend_ui")] diff --git a/diffsynth/extensions/FastBlend/cupy_kernels.py b/diffsynth/extensions/FastBlend/cupy_kernels.py new file mode 100644 index 0000000000000000000000000000000000000000..70e2790a2c67a2dd537f4188b38ebfc785f1fb34 --- /dev/null +++ b/diffsynth/extensions/FastBlend/cupy_kernels.py @@ -0,0 +1,119 @@ +import cupy as cp + +remapping_kernel = cp.RawKernel(r''' +extern "C" __global__ +void remap( + const int height, + const int width, + const int channel, + const int patch_size, + const int pad_size, + const float* source_style, + const int* nnf, + float* target_style +) { + const int r = (patch_size - 1) / 2; + const int x = blockDim.x * blockIdx.x + threadIdx.x; + const int y = blockDim.y * blockIdx.y + threadIdx.y; + if (x >= height or y >= width) return; + const int z = blockIdx.z * (height + pad_size * 2) * (width + pad_size * 2) * channel; + const int pid = (x + pad_size) * (width + pad_size * 2) + (y + pad_size); + const int min_px = x < r ? -x : -r; + const int max_px = x + r > height - 1 ? height - 1 - x : r; + const int min_py = y < r ? -y : -r; + const int max_py = y + r > width - 1 ? width - 1 - y : r; + int num = 0; + for (int px = min_px; px <= max_px; px++){ + for (int py = min_py; py <= max_py; py++){ + const int nid = (x + px) * width + y + py; + const int x_ = nnf[blockIdx.z * height * width * 2 + nid*2 + 0] - px; + const int y_ = nnf[blockIdx.z * height * width * 2 + nid*2 + 1] - py; + if (x_ < 0 or y_ < 0 or x_ >= height or y_ >= width)continue; + const int pid_ = (x_ + pad_size) * (width + pad_size * 2) + (y_ + pad_size); + num++; + for (int c = 0; c < channel; c++){ + target_style[z + pid * channel + c] += source_style[z + pid_ * channel + c]; + } + } + } + for (int c = 0; c < channel; c++){ + target_style[z + pid * channel + c] /= num; + } +} +''', 'remap') + + +patch_error_kernel = cp.RawKernel(r''' +extern "C" __global__ +void patch_error( + const int height, + const int width, + const int channel, + const int patch_size, + const int pad_size, + const float* source, + const int* nnf, + const float* target, + float* error +) { + const int r = (patch_size - 1) / 2; + const int x = blockDim.x * blockIdx.x + threadIdx.x; + const int y = blockDim.y * blockIdx.y + threadIdx.y; + const int z = blockIdx.z * (height + pad_size * 2) * (width + pad_size * 2) * channel; + if (x >= height or y >= width) return; + const int x_ = nnf[blockIdx.z * height * width * 2 + (x * width + y)*2 + 0]; + const int y_ = nnf[blockIdx.z * height * width * 2 + (x * width + y)*2 + 1]; + float e = 0; + for (int px = -r; px <= r; px++){ + for (int py = -r; py <= r; py++){ + const int pid = (x + pad_size + px) * (width + pad_size * 2) + y + pad_size + py; + const int pid_ = (x_ + pad_size + px) * (width + pad_size * 2) + y_ + pad_size + py; + for (int c = 0; c < channel; c++){ + const float diff = target[z + pid * channel + c] - source[z + pid_ * channel + c]; + e += diff * diff; + } + } + } + error[blockIdx.z * height * width + x * width + y] = e; +} +''', 'patch_error') + + +pairwise_patch_error_kernel = cp.RawKernel(r''' +extern "C" __global__ +void pairwise_patch_error( + const int height, + const int width, + const int channel, + const int patch_size, + const int pad_size, + const float* source_a, + const int* nnf_a, + const float* source_b, + const int* nnf_b, + float* error +) { + const int r = (patch_size - 1) / 2; + const int x = blockDim.x * blockIdx.x + threadIdx.x; + const int y = blockDim.y * blockIdx.y + threadIdx.y; + const int z = blockIdx.z * (height + pad_size * 2) * (width + pad_size * 2) * channel; + if (x >= height or y >= width) return; + const int z_nnf = blockIdx.z * height * width * 2 + (x * width + y) * 2; + const int x_a = nnf_a[z_nnf + 0]; + const int y_a = nnf_a[z_nnf + 1]; + const int x_b = nnf_b[z_nnf + 0]; + const int y_b = nnf_b[z_nnf + 1]; + float e = 0; + for (int px = -r; px <= r; px++){ + for (int py = -r; py <= r; py++){ + const int pid_a = (x_a + pad_size + px) * (width + pad_size * 2) + y_a + pad_size + py; + const int pid_b = (x_b + pad_size + px) * (width + pad_size * 2) + y_b + pad_size + py; + for (int c = 0; c < channel; c++){ + const float diff = source_a[z + pid_a * channel + c] - source_b[z + pid_b * channel + c]; + e += diff * diff; + } + } + } + error[blockIdx.z * height * width + x * width + y] = e; +} +''', 'pairwise_patch_error') diff --git a/diffsynth/extensions/FastBlend/data.py b/diffsynth/extensions/FastBlend/data.py new file mode 100644 index 0000000000000000000000000000000000000000..dcaddd77de9eaf208cd083dd522e5eaa6b58f783 --- /dev/null +++ b/diffsynth/extensions/FastBlend/data.py @@ -0,0 +1,146 @@ +import imageio, os +import numpy as np +from PIL import Image + + +def read_video(file_name): + reader = imageio.get_reader(file_name) + video = [] + for frame in reader: + frame = np.array(frame) + video.append(frame) + reader.close() + return video + + +def get_video_fps(file_name): + reader = imageio.get_reader(file_name) + fps = reader.get_meta_data()["fps"] + reader.close() + return fps + + +def save_video(frames_path, video_path, num_frames, fps): + writer = imageio.get_writer(video_path, fps=fps, quality=9) + for i in range(num_frames): + frame = np.array(Image.open(os.path.join(frames_path, "%05d.png" % i))) + writer.append_data(frame) + writer.close() + return video_path + + +class LowMemoryVideo: + def __init__(self, file_name): + self.reader = imageio.get_reader(file_name) + + def __len__(self): + return self.reader.count_frames() + + def __getitem__(self, item): + return np.array(self.reader.get_data(item)) + + def __del__(self): + self.reader.close() + + +def split_file_name(file_name): + result = [] + number = -1 + for i in file_name: + if ord(i)>=ord("0") and ord(i)<=ord("9"): + if number == -1: + number = 0 + number = number*10 + ord(i) - ord("0") + else: + if number != -1: + result.append(number) + number = -1 + result.append(i) + if number != -1: + result.append(number) + result = tuple(result) + return result + + +def search_for_images(folder): + file_list = [i for i in os.listdir(folder) if i.endswith(".jpg") or i.endswith(".png")] + file_list = [(split_file_name(file_name), file_name) for file_name in file_list] + file_list = [i[1] for i in sorted(file_list)] + file_list = [os.path.join(folder, i) for i in file_list] + return file_list + + +def read_images(folder): + file_list = search_for_images(folder) + frames = [np.array(Image.open(i)) for i in file_list] + return frames + + +class LowMemoryImageFolder: + def __init__(self, folder, file_list=None): + if file_list is None: + self.file_list = search_for_images(folder) + else: + self.file_list = [os.path.join(folder, file_name) for file_name in file_list] + + def __len__(self): + return len(self.file_list) + + def __getitem__(self, item): + return np.array(Image.open(self.file_list[item])) + + def __del__(self): + pass + + +class VideoData: + def __init__(self, video_file, image_folder, **kwargs): + if video_file is not None: + self.data_type = "video" + self.data = LowMemoryVideo(video_file, **kwargs) + elif image_folder is not None: + self.data_type = "images" + self.data = LowMemoryImageFolder(image_folder, **kwargs) + else: + raise ValueError("Cannot open video or image folder") + self.length = None + self.height = None + self.width = None + + def raw_data(self): + frames = [] + for i in range(self.__len__()): + frames.append(self.__getitem__(i)) + return frames + + def set_length(self, length): + self.length = length + + def set_shape(self, height, width): + self.height = height + self.width = width + + def __len__(self): + if self.length is None: + return len(self.data) + else: + return self.length + + def shape(self): + if self.height is not None and self.width is not None: + return self.height, self.width + else: + height, width, _ = self.__getitem__(0).shape + return height, width + + def __getitem__(self, item): + frame = self.data.__getitem__(item) + height, width, _ = frame.shape + if self.height is not None and self.width is not None: + if self.height != height or self.width != width: + frame = Image.fromarray(frame).resize((self.width, self.height)) + frame = np.array(frame) + return frame + + def __del__(self): + pass diff --git a/diffsynth/extensions/FastBlend/patch_match.py b/diffsynth/extensions/FastBlend/patch_match.py new file mode 100644 index 0000000000000000000000000000000000000000..8ba60036af43aa89e4811223af8f9f63b87ab6e1 --- /dev/null +++ b/diffsynth/extensions/FastBlend/patch_match.py @@ -0,0 +1,299 @@ +from .cupy_kernels import remapping_kernel, patch_error_kernel, pairwise_patch_error_kernel +import numpy as np +import cupy as cp +import cv2 +import torch +import torch.nn.functional as F + +class PatchMatcher: + def __init__( + self, height, width, channel, minimum_patch_size, + threads_per_block=8, num_iter=5, gpu_id=0, guide_weight=10.0, + random_search_steps=3, random_search_range=4, + use_mean_target_style=False, use_pairwise_patch_error=False, + tracking_window_size=0 + ): + self.height = height + self.width = width + self.channel = channel + self.minimum_patch_size = minimum_patch_size + self.threads_per_block = threads_per_block + self.num_iter = num_iter + self.gpu_id = gpu_id + self.guide_weight = guide_weight + self.random_search_steps = random_search_steps + self.random_search_range = random_search_range + self.use_mean_target_style = use_mean_target_style + self.use_pairwise_patch_error = use_pairwise_patch_error + self.tracking_window_size = tracking_window_size + + self.patch_size_list = [minimum_patch_size + i*2 for i in range(num_iter)][::-1] + self.pad_size = self.patch_size_list[0] // 2 + self.grid = ( + (height + threads_per_block - 1) // threads_per_block, + (width + threads_per_block - 1) // threads_per_block + ) + self.block = (threads_per_block, threads_per_block) + + def pad_image(self, image): + return cp.pad(image, ((0, 0), (self.pad_size, self.pad_size), (self.pad_size, self.pad_size), (0, 0))) + + def unpad_image(self, image): + return image[:, self.pad_size: -self.pad_size, self.pad_size: -self.pad_size, :] + + def apply_nnf_to_image(self, nnf, source): + batch_size = source.shape[0] + target = cp.zeros((batch_size, self.height + self.pad_size * 2, self.width + self.pad_size * 2, self.channel), dtype=cp.float32) + remapping_kernel( + self.grid + (batch_size,), + self.block, + (self.height, self.width, self.channel, self.patch_size, self.pad_size, source, nnf, target) + ) + return target + + def get_patch_error(self, source, nnf, target): + batch_size = source.shape[0] + error = cp.zeros((batch_size, self.height, self.width), dtype=cp.float32) + patch_error_kernel( + self.grid + (batch_size,), + self.block, + (self.height, self.width, self.channel, self.patch_size, self.pad_size, source, nnf, target, error) + ) + return error + + def get_pairwise_patch_error(self, source, nnf): + batch_size = source.shape[0]//2 + error = cp.zeros((batch_size, self.height, self.width), dtype=cp.float32) + source_a, nnf_a = source[0::2].copy(), nnf[0::2].copy() + source_b, nnf_b = source[1::2].copy(), nnf[1::2].copy() + pairwise_patch_error_kernel( + self.grid + (batch_size,), + self.block, + (self.height, self.width, self.channel, self.patch_size, self.pad_size, source_a, nnf_a, source_b, nnf_b, error) + ) + error = error.repeat(2, axis=0) + return error + + def get_error(self, source_guide, target_guide, source_style, target_style, nnf): + error_guide = self.get_patch_error(source_guide, nnf, target_guide) + if self.use_mean_target_style: + target_style = self.apply_nnf_to_image(nnf, source_style) + target_style = target_style.mean(axis=0, keepdims=True) + target_style = target_style.repeat(source_guide.shape[0], axis=0) + if self.use_pairwise_patch_error: + error_style = self.get_pairwise_patch_error(source_style, nnf) + else: + error_style = self.get_patch_error(source_style, nnf, target_style) + error = error_guide * self.guide_weight + error_style + return error + + def clamp_bound(self, nnf): + nnf[:,:,:,0] = cp.clip(nnf[:,:,:,0], 0, self.height-1) + nnf[:,:,:,1] = cp.clip(nnf[:,:,:,1], 0, self.width-1) + return nnf + + def random_step(self, nnf, r): + batch_size = nnf.shape[0] + step = cp.random.randint(-r, r+1, size=(batch_size, self.height, self.width, 2), dtype=cp.int32) + upd_nnf = self.clamp_bound(nnf + step) + return upd_nnf + + def neighboor_step(self, nnf, d): + if d==0: + upd_nnf = cp.concatenate([nnf[:, :1, :], nnf[:, :-1, :]], axis=1) + upd_nnf[:, :, :, 0] += 1 + elif d==1: + upd_nnf = cp.concatenate([nnf[:, :, :1], nnf[:, :, :-1]], axis=2) + upd_nnf[:, :, :, 1] += 1 + elif d==2: + upd_nnf = cp.concatenate([nnf[:, 1:, :], nnf[:, -1:, :]], axis=1) + upd_nnf[:, :, :, 0] -= 1 + elif d==3: + upd_nnf = cp.concatenate([nnf[:, :, 1:], nnf[:, :, -1:]], axis=2) + upd_nnf[:, :, :, 1] -= 1 + upd_nnf = self.clamp_bound(upd_nnf) + return upd_nnf + + def shift_nnf(self, nnf, d): + if d>0: + d = min(nnf.shape[0], d) + upd_nnf = cp.concatenate([nnf[d:]] + [nnf[-1:]] * d, axis=0) + else: + d = max(-nnf.shape[0], d) + upd_nnf = cp.concatenate([nnf[:1]] * (-d) + [nnf[:d]], axis=0) + return upd_nnf + + def track_step(self, nnf, d): + if self.use_pairwise_patch_error: + upd_nnf = cp.zeros_like(nnf) + upd_nnf[0::2] = self.shift_nnf(nnf[0::2], d) + upd_nnf[1::2] = self.shift_nnf(nnf[1::2], d) + else: + upd_nnf = self.shift_nnf(nnf, d) + return upd_nnf + + def C(self, n, m): + # not used + c = 1 + for i in range(1, n+1): + c *= i + for i in range(1, m+1): + c //= i + for i in range(1, n-m+1): + c //= i + return c + + def bezier_step(self, nnf, r): + # not used + n = r * 2 - 1 + upd_nnf = cp.zeros(shape=nnf.shape, dtype=cp.float32) + for i, d in enumerate(list(range(-r, 0)) + list(range(1, r+1))): + if d>0: + ctl_nnf = cp.concatenate([nnf[d:]] + [nnf[-1:]] * d, axis=0) + elif d<0: + ctl_nnf = cp.concatenate([nnf[:1]] * (-d) + [nnf[:d]], axis=0) + upd_nnf += ctl_nnf * (self.C(n, i) / 2**n) + upd_nnf = self.clamp_bound(upd_nnf).astype(nnf.dtype) + return upd_nnf + + def update(self, source_guide, target_guide, source_style, target_style, nnf, err, upd_nnf): + upd_err = self.get_error(source_guide, target_guide, source_style, target_style, upd_nnf) + upd_idx = (upd_err < err) + nnf[upd_idx] = upd_nnf[upd_idx] + err[upd_idx] = upd_err[upd_idx] + return nnf, err + + def propagation(self, source_guide, target_guide, source_style, target_style, nnf, err): + for d in cp.random.permutation(4): + upd_nnf = self.neighboor_step(nnf, d) + nnf, err = self.update(source_guide, target_guide, source_style, target_style, nnf, err, upd_nnf) + return nnf, err + + def random_search(self, source_guide, target_guide, source_style, target_style, nnf, err): + for i in range(self.random_search_steps): + upd_nnf = self.random_step(nnf, self.random_search_range) + nnf, err = self.update(source_guide, target_guide, source_style, target_style, nnf, err, upd_nnf) + return nnf, err + + def track(self, source_guide, target_guide, source_style, target_style, nnf, err): + for d in range(1, self.tracking_window_size + 1): + upd_nnf = self.track_step(nnf, d) + nnf, err = self.update(source_guide, target_guide, source_style, target_style, nnf, err, upd_nnf) + upd_nnf = self.track_step(nnf, -d) + nnf, err = self.update(source_guide, target_guide, source_style, target_style, nnf, err, upd_nnf) + return nnf, err + + def iteration(self, source_guide, target_guide, source_style, target_style, nnf, err): + nnf, err = self.propagation(source_guide, target_guide, source_style, target_style, nnf, err) + nnf, err = self.random_search(source_guide, target_guide, source_style, target_style, nnf, err) + nnf, err = self.track(source_guide, target_guide, source_style, target_style, nnf, err) + return nnf, err + + def estimate_nnf(self, source_guide, target_guide, source_style, nnf): + with cp.cuda.Device(self.gpu_id): + source_guide = self.pad_image(source_guide) + target_guide = self.pad_image(target_guide) + source_style = self.pad_image(source_style) + for it in range(self.num_iter): + self.patch_size = self.patch_size_list[it] + target_style = self.apply_nnf_to_image(nnf, source_style) + err = self.get_error(source_guide, target_guide, source_style, target_style, nnf) + nnf, err = self.iteration(source_guide, target_guide, source_style, target_style, nnf, err) + target_style = self.unpad_image(self.apply_nnf_to_image(nnf, source_style)) + return nnf, target_style + + +class PyramidPatchMatcher: + def __init__( + self, image_height, image_width, channel, minimum_patch_size, + threads_per_block=8, num_iter=5, gpu_id=0, guide_weight=10.0, + use_mean_target_style=False, use_pairwise_patch_error=False, + tracking_window_size=0, + initialize="identity" + ): + maximum_patch_size = minimum_patch_size + (num_iter - 1) * 2 + self.pyramid_level = int(np.log2(min(image_height, image_width) / maximum_patch_size)) + self.pyramid_heights = [] + self.pyramid_widths = [] + self.patch_matchers = [] + self.minimum_patch_size = minimum_patch_size + self.num_iter = num_iter + self.gpu_id = gpu_id + self.initialize = initialize + for level in range(self.pyramid_level): + height = image_height//(2**(self.pyramid_level - 1 - level)) + width = image_width//(2**(self.pyramid_level - 1 - level)) + self.pyramid_heights.append(height) + self.pyramid_widths.append(width) + self.patch_matchers.append(PatchMatcher( + height, width, channel, minimum_patch_size=minimum_patch_size, + threads_per_block=threads_per_block, num_iter=num_iter, gpu_id=gpu_id, guide_weight=guide_weight, + use_mean_target_style=use_mean_target_style, use_pairwise_patch_error=use_pairwise_patch_error, + tracking_window_size=tracking_window_size + )) + + def resample_image(self, images, level): + height, width = self.pyramid_heights[level], self.pyramid_widths[level] + images_torch = torch.as_tensor(images, device='cuda', dtype=torch.float32) + images_torch = images_torch.permute(0, 3, 1, 2) + images_resample = F.interpolate(images_torch, size=(height, width), mode='area', align_corners=None) + images_resample = images_resample.permute(0, 2, 3, 1).contiguous() + return cp.asarray(images_resample) + + def initialize_nnf(self, batch_size): + if self.initialize == "random": + height, width = self.pyramid_heights[0], self.pyramid_widths[0] + nnf = cp.stack([ + cp.random.randint(0, height, (batch_size, height, width), dtype=cp.int32), + cp.random.randint(0, width, (batch_size, height, width), dtype=cp.int32) + ], axis=3) + elif self.initialize == "identity": + height, width = self.pyramid_heights[0], self.pyramid_widths[0] + nnf = cp.stack([ + cp.repeat(cp.arange(height), width).reshape(height, width), + cp.tile(cp.arange(width), height).reshape(height, width) + ], axis=2) + nnf = cp.stack([nnf] * batch_size) + else: + raise NotImplementedError() + return nnf + + def update_nnf(self, nnf, level): + # upscale + nnf = nnf.repeat(2, axis=1).repeat(2, axis=2) * 2 + nnf[:, 1::2, :, 0] += 1 + nnf[:, :, 1::2, 1] += 1 + # check if scale is 2 + height, width = self.pyramid_heights[level], self.pyramid_widths[level] + if height != nnf.shape[0] * 2 or width != nnf.shape[1] * 2: + nnf_torch = torch.as_tensor(nnf, device='cuda', dtype=torch.float32) + nnf_torch = nnf_torch.permute(0, 3, 1, 2) + nnf_resized = F.interpolate(nnf_torch, size=(height, width), mode='bilinear', align_corners=False) + nnf_resized = nnf_resized.permute(0, 2, 3, 1) + nnf = cp.asarray(nnf_resized).astype(cp.int32) + nnf = self.patch_matchers[level].clamp_bound(nnf) + return nnf + + def apply_nnf_to_image(self, nnf, image): + with cp.cuda.Device(self.gpu_id): + image = self.patch_matchers[-1].pad_image(image) + image = self.patch_matchers[-1].apply_nnf_to_image(nnf, image) + return image + + def estimate_nnf(self, source_guide, target_guide, source_style): + with cp.cuda.Device(self.gpu_id): + if not isinstance(source_guide, cp.ndarray): + source_guide = cp.array(source_guide, dtype=cp.float32) + if not isinstance(target_guide, cp.ndarray): + target_guide = cp.array(target_guide, dtype=cp.float32) + if not isinstance(source_style, cp.ndarray): + source_style = cp.array(source_style, dtype=cp.float32) + for level in range(self.pyramid_level): + nnf = self.initialize_nnf(source_guide.shape[0]) if level==0 else self.update_nnf(nnf, level) + source_guide_ = self.resample_image(source_guide, level) + target_guide_ = self.resample_image(target_guide, level) + source_style_ = self.resample_image(source_style, level) + nnf, target_style = self.patch_matchers[level].estimate_nnf( + source_guide_, target_guide_, source_style_, nnf + ) + return nnf.get(), target_style.get() diff --git a/diffsynth/extensions/FastBlend/runners/__init__.py b/diffsynth/extensions/FastBlend/runners/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..078382729690d282436411661693ce22f3dcc033 --- /dev/null +++ b/diffsynth/extensions/FastBlend/runners/__init__.py @@ -0,0 +1,4 @@ +from .accurate import AccurateModeRunner +from .fast import FastModeRunner +from .balanced import BalancedModeRunner +from .interpolation import InterpolationModeRunner, InterpolationModeSingleFrameRunner diff --git a/diffsynth/extensions/FastBlend/runners/accurate.py b/diffsynth/extensions/FastBlend/runners/accurate.py new file mode 100644 index 0000000000000000000000000000000000000000..2e4a47f1981ebc1ec9a034a814dfc1130955c2e1 --- /dev/null +++ b/diffsynth/extensions/FastBlend/runners/accurate.py @@ -0,0 +1,35 @@ +from ..patch_match import PyramidPatchMatcher +import os +import numpy as np +from PIL import Image +from tqdm import tqdm + + +class AccurateModeRunner: + def __init__(self): + pass + + def run(self, frames_guide, frames_style, batch_size, window_size, ebsynth_config, desc="Accurate Mode", save_path=None): + patch_match_engine = PyramidPatchMatcher( + image_height=frames_style[0].shape[0], + image_width=frames_style[0].shape[1], + channel=3, + use_mean_target_style=True, + **ebsynth_config + ) + # run + n = len(frames_style) + for target in tqdm(range(n), desc=desc): + l, r = max(target - window_size, 0), min(target + window_size + 1, n) + remapped_frames = [] + for i in range(l, r, batch_size): + j = min(i + batch_size, r) + source_guide = np.stack([frames_guide[source] for source in range(i, j)]) + target_guide = np.stack([frames_guide[target]] * (j - i)) + source_style = np.stack([frames_style[source] for source in range(i, j)]) + _, target_style = patch_match_engine.estimate_nnf(source_guide, target_guide, source_style) + remapped_frames.append(target_style) + frame = np.concatenate(remapped_frames, axis=0).mean(axis=0) + frame = frame.clip(0, 255).astype("uint8") + if save_path is not None: + Image.fromarray(frame).save(os.path.join(save_path, "%05d.png" % target)) \ No newline at end of file diff --git a/diffsynth/extensions/FastBlend/runners/balanced.py b/diffsynth/extensions/FastBlend/runners/balanced.py new file mode 100644 index 0000000000000000000000000000000000000000..1c9a2bb7e438b49c89d0786e858ccf03302fab35 --- /dev/null +++ b/diffsynth/extensions/FastBlend/runners/balanced.py @@ -0,0 +1,46 @@ +from ..patch_match import PyramidPatchMatcher +import os +import numpy as np +from PIL import Image +from tqdm import tqdm + + +class BalancedModeRunner: + def __init__(self): + pass + + def run(self, frames_guide, frames_style, batch_size, window_size, ebsynth_config, desc="Balanced Mode", save_path=None): + patch_match_engine = PyramidPatchMatcher( + image_height=frames_style[0].shape[0], + image_width=frames_style[0].shape[1], + channel=3, + **ebsynth_config + ) + # tasks + n = len(frames_style) + tasks = [] + for target in range(n): + for source in range(target - window_size, target + window_size + 1): + if source >= 0 and source < n and source != target: + tasks.append((source, target)) + # run + frames = [(None, 1) for i in range(n)] + for batch_id in tqdm(range(0, len(tasks), batch_size), desc=desc): + tasks_batch = tasks[batch_id: min(batch_id+batch_size, len(tasks))] + source_guide = np.stack([frames_guide[source] for source, target in tasks_batch]) + target_guide = np.stack([frames_guide[target] for source, target in tasks_batch]) + source_style = np.stack([frames_style[source] for source, target in tasks_batch]) + _, target_style = patch_match_engine.estimate_nnf(source_guide, target_guide, source_style) + for (source, target), result in zip(tasks_batch, target_style): + frame, weight = frames[target] + if frame is None: + frame = frames_style[target] + frames[target] = ( + frame * (weight / (weight + 1)) + result / (weight + 1), + weight + 1 + ) + if weight + 1 == min(n, target + window_size + 1) - max(0, target - window_size): + frame = frame.clip(0, 255).astype("uint8") + if save_path is not None: + Image.fromarray(frame).save(os.path.join(save_path, "%05d.png" % target)) + frames[target] = (None, 1) diff --git a/diffsynth/extensions/FastBlend/runners/fast.py b/diffsynth/extensions/FastBlend/runners/fast.py new file mode 100644 index 0000000000000000000000000000000000000000..2ba5731475ab875929b14181e0c22f4fd466c591 --- /dev/null +++ b/diffsynth/extensions/FastBlend/runners/fast.py @@ -0,0 +1,141 @@ +from ..patch_match import PyramidPatchMatcher +import functools, os +import numpy as np +from PIL import Image +from tqdm import tqdm + + +class TableManager: + def __init__(self): + pass + + def task_list(self, n): + tasks = [] + max_level = 1 + while (1<=n: + break + meta_data = { + "source": i, + "target": j, + "level": level + 1 + } + tasks.append(meta_data) + tasks.sort(key=functools.cmp_to_key(lambda u, v: u["level"]-v["level"])) + return tasks + + def build_remapping_table(self, frames_guide, frames_style, patch_match_engine, batch_size, desc=""): + n = len(frames_guide) + tasks = self.task_list(n) + remapping_table = [[(frames_style[i], 1)] for i in range(n)] + for batch_id in tqdm(range(0, len(tasks), batch_size), desc=desc): + tasks_batch = tasks[batch_id: min(batch_id+batch_size, len(tasks))] + source_guide = np.stack([frames_guide[task["source"]] for task in tasks_batch]) + target_guide = np.stack([frames_guide[task["target"]] for task in tasks_batch]) + source_style = np.stack([frames_style[task["source"]] for task in tasks_batch]) + _, target_style = patch_match_engine.estimate_nnf(source_guide, target_guide, source_style) + for task, result in zip(tasks_batch, target_style): + target, level = task["target"], task["level"] + if len(remapping_table[target])==level: + remapping_table[target].append((result, 1)) + else: + frame, weight = remapping_table[target][level] + remapping_table[target][level] = ( + frame * (weight / (weight + 1)) + result / (weight + 1), + weight + 1 + ) + return remapping_table + + def remapping_table_to_blending_table(self, table): + for i in range(len(table)): + for j in range(1, len(table[i])): + frame_1, weight_1 = table[i][j-1] + frame_2, weight_2 = table[i][j] + frame = (frame_1 + frame_2) / 2 + weight = weight_1 + weight_2 + table[i][j] = (frame, weight) + return table + + def tree_query(self, leftbound, rightbound): + node_list = [] + node_index = rightbound + while node_index>=leftbound: + node_level = 0 + while (1<=leftbound: + node_level += 1 + node_list.append((node_index, node_level)) + node_index -= 1<0: + tasks = [] + for m in range(index_style[0]): + tasks.append((index_style[0], m, index_style[0])) + task_group.append(tasks) + # middle frames + for l, r in zip(index_style[:-1], index_style[1:]): + tasks = [] + for m in range(l, r): + tasks.append((l, m, r)) + task_group.append(tasks) + # last frame + tasks = [] + for m in range(index_style[-1], n): + tasks.append((index_style[-1], m, index_style[-1])) + task_group.append(tasks) + return task_group + + def run(self, frames_guide, frames_style, index_style, batch_size, ebsynth_config, save_path=None): + patch_match_engine = PyramidPatchMatcher( + image_height=frames_style[0].shape[0], + image_width=frames_style[0].shape[1], + channel=3, + use_mean_target_style=False, + use_pairwise_patch_error=True, + **ebsynth_config + ) + # task + index_dict = self.get_index_dict(index_style) + task_group = self.get_task_group(index_style, len(frames_guide)) + # run + for tasks in task_group: + index_start, index_end = min([i[1] for i in tasks]), max([i[1] for i in tasks]) + for batch_id in tqdm(range(0, len(tasks), batch_size), desc=f"Rendering frames {index_start}...{index_end}"): + tasks_batch = tasks[batch_id: min(batch_id+batch_size, len(tasks))] + source_guide, target_guide, source_style = [], [], [] + for l, m, r in tasks_batch: + # l -> m + source_guide.append(frames_guide[l]) + target_guide.append(frames_guide[m]) + source_style.append(frames_style[index_dict[l]]) + # r -> m + source_guide.append(frames_guide[r]) + target_guide.append(frames_guide[m]) + source_style.append(frames_style[index_dict[r]]) + source_guide = np.stack(source_guide) + target_guide = np.stack(target_guide) + source_style = np.stack(source_style) + _, target_style = patch_match_engine.estimate_nnf(source_guide, target_guide, source_style) + if save_path is not None: + for frame_l, frame_r, (l, m, r) in zip(target_style[0::2], target_style[1::2], tasks_batch): + weight_l, weight_r = self.get_weight(l, m, r) + frame = frame_l * weight_l + frame_r * weight_r + frame = frame.clip(0, 255).astype("uint8") + Image.fromarray(frame).save(os.path.join(save_path, "%05d.png" % m)) + + +class InterpolationModeSingleFrameRunner: + def __init__(self): + pass + + def run(self, frames_guide, frames_style, index_style, batch_size, ebsynth_config, save_path=None): + # check input + tracking_window_size = ebsynth_config["tracking_window_size"] + if tracking_window_size * 2 >= batch_size: + raise ValueError("batch_size should be larger than track_window_size * 2") + frame_style = frames_style[0] + frame_guide = frames_guide[index_style[0]] + patch_match_engine = PyramidPatchMatcher( + image_height=frame_style.shape[0], + image_width=frame_style.shape[1], + channel=3, + **ebsynth_config + ) + # run + frame_id, n = 0, len(frames_guide) + for i in tqdm(range(0, n, batch_size - tracking_window_size * 2), desc=f"Rendering frames 0...{n}"): + if i + batch_size > n: + l, r = max(n - batch_size, 0), n + else: + l, r = i, i + batch_size + source_guide = np.stack([frame_guide] * (r-l)) + target_guide = np.stack([frames_guide[i] for i in range(l, r)]) + source_style = np.stack([frame_style] * (r-l)) + _, target_style = patch_match_engine.estimate_nnf(source_guide, target_guide, source_style) + for i, frame in zip(range(l, r), target_style): + if i==frame_id: + frame = frame.clip(0, 255).astype("uint8") + Image.fromarray(frame).save(os.path.join(save_path, "%05d.png" % frame_id)) + frame_id += 1 + if r < n and r-frame_id <= tracking_window_size: + break diff --git a/diffsynth/extensions/ImageQualityMetric/BLIP/__init__.py b/diffsynth/extensions/ImageQualityMetric/BLIP/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..885dcf8f76ad77865054f0c033f8541ae08b1e04 --- /dev/null +++ b/diffsynth/extensions/ImageQualityMetric/BLIP/__init__.py @@ -0,0 +1 @@ +from .blip_pretrain import * diff --git a/diffsynth/extensions/ImageQualityMetric/BLIP/blip.py b/diffsynth/extensions/ImageQualityMetric/BLIP/blip.py new file mode 100644 index 0000000000000000000000000000000000000000..6b24c3c17fdeff6949c3692164362abb8d8d0989 --- /dev/null +++ b/diffsynth/extensions/ImageQualityMetric/BLIP/blip.py @@ -0,0 +1,77 @@ +''' + * Adapted from BLIP (https://github.com/salesforce/BLIP) +''' + +import warnings +warnings.filterwarnings("ignore") + +import torch +import os +from urllib.parse import urlparse +from timm.models.hub import download_cached_file +from transformers import BertTokenizer +from .vit import VisionTransformer, interpolate_pos_embed + + +def default_bert(): + current_dir = os.path.dirname(os.path.abspath(__file__)) + project_root = os.path.abspath(os.path.join(current_dir, '../../../../')) + model_path = os.path.join(project_root, 'models', 'QualityMetric') + return os.path.join(model_path, "bert-base-uncased") + + +def init_tokenizer(bert_model_path): + tokenizer = BertTokenizer.from_pretrained(bert_model_path) + tokenizer.add_special_tokens({'bos_token':'[DEC]'}) + tokenizer.add_special_tokens({'additional_special_tokens':['[ENC]']}) + tokenizer.enc_token_id = tokenizer.additional_special_tokens_ids[0] + return tokenizer + + +def create_vit(vit, image_size, use_grad_checkpointing=False, ckpt_layer=0, drop_path_rate=0): + + assert vit in ['base', 'large'], "vit parameter must be base or large" + if vit=='base': + vision_width = 768 + visual_encoder = VisionTransformer(img_size=image_size, patch_size=16, embed_dim=vision_width, depth=12, + num_heads=12, use_grad_checkpointing=use_grad_checkpointing, ckpt_layer=ckpt_layer, + drop_path_rate=0 or drop_path_rate + ) + elif vit=='large': + vision_width = 1024 + visual_encoder = VisionTransformer(img_size=image_size, patch_size=16, embed_dim=vision_width, depth=24, + num_heads=16, use_grad_checkpointing=use_grad_checkpointing, ckpt_layer=ckpt_layer, + drop_path_rate=0.1 or drop_path_rate + ) + return visual_encoder, vision_width + + +def is_url(url_or_filename): + parsed = urlparse(url_or_filename) + return parsed.scheme in ("http", "https") + +def load_checkpoint(model,url_or_filename): + if is_url(url_or_filename): + cached_file = download_cached_file(url_or_filename, check_hash=False, progress=True) + checkpoint = torch.load(cached_file, map_location='cpu') + elif os.path.isfile(url_or_filename): + checkpoint = torch.load(url_or_filename, map_location='cpu') + else: + raise RuntimeError('checkpoint url or path is invalid') + + state_dict = checkpoint['model'] + + state_dict['visual_encoder.pos_embed'] = interpolate_pos_embed(state_dict['visual_encoder.pos_embed'],model.visual_encoder) + if 'visual_encoder_m.pos_embed' in model.state_dict().keys(): + state_dict['visual_encoder_m.pos_embed'] = interpolate_pos_embed(state_dict['visual_encoder_m.pos_embed'], + model.visual_encoder_m) + for key in model.state_dict().keys(): + if key in state_dict.keys(): + if state_dict[key].shape!=model.state_dict()[key].shape: + print(key, ": ", state_dict[key].shape, ', ', model.state_dict()[key].shape) + del state_dict[key] + + msg = model.load_state_dict(state_dict,strict=False) + print('load checkpoint from %s'%url_or_filename) + return model,msg + diff --git a/diffsynth/extensions/ImageQualityMetric/BLIP/blip_pretrain.py b/diffsynth/extensions/ImageQualityMetric/BLIP/blip_pretrain.py new file mode 100644 index 0000000000000000000000000000000000000000..ba711e2776fd086190ca940248e022b4e083819a --- /dev/null +++ b/diffsynth/extensions/ImageQualityMetric/BLIP/blip_pretrain.py @@ -0,0 +1,44 @@ +''' + * Adapted from BLIP (https://github.com/salesforce/BLIP) +''' + +import transformers +transformers.logging.set_verbosity_error() + +from torch import nn +import os +from .med import BertConfig, BertModel +from .blip import create_vit, init_tokenizer + +class BLIP_Pretrain(nn.Module): + def __init__(self, + med_config = "med_config.json", + image_size = 224, + vit = 'base', + vit_grad_ckpt = False, + vit_ckpt_layer = 0, + embed_dim = 256, + queue_size = 57600, + momentum = 0.995, + bert_model_path = "" + ): + """ + Args: + med_config (str): path for the mixture of encoder-decoder model's configuration file + image_size (int): input image size + vit (str): model size of vision transformer + """ + super().__init__() + + self.visual_encoder, vision_width = create_vit(vit,image_size, vit_grad_ckpt, vit_ckpt_layer, 0) + + self.tokenizer = init_tokenizer(bert_model_path) + encoder_config = BertConfig.from_json_file(med_config) + encoder_config.encoder_width = vision_width + self.text_encoder = BertModel(config=encoder_config, add_pooling_layer=False) + + text_width = self.text_encoder.config.hidden_size + + self.vision_proj = nn.Linear(vision_width, embed_dim) + self.text_proj = nn.Linear(text_width, embed_dim) + diff --git a/diffsynth/extensions/ImageQualityMetric/BLIP/med.py b/diffsynth/extensions/ImageQualityMetric/BLIP/med.py new file mode 100644 index 0000000000000000000000000000000000000000..5905a346c8f3a511fc56d1e052467dcef3246e4b --- /dev/null +++ b/diffsynth/extensions/ImageQualityMetric/BLIP/med.py @@ -0,0 +1,947 @@ +''' + * Adapted from BLIP (https://github.com/salesforce/BLIP) + * Based on huggingface code base + * https://github.com/huggingface/transformers/blob/v4.15.0/src/transformers/models/bert +''' + +import math +from typing import Tuple + +import torch +from torch import Tensor, device, nn +import torch.utils.checkpoint +from torch import nn +from torch.nn import CrossEntropyLoss + +from transformers.activations import ACT2FN +from transformers.file_utils import ( + ModelOutput, +) +from transformers.modeling_outputs import ( + BaseModelOutputWithPastAndCrossAttentions, + BaseModelOutputWithPoolingAndCrossAttentions, + CausalLMOutputWithCrossAttentions, + MaskedLMOutput, + MultipleChoiceModelOutput, + NextSentencePredictorOutput, + QuestionAnsweringModelOutput, + SequenceClassifierOutput, + TokenClassifierOutput, +) +from transformers.modeling_utils import ( + PreTrainedModel, + apply_chunking_to_forward, + find_pruneable_heads_and_indices, + prune_linear_layer, +) +from transformers.utils import logging +from transformers.models.bert.configuration_bert import BertConfig + + +logger = logging.get_logger(__name__) + + +class BertEmbeddings(nn.Module): + """Construct the embeddings from word and position embeddings.""" + + def __init__(self, config): + super().__init__() + self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id) + self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size) + + # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load + # any TensorFlow checkpoint file + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + # position_ids (1, len position emb) is contiguous in memory and exported when serialized + self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1))) + self.position_embedding_type = getattr(config, "position_embedding_type", "absolute") + + self.config = config + + def forward( + self, input_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0 + ): + if input_ids is not None: + input_shape = input_ids.size() + else: + input_shape = inputs_embeds.size()[:-1] + + seq_length = input_shape[1] + + if position_ids is None: + position_ids = self.position_ids[:, past_key_values_length : seq_length + past_key_values_length] + + if inputs_embeds is None: + inputs_embeds = self.word_embeddings(input_ids) + + embeddings = inputs_embeds + + if self.position_embedding_type == "absolute": + position_embeddings = self.position_embeddings(position_ids) + embeddings += position_embeddings + embeddings = self.LayerNorm(embeddings) + embeddings = self.dropout(embeddings) + return embeddings + + +class BertSelfAttention(nn.Module): + def __init__(self, config, is_cross_attention): + super().__init__() + self.config = config + if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): + raise ValueError( + "The hidden size (%d) is not a multiple of the number of attention " + "heads (%d)" % (config.hidden_size, config.num_attention_heads) + ) + + self.num_attention_heads = config.num_attention_heads + self.attention_head_size = int(config.hidden_size / config.num_attention_heads) + self.all_head_size = self.num_attention_heads * self.attention_head_size + + self.query = nn.Linear(config.hidden_size, self.all_head_size) + if is_cross_attention: + self.key = nn.Linear(config.encoder_width, self.all_head_size) + self.value = nn.Linear(config.encoder_width, self.all_head_size) + else: + self.key = nn.Linear(config.hidden_size, self.all_head_size) + self.value = nn.Linear(config.hidden_size, self.all_head_size) + + self.dropout = nn.Dropout(config.attention_probs_dropout_prob) + self.position_embedding_type = getattr(config, "position_embedding_type", "absolute") + if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query": + self.max_position_embeddings = config.max_position_embeddings + self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size) + self.save_attention = False + + def save_attn_gradients(self, attn_gradients): + self.attn_gradients = attn_gradients + + def get_attn_gradients(self): + return self.attn_gradients + + def save_attention_map(self, attention_map): + self.attention_map = attention_map + + def get_attention_map(self): + return self.attention_map + + def transpose_for_scores(self, x): + new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) + x = x.view(*new_x_shape) + return x.permute(0, 2, 1, 3) + + def forward( + self, + hidden_states, + attention_mask=None, + head_mask=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + past_key_value=None, + output_attentions=False, + ): + mixed_query_layer = self.query(hidden_states) + + # If this is instantiated as a cross-attention module, the keys + # and values come from an encoder; the attention mask needs to be + # such that the encoder's padding tokens are not attended to. + is_cross_attention = encoder_hidden_states is not None + + if is_cross_attention: + key_layer = self.transpose_for_scores(self.key(encoder_hidden_states)) + value_layer = self.transpose_for_scores(self.value(encoder_hidden_states)) + attention_mask = encoder_attention_mask + elif past_key_value is not None: + key_layer = self.transpose_for_scores(self.key(hidden_states)) + value_layer = self.transpose_for_scores(self.value(hidden_states)) + key_layer = torch.cat([past_key_value[0], key_layer], dim=2) + value_layer = torch.cat([past_key_value[1], value_layer], dim=2) + else: + key_layer = self.transpose_for_scores(self.key(hidden_states)) + value_layer = self.transpose_for_scores(self.value(hidden_states)) + + query_layer = self.transpose_for_scores(mixed_query_layer) + + past_key_value = (key_layer, value_layer) + + # Take the dot product between "query" and "key" to get the raw attention scores. + attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) + + if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query": + seq_length = hidden_states.size()[1] + position_ids_l = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(-1, 1) + position_ids_r = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(1, -1) + distance = position_ids_l - position_ids_r + positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1) + positional_embedding = positional_embedding.to(dtype=query_layer.dtype) # fp16 compatibility + + if self.position_embedding_type == "relative_key": + relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding) + attention_scores = attention_scores + relative_position_scores + elif self.position_embedding_type == "relative_key_query": + relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding) + relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding) + attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key + + attention_scores = attention_scores / math.sqrt(self.attention_head_size) + if attention_mask is not None: + # Apply the attention mask is (precomputed for all layers in BertModel forward() function) + attention_scores = attention_scores + attention_mask + + # Normalize the attention scores to probabilities. + attention_probs = nn.Softmax(dim=-1)(attention_scores) + + if is_cross_attention and self.save_attention: + self.save_attention_map(attention_probs) + attention_probs.register_hook(self.save_attn_gradients) + + # This is actually dropping out entire tokens to attend to, which might + # seem a bit unusual, but is taken from the original Transformer paper. + attention_probs_dropped = self.dropout(attention_probs) + + # Mask heads if we want to + if head_mask is not None: + attention_probs_dropped = attention_probs_dropped * head_mask + + context_layer = torch.matmul(attention_probs_dropped, value_layer) + + context_layer = context_layer.permute(0, 2, 1, 3).contiguous() + new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) + context_layer = context_layer.view(*new_context_layer_shape) + + outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) + + outputs = outputs + (past_key_value,) + return outputs + + +class BertSelfOutput(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward(self, hidden_states, input_tensor): + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.LayerNorm(hidden_states + input_tensor) + return hidden_states + + +class BertAttention(nn.Module): + def __init__(self, config, is_cross_attention=False): + super().__init__() + self.self = BertSelfAttention(config, is_cross_attention) + self.output = BertSelfOutput(config) + self.pruned_heads = set() + + def prune_heads(self, heads): + if len(heads) == 0: + return + heads, index = find_pruneable_heads_and_indices( + heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads + ) + + # Prune linear layers + self.self.query = prune_linear_layer(self.self.query, index) + self.self.key = prune_linear_layer(self.self.key, index) + self.self.value = prune_linear_layer(self.self.value, index) + self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) + + # Update hyper params and store pruned heads + self.self.num_attention_heads = self.self.num_attention_heads - len(heads) + self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads + self.pruned_heads = self.pruned_heads.union(heads) + + def forward( + self, + hidden_states, + attention_mask=None, + head_mask=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + past_key_value=None, + output_attentions=False, + ): + self_outputs = self.self( + hidden_states, + attention_mask, + head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + ) + attention_output = self.output(self_outputs[0], hidden_states) + outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them + return outputs + + +class BertIntermediate(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.intermediate_size) + if isinstance(config.hidden_act, str): + self.intermediate_act_fn = ACT2FN[config.hidden_act] + else: + self.intermediate_act_fn = config.hidden_act + + def forward(self, hidden_states): + hidden_states = self.dense(hidden_states) + hidden_states = self.intermediate_act_fn(hidden_states) + return hidden_states + + +class BertOutput(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.intermediate_size, config.hidden_size) + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward(self, hidden_states, input_tensor): + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.LayerNorm(hidden_states + input_tensor) + return hidden_states + + +class BertLayer(nn.Module): + def __init__(self, config, layer_num): + super().__init__() + self.config = config + self.chunk_size_feed_forward = config.chunk_size_feed_forward + self.seq_len_dim = 1 + self.attention = BertAttention(config) + self.layer_num = layer_num + if self.config.add_cross_attention: + self.crossattention = BertAttention(config, is_cross_attention=self.config.add_cross_attention) + self.intermediate = BertIntermediate(config) + self.output = BertOutput(config) + + def forward( + self, + hidden_states, + attention_mask=None, + head_mask=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + past_key_value=None, + output_attentions=False, + mode=None, + ): + # decoder uni-directional self-attention cached key/values tuple is at positions 1,2 + self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None + self_attention_outputs = self.attention( + hidden_states, + attention_mask, + head_mask, + output_attentions=output_attentions, + past_key_value=self_attn_past_key_value, + ) + attention_output = self_attention_outputs[0] + + outputs = self_attention_outputs[1:-1] + present_key_value = self_attention_outputs[-1] + + if mode=='multimodal': + assert encoder_hidden_states is not None, "encoder_hidden_states must be given for cross-attention layers" + + cross_attention_outputs = self.crossattention( + attention_output, + attention_mask, + head_mask, + encoder_hidden_states, + encoder_attention_mask, + output_attentions=output_attentions, + ) + attention_output = cross_attention_outputs[0] + outputs = outputs + cross_attention_outputs[1:-1] # add cross attentions if we output attention weights + layer_output = apply_chunking_to_forward( + self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output + ) + outputs = (layer_output,) + outputs + + outputs = outputs + (present_key_value,) + + return outputs + + def feed_forward_chunk(self, attention_output): + intermediate_output = self.intermediate(attention_output) + layer_output = self.output(intermediate_output, attention_output) + return layer_output + + +class BertEncoder(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.layer = nn.ModuleList([BertLayer(config,i) for i in range(config.num_hidden_layers)]) + self.gradient_checkpointing = False + + def forward( + self, + hidden_states, + attention_mask=None, + head_mask=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + past_key_values=None, + use_cache=None, + output_attentions=False, + output_hidden_states=False, + return_dict=True, + mode='multimodal', + ): + all_hidden_states = () if output_hidden_states else None + all_self_attentions = () if output_attentions else None + all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None + + next_decoder_cache = () if use_cache else None + + for i in range(self.config.num_hidden_layers): + layer_module = self.layer[i] + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + layer_head_mask = head_mask[i] if head_mask is not None else None + past_key_value = past_key_values[i] if past_key_values is not None else None + + if self.gradient_checkpointing and self.training: + + if use_cache: + logger.warning( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." + ) + use_cache = False + + def create_custom_forward(module): + def custom_forward(*inputs): + return module(*inputs, past_key_value, output_attentions) + + return custom_forward + + layer_outputs = torch.utils.checkpoint.checkpoint( + create_custom_forward(layer_module), + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + mode=mode, + ) + else: + layer_outputs = layer_module( + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + mode=mode, + ) + + hidden_states = layer_outputs[0] + if use_cache: + next_decoder_cache += (layer_outputs[-1],) + if output_attentions: + all_self_attentions = all_self_attentions + (layer_outputs[1],) + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if not return_dict: + return tuple( + v + for v in [ + hidden_states, + next_decoder_cache, + all_hidden_states, + all_self_attentions, + all_cross_attentions, + ] + if v is not None + ) + return BaseModelOutputWithPastAndCrossAttentions( + last_hidden_state=hidden_states, + past_key_values=next_decoder_cache, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + cross_attentions=all_cross_attentions, + ) + + +class BertPooler(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.activation = nn.Tanh() + + def forward(self, hidden_states): + # We "pool" the model by simply taking the hidden state corresponding + # to the first token. + first_token_tensor = hidden_states[:, 0] + pooled_output = self.dense(first_token_tensor) + pooled_output = self.activation(pooled_output) + return pooled_output + + +class BertPredictionHeadTransform(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + if isinstance(config.hidden_act, str): + self.transform_act_fn = ACT2FN[config.hidden_act] + else: + self.transform_act_fn = config.hidden_act + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + + def forward(self, hidden_states): + hidden_states = self.dense(hidden_states) + hidden_states = self.transform_act_fn(hidden_states) + hidden_states = self.LayerNorm(hidden_states) + return hidden_states + + +class BertLMPredictionHead(nn.Module): + def __init__(self, config): + super().__init__() + self.transform = BertPredictionHeadTransform(config) + + # The output weights are the same as the input embeddings, but there is + # an output-only bias for each token. + self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + + self.bias = nn.Parameter(torch.zeros(config.vocab_size)) + + # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings` + self.decoder.bias = self.bias + + def forward(self, hidden_states): + hidden_states = self.transform(hidden_states) + hidden_states = self.decoder(hidden_states) + return hidden_states + + +class BertOnlyMLMHead(nn.Module): + def __init__(self, config): + super().__init__() + self.predictions = BertLMPredictionHead(config) + + def forward(self, sequence_output): + prediction_scores = self.predictions(sequence_output) + return prediction_scores + + +class BertPreTrainedModel(PreTrainedModel): + """ + An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained + models. + """ + + config_class = BertConfig + base_model_prefix = "bert" + _keys_to_ignore_on_load_missing = [r"position_ids"] + + def _init_weights(self, module): + """ Initialize the weights """ + if isinstance(module, (nn.Linear, nn.Embedding)): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + elif isinstance(module, nn.LayerNorm): + module.bias.data.zero_() + module.weight.data.fill_(1.0) + if isinstance(module, nn.Linear) and module.bias is not None: + module.bias.data.zero_() + + +class BertModel(BertPreTrainedModel): + """ + The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of + cross-attention is added between the self-attention layers, following the architecture described in `Attention is + all you need `__ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, + Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin. + argument and :obj:`add_cross_attention` set to :obj:`True`; an :obj:`encoder_hidden_states` is then expected as an + input to the forward pass. + """ + + def __init__(self, config, add_pooling_layer=True): + super().__init__(config) + self.config = config + + self.embeddings = BertEmbeddings(config) + + self.encoder = BertEncoder(config) + + self.pooler = BertPooler(config) if add_pooling_layer else None + + self.init_weights() + + + def get_input_embeddings(self): + return self.embeddings.word_embeddings + + def set_input_embeddings(self, value): + self.embeddings.word_embeddings = value + + def _prune_heads(self, heads_to_prune): + """ + Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base + class PreTrainedModel + """ + for layer, heads in heads_to_prune.items(): + self.encoder.layer[layer].attention.prune_heads(heads) + + + def get_extended_attention_mask(self, attention_mask: Tensor, input_shape: Tuple[int], device: device, is_decoder: bool) -> Tensor: + """ + Makes broadcastable attention and causal masks so that future and masked tokens are ignored. + + Arguments: + attention_mask (:obj:`torch.Tensor`): + Mask with ones indicating tokens to attend to, zeros for tokens to ignore. + input_shape (:obj:`Tuple[int]`): + The shape of the input to the model. + device: (:obj:`torch.device`): + The device of the input to the model. + + Returns: + :obj:`torch.Tensor` The extended attention mask, with a the same dtype as :obj:`attention_mask.dtype`. + """ + # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] + # ourselves in which case we just need to make it broadcastable to all heads. + if attention_mask.dim() == 3: + extended_attention_mask = attention_mask[:, None, :, :] + elif attention_mask.dim() == 2: + # Provided a padding mask of dimensions [batch_size, seq_length] + # - if the model is a decoder, apply a causal mask in addition to the padding mask + # - if the model is an encoder, make the mask broadcastable to [batch_size, num_heads, seq_length, seq_length] + if is_decoder: + batch_size, seq_length = input_shape + + seq_ids = torch.arange(seq_length, device=device) + causal_mask = seq_ids[None, None, :].repeat(batch_size, seq_length, 1) <= seq_ids[None, :, None] + # in case past_key_values are used we need to add a prefix ones mask to the causal mask + # causal and attention masks must have same type with pytorch version < 1.3 + causal_mask = causal_mask.to(attention_mask.dtype) + + if causal_mask.shape[1] < attention_mask.shape[1]: + prefix_seq_len = attention_mask.shape[1] - causal_mask.shape[1] + causal_mask = torch.cat( + [ + torch.ones((batch_size, seq_length, prefix_seq_len), device=device, dtype=causal_mask.dtype), + causal_mask, + ], + axis=-1, + ) + + extended_attention_mask = causal_mask[:, None, :, :] * attention_mask[:, None, None, :] + else: + extended_attention_mask = attention_mask[:, None, None, :] + else: + raise ValueError( + "Wrong shape for input_ids (shape {}) or attention_mask (shape {})".format( + input_shape, attention_mask.shape + ) + ) + + # Since attention_mask is 1.0 for positions we want to attend and 0.0 for + # masked positions, this operation will create a tensor which is 0.0 for + # positions we want to attend and -10000.0 for masked positions. + # Since we are adding it to the raw scores before the softmax, this is + # effectively the same as removing these entirely. + extended_attention_mask = extended_attention_mask.to(dtype=self.dtype) # fp16 compatibility + extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0 + return extended_attention_mask + + def forward( + self, + input_ids=None, + attention_mask=None, + position_ids=None, + head_mask=None, + inputs_embeds=None, + encoder_embeds=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + past_key_values=None, + use_cache=None, + output_attentions=None, + output_hidden_states=None, + return_dict=None, + is_decoder=False, + mode='multimodal', + ): + r""" + encoder_hidden_states (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`): + Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if + the model is configured as a decoder. + encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`): + Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in + the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``: + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): + Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. + If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids` + (those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)` + instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`. + use_cache (:obj:`bool`, `optional`): + If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up + decoding (see :obj:`past_key_values`). + """ + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if is_decoder: + use_cache = use_cache if use_cache is not None else self.config.use_cache + else: + use_cache = False + + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") + elif input_ids is not None: + input_shape = input_ids.size() + batch_size, seq_length = input_shape + device = input_ids.device + elif inputs_embeds is not None: + input_shape = inputs_embeds.size()[:-1] + batch_size, seq_length = input_shape + device = inputs_embeds.device + elif encoder_embeds is not None: + input_shape = encoder_embeds.size()[:-1] + batch_size, seq_length = input_shape + device = encoder_embeds.device + else: + raise ValueError("You have to specify either input_ids or inputs_embeds or encoder_embeds") + + # past_key_values_length + past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0 + + if attention_mask is None: + attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), device=device) + + # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] + # ourselves in which case we just need to make it broadcastable to all heads. + extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape, + device, is_decoder) + + # If a 2D or 3D attention mask is provided for the cross-attention + # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] + if encoder_hidden_states is not None: + if type(encoder_hidden_states) == list: + encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states[0].size() + else: + encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size() + encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length) + + if type(encoder_attention_mask) == list: + encoder_extended_attention_mask = [self.invert_attention_mask(mask) for mask in encoder_attention_mask] + elif encoder_attention_mask is None: + encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device) + encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask) + else: + encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask) + else: + encoder_extended_attention_mask = None + + # Prepare head mask if needed + # 1.0 in head_mask indicate we keep the head + # attention_probs has shape bsz x n_heads x N x N + # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] + # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] + head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) + + if encoder_embeds is None: + embedding_output = self.embeddings( + input_ids=input_ids, + position_ids=position_ids, + inputs_embeds=inputs_embeds, + past_key_values_length=past_key_values_length, + ) + else: + embedding_output = encoder_embeds + + encoder_outputs = self.encoder( + embedding_output, + attention_mask=extended_attention_mask, + head_mask=head_mask, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_extended_attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + mode=mode, + ) + sequence_output = encoder_outputs[0] + pooled_output = self.pooler(sequence_output) if self.pooler is not None else None + + if not return_dict: + return (sequence_output, pooled_output) + encoder_outputs[1:] + + return BaseModelOutputWithPoolingAndCrossAttentions( + last_hidden_state=sequence_output, + pooler_output=pooled_output, + past_key_values=encoder_outputs.past_key_values, + hidden_states=encoder_outputs.hidden_states, + attentions=encoder_outputs.attentions, + cross_attentions=encoder_outputs.cross_attentions, + ) + + + +class BertLMHeadModel(BertPreTrainedModel): + + _keys_to_ignore_on_load_unexpected = [r"pooler"] + _keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias"] + + def __init__(self, config): + super().__init__(config) + + self.bert = BertModel(config, add_pooling_layer=False) + self.cls = BertOnlyMLMHead(config) + + self.init_weights() + + def get_output_embeddings(self): + return self.cls.predictions.decoder + + def set_output_embeddings(self, new_embeddings): + self.cls.predictions.decoder = new_embeddings + + def forward( + self, + input_ids=None, + attention_mask=None, + position_ids=None, + head_mask=None, + inputs_embeds=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + labels=None, + past_key_values=None, + use_cache=None, + output_attentions=None, + output_hidden_states=None, + return_dict=None, + return_logits=False, + is_decoder=True, + reduction='mean', + mode='multimodal', + ): + r""" + encoder_hidden_states (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`): + Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if + the model is configured as a decoder. + encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`): + Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in + the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``: + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`): + Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in + ``[-100, 0, ..., config.vocab_size]`` (see ``input_ids`` docstring) Tokens with indices set to ``-100`` are + ignored (masked), the loss is only computed for the tokens with labels n ``[0, ..., config.vocab_size]`` + past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): + Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. + If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids` + (those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)` + instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`. + use_cache (:obj:`bool`, `optional`): + If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up + decoding (see :obj:`past_key_values`). + Returns: + Example:: + >>> from transformers import BertTokenizer, BertLMHeadModel, BertConfig + >>> import torch + >>> tokenizer = BertTokenizer.from_pretrained('bert-base-cased') + >>> config = BertConfig.from_pretrained("bert-base-cased") + >>> model = BertLMHeadModel.from_pretrained('bert-base-cased', config=config) + >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt") + >>> outputs = model(**inputs) + >>> prediction_logits = outputs.logits + """ + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + if labels is not None: + use_cache = False + + outputs = self.bert( + input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + is_decoder=is_decoder, + mode=mode, + ) + + sequence_output = outputs[0] + prediction_scores = self.cls(sequence_output) + + if return_logits: + return prediction_scores[:, :-1, :].contiguous() + + lm_loss = None + if labels is not None: + # we are doing next-token prediction; shift prediction scores and input ids by one + shifted_prediction_scores = prediction_scores[:, :-1, :].contiguous() + labels = labels[:, 1:].contiguous() + loss_fct = CrossEntropyLoss(reduction=reduction, label_smoothing=0.1) + lm_loss = loss_fct(shifted_prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) + if reduction=='none': + lm_loss = lm_loss.view(prediction_scores.size(0),-1).sum(1) + + if not return_dict: + output = (prediction_scores,) + outputs[2:] + return ((lm_loss,) + output) if lm_loss is not None else output + + return CausalLMOutputWithCrossAttentions( + loss=lm_loss, + logits=prediction_scores, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + cross_attentions=outputs.cross_attentions, + ) + + def prepare_inputs_for_generation(self, input_ids, past=None, attention_mask=None, **model_kwargs): + input_shape = input_ids.shape + # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly + if attention_mask is None: + attention_mask = input_ids.new_ones(input_shape) + + # cut decoder_input_ids if past is used + if past is not None: + input_ids = input_ids[:, -1:] + + return { + "input_ids": input_ids, + "attention_mask": attention_mask, + "past_key_values": past, + "encoder_hidden_states": model_kwargs.get("encoder_hidden_states", None), + "encoder_attention_mask": model_kwargs.get("encoder_attention_mask", None), + "is_decoder": True, + } + + def _reorder_cache(self, past, beam_idx): + reordered_past = () + for layer_past in past: + reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),) + return reordered_past diff --git a/diffsynth/extensions/ImageQualityMetric/BLIP/vit.py b/diffsynth/extensions/ImageQualityMetric/BLIP/vit.py new file mode 100644 index 0000000000000000000000000000000000000000..cef7b650a95f56266775cf0f18b28bc0f74987ab --- /dev/null +++ b/diffsynth/extensions/ImageQualityMetric/BLIP/vit.py @@ -0,0 +1,301 @@ +''' + * Adapted from BLIP (https://github.com/salesforce/BLIP) + * Based on timm code base + * https://github.com/rwightman/pytorch-image-models/tree/master/timm +''' + +import torch +import torch.nn as nn +import torch.nn.functional as F +from functools import partial + +from timm.models.vision_transformer import _cfg, PatchEmbed +from timm.models.registry import register_model +from timm.models.layers import trunc_normal_, DropPath +from timm.models.helpers import named_apply, adapt_input_conv + +# from fairscale.nn.checkpoint.checkpoint_activations import checkpoint_wrapper + +class Mlp(nn.Module): + """ MLP as used in Vision Transformer, MLP-Mixer and related networks + """ + def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + self.fc1 = nn.Linear(in_features, hidden_features) + self.act = act_layer() + self.fc2 = nn.Linear(hidden_features, out_features) + self.drop = nn.Dropout(drop) + + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + x = self.drop(x) + x = self.fc2(x) + x = self.drop(x) + return x + + +class Attention(nn.Module): + def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.): + super().__init__() + self.num_heads = num_heads + head_dim = dim // num_heads + # NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights + self.scale = qk_scale or head_dim ** -0.5 + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + self.attn_gradients = None + self.attention_map = None + + def save_attn_gradients(self, attn_gradients): + self.attn_gradients = attn_gradients + + def get_attn_gradients(self): + return self.attn_gradients + + def save_attention_map(self, attention_map): + self.attention_map = attention_map + + def get_attention_map(self): + return self.attention_map + + def forward(self, x, register_hook=False): + B, N, C = x.shape + qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) + q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple) + + attn = (q @ k.transpose(-2, -1)) * self.scale + attn = attn.softmax(dim=-1) + attn = self.attn_drop(attn) + + if register_hook: + self.save_attention_map(attn) + attn.register_hook(self.save_attn_gradients) + + x = (attn @ v).transpose(1, 2).reshape(B, N, C) + x = self.proj(x) + x = self.proj_drop(x) + return x + + +class Block(nn.Module): + + def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0., + drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, use_grad_checkpointing=False): + super().__init__() + self.norm1 = norm_layer(dim) + self.attn = Attention( + dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop) + # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here + self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() + self.norm2 = norm_layer(dim) + mlp_hidden_dim = int(dim * mlp_ratio) + self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) + + # if use_grad_checkpointing: + # self.attn = checkpoint_wrapper(self.attn) + # self.mlp = checkpoint_wrapper(self.mlp) + + def forward(self, x, register_hook=False): + x = x + self.drop_path(self.attn(self.norm1(x), register_hook=register_hook)) + x = x + self.drop_path(self.mlp(self.norm2(x))) + return x + + +class VisionTransformer(nn.Module): + """ Vision Transformer + A PyTorch impl of : `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale` - + https://arxiv.org/abs/2010.11929 + """ + def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, depth=12, + num_heads=12, mlp_ratio=4., qkv_bias=True, qk_scale=None, representation_size=None, + drop_rate=0., attn_drop_rate=0., drop_path_rate=0., norm_layer=None, + use_grad_checkpointing=False, ckpt_layer=0): + """ + Args: + img_size (int, tuple): input image size + patch_size (int, tuple): patch size + in_chans (int): number of input channels + num_classes (int): number of classes for classification head + embed_dim (int): embedding dimension + depth (int): depth of transformer + num_heads (int): number of attention heads + mlp_ratio (int): ratio of mlp hidden dim to embedding dim + qkv_bias (bool): enable bias for qkv if True + qk_scale (float): override default qk scale of head_dim ** -0.5 if set + representation_size (Optional[int]): enable and set representation layer (pre-logits) to this value if set + drop_rate (float): dropout rate + attn_drop_rate (float): attention dropout rate + drop_path_rate (float): stochastic depth rate + norm_layer: (nn.Module): normalization layer + """ + super().__init__() + self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models + norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6) + + self.patch_embed = PatchEmbed( + img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim) + + num_patches = self.patch_embed.num_patches + + self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) + self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim)) + self.pos_drop = nn.Dropout(p=drop_rate) + + dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule + self.blocks = nn.ModuleList([ + Block( + dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale, + drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer, + use_grad_checkpointing=(use_grad_checkpointing and i>=depth-ckpt_layer) + ) + for i in range(depth)]) + self.norm = norm_layer(embed_dim) + + trunc_normal_(self.pos_embed, std=.02) + trunc_normal_(self.cls_token, std=.02) + self.apply(self._init_weights) + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + trunc_normal_(m.weight, std=.02) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + @torch.jit.ignore + def no_weight_decay(self): + return {'pos_embed', 'cls_token'} + + def forward(self, x, register_blk=-1): + B = x.shape[0] + x = self.patch_embed(x) + + cls_tokens = self.cls_token.expand(B, -1, -1) # stole cls_tokens impl from Phil Wang, thanks + x = torch.cat((cls_tokens, x), dim=1) + + x = x + self.pos_embed[:,:x.size(1),:] + x = self.pos_drop(x) + + for i,blk in enumerate(self.blocks): + x = blk(x, register_blk==i) + x = self.norm(x) + + return x + + @torch.jit.ignore() + def load_pretrained(self, checkpoint_path, prefix=''): + _load_weights(self, checkpoint_path, prefix) + + +@torch.no_grad() +def _load_weights(model: VisionTransformer, checkpoint_path: str, prefix: str = ''): + """ Load weights from .npz checkpoints for official Google Brain Flax implementation + """ + import numpy as np + + def _n2p(w, t=True): + if w.ndim == 4 and w.shape[0] == w.shape[1] == w.shape[2] == 1: + w = w.flatten() + if t: + if w.ndim == 4: + w = w.transpose([3, 2, 0, 1]) + elif w.ndim == 3: + w = w.transpose([2, 0, 1]) + elif w.ndim == 2: + w = w.transpose([1, 0]) + return torch.from_numpy(w) + + w = np.load(checkpoint_path) + if not prefix and 'opt/target/embedding/kernel' in w: + prefix = 'opt/target/' + + if hasattr(model.patch_embed, 'backbone'): + # hybrid + backbone = model.patch_embed.backbone + stem_only = not hasattr(backbone, 'stem') + stem = backbone if stem_only else backbone.stem + stem.conv.weight.copy_(adapt_input_conv(stem.conv.weight.shape[1], _n2p(w[f'{prefix}conv_root/kernel']))) + stem.norm.weight.copy_(_n2p(w[f'{prefix}gn_root/scale'])) + stem.norm.bias.copy_(_n2p(w[f'{prefix}gn_root/bias'])) + if not stem_only: + for i, stage in enumerate(backbone.stages): + for j, block in enumerate(stage.blocks): + bp = f'{prefix}block{i + 1}/unit{j + 1}/' + for r in range(3): + getattr(block, f'conv{r + 1}').weight.copy_(_n2p(w[f'{bp}conv{r + 1}/kernel'])) + getattr(block, f'norm{r + 1}').weight.copy_(_n2p(w[f'{bp}gn{r + 1}/scale'])) + getattr(block, f'norm{r + 1}').bias.copy_(_n2p(w[f'{bp}gn{r + 1}/bias'])) + if block.downsample is not None: + block.downsample.conv.weight.copy_(_n2p(w[f'{bp}conv_proj/kernel'])) + block.downsample.norm.weight.copy_(_n2p(w[f'{bp}gn_proj/scale'])) + block.downsample.norm.bias.copy_(_n2p(w[f'{bp}gn_proj/bias'])) + embed_conv_w = _n2p(w[f'{prefix}embedding/kernel']) + else: + embed_conv_w = adapt_input_conv( + model.patch_embed.proj.weight.shape[1], _n2p(w[f'{prefix}embedding/kernel'])) + model.patch_embed.proj.weight.copy_(embed_conv_w) + model.patch_embed.proj.bias.copy_(_n2p(w[f'{prefix}embedding/bias'])) + model.cls_token.copy_(_n2p(w[f'{prefix}cls'], t=False)) + pos_embed_w = _n2p(w[f'{prefix}Transformer/posembed_input/pos_embedding'], t=False) + if pos_embed_w.shape != model.pos_embed.shape: + pos_embed_w = resize_pos_embed( # resize pos embedding when different size from pretrained weights + pos_embed_w, model.pos_embed, getattr(model, 'num_tokens', 1), model.patch_embed.grid_size) + model.pos_embed.copy_(pos_embed_w) + model.norm.weight.copy_(_n2p(w[f'{prefix}Transformer/encoder_norm/scale'])) + model.norm.bias.copy_(_n2p(w[f'{prefix}Transformer/encoder_norm/bias'])) +# if isinstance(model.head, nn.Linear) and model.head.bias.shape[0] == w[f'{prefix}head/bias'].shape[-1]: +# model.head.weight.copy_(_n2p(w[f'{prefix}head/kernel'])) +# model.head.bias.copy_(_n2p(w[f'{prefix}head/bias'])) +# if isinstance(getattr(model.pre_logits, 'fc', None), nn.Linear) and f'{prefix}pre_logits/bias' in w: +# model.pre_logits.fc.weight.copy_(_n2p(w[f'{prefix}pre_logits/kernel'])) +# model.pre_logits.fc.bias.copy_(_n2p(w[f'{prefix}pre_logits/bias'])) + for i, block in enumerate(model.blocks.children()): + block_prefix = f'{prefix}Transformer/encoderblock_{i}/' + mha_prefix = block_prefix + 'MultiHeadDotProductAttention_1/' + block.norm1.weight.copy_(_n2p(w[f'{block_prefix}LayerNorm_0/scale'])) + block.norm1.bias.copy_(_n2p(w[f'{block_prefix}LayerNorm_0/bias'])) + block.attn.qkv.weight.copy_(torch.cat([ + _n2p(w[f'{mha_prefix}{n}/kernel'], t=False).flatten(1).T for n in ('query', 'key', 'value')])) + block.attn.qkv.bias.copy_(torch.cat([ + _n2p(w[f'{mha_prefix}{n}/bias'], t=False).reshape(-1) for n in ('query', 'key', 'value')])) + block.attn.proj.weight.copy_(_n2p(w[f'{mha_prefix}out/kernel']).flatten(1)) + block.attn.proj.bias.copy_(_n2p(w[f'{mha_prefix}out/bias'])) + for r in range(2): + getattr(block.mlp, f'fc{r + 1}').weight.copy_(_n2p(w[f'{block_prefix}MlpBlock_3/Dense_{r}/kernel'])) + getattr(block.mlp, f'fc{r + 1}').bias.copy_(_n2p(w[f'{block_prefix}MlpBlock_3/Dense_{r}/bias'])) + block.norm2.weight.copy_(_n2p(w[f'{block_prefix}LayerNorm_2/scale'])) + block.norm2.bias.copy_(_n2p(w[f'{block_prefix}LayerNorm_2/bias'])) + + +def interpolate_pos_embed(pos_embed_checkpoint, visual_encoder): + # interpolate position embedding + embedding_size = pos_embed_checkpoint.shape[-1] + num_patches = visual_encoder.patch_embed.num_patches + num_extra_tokens = visual_encoder.pos_embed.shape[-2] - num_patches + # height (== width) for the checkpoint position embedding + orig_size = int((pos_embed_checkpoint.shape[-2] - num_extra_tokens) ** 0.5) + # height (== width) for the new position embedding + new_size = int(num_patches ** 0.5) + + if orig_size!=new_size: + # class_token and dist_token are kept unchanged + extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens] + # only the position tokens are interpolated + pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:] + pos_tokens = pos_tokens.reshape(-1, orig_size, orig_size, embedding_size).permute(0, 3, 1, 2) + pos_tokens = torch.nn.functional.interpolate( + pos_tokens, size=(new_size, new_size), mode='bicubic', align_corners=False) + pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2) + new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1) + print('reshape position embedding from %d to %d'%(orig_size ** 2,new_size ** 2)) + + return new_pos_embed + else: + return pos_embed_checkpoint \ No newline at end of file diff --git a/diffsynth/extensions/ImageQualityMetric/__init__.py b/diffsynth/extensions/ImageQualityMetric/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..fcfb7c02b0ce2b6a2fbe345d87c31e0d1bb3a128 --- /dev/null +++ b/diffsynth/extensions/ImageQualityMetric/__init__.py @@ -0,0 +1,148 @@ +from modelscope import snapshot_download +from typing_extensions import Literal, TypeAlias +import os +from diffsynth.extensions.ImageQualityMetric.aesthetic import AestheticScore +from diffsynth.extensions.ImageQualityMetric.imagereward import ImageRewardScore +from diffsynth.extensions.ImageQualityMetric.pickscore import PickScore +from diffsynth.extensions.ImageQualityMetric.clip import CLIPScore +from diffsynth.extensions.ImageQualityMetric.hps import HPScore_v2 +from diffsynth.extensions.ImageQualityMetric.mps import MPScore + + +preference_model_id: TypeAlias = Literal[ + "ImageReward", + "Aesthetic", + "PickScore", + "CLIP", + "HPSv2", + "HPSv2.1", + "MPS", +] +model_dict = { + "ImageReward": { + "model_id": "DiffSynth-Studio/QualityMetric_reward_pretrained", + "allow_file_pattern": [ + "ImageReward/ImageReward.safetensors", + "ImageReward/med_config.json", + "bert-base-uncased/config.json", + "bert-base-uncased/model.safetensors", + "bert-base-uncased/tokenizer.json", + "bert-base-uncased/tokenizer_config.json", + "bert-base-uncased/vocab.txt", + ], + "load_path": { + "imagereward": "ImageReward/ImageReward.safetensors", + "med_config": "ImageReward/med_config.json", + "bert_model_path": "bert-base-uncased", + }, + "model_class": ImageRewardScore + }, + "Aesthetic": { + "model_id": "DiffSynth-Studio/QualityMetric_reward_pretrained", + "allow_file_pattern": [ + "aesthetic-predictor/sac+logos+ava1-l14-linearMSE.safetensors", + "clip-vit-large-patch14/config.json", + "clip-vit-large-patch14/merges.txt", + "clip-vit-large-patch14/model.safetensors", + "clip-vit-large-patch14/preprocessor_config.json", + "clip-vit-large-patch14/special_tokens_map.json", + "clip-vit-large-patch14/tokenizer.json", + "clip-vit-large-patch14/tokenizer_config.json", + "clip-vit-large-patch14/vocab.json", + ], + "load_path": { + "aesthetic_predictor": "aesthetic-predictor/sac+logos+ava1-l14-linearMSE.safetensors", + "clip-large": "clip-vit-large-patch14", + }, + "model_class": AestheticScore + }, + "PickScore": { + "model_id": "DiffSynth-Studio/QualityMetric_reward_pretrained", + "allow_file_pattern": [ + "PickScore_v1/*", + "CLIP-ViT-H-14-laion2B-s32B-b79K/config.json", + "CLIP-ViT-H-14-laion2B-s32B-b79K/merges.txt", + "CLIP-ViT-H-14-laion2B-s32B-b79K/preprocessor_config.json", + "CLIP-ViT-H-14-laion2B-s32B-b79K/special_tokens_map.json", + "CLIP-ViT-H-14-laion2B-s32B-b79K/tokenizer.json", + "CLIP-ViT-H-14-laion2B-s32B-b79K/tokenizer_config.json", + "CLIP-ViT-H-14-laion2B-s32B-b79K/vocab.json", + ], + "load_path": { + "pickscore": "PickScore_v1", + "clip": "CLIP-ViT-H-14-laion2B-s32B-b79K", + }, + "model_class": PickScore + }, + "CLIP": { + "model_id": "DiffSynth-Studio/QualityMetric_reward_pretrained", + "allow_file_pattern": [ + "CLIP-ViT-H-14-laion2B-s32B-b79K/open_clip_pytorch_model.bin", + "bpe_simple_vocab_16e6.txt.gz", + ], + "load_path": { + "open_clip": "CLIP-ViT-H-14-laion2B-s32B-b79K/open_clip_pytorch_model.bin", + "open_clip_bpe": "bpe_simple_vocab_16e6.txt.gz", + }, + "model_class": CLIPScore + }, + "HPSv2": { + "model_id": "DiffSynth-Studio/QualityMetric_reward_pretrained", + "allow_file_pattern": [ + "HPS_v2/HPS_v2_compressed.safetensors", + "bpe_simple_vocab_16e6.txt.gz", + ], + "load_path": { + "hpsv2": "HPS_v2/HPS_v2_compressed.safetensors", + "open_clip_bpe": "bpe_simple_vocab_16e6.txt.gz", + }, + "model_class": HPScore_v2, + "extra_kwargs": {"model_version": "v2"} + }, + "HPSv2.1": { + "model_id": "DiffSynth-Studio/QualityMetric_reward_pretrained", + "allow_file_pattern": [ + "HPS_v2/HPS_v2.1_compressed.safetensors", + "bpe_simple_vocab_16e6.txt.gz", + ], + "load_path": { + "hpsv2.1": "HPS_v2/HPS_v2.1_compressed.safetensors", + "open_clip_bpe": "bpe_simple_vocab_16e6.txt.gz", + }, + "model_class": HPScore_v2, + "extra_kwargs": {"model_version": "v21"} + }, + "MPS": { + "model_id": "DiffSynth-Studio/QualityMetric_reward_pretrained", + "allow_file_pattern": [ + "MPS_overall_checkpoint/MPS_overall_checkpoint_diffsynth.safetensors", + "CLIP-ViT-H-14-laion2B-s32B-b79K/config.json", + "CLIP-ViT-H-14-laion2B-s32B-b79K/merges.txt", + "CLIP-ViT-H-14-laion2B-s32B-b79K/preprocessor_config.json", + "CLIP-ViT-H-14-laion2B-s32B-b79K/special_tokens_map.json", + "CLIP-ViT-H-14-laion2B-s32B-b79K/tokenizer.json", + "CLIP-ViT-H-14-laion2B-s32B-b79K/tokenizer_config.json", + "CLIP-ViT-H-14-laion2B-s32B-b79K/vocab.json", + ], + "load_path": { + "mps": "MPS_overall_checkpoint/MPS_overall_checkpoint_diffsynth.safetensors", + "clip": "CLIP-ViT-H-14-laion2B-s32B-b79K", + }, + "model_class": MPScore + }, +} + + +def download_preference_model(model_name: preference_model_id, cache_dir="models"): + metadata = model_dict[model_name] + snapshot_download(model_id=metadata["model_id"], allow_file_pattern=metadata["allow_file_pattern"], cache_dir=cache_dir) + load_path = metadata["load_path"] + load_path = {key: os.path.join(cache_dir, metadata["model_id"], path) for key, path in load_path.items()} + return load_path + + +def load_preference_model(model_name: preference_model_id, device = "cuda", path = None): + model_class = model_dict[model_name]["model_class"] + extra_kwargs = model_dict[model_name].get("extra_kwargs", {}) + preference_model = model_class(device=device, path=path, **extra_kwargs) + return preference_model diff --git a/diffsynth/extensions/ImageQualityMetric/aesthetic.py b/diffsynth/extensions/ImageQualityMetric/aesthetic.py new file mode 100644 index 0000000000000000000000000000000000000000..13da98a1f45ca7eea0411e18c307cc5d0154488f --- /dev/null +++ b/diffsynth/extensions/ImageQualityMetric/aesthetic.py @@ -0,0 +1,148 @@ +from typing import List, Optional +from PIL import Image +import torch +from transformers import AutoProcessor, AutoModel +from safetensors.torch import load_file +import os +from typing import Union, List +from .config import MODEL_PATHS + +class MLP(torch.nn.Module): + def __init__(self, input_size: int, xcol: str = "emb", ycol: str = "avg_rating"): + super().__init__() + self.input_size = input_size + self.xcol = xcol + self.ycol = ycol + self.layers = torch.nn.Sequential( + torch.nn.Linear(self.input_size, 1024), + #torch.nn.ReLU(), + torch.nn.Dropout(0.2), + torch.nn.Linear(1024, 128), + #torch.nn.ReLU(), + torch.nn.Dropout(0.2), + torch.nn.Linear(128, 64), + #torch.nn.ReLU(), + torch.nn.Dropout(0.1), + torch.nn.Linear(64, 16), + #torch.nn.ReLU(), + torch.nn.Linear(16, 1), + ) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + return self.layers(x) + + def training_step(self, batch: dict, batch_idx: int) -> torch.Tensor: + x = batch[self.xcol] + y = batch[self.ycol].reshape(-1, 1) + x_hat = self.layers(x) + loss = torch.nn.functional.mse_loss(x_hat, y) + return loss + + def validation_step(self, batch: dict, batch_idx: int) -> torch.Tensor: + x = batch[self.xcol] + y = batch[self.ycol].reshape(-1, 1) + x_hat = self.layers(x) + loss = torch.nn.functional.mse_loss(x_hat, y) + return loss + + def configure_optimizers(self) -> torch.optim.Optimizer: + return torch.optim.Adam(self.parameters(), lr=1e-3) + + +class AestheticScore(torch.nn.Module): + def __init__(self, device: torch.device, path: str = MODEL_PATHS): + super().__init__() + self.device = device + self.aes_model_path = path.get("aesthetic_predictor") + # Load the MLP model + self.model = MLP(768) + try: + if self.aes_model_path.endswith(".safetensors"): + state_dict = load_file(self.aes_model_path) + else: + state_dict = torch.load(self.aes_model_path) + self.model.load_state_dict(state_dict) + except Exception as e: + raise ValueError(f"Error loading model weights from {self.aes_model_path}: {e}") + + self.model.to(device) + self.model.eval() + + # Load the CLIP model and processor + clip_model_name = path.get('clip-large') + self.model2 = AutoModel.from_pretrained(clip_model_name).eval().to(device) + self.processor = AutoProcessor.from_pretrained(clip_model_name) + + def _calculate_score(self, image: torch.Tensor) -> float: + """Calculate the aesthetic score for a single image. + + Args: + image (torch.Tensor): The processed image tensor. + + Returns: + float: The aesthetic score. + """ + with torch.no_grad(): + # Get image embeddings + image_embs = self.model2.get_image_features(image) + image_embs = image_embs / torch.norm(image_embs, dim=-1, keepdim=True) + + # Compute score + score = self.model(image_embs).cpu().flatten().item() + + return score + + @torch.no_grad() + def score(self, images: Union[str, List[str], Image.Image, List[Image.Image]], prompt: str = "") -> List[float]: + """Score the images based on their aesthetic quality. + + Args: + images (Union[str, List[str], Image.Image, List[Image.Image]]): Path(s) to the image(s) or PIL image(s). + + Returns: + List[float]: List of scores for the images. + """ + try: + if isinstance(images, (str, Image.Image)): + # Single image + if isinstance(images, str): + pil_image = Image.open(images) + else: + pil_image = images + + # Prepare image inputs + image_inputs = self.processor( + images=pil_image, + padding=True, + truncation=True, + max_length=77, + return_tensors="pt", + ).to(self.device) + + return [self._calculate_score(image_inputs["pixel_values"])] + elif isinstance(images, list): + # Multiple images + scores = [] + for one_image in images: + if isinstance(one_image, str): + pil_image = Image.open(one_image) + elif isinstance(one_image, Image.Image): + pil_image = one_image + else: + raise TypeError("The type of parameter images is illegal.") + + # Prepare image inputs + image_inputs = self.processor( + images=pil_image, + padding=True, + truncation=True, + max_length=77, + return_tensors="pt", + ).to(self.device) + + scores.append(self._calculate_score(image_inputs["pixel_values"])) + return scores + else: + raise TypeError("The type of parameter images is illegal.") + except Exception as e: + raise RuntimeError(f"Error in scoring images: {e}") diff --git a/diffsynth/extensions/ImageQualityMetric/clip.py b/diffsynth/extensions/ImageQualityMetric/clip.py new file mode 100644 index 0000000000000000000000000000000000000000..f70941e0a45db61be87e21c347e97ad8bb390fff --- /dev/null +++ b/diffsynth/extensions/ImageQualityMetric/clip.py @@ -0,0 +1,97 @@ +from typing import List, Union +from PIL import Image +import torch +from .open_clip import create_model_and_transforms, get_tokenizer +from .config import MODEL_PATHS + +class CLIPScore(torch.nn.Module): + def __init__(self, device: torch.device, path: str = MODEL_PATHS): + super().__init__() + """Initialize the CLIPScore with a model and tokenizer. + + Args: + device (torch.device): The device to load the model on. + """ + self.device = device + + # Create model and transforms + self.model, _, self.preprocess_val = create_model_and_transforms( + "ViT-H-14", + # "laion2B-s32B-b79K", + pretrained=path.get("open_clip"), + precision="amp", + device=device, + jit=False, + force_quick_gelu=False, + force_custom_text=False, + force_patch_dropout=False, + force_image_size=None, + pretrained_image=False, + image_mean=None, + image_std=None, + light_augmentation=True, + aug_cfg={}, + output_dict=True, + with_score_predictor=False, + with_region_predictor=False, + ) + + # Initialize tokenizer + self.tokenizer = get_tokenizer("ViT-H-14", path["open_clip_bpe"]) + self.model = self.model.to(device) + self.model.eval() + + def _calculate_score(self, image: torch.Tensor, prompt: str) -> float: + """Calculate the CLIP score for a single image and prompt. + + Args: + image (torch.Tensor): The processed image tensor. + prompt (str): The prompt text. + + Returns: + float: The CLIP score. + """ + with torch.no_grad(): + # Process the prompt + text = self.tokenizer([prompt]).to(device=self.device, non_blocking=True) + + # Calculate the CLIP score + outputs = self.model(image, text) + image_features, text_features = outputs["image_features"], outputs["text_features"] + logits_per_image = image_features @ text_features.T + clip_score = torch.diagonal(logits_per_image).cpu().numpy() + + return clip_score[0].item() + + @torch.no_grad() + def score(self, images: Union[str, List[str], Image.Image, List[Image.Image]], prompt: str) -> List[float]: + """Score the images based on the prompt. + + Args: + images (Union[str, List[str], Image.Image, List[Image.Image]]): Path(s) to the image(s) or PIL image(s). + prompt (str): The prompt text. + + Returns: + List[float]: List of CLIP scores for the images. + """ + if isinstance(images, (str, Image.Image)): + # Single image + if isinstance(images, str): + image = self.preprocess_val(Image.open(images)).unsqueeze(0).to(device=self.device, non_blocking=True) + else: + image = self.preprocess_val(images).unsqueeze(0).to(device=self.device, non_blocking=True) + return [self._calculate_score(image, prompt)] + elif isinstance(images, list): + # Multiple images + scores = [] + for one_images in images: + if isinstance(one_images, str): + image = self.preprocess_val(Image.open(one_images)).unsqueeze(0).to(device=self.device, non_blocking=True) + elif isinstance(one_images, Image.Image): + image = self.preprocess_val(one_images).unsqueeze(0).to(device=self.device, non_blocking=True) + else: + raise TypeError("The type of parameter images is illegal.") + scores.append(self._calculate_score(image, prompt)) + return scores + else: + raise TypeError("The type of parameter images is illegal.") diff --git a/diffsynth/extensions/ImageQualityMetric/config.py b/diffsynth/extensions/ImageQualityMetric/config.py new file mode 100644 index 0000000000000000000000000000000000000000..60faadcb1e5554c8f8f29a64fc55c3150d8a8bbe --- /dev/null +++ b/diffsynth/extensions/ImageQualityMetric/config.py @@ -0,0 +1,23 @@ +import os + +current_dir = os.path.dirname(os.path.abspath(__file__)) +project_root = os.path.abspath(os.path.join(current_dir, '../../../')) +model_path = os.path.join(project_root, 'models', 'QualityMetric') + + +def get_model_path(model_name): + return os.path.join(model_path, model_name) + + +MODEL_PATHS = { + "aesthetic_predictor": get_model_path("aesthetic-predictor/sac+logos+ava1-l14-linearMSE.safetensors"), + "open_clip": get_model_path("CLIP-ViT-H-14-laion2B-s32B-b79K/open_clip_pytorch_model.bin"), + "hpsv2": get_model_path("HPS_v2/HPS_v2_compressed.safetensors"), + "hpsv2.1": get_model_path("HPS_v2/HPS_v2.1_compressed.safetensors"), + "imagereward": get_model_path("ImageReward/ImageReward.safetensors"), + "med_config": get_model_path("ImageReward/med_config.json"), + "clip": get_model_path("CLIP-ViT-H-14-laion2B-s32B-b79K"), + "clip-large": get_model_path("clip-vit-large-patch14"), + "mps": get_model_path("MPS_overall_checkpoint/MPS_overall_checkpoint_diffsynth.safetensors"), + "pickscore": get_model_path("PickScore_v1") +} \ No newline at end of file diff --git a/diffsynth/extensions/ImageQualityMetric/hps.py b/diffsynth/extensions/ImageQualityMetric/hps.py new file mode 100644 index 0000000000000000000000000000000000000000..a4b266bd261a95676ba700d38c3a63b143bbbb40 --- /dev/null +++ b/diffsynth/extensions/ImageQualityMetric/hps.py @@ -0,0 +1,118 @@ +from typing import List, Union +from PIL import Image +import torch +from .open_clip import create_model_and_transforms, get_tokenizer +from safetensors.torch import load_file +import os +from .config import MODEL_PATHS + +class HPScore_v2(torch.nn.Module): + def __init__(self, device: torch.device, path: str = MODEL_PATHS, model_version: str = "v2"): + super().__init__() + """Initialize the Selector with a model and tokenizer. + + Args: + device (torch.device): The device to load the model on. + model_version (str): The version of the model to load. Supports "v2" or "v21". Default is "v2". + """ + self.device = device + + if model_version == "v2": + safetensors_path = path.get("hpsv2") + elif model_version == "v21": + safetensors_path = path.get("hpsv2.1") + else: + raise ValueError(f"Unsupported model version: {model_version}. Choose 'v2' or 'v21'.") + + # Create model and transforms + model, _, self.preprocess_val = create_model_and_transforms( + "ViT-H-14", + # "laion2B-s32B-b79K", + pretrained=path.get("open_clip"), + precision="amp", + device=device, + jit=False, + force_quick_gelu=False, + force_custom_text=False, + force_patch_dropout=False, + force_image_size=None, + pretrained_image=False, + image_mean=None, + image_std=None, + light_augmentation=True, + aug_cfg={}, + output_dict=True, + with_score_predictor=False, + with_region_predictor=False, + ) + + # Load model weights + try: + state_dict = load_file(safetensors_path) + model.load_state_dict(state_dict) + except Exception as e: + raise ValueError(f"Error loading model weights from {safetensors_path}: {e}") + + # Initialize tokenizer and model + self.tokenizer = get_tokenizer("ViT-H-14", path["open_clip_bpe"]) + model = model.to(device) + model.eval() + self.model = model + + def _calculate_score(self, image: torch.Tensor, prompt: str) -> float: + """Calculate the HPS score for a single image and prompt. + + Args: + image (torch.Tensor): The processed image tensor. + prompt (str): The prompt text. + + Returns: + float: The HPS score. + """ + with torch.no_grad(): + # Process the prompt + text = self.tokenizer([prompt]).to(device=self.device, non_blocking=True) + + # Calculate the HPS score + outputs = self.model(image, text) + image_features, text_features = outputs["image_features"], outputs["text_features"] + logits_per_image = image_features @ text_features.T + hps_score = torch.diagonal(logits_per_image).cpu().numpy() + + return hps_score[0].item() + + @torch.no_grad() + def score(self, images: Union[str, List[str], Image.Image, List[Image.Image]], prompt: str) -> List[float]: + """Score the images based on the prompt. + + Args: + images (Union[str, List[str], Image.Image, List[Image.Image]]): Path(s) to the image(s) or PIL image(s). + prompt (str): The prompt text. + + Returns: + List[float]: List of HPS scores for the images. + """ + try: + if isinstance(images, (str, Image.Image)): + # Single image + if isinstance(images, str): + image = self.preprocess_val(Image.open(images)).unsqueeze(0).to(device=self.device, non_blocking=True) + else: + image = self.preprocess_val(images).unsqueeze(0).to(device=self.device, non_blocking=True) + return [self._calculate_score(image, prompt)] + elif isinstance(images, list): + # Multiple images + scores = [] + for one_images in images: + if isinstance(one_images, str): + image = self.preprocess_val(Image.open(one_images)).unsqueeze(0).to(device=self.device, non_blocking=True) + elif isinstance(one_images, Image.Image): + image = self.preprocess_val(one_images).unsqueeze(0).to(device=self.device, non_blocking=True) + else: + raise TypeError("The type of parameter images is illegal.") + scores.append(self._calculate_score(image, prompt)) + return scores + else: + raise TypeError("The type of parameter images is illegal.") + except Exception as e: + raise RuntimeError(f"Error in scoring images: {e}") diff --git a/diffsynth/extensions/ImageQualityMetric/imagereward.py b/diffsynth/extensions/ImageQualityMetric/imagereward.py new file mode 100644 index 0000000000000000000000000000000000000000..27607904b23fa1691c5a6966eb4030cd813567b0 --- /dev/null +++ b/diffsynth/extensions/ImageQualityMetric/imagereward.py @@ -0,0 +1,212 @@ +import os +import torch +from PIL import Image +from typing import List, Union +from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize +from .BLIP.blip_pretrain import BLIP_Pretrain +from torchvision.transforms import InterpolationMode +from safetensors.torch import load_file +from .config import MODEL_PATHS +BICUBIC = InterpolationMode.BICUBIC + +def _convert_image_to_rgb(image): + return image.convert("RGB") + +def _transform(n_px): + return Compose([ + Resize(n_px, interpolation=BICUBIC), + CenterCrop(n_px), + _convert_image_to_rgb, + ToTensor(), + Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)), + ]) + +class MLP(torch.nn.Module): + def __init__(self, input_size): + super().__init__() + self.input_size = input_size + + self.layers = torch.nn.Sequential( + torch.nn.Linear(self.input_size, 1024), + #nn.ReLU(), + torch.nn.Dropout(0.2), + torch.nn.Linear(1024, 128), + #nn.ReLU(), + torch.nn.Dropout(0.2), + torch.nn.Linear(128, 64), + #nn.ReLU(), + torch.nn.Dropout(0.1), + torch.nn.Linear(64, 16), + #nn.ReLU(), + torch.nn.Linear(16, 1) + ) + + # initial MLP param + for name, param in self.layers.named_parameters(): + if 'weight' in name: + torch.nn.init.normal_(param, mean=0.0, std=1.0/(self.input_size+1)) + if 'bias' in name: + torch.nn.init.constant_(param, val=0) + + def forward(self, input): + return self.layers(input) + +class ImageReward(torch.nn.Module): + def __init__(self, med_config, device='cpu', bert_model_path=""): + super().__init__() + self.device = device + + self.blip = BLIP_Pretrain(image_size=224, vit='large', med_config=med_config, bert_model_path=bert_model_path) + self.preprocess = _transform(224) + self.mlp = MLP(768) + + self.mean = 0.16717362830052426 + self.std = 1.0333394966054072 + + def score_grad(self, prompt_ids, prompt_attention_mask, image): + """Calculate the score with gradient for a single image and prompt. + + Args: + prompt_ids (torch.Tensor): Tokenized prompt IDs. + prompt_attention_mask (torch.Tensor): Attention mask for the prompt. + image (torch.Tensor): The processed image tensor. + + Returns: + torch.Tensor: The reward score. + """ + image_embeds = self.blip.visual_encoder(image) + image_atts = torch.ones(image_embeds.size()[:-1], dtype=torch.long).to(self.device) + text_output = self.blip.text_encoder( + prompt_ids, + attention_mask=prompt_attention_mask, + encoder_hidden_states=image_embeds, + encoder_attention_mask=image_atts, + return_dict=True, + ) + txt_features = text_output.last_hidden_state[:, 0, :] + rewards = self.mlp(txt_features) + rewards = (rewards - self.mean) / self.std + return rewards + + def score(self, images: Union[str, List[str], Image.Image, List[Image.Image]], prompt: str = "") -> List[float]: + """Score the images based on the prompt. + + Args: + prompt (str): The prompt text. + images (Union[str, List[str], Image.Image, List[Image.Image]]): Path(s) to the image(s) or PIL image(s). + + Returns: + List[float]: List of scores for the images. + """ + if isinstance(images, (str, Image.Image)): + # Single image + if isinstance(images, str): + pil_image = Image.open(images) + else: + pil_image = images + image = self.preprocess(pil_image).unsqueeze(0).to(self.device) + return [self._calculate_score(prompt, image).item()] + elif isinstance(images, list): + # Multiple images + scores = [] + for one_image in images: + if isinstance(one_image, str): + pil_image = Image.open(one_image) + elif isinstance(one_image, Image.Image): + pil_image = one_image + else: + raise TypeError("The type of parameter images is illegal.") + image = self.preprocess(pil_image).unsqueeze(0).to(self.device) + scores.append(self._calculate_score(prompt, image).item()) + return scores + else: + raise TypeError("The type of parameter images is illegal.") + + def _calculate_score(self, prompt: str, image: torch.Tensor) -> torch.Tensor: + """Calculate the score for a single image and prompt. + + Args: + prompt (str): The prompt text. + image (torch.Tensor): The processed image tensor. + + Returns: + torch.Tensor: The reward score. + """ + text_input = self.blip.tokenizer(prompt, padding='max_length', truncation=True, max_length=35, return_tensors="pt").to(self.device) + image_embeds = self.blip.visual_encoder(image) + image_atts = torch.ones(image_embeds.size()[:-1], dtype=torch.long).to(self.device) + text_output = self.blip.text_encoder( + text_input.input_ids, + attention_mask=text_input.attention_mask, + encoder_hidden_states=image_embeds, + encoder_attention_mask=image_atts, + return_dict=True, + ) + txt_features = text_output.last_hidden_state[:, 0, :].float() + rewards = self.mlp(txt_features) + rewards = (rewards - self.mean) / self.std + return rewards + + def inference_rank(self, prompt: str, generations_list: List[Union[str, Image.Image]]) -> tuple: + """Rank the images based on the prompt. + + Args: + prompt (str): The prompt text. + generations_list (List[Union[str, Image.Image]]): List of image paths or PIL images. + + Returns: + tuple: (indices, rewards) where indices are the ranks and rewards are the scores. + """ + text_input = self.blip.tokenizer(prompt, padding='max_length', truncation=True, max_length=35, return_tensors="pt").to(self.device) + txt_set = [] + for generation in generations_list: + if isinstance(generation, str): + pil_image = Image.open(generation) + elif isinstance(generation, Image.Image): + pil_image = generation + else: + raise TypeError("The type of parameter generations_list is illegal.") + image = self.preprocess(pil_image).unsqueeze(0).to(self.device) + image_embeds = self.blip.visual_encoder(image) + image_atts = torch.ones(image_embeds.size()[:-1], dtype=torch.long).to(self.device) + text_output = self.blip.text_encoder( + text_input.input_ids, + attention_mask=text_input.attention_mask, + encoder_hidden_states=image_embeds, + encoder_attention_mask=image_atts, + return_dict=True, + ) + txt_set.append(text_output.last_hidden_state[:, 0, :]) + txt_features = torch.cat(txt_set, 0).float() + rewards = self.mlp(txt_features) + rewards = (rewards - self.mean) / self.std + rewards = torch.squeeze(rewards) + _, rank = torch.sort(rewards, dim=0, descending=True) + _, indices = torch.sort(rank, dim=0) + indices = indices + 1 + return indices.detach().cpu().numpy().tolist(), rewards.detach().cpu().numpy().tolist() + + +class ImageRewardScore(torch.nn.Module): + def __init__(self, device: Union[str, torch.device], path: str = MODEL_PATHS): + super().__init__() + self.device = device if isinstance(device, torch.device) else torch.device(device) + model_path = path.get("imagereward") + med_config = path.get("med_config") + state_dict = load_file(model_path) + self.model = ImageReward(device=self.device, med_config=med_config, bert_model_path=path.get("bert_model_path")).to(self.device) + self.model.load_state_dict(state_dict, strict=False) + self.model.eval() + + @torch.no_grad() + def score(self, images: Union[str, List[str], Image.Image, List[Image.Image]], prompt: str) -> List[float]: + """Score the images based on the prompt. + + Args: + images (Union[str, List[str], Image.Image, List[Image.Image]]): Path(s) to the image(s) or PIL image(s). + prompt (str): The prompt text. + + Returns: + List[float]: List of scores for the images. + """ + return self.model.score(images, prompt) diff --git a/diffsynth/extensions/ImageQualityMetric/mps.py b/diffsynth/extensions/ImageQualityMetric/mps.py new file mode 100644 index 0000000000000000000000000000000000000000..d15aad4b81026a743911512bcc569520182b31c5 --- /dev/null +++ b/diffsynth/extensions/ImageQualityMetric/mps.py @@ -0,0 +1,129 @@ +import numpy as np +import torch +from PIL import Image +from io import BytesIO +from tqdm.auto import tqdm +from transformers import CLIPFeatureExtractor, CLIPImageProcessor +from transformers import CLIPConfig +from dataclasses import dataclass +from transformers import CLIPModel as HFCLIPModel +from safetensors.torch import load_file +from torch import nn, einsum + +from .trainer.models.base_model import BaseModelConfig + +from transformers import CLIPConfig +from transformers import AutoProcessor, AutoModel, AutoTokenizer +from typing import Any, Optional, Tuple, Union, List +import torch + +from .trainer.models.cross_modeling import Cross_model +from .trainer.models import clip_model +import torch.nn.functional as F +import gc +import json +from .config import MODEL_PATHS + +class MPScore(torch.nn.Module): + def __init__(self, device: Union[str, torch.device], path: str = MODEL_PATHS, condition: str = 'overall'): + super().__init__() + """Initialize the MPSModel with a processor, tokenizer, and model. + + Args: + device (Union[str, torch.device]): The device to load the model on. + """ + self.device = device + processor_name_or_path = path.get("clip") + self.image_processor = CLIPImageProcessor.from_pretrained(processor_name_or_path) + self.tokenizer = AutoTokenizer.from_pretrained(processor_name_or_path, trust_remote_code=True) + self.model = clip_model.CLIPModel(processor_name_or_path, config_file=True) + state_dict = load_file(path.get("mps")) + self.model.load_state_dict(state_dict, strict=False) + self.model.to(device) + self.condition = condition + + def _calculate_score(self, image: torch.Tensor, prompt: str) -> float: + """Calculate the reward score for a single image and prompt. + + Args: + image (torch.Tensor): The processed image tensor. + prompt (str): The prompt text. + + Returns: + float: The reward score. + """ + def _tokenize(caption): + input_ids = self.tokenizer( + caption, + max_length=self.tokenizer.model_max_length, + padding="max_length", + truncation=True, + return_tensors="pt" + ).input_ids + return input_ids + + text_input = _tokenize(prompt).to(self.device) + if self.condition == 'overall': + condition_prompt = 'light, color, clarity, tone, style, ambiance, artistry, shape, face, hair, hands, limbs, structure, instance, texture, quantity, attributes, position, number, location, word, things' + elif self.condition == 'aesthetics': + condition_prompt = 'light, color, clarity, tone, style, ambiance, artistry' + elif self.condition == 'quality': + condition_prompt = 'shape, face, hair, hands, limbs, structure, instance, texture' + elif self.condition == 'semantic': + condition_prompt = 'quantity, attributes, position, number, location' + else: + raise ValueError( + f"Unsupported condition: {self.condition}. Choose 'overall', 'aesthetics', 'quality', or 'semantic'.") + condition_batch = _tokenize(condition_prompt).repeat(text_input.shape[0], 1).to(self.device) + + with torch.no_grad(): + text_f, text_features = self.model.model.get_text_features(text_input) + + image_f = self.model.model.get_image_features(image.half()) + condition_f, _ = self.model.model.get_text_features(condition_batch) + + sim_text_condition = einsum('b i d, b j d -> b j i', text_f, condition_f) + sim_text_condition = torch.max(sim_text_condition, dim=1, keepdim=True)[0] + sim_text_condition = sim_text_condition / sim_text_condition.max() + mask = torch.where(sim_text_condition > 0.3, 0, float('-inf')) + mask = mask.repeat(1, image_f.shape[1], 1) + image_features = self.model.cross_model(image_f, text_f, mask.half())[:, 0, :] + + image_features = image_features / image_features.norm(dim=-1, keepdim=True) + text_features = text_features / text_features.norm(dim=-1, keepdim=True) + image_score = self.model.logit_scale.exp() * text_features @ image_features.T + + return image_score[0].cpu().numpy().item() + + @torch.no_grad() + def score(self, images: Union[str, List[str], Image.Image, List[Image.Image]], prompt: str) -> List[float]: + """Score the images based on the prompt. + + Args: + images (Union[str, List[str], Image.Image, List[Image.Image]]): Path(s) to the image(s) or PIL image(s). + prompt (str): The prompt text. + + Returns: + List[float]: List of reward scores for the images. + """ + if isinstance(images, (str, Image.Image)): + # Single image + if isinstance(images, str): + image = self.image_processor(Image.open(images), return_tensors="pt")["pixel_values"].to(self.device) + else: + image = self.image_processor(images, return_tensors="pt")["pixel_values"].to(self.device) + return [self._calculate_score(image, prompt)] + elif isinstance(images, list): + # Multiple images + scores = [] + for one_images in images: + if isinstance(one_images, str): + image = self.image_processor(Image.open(one_images), return_tensors="pt")["pixel_values"].to(self.device) + elif isinstance(one_images, Image.Image): + image = self.image_processor(one_images, return_tensors="pt")["pixel_values"].to(self.device) + else: + raise TypeError("The type of parameter images is illegal.") + scores.append(self._calculate_score(image, prompt)) + return scores + else: + raise TypeError("The type of parameter images is illegal.") diff --git a/diffsynth/extensions/ImageQualityMetric/open_clip/__init__.py b/diffsynth/extensions/ImageQualityMetric/open_clip/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..1560db0b543b7b8857f39d7de435c834380666ab --- /dev/null +++ b/diffsynth/extensions/ImageQualityMetric/open_clip/__init__.py @@ -0,0 +1,14 @@ +from .coca_model import CoCa +from .constants import OPENAI_DATASET_MEAN, OPENAI_DATASET_STD +from .factory import create_model, create_model_and_transforms, create_model_from_pretrained, get_tokenizer, create_loss +from .factory import list_models, add_model_config, get_model_config, load_checkpoint +from .loss import ClipLoss, DistillClipLoss, CoCaLoss +from .model import CLIP, CustomTextCLIP, CLIPTextCfg, CLIPVisionCfg, \ + convert_weights_to_lp, convert_weights_to_fp16, trace_model, get_cast_dtype +from .openai import load_openai_model, list_openai_models +from .pretrained import list_pretrained, list_pretrained_models_by_tag, list_pretrained_tags_by_model, \ + get_pretrained_url, download_pretrained_from_url, is_pretrained_cfg, get_pretrained_cfg, download_pretrained +from .push_to_hf_hub import push_pretrained_to_hf_hub, push_to_hf_hub +from .tokenizer import SimpleTokenizer +from .transform import image_transform, AugmentationCfg +from .utils import freeze_batch_norm_2d diff --git a/diffsynth/extensions/ImageQualityMetric/open_clip/coca_model.py b/diffsynth/extensions/ImageQualityMetric/open_clip/coca_model.py new file mode 100644 index 0000000000000000000000000000000000000000..039453af70d1c865dd7cc6016f732aff2f7dc3d2 --- /dev/null +++ b/diffsynth/extensions/ImageQualityMetric/open_clip/coca_model.py @@ -0,0 +1,458 @@ +from typing import Optional + +import torch +from torch import nn +from torch.nn import functional as F +import numpy as np +from dataclasses import dataclass + +from .transformer import ( + LayerNormFp32, + LayerNorm, + QuickGELU, + MultimodalTransformer, +) +from .model import CLIPTextCfg, CLIPVisionCfg, _build_vision_tower, _build_text_tower + +try: + from transformers import ( + BeamSearchScorer, + LogitsProcessorList, + TopPLogitsWarper, + TopKLogitsWarper, + RepetitionPenaltyLogitsProcessor, + MinLengthLogitsProcessor, + MaxLengthCriteria, + StoppingCriteriaList + ) + + GENERATION_TYPES = { + "top_k": TopKLogitsWarper, + "top_p": TopPLogitsWarper, + "beam_search": "beam_search" + } + _has_transformers = True +except ImportError as e: + GENERATION_TYPES = { + "top_k": None, + "top_p": None, + "beam_search": "beam_search" + } + _has_transformers = False + + +@dataclass +class MultimodalCfg(CLIPTextCfg): + mlp_ratio: int = 4 + dim_head: int = 64 + heads: int = 8 + n_queries: int = 256 + attn_pooler_heads: int = 8 + + +def _build_text_decoder_tower( + embed_dim, + multimodal_cfg, + quick_gelu: bool = False, + cast_dtype: Optional[torch.dtype] = None, +): + multimodal_cfg = MultimodalCfg(**multimodal_cfg) if isinstance(multimodal_cfg, dict) else multimodal_cfg + act_layer = QuickGELU if quick_gelu else nn.GELU + norm_layer = ( + LayerNormFp32 if cast_dtype in (torch.float16, torch.bfloat16) else LayerNorm + ) + + decoder = MultimodalTransformer( + context_length=multimodal_cfg.context_length, + width=multimodal_cfg.width, + heads=multimodal_cfg.heads, + layers=multimodal_cfg.layers, + ls_init_value=multimodal_cfg.ls_init_value, + output_dim=embed_dim, + act_layer=act_layer, + norm_layer=norm_layer, + ) + + return decoder + + +class CoCa(nn.Module): + def __init__( + self, + embed_dim, + multimodal_cfg: MultimodalCfg, + text_cfg: CLIPTextCfg, + vision_cfg: CLIPVisionCfg, + quick_gelu: bool = False, + cast_dtype: Optional[torch.dtype] = None, + pad_id: int = 0, + ): + super().__init__() + multimodal_cfg = MultimodalCfg(**multimodal_cfg) if isinstance(multimodal_cfg, dict) else multimodal_cfg + text_cfg = CLIPTextCfg(**text_cfg) if isinstance(text_cfg, dict) else text_cfg + vision_cfg = CLIPVisionCfg(**vision_cfg) if isinstance(vision_cfg, dict) else vision_cfg + + self.text = _build_text_tower( + embed_dim=embed_dim, + text_cfg=text_cfg, + quick_gelu=quick_gelu, + cast_dtype=cast_dtype, + ) + + vocab_size = ( + text_cfg.vocab_size # for hf models + if hasattr(text_cfg, "hf_model_name") and text_cfg.hf_model_name is not None + else text_cfg.vocab_size + ) + + self.visual = _build_vision_tower( + embed_dim=embed_dim, + vision_cfg=vision_cfg, + quick_gelu=quick_gelu, + cast_dtype=cast_dtype, + ) + + self.text_decoder = _build_text_decoder_tower( + vocab_size, + multimodal_cfg=multimodal_cfg, + quick_gelu=quick_gelu, + cast_dtype=cast_dtype, + ) + + self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07)) + self.pad_id = pad_id + + @torch.jit.ignore + def set_grad_checkpointing(self, enable=True): + self.visual.set_grad_checkpointing(enable) + self.text.set_grad_checkpointing(enable) + self.text_decoder.set_grad_checkpointing(enable) + + def _encode_image(self, images, normalize=True): + image_latent, tokens_embs = self.visual(images) + image_latent = F.normalize(image_latent, dim=-1) if normalize else image_latent + return image_latent, tokens_embs + + def _encode_text(self, text, normalize=True, embed_cls=True): + text = text[:, :-1] if embed_cls else text # make space for CLS token + text_latent, token_emb = self.text(text) + text_latent = F.normalize(text_latent, dim=-1) if normalize else text_latent + return text_latent, token_emb + + def encode_image(self, images, normalize=True): + image_latent, _ = self._encode_image(images, normalize=normalize) + return image_latent + + def encode_text(self, text, normalize=True, embed_cls=True): + text_latent, _ = self._encode_text(text, normalize=normalize, embed_cls=embed_cls) + return text_latent + + def forward(self, image, text, embed_cls=True, image_latent=None, image_embs=None): + text_latent, token_embs = self._encode_text(text, embed_cls=embed_cls) + if image_latent is None or image_embs is None: + image_latent, image_embs = self._encode_image(image) + + # TODO: add assertion to avoid bugs? + labels = text[:, -token_embs.shape[1]:] + + logits = self.text_decoder(image_embs, token_embs) + return { + "image_features": image_latent, + "text_features": text_latent, + "logits": logits, + "labels": labels, + "logit_scale": self.logit_scale.exp() + } + + def generate( + self, + image, + text=None, + seq_len=30, + max_seq_len=77, + temperature=1., + generation_type="beam_search", + top_p=0.1, # keep tokens in the 1 - top_p quantile + top_k=1, # keeps the top_k most probable tokens + pad_token_id=None, + eos_token_id=None, + sot_token_id=None, + num_beams=6, + num_beam_groups=3, + min_seq_len=5, + stopping_criteria=None, + repetition_penalty=1.0, + fixed_output_length=False # if True output.shape == (batch_size, seq_len) + ): + # taking many ideas and components from HuggingFace GenerationMixin + # https://huggingface.co/docs/transformers/main/en/main_classes/text_generation + assert _has_transformers, "Please install transformers for generate functionality. `pip install transformers`." + assert seq_len > min_seq_len, "seq_len must be larger than min_seq_len" + + with torch.no_grad(): + sot_token_id = 49406 if sot_token_id is None else sot_token_id + eos_token_id = 49407 if eos_token_id is None else eos_token_id + pad_token_id = self.pad_id if pad_token_id is None else pad_token_id + logit_processor = LogitsProcessorList( + [ + MinLengthLogitsProcessor(min_seq_len, eos_token_id), + RepetitionPenaltyLogitsProcessor(repetition_penalty), + ] + ) + + if stopping_criteria is None: + stopping_criteria = [MaxLengthCriteria(max_length=seq_len)] + + stopping_criteria = StoppingCriteriaList( + stopping_criteria + ) + + device = image.device + + if generation_type == "beam_search": + output = self._generate_beamsearch( + image_inputs = image, + pad_token_id=pad_token_id, + eos_token_id=eos_token_id, + sot_token_id=sot_token_id, + num_beams=num_beams, + num_beam_groups=num_beam_groups, + min_seq_len=min_seq_len, + stopping_criteria=stopping_criteria, + logit_processor=logit_processor, + ) + if fixed_output_length and output.shape[1] < seq_len: + return torch.cat( + (output, torch.ones(output.shape[0], seq_len-output.shape[1], device=device, dtype=output.dtype) * self.pad_id), + dim=1 + ) + return output + + elif generation_type == "top_p": + logit_warper = GENERATION_TYPES[generation_type](top_p) + elif generation_type == "top_k": + logit_warper = GENERATION_TYPES[generation_type](top_k) + else: + raise ValueError( + f"generation_type has to be one of " + f"{'| ' + ' | '.join(list(GENERATION_TYPES.keys())) + ' |'}." + ) + + image_latent, image_embs = self._encode_image(image) + + if text is None: + text = torch.ones((image.shape[0], 1), device=device, dtype=torch.long) * sot_token_id + + was_training = self.training + num_dims = len(text.shape) + + if num_dims == 1: + text = text[None, :] + + cur_len = text.shape[1] + self.eval() + out = text + + while True: + x = out[:, -max_seq_len:] + cur_len = x.shape[1] + logits = self(image, x, image_latent=image_latent, image_embs=image_embs, embed_cls=False)["logits"][:, -1] + mask = (out[:, -1] == eos_token_id) | (out[:, -1] == pad_token_id) + sample = torch.ones((out.shape[0], 1), device=device, dtype=torch.long) * pad_token_id + + if mask.all(): + if not fixed_output_length: + break + else: + logits = logits[~mask, :] + filtered_logits = logit_processor(x[~mask, :], logits) + filtered_logits = logit_warper(x[~mask, :], filtered_logits) + probs = F.softmax(filtered_logits / temperature, dim=-1) + + if (cur_len + 1 == seq_len): + sample[~mask, :] = torch.ones((sum(~mask), 1), device=device, dtype=torch.long) * eos_token_id + else: + sample[~mask, :] = torch.multinomial(probs, 1) + + out = torch.cat((out, sample), dim=-1) + + cur_len += 1 + + if stopping_criteria(out, None): + break + + if num_dims == 1: + out = out.squeeze(0) + + self.train(was_training) + return out + + def _generate_beamsearch( + self, + image_inputs, + pad_token_id=None, + eos_token_id=None, + sot_token_id=None, + num_beams=6, + num_beam_groups=3, + min_seq_len=5, + stopping_criteria=None, + logit_processor=None, + logit_warper=None, + ): + device = image_inputs.device + batch_size = image_inputs.shape[0] + image_inputs = torch.repeat_interleave(image_inputs, num_beams, dim=0) + image_latent, image_embs = self._encode_image(image_inputs) + + input_ids = torch.ones((batch_size * num_beams, 1), device=device, dtype=torch.long) + input_ids = input_ids * sot_token_id + beam_scorer = BeamSearchScorer( + batch_size=batch_size, + num_beams=num_beams, + device=device, + num_beam_groups=num_beam_groups, + ) + # instantiate logits processors + logits_processor = ( + LogitsProcessorList([MinLengthLogitsProcessor(min_seq_len, eos_token_id=eos_token_id)]) + if logit_processor is None + else logit_processor + ) + + batch_size = len(beam_scorer._beam_hyps) + num_beams = beam_scorer.num_beams + num_beam_groups = beam_scorer.num_beam_groups + num_sub_beams = num_beams // num_beam_groups + batch_beam_size, cur_len = input_ids.shape + beam_indices = None + + if num_beams * batch_size != batch_beam_size: + raise ValueError( + f"Batch dimension of `input_ids` should be {num_beams * batch_size}, but is {batch_beam_size}." + ) + + beam_scores = torch.full((batch_size, num_beams), -1e9, dtype=torch.float, device=device) + # initialise score of first beam of each group with 0 and the rest with 1e-9. This ensures that the beams in + # the same group don't produce same tokens everytime. + beam_scores[:, ::num_sub_beams] = 0 + beam_scores = beam_scores.view((batch_size * num_beams,)) + + while True: + + # predicted tokens in cur_len step + current_tokens = torch.zeros(batch_size * num_beams, dtype=input_ids.dtype, device=device) + + # indices which will form the beams in the next time step + reordering_indices = torch.zeros(batch_size * num_beams, dtype=torch.long, device=device) + + # do one decoder step on all beams of all sentences in batch + model_inputs = prepare_inputs_for_generation(input_ids=input_ids, image_inputs=image_inputs) + outputs = self( + model_inputs['images'], + model_inputs['text'], + embed_cls=False, + image_latent=image_latent, + image_embs=image_embs + ) + + for beam_group_idx in range(num_beam_groups): + group_start_idx = beam_group_idx * num_sub_beams + group_end_idx = min(group_start_idx + num_sub_beams, num_beams) + group_size = group_end_idx - group_start_idx + + # indices of beams of current group among all sentences in batch + batch_group_indices = [] + + for batch_idx in range(batch_size): + batch_group_indices.extend( + [batch_idx * num_beams + idx for idx in range(group_start_idx, group_end_idx)] + ) + group_input_ids = input_ids[batch_group_indices] + + # select outputs of beams of currentg group only + next_token_logits = outputs['logits'][batch_group_indices, -1, :] + vocab_size = next_token_logits.shape[-1] + + next_token_scores_processed = logits_processor( + group_input_ids, next_token_logits, current_tokens=current_tokens, beam_group_idx=beam_group_idx + ) + next_token_scores = next_token_scores_processed + beam_scores[batch_group_indices].unsqueeze(-1) + next_token_scores = next_token_scores.expand_as(next_token_scores_processed) + + # reshape for beam search + next_token_scores = next_token_scores.view(batch_size, group_size * vocab_size) + + next_token_scores, next_tokens = torch.topk( + next_token_scores, 2 * group_size, dim=1, largest=True, sorted=True + ) + + next_indices = torch.div(next_tokens, vocab_size, rounding_mode="floor") + next_tokens = next_tokens % vocab_size + + # stateless + process_beam_indices = sum(beam_indices, ()) if beam_indices is not None else None + beam_outputs = beam_scorer.process( + group_input_ids, + next_token_scores, + next_tokens, + next_indices, + pad_token_id=pad_token_id, + eos_token_id=eos_token_id, + beam_indices=process_beam_indices, + ) + beam_scores[batch_group_indices] = beam_outputs["next_beam_scores"] + beam_next_tokens = beam_outputs["next_beam_tokens"] + beam_idx = beam_outputs["next_beam_indices"] + + input_ids[batch_group_indices] = group_input_ids[beam_idx] + group_input_ids = torch.cat([group_input_ids[beam_idx, :], beam_next_tokens.unsqueeze(-1)], dim=-1) + current_tokens[batch_group_indices] = group_input_ids[:, -1] + + # (beam_idx // group_size) -> batch_idx + # (beam_idx % group_size) -> offset of idx inside the group + reordering_indices[batch_group_indices] = ( + num_beams * torch.div(beam_idx, group_size, rounding_mode="floor") + group_start_idx + (beam_idx % group_size) + ) + + input_ids = torch.cat([input_ids, current_tokens.unsqueeze(-1)], dim=-1) + + # increase cur_len + cur_len = cur_len + 1 + if beam_scorer.is_done or stopping_criteria(input_ids, None): + break + + final_beam_indices = sum(beam_indices, ()) if beam_indices is not None else None + sequence_outputs = beam_scorer.finalize( + input_ids, + beam_scores, + next_tokens, + next_indices, + pad_token_id=pad_token_id, + eos_token_id=eos_token_id, + max_length=stopping_criteria.max_length, + beam_indices=final_beam_indices, + ) + return sequence_outputs['sequences'] + + +def prepare_inputs_for_generation(input_ids, image_inputs, past=None, **kwargs): + if past: + input_ids = input_ids[:, -1].unsqueeze(-1) + + attention_mask = kwargs.get("attention_mask", None) + position_ids = kwargs.get("position_ids", None) + + if attention_mask is not None and position_ids is None: + # create position_ids on the fly for batch generation + position_ids = attention_mask.long().cumsum(-1) - 1 + position_ids.masked_fill_(attention_mask == 0, 1) + else: + position_ids = None + return { + "text": input_ids, + "images": image_inputs, + "past_key_values": past, + "position_ids": position_ids, + "attention_mask": attention_mask, + } diff --git a/diffsynth/extensions/ImageQualityMetric/open_clip/constants.py b/diffsynth/extensions/ImageQualityMetric/open_clip/constants.py new file mode 100644 index 0000000000000000000000000000000000000000..a670bb3fab442baeb9af53b91c312e6982af57ee --- /dev/null +++ b/diffsynth/extensions/ImageQualityMetric/open_clip/constants.py @@ -0,0 +1,2 @@ +OPENAI_DATASET_MEAN = (0.48145466, 0.4578275, 0.40821073) +OPENAI_DATASET_STD = (0.26862954, 0.26130258, 0.27577711) diff --git a/diffsynth/extensions/ImageQualityMetric/open_clip/factory.py b/diffsynth/extensions/ImageQualityMetric/open_clip/factory.py new file mode 100644 index 0000000000000000000000000000000000000000..5bd51a1bb6b69e0e69147c8b7cb8d7bd4899b349 --- /dev/null +++ b/diffsynth/extensions/ImageQualityMetric/open_clip/factory.py @@ -0,0 +1,433 @@ +import json +import logging +import os +import pathlib +import re +from copy import deepcopy +from pathlib import Path +# from turtle import forward +from typing import Any, Dict, Optional, Tuple, Union + +import torch + +from .constants import OPENAI_DATASET_MEAN, OPENAI_DATASET_STD +from .model import CLIP, CustomTextCLIP, convert_weights_to_lp, convert_to_custom_text_state_dict,\ + resize_pos_embed, get_cast_dtype +from .coca_model import CoCa +from .loss import ClipLoss, DistillClipLoss, CoCaLoss +from .openai import load_openai_model +from .pretrained import is_pretrained_cfg, get_pretrained_cfg, download_pretrained, list_pretrained_tags_by_model, download_pretrained_from_hf +from .transform import image_transform, AugmentationCfg +from .tokenizer import HFTokenizer, SimpleTokenizer + + +HF_HUB_PREFIX = 'hf-hub:' +_MODEL_CONFIG_PATHS = [Path(__file__).parent / f"model_configs/"] +_MODEL_CONFIGS = {} # directory (model_name: config) of model architecture configs + + +def _natural_key(string_): + return [int(s) if s.isdigit() else s for s in re.split(r'(\d+)', string_.lower())] + + +def _rescan_model_configs(): + global _MODEL_CONFIGS + + config_ext = ('.json',) + config_files = [] + for config_path in _MODEL_CONFIG_PATHS: + if config_path.is_file() and config_path.suffix in config_ext: + config_files.append(config_path) + elif config_path.is_dir(): + for ext in config_ext: + config_files.extend(config_path.glob(f'*{ext}')) + + for cf in config_files: + with open(cf, 'r') as f: + model_cfg = json.load(f) + if all(a in model_cfg for a in ('embed_dim', 'vision_cfg', 'text_cfg')): + _MODEL_CONFIGS[cf.stem] = model_cfg + + _MODEL_CONFIGS = {k: v for k, v in sorted(_MODEL_CONFIGS.items(), key=lambda x: _natural_key(x[0]))} + + +_rescan_model_configs() # initial populate of model config registry + + +def list_models(): + """ enumerate available model architectures based on config files """ + return list(_MODEL_CONFIGS.keys()) + + +def add_model_config(path): + """ add model config path or file and update registry """ + if not isinstance(path, Path): + path = Path(path) + _MODEL_CONFIG_PATHS.append(path) + _rescan_model_configs() + + +def get_model_config(model_name): + if model_name in _MODEL_CONFIGS: + return deepcopy(_MODEL_CONFIGS[model_name]) + else: + return None + + +def get_tokenizer(model_name, open_clip_bpe_path=None): + if model_name.startswith(HF_HUB_PREFIX): + tokenizer = HFTokenizer(model_name[len(HF_HUB_PREFIX):]) + else: + config = get_model_config(model_name) + tokenizer = HFTokenizer( + config['text_cfg']['hf_tokenizer_name']) if 'hf_tokenizer_name' in config['text_cfg'] else SimpleTokenizer(open_clip_bpe_path) + return tokenizer + + +def load_state_dict(checkpoint_path: str, map_location='cpu'): + checkpoint = torch.load(checkpoint_path, map_location=map_location) + if isinstance(checkpoint, dict) and 'state_dict' in checkpoint: + state_dict = checkpoint['state_dict'] + else: + state_dict = checkpoint + if next(iter(state_dict.items()))[0].startswith('module'): + state_dict = {k[7:]: v for k, v in state_dict.items()} + return state_dict + + +def load_checkpoint(model, checkpoint_path, strict=True): + state_dict = load_state_dict(checkpoint_path) + # detect old format and make compatible with new format + if 'positional_embedding' in state_dict and not hasattr(model, 'positional_embedding'): + state_dict = convert_to_custom_text_state_dict(state_dict) + resize_pos_embed(state_dict, model) + incompatible_keys = model.load_state_dict(state_dict, strict=strict) + return incompatible_keys + + +def create_model( + model_name: str, + pretrained: Optional[str] = None, + precision: str = 'fp32', + device: Union[str, torch.device] = 'cpu', + jit: bool = False, + force_quick_gelu: bool = False, + force_custom_text: bool = False, + force_patch_dropout: Optional[float] = None, + force_image_size: Optional[Union[int, Tuple[int, int]]] = None, + pretrained_image: bool = False, + pretrained_hf: bool = True, + cache_dir: Optional[str] = None, + output_dict: Optional[bool] = None, + require_pretrained: bool = False, +): + has_hf_hub_prefix = model_name.startswith(HF_HUB_PREFIX) + if has_hf_hub_prefix: + model_id = model_name[len(HF_HUB_PREFIX):] + checkpoint_path = download_pretrained_from_hf(model_id, cache_dir=cache_dir) + config_path = download_pretrained_from_hf(model_id, filename='open_clip_config.json', cache_dir=cache_dir) + + with open(config_path, 'r', encoding='utf-8') as f: + config = json.load(f) + pretrained_cfg = config['preprocess_cfg'] + model_cfg = config['model_cfg'] + else: + model_name = model_name.replace('/', '-') # for callers using old naming with / in ViT names + checkpoint_path = None + pretrained_cfg = {} + model_cfg = None + + if isinstance(device, str): + device = torch.device(device) + + if pretrained and pretrained.lower() == 'openai': + logging.info(f'Loading pretrained {model_name} from OpenAI.') + model = load_openai_model( + model_name, + precision=precision, + device=device, + jit=jit, + cache_dir=cache_dir, + ) + + # to always output dict even if it is clip + if output_dict and hasattr(model, "output_dict"): + model.output_dict = True + else: + model_cfg = model_cfg or get_model_config(model_name) + if model_cfg is not None: + logging.info(f'Loaded {model_name} model config.') + else: + logging.error(f'Model config for {model_name} not found; available models {list_models()}.') + raise RuntimeError(f'Model config for {model_name} not found.') + + if force_quick_gelu: + # override for use of QuickGELU on non-OpenAI transformer models + model_cfg["quick_gelu"] = True + + if force_patch_dropout is not None: + # override the default patch dropout value + model_cfg["vision_cfg"]["patch_dropout"] = force_patch_dropout + + if force_image_size is not None: + # override model config's image size + model_cfg["vision_cfg"]["image_size"] = force_image_size + + if pretrained_image: + if 'timm_model_name' in model_cfg.get('vision_cfg', {}): + # pretrained weight loading for timm models set via vision_cfg + model_cfg['vision_cfg']['timm_model_pretrained'] = True + else: + assert False, 'pretrained image towers currently only supported for timm models' + + cast_dtype = get_cast_dtype(precision) + is_hf_model = 'hf_model_name' in model_cfg.get('text_cfg', {}) + custom_text = model_cfg.pop('custom_text', False) or force_custom_text or is_hf_model + + if custom_text: + if is_hf_model: + model_cfg['text_cfg']['hf_model_pretrained'] = pretrained_hf + if "coca" in model_name: + model = CoCa(**model_cfg, cast_dtype=cast_dtype) + else: + model = CustomTextCLIP(**model_cfg, cast_dtype=cast_dtype) + else: + model = CLIP(**model_cfg, cast_dtype=cast_dtype) + + pretrained_loaded = False + if pretrained: + checkpoint_path = '' + pretrained_cfg = get_pretrained_cfg(model_name, pretrained) + if pretrained_cfg: + checkpoint_path = download_pretrained(pretrained_cfg, cache_dir=cache_dir) + elif os.path.exists(pretrained): + checkpoint_path = pretrained + + if checkpoint_path: + logging.info(f'Loading pretrained {model_name} weights ({pretrained}).') + load_checkpoint(model, checkpoint_path) + else: + error_str = ( + f'Pretrained weights ({pretrained}) not found for model {model_name}.' + f'Available pretrained tags ({list_pretrained_tags_by_model(model_name)}.') + logging.warning(error_str) + raise RuntimeError(error_str) + pretrained_loaded = True + elif has_hf_hub_prefix: + logging.info(f'Loading pretrained {model_name} weights ({pretrained}).') + load_checkpoint(model, checkpoint_path) + pretrained_loaded = True + + if require_pretrained and not pretrained_loaded: + # callers of create_model_from_pretrained always expect pretrained weights + raise RuntimeError( + f'Pretrained weights were required for (model: {model_name}, pretrained: {pretrained}) but not loaded.') + + model.to(device=device) + if precision in ("fp16", "bf16"): + convert_weights_to_lp(model, dtype=torch.bfloat16 if precision == 'bf16' else torch.float16) + + # set image / mean metadata from pretrained_cfg if available, or use default + model.visual.image_mean = pretrained_cfg.get('mean', None) or OPENAI_DATASET_MEAN + model.visual.image_std = pretrained_cfg.get('std', None) or OPENAI_DATASET_STD + + # to always output dict even if it is clip + if output_dict and hasattr(model, "output_dict"): + model.output_dict = True + + if jit: + model = torch.jit.script(model) + + return model + + +def create_loss(args): + if args.distill: + return DistillClipLoss( + local_loss=args.local_loss, + gather_with_grad=args.gather_with_grad, + cache_labels=True, + rank=args.rank, + world_size=args.world_size, + use_horovod=args.horovod, + ) + elif "coca" in args.model.lower(): + return CoCaLoss( + caption_loss_weight=args.coca_caption_loss_weight, + clip_loss_weight=args.coca_contrastive_loss_weight, + local_loss=args.local_loss, + gather_with_grad=args.gather_with_grad, + cache_labels=True, + rank=args.rank, + world_size=args.world_size, + use_horovod=args.horovod, + ) + return ClipLoss( + local_loss=args.local_loss, + gather_with_grad=args.gather_with_grad, + cache_labels=True, + rank=args.rank, + world_size=args.world_size, + use_horovod=args.horovod, + ) + +class MLP(torch.nn.Module): + def __init__(self, input_size): + super().__init__() + self.input_size = input_size + self.layers = torch.nn.Sequential( + torch.nn.Linear(self.input_size, 1024), + torch.nn.Dropout(0.2), + torch.nn.Linear(1024, 128), + torch.nn.Dropout(0.2), + torch.nn.Linear(128, 64), + torch.nn.Dropout(0.1), + torch.nn.Linear(64, 16), + torch.nn.Linear(16, 1) + ) + + def forward(self, x): + return self.layers(x) + +# class semantic_head(torch.nn.Module): +# def __init__(self, input_size): +# super().__init__() +# self.input_size = input_size # for ViT-L-14 is 1024 +# self.seg_head = torch.nn.Sequential( +# torch.nn.Linear(input_size, 128), +# torch.nn.Dropout(0.2), +# torch.nn.Linear(128, 64), +# torch.nn.Dropout(0.1), +# torch.nn.Linear(64, 16), +# torch.nn.Linear(16, 1), +# ) +# self.sigmoid = torch.nn.Sigmoid() + +# def forward(self, x): +# return self.sigmoid(self.seg_head(x)) + +def create_model_and_transforms( + model_name: str, + pretrained: Optional[str] = None, + precision: str = 'fp32', + device: Union[str, torch.device] = 'cpu', + jit: bool = False, + force_quick_gelu: bool = False, + force_custom_text: bool = False, + force_patch_dropout: Optional[float] = None, + force_image_size: Optional[Union[int, Tuple[int, int]]] = None, + pretrained_image: bool = False, + pretrained_hf: bool = True, + image_mean: Optional[Tuple[float, ...]] = None, + image_std: Optional[Tuple[float, ...]] = None, + aug_cfg: Optional[Union[Dict[str, Any], AugmentationCfg]] = None, + cache_dir: Optional[str] = None, + light_augmentation = False, + output_dict: Optional[bool] = None, + with_score_predictor: bool = False, + with_region_predictor: bool = False +): + model = create_model( + model_name, + pretrained, + precision=precision, + device=device, + jit=jit, + force_quick_gelu=force_quick_gelu, + force_custom_text=force_custom_text, + force_patch_dropout=force_patch_dropout, + force_image_size=force_image_size, + pretrained_image=pretrained_image, + pretrained_hf=pretrained_hf, + cache_dir=cache_dir, + output_dict=output_dict, + ) + + image_mean = image_mean or getattr(model.visual, 'image_mean', None) + image_std = image_std or getattr(model.visual, 'image_std', None) + + if with_score_predictor: + model.score_predictor = MLP(model.visual.proj.size(1)).to(device=device, dtype=model.visual.proj.dtype) + + if with_region_predictor: + # model.region_predictor = semantic_head(model.visual.proj.size(1)).to(device=device, dtype=model.visual.proj.dtype) + model.region_predictor = torch.nn.Linear(model.visual.proj.size(0), 1).to(device=device, dtype=model.visual.proj.dtype) + # preprocess_train = image_transform_region( + # model.visual.image_size, + # is_train=True, + # mean=image_mean, + # std=image_std + # ) + # preprocess_val = image_transform_region( + # model.visual.image_size, + # is_train=False, + # mean=image_mean, + # std=image_std + # ) + + if light_augmentation: + preprocess_val = image_transform( + model.visual.image_size, + is_train=False, + mean=image_mean, + std=image_std, + resize_longest_max=True, + ) + preprocess_train = preprocess_val + else: + preprocess_train = image_transform( + model.visual.image_size, + is_train=True, + mean=image_mean, + std=image_std + ) + preprocess_val = image_transform( + model.visual.image_size, + is_train=False, + mean=image_mean, + std=image_std + ) + + return model, preprocess_train, preprocess_val + + +def create_model_from_pretrained( + model_name: str, + pretrained: Optional[str] = None, + precision: str = 'fp32', + device: Union[str, torch.device] = 'cpu', + jit: bool = False, + force_quick_gelu: bool = False, + force_custom_text: bool = False, + force_image_size: Optional[Union[int, Tuple[int, int]]] = None, + return_transform: bool = True, + image_mean: Optional[Tuple[float, ...]] = None, + image_std: Optional[Tuple[float, ...]] = None, + cache_dir: Optional[str] = None, +): + model = create_model( + model_name, + pretrained, + precision=precision, + device=device, + jit=jit, + force_quick_gelu=force_quick_gelu, + force_custom_text=force_custom_text, + force_image_size=force_image_size, + cache_dir=cache_dir, + require_pretrained=True, + ) + + if not return_transform: + return model + + image_mean = image_mean or getattr(model.visual, 'image_mean', None) + image_std = image_std or getattr(model.visual, 'image_std', None) + preprocess = image_transform( + model.visual.image_size, + is_train=False, + mean=image_mean, + std=image_std, + ) + + return model, preprocess diff --git a/diffsynth/extensions/ImageQualityMetric/open_clip/generation_utils.py b/diffsynth/extensions/ImageQualityMetric/open_clip/generation_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/diffsynth/extensions/ImageQualityMetric/open_clip/hf_configs.py b/diffsynth/extensions/ImageQualityMetric/open_clip/hf_configs.py new file mode 100644 index 0000000000000000000000000000000000000000..e236222bafce0358445ea16953ca0b2d5a84758a --- /dev/null +++ b/diffsynth/extensions/ImageQualityMetric/open_clip/hf_configs.py @@ -0,0 +1,45 @@ +# HF architecture dict: +arch_dict = { + # https://huggingface.co/docs/transformers/model_doc/roberta#roberta + "roberta": { + "config_names": { + "context_length": "max_position_embeddings", + "vocab_size": "vocab_size", + "width": "hidden_size", + "heads": "num_attention_heads", + "layers": "num_hidden_layers", + "layer_attr": "layer", + "token_embeddings_attr": "embeddings" + }, + "pooler": "mean_pooler", + }, + # https://huggingface.co/docs/transformers/model_doc/xlm-roberta#transformers.XLMRobertaConfig + "xlm-roberta": { + "config_names": { + "context_length": "max_position_embeddings", + "vocab_size": "vocab_size", + "width": "hidden_size", + "heads": "num_attention_heads", + "layers": "num_hidden_layers", + "layer_attr": "layer", + "token_embeddings_attr": "embeddings" + }, + "pooler": "mean_pooler", + }, + # https://huggingface.co/docs/transformers/model_doc/mt5#mt5 + "mt5": { + "config_names": { + # unlimited seqlen + # https://github.com/google-research/text-to-text-transfer-transformer/issues/273 + # https://github.com/huggingface/transformers/blob/v4.24.0/src/transformers/models/t5/modeling_t5.py#L374 + "context_length": "", + "vocab_size": "vocab_size", + "width": "d_model", + "heads": "num_heads", + "layers": "num_layers", + "layer_attr": "block", + "token_embeddings_attr": "embed_tokens" + }, + "pooler": "mean_pooler", + }, +} diff --git a/diffsynth/extensions/ImageQualityMetric/open_clip/hf_model.py b/diffsynth/extensions/ImageQualityMetric/open_clip/hf_model.py new file mode 100644 index 0000000000000000000000000000000000000000..fbccc812757bf10b122ff14096980e0e38d1d221 --- /dev/null +++ b/diffsynth/extensions/ImageQualityMetric/open_clip/hf_model.py @@ -0,0 +1,176 @@ +""" huggingface model adapter + +Wraps HuggingFace transformers (https://github.com/huggingface/transformers) models for use as a text tower in CLIP model. +""" + +import re + +import torch +import torch.nn as nn +from torch import TensorType + +try: + import transformers + from transformers import AutoModel, AutoTokenizer, AutoConfig, PretrainedConfig + from transformers.modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling, \ + BaseModelOutputWithPoolingAndCrossAttentions +except ImportError as e: + transformers = None + + + class BaseModelOutput: + pass + + + class PretrainedConfig: + pass + +from .hf_configs import arch_dict + + +# utils +def _camel2snake(s): + return re.sub(r'(? torch.Tensor: + # calculated ground-truth and cache if enabled + if self.prev_num_logits != num_logits or device not in self.labels: + labels = torch.arange(num_logits, device=device, dtype=torch.long) + if self.world_size > 1 and self.local_loss: + labels = labels + num_logits * self.rank + if self.cache_labels: + self.labels[device] = labels + self.prev_num_logits = num_logits + else: + labels = self.labels[device] + return labels + + def get_logits(self, image_features, text_features, logit_scale): + if self.world_size > 1: + all_image_features, all_text_features = gather_features( + image_features, text_features, + self.local_loss, self.gather_with_grad, self.rank, self.world_size, self.use_horovod) + + if self.local_loss: + logits_per_image = logit_scale * image_features @ all_text_features.T + logits_per_text = logit_scale * text_features @ all_image_features.T + else: + logits_per_image = logit_scale * all_image_features @ all_text_features.T + logits_per_text = logits_per_image.T + else: + logits_per_image = logit_scale * image_features @ text_features.T + logits_per_text = logit_scale * text_features @ image_features.T + + return logits_per_image, logits_per_text + + def forward(self, image_features, text_features, logit_scale, output_dict=False): + device = image_features.device + logits_per_image, logits_per_text = self.get_logits(image_features, text_features, logit_scale) + + labels = self.get_ground_truth(device, logits_per_image.shape[0]) + + total_loss = ( + F.cross_entropy(logits_per_image, labels) + + F.cross_entropy(logits_per_text, labels) + ) / 2 + return total_loss + +class PreferenceLoss(nn.Module): + + def forward(self, logits_per_image, num_images, labels): + + paired_logits_list = [logit[:,i] for i, logit in enumerate(logits_per_image.split(num_images.tolist()))] + paired_logits = pad_sequence(paired_logits_list, batch_first=True, padding_value=-999) + + ce_loss = F.cross_entropy(paired_logits, labels) + return ce_loss + +class HPSLoss(nn.Module): + + def forward(self, text_logits, labels): + + device = text_logits.device + text_0_logits, text_1_logits = text_logits.chunk(2, dim=-1) + label_0, label_1 = labels.chunk(2, dim=-1) + + index = torch.arange(text_0_logits.shape[0], device=device, dtype=torch.long) + text_0_logits = text_0_logits[index, index] + text_1_logits = text_1_logits[index, index] + text_logits = torch.stack([text_0_logits, text_1_logits], dim=-1) + text_0_labels = torch.zeros(text_logits.shape[0], device=device, dtype=torch.long) + text_1_labels = text_0_labels + 1 + + text_0_loss = torch.nn.functional.cross_entropy(text_logits, text_0_labels, reduction="none") + text_1_loss = torch.nn.functional.cross_entropy(text_logits, text_1_labels, reduction="none") + + text_loss = label_0 * text_0_loss + label_1 * text_1_loss + + # absolute_example_weight = 1 / num_per_prompt + # denominator = absolute_example_weight.sum() + # weight_per_example = absolute_example_weight / denominator + # text_loss *= weight_per_example + + text_loss = text_loss.sum() + return text_loss + +class RankingLoss(nn.Module): + + def forward(self, logits_per_image, num_images, labels, margin = 1.0): + paired_logits_list = [logit[:,i] for i, logit in enumerate(logits_per_image.split(num_images.tolist()))] + label_list = [label for label in labels.split(num_images.tolist())] + # ranked_logits = [torch.index_select(paired_logits_list[i], 0, rank) for i, rank in enumerate(label_list)] + + paired_logits = pad_sequence(paired_logits_list, batch_first=True, padding_value=-1) + padded_labels = pad_sequence(label_list, batch_first=True, padding_value=10) + + # regulized_logits = torch.log(torch.sigmoid(paired_logits)) + + diff = paired_logits.unsqueeze(1) - paired_logits.unsqueeze(2) + # diff = paired_logits.unsqueeze(1) - paired_logits.unsqueeze(2) + # diff_label = torch.clamp(padded_labels.unsqueeze(1) - padded_labels.unsqueeze(2), min=-1, max=1) + diff_label = - (padded_labels.unsqueeze(1) - padded_labels.unsqueeze(2)) + mask = torch.triu(torch.ones(diff.shape[1], diff.shape[1]), diagonal=1).bool().detach() + + loss = torch.clamp(margin - torch.mul(diff[:, ~mask],diff_label[:,~mask]), min=0).mean() + return loss + +class CoCaLoss(ClipLoss): + def __init__( + self, + caption_loss_weight, + clip_loss_weight, + pad_id=0, # pad_token for open_clip custom tokenizer + local_loss=False, + gather_with_grad=False, + cache_labels=False, + rank=0, + world_size=1, + use_horovod=False, + ): + super().__init__( + local_loss=local_loss, + gather_with_grad=gather_with_grad, + cache_labels=cache_labels, + rank=rank, + world_size=world_size, + use_horovod=use_horovod + ) + + self.clip_loss_weight = clip_loss_weight + self.caption_loss_weight = caption_loss_weight + self.caption_loss = nn.CrossEntropyLoss(ignore_index=pad_id) + + def forward(self, image_features, text_features, logits, labels, logit_scale, output_dict=False): + clip_loss = super().forward(image_features, text_features, logit_scale) + clip_loss = self.clip_loss_weight * clip_loss + + caption_loss = self.caption_loss( + logits.permute(0, 2, 1), + labels, + ) + caption_loss = caption_loss * self.caption_loss_weight + + if output_dict: + return {"contrastive_loss": clip_loss, "caption_loss": caption_loss} + + return clip_loss, caption_loss + + +class DistillClipLoss(ClipLoss): + + def dist_loss(self, teacher_logits, student_logits): + return -(teacher_logits.softmax(dim=1) * student_logits.log_softmax(dim=1)).sum(dim=1).mean(dim=0) + + def forward( + self, + image_features, + text_features, + logit_scale, + dist_image_features, + dist_text_features, + dist_logit_scale, + output_dict=False, + ): + logits_per_image, logits_per_text = \ + self.get_logits(image_features, text_features, logit_scale) + + dist_logits_per_image, dist_logits_per_text = \ + self.get_logits(dist_image_features, dist_text_features, dist_logit_scale) + + labels = self.get_ground_truth(image_features.device, logits_per_image.shape[0]) + + contrastive_loss = ( + F.cross_entropy(logits_per_image, labels) + + F.cross_entropy(logits_per_text, labels) + ) / 2 + + distill_loss = ( + self.dist_loss(dist_logits_per_image, logits_per_image) + + self.dist_loss(dist_logits_per_text, logits_per_text) + ) / 2 + + if output_dict: + return {"contrastive_loss": contrastive_loss, "distill_loss": distill_loss} + + return contrastive_loss, distill_loss diff --git a/diffsynth/extensions/ImageQualityMetric/open_clip/model.py b/diffsynth/extensions/ImageQualityMetric/open_clip/model.py new file mode 100644 index 0000000000000000000000000000000000000000..e347c42fc8df6464ca28e59adadba61e53a38add --- /dev/null +++ b/diffsynth/extensions/ImageQualityMetric/open_clip/model.py @@ -0,0 +1,461 @@ +""" CLIP Model + +Adapted from https://github.com/openai/CLIP. Originally MIT License, Copyright (c) 2021 OpenAI. +""" +from dataclasses import dataclass +import logging +import math +from typing import Optional, Tuple, Union + +import numpy as np +import torch +import torch.nn.functional as F +from torch import nn +from torch.utils.checkpoint import checkpoint + +from .hf_model import HFTextEncoder +from .modified_resnet import ModifiedResNet +from .timm_model import TimmModel +from .transformer import LayerNormFp32, LayerNorm, QuickGELU, Attention, VisionTransformer, TextTransformer +from .utils import to_2tuple + + +@dataclass +class CLIPVisionCfg: + layers: Union[Tuple[int, int, int, int], int] = 12 + width: int = 768 + head_width: int = 64 + mlp_ratio: float = 4.0 + patch_size: int = 16 + image_size: Union[Tuple[int, int], int] = 224 + ls_init_value: Optional[float] = None # layer scale initial value + patch_dropout: float = 0. # what fraction of patches to dropout during training (0 would mean disabled and no patches dropped) - 0.5 to 0.75 recommended in the paper for optimal results + input_patchnorm: bool = False # whether to use dual patchnorm - would only apply the input layernorm on each patch, as post-layernorm already exist in original clip vit design + global_average_pool: bool = False # whether to global average pool the last embedding layer, instead of using CLS token (https://arxiv.org/abs/2205.01580) + attentional_pool: bool = False # whether to use attentional pooler in the last embedding layer + n_queries: int = 256 # n_queries for attentional pooler + attn_pooler_heads: int = 8 # n heads for attentional_pooling + timm_model_name: str = None # a valid model name overrides layers, width, patch_size + timm_model_pretrained: bool = False # use (imagenet) pretrained weights for named model + timm_pool: str = 'avg' # feature pooling for timm model ('abs_attn', 'rot_attn', 'avg', '') + timm_proj: str = 'linear' # linear projection for timm model output ('linear', 'mlp', '') + timm_proj_bias: bool = False # enable bias final projection + timm_drop: float = 0. # head dropout + timm_drop_path: Optional[float] = None # backbone stochastic depth + output_tokens: bool = False + + +@dataclass +class CLIPTextCfg: + context_length: int = 77 + vocab_size: int = 49408 + width: int = 512 + heads: int = 8 + layers: int = 12 + ls_init_value: Optional[float] = None # layer scale initial value + hf_model_name: str = None + hf_tokenizer_name: str = None + hf_model_pretrained: bool = True + proj: str = 'mlp' + pooler_type: str = 'mean_pooler' + embed_cls: bool = False + pad_id: int = 0 + output_tokens: bool = False + + +def get_cast_dtype(precision: str): + cast_dtype = None + if precision == 'bf16': + cast_dtype = torch.bfloat16 + elif precision == 'fp16': + cast_dtype = torch.float16 + return cast_dtype + + +def _build_vision_tower( + embed_dim: int, + vision_cfg: CLIPVisionCfg, + quick_gelu: bool = False, + cast_dtype: Optional[torch.dtype] = None +): + if isinstance(vision_cfg, dict): + vision_cfg = CLIPVisionCfg(**vision_cfg) + + # OpenAI models are pretrained w/ QuickGELU but native nn.GELU is both faster and more + # memory efficient in recent PyTorch releases (>= 1.10). + # NOTE: timm models always use native GELU regardless of quick_gelu flag. + act_layer = QuickGELU if quick_gelu else nn.GELU + + if vision_cfg.timm_model_name: + visual = TimmModel( + vision_cfg.timm_model_name, + pretrained=vision_cfg.timm_model_pretrained, + pool=vision_cfg.timm_pool, + proj=vision_cfg.timm_proj, + proj_bias=vision_cfg.timm_proj_bias, + drop=vision_cfg.timm_drop, + drop_path=vision_cfg.timm_drop_path, + embed_dim=embed_dim, + image_size=vision_cfg.image_size, + ) + act_layer = nn.GELU # so that text transformer doesn't use QuickGELU w/ timm models + elif isinstance(vision_cfg.layers, (tuple, list)): + vision_heads = vision_cfg.width * 32 // vision_cfg.head_width + visual = ModifiedResNet( + layers=vision_cfg.layers, + output_dim=embed_dim, + heads=vision_heads, + image_size=vision_cfg.image_size, + width=vision_cfg.width, + ) + else: + vision_heads = vision_cfg.width // vision_cfg.head_width + norm_layer = LayerNormFp32 if cast_dtype in (torch.float16, torch.bfloat16) else LayerNorm + visual = VisionTransformer( + image_size=vision_cfg.image_size, + patch_size=vision_cfg.patch_size, + width=vision_cfg.width, + layers=vision_cfg.layers, + heads=vision_heads, + mlp_ratio=vision_cfg.mlp_ratio, + ls_init_value=vision_cfg.ls_init_value, + patch_dropout=vision_cfg.patch_dropout, + input_patchnorm=vision_cfg.input_patchnorm, + global_average_pool=vision_cfg.global_average_pool, + attentional_pool=vision_cfg.attentional_pool, + n_queries=vision_cfg.n_queries, + attn_pooler_heads=vision_cfg.attn_pooler_heads, + output_tokens=vision_cfg.output_tokens, + output_dim=embed_dim, + act_layer=act_layer, + norm_layer=norm_layer, + ) + + return visual + + +def _build_text_tower( + embed_dim: int, + text_cfg: CLIPTextCfg, + quick_gelu: bool = False, + cast_dtype: Optional[torch.dtype] = None, +): + if isinstance(text_cfg, dict): + text_cfg = CLIPTextCfg(**text_cfg) + + if text_cfg.hf_model_name: + text = HFTextEncoder( + text_cfg.hf_model_name, + output_dim=embed_dim, + proj=text_cfg.proj, + pooler_type=text_cfg.pooler_type, + pretrained=text_cfg.hf_model_pretrained, + output_tokens=text_cfg.output_tokens, + ) + else: + act_layer = QuickGELU if quick_gelu else nn.GELU + norm_layer = LayerNormFp32 if cast_dtype in (torch.float16, torch.bfloat16) else LayerNorm + + text = TextTransformer( + context_length=text_cfg.context_length, + vocab_size=text_cfg.vocab_size, + width=text_cfg.width, + heads=text_cfg.heads, + layers=text_cfg.layers, + ls_init_value=text_cfg.ls_init_value, + output_dim=embed_dim, + embed_cls=text_cfg.embed_cls, + output_tokens=text_cfg.output_tokens, + pad_id=text_cfg.pad_id, + act_layer=act_layer, + norm_layer=norm_layer, + ) + return text + + +class CLIP(nn.Module): + output_dict: torch.jit.Final[bool] + + def __init__( + self, + embed_dim: int, + vision_cfg: CLIPVisionCfg, + text_cfg: CLIPTextCfg, + quick_gelu: bool = False, + cast_dtype: Optional[torch.dtype] = None, + output_dict: bool = False, + ): + super().__init__() + self.output_dict = output_dict + self.visual = _build_vision_tower(embed_dim, vision_cfg, quick_gelu, cast_dtype) + + text = _build_text_tower(embed_dim, text_cfg, quick_gelu, cast_dtype) + self.transformer = text.transformer + self.vocab_size = text.vocab_size + self.token_embedding = text.token_embedding + self.positional_embedding = text.positional_embedding + self.ln_final = text.ln_final + self.text_projection = text.text_projection + self.register_buffer('attn_mask', text.attn_mask, persistent=False) + + self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07)) + + def lock_image_tower(self, unlocked_groups=0, freeze_bn_stats=False): + # lock image tower as per LiT - https://arxiv.org/abs/2111.07991 + self.visual.lock(unlocked_groups=unlocked_groups, freeze_bn_stats=freeze_bn_stats) + + def lock_text_tower(self, unlocked_layers: int = 0, freeze_layer_norm: bool = True): + locked_layers = [] + locked_layers.append(self.token_embedding) + self.positional_embedding.requires_grad = False + if unlocked_layers > 0: + locked_layers.append(self.transformer.resblocks[:-unlocked_layers]) + else: + locked_layers.append(self.transformer) + locked_layers.append(self.ln_final) + self.text_projection.requires_grad = False + + # freeze layers + for module in locked_layers: + for n, p in module.named_parameters(): + p.requires_grad = (not freeze_layer_norm) if "LayerNorm" in n.split(".") else False + + @torch.jit.ignore + def set_grad_checkpointing(self, enable=True): + self.visual.set_grad_checkpointing(enable) + self.transformer.grad_checkpointing = enable + + def encode_image(self, image, normalize: bool = False): + features = self.visual(image) + return F.normalize(features, dim=-1) if normalize else features + + def encode_text(self, text, normalize: bool = False): + cast_dtype = self.transformer.get_cast_dtype() + + x = self.token_embedding(text).to(cast_dtype) # [batch_size, n_ctx, d_model] + + x = x + self.positional_embedding.to(cast_dtype) + x = x.permute(1, 0, 2) # NLD -> LND + x = self.transformer(x, attn_mask=self.attn_mask) + x = x.permute(1, 0, 2) # LND -> NLD + x = self.ln_final(x) # [batch_size, n_ctx, transformer.width] + # take features from the eot embedding (eot_token is the highest number in each sequence) + x = x[torch.arange(x.shape[0]), text.argmax(dim=-1)] @ self.text_projection + return F.normalize(x, dim=-1) if normalize else x + + def forward(self, image, text): + image_features = self.encode_image(image, normalize=True) + text_features = self.encode_text(text, normalize=True) + if self.output_dict: + return { + "image_features": image_features, + "text_features": text_features, + "logit_scale": self.logit_scale.exp() + } + return image_features, text_features, self.logit_scale.exp() + + +class CustomTextCLIP(nn.Module): + output_dict: torch.jit.Final[bool] + + def __init__( + self, + embed_dim: int, + vision_cfg: CLIPVisionCfg, + text_cfg: CLIPTextCfg, + quick_gelu: bool = False, + cast_dtype: Optional[torch.dtype] = None, + output_dict: bool = False, + ): + super().__init__() + self.output_dict = output_dict + self.visual = _build_vision_tower(embed_dim, vision_cfg, quick_gelu, cast_dtype) + self.text = _build_text_tower(embed_dim, text_cfg, quick_gelu, cast_dtype) + self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07)) + + def lock_image_tower(self, unlocked_groups=0, freeze_bn_stats=False): + # lock image tower as per LiT - https://arxiv.org/abs/2111.07991 + self.visual.lock(unlocked_groups=unlocked_groups, freeze_bn_stats=freeze_bn_stats) + + def lock_text_tower(self, unlocked_layers: int = 0, freeze_layer_norm: bool = True): + self.text.lock(unlocked_layers, freeze_layer_norm) + + @torch.jit.ignore + def set_grad_checkpointing(self, enable=True): + self.visual.set_grad_checkpointing(enable) + self.text.set_grad_checkpointing(enable) + + def encode_image(self, image, normalize: bool = False): + features = self.visual(image) + return F.normalize(features, dim=-1) if normalize else features + + def encode_text(self, text, normalize: bool = False): + features = self.text(text) + return F.normalize(features, dim=-1) if normalize else features + + def forward(self, image, text): + image_features = self.encode_image(image, normalize=True) + text_features = self.encode_text(text, normalize=True) + if self.output_dict: + return { + "image_features": image_features, + "text_features": text_features, + "logit_scale": self.logit_scale.exp() + } + return image_features, text_features, self.logit_scale.exp() + + +def convert_weights_to_lp(model: nn.Module, dtype=torch.float16): + """Convert applicable model parameters to low-precision (bf16 or fp16)""" + + def _convert_weights(l): + if isinstance(l, (nn.Conv1d, nn.Conv2d, nn.Linear)): + l.weight.data = l.weight.data.to(dtype) + if l.bias is not None: + l.bias.data = l.bias.data.to(dtype) + + if isinstance(l, (nn.MultiheadAttention, Attention)): + for attr in [*[f"{s}_proj_weight" for s in ["in", "q", "k", "v"]], "in_proj_bias", "bias_k", "bias_v"]: + tensor = getattr(l, attr) + if tensor is not None: + tensor.data = tensor.data.to(dtype) + + for name in ["text_projection", "proj"]: + if hasattr(l, name): + attr = getattr(l, name) + if attr is not None: + attr.data = attr.data.to(dtype) + + model.apply(_convert_weights) + + +convert_weights_to_fp16 = convert_weights_to_lp # backwards compat + + +# used to maintain checkpoint compatibility +def convert_to_custom_text_state_dict(state_dict: dict): + if 'text_projection' in state_dict: + # old format state_dict, move text tower -> .text + new_state_dict = {} + for k, v in state_dict.items(): + if any(k.startswith(p) for p in ( + 'text_projection', + 'positional_embedding', + 'token_embedding', + 'transformer', + 'ln_final', + )): + k = 'text.' + k + new_state_dict[k] = v + return new_state_dict + return state_dict + + +def build_model_from_openai_state_dict( + state_dict: dict, + quick_gelu=True, + cast_dtype=torch.float16, +): + vit = "visual.proj" in state_dict + + if vit: + vision_width = state_dict["visual.conv1.weight"].shape[0] + vision_layers = len( + [k for k in state_dict.keys() if k.startswith("visual.") and k.endswith(".attn.in_proj_weight")]) + vision_patch_size = state_dict["visual.conv1.weight"].shape[-1] + grid_size = round((state_dict["visual.positional_embedding"].shape[0] - 1) ** 0.5) + image_size = vision_patch_size * grid_size + else: + counts: list = [ + len(set(k.split(".")[2] for k in state_dict if k.startswith(f"visual.layer{b}"))) for b in [1, 2, 3, 4]] + vision_layers = tuple(counts) + vision_width = state_dict["visual.layer1.0.conv1.weight"].shape[0] + output_width = round((state_dict["visual.attnpool.positional_embedding"].shape[0] - 1) ** 0.5) + vision_patch_size = None + assert output_width ** 2 + 1 == state_dict["visual.attnpool.positional_embedding"].shape[0] + image_size = output_width * 32 + + embed_dim = state_dict["text_projection"].shape[1] + context_length = state_dict["positional_embedding"].shape[0] + vocab_size = state_dict["token_embedding.weight"].shape[0] + transformer_width = state_dict["ln_final.weight"].shape[0] + transformer_heads = transformer_width // 64 + transformer_layers = len(set(k.split(".")[2] for k in state_dict if k.startswith(f"transformer.resblocks"))) + + vision_cfg = CLIPVisionCfg( + layers=vision_layers, + width=vision_width, + patch_size=vision_patch_size, + image_size=image_size, + ) + text_cfg = CLIPTextCfg( + context_length=context_length, + vocab_size=vocab_size, + width=transformer_width, + heads=transformer_heads, + layers=transformer_layers, + ) + model = CLIP( + embed_dim, + vision_cfg=vision_cfg, + text_cfg=text_cfg, + quick_gelu=quick_gelu, # OpenAI models were trained with QuickGELU + cast_dtype=cast_dtype, + ) + + for key in ["input_resolution", "context_length", "vocab_size"]: + state_dict.pop(key, None) + + convert_weights_to_fp16(model) # OpenAI state dicts are partially converted to float16 + model.load_state_dict(state_dict) + return model.eval() + + +def trace_model(model, batch_size=256, device=torch.device('cpu')): + model.eval() + image_size = model.visual.image_size + example_images = torch.ones((batch_size, 3, image_size, image_size), device=device) + example_text = torch.zeros((batch_size, model.context_length), dtype=torch.int, device=device) + model = torch.jit.trace_module( + model, + inputs=dict( + forward=(example_images, example_text), + encode_text=(example_text,), + encode_image=(example_images,) + )) + model.visual.image_size = image_size + return model + + +def resize_pos_embed(state_dict, model, interpolation: str = 'bicubic', antialias: bool = True): + # Rescale the grid of position embeddings when loading from state_dict + old_pos_embed = state_dict.get('visual.positional_embedding', None) + if old_pos_embed is None or not hasattr(model.visual, 'grid_size'): + return + grid_size = to_2tuple(model.visual.grid_size) + extra_tokens = 1 # FIXME detect different token configs (ie no class token, or more) + new_seq_len = grid_size[0] * grid_size[1] + extra_tokens + if new_seq_len == old_pos_embed.shape[0]: + return + + if extra_tokens: + pos_emb_tok, pos_emb_img = old_pos_embed[:extra_tokens], old_pos_embed[extra_tokens:] + else: + pos_emb_tok, pos_emb_img = None, old_pos_embed + old_grid_size = to_2tuple(int(math.sqrt(len(pos_emb_img)))) + + logging.info('Resizing position embedding grid-size from %s to %s', old_grid_size, grid_size) + pos_emb_img = pos_emb_img.reshape(1, old_grid_size[0], old_grid_size[1], -1).permute(0, 3, 1, 2) + pos_emb_img = F.interpolate( + pos_emb_img, + size=grid_size, + mode=interpolation, + antialias=antialias, + align_corners=False, + ) + pos_emb_img = pos_emb_img.permute(0, 2, 3, 1).reshape(1, grid_size[0] * grid_size[1], -1)[0] + if pos_emb_tok is not None: + new_pos_embed = torch.cat([pos_emb_tok, pos_emb_img], dim=0) + else: + new_pos_embed = pos_emb_img + state_dict['visual.positional_embedding'] = new_pos_embed diff --git a/diffsynth/extensions/ImageQualityMetric/open_clip/model_configs/ViT-H-14.json b/diffsynth/extensions/ImageQualityMetric/open_clip/model_configs/ViT-H-14.json new file mode 100644 index 0000000000000000000000000000000000000000..3e3a7e934e7f02e41f4829996c4950e05f015a74 --- /dev/null +++ b/diffsynth/extensions/ImageQualityMetric/open_clip/model_configs/ViT-H-14.json @@ -0,0 +1,17 @@ +{ + "embed_dim": 1024, + "vision_cfg": { + "image_size": 224, + "layers": 32, + "width": 1280, + "head_width": 80, + "patch_size": 14 + }, + "text_cfg": { + "context_length": 77, + "vocab_size": 49408, + "width": 1024, + "heads": 16, + "layers": 24 + } +} \ No newline at end of file diff --git a/diffsynth/extensions/ImageQualityMetric/open_clip/modified_resnet.py b/diffsynth/extensions/ImageQualityMetric/open_clip/modified_resnet.py new file mode 100644 index 0000000000000000000000000000000000000000..6a8d3aeda91ecb394303becbbfccc8acd8cddcd9 --- /dev/null +++ b/diffsynth/extensions/ImageQualityMetric/open_clip/modified_resnet.py @@ -0,0 +1,181 @@ +from collections import OrderedDict + +import torch +from torch import nn +from torch.nn import functional as F + +from .utils import freeze_batch_norm_2d + + +class Bottleneck(nn.Module): + expansion = 4 + + def __init__(self, inplanes, planes, stride=1): + super().__init__() + + # all conv layers have stride 1. an avgpool is performed after the second convolution when stride > 1 + self.conv1 = nn.Conv2d(inplanes, planes, 1, bias=False) + self.bn1 = nn.BatchNorm2d(planes) + self.act1 = nn.ReLU(inplace=True) + + self.conv2 = nn.Conv2d(planes, planes, 3, padding=1, bias=False) + self.bn2 = nn.BatchNorm2d(planes) + self.act2 = nn.ReLU(inplace=True) + + self.avgpool = nn.AvgPool2d(stride) if stride > 1 else nn.Identity() + + self.conv3 = nn.Conv2d(planes, planes * self.expansion, 1, bias=False) + self.bn3 = nn.BatchNorm2d(planes * self.expansion) + self.act3 = nn.ReLU(inplace=True) + + self.downsample = None + self.stride = stride + + if stride > 1 or inplanes != planes * Bottleneck.expansion: + # downsampling layer is prepended with an avgpool, and the subsequent convolution has stride 1 + self.downsample = nn.Sequential(OrderedDict([ + ("-1", nn.AvgPool2d(stride)), + ("0", nn.Conv2d(inplanes, planes * self.expansion, 1, stride=1, bias=False)), + ("1", nn.BatchNorm2d(planes * self.expansion)) + ])) + + def forward(self, x: torch.Tensor): + identity = x + + out = self.act1(self.bn1(self.conv1(x))) + out = self.act2(self.bn2(self.conv2(out))) + out = self.avgpool(out) + out = self.bn3(self.conv3(out)) + + if self.downsample is not None: + identity = self.downsample(x) + + out += identity + out = self.act3(out) + return out + + +class AttentionPool2d(nn.Module): + def __init__(self, spacial_dim: int, embed_dim: int, num_heads: int, output_dim: int = None): + super().__init__() + self.positional_embedding = nn.Parameter(torch.randn(spacial_dim ** 2 + 1, embed_dim) / embed_dim ** 0.5) + self.k_proj = nn.Linear(embed_dim, embed_dim) + self.q_proj = nn.Linear(embed_dim, embed_dim) + self.v_proj = nn.Linear(embed_dim, embed_dim) + self.c_proj = nn.Linear(embed_dim, output_dim or embed_dim) + self.num_heads = num_heads + + def forward(self, x): + x = x.reshape(x.shape[0], x.shape[1], x.shape[2] * x.shape[3]).permute(2, 0, 1) # NCHW -> (HW)NC + x = torch.cat([x.mean(dim=0, keepdim=True), x], dim=0) # (HW+1)NC + x = x + self.positional_embedding[:, None, :].to(x.dtype) # (HW+1)NC + x, _ = F.multi_head_attention_forward( + query=x, key=x, value=x, + embed_dim_to_check=x.shape[-1], + num_heads=self.num_heads, + q_proj_weight=self.q_proj.weight, + k_proj_weight=self.k_proj.weight, + v_proj_weight=self.v_proj.weight, + in_proj_weight=None, + in_proj_bias=torch.cat([self.q_proj.bias, self.k_proj.bias, self.v_proj.bias]), + bias_k=None, + bias_v=None, + add_zero_attn=False, + dropout_p=0., + out_proj_weight=self.c_proj.weight, + out_proj_bias=self.c_proj.bias, + use_separate_proj_weight=True, + training=self.training, + need_weights=False + ) + + return x[0] + + +class ModifiedResNet(nn.Module): + """ + A ResNet class that is similar to torchvision's but contains the following changes: + - There are now 3 "stem" convolutions as opposed to 1, with an average pool instead of a max pool. + - Performs anti-aliasing strided convolutions, where an avgpool is prepended to convolutions with stride > 1 + - The final pooling layer is a QKV attention instead of an average pool + """ + + def __init__(self, layers, output_dim, heads, image_size=224, width=64): + super().__init__() + self.output_dim = output_dim + self.image_size = image_size + + # the 3-layer stem + self.conv1 = nn.Conv2d(3, width // 2, kernel_size=3, stride=2, padding=1, bias=False) + self.bn1 = nn.BatchNorm2d(width // 2) + self.act1 = nn.ReLU(inplace=True) + self.conv2 = nn.Conv2d(width // 2, width // 2, kernel_size=3, padding=1, bias=False) + self.bn2 = nn.BatchNorm2d(width // 2) + self.act2 = nn.ReLU(inplace=True) + self.conv3 = nn.Conv2d(width // 2, width, kernel_size=3, padding=1, bias=False) + self.bn3 = nn.BatchNorm2d(width) + self.act3 = nn.ReLU(inplace=True) + self.avgpool = nn.AvgPool2d(2) + + # residual layers + self._inplanes = width # this is a *mutable* variable used during construction + self.layer1 = self._make_layer(width, layers[0]) + self.layer2 = self._make_layer(width * 2, layers[1], stride=2) + self.layer3 = self._make_layer(width * 4, layers[2], stride=2) + self.layer4 = self._make_layer(width * 8, layers[3], stride=2) + + embed_dim = width * 32 # the ResNet feature dimension + self.attnpool = AttentionPool2d(image_size // 32, embed_dim, heads, output_dim) + + self.init_parameters() + + def _make_layer(self, planes, blocks, stride=1): + layers = [Bottleneck(self._inplanes, planes, stride)] + + self._inplanes = planes * Bottleneck.expansion + for _ in range(1, blocks): + layers.append(Bottleneck(self._inplanes, planes)) + + return nn.Sequential(*layers) + + def init_parameters(self): + if self.attnpool is not None: + std = self.attnpool.c_proj.in_features ** -0.5 + nn.init.normal_(self.attnpool.q_proj.weight, std=std) + nn.init.normal_(self.attnpool.k_proj.weight, std=std) + nn.init.normal_(self.attnpool.v_proj.weight, std=std) + nn.init.normal_(self.attnpool.c_proj.weight, std=std) + + for resnet_block in [self.layer1, self.layer2, self.layer3, self.layer4]: + for name, param in resnet_block.named_parameters(): + if name.endswith("bn3.weight"): + nn.init.zeros_(param) + + def lock(self, unlocked_groups=0, freeze_bn_stats=False): + assert unlocked_groups == 0, 'partial locking not currently supported for this model' + for param in self.parameters(): + param.requires_grad = False + if freeze_bn_stats: + freeze_batch_norm_2d(self) + + @torch.jit.ignore + def set_grad_checkpointing(self, enable=True): + # FIXME support for non-transformer + pass + + def stem(self, x): + x = self.act1(self.bn1(self.conv1(x))) + x = self.act2(self.bn2(self.conv2(x))) + x = self.act3(self.bn3(self.conv3(x))) + x = self.avgpool(x) + return x + + def forward(self, x): + x = self.stem(x) + x = self.layer1(x) + x = self.layer2(x) + x = self.layer3(x) + x = self.layer4(x) + x = self.attnpool(x) + + return x diff --git a/diffsynth/extensions/ImageQualityMetric/open_clip/openai.py b/diffsynth/extensions/ImageQualityMetric/open_clip/openai.py new file mode 100644 index 0000000000000000000000000000000000000000..cc4e13e876d6a7a3463b457e62c517cb063b1356 --- /dev/null +++ b/diffsynth/extensions/ImageQualityMetric/open_clip/openai.py @@ -0,0 +1,144 @@ +""" OpenAI pretrained model functions + +Adapted from https://github.com/openai/CLIP. Originally MIT License, Copyright (c) 2021 OpenAI. +""" + +import os +import warnings +from typing import List, Optional, Union + +import torch + +from .model import build_model_from_openai_state_dict, convert_weights_to_lp, get_cast_dtype +from .pretrained import get_pretrained_url, list_pretrained_models_by_tag, download_pretrained_from_url + +__all__ = ["list_openai_models", "load_openai_model"] + + +def list_openai_models() -> List[str]: + """Returns the names of available CLIP models""" + return list_pretrained_models_by_tag('openai') + + +def load_openai_model( + name: str, + precision: Optional[str] = None, + device: Optional[Union[str, torch.device]] = None, + jit: bool = True, + cache_dir: Optional[str] = None, +): + """Load a CLIP model + + Parameters + ---------- + name : str + A model name listed by `clip.available_models()`, or the path to a model checkpoint containing the state_dict + precision: str + Model precision, if None defaults to 'fp32' if device == 'cpu' else 'fp16'. + device : Union[str, torch.device] + The device to put the loaded model + jit : bool + Whether to load the optimized JIT model (default) or more hackable non-JIT model. + cache_dir : Optional[str] + The directory to cache the downloaded model weights + + Returns + ------- + model : torch.nn.Module + The CLIP model + preprocess : Callable[[PIL.Image], torch.Tensor] + A torchvision transform that converts a PIL image into a tensor that the returned model can take as its input + """ + if device is None: + device = "cuda" if torch.cuda.is_available() else "cpu" + if precision is None: + precision = 'fp32' if device == 'cpu' else 'fp16' + + if get_pretrained_url(name, 'openai'): + model_path = download_pretrained_from_url(get_pretrained_url(name, 'openai'), cache_dir=cache_dir) + elif os.path.isfile(name): + model_path = name + else: + raise RuntimeError(f"Model {name} not found; available models = {list_openai_models()}") + + try: + # loading JIT archive + model = torch.jit.load(model_path, map_location=device if jit else "cpu").eval() + state_dict = None + except RuntimeError: + # loading saved state dict + if jit: + warnings.warn(f"File {model_path} is not a JIT archive. Loading as a state dict instead") + jit = False + state_dict = torch.load(model_path, map_location="cpu") + + if not jit: + # Build a non-jit model from the OpenAI jitted model state dict + cast_dtype = get_cast_dtype(precision) + try: + model = build_model_from_openai_state_dict(state_dict or model.state_dict(), cast_dtype=cast_dtype) + except KeyError: + sd = {k[7:]: v for k, v in state_dict["state_dict"].items()} + model = build_model_from_openai_state_dict(sd, cast_dtype=cast_dtype) + + # model from OpenAI state dict is in manually cast fp16 mode, must be converted for AMP/fp32/bf16 use + model = model.to(device) + if precision.startswith('amp') or precision == 'fp32': + model.float() + elif precision == 'bf16': + convert_weights_to_lp(model, dtype=torch.bfloat16) + + return model + + # patch the device names + device_holder = torch.jit.trace(lambda: torch.ones([]).to(torch.device(device)), example_inputs=[]) + device_node = [n for n in device_holder.graph.findAllNodes("prim::Constant") if "Device" in repr(n)][-1] + + def patch_device(module): + try: + graphs = [module.graph] if hasattr(module, "graph") else [] + except RuntimeError: + graphs = [] + + if hasattr(module, "forward1"): + graphs.append(module.forward1.graph) + + for graph in graphs: + for node in graph.findAllNodes("prim::Constant"): + if "value" in node.attributeNames() and str(node["value"]).startswith("cuda"): + node.copyAttributes(device_node) + + model.apply(patch_device) + patch_device(model.encode_image) + patch_device(model.encode_text) + + # patch dtype to float32 (typically for CPU) + if precision == 'fp32': + float_holder = torch.jit.trace(lambda: torch.ones([]).float(), example_inputs=[]) + float_input = list(float_holder.graph.findNode("aten::to").inputs())[1] + float_node = float_input.node() + + def patch_float(module): + try: + graphs = [module.graph] if hasattr(module, "graph") else [] + except RuntimeError: + graphs = [] + + if hasattr(module, "forward1"): + graphs.append(module.forward1.graph) + + for graph in graphs: + for node in graph.findAllNodes("aten::to"): + inputs = list(node.inputs()) + for i in [1, 2]: # dtype can be the second or third argument to aten::to() + if inputs[i].node()["value"] == 5: + inputs[i].node().copyAttributes(float_node) + + model.apply(patch_float) + patch_float(model.encode_image) + patch_float(model.encode_text) + model.float() + + # ensure image_size attr available at consistent location for both jit and non-jit + model.visual.image_size = model.input_resolution.item() + return model diff --git a/diffsynth/extensions/ImageQualityMetric/open_clip/pretrained.py b/diffsynth/extensions/ImageQualityMetric/open_clip/pretrained.py new file mode 100644 index 0000000000000000000000000000000000000000..87e7e527497d643fdf6ac931ac73b6e887a90d0d --- /dev/null +++ b/diffsynth/extensions/ImageQualityMetric/open_clip/pretrained.py @@ -0,0 +1,376 @@ +import hashlib +import os +import urllib +import warnings +from functools import partial +from typing import Dict, Union + +from tqdm import tqdm + +from .version import __version__ + +try: + from huggingface_hub import hf_hub_download + hf_hub_download = partial(hf_hub_download, library_name="open_clip", library_version=__version__) + _has_hf_hub = True +except ImportError: + hf_hub_download = None + _has_hf_hub = False + + +def _pcfg(url='', hf_hub='', mean=None, std=None): + return dict( + url=url, + hf_hub=hf_hub, + mean=mean, + std=std, + ) + + +_RN50 = dict( + openai=_pcfg( + "https://openaipublic.azureedge.net/clip/models/afeb0e10f9e5a86da6080e35cf09123aca3b358a0c3e3b6c78a7b63bc04b6762/RN50.pt"), + yfcc15m=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn50-quickgelu-yfcc15m-455df137.pt"), + cc12m=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn50-quickgelu-cc12m-f000538c.pt"), +) + +_RN50_quickgelu = dict( + openai=_pcfg( + "https://openaipublic.azureedge.net/clip/models/afeb0e10f9e5a86da6080e35cf09123aca3b358a0c3e3b6c78a7b63bc04b6762/RN50.pt"), + yfcc15m=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn50-quickgelu-yfcc15m-455df137.pt"), + cc12m=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn50-quickgelu-cc12m-f000538c.pt"), +) + +_RN101 = dict( + openai=_pcfg( + "https://openaipublic.azureedge.net/clip/models/8fa8567bab74a42d41c5915025a8e4538c3bdbe8804a470a72f30b0d94fab599/RN101.pt"), + yfcc15m=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn101-quickgelu-yfcc15m-3e04b30e.pt"), +) + +_RN101_quickgelu = dict( + openai=_pcfg( + "https://openaipublic.azureedge.net/clip/models/8fa8567bab74a42d41c5915025a8e4538c3bdbe8804a470a72f30b0d94fab599/RN101.pt"), + yfcc15m=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/rn101-quickgelu-yfcc15m-3e04b30e.pt"), +) + +_RN50x4 = dict( + openai=_pcfg( + "https://openaipublic.azureedge.net/clip/models/7e526bd135e493cef0776de27d5f42653e6b4c8bf9e0f653bb11773263205fdd/RN50x4.pt"), +) + +_RN50x16 = dict( + openai=_pcfg( + "https://openaipublic.azureedge.net/clip/models/52378b407f34354e150460fe41077663dd5b39c54cd0bfd2b27167a4a06ec9aa/RN50x16.pt"), +) + +_RN50x64 = dict( + openai=_pcfg( + "https://openaipublic.azureedge.net/clip/models/be1cfb55d75a9666199fb2206c106743da0f6468c9d327f3e0d0a543a9919d9c/RN50x64.pt"), +) + +_VITB32 = dict( + openai=_pcfg( + "https://openaipublic.azureedge.net/clip/models/40d365715913c9da98579312b702a82c18be219cc2a73407c4526f58eba950af/ViT-B-32.pt"), + laion400m_e31=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_e31-d867053b.pt"), + laion400m_e32=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_e32-46683a32.pt"), + laion2b_e16=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-laion2b_e16-af8dbd0c.pth"), + laion2b_s34b_b79k=_pcfg(hf_hub='laion/CLIP-ViT-B-32-laion2B-s34B-b79K/') +) + +_VITB32_quickgelu = dict( + openai=_pcfg( + "https://openaipublic.azureedge.net/clip/models/40d365715913c9da98579312b702a82c18be219cc2a73407c4526f58eba950af/ViT-B-32.pt"), + laion400m_e31=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_e31-d867053b.pt"), + laion400m_e32=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_32-quickgelu-laion400m_e32-46683a32.pt"), +) + +_VITB16 = dict( + openai=_pcfg( + "https://openaipublic.azureedge.net/clip/models/5806e77cd80f8b59890b7e101eabd078d9fb84e6937f9e85e4ecb61988df416f/ViT-B-16.pt"), + laion400m_e31=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_16-laion400m_e31-00efa78f.pt"), + laion400m_e32=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_16-laion400m_e32-55e67d44.pt"), + # laion400m_32k=_pcfg( + # url="", + # mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)), + # laion400m_64k=_pcfg( + # url="", + # mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)), + laion2b_s34b_b88k=_pcfg(hf_hub='laion/CLIP-ViT-B-16-laion2B-s34B-b88K/'), +) + +_VITB16_PLUS_240 = dict( + laion400m_e31=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_16_plus_240-laion400m_e31-8fb26589.pt"), + laion400m_e32=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_b_16_plus_240-laion400m_e32-699c4b84.pt"), +) + +_VITL14 = dict( + openai=_pcfg( + "https://openaipublic.azureedge.net/clip/models/b8cca3fd41ae0c99ba7e8951adf17d267cdb84cd88be6f7c2e0eca1737a03836/ViT-L-14.pt"), + laion400m_e31=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_l_14-laion400m_e31-69988bb6.pt"), + laion400m_e32=_pcfg( + "https://github.com/mlfoundations/open_clip/releases/download/v0.2-weights/vit_l_14-laion400m_e32-3d133497.pt"), + laion2b_s32b_b82k=_pcfg( + hf_hub='laion/CLIP-ViT-L-14-laion2B-s32B-b82K/', + mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)), +) + +_VITL14_336 = dict( + openai=_pcfg( + "https://openaipublic.azureedge.net/clip/models/3035c92b350959924f9f00213499208652fc7ea050643e8b385c2dac08641f02/ViT-L-14-336px.pt"), +) + +_VITH14 = dict( + laion2b_s32b_b79k=_pcfg(hf_hub='laion/CLIP-ViT-H-14-laion2B-s32B-b79K/'), +) + +_VITg14 = dict( + laion2b_s12b_b42k=_pcfg(hf_hub='laion/CLIP-ViT-g-14-laion2B-s12B-b42K/'), + laion2b_s34b_b88k=_pcfg(hf_hub='laion/CLIP-ViT-g-14-laion2B-s34B-b88K/'), +) + +_VITbigG14 = dict( + laion2b_s39b_b160k=_pcfg(hf_hub='laion/CLIP-ViT-bigG-14-laion2B-39B-b160k/'), +) + +_robertaViTB32 = dict( + laion2b_s12b_b32k=_pcfg(hf_hub='laion/CLIP-ViT-B-32-roberta-base-laion2B-s12B-b32k/'), +) + +_xlmRobertaBaseViTB32 = dict( + laion5b_s13b_b90k=_pcfg(hf_hub='laion/CLIP-ViT-B-32-xlm-roberta-base-laion5B-s13B-b90k/'), +) + +_xlmRobertaLargeFrozenViTH14 = dict( + frozen_laion5b_s13b_b90k=_pcfg(hf_hub='laion/CLIP-ViT-H-14-frozen-xlm-roberta-large-laion5B-s13B-b90k/'), +) + +_convnext_base = dict( + laion400m_s13b_b51k=_pcfg(hf_hub='laion/CLIP-convnext_base-laion400M-s13B-b51K/'), +) + +_convnext_base_w = dict( + laion2b_s13b_b82k=_pcfg(hf_hub='laion/CLIP-convnext_base_w-laion2B-s13B-b82K/'), + laion2b_s13b_b82k_augreg=_pcfg(hf_hub='laion/CLIP-convnext_base_w-laion2B-s13B-b82K-augreg/'), + laion_aesthetic_s13b_b82k=_pcfg(hf_hub='laion/CLIP-convnext_base_w-laion_aesthetic-s13B-b82K/'), +) + +_convnext_base_w_320 = dict( + laion_aesthetic_s13b_b82k=_pcfg(hf_hub='laion/CLIP-convnext_base_w_320-laion_aesthetic-s13B-b82K/'), + laion_aesthetic_s13b_b82k_augreg=_pcfg(hf_hub='laion/CLIP-convnext_base_w_320-laion_aesthetic-s13B-b82K-augreg/'), +) + +_convnext_large_d = dict( + laion2b_s26b_b102k_augreg=_pcfg(hf_hub='laion/CLIP-convnext_large_d.laion2B-s26B-b102K-augreg/'), +) + +_convnext_large_d_320 = dict( + laion2b_s29b_b131k_ft=_pcfg(hf_hub='laion/CLIP-convnext_large_d_320.laion2B-s29B-b131K-ft/'), + laion2b_s29b_b131k_ft_soup=_pcfg(hf_hub='laion/CLIP-convnext_large_d_320.laion2B-s29B-b131K-ft-soup/'), +) + +_convnext_xxlarge = dict( + laion2b_s34b_b82k_augreg=_pcfg(hf_hub='laion/CLIP-convnext_xxlarge-laion2B-s34B-b82K-augreg/'), + laion2b_s34b_b82k_augreg_rewind=_pcfg(hf_hub='laion/CLIP-convnext_xxlarge-laion2B-s34B-b82K-augreg-rewind/'), + laion2b_s34b_b82k_augreg_soup=_pcfg(hf_hub='laion/CLIP-convnext_xxlarge-laion2B-s34B-b82K-augreg-soup/'), +) + +_coca_VITB32 = dict( + laion2b_s13b_b90k=_pcfg(hf_hub='laion/CoCa-ViT-B-32-laion2B-s13B-b90k/'), + mscoco_finetuned_laion2b_s13b_b90k=_pcfg(hf_hub='laion/mscoco_finetuned_CoCa-ViT-B-32-laion2B-s13B-b90k/') +) + +_coca_VITL14 = dict( + laion2b_s13b_b90k=_pcfg(hf_hub='laion/CoCa-ViT-L-14-laion2B-s13B-b90k/'), + mscoco_finetuned_laion2b_s13b_b90k=_pcfg(hf_hub='laion/mscoco_finetuned_CoCa-ViT-L-14-laion2B-s13B-b90k/') +) + + +_PRETRAINED = { + "RN50": _RN50, + "RN50-quickgelu": _RN50_quickgelu, + "RN101": _RN101, + "RN101-quickgelu": _RN101_quickgelu, + "RN50x4": _RN50x4, + "RN50x16": _RN50x16, + "RN50x64": _RN50x64, + "ViT-B-32": _VITB32, + "ViT-B-32-quickgelu": _VITB32_quickgelu, + "ViT-B-16": _VITB16, + "ViT-B-16-plus-240": _VITB16_PLUS_240, + "ViT-L-14": _VITL14, + "ViT-L-14-336": _VITL14_336, + "ViT-H-14": _VITH14, + "ViT-g-14": _VITg14, + "ViT-bigG-14": _VITbigG14, + "roberta-ViT-B-32": _robertaViTB32, + "xlm-roberta-base-ViT-B-32": _xlmRobertaBaseViTB32, + "xlm-roberta-large-ViT-H-14": _xlmRobertaLargeFrozenViTH14, + "convnext_base": _convnext_base, + "convnext_base_w": _convnext_base_w, + "convnext_base_w_320": _convnext_base_w_320, + "convnext_large_d": _convnext_large_d, + "convnext_large_d_320": _convnext_large_d_320, + "convnext_xxlarge": _convnext_xxlarge, + "coca_ViT-B-32": _coca_VITB32, + "coca_ViT-L-14": _coca_VITL14, +} + + +def _clean_tag(tag: str): + # normalize pretrained tags + return tag.lower().replace('-', '_') + + +def list_pretrained(as_str: bool = False): + """ returns list of pretrained models + Returns a tuple (model_name, pretrain_tag) by default or 'name:tag' if as_str == True + """ + return [':'.join([k, t]) if as_str else (k, t) for k in _PRETRAINED.keys() for t in _PRETRAINED[k].keys()] + + +def list_pretrained_models_by_tag(tag: str): + """ return all models having the specified pretrain tag """ + models = [] + tag = _clean_tag(tag) + for k in _PRETRAINED.keys(): + if tag in _PRETRAINED[k]: + models.append(k) + return models + + +def list_pretrained_tags_by_model(model: str): + """ return all pretrain tags for the specified model architecture """ + tags = [] + if model in _PRETRAINED: + tags.extend(_PRETRAINED[model].keys()) + return tags + + +def is_pretrained_cfg(model: str, tag: str): + if model not in _PRETRAINED: + return False + return _clean_tag(tag) in _PRETRAINED[model] + + +def get_pretrained_cfg(model: str, tag: str): + if model not in _PRETRAINED: + return {} + model_pretrained = _PRETRAINED[model] + return model_pretrained.get(_clean_tag(tag), {}) + + +def get_pretrained_url(model: str, tag: str): + cfg = get_pretrained_cfg(model, _clean_tag(tag)) + return cfg.get('url', '') + + +def download_pretrained_from_url( + url: str, + cache_dir: Union[str, None] = None, +): + if not cache_dir: + cache_dir = os.path.expanduser("~/.cache/clip") + os.makedirs(cache_dir, exist_ok=True) + filename = os.path.basename(url) + + if 'openaipublic' in url: + expected_sha256 = url.split("/")[-2] + elif 'mlfoundations' in url: + expected_sha256 = os.path.splitext(filename)[0].split("-")[-1] + else: + expected_sha256 = '' + + download_target = os.path.join(cache_dir, filename) + + if os.path.exists(download_target) and not os.path.isfile(download_target): + raise RuntimeError(f"{download_target} exists and is not a regular file") + + if os.path.isfile(download_target): + if expected_sha256: + if hashlib.sha256(open(download_target, "rb").read()).hexdigest().startswith(expected_sha256): + return download_target + else: + warnings.warn(f"{download_target} exists, but the SHA256 checksum does not match; re-downloading the file") + else: + return download_target + + with urllib.request.urlopen(url) as source, open(download_target, "wb") as output: + with tqdm(total=int(source.headers.get("Content-Length")), ncols=80, unit='iB', unit_scale=True) as loop: + while True: + buffer = source.read(8192) + if not buffer: + break + + output.write(buffer) + loop.update(len(buffer)) + + if expected_sha256 and not hashlib.sha256(open(download_target, "rb").read()).hexdigest().startswith(expected_sha256): + raise RuntimeError(f"Model has been downloaded but the SHA256 checksum does not not match") + + return download_target + + +def has_hf_hub(necessary=False): + if not _has_hf_hub and necessary: + # if no HF Hub module installed, and it is necessary to continue, raise error + raise RuntimeError( + 'Hugging Face hub model specified but package not installed. Run `pip install huggingface_hub`.') + return _has_hf_hub + + +def download_pretrained_from_hf( + model_id: str, + filename: str = 'open_clip_pytorch_model.bin', + revision=None, + cache_dir: Union[str, None] = None, +): + has_hf_hub(True) + cached_file = hf_hub_download(model_id, filename, revision=revision, cache_dir=cache_dir) + return cached_file + + +def download_pretrained( + cfg: Dict, + force_hf_hub: bool = False, + cache_dir: Union[str, None] = None, +): + target = '' + if not cfg: + return target + + download_url = cfg.get('url', '') + download_hf_hub = cfg.get('hf_hub', '') + if download_hf_hub and force_hf_hub: + # use HF hub even if url exists + download_url = '' + + if download_url: + target = download_pretrained_from_url(download_url, cache_dir=cache_dir) + elif download_hf_hub: + has_hf_hub(True) + # we assume the hf_hub entries in pretrained config combine model_id + filename in + # 'org/model_name/filename.pt' form. To specify just the model id w/o filename and + # use 'open_clip_pytorch_model.bin' default, there must be a trailing slash 'org/model_name/'. + model_id, filename = os.path.split(download_hf_hub) + if filename: + target = download_pretrained_from_hf(model_id, filename=filename, cache_dir=cache_dir) + else: + target = download_pretrained_from_hf(model_id, cache_dir=cache_dir) + + return target diff --git a/diffsynth/extensions/ImageQualityMetric/open_clip/push_to_hf_hub.py b/diffsynth/extensions/ImageQualityMetric/open_clip/push_to_hf_hub.py new file mode 100644 index 0000000000000000000000000000000000000000..23c0631c81dcb43829b7374fac09406ecefcb436 --- /dev/null +++ b/diffsynth/extensions/ImageQualityMetric/open_clip/push_to_hf_hub.py @@ -0,0 +1,243 @@ +import argparse +import json +from pathlib import Path +from tempfile import TemporaryDirectory +from typing import Optional, Tuple + +import torch + +try: + from huggingface_hub import ( + create_repo, + get_hf_file_metadata, + hf_hub_download, + hf_hub_url, + repo_type_and_id_from_hf_id, + upload_folder, + ) + from huggingface_hub.utils import EntryNotFoundError + _has_hf_hub = True +except ImportError: + _has_hf_hub = False + +from .factory import create_model_from_pretrained, get_model_config, get_tokenizer +from .tokenizer import HFTokenizer + + +def save_config_for_hf( + model, + config_path: str, + model_config: Optional[dict] +): + preprocess_cfg = { + 'mean': model.visual.image_mean, + 'std': model.visual.image_std, + } + hf_config = { + 'model_cfg': model_config, + 'preprocess_cfg': preprocess_cfg, + } + + with config_path.open('w') as f: + json.dump(hf_config, f, indent=2) + + +def save_for_hf( + model, + tokenizer: HFTokenizer, + model_config: dict, + save_directory: str, + weights_filename='open_clip_pytorch_model.bin', + config_filename='open_clip_config.json', +): + save_directory = Path(save_directory) + save_directory.mkdir(exist_ok=True, parents=True) + + weights_path = save_directory / weights_filename + torch.save(model.state_dict(), weights_path) + + tokenizer.save_pretrained(save_directory) + + config_path = save_directory / config_filename + save_config_for_hf(model, config_path, model_config=model_config) + + +def push_to_hf_hub( + model, + tokenizer, + model_config: Optional[dict], + repo_id: str, + commit_message: str = 'Add model', + token: Optional[str] = None, + revision: Optional[str] = None, + private: bool = False, + create_pr: bool = False, + model_card: Optional[dict] = None, +): + if not isinstance(tokenizer, HFTokenizer): + # default CLIP tokenizers use https://huggingface.co/openai/clip-vit-large-patch14 + tokenizer = HFTokenizer('openai/clip-vit-large-patch14') + + # Create repo if it doesn't exist yet + repo_url = create_repo(repo_id, token=token, private=private, exist_ok=True) + + # Infer complete repo_id from repo_url + # Can be different from the input `repo_id` if repo_owner was implicit + _, repo_owner, repo_name = repo_type_and_id_from_hf_id(repo_url) + repo_id = f"{repo_owner}/{repo_name}" + + # Check if README file already exist in repo + try: + get_hf_file_metadata(hf_hub_url(repo_id=repo_id, filename="README.md", revision=revision)) + has_readme = True + except EntryNotFoundError: + has_readme = False + + # Dump model and push to Hub + with TemporaryDirectory() as tmpdir: + # Save model weights and config. + save_for_hf( + model, + tokenizer=tokenizer, + model_config=model_config, + save_directory=tmpdir, + ) + + # Add readme if it does not exist + if not has_readme: + model_card = model_card or {} + model_name = repo_id.split('/')[-1] + readme_path = Path(tmpdir) / "README.md" + readme_text = generate_readme(model_card, model_name) + readme_path.write_text(readme_text) + + # Upload model and return + return upload_folder( + repo_id=repo_id, + folder_path=tmpdir, + revision=revision, + create_pr=create_pr, + commit_message=commit_message, + ) + + +def push_pretrained_to_hf_hub( + model_name, + pretrained: str, + repo_id: str, + image_mean: Optional[Tuple[float, ...]] = None, + image_std: Optional[Tuple[float, ...]] = None, + commit_message: str = 'Add model', + token: Optional[str] = None, + revision: Optional[str] = None, + private: bool = False, + create_pr: bool = False, + model_card: Optional[dict] = None, +): + model, preprocess_eval = create_model_from_pretrained( + model_name, + pretrained=pretrained, + image_mean=image_mean, + image_std=image_std, + ) + + model_config = get_model_config(model_name) + assert model_config + + tokenizer = get_tokenizer(model_name) + + push_to_hf_hub( + model=model, + tokenizer=tokenizer, + model_config=model_config, + repo_id=repo_id, + commit_message=commit_message, + token=token, + revision=revision, + private=private, + create_pr=create_pr, + model_card=model_card, + ) + + +def generate_readme(model_card: dict, model_name: str): + readme_text = "---\n" + readme_text += "tags:\n- zero-shot-image-classification\n- clip\n" + readme_text += "library_tag: open_clip\n" + readme_text += f"license: {model_card.get('license', 'mit')}\n" + if 'details' in model_card and 'Dataset' in model_card['details']: + readme_text += 'datasets:\n' + readme_text += f"- {model_card['details']['Dataset'].lower()}\n" + readme_text += "---\n" + readme_text += f"# Model card for {model_name}\n" + if 'description' in model_card: + readme_text += f"\n{model_card['description']}\n" + if 'details' in model_card: + readme_text += f"\n## Model Details\n" + for k, v in model_card['details'].items(): + if isinstance(v, (list, tuple)): + readme_text += f"- **{k}:**\n" + for vi in v: + readme_text += f" - {vi}\n" + elif isinstance(v, dict): + readme_text += f"- **{k}:**\n" + for ki, vi in v.items(): + readme_text += f" - {ki}: {vi}\n" + else: + readme_text += f"- **{k}:** {v}\n" + if 'usage' in model_card: + readme_text += f"\n## Model Usage\n" + readme_text += model_card['usage'] + readme_text += '\n' + + if 'comparison' in model_card: + readme_text += f"\n## Model Comparison\n" + readme_text += model_card['comparison'] + readme_text += '\n' + + if 'citation' in model_card: + readme_text += f"\n## Citation\n" + if not isinstance(model_card['citation'], (list, tuple)): + citations = [model_card['citation']] + else: + citations = model_card['citation'] + for c in citations: + readme_text += f"```bibtex\n{c}\n```\n" + + return readme_text + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Push to Hugging Face Hub") + parser.add_argument( + "--model", type=str, help="Name of the model to use.", + ) + parser.add_argument( + "--pretrained", type=str, + help="Use a pretrained CLIP model weights with the specified tag or file path.", + ) + parser.add_argument( + "--repo-id", type=str, + help="Destination HF Hub repo-id ie 'organization/model_id'.", + ) + parser.add_argument( + '--image-mean', type=float, nargs='+', default=None, metavar='MEAN', + help='Override default image mean value of dataset') + parser.add_argument( + '--image-std', type=float, nargs='+', default=None, metavar='STD', + help='Override default image std deviation of of dataset') + args = parser.parse_args() + + print(f'Saving model {args.model} with pretrained weights {args.pretrained} to Hugging Face Hub at {args.repo_id}') + + # FIXME add support to pass model_card json / template from file via cmd line + + push_pretrained_to_hf_hub( + args.model, + args.pretrained, + args.repo_id, + image_mean=args.image_mean, # override image mean/std if trained w/ non defaults + image_std=args.image_std, + ) + + print(f'{args.model} saved.') diff --git a/diffsynth/extensions/ImageQualityMetric/open_clip/timm_model.py b/diffsynth/extensions/ImageQualityMetric/open_clip/timm_model.py new file mode 100644 index 0000000000000000000000000000000000000000..dc71a693f9a42ec01fd88d307661bc382b4d05bc --- /dev/null +++ b/diffsynth/extensions/ImageQualityMetric/open_clip/timm_model.py @@ -0,0 +1,127 @@ +""" timm model adapter + +Wraps timm (https://github.com/rwightman/pytorch-image-models) models for use as a vision tower in CLIP model. +""" +import logging +from collections import OrderedDict + +import torch +import torch.nn as nn + +try: + import timm + from timm.models.layers import Mlp, to_2tuple + try: + # old timm imports < 0.8.1 + from timm.models.layers.attention_pool2d import RotAttentionPool2d + from timm.models.layers.attention_pool2d import AttentionPool2d as AbsAttentionPool2d + except ImportError: + # new timm imports >= 0.8.1 + from timm.layers import RotAttentionPool2d + from timm.layers import AttentionPool2d as AbsAttentionPool2d +except ImportError: + timm = None + +from .utils import freeze_batch_norm_2d + + +class TimmModel(nn.Module): + """ timm model adapter + # FIXME this adapter is a work in progress, may change in ways that break weight compat + """ + + def __init__( + self, + model_name, + embed_dim, + image_size=224, + pool='avg', + proj='linear', + proj_bias=False, + drop=0., + drop_path=None, + pretrained=False, + ): + super().__init__() + if timm is None: + raise RuntimeError("Please `pip install timm` to use timm models.") + + self.image_size = to_2tuple(image_size) + timm_kwargs = {} + if drop_path is not None: + timm_kwargs['drop_path_rate'] = drop_path + self.trunk = timm.create_model(model_name, pretrained=pretrained, **timm_kwargs) + feat_size = self.trunk.default_cfg.get('pool_size', None) + feature_ndim = 1 if not feat_size else 2 + if pool in ('abs_attn', 'rot_attn'): + assert feature_ndim == 2 + # if attn pooling used, remove both classifier and default pool + self.trunk.reset_classifier(0, global_pool='') + else: + # reset global pool if pool config set, otherwise leave as network default + reset_kwargs = dict(global_pool=pool) if pool else {} + self.trunk.reset_classifier(0, **reset_kwargs) + prev_chs = self.trunk.num_features + + head_layers = OrderedDict() + if pool == 'abs_attn': + head_layers['pool'] = AbsAttentionPool2d(prev_chs, feat_size=feat_size, out_features=embed_dim) + prev_chs = embed_dim + elif pool == 'rot_attn': + head_layers['pool'] = RotAttentionPool2d(prev_chs, out_features=embed_dim) + prev_chs = embed_dim + else: + assert proj, 'projection layer needed if non-attention pooling is used.' + + # NOTE attention pool ends with a projection layer, so proj should usually be set to '' if such pooling is used + if proj == 'linear': + head_layers['drop'] = nn.Dropout(drop) + head_layers['proj'] = nn.Linear(prev_chs, embed_dim, bias=proj_bias) + elif proj == 'mlp': + head_layers['mlp'] = Mlp(prev_chs, 2 * embed_dim, embed_dim, drop=(drop, 0), bias=(True, proj_bias)) + + self.head = nn.Sequential(head_layers) + + def lock(self, unlocked_groups=0, freeze_bn_stats=False): + """ lock modules + Args: + unlocked_groups (int): leave last n layer groups unlocked (default: 0) + """ + if not unlocked_groups: + # lock full model + for param in self.trunk.parameters(): + param.requires_grad = False + if freeze_bn_stats: + freeze_batch_norm_2d(self.trunk) + else: + # NOTE: partial freeze requires latest timm (master) branch and is subject to change + try: + # FIXME import here until API stable and in an official release + from timm.models.helpers import group_parameters, group_modules + except ImportError: + raise RuntimeError( + 'Please install latest timm `pip install git+https://github.com/rwightman/pytorch-image-models`') + matcher = self.trunk.group_matcher() + gparams = group_parameters(self.trunk, matcher) + max_layer_id = max(gparams.keys()) + max_layer_id = max_layer_id - unlocked_groups + for group_idx in range(max_layer_id + 1): + group = gparams[group_idx] + for param in group: + self.trunk.get_parameter(param).requires_grad = False + if freeze_bn_stats: + gmodules = group_modules(self.trunk, matcher, reverse=True) + gmodules = {k for k, v in gmodules.items() if v <= max_layer_id} + freeze_batch_norm_2d(self.trunk, gmodules) + + @torch.jit.ignore + def set_grad_checkpointing(self, enable=True): + try: + self.trunk.set_grad_checkpointing(enable) + except Exception as e: + logging.warning('grad checkpointing not supported for this timm image tower, continuing without...') + + def forward(self, x): + x = self.trunk(x) + x = self.head(x) + return x diff --git a/diffsynth/extensions/ImageQualityMetric/open_clip/tokenizer.py b/diffsynth/extensions/ImageQualityMetric/open_clip/tokenizer.py new file mode 100644 index 0000000000000000000000000000000000000000..22ec4880b13ec73594d5c19b3d3be83aadb55aba --- /dev/null +++ b/diffsynth/extensions/ImageQualityMetric/open_clip/tokenizer.py @@ -0,0 +1,211 @@ +""" CLIP tokenizer + +Copied from https://github.com/openai/CLIP. Originally MIT License, Copyright (c) 2021 OpenAI. +""" +import gzip +import html +import os +from functools import lru_cache +from typing import Union, List + +import ftfy +import regex as re +import torch + +# https://stackoverflow.com/q/62691279 +import os +os.environ["TOKENIZERS_PARALLELISM"] = "false" + + +@lru_cache() +def default_bpe(): + current_dir = os.path.dirname(os.path.abspath(__file__)) + project_root = os.path.abspath(os.path.join(current_dir, '../../../../')) + quality_metric_path = os.path.join(project_root, 'models', 'QualityMetric') + return os.path.join(quality_metric_path, "bpe_simple_vocab_16e6.txt.gz") + + +@lru_cache() +def bytes_to_unicode(): + """ + Returns list of utf-8 byte and a corresponding list of unicode strings. + The reversible bpe codes work on unicode strings. + This means you need a large # of unicode characters in your vocab if you want to avoid UNKs. + When you're at something like a 10B token dataset you end up needing around 5K for decent coverage. + This is a significant percentage of your normal, say, 32K bpe vocab. + To avoid that, we want lookup tables between utf-8 bytes and unicode strings. + And avoids mapping to whitespace/control characters the bpe code barfs on. + """ + bs = list(range(ord("!"), ord("~")+1))+list(range(ord("¡"), ord("¬")+1))+list(range(ord("®"), ord("ÿ")+1)) + cs = bs[:] + n = 0 + for b in range(2**8): + if b not in bs: + bs.append(b) + cs.append(2**8+n) + n += 1 + cs = [chr(n) for n in cs] + return dict(zip(bs, cs)) + + +def get_pairs(word): + """Return set of symbol pairs in a word. + Word is represented as tuple of symbols (symbols being variable-length strings). + """ + pairs = set() + prev_char = word[0] + for char in word[1:]: + pairs.add((prev_char, char)) + prev_char = char + return pairs + + +def basic_clean(text): + text = ftfy.fix_text(text) + text = html.unescape(html.unescape(text)) + return text.strip() + + +def whitespace_clean(text): + text = re.sub(r'\s+', ' ', text) + text = text.strip() + return text + + +class SimpleTokenizer(object): + def __init__(self, bpe_path: str = default_bpe(), special_tokens=None): + self.byte_encoder = bytes_to_unicode() + self.byte_decoder = {v: k for k, v in self.byte_encoder.items()} + merges = gzip.open(bpe_path).read().decode("utf-8").split('\n') + merges = merges[1:49152-256-2+1] + merges = [tuple(merge.split()) for merge in merges] + vocab = list(bytes_to_unicode().values()) + vocab = vocab + [v+'' for v in vocab] + for merge in merges: + vocab.append(''.join(merge)) + if not special_tokens: + special_tokens = ['', ''] + else: + special_tokens = ['', ''] + special_tokens + vocab.extend(special_tokens) + self.encoder = dict(zip(vocab, range(len(vocab)))) + self.decoder = {v: k for k, v in self.encoder.items()} + self.bpe_ranks = dict(zip(merges, range(len(merges)))) + self.cache = {t:t for t in special_tokens} + special = "|".join(special_tokens) + self.pat = re.compile(special + r"""|'s|'t|'re|'ve|'m|'ll|'d|[\p{L}]+|[\p{N}]|[^\s\p{L}\p{N}]+""", re.IGNORECASE) + + self.vocab_size = len(self.encoder) + self.all_special_ids = [self.encoder[t] for t in special_tokens] + + def bpe(self, token): + if token in self.cache: + return self.cache[token] + word = tuple(token[:-1]) + ( token[-1] + '',) + pairs = get_pairs(word) + + if not pairs: + return token+'' + + while True: + bigram = min(pairs, key = lambda pair: self.bpe_ranks.get(pair, float('inf'))) + if bigram not in self.bpe_ranks: + break + first, second = bigram + new_word = [] + i = 0 + while i < len(word): + try: + j = word.index(first, i) + new_word.extend(word[i:j]) + i = j + except: + new_word.extend(word[i:]) + break + + if word[i] == first and i < len(word)-1 and word[i+1] == second: + new_word.append(first+second) + i += 2 + else: + new_word.append(word[i]) + i += 1 + new_word = tuple(new_word) + word = new_word + if len(word) == 1: + break + else: + pairs = get_pairs(word) + word = ' '.join(word) + self.cache[token] = word + return word + + def encode(self, text): + bpe_tokens = [] + text = whitespace_clean(basic_clean(text)).lower() + for token in re.findall(self.pat, text): + token = ''.join(self.byte_encoder[b] for b in token.encode('utf-8')) + bpe_tokens.extend(self.encoder[bpe_token] for bpe_token in self.bpe(token).split(' ')) + return bpe_tokens + + def decode(self, tokens): + text = ''.join([self.decoder[token] for token in tokens]) + text = bytearray([self.byte_decoder[c] for c in text]).decode('utf-8', errors="replace").replace('', ' ') + return text + + def __call__(self, texts: Union[str, List[str]], context_length: int = 77) -> torch.LongTensor: + """ + Returns the tokenized representation of given input string(s) + + Parameters + ---------- + texts : Union[str, List[str]] + An input string or a list of input strings to tokenize + context_length : int + The context length to use; all CLIP models use 77 as the context length + + Returns + ------- + A two-dimensional tensor containing the resulting tokens, shape = [number of input strings, context_length] + """ + if isinstance(texts, str): + texts = [texts] + + sot_token = self.encoder[""] + eot_token = self.encoder[""] + all_tokens = [[sot_token] + self.encode(text) + [eot_token] for text in texts] + result = torch.zeros(len(all_tokens), context_length, dtype=torch.long) + + for i, tokens in enumerate(all_tokens): + if len(tokens) > context_length: + tokens = tokens[:context_length] # Truncate + tokens[-1] = eot_token + result[i, :len(tokens)] = torch.tensor(tokens) + + return result + + + +class HFTokenizer: + """HuggingFace tokenizer wrapper""" + + def __init__(self, tokenizer_name: str): + from transformers import AutoTokenizer + self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_name) + + def save_pretrained(self, dest): + self.tokenizer.save_pretrained(dest) + + def __call__(self, texts: Union[str, List[str]], context_length: int = 77) -> torch.Tensor: + # same cleaning as for default tokenizer, except lowercasing + # adding lower (for case-sensitive tokenizers) will make it more robust but less sensitive to nuance + if isinstance(texts, str): + texts = [texts] + texts = [whitespace_clean(basic_clean(text)) for text in texts] + input_ids = self.tokenizer( + texts, + return_tensors='pt', + max_length=context_length, + padding='max_length', + truncation=True, + ).input_ids + return input_ids diff --git a/diffsynth/extensions/ImageQualityMetric/open_clip/transform.py b/diffsynth/extensions/ImageQualityMetric/open_clip/transform.py new file mode 100644 index 0000000000000000000000000000000000000000..fe4e21fa5b515f2412049f9274bd06fbe77fb9b9 --- /dev/null +++ b/diffsynth/extensions/ImageQualityMetric/open_clip/transform.py @@ -0,0 +1,216 @@ +import warnings +from dataclasses import dataclass, asdict +from typing import Any, Dict, Optional, Sequence, Tuple, Union + +import torch +import torch.nn as nn +import torchvision.transforms.functional as F +from functools import partial +from torchvision.transforms import Normalize, Compose, RandomResizedCrop, InterpolationMode, ToTensor, Resize, \ + CenterCrop + +from .constants import OPENAI_DATASET_MEAN, OPENAI_DATASET_STD + + +@dataclass +class AugmentationCfg: + scale: Tuple[float, float] = (0.9, 1.0) + ratio: Optional[Tuple[float, float]] = None + color_jitter: Optional[Union[float, Tuple[float, float, float]]] = None + interpolation: Optional[str] = None + re_prob: Optional[float] = None + re_count: Optional[int] = None + use_timm: bool = False + + +class ResizeMaxSize(nn.Module): + + def __init__(self, max_size, interpolation=InterpolationMode.BICUBIC, fn='max', fill=0): + super().__init__() + if not isinstance(max_size, int): + raise TypeError(f"Size should be int. Got {type(max_size)}") + self.max_size = max_size + self.interpolation = interpolation + self.fn = min if fn == 'min' else min + self.fill = fill + + def forward(self, img): + if isinstance(img, torch.Tensor): + height, width = img.shape[1:] + else: + width, height = img.size + scale = self.max_size / float(max(height, width)) + if scale != 1.0: + new_size = tuple(round(dim * scale) for dim in (height, width)) + img = F.resize(img, new_size, self.interpolation) + pad_h = self.max_size - new_size[0] + pad_w = self.max_size - new_size[1] + img = F.pad(img, padding=[pad_w//2, pad_h//2, pad_w - pad_w//2, pad_h - pad_h//2], fill=self.fill) + return img + + +def _convert_to_rgb_or_rgba(image): + if image.mode == 'RGBA': + return image + else: + return image.convert('RGB') + +# def transform_and_split(merged, transform_fn, normalize_fn): +# transformed = transform_fn(merged) +# crop_img, crop_label = torch.split(transformed, [3,1], dim=0) + +# # crop_img = _convert_to_rgb(crop_img) +# crop_img = normalize_fn(ToTensor()(crop_img)) +# return crop_img, crop_label + +class MaskAwareNormalize(nn.Module): + def __init__(self, mean, std): + super().__init__() + self.normalize = Normalize(mean=mean, std=std) + + def forward(self, tensor): + if tensor.shape[0] == 4: + return torch.cat([self.normalize(tensor[:3]), tensor[3:]], dim=0) + else: + return self.normalize(tensor) + +def image_transform( + image_size: int, + is_train: bool, + mean: Optional[Tuple[float, ...]] = None, + std: Optional[Tuple[float, ...]] = None, + resize_longest_max: bool = False, + fill_color: int = 0, + aug_cfg: Optional[Union[Dict[str, Any], AugmentationCfg]] = None, +): + mean = mean or OPENAI_DATASET_MEAN + if not isinstance(mean, (list, tuple)): + mean = (mean,) * 3 + + std = std or OPENAI_DATASET_STD + if not isinstance(std, (list, tuple)): + std = (std,) * 3 + + if isinstance(image_size, (list, tuple)) and image_size[0] == image_size[1]: + # for square size, pass size as int so that Resize() uses aspect preserving shortest edge + image_size = image_size[0] + + if isinstance(aug_cfg, dict): + aug_cfg = AugmentationCfg(**aug_cfg) + else: + aug_cfg = aug_cfg or AugmentationCfg() + normalize = MaskAwareNormalize(mean=mean, std=std) + if is_train: + aug_cfg_dict = {k: v for k, v in asdict(aug_cfg).items() if v is not None} + use_timm = aug_cfg_dict.pop('use_timm', False) + if use_timm: + assert False, "not tested for augmentation with mask" + from timm.data import create_transform # timm can still be optional + if isinstance(image_size, (tuple, list)): + assert len(image_size) >= 2 + input_size = (3,) + image_size[-2:] + else: + input_size = (3, image_size, image_size) + # by default, timm aug randomly alternates bicubic & bilinear for better robustness at inference time + aug_cfg_dict.setdefault('interpolation', 'random') + aug_cfg_dict.setdefault('color_jitter', None) # disable by default + train_transform = create_transform( + input_size=input_size, + is_training=True, + hflip=0., + mean=mean, + std=std, + re_mode='pixel', + **aug_cfg_dict, + ) + else: + train_transform = Compose([ + _convert_to_rgb_or_rgba, + ToTensor(), + RandomResizedCrop( + image_size, + scale=aug_cfg_dict.pop('scale'), + interpolation=InterpolationMode.BICUBIC, + ), + normalize, + ]) + if aug_cfg_dict: + warnings.warn(f'Unused augmentation cfg items, specify `use_timm` to use ({list(aug_cfg_dict.keys())}).') + return train_transform + else: + transforms = [ + _convert_to_rgb_or_rgba, + ToTensor(), + ] + if resize_longest_max: + transforms.extend([ + ResizeMaxSize(image_size, fill=fill_color) + ]) + else: + transforms.extend([ + Resize(image_size, interpolation=InterpolationMode.BICUBIC), + CenterCrop(image_size), + ]) + transforms.extend([ + normalize, + ]) + return Compose(transforms) + + +# def image_transform_region( +# image_size: int, +# is_train: bool, +# mean: Optional[Tuple[float, ...]] = None, +# std: Optional[Tuple[float, ...]] = None, +# resize_longest_max: bool = False, +# fill_color: int = 0, +# aug_cfg: Optional[Union[Dict[str, Any], AugmentationCfg]] = None, +# ): +# mean = mean or OPENAI_DATASET_MEAN +# if not isinstance(mean, (list, tuple)): +# mean = (mean,) * 3 + +# std = std or OPENAI_DATASET_STD +# if not isinstance(std, (list, tuple)): +# std = (std,) * 3 + +# if isinstance(image_size, (list, tuple)) and image_size[0] == image_size[1]: +# # for square size, pass size as int so that Resize() uses aspect preserving shortest edge +# image_size = image_size[0] + +# if isinstance(aug_cfg, dict): +# aug_cfg = AugmentationCfg(**aug_cfg) +# else: +# aug_cfg = aug_cfg or AugmentationCfg() +# normalize = Normalize(mean=mean, std=std) +# if is_train: +# aug_cfg_dict = {k: v for k, v in asdict(aug_cfg).items() if v is not None} + +# transform = Compose([ +# RandomResizedCrop( +# image_size, +# scale=aug_cfg_dict.pop('scale'), +# interpolation=InterpolationMode.BICUBIC, +# ), +# ]) +# train_transform = Compose([ +# partial(transform_and_split, transform_fn=transform,normalize_fn=normalize) +# ]) +# return train_transform +# else: +# if resize_longest_max: +# transform = [ +# ResizeMaxSize(image_size, fill=fill_color) +# ] +# val_transform = Compose([ +# partial(transform_and_split, transform_fn=transform,normalize_fn=normalize), +# ]) +# else: +# transform = [ +# Resize(image_size, interpolation=InterpolationMode.BICUBIC), +# CenterCrop(image_size), +# ] +# val_transform = Compose([ +# partial(transform_and_split, transform_fn=transform,normalize_fn=normalize), +# ]) +# return val_transform \ No newline at end of file diff --git a/diffsynth/extensions/ImageQualityMetric/open_clip/transformer.py b/diffsynth/extensions/ImageQualityMetric/open_clip/transformer.py new file mode 100644 index 0000000000000000000000000000000000000000..7465c1b20bf388a17e0f4f80f7b8eee3b564af92 --- /dev/null +++ b/diffsynth/extensions/ImageQualityMetric/open_clip/transformer.py @@ -0,0 +1,727 @@ +from collections import OrderedDict +import math +from typing import Callable, Optional, Sequence, Tuple + +import torch +from torch import nn +from torch.nn import functional as F +from torch.utils.checkpoint import checkpoint + +from .utils import to_2tuple + + +class LayerNormFp32(nn.LayerNorm): + """Subclass torch's LayerNorm to handle fp16 (by casting to float32 and back).""" + + def forward(self, x: torch.Tensor): + orig_type = x.dtype + x = F.layer_norm(x.to(torch.float32), self.normalized_shape, self.weight, self.bias, self.eps) + return x.to(orig_type) + + +class LayerNorm(nn.LayerNorm): + """Subclass torch's LayerNorm (with cast back to input dtype).""" + + def forward(self, x: torch.Tensor): + orig_type = x.dtype + x = F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps) + return x.to(orig_type) + + +class QuickGELU(nn.Module): + # NOTE This is slower than nn.GELU or nn.SiLU and uses more GPU memory + def forward(self, x: torch.Tensor): + return x * torch.sigmoid(1.702 * x) + + +class LayerScale(nn.Module): + def __init__(self, dim, init_values=1e-5, inplace=False): + super().__init__() + self.inplace = inplace + self.gamma = nn.Parameter(init_values * torch.ones(dim)) + + def forward(self, x): + return x.mul_(self.gamma) if self.inplace else x * self.gamma + + +class PatchDropout(nn.Module): + """ + https://arxiv.org/abs/2212.00794 + """ + + def __init__(self, prob, exclude_first_token=True): + super().__init__() + assert 0 <= prob < 1. + self.prob = prob + self.exclude_first_token = exclude_first_token # exclude CLS token + + def forward(self, x): + if not self.training or self.prob == 0.: + return x + + if self.exclude_first_token: + cls_tokens, x = x[:, :1], x[:, 1:] + else: + cls_tokens = torch.jit.annotate(torch.Tensor, x[:, :1]) + + batch = x.size()[0] + num_tokens = x.size()[1] + + batch_indices = torch.arange(batch) + batch_indices = batch_indices[..., None] + + keep_prob = 1 - self.prob + num_patches_keep = max(1, int(num_tokens * keep_prob)) + + rand = torch.randn(batch, num_tokens) + patch_indices_keep = rand.topk(num_patches_keep, dim=-1).indices + + x = x[batch_indices, patch_indices_keep] + + if self.exclude_first_token: + x = torch.cat((cls_tokens, x), dim=1) + + return x + + +class Attention(nn.Module): + def __init__( + self, + dim, + num_heads=8, + qkv_bias=True, + scaled_cosine=False, + scale_heads=False, + logit_scale_max=math.log(1. / 0.01), + attn_drop=0., + proj_drop=0. + ): + super().__init__() + self.scaled_cosine = scaled_cosine + self.scale_heads = scale_heads + assert dim % num_heads == 0, 'dim should be divisible by num_heads' + self.num_heads = num_heads + self.head_dim = dim // num_heads + self.scale = self.head_dim ** -0.5 + self.logit_scale_max = logit_scale_max + + # keeping in_proj in this form (instead of nn.Linear) to match weight scheme of original + self.in_proj_weight = nn.Parameter(torch.randn((dim * 3, dim)) * self.scale) + if qkv_bias: + self.in_proj_bias = nn.Parameter(torch.zeros(dim * 3)) + else: + self.in_proj_bias = None + + if self.scaled_cosine: + self.logit_scale = nn.Parameter(torch.log(10 * torch.ones((num_heads, 1, 1)))) + else: + self.logit_scale = None + self.attn_drop = nn.Dropout(attn_drop) + if self.scale_heads: + self.head_scale = nn.Parameter(torch.ones((num_heads, 1, 1))) + else: + self.head_scale = None + self.out_proj = nn.Linear(dim, dim) + self.out_drop = nn.Dropout(proj_drop) + + def forward(self, x, attn_mask: Optional[torch.Tensor] = None): + L, N, C = x.shape + q, k, v = F.linear(x, self.in_proj_weight, self.in_proj_bias).chunk(3, dim=-1) + q = q.contiguous().view(L, N * self.num_heads, -1).transpose(0, 1) + k = k.contiguous().view(L, N * self.num_heads, -1).transpose(0, 1) + v = v.contiguous().view(L, N * self.num_heads, -1).transpose(0, 1) + + if self.logit_scale is not None: + attn = torch.bmm(F.normalize(q, dim=-1), F.normalize(k, dim=-1).transpose(-1, -2)) + logit_scale = torch.clamp(self.logit_scale, max=self.logit_scale_max).exp() + attn = attn.view(N, self.num_heads, L, L) * logit_scale + attn = attn.view(-1, L, L) + else: + q = q * self.scale + attn = torch.bmm(q, k.transpose(-1, -2)) + + if attn_mask is not None: + if attn_mask.dtype == torch.bool: + new_attn_mask = torch.zeros_like(attn_mask, dtype=q.dtype) + new_attn_mask.masked_fill_(attn_mask, float("-inf")) + attn_mask = new_attn_mask + attn += attn_mask + + attn = attn.softmax(dim=-1) + attn = self.attn_drop(attn) + + x = torch.bmm(attn, v) + if self.head_scale is not None: + x = x.view(N, self.num_heads, L, C) * self.head_scale + x = x.view(-1, L, C) + x = x.transpose(0, 1).reshape(L, N, C) + x = self.out_proj(x) + x = self.out_drop(x) + return x + + +class AttentionalPooler(nn.Module): + def __init__( + self, + d_model: int, + context_dim: int, + n_head: int = 8, + n_queries: int = 256, + norm_layer: Callable = LayerNorm + ): + super().__init__() + self.query = nn.Parameter(torch.randn(n_queries, d_model)) + self.attn = nn.MultiheadAttention(d_model, n_head, kdim=context_dim, vdim=context_dim) + self.ln_q = norm_layer(d_model) + self.ln_k = norm_layer(context_dim) + + def forward(self, x: torch.Tensor): + x = self.ln_k(x).permute(1, 0, 2) # NLD -> LND + N = x.shape[1] + q = self.ln_q(self.query) + out = self.attn(self._repeat(q, N), x, x, need_weights=False)[0] + return out.permute(1, 0, 2) # LND -> NLD + + def _repeat(self, query, N: int): + return query.unsqueeze(1).repeat(1, N, 1) + + +class ResidualAttentionBlock(nn.Module): + def __init__( + self, + d_model: int, + n_head: int, + mlp_ratio: float = 4.0, + ls_init_value: float = None, + act_layer: Callable = nn.GELU, + norm_layer: Callable = LayerNorm, + is_cross_attention: bool = False, + ): + super().__init__() + + self.ln_1 = norm_layer(d_model) + self.attn = nn.MultiheadAttention(d_model, n_head) + self.ls_1 = LayerScale(d_model, ls_init_value) if ls_init_value is not None else nn.Identity() + if is_cross_attention: + self.ln_1_kv = norm_layer(d_model) + + self.ln_2 = norm_layer(d_model) + mlp_width = int(d_model * mlp_ratio) + self.mlp = nn.Sequential(OrderedDict([ + ("c_fc", nn.Linear(d_model, mlp_width)), + ("gelu", act_layer()), + ("c_proj", nn.Linear(mlp_width, d_model)) + ])) + self.ls_2 = LayerScale(d_model, ls_init_value) if ls_init_value is not None else nn.Identity() + + def attention( + self, + q_x: torch.Tensor, + k_x: Optional[torch.Tensor] = None, + v_x: Optional[torch.Tensor] = None, + attn_mask: Optional[torch.Tensor] = None, + ): + k_x = k_x if k_x is not None else q_x + v_x = v_x if v_x is not None else q_x + + attn_mask = attn_mask.to(q_x.dtype) if attn_mask is not None else None + return self.attn( + q_x, k_x, v_x, need_weights=False, attn_mask=attn_mask + )[0] + + def forward( + self, + q_x: torch.Tensor, + k_x: Optional[torch.Tensor] = None, + v_x: Optional[torch.Tensor] = None, + attn_mask: Optional[torch.Tensor] = None, + ): + k_x = self.ln_1_kv(k_x) if hasattr(self, "ln_1_kv") and k_x is not None else None + v_x = self.ln_1_kv(v_x) if hasattr(self, "ln_1_kv") and v_x is not None else None + + x = q_x + self.ls_1(self.attention(q_x=self.ln_1(q_x), k_x=k_x, v_x=v_x, attn_mask=attn_mask)) + x = x + self.ls_2(self.mlp(self.ln_2(x))) + return x + + +class CustomResidualAttentionBlock(nn.Module): + def __init__( + self, + d_model: int, + n_head: int, + mlp_ratio: float = 4.0, + ls_init_value: float = None, + act_layer: Callable = nn.GELU, + norm_layer: Callable = LayerNorm, + scale_cosine_attn: bool = False, + scale_heads: bool = False, + scale_attn: bool = False, + scale_fc: bool = False, + ): + super().__init__() + + self.ln_1 = norm_layer(d_model) + self.attn = Attention( + d_model, n_head, + scaled_cosine=scale_cosine_attn, + scale_heads=scale_heads, + ) + self.ln_attn = norm_layer(d_model) if scale_attn else nn.Identity() + self.ls_1 = LayerScale(d_model, ls_init_value) if ls_init_value is not None else nn.Identity() + + self.ln_2 = norm_layer(d_model) + mlp_width = int(d_model * mlp_ratio) + self.mlp = nn.Sequential(OrderedDict([ + ("c_fc", nn.Linear(d_model, mlp_width)), + ('ln', norm_layer(mlp_width) if scale_fc else nn.Identity()), + ("gelu", act_layer()), + ("c_proj", nn.Linear(mlp_width, d_model)) + ])) + self.ls_2 = LayerScale(d_model, ls_init_value) if ls_init_value is not None else nn.Identity() + + def forward(self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] = None): + x = x + self.ls_1(self.ln_attn(self.attn(self.ln_1(x), attn_mask=attn_mask))) + x = x + self.ls_2(self.mlp(self.ln_2(x))) + return x + + +class Transformer(nn.Module): + def __init__( + self, + width: int, + layers: int, + heads: int, + mlp_ratio: float = 4.0, + ls_init_value: float = None, + act_layer: Callable = nn.GELU, + norm_layer: Callable = LayerNorm, + ): + super().__init__() + self.width = width + self.layers = layers + self.grad_checkpointing = False + + self.resblocks = nn.ModuleList([ + ResidualAttentionBlock( + width, heads, mlp_ratio, ls_init_value=ls_init_value, act_layer=act_layer, norm_layer=norm_layer) + for _ in range(layers) + ]) + + def get_cast_dtype(self) -> torch.dtype: + return self.resblocks[0].mlp.c_fc.weight.dtype + + def forward(self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] = None): + for r in self.resblocks: + if self.grad_checkpointing and not torch.jit.is_scripting(): + # TODO: handle kwargs https://github.com/pytorch/pytorch/issues/79887#issuecomment-1161758372 + x = checkpoint(r, x, None, None, attn_mask) + else: + x = r(x, attn_mask=attn_mask) + return x + + +class VisionTransformer(nn.Module): + output_tokens: torch.jit.Final[bool] + + def __init__( + self, + image_size: int, + patch_size: int, + width: int, + layers: int, + heads: int, + mlp_ratio: float, + ls_init_value: float = None, + global_average_pool: bool = False, + attentional_pool: bool = False, + n_queries: int = 256, + attn_pooler_heads: int = 8, + output_dim: int = 512, + patch_dropout: float = 0., + input_patchnorm: bool = False, + act_layer: Callable = nn.GELU, + norm_layer: Callable = LayerNorm, + output_tokens: bool = False + ): + super().__init__() + self.output_tokens = output_tokens + image_height, image_width = self.image_size = to_2tuple(image_size) + patch_height, patch_width = self.patch_size = to_2tuple(patch_size) + self.grid_size = (image_height // patch_height, image_width // patch_width) + self.output_dim = output_dim + + # whether to layernorm each patch, as done in dual patchnorm paper - https://arxiv.org/abs/2302.01327v1 + self.input_patchnorm = input_patchnorm + + if input_patchnorm: + patch_input_dim = patch_height * patch_width * 3 + self.patchnorm_pre_ln = LayerNorm(patch_input_dim) + self.conv1 = nn.Linear(patch_input_dim, width) + else: + self.patchnorm_pre_ln = nn.Identity() + self.conv1 = nn.Conv2d(in_channels=3, out_channels=width, kernel_size=patch_size, stride=patch_size, bias=False) + + # class embeddings and positional embeddings + scale = width ** -0.5 + self.class_embedding = nn.Parameter(scale * torch.randn(width)) + self.positional_embedding = nn.Parameter(scale * torch.randn(self.grid_size[0] * self.grid_size[1] + 1, width)) + + # setting a patch_dropout of 0. would mean it is disabled and this function would be the identity fn + self.patch_dropout = PatchDropout(patch_dropout) if patch_dropout > 0. else nn.Identity() + + self.ln_pre = norm_layer(width) + self.transformer = Transformer( + width, + layers, + heads, + mlp_ratio, + ls_init_value=ls_init_value, + act_layer=act_layer, + norm_layer=norm_layer, + ) + + self.global_average_pool = global_average_pool + if attentional_pool: + self.attn_pool = AttentionalPooler(output_dim, width, n_head=attn_pooler_heads, n_queries=n_queries) + self.ln_post = norm_layer(output_dim) + self.proj = nn.Parameter(scale * torch.randn(output_dim, output_dim)) + else: + self.attn_pool = None + self.ln_post = norm_layer(width) + self.proj = nn.Parameter(scale * torch.randn(width, output_dim)) + + self.init_parameters() + + def lock(self, unlocked_groups=0, freeze_bn_stats=False): + for param in self.parameters(): + param.requires_grad = False + + if unlocked_groups != 0: + groups = [ + [ + self.conv1, + self.class_embedding, + self.positional_embedding, + self.ln_pre, + ], + *self.transformer.resblocks[:-1], + [ + self.transformer.resblocks[-1], + self.ln_post, + ], + self.proj, + ] + + def _unlock(x): + if isinstance(x, Sequence): + for g in x: + _unlock(g) + else: + if isinstance(x, torch.nn.Parameter): + x.requires_grad = True + else: + for p in x.parameters(): + p.requires_grad = True + + _unlock(groups[-unlocked_groups:]) + + def init_parameters(self): + # FIXME OpenAI CLIP did not define an init for the VisualTransformer + # TODO experiment if default PyTorch init, below, or alternate init is best. + + # nn.init.normal_(self.class_embedding, std=self.scale) + # nn.init.normal_(self.positional_embedding, std=self.scale) + # + # proj_std = (self.transformer.width ** -0.5) * ((2 * self.transformer.layers) ** -0.5) + # attn_std = self.transformer.width ** -0.5 + # fc_std = (2 * self.transformer.width) ** -0.5 + # for block in self.transformer.resblocks: + # nn.init.normal_(block.attn.in_proj_weight, std=attn_std) + # nn.init.normal_(block.attn.out_proj.weight, std=proj_std) + # nn.init.normal_(block.mlp.c_fc.weight, std=fc_std) + # nn.init.normal_(block.mlp.c_proj.weight, std=proj_std) + # + # if self.text_projection is not None: + # nn.init.normal_(self.text_projection, std=self.scale) + pass + + @torch.jit.ignore + def set_grad_checkpointing(self, enable=True): + self.transformer.grad_checkpointing = enable + + def _global_pool(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: + if self.global_average_pool: + return x.mean(dim=1), x + else: + return x[:, 0], x[:, 1:] + + def forward(self, x: torch.Tensor, skip_pool: bool = False): + + # to patches - whether to use dual patchnorm - https://arxiv.org/abs/2302.01327v1 + if self.input_patchnorm: + # einops - rearrange(x, 'b c (h p1) (w p2) -> b (h w) (c p1 p2)') + x = x.reshape(x.shape[0], x.shape[1], self.grid_size[0], self.patch_size[0], self.grid_size[1], self.patch_size[1]) + x = x.permute(0, 2, 4, 1, 3, 5) + x = x.reshape(x.shape[0], self.grid_size[0] * self.grid_size[1], -1) + x = self.patchnorm_pre_ln(x) + x = self.conv1(x) + else: + x = self.conv1(x) # shape = [*, width, grid, grid] + x = x.reshape(x.shape[0], x.shape[1], -1) # shape = [*, width, grid ** 2] + x = x.permute(0, 2, 1) # shape = [*, grid ** 2, width] + + # class embeddings and positional embeddings + x = torch.cat( + [self.class_embedding.to(x.dtype) + torch.zeros(x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device), + x], dim=1) # shape = [*, grid ** 2 + 1, width] + x = x + self.positional_embedding.to(x.dtype) + + # a patch_dropout of 0. would mean it is disabled and this function would do nothing but return what was passed in + x = self.patch_dropout(x) + x = self.ln_pre(x) + + x = x.permute(1, 0, 2) # NLD -> LND + x = self.transformer(x) + x = x.permute(1, 0, 2) # LND -> NLD + + if skip_pool: + return x + + if self.attn_pool is not None: + x = self.attn_pool(x) + x = self.ln_post(x) + pooled, tokens = self._global_pool(x) + else: + pooled, tokens = self._global_pool(x) + pooled = self.ln_post(pooled) + + if self.proj is not None: + pooled = pooled @ self.proj + + if self.output_tokens: + return pooled, tokens + + return pooled + + +class TextTransformer(nn.Module): + output_tokens: torch.jit.Final[bool] + + def __init__( + self, + context_length: int = 77, + vocab_size: int = 49408, + width: int = 512, + heads: int = 8, + layers: int = 12, + ls_init_value: float = None, + output_dim: int = 512, + act_layer: Callable = nn.GELU, + norm_layer: Callable = LayerNorm, + embed_cls: bool = False, + pad_id: int = 0, + output_tokens: bool = False, + ): + super().__init__() + self.output_tokens = output_tokens + self.num_pos = self.context_length = context_length + self.vocab_size = vocab_size + self.width = width + self.output_dim = output_dim + self.heads = heads + self.pad_id = pad_id + + self.text_projection = nn.Parameter(torch.empty(width, output_dim)) + + if embed_cls: + self.cls_emb = nn.Parameter(torch.empty(width)) + self.num_pos += 1 + else: + self.cls_emb = None + + self.token_embedding = nn.Embedding(vocab_size, width) + self.positional_embedding = nn.Parameter(torch.empty(self.num_pos, width)) + self.transformer = Transformer( + width=width, + layers=layers, + heads=heads, + ls_init_value=ls_init_value, + act_layer=act_layer, + norm_layer=norm_layer, + ) + self.ln_final = norm_layer(width) + + self.register_buffer('attn_mask', self.build_attention_mask(), persistent=False) + + self.init_parameters() + + def init_parameters(self): + nn.init.normal_(self.token_embedding.weight, std=0.02) + nn.init.normal_(self.positional_embedding, std=0.01) + if self.cls_emb is not None: + nn.init.normal_(self.cls_emb, std=0.01) + + proj_std = (self.transformer.width ** -0.5) * ((2 * self.transformer.layers) ** -0.5) + attn_std = self.transformer.width ** -0.5 + fc_std = (2 * self.transformer.width) ** -0.5 + for block in self.transformer.resblocks: + nn.init.normal_(block.attn.in_proj_weight, std=attn_std) + nn.init.normal_(block.attn.out_proj.weight, std=proj_std) + nn.init.normal_(block.mlp.c_fc.weight, std=fc_std) + nn.init.normal_(block.mlp.c_proj.weight, std=proj_std) + + if self.text_projection is not None: + nn.init.normal_(self.text_projection, std=self.transformer.width ** -0.5) + + @torch.jit.ignore + def set_grad_checkpointing(self, enable=True): + self.transformer.grad_checkpointing = enable + + def build_attention_mask(self): + # lazily create causal attention mask, with full attention between the tokens + # pytorch uses additive attention mask; fill with -inf + mask = torch.empty(self.num_pos, self.num_pos) + mask.fill_(float("-inf")) + mask.triu_(1) # zero out the lower diagonal + return mask + + def build_cls_mask(self, text, cast_dtype: torch.dtype): + cls_mask = (text != self.pad_id).unsqueeze(1) + cls_mask = F.pad(cls_mask, (1, 0, cls_mask.shape[2], 0), value=1.0) + additive_mask = torch.empty(cls_mask.shape, dtype=cast_dtype, device=cls_mask.device) + additive_mask.fill_(0) + additive_mask.masked_fill_(~cls_mask, float("-inf")) + additive_mask = torch.repeat_interleave(additive_mask, self.heads, 0) + return additive_mask + + def _repeat(self, t, N: int): + return t.reshape(1, 1, -1).repeat(N, 1, 1) + + def forward(self, text): + cast_dtype = self.transformer.get_cast_dtype() + seq_len = text.shape[1] + + x = self.token_embedding(text).to(cast_dtype) # [batch_size, n_ctx, d_model] + attn_mask = self.attn_mask + if self.cls_emb is not None: + seq_len += 1 + x = torch.cat([x, self._repeat(self.cls_emb, x.shape[0])], dim=1) + cls_mask = self.build_cls_mask(text, cast_dtype) + attn_mask = attn_mask[None, :seq_len, :seq_len] + cls_mask[:, :seq_len, :seq_len] + + x = x + self.positional_embedding[:seq_len].to(cast_dtype) + x = x.permute(1, 0, 2) # NLD -> LND + x = self.transformer(x, attn_mask=attn_mask) + x = x.permute(1, 0, 2) # LND -> NLD + + # x.shape = [batch_size, n_ctx, transformer.width] + # take features from the eot embedding (eot_token is the highest number in each sequence) + if self.cls_emb is not None: + pooled, tokens = x[:, -1], x[:, :-1] + pooled = self.ln_final(pooled) + else: + x = self.ln_final(x) + pooled, tokens = x[torch.arange(x.shape[0]), text.argmax(dim=-1)], x + + if self.text_projection is not None: + pooled = pooled @ self.text_projection + + if self.output_tokens: + return pooled, tokens + + return pooled + + +class MultimodalTransformer(Transformer): + def __init__( + self, + width: int, + layers: int, + heads: int, + context_length: int = 77, + mlp_ratio: float = 4.0, + ls_init_value: float = None, + act_layer: Callable = nn.GELU, + norm_layer: Callable = LayerNorm, + output_dim: int = 512, + ): + + super().__init__( + width=width, + layers=layers, + heads=heads, + mlp_ratio=mlp_ratio, + ls_init_value=ls_init_value, + act_layer=act_layer, + norm_layer=norm_layer, + ) + self.context_length = context_length + self.cross_attn = nn.ModuleList([ + ResidualAttentionBlock( + width, + heads, + mlp_ratio, + ls_init_value=ls_init_value, + act_layer=act_layer, + norm_layer=norm_layer, + is_cross_attention=True, + ) + for _ in range(layers) + ]) + + self.register_buffer('attn_mask', self.build_attention_mask(), persistent=False) + + self.ln_final = norm_layer(width) + self.text_projection = nn.Parameter(torch.empty(width, output_dim)) + + def init_parameters(self): + proj_std = (self.transformer.width ** -0.5) * ((2 * self.transformer.layers) ** -0.5) + attn_std = self.transformer.width ** -0.5 + fc_std = (2 * self.transformer.width) ** -0.5 + for block in self.transformer.resblocks: + nn.init.normal_(block.attn.in_proj_weight, std=attn_std) + nn.init.normal_(block.attn.out_proj.weight, std=proj_std) + nn.init.normal_(block.mlp.c_fc.weight, std=fc_std) + nn.init.normal_(block.mlp.c_proj.weight, std=proj_std) + for block in self.transformer.cross_attn: + nn.init.normal_(block.attn.in_proj_weight, std=attn_std) + nn.init.normal_(block.attn.out_proj.weight, std=proj_std) + nn.init.normal_(block.mlp.c_fc.weight, std=fc_std) + nn.init.normal_(block.mlp.c_proj.weight, std=proj_std) + + if self.text_projection is not None: + nn.init.normal_(self.text_projection, std=self.transformer.width ** -0.5) + + def build_attention_mask(self): + # lazily create causal attention mask, with full attention between the tokens + # pytorch uses additive attention mask; fill with -inf + mask = torch.empty(self.context_length, self.context_length) + mask.fill_(float("-inf")) + mask.triu_(1) # zero out the lower diagonal + return mask + + def forward(self, image_embs, text_embs): + text_embs = text_embs.permute(1, 0, 2) # NLD -> LNDsq + image_embs = image_embs.permute(1, 0, 2) # NLD -> LND + seq_len = text_embs.shape[0] + + for resblock, cross_attn in zip(self.resblocks, self.cross_attn): + if self.grad_checkpointing and not torch.jit.is_scripting(): + # TODO: handle kwargs https://github.com/pytorch/pytorch/issues/79887#issuecomment-1161758372 + text_embs = checkpoint(resblock, text_embs, None, None, self.attn_mask[:seq_len, :seq_len]) + text_embs = checkpoint(cross_attn, text_embs, image_embs, image_embs, None) + else: + text_embs = resblock(text_embs, attn_mask=self.attn_mask[:seq_len, :seq_len]) + text_embs = cross_attn(text_embs, k_x=image_embs, v_x=image_embs) + + x = text_embs.permute(1, 0, 2) # LND -> NLD + x = self.ln_final(x) + + if self.text_projection is not None: + x = x @ self.text_projection + + return x + + @torch.jit.ignore + def set_grad_checkpointing(self, enable=True): + self.grad_checkpointing = enable diff --git a/diffsynth/extensions/ImageQualityMetric/open_clip/utils.py b/diffsynth/extensions/ImageQualityMetric/open_clip/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..51e80c5e296b24cae130ab0459baf268e0db7673 --- /dev/null +++ b/diffsynth/extensions/ImageQualityMetric/open_clip/utils.py @@ -0,0 +1,60 @@ +from itertools import repeat +import collections.abc + +from torch import nn as nn +from torchvision.ops.misc import FrozenBatchNorm2d + + +def freeze_batch_norm_2d(module, module_match={}, name=''): + """ + Converts all `BatchNorm2d` and `SyncBatchNorm` layers of provided module into `FrozenBatchNorm2d`. If `module` is + itself an instance of either `BatchNorm2d` or `SyncBatchNorm`, it is converted into `FrozenBatchNorm2d` and + returned. Otherwise, the module is walked recursively and submodules are converted in place. + + Args: + module (torch.nn.Module): Any PyTorch module. + module_match (dict): Dictionary of full module names to freeze (all if empty) + name (str): Full module name (prefix) + + Returns: + torch.nn.Module: Resulting module + + Inspired by https://github.com/pytorch/pytorch/blob/a5895f85be0f10212791145bfedc0261d364f103/torch/nn/modules/batchnorm.py#L762 + """ + res = module + is_match = True + if module_match: + is_match = name in module_match + if is_match and isinstance(module, (nn.modules.batchnorm.BatchNorm2d, nn.modules.batchnorm.SyncBatchNorm)): + res = FrozenBatchNorm2d(module.num_features) + res.num_features = module.num_features + res.affine = module.affine + if module.affine: + res.weight.data = module.weight.data.clone().detach() + res.bias.data = module.bias.data.clone().detach() + res.running_mean.data = module.running_mean.data + res.running_var.data = module.running_var.data + res.eps = module.eps + else: + for child_name, child in module.named_children(): + full_child_name = '.'.join([name, child_name]) if name else child_name + new_child = freeze_batch_norm_2d(child, module_match, full_child_name) + if new_child is not child: + res.add_module(child_name, new_child) + return res + + +# From PyTorch internals +def _ntuple(n): + def parse(x): + if isinstance(x, collections.abc.Iterable): + return x + return tuple(repeat(x, n)) + return parse + + +to_1tuple = _ntuple(1) +to_2tuple = _ntuple(2) +to_3tuple = _ntuple(3) +to_4tuple = _ntuple(4) +to_ntuple = lambda n, x: _ntuple(n)(x) diff --git a/diffsynth/extensions/ImageQualityMetric/open_clip/version.py b/diffsynth/extensions/ImageQualityMetric/open_clip/version.py new file mode 100644 index 0000000000000000000000000000000000000000..48aa744fb053599044caf0253b889b5cfe5b78e7 --- /dev/null +++ b/diffsynth/extensions/ImageQualityMetric/open_clip/version.py @@ -0,0 +1 @@ +__version__ = '2.16.0' diff --git a/diffsynth/extensions/ImageQualityMetric/pickscore.py b/diffsynth/extensions/ImageQualityMetric/pickscore.py new file mode 100644 index 0000000000000000000000000000000000000000..7370e099724997d98f1c4ad3fc5f14c861202665 --- /dev/null +++ b/diffsynth/extensions/ImageQualityMetric/pickscore.py @@ -0,0 +1,112 @@ +import torch +from PIL import Image +from transformers import AutoProcessor, AutoModel +from typing import List, Union +import os +from .config import MODEL_PATHS + +class PickScore(torch.nn.Module): + def __init__(self, device: Union[str, torch.device], path: str = MODEL_PATHS): + super().__init__() + """Initialize the Selector with a processor and model. + + Args: + device (Union[str, torch.device]): The device to load the model on. + """ + self.device = device if isinstance(device, torch.device) else torch.device(device) + processor_name_or_path = path.get("clip") + model_pretrained_name_or_path = path.get("pickscore") + self.processor = AutoProcessor.from_pretrained(processor_name_or_path) + self.model = AutoModel.from_pretrained(model_pretrained_name_or_path).eval().to(self.device) + + def _calculate_score(self, image: torch.Tensor, prompt: str, softmax: bool = False) -> float: + """Calculate the score for a single image and prompt. + + Args: + image (torch.Tensor): The processed image tensor. + prompt (str): The prompt text. + softmax (bool): Whether to apply softmax to the scores. + + Returns: + float: The score for the image. + """ + with torch.no_grad(): + # Prepare text inputs + text_inputs = self.processor( + text=prompt, + padding=True, + truncation=True, + max_length=77, + return_tensors="pt", + ).to(self.device) + + # Embed images and text + image_embs = self.model.get_image_features(pixel_values=image) + image_embs = image_embs / torch.norm(image_embs, dim=-1, keepdim=True) + text_embs = self.model.get_text_features(**text_inputs) + text_embs = text_embs / torch.norm(text_embs, dim=-1, keepdim=True) + + # Compute score + score = (text_embs @ image_embs.T)[0] + if softmax: + # Apply logit scale and softmax + score = torch.softmax(self.model.logit_scale.exp() * score, dim=-1) + + return score.cpu().item() + + @torch.no_grad() + def score(self, images: Union[str, List[str], Image.Image, List[Image.Image]], prompt: str, softmax: bool = False) -> List[float]: + """Score the images based on the prompt. + + Args: + images (Union[str, List[str], Image.Image, List[Image.Image]]): Path(s) to the image(s) or PIL image(s). + prompt (str): The prompt text. + softmax (bool): Whether to apply softmax to the scores. + + Returns: + List[float]: List of scores for the images. + """ + try: + if isinstance(images, (str, Image.Image)): + # Single image + if isinstance(images, str): + pil_image = Image.open(images) + else: + pil_image = images + + # Prepare image inputs + image_inputs = self.processor( + images=pil_image, + padding=True, + truncation=True, + max_length=77, + return_tensors="pt", + ).to(self.device) + + return [self._calculate_score(image_inputs["pixel_values"], prompt, softmax)] + elif isinstance(images, list): + # Multiple images + scores = [] + for one_image in images: + if isinstance(one_image, str): + pil_image = Image.open(one_image) + elif isinstance(one_image, Image.Image): + pil_image = one_image + else: + raise TypeError("The type of parameter images is illegal.") + + # Prepare image inputs + image_inputs = self.processor( + images=pil_image, + padding=True, + truncation=True, + max_length=77, + return_tensors="pt", + ).to(self.device) + + scores.append(self._calculate_score(image_inputs["pixel_values"], prompt, softmax)) + return scores + else: + raise TypeError("The type of parameter images is illegal.") + except Exception as e: + raise RuntimeError(f"Error in scoring images: {e}") diff --git a/diffsynth/extensions/ImageQualityMetric/trainer/__init__.py b/diffsynth/extensions/ImageQualityMetric/trainer/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..cf4f59d6c0977e578ab67ec92c916c7e38842715 --- /dev/null +++ b/diffsynth/extensions/ImageQualityMetric/trainer/__init__.py @@ -0,0 +1 @@ +from .models import * \ No newline at end of file diff --git a/diffsynth/extensions/RIFE/__init__.py b/diffsynth/extensions/RIFE/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e76c391f0b085b3628592990a868ac09f37cced7 --- /dev/null +++ b/diffsynth/extensions/RIFE/__init__.py @@ -0,0 +1,242 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +import numpy as np +from PIL import Image + + +def warp(tenInput, tenFlow, device): + backwarp_tenGrid = {} + k = (str(tenFlow.device), str(tenFlow.size())) + if k not in backwarp_tenGrid: + tenHorizontal = torch.linspace(-1.0, 1.0, tenFlow.shape[3], device=device).view( + 1, 1, 1, tenFlow.shape[3]).expand(tenFlow.shape[0], -1, tenFlow.shape[2], -1) + tenVertical = torch.linspace(-1.0, 1.0, tenFlow.shape[2], device=device).view( + 1, 1, tenFlow.shape[2], 1).expand(tenFlow.shape[0], -1, -1, tenFlow.shape[3]) + backwarp_tenGrid[k] = torch.cat( + [tenHorizontal, tenVertical], 1).to(device) + + tenFlow = torch.cat([tenFlow[:, 0:1, :, :] / ((tenInput.shape[3] - 1.0) / 2.0), + tenFlow[:, 1:2, :, :] / ((tenInput.shape[2] - 1.0) / 2.0)], 1) + + g = (backwarp_tenGrid[k] + tenFlow).permute(0, 2, 3, 1) + return torch.nn.functional.grid_sample(input=tenInput, grid=g, mode='bilinear', padding_mode='border', align_corners=True) + + +def conv(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1): + return nn.Sequential( + nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride, + padding=padding, dilation=dilation, bias=True), + nn.PReLU(out_planes) + ) + + +class IFBlock(nn.Module): + def __init__(self, in_planes, c=64): + super(IFBlock, self).__init__() + self.conv0 = nn.Sequential(conv(in_planes, c//2, 3, 2, 1), conv(c//2, c, 3, 2, 1),) + self.convblock0 = nn.Sequential(conv(c, c), conv(c, c)) + self.convblock1 = nn.Sequential(conv(c, c), conv(c, c)) + self.convblock2 = nn.Sequential(conv(c, c), conv(c, c)) + self.convblock3 = nn.Sequential(conv(c, c), conv(c, c)) + self.conv1 = nn.Sequential(nn.ConvTranspose2d(c, c//2, 4, 2, 1), nn.PReLU(c//2), nn.ConvTranspose2d(c//2, 4, 4, 2, 1)) + self.conv2 = nn.Sequential(nn.ConvTranspose2d(c, c//2, 4, 2, 1), nn.PReLU(c//2), nn.ConvTranspose2d(c//2, 1, 4, 2, 1)) + + def forward(self, x, flow, scale=1): + x = F.interpolate(x, scale_factor= 1. / scale, mode="bilinear", align_corners=False, recompute_scale_factor=False) + flow = F.interpolate(flow, scale_factor= 1. / scale, mode="bilinear", align_corners=False, recompute_scale_factor=False) * 1. / scale + feat = self.conv0(torch.cat((x, flow), 1)) + feat = self.convblock0(feat) + feat + feat = self.convblock1(feat) + feat + feat = self.convblock2(feat) + feat + feat = self.convblock3(feat) + feat + flow = self.conv1(feat) + mask = self.conv2(feat) + flow = F.interpolate(flow, scale_factor=scale, mode="bilinear", align_corners=False, recompute_scale_factor=False) * scale + mask = F.interpolate(mask, scale_factor=scale, mode="bilinear", align_corners=False, recompute_scale_factor=False) + return flow, mask + + +class IFNet(nn.Module): + def __init__(self, **kwargs): + super(IFNet, self).__init__() + self.block0 = IFBlock(7+4, c=90) + self.block1 = IFBlock(7+4, c=90) + self.block2 = IFBlock(7+4, c=90) + self.block_tea = IFBlock(10+4, c=90) + + def forward(self, x, scale_list=[4, 2, 1], training=False): + if training == False: + channel = x.shape[1] // 2 + img0 = x[:, :channel] + img1 = x[:, channel:] + flow_list = [] + merged = [] + mask_list = [] + warped_img0 = img0 + warped_img1 = img1 + flow = (x[:, :4]).detach() * 0 + mask = (x[:, :1]).detach() * 0 + block = [self.block0, self.block1, self.block2] + for i in range(3): + f0, m0 = block[i](torch.cat((warped_img0[:, :3], warped_img1[:, :3], mask), 1), flow, scale=scale_list[i]) + f1, m1 = block[i](torch.cat((warped_img1[:, :3], warped_img0[:, :3], -mask), 1), torch.cat((flow[:, 2:4], flow[:, :2]), 1), scale=scale_list[i]) + flow = flow + (f0 + torch.cat((f1[:, 2:4], f1[:, :2]), 1)) / 2 + mask = mask + (m0 + (-m1)) / 2 + mask_list.append(mask) + flow_list.append(flow) + warped_img0 = warp(img0, flow[:, :2], device=x.device) + warped_img1 = warp(img1, flow[:, 2:4], device=x.device) + merged.append((warped_img0, warped_img1)) + ''' + c0 = self.contextnet(img0, flow[:, :2]) + c1 = self.contextnet(img1, flow[:, 2:4]) + tmp = self.unet(img0, img1, warped_img0, warped_img1, mask, flow, c0, c1) + res = tmp[:, 1:4] * 2 - 1 + ''' + for i in range(3): + mask_list[i] = torch.sigmoid(mask_list[i]) + merged[i] = merged[i][0] * mask_list[i] + merged[i][1] * (1 - mask_list[i]) + return flow_list, mask_list[2], merged + + @staticmethod + def state_dict_converter(): + return IFNetStateDictConverter() + + +class IFNetStateDictConverter: + def __init__(self): + pass + + def from_diffusers(self, state_dict): + state_dict_ = {k.replace("module.", ""): v for k, v in state_dict.items()} + return state_dict_ + + def from_civitai(self, state_dict): + return self.from_diffusers(state_dict), {"upcast_to_float32": True} + + +class RIFEInterpolater: + def __init__(self, model, device="cuda"): + self.model = model + self.device = device + # IFNet only does not support float16 + self.torch_dtype = torch.float32 + + @staticmethod + def from_model_manager(model_manager): + return RIFEInterpolater(model_manager.fetch_model("rife"), device=model_manager.device) + + def process_image(self, image): + width, height = image.size + if width % 32 != 0 or height % 32 != 0: + width = (width + 31) // 32 + height = (height + 31) // 32 + image = image.resize((width, height)) + image = torch.Tensor(np.array(image, dtype=np.float32)[:, :, [2,1,0]] / 255).permute(2, 0, 1) + return image + + def process_images(self, images): + images = [self.process_image(image) for image in images] + images = torch.stack(images) + return images + + def decode_images(self, images): + images = (images[:, [2,1,0]].permute(0, 2, 3, 1) * 255).clip(0, 255).numpy().astype(np.uint8) + images = [Image.fromarray(image) for image in images] + return images + + def add_interpolated_images(self, images, interpolated_images): + output_images = [] + for image, interpolated_image in zip(images, interpolated_images): + output_images.append(image) + output_images.append(interpolated_image) + output_images.append(images[-1]) + return output_images + + + @torch.no_grad() + def interpolate_(self, images, scale=1.0): + input_tensor = self.process_images(images) + input_tensor = torch.cat((input_tensor[:-1], input_tensor[1:]), dim=1) + input_tensor = input_tensor.to(device=self.device, dtype=self.torch_dtype) + flow, mask, merged = self.model(input_tensor, [4/scale, 2/scale, 1/scale]) + output_images = self.decode_images(merged[2].cpu()) + if output_images[0].size != images[0].size: + output_images = [image.resize(images[0].size) for image in output_images] + return output_images + + + @torch.no_grad() + def interpolate(self, images, scale=1.0, batch_size=4, num_iter=1, progress_bar=lambda x:x): + # Preprocess + processed_images = self.process_images(images) + + for iter in range(num_iter): + # Input + input_tensor = torch.cat((processed_images[:-1], processed_images[1:]), dim=1) + + # Interpolate + output_tensor = [] + for batch_id in progress_bar(range(0, input_tensor.shape[0], batch_size)): + batch_id_ = min(batch_id + batch_size, input_tensor.shape[0]) + batch_input_tensor = input_tensor[batch_id: batch_id_] + batch_input_tensor = batch_input_tensor.to(device=self.device, dtype=self.torch_dtype) + flow, mask, merged = self.model(batch_input_tensor, [4/scale, 2/scale, 1/scale]) + output_tensor.append(merged[2].cpu()) + + # Output + output_tensor = torch.concat(output_tensor, dim=0).clip(0, 1) + processed_images = self.add_interpolated_images(processed_images, output_tensor) + processed_images = torch.stack(processed_images) + + # To images + output_images = self.decode_images(processed_images) + if output_images[0].size != images[0].size: + output_images = [image.resize(images[0].size) for image in output_images] + return output_images + + +class RIFESmoother(RIFEInterpolater): + def __init__(self, model, device="cuda"): + super(RIFESmoother, self).__init__(model, device=device) + + @staticmethod + def from_model_manager(model_manager): + return RIFEInterpolater(model_manager.fetch_model("rife"), device=model_manager.device) + + def process_tensors(self, input_tensor, scale=1.0, batch_size=4): + output_tensor = [] + for batch_id in range(0, input_tensor.shape[0], batch_size): + batch_id_ = min(batch_id + batch_size, input_tensor.shape[0]) + batch_input_tensor = input_tensor[batch_id: batch_id_] + batch_input_tensor = batch_input_tensor.to(device=self.device, dtype=self.torch_dtype) + flow, mask, merged = self.model(batch_input_tensor, [4/scale, 2/scale, 1/scale]) + output_tensor.append(merged[2].cpu()) + output_tensor = torch.concat(output_tensor, dim=0) + return output_tensor + + @torch.no_grad() + def __call__(self, rendered_frames, scale=1.0, batch_size=4, num_iter=1, **kwargs): + # Preprocess + processed_images = self.process_images(rendered_frames) + + for iter in range(num_iter): + # Input + input_tensor = torch.cat((processed_images[:-2], processed_images[2:]), dim=1) + + # Interpolate + output_tensor = self.process_tensors(input_tensor, scale=scale, batch_size=batch_size) + + # Blend + input_tensor = torch.cat((processed_images[1:-1], output_tensor), dim=1) + output_tensor = self.process_tensors(input_tensor, scale=scale, batch_size=batch_size) + + # Add to frames + processed_images[1:-1] = output_tensor + + # To images + output_images = self.decode_images(processed_images) + if output_images[0].size != rendered_frames[0].size: + output_images = [image.resize(rendered_frames[0].size) for image in output_images] + return output_images diff --git a/diffsynth/extensions/__init__.py b/diffsynth/extensions/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/diffsynth/lora/__init__.py b/diffsynth/lora/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..33bd89c21b74ec0bba8b7b69468a3901e45360fd --- /dev/null +++ b/diffsynth/lora/__init__.py @@ -0,0 +1,45 @@ +import torch + + + +class GeneralLoRALoader: + def __init__(self, device="cpu", torch_dtype=torch.float32): + self.device = device + self.torch_dtype = torch_dtype + + + def get_name_dict(self, lora_state_dict): + lora_name_dict = {} + for key in lora_state_dict: + if ".lora_B." not in key: + continue + keys = key.split(".") + if len(keys) > keys.index("lora_B") + 2: + keys.pop(keys.index("lora_B") + 1) + keys.pop(keys.index("lora_B")) + if keys[0] == "diffusion_model": + keys.pop(0) + keys.pop(-1) + target_name = ".".join(keys) + lora_name_dict[target_name] = (key, key.replace(".lora_B.", ".lora_A.")) + return lora_name_dict + + + def load(self, model: torch.nn.Module, state_dict_lora, alpha=1.0): + updated_num = 0 + lora_name_dict = self.get_name_dict(state_dict_lora) + for name, module in model.named_modules(): + if name in lora_name_dict: + weight_up = state_dict_lora[lora_name_dict[name][0]].to(device=self.device, dtype=self.torch_dtype) + weight_down = state_dict_lora[lora_name_dict[name][1]].to(device=self.device, dtype=self.torch_dtype) + if len(weight_up.shape) == 4: + weight_up = weight_up.squeeze(3).squeeze(2) + weight_down = weight_down.squeeze(3).squeeze(2) + weight_lora = alpha * torch.mm(weight_up, weight_down).unsqueeze(2).unsqueeze(3) + else: + weight_lora = alpha * torch.mm(weight_up, weight_down) + state_dict = module.state_dict() + state_dict["weight"] = state_dict["weight"].to(device=self.device, dtype=self.torch_dtype) + weight_lora + module.load_state_dict(state_dict) + updated_num += 1 + print(f"{updated_num} tensors are updated by LoRA.") diff --git a/diffsynth/lora/flux_lora.py b/diffsynth/lora/flux_lora.py new file mode 100644 index 0000000000000000000000000000000000000000..cb53b7342221dd749c9a704a09f6669f08f684b7 --- /dev/null +++ b/diffsynth/lora/flux_lora.py @@ -0,0 +1,324 @@ +import torch, math +from . import GeneralLoRALoader +from ..utils import ModelConfig +from ..models.utils import load_state_dict +from typing import Union + + +class FluxLoRALoader(GeneralLoRALoader): + def __init__(self, device="cpu", torch_dtype=torch.float32): + super().__init__(device=device, torch_dtype=torch_dtype) + + self.diffusers_rename_dict = { + "transformer.single_transformer_blocks.blockid.attn.to_k.lora_A.weight":"single_blocks.blockid.a_to_k.lora_A.default.weight", + "transformer.single_transformer_blocks.blockid.attn.to_k.lora_B.weight":"single_blocks.blockid.a_to_k.lora_B.default.weight", + "transformer.single_transformer_blocks.blockid.attn.to_q.lora_A.weight":"single_blocks.blockid.a_to_q.lora_A.default.weight", + "transformer.single_transformer_blocks.blockid.attn.to_q.lora_B.weight":"single_blocks.blockid.a_to_q.lora_B.default.weight", + "transformer.single_transformer_blocks.blockid.attn.to_v.lora_A.weight":"single_blocks.blockid.a_to_v.lora_A.default.weight", + "transformer.single_transformer_blocks.blockid.attn.to_v.lora_B.weight":"single_blocks.blockid.a_to_v.lora_B.default.weight", + "transformer.single_transformer_blocks.blockid.norm.linear.lora_A.weight":"single_blocks.blockid.norm.linear.lora_A.default.weight", + "transformer.single_transformer_blocks.blockid.norm.linear.lora_B.weight":"single_blocks.blockid.norm.linear.lora_B.default.weight", + "transformer.single_transformer_blocks.blockid.proj_mlp.lora_A.weight":"single_blocks.blockid.proj_in_besides_attn.lora_A.default.weight", + "transformer.single_transformer_blocks.blockid.proj_mlp.lora_B.weight":"single_blocks.blockid.proj_in_besides_attn.lora_B.default.weight", + "transformer.single_transformer_blocks.blockid.proj_out.lora_A.weight":"single_blocks.blockid.proj_out.lora_A.default.weight", + "transformer.single_transformer_blocks.blockid.proj_out.lora_B.weight":"single_blocks.blockid.proj_out.lora_B.default.weight", + "transformer.transformer_blocks.blockid.attn.add_k_proj.lora_A.weight":"blocks.blockid.attn.b_to_k.lora_A.default.weight", + "transformer.transformer_blocks.blockid.attn.add_k_proj.lora_B.weight":"blocks.blockid.attn.b_to_k.lora_B.default.weight", + "transformer.transformer_blocks.blockid.attn.add_q_proj.lora_A.weight":"blocks.blockid.attn.b_to_q.lora_A.default.weight", + "transformer.transformer_blocks.blockid.attn.add_q_proj.lora_B.weight":"blocks.blockid.attn.b_to_q.lora_B.default.weight", + "transformer.transformer_blocks.blockid.attn.add_v_proj.lora_A.weight":"blocks.blockid.attn.b_to_v.lora_A.default.weight", + "transformer.transformer_blocks.blockid.attn.add_v_proj.lora_B.weight":"blocks.blockid.attn.b_to_v.lora_B.default.weight", + "transformer.transformer_blocks.blockid.attn.to_add_out.lora_A.weight":"blocks.blockid.attn.b_to_out.lora_A.default.weight", + "transformer.transformer_blocks.blockid.attn.to_add_out.lora_B.weight":"blocks.blockid.attn.b_to_out.lora_B.default.weight", + "transformer.transformer_blocks.blockid.attn.to_k.lora_A.weight":"blocks.blockid.attn.a_to_k.lora_A.default.weight", + "transformer.transformer_blocks.blockid.attn.to_k.lora_B.weight":"blocks.blockid.attn.a_to_k.lora_B.default.weight", + "transformer.transformer_blocks.blockid.attn.to_out.0.lora_A.weight":"blocks.blockid.attn.a_to_out.lora_A.default.weight", + "transformer.transformer_blocks.blockid.attn.to_out.0.lora_B.weight":"blocks.blockid.attn.a_to_out.lora_B.default.weight", + "transformer.transformer_blocks.blockid.attn.to_q.lora_A.weight":"blocks.blockid.attn.a_to_q.lora_A.default.weight", + "transformer.transformer_blocks.blockid.attn.to_q.lora_B.weight":"blocks.blockid.attn.a_to_q.lora_B.default.weight", + "transformer.transformer_blocks.blockid.attn.to_v.lora_A.weight":"blocks.blockid.attn.a_to_v.lora_A.default.weight", + "transformer.transformer_blocks.blockid.attn.to_v.lora_B.weight":"blocks.blockid.attn.a_to_v.lora_B.default.weight", + "transformer.transformer_blocks.blockid.ff.net.0.proj.lora_A.weight":"blocks.blockid.ff_a.0.lora_A.default.weight", + "transformer.transformer_blocks.blockid.ff.net.0.proj.lora_B.weight":"blocks.blockid.ff_a.0.lora_B.default.weight", + "transformer.transformer_blocks.blockid.ff.net.2.lora_A.weight":"blocks.blockid.ff_a.2.lora_A.default.weight", + "transformer.transformer_blocks.blockid.ff.net.2.lora_B.weight":"blocks.blockid.ff_a.2.lora_B.default.weight", + "transformer.transformer_blocks.blockid.ff_context.net.0.proj.lora_A.weight":"blocks.blockid.ff_b.0.lora_A.default.weight", + "transformer.transformer_blocks.blockid.ff_context.net.0.proj.lora_B.weight":"blocks.blockid.ff_b.0.lora_B.default.weight", + "transformer.transformer_blocks.blockid.ff_context.net.2.lora_A.weight":"blocks.blockid.ff_b.2.lora_A.default.weight", + "transformer.transformer_blocks.blockid.ff_context.net.2.lora_B.weight":"blocks.blockid.ff_b.2.lora_B.default.weight", + "transformer.transformer_blocks.blockid.norm1.linear.lora_A.weight":"blocks.blockid.norm1_a.linear.lora_A.default.weight", + "transformer.transformer_blocks.blockid.norm1.linear.lora_B.weight":"blocks.blockid.norm1_a.linear.lora_B.default.weight", + "transformer.transformer_blocks.blockid.norm1_context.linear.lora_A.weight":"blocks.blockid.norm1_b.linear.lora_A.default.weight", + "transformer.transformer_blocks.blockid.norm1_context.linear.lora_B.weight":"blocks.blockid.norm1_b.linear.lora_B.default.weight", + } + + self.civitai_rename_dict = { + "lora_unet_double_blocks_blockid_img_mod_lin.lora_down.weight": "blocks.blockid.norm1_a.linear.lora_A.default.weight", + "lora_unet_double_blocks_blockid_img_mod_lin.lora_up.weight": "blocks.blockid.norm1_a.linear.lora_B.default.weight", + "lora_unet_double_blocks_blockid_txt_mod_lin.lora_down.weight": "blocks.blockid.norm1_b.linear.lora_A.default.weight", + "lora_unet_double_blocks_blockid_txt_mod_lin.lora_up.weight": "blocks.blockid.norm1_b.linear.lora_B.default.weight", + "lora_unet_double_blocks_blockid_img_attn_qkv.lora_down.weight": "blocks.blockid.attn.a_to_qkv.lora_A.default.weight", + "lora_unet_double_blocks_blockid_img_attn_qkv.lora_up.weight": "blocks.blockid.attn.a_to_qkv.lora_B.default.weight", + "lora_unet_double_blocks_blockid_txt_attn_qkv.lora_down.weight": "blocks.blockid.attn.b_to_qkv.lora_A.default.weight", + "lora_unet_double_blocks_blockid_txt_attn_qkv.lora_up.weight": "blocks.blockid.attn.b_to_qkv.lora_B.default.weight", + "lora_unet_double_blocks_blockid_img_attn_proj.lora_down.weight": "blocks.blockid.attn.a_to_out.lora_A.default.weight", + "lora_unet_double_blocks_blockid_img_attn_proj.lora_up.weight": "blocks.blockid.attn.a_to_out.lora_B.default.weight", + "lora_unet_double_blocks_blockid_txt_attn_proj.lora_down.weight": "blocks.blockid.attn.b_to_out.lora_A.default.weight", + "lora_unet_double_blocks_blockid_txt_attn_proj.lora_up.weight": "blocks.blockid.attn.b_to_out.lora_B.default.weight", + "lora_unet_double_blocks_blockid_img_mlp_0.lora_down.weight": "blocks.blockid.ff_a.0.lora_A.default.weight", + "lora_unet_double_blocks_blockid_img_mlp_0.lora_up.weight": "blocks.blockid.ff_a.0.lora_B.default.weight", + "lora_unet_double_blocks_blockid_img_mlp_2.lora_down.weight": "blocks.blockid.ff_a.2.lora_A.default.weight", + "lora_unet_double_blocks_blockid_img_mlp_2.lora_up.weight": "blocks.blockid.ff_a.2.lora_B.default.weight", + "lora_unet_double_blocks_blockid_txt_mlp_0.lora_down.weight": "blocks.blockid.ff_b.0.lora_A.default.weight", + "lora_unet_double_blocks_blockid_txt_mlp_0.lora_up.weight": "blocks.blockid.ff_b.0.lora_B.default.weight", + "lora_unet_double_blocks_blockid_txt_mlp_2.lora_down.weight": "blocks.blockid.ff_b.2.lora_A.default.weight", + "lora_unet_double_blocks_blockid_txt_mlp_2.lora_up.weight": "blocks.blockid.ff_b.2.lora_B.default.weight", + "lora_unet_single_blocks_blockid_modulation_lin.lora_down.weight": "single_blocks.blockid.norm.linear.lora_A.default.weight", + "lora_unet_single_blocks_blockid_modulation_lin.lora_up.weight": "single_blocks.blockid.norm.linear.lora_B.default.weight", + "lora_unet_single_blocks_blockid_linear1.lora_down.weight": "single_blocks.blockid.to_qkv_mlp.lora_A.default.weight", + "lora_unet_single_blocks_blockid_linear1.lora_up.weight": "single_blocks.blockid.to_qkv_mlp.lora_B.default.weight", + "lora_unet_single_blocks_blockid_linear2.lora_down.weight": "single_blocks.blockid.proj_out.lora_A.default.weight", + "lora_unet_single_blocks_blockid_linear2.lora_up.weight": "single_blocks.blockid.proj_out.lora_B.default.weight", + } + + def load(self, model: torch.nn.Module, state_dict_lora, alpha=1.0): + super().load(model, state_dict_lora, alpha) + + + def convert_state_dict(self,state_dict): + + def guess_block_id(name,model_resource): + if model_resource == 'civitai': + names = name.split("_") + for i in names: + if i.isdigit(): + return i, name.replace(f"_{i}_", "_blockid_") + if model_resource == 'diffusers': + names = name.split(".") + for i in names: + if i.isdigit(): + return i, name.replace(f"transformer_blocks.{i}.", "transformer_blocks.blockid.") + return None, None + + def guess_resource(state_dict): + for k in state_dict: + if "lora_unet_" in k: + return 'civitai' + elif k.startswith("transformer."): + return 'diffusers' + else: + None + + model_resource = guess_resource(state_dict) + if model_resource is None: + return state_dict + + rename_dict = self.diffusers_rename_dict if model_resource == 'diffusers' else self.civitai_rename_dict + def guess_alpha(state_dict): + for name, param in state_dict.items(): + if ".alpha" in name: + for suffix in [".lora_down.weight", ".lora_A.weight"]: + name_ = name.replace(".alpha", suffix) + if name_ in state_dict: + lora_alpha = param.item() / state_dict[name_].shape[0] + lora_alpha = math.sqrt(lora_alpha) + return lora_alpha + + return 1 + + alpha = guess_alpha(state_dict) + + state_dict_ = {} + for name, param in state_dict.items(): + block_id, source_name = guess_block_id(name,model_resource) + if alpha != 1: + param *= alpha + if source_name in rename_dict: + target_name = rename_dict[source_name] + target_name = target_name.replace(".blockid.", f".{block_id}.") + state_dict_[target_name] = param + else: + state_dict_[name] = param + + if model_resource == 'diffusers': + for name in list(state_dict_.keys()): + if "single_blocks." in name and ".a_to_q." in name: + mlp = state_dict_.get(name.replace(".a_to_q.", ".proj_in_besides_attn."), None) + if mlp is None: + dim = 4 + if 'lora_A' in name: + dim = 1 + mlp = torch.zeros(dim * state_dict_[name].shape[0], + *state_dict_[name].shape[1:], + dtype=state_dict_[name].dtype) + else: + state_dict_.pop(name.replace(".a_to_q.", ".proj_in_besides_attn.")) + if 'lora_A' in name: + param = torch.concat([ + state_dict_.pop(name), + state_dict_.pop(name.replace(".a_to_q.", ".a_to_k.")), + state_dict_.pop(name.replace(".a_to_q.", ".a_to_v.")), + mlp, + ], dim=0) + elif 'lora_B' in name: + d, r = state_dict_[name].shape + param = torch.zeros((3*d+mlp.shape[0], 3*r+mlp.shape[1]), dtype=state_dict_[name].dtype, device=state_dict_[name].device) + param[:d, :r] = state_dict_.pop(name) + param[d:2*d, r:2*r] = state_dict_.pop(name.replace(".a_to_q.", ".a_to_k.")) + param[2*d:3*d, 2*r:3*r] = state_dict_.pop(name.replace(".a_to_q.", ".a_to_v.")) + param[3*d:, 3*r:] = mlp + else: + param = torch.concat([ + state_dict_.pop(name), + state_dict_.pop(name.replace(".a_to_q.", ".a_to_k.")), + state_dict_.pop(name.replace(".a_to_q.", ".a_to_v.")), + mlp, + ], dim=0) + name_ = name.replace(".a_to_q.", ".to_qkv_mlp.") + state_dict_[name_] = param + for name in list(state_dict_.keys()): + for component in ["a", "b"]: + if f".{component}_to_q." in name: + name_ = name.replace(f".{component}_to_q.", f".{component}_to_qkv.") + concat_dim = 0 + if 'lora_A' in name: + param = torch.concat([ + state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_q.")], + state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_k.")], + state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_v.")], + ], dim=0) + elif 'lora_B' in name: + origin = state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_q.")] + d, r = origin.shape + # print(d, r) + param = torch.zeros((3*d, 3*r), dtype=origin.dtype, device=origin.device) + param[:d, :r] = state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_q.")] + param[d:2*d, r:2*r] = state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_k.")] + param[2*d:3*d, 2*r:3*r] = state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_v.")] + else: + param = torch.concat([ + state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_q.")], + state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_k.")], + state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_v.")], + ], dim=0) + state_dict_[name_] = param + state_dict_.pop(name.replace(f".{component}_to_q.", f".{component}_to_q.")) + state_dict_.pop(name.replace(f".{component}_to_q.", f".{component}_to_k.")) + state_dict_.pop(name.replace(f".{component}_to_q.", f".{component}_to_v.")) + return state_dict_ + + +class LoraMerger(torch.nn.Module): + def __init__(self, dim): + super().__init__() + self.weight_base = torch.nn.Parameter(torch.randn((dim,))) + self.weight_lora = torch.nn.Parameter(torch.randn((dim,))) + self.weight_cross = torch.nn.Parameter(torch.randn((dim,))) + self.weight_out = torch.nn.Parameter(torch.ones((dim,))) + self.bias = torch.nn.Parameter(torch.randn((dim,))) + self.activation = torch.nn.Sigmoid() + self.norm_base = torch.nn.LayerNorm(dim, eps=1e-5) + self.norm_lora = torch.nn.LayerNorm(dim, eps=1e-5) + + def forward(self, base_output, lora_outputs): + norm_base_output = self.norm_base(base_output) + norm_lora_outputs = self.norm_lora(lora_outputs) + gate = self.activation( + norm_base_output * self.weight_base \ + + norm_lora_outputs * self.weight_lora \ + + norm_base_output * norm_lora_outputs * self.weight_cross + self.bias + ) + output = base_output + (self.weight_out * gate * lora_outputs).sum(dim=0) + return output + + +class FluxLoraPatcher(torch.nn.Module): + def __init__(self, lora_patterns=None): + super().__init__() + if lora_patterns is None: + lora_patterns = self.default_lora_patterns() + model_dict = {} + for lora_pattern in lora_patterns: + name, dim = lora_pattern["name"], lora_pattern["dim"] + model_dict[name.replace(".", "___")] = LoraMerger(dim) + self.model_dict = torch.nn.ModuleDict(model_dict) + + def default_lora_patterns(self): + lora_patterns = [] + lora_dict = { + "attn.a_to_qkv": 9216, "attn.a_to_out": 3072, "ff_a.0": 12288, "ff_a.2": 3072, "norm1_a.linear": 18432, + "attn.b_to_qkv": 9216, "attn.b_to_out": 3072, "ff_b.0": 12288, "ff_b.2": 3072, "norm1_b.linear": 18432, + } + for i in range(19): + for suffix in lora_dict: + lora_patterns.append({ + "name": f"blocks.{i}.{suffix}", + "dim": lora_dict[suffix] + }) + lora_dict = {"to_qkv_mlp": 21504, "proj_out": 3072, "norm.linear": 9216} + for i in range(38): + for suffix in lora_dict: + lora_patterns.append({ + "name": f"single_blocks.{i}.{suffix}", + "dim": lora_dict[suffix] + }) + return lora_patterns + + def forward(self, base_output, lora_outputs, name): + return self.model_dict[name.replace(".", "___")](base_output, lora_outputs) + + @staticmethod + def state_dict_converter(): + return FluxLoraPatcherStateDictConverter() + + +class FluxLoraPatcherStateDictConverter: + def __init__(self): + pass + + def from_civitai(self, state_dict): + return state_dict + + +class FluxLoRAFuser: + def __init__(self, device="cuda", torch_dtype=torch.bfloat16): + self.device = device + self.torch_dtype = torch_dtype + + def Matrix_Decomposition_lowrank(self, A, k): + U, S, V = torch.svd_lowrank(A.float(), q=k) + S_k = torch.diag(S[:k]) + U_hat = U @ S_k + return U_hat, V.t() + + def LoRA_State_Dicts_Decomposition(self, lora_state_dicts=[], q=4): + lora_1 = lora_state_dicts[0] + state_dict_ = {} + for k,v in lora_1.items(): + if 'lora_A.' in k: + lora_B_name = k.replace('lora_A.', 'lora_B.') + lora_B = lora_1[lora_B_name] + weight = torch.mm(lora_B, v) + for lora_dict in lora_state_dicts[1:]: + lora_A_ = lora_dict[k] + lora_B_ = lora_dict[lora_B_name] + weight_ = torch.mm(lora_B_, lora_A_) + weight += weight_ + new_B, new_A = self.Matrix_Decomposition_lowrank(weight, q) + state_dict_[lora_B_name] = new_B.to(dtype=torch.bfloat16) + state_dict_[k] = new_A.to(dtype=torch.bfloat16) + return state_dict_ + + def __call__(self, lora_configs: list[Union[ModelConfig, str]]): + loras = [] + loader = FluxLoRALoader(torch_dtype=self.torch_dtype, device=self.device) + for lora_config in lora_configs: + if isinstance(lora_config, str): + lora = load_state_dict(lora_config, torch_dtype=self.torch_dtype, device=self.device) + else: + lora_config.download_if_necessary() + lora = load_state_dict(lora_config.path, torch_dtype=self.torch_dtype, device=self.device) + lora = loader.convert_state_dict(lora) + loras.append(lora) + lora = self.LoRA_State_Dicts_Decomposition(loras) + return lora diff --git a/diffsynth/pipelines/__init__.py b/diffsynth/pipelines/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e2ad5516a01787b9e2ce5ba54228466dd7b57d8e --- /dev/null +++ b/diffsynth/pipelines/__init__.py @@ -0,0 +1,15 @@ +from .sd_image import SDImagePipeline +from .sd_video import SDVideoPipeline +from .sdxl_image import SDXLImagePipeline +from .sdxl_video import SDXLVideoPipeline +from .sd3_image import SD3ImagePipeline +from .hunyuan_image import HunyuanDiTImagePipeline +from .svd_video import SVDVideoPipeline +from .flux_image import FluxImagePipeline +from .cog_video import CogVideoPipeline +from .omnigen_image import OmnigenImagePipeline +from .pipeline_runner import SDVideoPipelineRunner +from .hunyuan_video import HunyuanVideoPipeline +from .step_video import StepVideoPipeline +from .wan_video import WanVideoPipeline +KolorsImagePipeline = SDXLImagePipeline diff --git a/diffsynth/pipelines/base.py b/diffsynth/pipelines/base.py new file mode 100644 index 0000000000000000000000000000000000000000..2a4f01cff55dc0fcca02dc5234227bd65efc7434 --- /dev/null +++ b/diffsynth/pipelines/base.py @@ -0,0 +1,127 @@ +import torch +import numpy as np +from PIL import Image +from torchvision.transforms import GaussianBlur + + + +class BasePipeline(torch.nn.Module): + + def __init__(self, device="cuda", torch_dtype=torch.float16, height_division_factor=64, width_division_factor=64): + super().__init__() + self.device = device + self.torch_dtype = torch_dtype + self.height_division_factor = height_division_factor + self.width_division_factor = width_division_factor + self.cpu_offload = False + self.model_names = [] + + + def check_resize_height_width(self, height, width): + if height % self.height_division_factor != 0: + height = (height + self.height_division_factor - 1) // self.height_division_factor * self.height_division_factor + print(f"The height cannot be evenly divided by {self.height_division_factor}. We round it up to {height}.") + if width % self.width_division_factor != 0: + width = (width + self.width_division_factor - 1) // self.width_division_factor * self.width_division_factor + print(f"The width cannot be evenly divided by {self.width_division_factor}. We round it up to {width}.") + return height, width + + + def preprocess_image(self, image): + image = torch.Tensor(np.array(image, dtype=np.float32) * (2 / 255) - 1).permute(2, 0, 1).unsqueeze(0) + return image + + + def preprocess_images(self, images): + return [self.preprocess_image(image) for image in images] + + + def vae_output_to_image(self, vae_output): + image = vae_output[0].cpu().float().permute(1, 2, 0).numpy() + image = Image.fromarray(((image / 2 + 0.5).clip(0, 1) * 255).astype("uint8")) + return image + + + def vae_output_to_video(self, vae_output): + video = vae_output.cpu().permute(1, 2, 0).numpy() + video = [Image.fromarray(((image / 2 + 0.5).clip(0, 1) * 255).astype("uint8")) for image in video] + return video + + + def merge_latents(self, value, latents, masks, scales, blur_kernel_size=33, blur_sigma=10.0): + if len(latents) > 0: + blur = GaussianBlur(kernel_size=blur_kernel_size, sigma=blur_sigma) + height, width = value.shape[-2:] + weight = torch.ones_like(value) + for latent, mask, scale in zip(latents, masks, scales): + mask = self.preprocess_image(mask.resize((width, height))).mean(dim=1, keepdim=True) > 0 + mask = mask.repeat(1, latent.shape[1], 1, 1).to(dtype=latent.dtype, device=latent.device) + mask = blur(mask) + value += latent * mask * scale + weight += mask * scale + value /= weight + return value + + + def control_noise_via_local_prompts(self, prompt_emb_global, prompt_emb_locals, masks, mask_scales, inference_callback, special_kwargs=None, special_local_kwargs_list=None): + if special_kwargs is None: + noise_pred_global = inference_callback(prompt_emb_global) + else: + noise_pred_global = inference_callback(prompt_emb_global, special_kwargs) + if special_local_kwargs_list is None: + noise_pred_locals = [inference_callback(prompt_emb_local) for prompt_emb_local in prompt_emb_locals] + else: + noise_pred_locals = [inference_callback(prompt_emb_local, special_kwargs) for prompt_emb_local, special_kwargs in zip(prompt_emb_locals, special_local_kwargs_list)] + noise_pred = self.merge_latents(noise_pred_global, noise_pred_locals, masks, mask_scales) + return noise_pred + + + def extend_prompt(self, prompt, local_prompts, masks, mask_scales): + local_prompts = local_prompts or [] + masks = masks or [] + mask_scales = mask_scales or [] + extended_prompt_dict = self.prompter.extend_prompt(prompt) + prompt = extended_prompt_dict.get("prompt", prompt) + local_prompts += extended_prompt_dict.get("prompts", []) + masks += extended_prompt_dict.get("masks", []) + mask_scales += [100.0] * len(extended_prompt_dict.get("masks", [])) + return prompt, local_prompts, masks, mask_scales + + + def enable_cpu_offload(self): + self.cpu_offload = True + + + def load_models_to_device(self, loadmodel_names=[]): + # only load models to device if cpu_offload is enabled + if not self.cpu_offload: + return + # offload the unneeded models to cpu + for model_name in self.model_names: + if model_name not in loadmodel_names: + model = getattr(self, model_name) + if model is not None: + if hasattr(model, "vram_management_enabled") and model.vram_management_enabled: + for module in model.modules(): + if hasattr(module, "offload"): + module.offload() + else: + model.cpu() + # load the needed models to device + for model_name in loadmodel_names: + model = getattr(self, model_name) + if model is not None: + if hasattr(model, "vram_management_enabled") and model.vram_management_enabled: + for module in model.modules(): + if hasattr(module, "onload"): + module.onload() + else: + model.to(self.device) + # fresh the cuda cache + torch.cuda.empty_cache() + + + def generate_noise(self, shape, seed=None, device="cpu", dtype=torch.float16): + generator = None if seed is None else torch.Generator(device).manual_seed(seed) + noise = torch.randn(shape, generator=generator, device=device, dtype=dtype) + return noise diff --git a/diffsynth/pipelines/cog_video.py b/diffsynth/pipelines/cog_video.py new file mode 100644 index 0000000000000000000000000000000000000000..f42d295187e718617cc7d4e327067700f2a689fd --- /dev/null +++ b/diffsynth/pipelines/cog_video.py @@ -0,0 +1,135 @@ +from ..models import ModelManager, FluxTextEncoder2, CogDiT, CogVAEEncoder, CogVAEDecoder +from ..prompters import CogPrompter +from ..schedulers import EnhancedDDIMScheduler +from .base import BasePipeline +import torch +from tqdm import tqdm +from PIL import Image +import numpy as np +from einops import rearrange + + + +class CogVideoPipeline(BasePipeline): + + def __init__(self, device="cuda", torch_dtype=torch.float16): + super().__init__(device=device, torch_dtype=torch_dtype, height_division_factor=16, width_division_factor=16) + self.scheduler = EnhancedDDIMScheduler(rescale_zero_terminal_snr=True, prediction_type="v_prediction") + self.prompter = CogPrompter() + # models + self.text_encoder: FluxTextEncoder2 = None + self.dit: CogDiT = None + self.vae_encoder: CogVAEEncoder = None + self.vae_decoder: CogVAEDecoder = None + + + def fetch_models(self, model_manager: ModelManager, prompt_refiner_classes=[]): + self.text_encoder = model_manager.fetch_model("flux_text_encoder_2") + self.dit = model_manager.fetch_model("cog_dit") + self.vae_encoder = model_manager.fetch_model("cog_vae_encoder") + self.vae_decoder = model_manager.fetch_model("cog_vae_decoder") + self.prompter.fetch_models(self.text_encoder) + self.prompter.load_prompt_refiners(model_manager, prompt_refiner_classes) + + + @staticmethod + def from_model_manager(model_manager: ModelManager, prompt_refiner_classes=[]): + pipe = CogVideoPipeline( + device=model_manager.device, + torch_dtype=model_manager.torch_dtype + ) + pipe.fetch_models(model_manager, prompt_refiner_classes) + return pipe + + + def tensor2video(self, frames): + frames = rearrange(frames, "C T H W -> T H W C") + frames = ((frames.float() + 1) * 127.5).clip(0, 255).cpu().numpy().astype(np.uint8) + frames = [Image.fromarray(frame) for frame in frames] + return frames + + + def encode_prompt(self, prompt, positive=True): + prompt_emb = self.prompter.encode_prompt(prompt, device=self.device, positive=positive) + return {"prompt_emb": prompt_emb} + + + def prepare_extra_input(self, latents): + return {"image_rotary_emb": self.dit.prepare_rotary_positional_embeddings(latents.shape[3], latents.shape[4], latents.shape[2], device=self.device)} + + + @torch.no_grad() + def __call__( + self, + prompt, + negative_prompt="", + input_video=None, + cfg_scale=7.0, + denoising_strength=1.0, + num_frames=49, + height=480, + width=720, + num_inference_steps=20, + tiled=False, + tile_size=(60, 90), + tile_stride=(30, 45), + seed=None, + progress_bar_cmd=tqdm, + progress_bar_st=None, + ): + height, width = self.check_resize_height_width(height, width) + + # Tiler parameters + tiler_kwargs = {"tiled": tiled, "tile_size": tile_size, "tile_stride": tile_stride} + + # Prepare scheduler + self.scheduler.set_timesteps(num_inference_steps, denoising_strength=denoising_strength) + + # Prepare latent tensors + noise = self.generate_noise((1, 16, num_frames // 4 + 1, height//8, width//8), seed=seed, device="cpu", dtype=self.torch_dtype) + + if denoising_strength == 1.0: + latents = noise.clone() + else: + input_video = self.preprocess_images(input_video) + input_video = torch.stack(input_video, dim=2) + latents = self.vae_encoder.encode_video(input_video, **tiler_kwargs, progress_bar=progress_bar_cmd).to(dtype=self.torch_dtype) + latents = self.scheduler.add_noise(latents, noise, self.scheduler.timesteps[0]) + if not tiled: latents = latents.to(self.device) + + # Encode prompt + prompt_emb_posi = self.encode_prompt(prompt, positive=True) + if cfg_scale != 1.0: + prompt_emb_nega = self.encode_prompt(negative_prompt, positive=False) + + # Extra input + extra_input = self.prepare_extra_input(latents) + + # Denoise + for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)): + timestep = timestep.unsqueeze(0).to(self.device) + + # Classifier-free guidance + noise_pred_posi = self.dit( + latents, timestep=timestep, **prompt_emb_posi, **tiler_kwargs, **extra_input + ) + if cfg_scale != 1.0: + noise_pred_nega = self.dit( + latents, timestep=timestep, **prompt_emb_nega, **tiler_kwargs, **extra_input + ) + noise_pred = noise_pred_nega + cfg_scale * (noise_pred_posi - noise_pred_nega) + else: + noise_pred = noise_pred_posi + + # DDIM + latents = self.scheduler.step(noise_pred, self.scheduler.timesteps[progress_id], latents) + + # Update progress bar + if progress_bar_st is not None: + progress_bar_st.progress(progress_id / len(self.scheduler.timesteps)) + + # Decode image + video = self.vae_decoder.decode_video(latents.to("cpu"), **tiler_kwargs, progress_bar=progress_bar_cmd) + video = self.tensor2video(video[0]) + + return video diff --git a/diffsynth/pipelines/dancer.py b/diffsynth/pipelines/dancer.py new file mode 100644 index 0000000000000000000000000000000000000000..593b57c8363f94e312debf7c7f69bf6decdb7dbd --- /dev/null +++ b/diffsynth/pipelines/dancer.py @@ -0,0 +1,236 @@ +import torch +from ..models import SDUNet, SDMotionModel, SDXLUNet, SDXLMotionModel +from ..models.sd_unet import PushBlock, PopBlock +from ..controlnets import MultiControlNetManager + + +def lets_dance( + unet: SDUNet, + motion_modules: SDMotionModel = None, + controlnet: MultiControlNetManager = None, + sample = None, + timestep = None, + encoder_hidden_states = None, + ipadapter_kwargs_list = {}, + controlnet_frames = None, + unet_batch_size = 1, + controlnet_batch_size = 1, + cross_frame_attention = False, + tiled=False, + tile_size=64, + tile_stride=32, + device = "cuda", + vram_limit_level = 0, +): + # 0. Text embedding alignment (only for video processing) + if encoder_hidden_states.shape[0] != sample.shape[0]: + encoder_hidden_states = encoder_hidden_states.repeat(sample.shape[0], 1, 1, 1) + + # 1. ControlNet + # This part will be repeated on overlapping frames if animatediff_batch_size > animatediff_stride. + # I leave it here because I intend to do something interesting on the ControlNets. + controlnet_insert_block_id = 30 + if controlnet is not None and controlnet_frames is not None: + res_stacks = [] + # process controlnet frames with batch + for batch_id in range(0, sample.shape[0], controlnet_batch_size): + batch_id_ = min(batch_id + controlnet_batch_size, sample.shape[0]) + res_stack = controlnet( + sample[batch_id: batch_id_], + timestep, + encoder_hidden_states[batch_id: batch_id_], + controlnet_frames[:, batch_id: batch_id_], + tiled=tiled, tile_size=tile_size, tile_stride=tile_stride + ) + if vram_limit_level >= 1: + res_stack = [res.cpu() for res in res_stack] + res_stacks.append(res_stack) + # concat the residual + additional_res_stack = [] + for i in range(len(res_stacks[0])): + res = torch.concat([res_stack[i] for res_stack in res_stacks], dim=0) + additional_res_stack.append(res) + else: + additional_res_stack = None + + # 2. time + time_emb = unet.time_proj(timestep).to(sample.dtype) + time_emb = unet.time_embedding(time_emb) + + # 3. pre-process + height, width = sample.shape[2], sample.shape[3] + hidden_states = unet.conv_in(sample) + text_emb = encoder_hidden_states + res_stack = [hidden_states.cpu() if vram_limit_level>=1 else hidden_states] + + # 4. blocks + for block_id, block in enumerate(unet.blocks): + # 4.1 UNet + if isinstance(block, PushBlock): + hidden_states, time_emb, text_emb, res_stack = block(hidden_states, time_emb, text_emb, res_stack) + if vram_limit_level>=1: + res_stack[-1] = res_stack[-1].cpu() + elif isinstance(block, PopBlock): + if vram_limit_level>=1: + res_stack[-1] = res_stack[-1].to(device) + hidden_states, time_emb, text_emb, res_stack = block(hidden_states, time_emb, text_emb, res_stack) + else: + hidden_states_input = hidden_states + hidden_states_output = [] + for batch_id in range(0, sample.shape[0], unet_batch_size): + batch_id_ = min(batch_id + unet_batch_size, sample.shape[0]) + hidden_states, _, _, _ = block( + hidden_states_input[batch_id: batch_id_], + time_emb, + text_emb[batch_id: batch_id_], + res_stack, + cross_frame_attention=cross_frame_attention, + ipadapter_kwargs_list=ipadapter_kwargs_list.get(block_id, {}), + tiled=tiled, tile_size=tile_size, tile_stride=tile_stride + ) + hidden_states_output.append(hidden_states) + hidden_states = torch.concat(hidden_states_output, dim=0) + # 4.2 AnimateDiff + if motion_modules is not None: + if block_id in motion_modules.call_block_id: + motion_module_id = motion_modules.call_block_id[block_id] + hidden_states, time_emb, text_emb, res_stack = motion_modules.motion_modules[motion_module_id]( + hidden_states, time_emb, text_emb, res_stack, + batch_size=1 + ) + # 4.3 ControlNet + if block_id == controlnet_insert_block_id and additional_res_stack is not None: + hidden_states += additional_res_stack.pop().to(device) + if vram_limit_level>=1: + res_stack = [(res.to(device) + additional_res.to(device)).cpu() for res, additional_res in zip(res_stack, additional_res_stack)] + else: + res_stack = [res + additional_res for res, additional_res in zip(res_stack, additional_res_stack)] + + # 5. output + hidden_states = unet.conv_norm_out(hidden_states) + hidden_states = unet.conv_act(hidden_states) + hidden_states = unet.conv_out(hidden_states) + + return hidden_states + + + + +def lets_dance_xl( + unet: SDXLUNet, + motion_modules: SDXLMotionModel = None, + controlnet: MultiControlNetManager = None, + sample = None, + add_time_id = None, + add_text_embeds = None, + timestep = None, + encoder_hidden_states = None, + ipadapter_kwargs_list = {}, + controlnet_frames = None, + unet_batch_size = 1, + controlnet_batch_size = 1, + cross_frame_attention = False, + tiled=False, + tile_size=64, + tile_stride=32, + device = "cuda", + vram_limit_level = 0, +): + # 0. Text embedding alignment (only for video processing) + if encoder_hidden_states.shape[0] != sample.shape[0]: + encoder_hidden_states = encoder_hidden_states.repeat(sample.shape[0], 1, 1, 1) + if add_text_embeds.shape[0] != sample.shape[0]: + add_text_embeds = add_text_embeds.repeat(sample.shape[0], 1) + + # 1. ControlNet + controlnet_insert_block_id = 22 + if controlnet is not None and controlnet_frames is not None: + res_stacks = [] + # process controlnet frames with batch + for batch_id in range(0, sample.shape[0], controlnet_batch_size): + batch_id_ = min(batch_id + controlnet_batch_size, sample.shape[0]) + res_stack = controlnet( + sample[batch_id: batch_id_], + timestep, + encoder_hidden_states[batch_id: batch_id_], + controlnet_frames[:, batch_id: batch_id_], + add_time_id=add_time_id, + add_text_embeds=add_text_embeds, + tiled=tiled, tile_size=tile_size, tile_stride=tile_stride, + unet=unet, # for Kolors, some modules in ControlNets will be replaced. + ) + if vram_limit_level >= 1: + res_stack = [res.cpu() for res in res_stack] + res_stacks.append(res_stack) + # concat the residual + additional_res_stack = [] + for i in range(len(res_stacks[0])): + res = torch.concat([res_stack[i] for res_stack in res_stacks], dim=0) + additional_res_stack.append(res) + else: + additional_res_stack = None + + # 2. time + t_emb = unet.time_proj(timestep).to(sample.dtype) + t_emb = unet.time_embedding(t_emb) + + time_embeds = unet.add_time_proj(add_time_id) + time_embeds = time_embeds.reshape((add_text_embeds.shape[0], -1)) + add_embeds = torch.concat([add_text_embeds, time_embeds], dim=-1) + add_embeds = add_embeds.to(sample.dtype) + add_embeds = unet.add_time_embedding(add_embeds) + + time_emb = t_emb + add_embeds + + # 3. pre-process + height, width = sample.shape[2], sample.shape[3] + hidden_states = unet.conv_in(sample) + text_emb = encoder_hidden_states if unet.text_intermediate_proj is None else unet.text_intermediate_proj(encoder_hidden_states) + res_stack = [hidden_states] + + # 4. blocks + for block_id, block in enumerate(unet.blocks): + # 4.1 UNet + if isinstance(block, PushBlock): + hidden_states, time_emb, text_emb, res_stack = block(hidden_states, time_emb, text_emb, res_stack) + if vram_limit_level>=1: + res_stack[-1] = res_stack[-1].cpu() + elif isinstance(block, PopBlock): + if vram_limit_level>=1: + res_stack[-1] = res_stack[-1].to(device) + hidden_states, time_emb, text_emb, res_stack = block(hidden_states, time_emb, text_emb, res_stack) + else: + hidden_states_input = hidden_states + hidden_states_output = [] + for batch_id in range(0, sample.shape[0], unet_batch_size): + batch_id_ = min(batch_id + unet_batch_size, sample.shape[0]) + hidden_states, _, _, _ = block( + hidden_states_input[batch_id: batch_id_], + time_emb[batch_id: batch_id_], + text_emb[batch_id: batch_id_], + res_stack, + cross_frame_attention=cross_frame_attention, + ipadapter_kwargs_list=ipadapter_kwargs_list.get(block_id, {}), + tiled=tiled, tile_size=tile_size, tile_stride=tile_stride, + ) + hidden_states_output.append(hidden_states) + hidden_states = torch.concat(hidden_states_output, dim=0) + # 4.2 AnimateDiff + if motion_modules is not None: + if block_id in motion_modules.call_block_id: + motion_module_id = motion_modules.call_block_id[block_id] + hidden_states, time_emb, text_emb, res_stack = motion_modules.motion_modules[motion_module_id]( + hidden_states, time_emb, text_emb, res_stack, + batch_size=1 + ) + # 4.3 ControlNet + if block_id == controlnet_insert_block_id and additional_res_stack is not None: + hidden_states += additional_res_stack.pop().to(device) + res_stack = [res + additional_res for res, additional_res in zip(res_stack, additional_res_stack)] + + # 5. output + hidden_states = unet.conv_norm_out(hidden_states) + hidden_states = unet.conv_act(hidden_states) + hidden_states = unet.conv_out(hidden_states) + + return hidden_states \ No newline at end of file diff --git a/diffsynth/pipelines/flux_image.py b/diffsynth/pipelines/flux_image.py new file mode 100644 index 0000000000000000000000000000000000000000..55a84c06d81bfa6a1a663a08db375de12a3aea57 --- /dev/null +++ b/diffsynth/pipelines/flux_image.py @@ -0,0 +1,823 @@ +from ..models import ModelManager, FluxDiT, SD3TextEncoder1, FluxTextEncoder2, FluxVAEDecoder, FluxVAEEncoder, FluxIpAdapter +from ..models.step1x_connector import Qwen2Connector +from ..controlnets import FluxMultiControlNetManager, ControlNetUnit, ControlNetConfigUnit, Annotator +from ..prompters import FluxPrompter +from ..schedulers import FlowMatchScheduler +from .base import BasePipeline +from typing import List +import torch +from tqdm import tqdm +import numpy as np +from PIL import Image +from ..models.tiler import FastTileWorker +from transformers import SiglipVisionModel +from copy import deepcopy +from transformers.models.t5.modeling_t5 import T5LayerNorm, T5DenseActDense, T5DenseGatedActDense +from ..models.flux_dit import RMSNorm +from ..vram_management import enable_vram_management, AutoWrappedModule, AutoWrappedLinear + + +class FluxImagePipeline(BasePipeline): + + def __init__(self, device="cuda", torch_dtype=torch.float16): + super().__init__(device=device, torch_dtype=torch_dtype, height_division_factor=16, width_division_factor=16) + self.scheduler = FlowMatchScheduler() + self.prompter = FluxPrompter() + # models + self.text_encoder_1: SD3TextEncoder1 = None + self.text_encoder_2: FluxTextEncoder2 = None + self.dit: FluxDiT = None + self.vae_decoder: FluxVAEDecoder = None + self.vae_encoder: FluxVAEEncoder = None + self.controlnet: FluxMultiControlNetManager = None + self.ipadapter: FluxIpAdapter = None + self.ipadapter_image_encoder: SiglipVisionModel = None + self.infinityou_processor: InfinitYou = None + self.qwenvl = None + self.step1x_connector: Qwen2Connector = None + self.model_names = ['text_encoder_1', 'text_encoder_2', 'dit', 'vae_decoder', 'vae_encoder', 'controlnet', 'ipadapter', 'ipadapter_image_encoder', 'qwenvl', 'step1x_connector'] + + + def enable_vram_management(self, num_persistent_param_in_dit=None): + if self.text_encoder_1 is not None: + dtype = next(iter(self.text_encoder_1.parameters())).dtype + enable_vram_management( + self.text_encoder_1, + module_map = { + torch.nn.Linear: AutoWrappedLinear, + torch.nn.Embedding: AutoWrappedModule, + torch.nn.LayerNorm: AutoWrappedModule, + }, + module_config = dict( + offload_dtype=dtype, + offload_device="cpu", + onload_dtype=dtype, + onload_device="cpu", + computation_dtype=self.torch_dtype, + computation_device=self.device, + ), + ) + if self.text_encoder_2 is not None: + dtype = next(iter(self.text_encoder_2.parameters())).dtype + enable_vram_management( + self.text_encoder_2, + module_map = { + torch.nn.Linear: AutoWrappedLinear, + torch.nn.Embedding: AutoWrappedModule, + T5LayerNorm: AutoWrappedModule, + T5DenseActDense: AutoWrappedModule, + T5DenseGatedActDense: AutoWrappedModule, + }, + module_config = dict( + offload_dtype=dtype, + offload_device="cpu", + onload_dtype=dtype, + onload_device="cpu", + computation_dtype=self.torch_dtype, + computation_device=self.device, + ), + ) + if self.dit is not None: + dtype = next(iter(self.dit.parameters())).dtype + enable_vram_management( + self.dit, + module_map = { + RMSNorm: AutoWrappedModule, + torch.nn.Linear: AutoWrappedLinear, + }, + module_config = dict( + offload_dtype=dtype, + offload_device="cpu", + onload_dtype=dtype, + onload_device="cuda", + computation_dtype=self.torch_dtype, + computation_device=self.device, + ), + max_num_param=num_persistent_param_in_dit, + overflow_module_config = dict( + offload_dtype=dtype, + offload_device="cpu", + onload_dtype=dtype, + onload_device="cpu", + computation_dtype=self.torch_dtype, + computation_device=self.device, + ), + ) + if self.vae_decoder is not None: + dtype = next(iter(self.vae_decoder.parameters())).dtype + enable_vram_management( + self.vae_decoder, + module_map = { + torch.nn.Linear: AutoWrappedLinear, + torch.nn.Conv2d: AutoWrappedModule, + torch.nn.GroupNorm: AutoWrappedModule, + }, + module_config = dict( + offload_dtype=dtype, + offload_device="cpu", + onload_dtype=dtype, + onload_device="cpu", + computation_dtype=self.torch_dtype, + computation_device=self.device, + ), + ) + if self.vae_encoder is not None: + dtype = next(iter(self.vae_encoder.parameters())).dtype + enable_vram_management( + self.vae_encoder, + module_map = { + torch.nn.Linear: AutoWrappedLinear, + torch.nn.Conv2d: AutoWrappedModule, + torch.nn.GroupNorm: AutoWrappedModule, + }, + module_config = dict( + offload_dtype=dtype, + offload_device="cpu", + onload_dtype=dtype, + onload_device="cpu", + computation_dtype=self.torch_dtype, + computation_device=self.device, + ), + ) + self.enable_cpu_offload() + + + def denoising_model(self): + return self.dit + + + def fetch_models(self, model_manager: ModelManager, controlnet_config_units: List[ControlNetConfigUnit]=[], prompt_refiner_classes=[], prompt_extender_classes=[]): + self.text_encoder_1 = model_manager.fetch_model("sd3_text_encoder_1") + self.text_encoder_2 = model_manager.fetch_model("flux_text_encoder_2") + self.dit = model_manager.fetch_model("flux_dit") + self.vae_decoder = model_manager.fetch_model("flux_vae_decoder") + self.vae_encoder = model_manager.fetch_model("flux_vae_encoder") + self.prompter.fetch_models(self.text_encoder_1, self.text_encoder_2) + self.prompter.load_prompt_refiners(model_manager, prompt_refiner_classes) + self.prompter.load_prompt_extenders(model_manager, prompt_extender_classes) + + # ControlNets + controlnet_units = [] + for config in controlnet_config_units: + controlnet_unit = ControlNetUnit( + Annotator(config.processor_id, device=self.device, skip_processor=config.skip_processor), + model_manager.fetch_model("flux_controlnet", config.model_path), + config.scale + ) + controlnet_units.append(controlnet_unit) + self.controlnet = FluxMultiControlNetManager(controlnet_units) + + # IP-Adapters + self.ipadapter = model_manager.fetch_model("flux_ipadapter") + self.ipadapter_image_encoder = model_manager.fetch_model("siglip_vision_model") + + # InfiniteYou + self.image_proj_model = model_manager.fetch_model("infiniteyou_image_projector") + if self.image_proj_model is not None: + self.infinityou_processor = InfinitYou(device=self.device) + + # Step1x + self.qwenvl = model_manager.fetch_model("qwenvl") + self.step1x_connector = model_manager.fetch_model("step1x_connector") + + + @staticmethod + def from_model_manager(model_manager: ModelManager, controlnet_config_units: List[ControlNetConfigUnit]=[], prompt_refiner_classes=[], prompt_extender_classes=[], device=None, torch_dtype=None): + pipe = FluxImagePipeline( + device=model_manager.device if device is None else device, + torch_dtype=model_manager.torch_dtype if torch_dtype is None else torch_dtype, + ) + pipe.fetch_models(model_manager, controlnet_config_units, prompt_refiner_classes, prompt_extender_classes) + return pipe + + + def encode_image(self, image, tiled=False, tile_size=64, tile_stride=32): + latents = self.vae_encoder(image, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) + return latents + + + def decode_image(self, latent, tiled=False, tile_size=64, tile_stride=32): + image = self.vae_decoder(latent.to(self.device), tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) + image = self.vae_output_to_image(image) + return image + + + def encode_prompt(self, prompt, positive=True, t5_sequence_length=512): + if self.text_encoder_1 is not None and self.text_encoder_2 is not None: + prompt_emb, pooled_prompt_emb, text_ids = self.prompter.encode_prompt( + prompt, device=self.device, positive=positive, t5_sequence_length=t5_sequence_length + ) + return {"prompt_emb": prompt_emb, "pooled_prompt_emb": pooled_prompt_emb, "text_ids": text_ids} + else: + return {} + + + def prepare_extra_input(self, latents=None, guidance=1.0): + latent_image_ids = self.dit.prepare_image_ids(latents) + guidance = torch.Tensor([guidance] * latents.shape[0]).to(device=latents.device, dtype=latents.dtype) + return {"image_ids": latent_image_ids, "guidance": guidance} + + + def apply_controlnet_mask_on_latents(self, latents, mask): + mask = (self.preprocess_image(mask) + 1) / 2 + mask = mask.mean(dim=1, keepdim=True) + mask = mask.to(dtype=self.torch_dtype, device=self.device) + mask = 1 - torch.nn.functional.interpolate(mask, size=latents.shape[-2:]) + latents = torch.concat([latents, mask], dim=1) + return latents + + + def apply_controlnet_mask_on_image(self, image, mask): + mask = mask.resize(image.size) + mask = self.preprocess_image(mask).mean(dim=[0, 1]) + image = np.array(image) + image[mask > 0] = 0 + image = Image.fromarray(image) + return image + + + def prepare_controlnet_input(self, controlnet_image, controlnet_inpaint_mask, tiler_kwargs): + if isinstance(controlnet_image, Image.Image): + controlnet_image = [controlnet_image] * len(self.controlnet.processors) + + controlnet_frames = [] + for i in range(len(self.controlnet.processors)): + # image annotator + image = self.controlnet.process_image(controlnet_image[i], processor_id=i)[0] + if controlnet_inpaint_mask is not None and self.controlnet.processors[i].processor_id == "inpaint": + image = self.apply_controlnet_mask_on_image(image, controlnet_inpaint_mask) + + # image to tensor + image = self.preprocess_image(image).to(device=self.device, dtype=self.torch_dtype) + + # vae encoder + image = self.encode_image(image, **tiler_kwargs) + if controlnet_inpaint_mask is not None and self.controlnet.processors[i].processor_id == "inpaint": + image = self.apply_controlnet_mask_on_latents(image, controlnet_inpaint_mask) + + # store it + controlnet_frames.append(image) + return controlnet_frames + + + def prepare_ipadapter_inputs(self, images, height=384, width=384): + images = [image.convert("RGB").resize((width, height), resample=3) for image in images] + images = [self.preprocess_image(image).to(device=self.device, dtype=self.torch_dtype) for image in images] + return torch.cat(images, dim=0) + + + def inpaint_fusion(self, latents, inpaint_latents, pred_noise, fg_mask, bg_mask, progress_id, background_weight=0.): + # inpaint noise + inpaint_noise = (latents - inpaint_latents) / self.scheduler.sigmas[progress_id] + # merge noise + weight = torch.ones_like(inpaint_noise) + inpaint_noise[fg_mask] = pred_noise[fg_mask] + inpaint_noise[bg_mask] += pred_noise[bg_mask] * background_weight + weight[bg_mask] += background_weight + inpaint_noise /= weight + return inpaint_noise + + + def preprocess_masks(self, masks, height, width, dim): + out_masks = [] + for mask in masks: + mask = self.preprocess_image(mask.resize((width, height), resample=Image.NEAREST)).mean(dim=1, keepdim=True) > 0 + mask = mask.repeat(1, dim, 1, 1).to(device=self.device, dtype=self.torch_dtype) + out_masks.append(mask) + return out_masks + + + def prepare_entity_inputs(self, entity_prompts, entity_masks, width, height, t5_sequence_length=512, enable_eligen_inpaint=False): + fg_mask, bg_mask = None, None + if enable_eligen_inpaint: + masks_ = deepcopy(entity_masks) + fg_masks = torch.cat([self.preprocess_image(mask.resize((width//8, height//8))).mean(dim=1, keepdim=True) for mask in masks_]) + fg_masks = (fg_masks > 0).float() + fg_mask = fg_masks.sum(dim=0, keepdim=True).repeat(1, 16, 1, 1) > 0 + bg_mask = ~fg_mask + entity_masks = self.preprocess_masks(entity_masks, height//8, width//8, 1) + entity_masks = torch.cat(entity_masks, dim=0).unsqueeze(0) # b, n_mask, c, h, w + entity_prompts = self.encode_prompt(entity_prompts, t5_sequence_length=t5_sequence_length)['prompt_emb'].unsqueeze(0) + return entity_prompts, entity_masks, fg_mask, bg_mask + + + def prepare_latents(self, input_image, height, width, seed, tiled, tile_size, tile_stride): + if input_image is not None: + self.load_models_to_device(['vae_encoder']) + image = self.preprocess_image(input_image).to(device=self.device, dtype=self.torch_dtype) + input_latents = self.encode_image(image, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) + noise = self.generate_noise((1, 16, height//8, width//8), seed=seed, device=self.device, dtype=self.torch_dtype) + latents = self.scheduler.add_noise(input_latents, noise, timestep=self.scheduler.timesteps[0]) + else: + latents = self.generate_noise((1, 16, height//8, width//8), seed=seed, device=self.device, dtype=self.torch_dtype) + input_latents = None + return latents, input_latents + + + def prepare_ipadapter(self, ipadapter_images, ipadapter_scale): + if ipadapter_images is not None: + self.load_models_to_device(['ipadapter_image_encoder']) + ipadapter_images = self.prepare_ipadapter_inputs(ipadapter_images) + ipadapter_image_encoding = self.ipadapter_image_encoder(ipadapter_images).pooler_output + self.load_models_to_device(['ipadapter']) + ipadapter_kwargs_list_posi = {"ipadapter_kwargs_list": self.ipadapter(ipadapter_image_encoding, scale=ipadapter_scale)} + ipadapter_kwargs_list_nega = {"ipadapter_kwargs_list": self.ipadapter(torch.zeros_like(ipadapter_image_encoding))} + else: + ipadapter_kwargs_list_posi, ipadapter_kwargs_list_nega = {"ipadapter_kwargs_list": {}}, {"ipadapter_kwargs_list": {}} + return ipadapter_kwargs_list_posi, ipadapter_kwargs_list_nega + + + def prepare_controlnet(self, controlnet_image, masks, controlnet_inpaint_mask, tiler_kwargs, enable_controlnet_on_negative): + if controlnet_image is not None: + self.load_models_to_device(['vae_encoder']) + controlnet_kwargs_posi = {"controlnet_frames": self.prepare_controlnet_input(controlnet_image, controlnet_inpaint_mask, tiler_kwargs)} + if len(masks) > 0 and controlnet_inpaint_mask is not None: + print("The controlnet_inpaint_mask will be overridden by masks.") + local_controlnet_kwargs = [{"controlnet_frames": self.prepare_controlnet_input(controlnet_image, mask, tiler_kwargs)} for mask in masks] + else: + local_controlnet_kwargs = None + else: + controlnet_kwargs_posi, local_controlnet_kwargs = {"controlnet_frames": None}, [{}] * len(masks) + controlnet_kwargs_nega = controlnet_kwargs_posi if enable_controlnet_on_negative else {} + return controlnet_kwargs_posi, controlnet_kwargs_nega, local_controlnet_kwargs + + + def prepare_eligen(self, prompt_emb_nega, eligen_entity_prompts, eligen_entity_masks, width, height, t5_sequence_length, enable_eligen_inpaint, enable_eligen_on_negative, cfg_scale): + if eligen_entity_masks is not None: + entity_prompt_emb_posi, entity_masks_posi, fg_mask, bg_mask = self.prepare_entity_inputs(eligen_entity_prompts, eligen_entity_masks, width, height, t5_sequence_length, enable_eligen_inpaint) + if enable_eligen_on_negative and cfg_scale != 1.0: + entity_prompt_emb_nega = prompt_emb_nega['prompt_emb'].unsqueeze(1).repeat(1, entity_masks_posi.shape[1], 1, 1) + entity_masks_nega = entity_masks_posi + else: + entity_prompt_emb_nega, entity_masks_nega = None, None + else: + entity_prompt_emb_posi, entity_masks_posi, entity_prompt_emb_nega, entity_masks_nega = None, None, None, None + fg_mask, bg_mask = None, None + eligen_kwargs_posi = {"entity_prompt_emb": entity_prompt_emb_posi, "entity_masks": entity_masks_posi} + eligen_kwargs_nega = {"entity_prompt_emb": entity_prompt_emb_nega, "entity_masks": entity_masks_nega} + return eligen_kwargs_posi, eligen_kwargs_nega, fg_mask, bg_mask + + + def prepare_prompts(self, prompt, local_prompts, masks, mask_scales, t5_sequence_length, negative_prompt, cfg_scale): + # Extend prompt + self.load_models_to_device(['text_encoder_1', 'text_encoder_2']) + prompt, local_prompts, masks, mask_scales = self.extend_prompt(prompt, local_prompts, masks, mask_scales) + + # Encode prompts + prompt_emb_posi = self.encode_prompt(prompt, t5_sequence_length=t5_sequence_length) + prompt_emb_nega = self.encode_prompt(negative_prompt, positive=False, t5_sequence_length=t5_sequence_length) if cfg_scale != 1.0 else None + prompt_emb_locals = [self.encode_prompt(prompt_local, t5_sequence_length=t5_sequence_length) for prompt_local in local_prompts] + return prompt_emb_posi, prompt_emb_nega, prompt_emb_locals + + + def prepare_infinite_you(self, id_image, controlnet_image, infinityou_guidance, height, width): + if self.infinityou_processor is not None and id_image is not None: + return self.infinityou_processor.prepare_infinite_you(self.image_proj_model, id_image, controlnet_image, infinityou_guidance, height, width) + else: + return {}, controlnet_image + + + def prepare_flex_kwargs(self, latents, flex_inpaint_image=None, flex_inpaint_mask=None, flex_control_image=None, flex_control_strength=0.5, flex_control_stop=0.5, tiled=False, tile_size=64, tile_stride=32): + if self.dit.input_dim == 196: + if flex_inpaint_image is None: + flex_inpaint_image = torch.zeros_like(latents) + else: + flex_inpaint_image = self.preprocess_image(flex_inpaint_image).to(device=self.device, dtype=self.torch_dtype) + flex_inpaint_image = self.encode_image(flex_inpaint_image, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) + if flex_inpaint_mask is None: + flex_inpaint_mask = torch.ones_like(latents)[:, 0:1, :, :] + else: + flex_inpaint_mask = flex_inpaint_mask.resize((latents.shape[3], latents.shape[2])) + flex_inpaint_mask = self.preprocess_image(flex_inpaint_mask).to(device=self.device, dtype=self.torch_dtype) + flex_inpaint_mask = (flex_inpaint_mask[:, 0:1, :, :] + 1) / 2 + flex_inpaint_image = flex_inpaint_image * (1 - flex_inpaint_mask) + if flex_control_image is None: + flex_control_image = torch.zeros_like(latents) + else: + flex_control_image = self.preprocess_image(flex_control_image).to(device=self.device, dtype=self.torch_dtype) + flex_control_image = self.encode_image(flex_control_image, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) * flex_control_strength + flex_condition = torch.concat([flex_inpaint_image, flex_inpaint_mask, flex_control_image], dim=1) + flex_uncondition = torch.concat([flex_inpaint_image, flex_inpaint_mask, torch.zeros_like(flex_control_image)], dim=1) + flex_control_stop_timestep = self.scheduler.timesteps[int(flex_control_stop * (len(self.scheduler.timesteps) - 1))] + flex_kwargs = {"flex_condition": flex_condition, "flex_uncondition": flex_uncondition, "flex_control_stop_timestep": flex_control_stop_timestep} + else: + flex_kwargs = {} + return flex_kwargs + + + def prepare_step1x_kwargs(self, prompt, negative_prompt, image): + if image is None: + return {}, {} + self.load_models_to_device(["qwenvl", "vae_encoder"]) + captions = [prompt, negative_prompt] + ref_images = [image, image] + embs, masks = self.qwenvl(captions, ref_images) + image = self.preprocess_image(image).to(device=self.device, dtype=self.torch_dtype) + image = self.encode_image(image) + return {"step1x_llm_embedding": embs[0:1], "step1x_mask": masks[0:1], "step1x_reference_latents": image}, {"step1x_llm_embedding": embs[1:2], "step1x_mask": masks[1:2], "step1x_reference_latents": image} + + + @torch.no_grad() + def __call__( + self, + # Prompt + prompt, + negative_prompt="", + cfg_scale=1.0, + embedded_guidance=3.5, + t5_sequence_length=512, + # Image + input_image=None, + denoising_strength=1.0, + height=1024, + width=1024, + seed=None, + # Steps + num_inference_steps=30, + # local prompts + local_prompts=(), + masks=(), + mask_scales=(), + # ControlNet + controlnet_image=None, + controlnet_inpaint_mask=None, + enable_controlnet_on_negative=False, + # IP-Adapter + ipadapter_images=None, + ipadapter_scale=1.0, + # EliGen + eligen_entity_prompts=None, + eligen_entity_masks=None, + enable_eligen_on_negative=False, + enable_eligen_inpaint=False, + # InfiniteYou + infinityou_id_image=None, + infinityou_guidance=1.0, + # Flex + flex_inpaint_image=None, + flex_inpaint_mask=None, + flex_control_image=None, + flex_control_strength=0.5, + flex_control_stop=0.5, + # Step1x + step1x_reference_image=None, + # TeaCache + tea_cache_l1_thresh=None, + # Tile + tiled=False, + tile_size=128, + tile_stride=64, + # Progress bar + progress_bar_cmd=tqdm, + progress_bar_st=None, + ): + height, width = self.check_resize_height_width(height, width) + + # Tiler parameters + tiler_kwargs = {"tiled": tiled, "tile_size": tile_size, "tile_stride": tile_stride} + + # Prepare scheduler + self.scheduler.set_timesteps(num_inference_steps, denoising_strength) + + # Prepare latent tensors + latents, input_latents = self.prepare_latents(input_image, height, width, seed, tiled, tile_size, tile_stride) + + # Prompt + prompt_emb_posi, prompt_emb_nega, prompt_emb_locals = self.prepare_prompts(prompt, local_prompts, masks, mask_scales, t5_sequence_length, negative_prompt, cfg_scale) + + # Extra input + extra_input = self.prepare_extra_input(latents, guidance=embedded_guidance) + + # InfiniteYou + infiniteyou_kwargs, controlnet_image = self.prepare_infinite_you(infinityou_id_image, controlnet_image, infinityou_guidance, height, width) + + # Entity control + eligen_kwargs_posi, eligen_kwargs_nega, fg_mask, bg_mask = self.prepare_eligen(prompt_emb_nega, eligen_entity_prompts, eligen_entity_masks, width, height, t5_sequence_length, enable_eligen_inpaint, enable_eligen_on_negative, cfg_scale) + + # IP-Adapter + ipadapter_kwargs_list_posi, ipadapter_kwargs_list_nega = self.prepare_ipadapter(ipadapter_images, ipadapter_scale) + + # ControlNets + controlnet_kwargs_posi, controlnet_kwargs_nega, local_controlnet_kwargs = self.prepare_controlnet(controlnet_image, masks, controlnet_inpaint_mask, tiler_kwargs, enable_controlnet_on_negative) + + # Flex + flex_kwargs = self.prepare_flex_kwargs(latents, flex_inpaint_image, flex_inpaint_mask, flex_control_image, flex_control_strength=flex_control_strength, flex_control_stop=flex_control_stop, **tiler_kwargs) + + # Step1x + step1x_kwargs_posi, step1x_kwargs_nega = self.prepare_step1x_kwargs(prompt, negative_prompt, image=step1x_reference_image) + + # TeaCache + tea_cache_kwargs = {"tea_cache": TeaCache(num_inference_steps, rel_l1_thresh=tea_cache_l1_thresh) if tea_cache_l1_thresh is not None else None} + + # Denoise + self.load_models_to_device(['dit', 'controlnet', 'step1x_connector']) + for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)): + timestep = timestep.unsqueeze(0).to(self.device) + + # Positive side + inference_callback = lambda prompt_emb_posi, controlnet_kwargs: lets_dance_flux( + dit=self.dit, controlnet=self.controlnet, step1x_connector=self.step1x_connector, + hidden_states=latents, timestep=timestep, + **prompt_emb_posi, **tiler_kwargs, **extra_input, **controlnet_kwargs, **ipadapter_kwargs_list_posi, **eligen_kwargs_posi, **tea_cache_kwargs, **infiniteyou_kwargs, **flex_kwargs, **step1x_kwargs_posi, + ) + noise_pred_posi = self.control_noise_via_local_prompts( + prompt_emb_posi, prompt_emb_locals, masks, mask_scales, inference_callback, + special_kwargs=controlnet_kwargs_posi, special_local_kwargs_list=local_controlnet_kwargs + ) + + # Inpaint + if enable_eligen_inpaint: + noise_pred_posi = self.inpaint_fusion(latents, input_latents, noise_pred_posi, fg_mask, bg_mask, progress_id) + + # Classifier-free guidance + if cfg_scale != 1.0: + # Negative side + noise_pred_nega = lets_dance_flux( + dit=self.dit, controlnet=self.controlnet, step1x_connector=self.step1x_connector, + hidden_states=latents, timestep=timestep, + **prompt_emb_nega, **tiler_kwargs, **extra_input, **controlnet_kwargs_nega, **ipadapter_kwargs_list_nega, **eligen_kwargs_nega, **infiniteyou_kwargs, **flex_kwargs, **step1x_kwargs_nega, + ) + noise_pred = noise_pred_nega + cfg_scale * (noise_pred_posi - noise_pred_nega) + else: + noise_pred = noise_pred_posi + + # Iterate + latents = self.scheduler.step(noise_pred, self.scheduler.timesteps[progress_id], latents) + + # UI + if progress_bar_st is not None: + progress_bar_st.progress(progress_id / len(self.scheduler.timesteps)) + + # Decode image + self.load_models_to_device(['vae_decoder']) + image = self.decode_image(latents, **tiler_kwargs) + + # Offload all models + self.load_models_to_device([]) + return image + + + +class InfinitYou: + def __init__(self, device="cuda", torch_dtype=torch.bfloat16): + from facexlib.recognition import init_recognition_model + from insightface.app import FaceAnalysis + self.device = device + self.torch_dtype = torch_dtype + insightface_root_path = 'models/InfiniteYou/insightface' + self.app_640 = FaceAnalysis(name='antelopev2', root=insightface_root_path, providers=['CUDAExecutionProvider', 'CPUExecutionProvider']) + self.app_640.prepare(ctx_id=0, det_size=(640, 640)) + self.app_320 = FaceAnalysis(name='antelopev2', root=insightface_root_path, providers=['CUDAExecutionProvider', 'CPUExecutionProvider']) + self.app_320.prepare(ctx_id=0, det_size=(320, 320)) + self.app_160 = FaceAnalysis(name='antelopev2', root=insightface_root_path, providers=['CUDAExecutionProvider', 'CPUExecutionProvider']) + self.app_160.prepare(ctx_id=0, det_size=(160, 160)) + self.arcface_model = init_recognition_model('arcface', device=self.device) + + def _detect_face(self, id_image_cv2): + face_info = self.app_640.get(id_image_cv2) + if len(face_info) > 0: + return face_info + face_info = self.app_320.get(id_image_cv2) + if len(face_info) > 0: + return face_info + face_info = self.app_160.get(id_image_cv2) + return face_info + + def extract_arcface_bgr_embedding(self, in_image, landmark): + from insightface.utils import face_align + arc_face_image = face_align.norm_crop(in_image, landmark=np.array(landmark), image_size=112) + arc_face_image = torch.from_numpy(arc_face_image).unsqueeze(0).permute(0, 3, 1, 2) / 255. + arc_face_image = 2 * arc_face_image - 1 + arc_face_image = arc_face_image.contiguous().to(self.device) + face_emb = self.arcface_model(arc_face_image)[0] # [512], normalized + return face_emb + + def prepare_infinite_you(self, model, id_image, controlnet_image, infinityou_guidance, height, width): + import cv2 + if id_image is None: + return {'id_emb': None}, controlnet_image + id_image_cv2 = cv2.cvtColor(np.array(id_image), cv2.COLOR_RGB2BGR) + face_info = self._detect_face(id_image_cv2) + if len(face_info) == 0: + raise ValueError('No face detected in the input ID image') + landmark = sorted(face_info, key=lambda x:(x['bbox'][2]-x['bbox'][0])*(x['bbox'][3]-x['bbox'][1]))[-1]['kps'] # only use the maximum face + id_emb = self.extract_arcface_bgr_embedding(id_image_cv2, landmark) + id_emb = model(id_emb.unsqueeze(0).reshape([1, -1, 512]).to(dtype=self.torch_dtype)) + if controlnet_image is None: + controlnet_image = Image.fromarray(np.zeros([height, width, 3]).astype(np.uint8)) + infinityou_guidance = torch.Tensor([infinityou_guidance]).to(device=self.device, dtype=self.torch_dtype) + return {'id_emb': id_emb, 'infinityou_guidance': infinityou_guidance}, controlnet_image + + +class TeaCache: + def __init__(self, num_inference_steps, rel_l1_thresh): + self.num_inference_steps = num_inference_steps + self.step = 0 + self.accumulated_rel_l1_distance = 0 + self.previous_modulated_input = None + self.rel_l1_thresh = rel_l1_thresh + self.previous_residual = None + self.previous_hidden_states = None + + def check(self, dit: FluxDiT, hidden_states, conditioning): + inp = hidden_states.clone() + temb_ = conditioning.clone() + modulated_inp, _, _, _, _ = dit.blocks[0].norm1_a(inp, emb=temb_) + if self.step == 0 or self.step == self.num_inference_steps - 1: + should_calc = True + self.accumulated_rel_l1_distance = 0 + else: + coefficients = [4.98651651e+02, -2.83781631e+02, 5.58554382e+01, -3.82021401e+00, 2.64230861e-01] + rescale_func = np.poly1d(coefficients) + self.accumulated_rel_l1_distance += rescale_func(((modulated_inp-self.previous_modulated_input).abs().mean() / self.previous_modulated_input.abs().mean()).cpu().item()) + if self.accumulated_rel_l1_distance < self.rel_l1_thresh: + should_calc = False + else: + should_calc = True + self.accumulated_rel_l1_distance = 0 + self.previous_modulated_input = modulated_inp + self.step += 1 + if self.step == self.num_inference_steps: + self.step = 0 + if should_calc: + self.previous_hidden_states = hidden_states.clone() + return not should_calc + + def store(self, hidden_states): + self.previous_residual = hidden_states - self.previous_hidden_states + self.previous_hidden_states = None + + def update(self, hidden_states): + hidden_states = hidden_states + self.previous_residual + return hidden_states + + +def lets_dance_flux( + dit: FluxDiT, + controlnet: FluxMultiControlNetManager = None, + step1x_connector: Qwen2Connector = None, + hidden_states=None, + timestep=None, + prompt_emb=None, + pooled_prompt_emb=None, + guidance=None, + text_ids=None, + image_ids=None, + controlnet_frames=None, + tiled=False, + tile_size=128, + tile_stride=64, + entity_prompt_emb=None, + entity_masks=None, + ipadapter_kwargs_list={}, + id_emb=None, + infinityou_guidance=None, + flex_condition=None, + flex_uncondition=None, + flex_control_stop_timestep=None, + step1x_llm_embedding=None, + step1x_mask=None, + step1x_reference_latents=None, + tea_cache: TeaCache = None, + **kwargs +): + if tiled: + def flux_forward_fn(hl, hr, wl, wr): + tiled_controlnet_frames = [f[:, :, hl: hr, wl: wr] for f in controlnet_frames] if controlnet_frames is not None else None + return lets_dance_flux( + dit=dit, + controlnet=controlnet, + hidden_states=hidden_states[:, :, hl: hr, wl: wr], + timestep=timestep, + prompt_emb=prompt_emb, + pooled_prompt_emb=pooled_prompt_emb, + guidance=guidance, + text_ids=text_ids, + image_ids=None, + controlnet_frames=tiled_controlnet_frames, + tiled=False, + **kwargs + ) + return FastTileWorker().tiled_forward( + flux_forward_fn, + hidden_states, + tile_size=tile_size, + tile_stride=tile_stride, + tile_device=hidden_states.device, + tile_dtype=hidden_states.dtype + ) + + + # ControlNet + if controlnet is not None and controlnet_frames is not None: + controlnet_extra_kwargs = { + "hidden_states": hidden_states, + "timestep": timestep, + "prompt_emb": prompt_emb, + "pooled_prompt_emb": pooled_prompt_emb, + "guidance": guidance, + "text_ids": text_ids, + "image_ids": image_ids, + "tiled": tiled, + "tile_size": tile_size, + "tile_stride": tile_stride, + } + if id_emb is not None: + controlnet_text_ids = torch.zeros(id_emb.shape[0], id_emb.shape[1], 3).to(device=hidden_states.device, dtype=hidden_states.dtype) + controlnet_extra_kwargs.update({"prompt_emb": id_emb, 'text_ids': controlnet_text_ids, 'guidance': infinityou_guidance}) + controlnet_res_stack, controlnet_single_res_stack = controlnet( + controlnet_frames, **controlnet_extra_kwargs + ) + + # Flex + if flex_condition is not None: + if timestep.tolist()[0] >= flex_control_stop_timestep: + hidden_states = torch.concat([hidden_states, flex_condition], dim=1) + else: + hidden_states = torch.concat([hidden_states, flex_uncondition], dim=1) + + # Step1x + if step1x_llm_embedding is not None: + prompt_emb, pooled_prompt_emb = step1x_connector(step1x_llm_embedding, timestep / 1000, step1x_mask) + text_ids = torch.zeros((1, prompt_emb.shape[1], 3), dtype=prompt_emb.dtype, device=prompt_emb.device) + + if image_ids is None: + image_ids = dit.prepare_image_ids(hidden_states) + + conditioning = dit.time_embedder(timestep, hidden_states.dtype) + dit.pooled_text_embedder(pooled_prompt_emb) + if dit.guidance_embedder is not None: + guidance = guidance * 1000 + conditioning = conditioning + dit.guidance_embedder(guidance, hidden_states.dtype) + + height, width = hidden_states.shape[-2:] + hidden_states = dit.patchify(hidden_states) + + # Step1x + if step1x_reference_latents is not None: + step1x_reference_image_ids = dit.prepare_image_ids(step1x_reference_latents) + step1x_reference_latents = dit.patchify(step1x_reference_latents) + image_ids = torch.concat([image_ids, step1x_reference_image_ids], dim=-2) + hidden_states = torch.concat([hidden_states, step1x_reference_latents], dim=1) + + hidden_states = dit.x_embedder(hidden_states) + + if entity_prompt_emb is not None and entity_masks is not None: + prompt_emb, image_rotary_emb, attention_mask = dit.process_entity_masks(hidden_states, prompt_emb, entity_prompt_emb, entity_masks, text_ids, image_ids, 16) + else: + prompt_emb = dit.context_embedder(prompt_emb) + image_rotary_emb = dit.pos_embedder(torch.cat((text_ids, image_ids), dim=1)) + attention_mask = None + + # TeaCache + if tea_cache is not None: + tea_cache_update = tea_cache.check(dit, hidden_states, conditioning) + else: + tea_cache_update = False + + if tea_cache_update: + hidden_states = tea_cache.update(hidden_states) + else: + # Joint Blocks + for block_id, block in enumerate(dit.blocks): + hidden_states, prompt_emb = block( + hidden_states, + prompt_emb, + conditioning, + image_rotary_emb, + attention_mask, + ipadapter_kwargs_list=ipadapter_kwargs_list.get(block_id, None) + ) + # ControlNet + if controlnet is not None and controlnet_frames is not None: + hidden_states = hidden_states + controlnet_res_stack[block_id] + + # Single Blocks + hidden_states = torch.cat([prompt_emb, hidden_states], dim=1) + num_joint_blocks = len(dit.blocks) + for block_id, block in enumerate(dit.single_blocks): + hidden_states, prompt_emb = block( + hidden_states, + prompt_emb, + conditioning, + image_rotary_emb, + attention_mask, + ipadapter_kwargs_list=ipadapter_kwargs_list.get(block_id + num_joint_blocks, None) + ) + # ControlNet + if controlnet is not None and controlnet_frames is not None: + hidden_states[:, prompt_emb.shape[1]:] = hidden_states[:, prompt_emb.shape[1]:] + controlnet_single_res_stack[block_id] + hidden_states = hidden_states[:, prompt_emb.shape[1]:] + + if tea_cache is not None: + tea_cache.store(hidden_states) + + hidden_states = dit.final_norm_out(hidden_states, conditioning) + hidden_states = dit.final_proj_out(hidden_states) + + # Step1x + if step1x_reference_latents is not None: + hidden_states = hidden_states[:, :hidden_states.shape[1] // 2] + + hidden_states = dit.unpatchify(hidden_states, height, width) + + return hidden_states diff --git a/diffsynth/pipelines/flux_image_new.py b/diffsynth/pipelines/flux_image_new.py new file mode 100644 index 0000000000000000000000000000000000000000..8cf73a65b49e3fa6ab7e424e31cff8680dc65806 --- /dev/null +++ b/diffsynth/pipelines/flux_image_new.py @@ -0,0 +1,1318 @@ +import torch, warnings, glob, os, types +import numpy as np +from PIL import Image +from einops import repeat, reduce +from typing import Optional, Union +from dataclasses import dataclass +from modelscope import snapshot_download +from einops import rearrange +import numpy as np +from PIL import Image +from tqdm import tqdm +from typing import Optional +from typing_extensions import Literal + +from ..schedulers import FlowMatchScheduler +from ..prompters import FluxPrompter +from ..models import ModelManager, load_state_dict, SD3TextEncoder1, FluxTextEncoder2, FluxDiT, FluxVAEEncoder, FluxVAEDecoder +from ..models.step1x_connector import Qwen2Connector +from ..models.flux_controlnet import FluxControlNet +from ..models.flux_ipadapter import FluxIpAdapter +from ..models.flux_value_control import MultiValueEncoder +from ..models.flux_infiniteyou import InfiniteYouImageProjector +from ..models.flux_lora_encoder import FluxLoRAEncoder, LoRALayerBlock +from ..models.tiler import FastTileWorker +from ..models.nexus_gen import NexusGenAutoregressiveModel +from ..models.nexus_gen_projector import NexusGenAdapter, NexusGenImageEmbeddingMerger +from ..utils import BasePipeline, ModelConfig, PipelineUnitRunner, PipelineUnit +from ..lora.flux_lora import FluxLoRALoader, FluxLoraPatcher, FluxLoRAFuser + +from ..models.flux_dit import RMSNorm +from ..vram_management import gradient_checkpoint_forward, enable_vram_management, AutoWrappedModule, AutoWrappedLinear + +def visualize_inputs(inputs: dict): + # save the image for debug + print(f"### prompt = {inputs.get('prompt', '')} ###") + # img = inputs['input_image'].detach().cpu().numpy().transpose(0, 2, 3, 1)[0] + img = inputs['input_image'].save("debug_input_image.png") + img_vae = inputs['input_latents'].to(torch.float32).detach().cpu().numpy().transpose(0, 2, 3, 1)[0, :, :, :3] + img_vae = ((img_vae + 1) * 127.5).astype(np.uint8) + Image.fromarray(img_vae).save("debug_input_image_vae.png") + print("### input image saved to debug_input_image.png ###") + +@dataclass +class ControlNetInput: + controlnet_id: int = 0 + scale: float = 1.0 + start: float = 1.0 + end: float = 0.0 + image: Image.Image = None + inpaint_mask: Image.Image = None + processor_id: str = None + + + +class MultiControlNet(torch.nn.Module): + def __init__(self, models: list[FluxControlNet]): + super().__init__() + self.models = torch.nn.ModuleList(models) + + def process_single_controlnet(self, controlnet_input: ControlNetInput, conditioning: torch.Tensor, **kwargs): + model = self.models[controlnet_input.controlnet_id] + res_stack, single_res_stack = model( + controlnet_conditioning=conditioning, + processor_id=controlnet_input.processor_id, + **kwargs + ) + res_stack = [res * controlnet_input.scale for res in res_stack] + single_res_stack = [res * controlnet_input.scale for res in single_res_stack] + return res_stack, single_res_stack + + def forward(self, conditionings: list[torch.Tensor], controlnet_inputs: list[ControlNetInput], progress_id, num_inference_steps, **kwargs): + res_stack, single_res_stack = None, None + for controlnet_input, conditioning in zip(controlnet_inputs, conditionings): + progress = (num_inference_steps - 1 - progress_id) / max(num_inference_steps - 1, 1) + if progress > controlnet_input.start or progress < controlnet_input.end: + continue + res_stack_, single_res_stack_ = self.process_single_controlnet(controlnet_input, conditioning, **kwargs) + if res_stack is None: + res_stack = res_stack_ + single_res_stack = single_res_stack_ + else: + res_stack = [i + j for i, j in zip(res_stack, res_stack_)] + single_res_stack = [i + j for i, j in zip(single_res_stack, single_res_stack_)] + return res_stack, single_res_stack + + + +class FluxImagePipeline(BasePipeline): + + def __init__(self, device="cuda", torch_dtype=torch.bfloat16): + super().__init__( + device=device, torch_dtype=torch_dtype, + height_division_factor=16, width_division_factor=16, + ) + self.scheduler = FlowMatchScheduler() + self.prompter = FluxPrompter() + self.text_encoder_1: SD3TextEncoder1 = None + self.text_encoder_2: FluxTextEncoder2 = None + self.dit: FluxDiT = None + self.vae_decoder: FluxVAEDecoder = None + self.vae_encoder: FluxVAEEncoder = None + self.controlnet: MultiControlNet = None + self.ipadapter: FluxIpAdapter = None + self.ipadapter_image_encoder = None + self.qwenvl = None + self.step1x_connector: Qwen2Connector = None + self.nexus_gen: NexusGenAutoregressiveModel = None + self.nexus_gen_generation_adapter: NexusGenAdapter = None + self.nexus_gen_editing_adapter: NexusGenImageEmbeddingMerger = None + self.value_controller: MultiValueEncoder = None + self.infinityou_processor: InfinitYou = None + self.image_proj_model: InfiniteYouImageProjector = None + self.lora_patcher: FluxLoraPatcher = None + self.lora_encoder: FluxLoRAEncoder = None + self.unit_runner = PipelineUnitRunner() + self.in_iteration_models = ("dit", "step1x_connector", "controlnet", "lora_patcher") + self.units = [ + FluxImageUnit_ShapeChecker(), + FluxImageUnit_NoiseInitializer(), + FluxImageUnit_PromptEmbedder(), + FluxImageUnit_InputImageEmbedder(), + FluxImageUnit_ImageIDs(), + FluxImageUnit_EmbeddedGuidanceEmbedder(), + FluxImageUnit_Kontext(), + FluxImageUnit_InfiniteYou(), + FluxImageUnit_ControlNet(), + FluxImageUnit_IPAdapter(), + FluxImageUnit_EntityControl(), + FluxImageUnit_NexusGen(), + FluxImageUnit_TeaCache(), + FluxImageUnit_Flex(), + FluxImageUnit_Step1x(), + FluxImageUnit_ValueControl(), + FluxImageUnit_LoRAEncode(), + ] + self.model_fn = model_fn_flux_image + + + def load_lora( + self, + module: torch.nn.Module, + lora_config: Union[ModelConfig, str] = None, + alpha=1, + hotload=False, + state_dict=None, + ): + if state_dict is None: + if isinstance(lora_config, str): + lora = load_state_dict(lora_config, torch_dtype=self.torch_dtype, device=self.device) + else: + lora_config.download_if_necessary() + lora = load_state_dict(lora_config.path, torch_dtype=self.torch_dtype, device=self.device) + else: + lora = state_dict + loader = FluxLoRALoader(torch_dtype=self.torch_dtype, device=self.device) + lora = loader.convert_state_dict(lora) + if hotload: + for name, module in module.named_modules(): + if isinstance(module, AutoWrappedLinear): + lora_a_name = f'{name}.lora_A.default.weight' + lora_b_name = f'{name}.lora_B.default.weight' + if lora_a_name in lora and lora_b_name in lora: + module.lora_A_weights.append(lora[lora_a_name] * alpha) + module.lora_B_weights.append(lora[lora_b_name]) + else: + loader.load(module, lora, alpha=alpha) + + + def load_loras( + self, + module: torch.nn.Module, + lora_configs: list[Union[ModelConfig, str]], + alpha=1, + hotload=False, + extra_fused_lora=False, + ): + for lora_config in lora_configs: + self.load_lora(module, lora_config, hotload=hotload, alpha=alpha) + if extra_fused_lora: + lora_fuser = FluxLoRAFuser(device="cuda", torch_dtype=torch.bfloat16) + fused_lora = lora_fuser(lora_configs) + self.load_lora(module, state_dict=fused_lora, hotload=hotload, alpha=alpha) + + + def clear_lora(self): + for name, module in self.named_modules(): + if isinstance(module, AutoWrappedLinear): + if hasattr(module, "lora_A_weights"): + module.lora_A_weights.clear() + if hasattr(module, "lora_B_weights"): + module.lora_B_weights.clear() + + + def training_loss(self, **inputs): + timestep_id = torch.randint(0, self.scheduler.num_train_timesteps, (1,)) + timestep = self.scheduler.timesteps[timestep_id].to(dtype=self.torch_dtype, device=self.device) + + inputs["latents"] = self.scheduler.add_noise(inputs["input_latents"], inputs["noise"], timestep) + training_target = self.scheduler.training_target(inputs["input_latents"], inputs["noise"], timestep) + + noise_pred = self.model_fn(**inputs, timestep=timestep) + + loss = torch.nn.functional.mse_loss(noise_pred.float(), training_target.float()) + loss = loss * self.scheduler.training_weight(timestep) + return loss + + + def _enable_vram_management_with_default_config(self, model, vram_limit): + if model is not None: + dtype = next(iter(model.parameters())).dtype + enable_vram_management( + model, + module_map = { + torch.nn.Linear: AutoWrappedLinear, + torch.nn.Embedding: AutoWrappedModule, + torch.nn.LayerNorm: AutoWrappedModule, + torch.nn.Conv2d: AutoWrappedModule, + torch.nn.GroupNorm: AutoWrappedModule, + RMSNorm: AutoWrappedModule, + LoRALayerBlock: AutoWrappedModule, + }, + module_config = dict( + offload_dtype=dtype, + offload_device="cpu", + onload_dtype=dtype, + onload_device="cpu", + computation_dtype=self.torch_dtype, + computation_device=self.device, + ), + vram_limit=vram_limit, + ) + + + def enable_lora_magic(self): + if self.dit is not None: + if not (hasattr(self.dit, "vram_management_enabled") and self.dit.vram_management_enabled): + dtype = next(iter(self.dit.parameters())).dtype + enable_vram_management( + self.dit, + module_map = { + torch.nn.Linear: AutoWrappedLinear, + }, + module_config = dict( + offload_dtype=dtype, + offload_device=self.device, + onload_dtype=dtype, + onload_device=self.device, + computation_dtype=self.torch_dtype, + computation_device=self.device, + ), + vram_limit=None, + ) + if self.lora_patcher is not None: + for name, module in self.dit.named_modules(): + if isinstance(module, AutoWrappedLinear): + merger_name = name.replace(".", "___") + if merger_name in self.lora_patcher.model_dict: + module.lora_merger = self.lora_patcher.model_dict[merger_name] + + + def enable_vram_management(self, num_persistent_param_in_dit=None, vram_limit=None, vram_buffer=0.5): + self.vram_management_enabled = True + if num_persistent_param_in_dit is not None: + vram_limit = None + else: + if vram_limit is None: + vram_limit = self.get_vram() + vram_limit = vram_limit - vram_buffer + + # Default config + default_vram_management_models = ["text_encoder_1", "vae_decoder", "vae_encoder", "controlnet", "image_proj_model", "ipadapter", "lora_patcher", "value_controller", "step1x_connector", "lora_encoder"] + for model_name in default_vram_management_models: + self._enable_vram_management_with_default_config(getattr(self, model_name), vram_limit) + + # Special config + if self.text_encoder_2 is not None: + from transformers.models.t5.modeling_t5 import T5LayerNorm, T5DenseActDense, T5DenseGatedActDense + dtype = next(iter(self.text_encoder_2.parameters())).dtype + enable_vram_management( + self.text_encoder_2, + module_map = { + torch.nn.Linear: AutoWrappedLinear, + torch.nn.Embedding: AutoWrappedModule, + T5LayerNorm: AutoWrappedModule, + T5DenseActDense: AutoWrappedModule, + T5DenseGatedActDense: AutoWrappedModule, + }, + module_config = dict( + offload_dtype=dtype, + offload_device="cpu", + onload_dtype=dtype, + onload_device="cpu", + computation_dtype=self.torch_dtype, + computation_device=self.device, + ), + vram_limit=vram_limit, + ) + if self.dit is not None: + dtype = next(iter(self.dit.parameters())).dtype + device = "cpu" if vram_limit is not None else self.device + enable_vram_management( + self.dit, + module_map = { + RMSNorm: AutoWrappedModule, + torch.nn.Linear: AutoWrappedLinear, + }, + module_config = dict( + offload_dtype=dtype, + offload_device="cpu", + onload_dtype=dtype, + onload_device=device, + computation_dtype=self.torch_dtype, + computation_device=self.device, + ), + max_num_param=num_persistent_param_in_dit, + overflow_module_config = dict( + offload_dtype=dtype, + offload_device="cpu", + onload_dtype=dtype, + onload_device="cpu", + computation_dtype=self.torch_dtype, + computation_device=self.device, + ), + vram_limit=vram_limit, + ) + if self.ipadapter_image_encoder is not None: + from transformers.models.siglip.modeling_siglip import SiglipVisionEmbeddings, SiglipEncoder, SiglipMultiheadAttentionPoolingHead + dtype = next(iter(self.ipadapter_image_encoder.parameters())).dtype + enable_vram_management( + self.ipadapter_image_encoder, + module_map = { + SiglipVisionEmbeddings: AutoWrappedModule, + SiglipEncoder: AutoWrappedModule, + SiglipMultiheadAttentionPoolingHead: AutoWrappedModule, + torch.nn.MultiheadAttention: AutoWrappedModule, + torch.nn.Linear: AutoWrappedLinear, + torch.nn.LayerNorm: AutoWrappedModule, + }, + module_config = dict( + offload_dtype=dtype, + offload_device="cpu", + onload_dtype=dtype, + onload_device="cpu", + computation_dtype=self.torch_dtype, + computation_device=self.device, + ), + vram_limit=vram_limit, + ) + if self.qwenvl is not None: + from transformers.models.qwen2_5_vl.modeling_qwen2_5_vl import ( + Qwen2_5_VisionPatchEmbed, Qwen2_5_VLVisionBlock, Qwen2_5_VLPatchMerger, + Qwen2_5_VLDecoderLayer, Qwen2_5_VisionRotaryEmbedding, Qwen2_5_VLRotaryEmbedding, Qwen2RMSNorm + ) + dtype = next(iter(self.qwenvl.parameters())).dtype + enable_vram_management( + self.qwenvl, + module_map = { + Qwen2_5_VisionPatchEmbed: AutoWrappedModule, + Qwen2_5_VLVisionBlock: AutoWrappedModule, + Qwen2_5_VLPatchMerger: AutoWrappedModule, + Qwen2_5_VLDecoderLayer: AutoWrappedModule, + Qwen2_5_VisionRotaryEmbedding: AutoWrappedModule, + Qwen2_5_VLRotaryEmbedding: AutoWrappedModule, + Qwen2RMSNorm: AutoWrappedModule, + torch.nn.Embedding: AutoWrappedModule, + torch.nn.Linear: AutoWrappedLinear, + torch.nn.LayerNorm: AutoWrappedModule, + }, + module_config = dict( + offload_dtype=dtype, + offload_device="cpu", + onload_dtype=dtype, + onload_device="cpu", + computation_dtype=self.torch_dtype, + computation_device=self.device, + ), + vram_limit=vram_limit, + ) + + + @staticmethod + def from_pretrained( + torch_dtype: torch.dtype = torch.bfloat16, + device: Union[str, torch.device] = "cuda", + model_configs: list[ModelConfig] = [], + nexus_gen_processor_config: ModelConfig = ModelConfig(model_id="DiffSynth-Studio/Nexus-GenV2", origin_file_pattern="processor/"), + ): + # Download and load models + model_manager = ModelManager() + for model_config in model_configs: + model_config.download_if_necessary() + model_manager.load_model( + model_config.path, + device=model_config.offload_device or device, + torch_dtype=model_config.offload_dtype or torch_dtype + ) + + # Initialize pipeline + pipe = FluxImagePipeline(device=device, torch_dtype=torch_dtype) + pipe.text_encoder_1 = model_manager.fetch_model("sd3_text_encoder_1") + pipe.text_encoder_2 = model_manager.fetch_model("flux_text_encoder_2") + pipe.dit = model_manager.fetch_model("flux_dit") + pipe.vae_decoder = model_manager.fetch_model("flux_vae_decoder") + pipe.vae_encoder = model_manager.fetch_model("flux_vae_encoder") + pipe.prompter.fetch_models(pipe.text_encoder_1, pipe.text_encoder_2) + pipe.ipadapter = model_manager.fetch_model("flux_ipadapter") + pipe.ipadapter_image_encoder = model_manager.fetch_model("siglip_vision_model") + pipe.qwenvl = model_manager.fetch_model("qwenvl") + pipe.step1x_connector = model_manager.fetch_model("step1x_connector") + pipe.image_proj_model = model_manager.fetch_model("infiniteyou_image_projector") + if pipe.image_proj_model is not None: + pipe.infinityou_processor = InfinitYou(device=device) + pipe.lora_patcher = model_manager.fetch_model("flux_lora_patcher") + pipe.lora_encoder = model_manager.fetch_model("flux_lora_encoder") + pipe.nexus_gen = model_manager.fetch_model("nexus_gen_llm") + pipe.nexus_gen_generation_adapter = model_manager.fetch_model("nexus_gen_generation_adapter") + pipe.nexus_gen_editing_adapter = model_manager.fetch_model("nexus_gen_editing_adapter") + if nexus_gen_processor_config is not None and pipe.nexus_gen is not None: + nexus_gen_processor_config.download_if_necessary() + pipe.nexus_gen.load_processor(nexus_gen_processor_config.path) + + # ControlNet + controlnets = [] + for model_name, model in zip(model_manager.model_name, model_manager.model): + if model_name == "flux_controlnet": + controlnets.append(model) + if len(controlnets) > 0: + pipe.controlnet = MultiControlNet(controlnets) + + # Value Controller + value_controllers = [] + for model_name, model in zip(model_manager.model_name, model_manager.model): + if model_name == "flux_value_controller": + value_controllers.append(model) + if len(value_controllers) > 0: + pipe.value_controller = MultiValueEncoder(value_controllers) + + return pipe + + + @torch.no_grad() + def __call__( + self, + # Prompt + prompt: str, + negative_prompt: str = "", + cfg_scale: float = 1.0, + embedded_guidance: float = 3.5, + t5_sequence_length: int = 512, + # Image + input_image: Image.Image = None, + denoising_strength: float = 1.0, + # Shape + height: int = 1024, + width: int = 1024, + # Randomness + seed: int = None, + rand_device: str = "cpu", + # Scheduler + sigma_shift: float = None, + # Steps + num_inference_steps: int = 30, + # local prompts + multidiffusion_prompts=(), + multidiffusion_masks=(), + multidiffusion_scales=(), + # Kontext + kontext_images: Union[list[Image.Image], Image.Image] = None, + # ControlNet + controlnet_inputs: list[ControlNetInput] = None, + # IP-Adapter + ipadapter_images: Union[list[Image.Image], Image.Image] = None, + ipadapter_scale: float = 1.0, + # EliGen + eligen_entity_prompts: list[str] = None, + eligen_entity_masks: list[Image.Image] = None, + eligen_enable_on_negative: bool = False, + eligen_enable_inpaint: bool = False, + # InfiniteYou + infinityou_id_image: Image.Image = None, + infinityou_guidance: float = 1.0, + # Flex + flex_inpaint_image: Image.Image = None, + flex_inpaint_mask: Image.Image = None, + flex_control_image: Image.Image = None, + flex_control_strength: float = 0.5, + flex_control_stop: float = 0.5, + # Value Controller + value_controller_inputs: Union[list[float], float] = None, + # Step1x + step1x_reference_image: Image.Image = None, + # NexusGen + nexus_gen_reference_image: Image.Image = None, + # LoRA Encoder + lora_encoder_inputs: Union[list[ModelConfig], ModelConfig, str] = None, + lora_encoder_scale: float = 1.0, + # TeaCache + tea_cache_l1_thresh: float = None, + # Tile + tiled: bool = False, + tile_size: int = 128, + tile_stride: int = 64, + # Progress bar + progress_bar_cmd = tqdm, + ): + # Scheduler + self.scheduler.set_timesteps(num_inference_steps, denoising_strength=denoising_strength, shift=sigma_shift) + + inputs_posi = { + "prompt": prompt, + } + inputs_nega = { + "negative_prompt": negative_prompt, + } + inputs_shared = { + "cfg_scale": cfg_scale, "embedded_guidance": embedded_guidance, "t5_sequence_length": t5_sequence_length, + "input_image": input_image, "denoising_strength": denoising_strength, + "height": height, "width": width, + "seed": seed, "rand_device": rand_device, + "sigma_shift": sigma_shift, "num_inference_steps": num_inference_steps, + "multidiffusion_prompts": multidiffusion_prompts, "multidiffusion_masks": multidiffusion_masks, "multidiffusion_scales": multidiffusion_scales, + "kontext_images": kontext_images, + "controlnet_inputs": controlnet_inputs, + "ipadapter_images": ipadapter_images, "ipadapter_scale": ipadapter_scale, + "eligen_entity_prompts": eligen_entity_prompts, "eligen_entity_masks": eligen_entity_masks, "eligen_enable_on_negative": eligen_enable_on_negative, "eligen_enable_inpaint": eligen_enable_inpaint, + "infinityou_id_image": infinityou_id_image, "infinityou_guidance": infinityou_guidance, + "flex_inpaint_image": flex_inpaint_image, "flex_inpaint_mask": flex_inpaint_mask, "flex_control_image": flex_control_image, "flex_control_strength": flex_control_strength, "flex_control_stop": flex_control_stop, + "value_controller_inputs": value_controller_inputs, + "step1x_reference_image": step1x_reference_image, + "nexus_gen_reference_image": nexus_gen_reference_image, + "lora_encoder_inputs": lora_encoder_inputs, "lora_encoder_scale": lora_encoder_scale, + "tea_cache_l1_thresh": tea_cache_l1_thresh, + "tiled": tiled, "tile_size": tile_size, "tile_stride": tile_stride, + "progress_bar_cmd": progress_bar_cmd, + } + for unit in self.units: + inputs_shared, inputs_posi, inputs_nega = self.unit_runner(unit, self, inputs_shared, inputs_posi, inputs_nega) + + # Denoise + self.load_models_to_device(self.in_iteration_models) + models = {name: getattr(self, name) for name in self.in_iteration_models} + for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)): + timestep = timestep.unsqueeze(0).to(dtype=self.torch_dtype, device=self.device) + + # Inference + noise_pred_posi = self.model_fn(**models, **inputs_shared, **inputs_posi, timestep=timestep, progress_id=progress_id) + if cfg_scale != 1.0: + noise_pred_nega = self.model_fn(**models, **inputs_shared, **inputs_nega, timestep=timestep, progress_id=progress_id) + noise_pred = noise_pred_nega + cfg_scale * (noise_pred_posi - noise_pred_nega) + else: + noise_pred = noise_pred_posi + + # Scheduler + inputs_shared["latents"] = self.scheduler.step(noise_pred, self.scheduler.timesteps[progress_id], inputs_shared["latents"]) + + # Decode + self.load_models_to_device(['vae_decoder']) + image = self.vae_decoder(inputs_shared["latents"], device=self.device, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) + image = self.vae_output_to_image(image) + self.load_models_to_device([]) + + return image + + + +class FluxImageUnit_ShapeChecker(PipelineUnit): + def __init__(self): + super().__init__(input_params=("height", "width")) + + def process(self, pipe: FluxImagePipeline, height, width): + height, width = pipe.check_resize_height_width(height, width) + return {"height": height, "width": width} + + + +class FluxImageUnit_NoiseInitializer(PipelineUnit): + def __init__(self): + super().__init__(input_params=("height", "width", "seed", "rand_device")) + + def process(self, pipe: FluxImagePipeline, height, width, seed, rand_device): + noise = pipe.generate_noise((1, 16, height//8, width//8), seed=seed, rand_device=rand_device) + return {"noise": noise} + + + +class FluxImageUnit_InputImageEmbedder(PipelineUnit): + def __init__(self): + super().__init__( + input_params=("input_image", "noise", "tiled", "tile_size", "tile_stride"), + onload_model_names=("vae_encoder",) + ) + + def process(self, pipe: FluxImagePipeline, input_image, noise, tiled, tile_size, tile_stride): + if input_image is None: + return {"latents": noise, "input_latents": None} + pipe.load_models_to_device(['vae_encoder']) + image = pipe.preprocess_image(input_image).to(device=pipe.device, dtype=pipe.torch_dtype) + input_latents = pipe.vae_encoder(image, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) + if pipe.scheduler.training: + return {"latents": noise, "input_latents": input_latents} + else: + latents = pipe.scheduler.add_noise(input_latents, noise, timestep=pipe.scheduler.timesteps[0]) + return {"latents": latents, "input_latents": None} + + + +class FluxImageUnit_PromptEmbedder(PipelineUnit): + def __init__(self): + super().__init__( + seperate_cfg=True, + input_params_posi={"prompt": "prompt", "positive": "positive"}, + input_params_nega={"prompt": "negative_prompt", "positive": "positive"}, + input_params=("t5_sequence_length",), + onload_model_names=("text_encoder_1", "text_encoder_2") + ) + + def process(self, pipe: FluxImagePipeline, prompt, t5_sequence_length, positive) -> dict: + if pipe.text_encoder_1 is not None and pipe.text_encoder_2 is not None: + prompt_emb, pooled_prompt_emb, text_ids = pipe.prompter.encode_prompt( + prompt, device=pipe.device, positive=positive, t5_sequence_length=t5_sequence_length + ) + return {"prompt_emb": prompt_emb, "pooled_prompt_emb": pooled_prompt_emb, "text_ids": text_ids} + else: + return {} + + +class FluxImageUnit_ImageIDs(PipelineUnit): + def __init__(self): + super().__init__(input_params=("latents",)) + + def process(self, pipe: FluxImagePipeline, latents): + latent_image_ids = pipe.dit.prepare_image_ids(latents) + return {"image_ids": latent_image_ids} + + + +class FluxImageUnit_EmbeddedGuidanceEmbedder(PipelineUnit): + def __init__(self): + super().__init__(input_params=("embedded_guidance", "latents")) + + def process(self, pipe: FluxImagePipeline, embedded_guidance, latents): + guidance = torch.Tensor([embedded_guidance] * latents.shape[0]).to(device=latents.device, dtype=latents.dtype) + return {"guidance": guidance} + + + +class FluxImageUnit_Kontext(PipelineUnit): + def __init__(self): + super().__init__(input_params=("kontext_images", "tiled", "tile_size", "tile_stride")) + + def process(self, pipe: FluxImagePipeline, kontext_images, tiled, tile_size, tile_stride): + if kontext_images is None: + return {} + if not isinstance(kontext_images, list): + kontext_images = [kontext_images] + + kontext_latents = [] + kontext_image_ids = [] + for kontext_image in kontext_images: + kontext_image = pipe.preprocess_image(kontext_image) + kontext_latent = pipe.vae_encoder(kontext_image, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) + image_ids = pipe.dit.prepare_image_ids(kontext_latent) + image_ids[..., 0] = 1 + kontext_image_ids.append(image_ids) + kontext_latent = pipe.dit.patchify(kontext_latent) + kontext_latents.append(kontext_latent) + kontext_latents = torch.concat(kontext_latents, dim=1) + kontext_image_ids = torch.concat(kontext_image_ids, dim=-2) + return {"kontext_latents": kontext_latents, "kontext_image_ids": kontext_image_ids} + + + +class FluxImageUnit_ControlNet(PipelineUnit): + def __init__(self): + super().__init__( + input_params=("controlnet_inputs", "tiled", "tile_size", "tile_stride"), + onload_model_names=("vae_encoder",) + ) + + def apply_controlnet_mask_on_latents(self, pipe, latents, mask): + mask = (pipe.preprocess_image(mask) + 1) / 2 + mask = mask.mean(dim=1, keepdim=True) + mask = 1 - torch.nn.functional.interpolate(mask, size=latents.shape[-2:]) + latents = torch.concat([latents, mask], dim=1) + return latents + + def apply_controlnet_mask_on_image(self, pipe, image, mask): + mask = mask.resize(image.size) + mask = pipe.preprocess_image(mask).mean(dim=[0, 1]).cpu() + image = np.array(image) + image[mask > 0] = 0 + image = Image.fromarray(image) + return image + + def process(self, pipe: FluxImagePipeline, controlnet_inputs: list[ControlNetInput], tiled, tile_size, tile_stride): + if controlnet_inputs is None: + return {} + pipe.load_models_to_device(['vae_encoder']) + conditionings = [] + for controlnet_input in controlnet_inputs: + image = controlnet_input.image + if controlnet_input.inpaint_mask is not None: + image = self.apply_controlnet_mask_on_image(pipe, image, controlnet_input.inpaint_mask) + + image = pipe.preprocess_image(image).to(device=pipe.device, dtype=pipe.torch_dtype) + image = pipe.vae_encoder(image, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) + + if controlnet_input.inpaint_mask is not None: + image = self.apply_controlnet_mask_on_latents(pipe, image, controlnet_input.inpaint_mask) + conditionings.append(image) + return {"controlnet_conditionings": conditionings} + + + +class FluxImageUnit_IPAdapter(PipelineUnit): + def __init__(self): + super().__init__( + take_over=True, + onload_model_names=("ipadapter_image_encoder", "ipadapter") + ) + + def process(self, pipe: FluxImagePipeline, inputs_shared, inputs_posi, inputs_nega): + ipadapter_images, ipadapter_scale = inputs_shared.get("ipadapter_images", None), inputs_shared.get("ipadapter_scale", 1.0) + if ipadapter_images is None: + return inputs_shared, inputs_posi, inputs_nega + if not isinstance(ipadapter_images, list): + ipadapter_images = [ipadapter_images] + + pipe.load_models_to_device(self.onload_model_names) + images = [image.convert("RGB").resize((384, 384), resample=3) for image in ipadapter_images] + images = [pipe.preprocess_image(image).to(device=pipe.device, dtype=pipe.torch_dtype) for image in images] + ipadapter_images = torch.cat(images, dim=0) + ipadapter_image_encoding = pipe.ipadapter_image_encoder(ipadapter_images).pooler_output + + inputs_posi.update({"ipadapter_kwargs_list": pipe.ipadapter(ipadapter_image_encoding, scale=ipadapter_scale)}) + if inputs_shared.get("cfg_scale", 1.0) != 1.0: + inputs_nega.update({"ipadapter_kwargs_list": pipe.ipadapter(torch.zeros_like(ipadapter_image_encoding))}) + return inputs_shared, inputs_posi, inputs_nega + + + +class FluxImageUnit_EntityControl(PipelineUnit): + def __init__(self): + super().__init__( + take_over=True, + onload_model_names=("text_encoder_1", "text_encoder_2") + ) + + def preprocess_masks(self, pipe, masks, height, width, dim): + out_masks = [] + for mask in masks: + mask = pipe.preprocess_image(mask.resize((width, height), resample=Image.NEAREST)).mean(dim=1, keepdim=True) > 0 + mask = mask.repeat(1, dim, 1, 1).to(device=pipe.device, dtype=pipe.torch_dtype) + out_masks.append(mask) + return out_masks + + def prepare_entity_inputs(self, pipe, entity_prompts, entity_masks, width, height, t5_sequence_length=512): + entity_masks = self.preprocess_masks(pipe, entity_masks, height//8, width//8, 1) + entity_masks = torch.cat(entity_masks, dim=0).unsqueeze(0) # b, n_mask, c, h, w + + prompt_emb, _, _ = pipe.prompter.encode_prompt( + entity_prompts, device=pipe.device, t5_sequence_length=t5_sequence_length + ) + return prompt_emb.unsqueeze(0), entity_masks + + def prepare_eligen(self, pipe, prompt_emb_nega, eligen_entity_prompts, eligen_entity_masks, width, height, t5_sequence_length, enable_eligen_on_negative, cfg_scale): + entity_prompt_emb_posi, entity_masks_posi = self.prepare_entity_inputs(pipe, eligen_entity_prompts, eligen_entity_masks, width, height, t5_sequence_length) + if enable_eligen_on_negative and cfg_scale != 1.0: + entity_prompt_emb_nega = prompt_emb_nega['prompt_emb'].unsqueeze(1).repeat(1, entity_masks_posi.shape[1], 1, 1) + entity_masks_nega = entity_masks_posi + else: + entity_prompt_emb_nega, entity_masks_nega = None, None + eligen_kwargs_posi = {"entity_prompt_emb": entity_prompt_emb_posi, "entity_masks": entity_masks_posi} + eligen_kwargs_nega = {"entity_prompt_emb": entity_prompt_emb_nega, "entity_masks": entity_masks_nega} + return eligen_kwargs_posi, eligen_kwargs_nega + + def process(self, pipe: FluxImagePipeline, inputs_shared, inputs_posi, inputs_nega): + eligen_entity_prompts, eligen_entity_masks = inputs_shared.get("eligen_entity_prompts", None), inputs_shared.get("eligen_entity_masks", None) + if eligen_entity_prompts is None or eligen_entity_masks is None: + return inputs_shared, inputs_posi, inputs_nega + pipe.load_models_to_device(self.onload_model_names) + eligen_enable_on_negative = inputs_shared.get("eligen_enable_on_negative", False) + eligen_kwargs_posi, eligen_kwargs_nega = self.prepare_eligen(pipe, inputs_nega, + eligen_entity_prompts, eligen_entity_masks, inputs_shared["width"], inputs_shared["height"], + inputs_shared["t5_sequence_length"], eligen_enable_on_negative, inputs_shared["cfg_scale"]) + inputs_posi.update(eligen_kwargs_posi) + if inputs_shared.get("cfg_scale", 1.0) != 1.0: + inputs_nega.update(eligen_kwargs_nega) + return inputs_shared, inputs_posi, inputs_nega + + +class FluxImageUnit_NexusGen(PipelineUnit): + def __init__(self): + super().__init__( + take_over=True, + onload_model_names=("nexus_gen", "nexus_gen_generation_adapter", "nexus_gen_editing_adapter"), + ) + + def process(self, pipe: FluxImagePipeline, inputs_shared, inputs_posi, inputs_nega): + if pipe.nexus_gen is None: + return inputs_shared, inputs_posi, inputs_nega + pipe.load_models_to_device(self.onload_model_names) + if inputs_shared.get("nexus_gen_reference_image", None) is None: + assert pipe.nexus_gen_generation_adapter is not None, "NexusGen requires a generation adapter to be set." + embed = pipe.nexus_gen(inputs_posi["prompt"])[0].unsqueeze(0) + inputs_posi["prompt_emb"] = pipe.nexus_gen_generation_adapter(embed) + inputs_posi['text_ids'] = torch.zeros(embed.shape[0], embed.shape[1], 3).to(device=pipe.device, dtype=pipe.torch_dtype) + else: + assert pipe.nexus_gen_editing_adapter is not None, "NexusGen requires an editing adapter to be set." + embed, ref_embed, grids = pipe.nexus_gen(inputs_posi["prompt"], inputs_shared["nexus_gen_reference_image"]) + embeds_grid = grids[1:2].to(device=pipe.device, dtype=torch.long) + ref_embeds_grid = grids[0:1].to(device=pipe.device, dtype=torch.long) + + inputs_posi["prompt_emb"] = pipe.nexus_gen_editing_adapter(embed.unsqueeze(0), embeds_grid, ref_embed.unsqueeze(0), ref_embeds_grid) + inputs_posi["text_ids"] = self.get_editing_text_ids( + inputs_shared["latents"], + embeds_grid[0][1].item(), embeds_grid[0][2].item(), + ref_embeds_grid[0][1].item(), ref_embeds_grid[0][2].item(), + ) + return inputs_shared, inputs_posi, inputs_nega + + + def get_editing_text_ids(self, latents, target_embed_height, target_embed_width, ref_embed_height, ref_embed_width): + # prepare text ids for target and reference embeddings + batch_size, height, width = latents.shape[0], target_embed_height, target_embed_width + embed_ids = torch.zeros(height // 2, width // 2, 3) + scale_factor_height, scale_factor_width = latents.shape[-2] / height, latents.shape[-1] / width + embed_ids[..., 1] = embed_ids[..., 1] + torch.arange(height // 2)[:, None] * scale_factor_height + embed_ids[..., 2] = embed_ids[..., 2] + torch.arange(width // 2)[None, :] * scale_factor_width + embed_ids = embed_ids[None, :].repeat(batch_size, 1, 1, 1).reshape(batch_size, height // 2 * width // 2, 3) + embed_text_ids = embed_ids.to(device=latents.device, dtype=latents.dtype) + + batch_size, height, width = latents.shape[0], ref_embed_height, ref_embed_width + ref_embed_ids = torch.zeros(height // 2, width // 2, 3) + scale_factor_height, scale_factor_width = latents.shape[-2] / height, latents.shape[-1] / width + ref_embed_ids[..., 0] = ref_embed_ids[..., 0] + 1.0 + ref_embed_ids[..., 1] = ref_embed_ids[..., 1] + torch.arange(height // 2)[:, None] * scale_factor_height + ref_embed_ids[..., 2] = ref_embed_ids[..., 2] + torch.arange(width // 2)[None, :] * scale_factor_width + ref_embed_ids = ref_embed_ids[None, :].repeat(batch_size, 1, 1, 1).reshape(batch_size, height // 2 * width // 2, 3) + ref_embed_text_ids = ref_embed_ids.to(device=latents.device, dtype=latents.dtype) + + text_ids = torch.cat([embed_text_ids, ref_embed_text_ids], dim=1) + return text_ids + + +class FluxImageUnit_Step1x(PipelineUnit): + def __init__(self): + super().__init__(take_over=True,onload_model_names=("qwenvl","vae_encoder")) + + def process(self, pipe: FluxImagePipeline, inputs_shared: dict, inputs_posi: dict, inputs_nega: dict): + image = inputs_shared.get("step1x_reference_image",None) + if image is None: + return inputs_shared, inputs_posi, inputs_nega + else: + pipe.load_models_to_device(self.onload_model_names) + prompt = inputs_posi["prompt"] + nega_prompt = inputs_nega["negative_prompt"] + captions = [prompt, nega_prompt] + ref_images = [image, image] + embs, masks = pipe.qwenvl(captions, ref_images) + image = pipe.preprocess_image(image).to(device=pipe.device, dtype=pipe.torch_dtype) + image = pipe.vae_encoder(image) + inputs_posi.update({"step1x_llm_embedding": embs[0:1], "step1x_mask": masks[0:1], "step1x_reference_latents": image}) + if inputs_shared.get("cfg_scale", 1) != 1: + inputs_nega.update({"step1x_llm_embedding": embs[1:2], "step1x_mask": masks[1:2], "step1x_reference_latents": image}) + return inputs_shared, inputs_posi, inputs_nega + + +class FluxImageUnit_TeaCache(PipelineUnit): + def __init__(self): + super().__init__(input_params=("num_inference_steps","tea_cache_l1_thresh")) + + def process(self, pipe: FluxImagePipeline, num_inference_steps, tea_cache_l1_thresh): + if tea_cache_l1_thresh is None: + return {} + else: + return {"tea_cache": TeaCache(num_inference_steps=num_inference_steps, rel_l1_thresh=tea_cache_l1_thresh)} + +class FluxImageUnit_Flex(PipelineUnit): + def __init__(self): + super().__init__( + input_params=("latents", "flex_inpaint_image", "flex_inpaint_mask", "flex_control_image", "flex_control_strength", "flex_control_stop", "tiled", "tile_size", "tile_stride"), + onload_model_names=("vae_encoder",) + ) + + def process(self, pipe: FluxImagePipeline, latents, flex_inpaint_image, flex_inpaint_mask, flex_control_image, flex_control_strength, flex_control_stop, tiled, tile_size, tile_stride): + if pipe.dit.input_dim == 196: + if flex_control_stop is None: + flex_control_stop = 1 + pipe.load_models_to_device(self.onload_model_names) + if flex_inpaint_image is None: + flex_inpaint_image = torch.zeros_like(latents) + else: + flex_inpaint_image = pipe.preprocess_image(flex_inpaint_image).to(device=pipe.device, dtype=pipe.torch_dtype) + flex_inpaint_image = pipe.vae_encoder(flex_inpaint_image, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) + if flex_inpaint_mask is None: + flex_inpaint_mask = torch.ones_like(latents)[:, 0:1, :, :] + else: + flex_inpaint_mask = flex_inpaint_mask.resize((latents.shape[3], latents.shape[2])) + flex_inpaint_mask = pipe.preprocess_image(flex_inpaint_mask).to(device=pipe.device, dtype=pipe.torch_dtype) + flex_inpaint_mask = (flex_inpaint_mask[:, 0:1, :, :] + 1) / 2 + flex_inpaint_image = flex_inpaint_image * (1 - flex_inpaint_mask) + if flex_control_image is None: + flex_control_image = torch.zeros_like(latents) + else: + flex_control_image = pipe.preprocess_image(flex_control_image).to(device=pipe.device, dtype=pipe.torch_dtype) + flex_control_image = pipe.vae_encoder(flex_control_image, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) * flex_control_strength + flex_condition = torch.concat([flex_inpaint_image, flex_inpaint_mask, flex_control_image], dim=1) + flex_uncondition = torch.concat([flex_inpaint_image, flex_inpaint_mask, torch.zeros_like(flex_control_image)], dim=1) + flex_control_stop_timestep = pipe.scheduler.timesteps[int(flex_control_stop * (len(pipe.scheduler.timesteps) - 1))] + return {"flex_condition": flex_condition, "flex_uncondition": flex_uncondition, "flex_control_stop_timestep": flex_control_stop_timestep} + else: + return {} + + + +class FluxImageUnit_InfiniteYou(PipelineUnit): + def __init__(self): + super().__init__( + input_params=("infinityou_id_image", "infinityou_guidance"), + onload_model_names=("infinityou_processor",) + ) + + def process(self, pipe: FluxImagePipeline, infinityou_id_image, infinityou_guidance): + pipe.load_models_to_device("infinityou_processor") + if infinityou_id_image is not None: + return pipe.infinityou_processor.prepare_infinite_you(pipe.image_proj_model, infinityou_id_image, infinityou_guidance, pipe.device) + else: + return {} + + + +class FluxImageUnit_ValueControl(PipelineUnit): + def __init__(self): + super().__init__( + seperate_cfg=True, + input_params_posi={"prompt_emb": "prompt_emb", "text_ids": "text_ids"}, + input_params_nega={"prompt_emb": "prompt_emb", "text_ids": "text_ids"}, + input_params=("value_controller_inputs",), + onload_model_names=("value_controller",) + ) + + def add_to_text_embedding(self, prompt_emb, text_ids, value_emb): + prompt_emb = torch.concat([prompt_emb, value_emb], dim=1) + extra_text_ids = torch.zeros((value_emb.shape[0], value_emb.shape[1], 3), device=value_emb.device, dtype=value_emb.dtype) + text_ids = torch.concat([text_ids, extra_text_ids], dim=1) + return prompt_emb, text_ids + + def process(self, pipe: FluxImagePipeline, prompt_emb, text_ids, value_controller_inputs): + if value_controller_inputs is None: + return {} + if not isinstance(value_controller_inputs, list): + value_controller_inputs = [value_controller_inputs] + value_controller_inputs = torch.tensor(value_controller_inputs).to(dtype=pipe.torch_dtype, device=pipe.device) + pipe.load_models_to_device(["value_controller"]) + value_emb = pipe.value_controller(value_controller_inputs, pipe.torch_dtype) + value_emb = value_emb.unsqueeze(0) + prompt_emb, text_ids = self.add_to_text_embedding(prompt_emb, text_ids, value_emb) + return {"prompt_emb": prompt_emb, "text_ids": text_ids} + + + +class InfinitYou(torch.nn.Module): + def __init__(self, device="cuda", torch_dtype=torch.bfloat16): + super().__init__() + from facexlib.recognition import init_recognition_model + from insightface.app import FaceAnalysis + self.device = device + self.torch_dtype = torch_dtype + insightface_root_path = 'models/ByteDance/InfiniteYou/supports/insightface' + self.app_640 = FaceAnalysis(name='antelopev2', root=insightface_root_path, providers=['CUDAExecutionProvider', 'CPUExecutionProvider']) + self.app_640.prepare(ctx_id=0, det_size=(640, 640)) + self.app_320 = FaceAnalysis(name='antelopev2', root=insightface_root_path, providers=['CUDAExecutionProvider', 'CPUExecutionProvider']) + self.app_320.prepare(ctx_id=0, det_size=(320, 320)) + self.app_160 = FaceAnalysis(name='antelopev2', root=insightface_root_path, providers=['CUDAExecutionProvider', 'CPUExecutionProvider']) + self.app_160.prepare(ctx_id=0, det_size=(160, 160)) + self.arcface_model = init_recognition_model('arcface', device=self.device).to(torch_dtype) + + def _detect_face(self, id_image_cv2): + face_info = self.app_640.get(id_image_cv2) + if len(face_info) > 0: + return face_info + face_info = self.app_320.get(id_image_cv2) + if len(face_info) > 0: + return face_info + face_info = self.app_160.get(id_image_cv2) + return face_info + + def extract_arcface_bgr_embedding(self, in_image, landmark, device): + from insightface.utils import face_align + arc_face_image = face_align.norm_crop(in_image, landmark=np.array(landmark), image_size=112) + arc_face_image = torch.from_numpy(arc_face_image).unsqueeze(0).permute(0, 3, 1, 2) / 255. + arc_face_image = 2 * arc_face_image - 1 + arc_face_image = arc_face_image.contiguous().to(device=device, dtype=self.torch_dtype) + face_emb = self.arcface_model(arc_face_image)[0] # [512], normalized + return face_emb + + def prepare_infinite_you(self, model, id_image, infinityou_guidance, device): + import cv2 + if id_image is None: + return {'id_emb': None} + id_image_cv2 = cv2.cvtColor(np.array(id_image), cv2.COLOR_RGB2BGR) + face_info = self._detect_face(id_image_cv2) + if len(face_info) == 0: + raise ValueError('No face detected in the input ID image') + landmark = sorted(face_info, key=lambda x:(x['bbox'][2]-x['bbox'][0])*(x['bbox'][3]-x['bbox'][1]))[-1]['kps'] # only use the maximum face + id_emb = self.extract_arcface_bgr_embedding(id_image_cv2, landmark, device) + id_emb = model(id_emb.unsqueeze(0).reshape([1, -1, 512]).to(dtype=self.torch_dtype)) + infinityou_guidance = torch.Tensor([infinityou_guidance]).to(device=device, dtype=self.torch_dtype) + return {'id_emb': id_emb, 'infinityou_guidance': infinityou_guidance} + + + +class FluxImageUnit_LoRAEncode(PipelineUnit): + def __init__(self): + super().__init__( + take_over=True, + onload_model_names=("lora_encoder",) + ) + + def parse_lora_encoder_inputs(self, lora_encoder_inputs): + if not isinstance(lora_encoder_inputs, list): + lora_encoder_inputs = [lora_encoder_inputs] + lora_configs = [] + for lora_encoder_input in lora_encoder_inputs: + if isinstance(lora_encoder_input, str): + lora_encoder_input = ModelConfig(path=lora_encoder_input) + lora_encoder_input.download_if_necessary() + lora_configs.append(lora_encoder_input) + return lora_configs + + def load_lora(self, lora_config, dtype, device): + loader = FluxLoRALoader(torch_dtype=dtype, device=device) + lora = load_state_dict(lora_config.path, torch_dtype=dtype, device=device) + lora = loader.convert_state_dict(lora) + return lora + + def lora_embedding(self, pipe, lora_encoder_inputs): + lora_emb = [] + for lora_config in self.parse_lora_encoder_inputs(lora_encoder_inputs): + lora = self.load_lora(lora_config, pipe.torch_dtype, pipe.device) + lora_emb.append(pipe.lora_encoder(lora)) + lora_emb = torch.concat(lora_emb, dim=1) + return lora_emb + + def add_to_text_embedding(self, prompt_emb, text_ids, lora_emb): + prompt_emb = torch.concat([prompt_emb, lora_emb], dim=1) + extra_text_ids = torch.zeros((lora_emb.shape[0], lora_emb.shape[1], 3), device=lora_emb.device, dtype=lora_emb.dtype) + text_ids = torch.concat([text_ids, extra_text_ids], dim=1) + return prompt_emb, text_ids + + def process(self, pipe: FluxImagePipeline, inputs_shared, inputs_posi, inputs_nega): + if inputs_shared.get("lora_encoder_inputs", None) is None: + return inputs_shared, inputs_posi, inputs_nega + + # Encode + pipe.load_models_to_device(["lora_encoder"]) + lora_encoder_inputs = inputs_shared["lora_encoder_inputs"] + lora_emb = self.lora_embedding(pipe, lora_encoder_inputs) + + # Scale + lora_encoder_scale = inputs_shared.get("lora_encoder_scale", None) + if lora_encoder_scale is not None: + lora_emb = lora_emb * lora_encoder_scale + + # Add to prompt embedding + inputs_posi["prompt_emb"], inputs_posi["text_ids"] = self.add_to_text_embedding( + inputs_posi["prompt_emb"], inputs_posi["text_ids"], lora_emb) + return inputs_shared, inputs_posi, inputs_nega + + + +class TeaCache: + def __init__(self, num_inference_steps, rel_l1_thresh): + self.num_inference_steps = num_inference_steps + self.step = 0 + self.accumulated_rel_l1_distance = 0 + self.previous_modulated_input = None + self.rel_l1_thresh = rel_l1_thresh + self.previous_residual = None + self.previous_hidden_states = None + + def check(self, dit: FluxDiT, hidden_states, conditioning): + inp = hidden_states.clone() + temb_ = conditioning.clone() + modulated_inp, _, _, _, _ = dit.blocks[0].norm1_a(inp, emb=temb_) + if self.step == 0 or self.step == self.num_inference_steps - 1: + should_calc = True + self.accumulated_rel_l1_distance = 0 + else: + coefficients = [4.98651651e+02, -2.83781631e+02, 5.58554382e+01, -3.82021401e+00, 2.64230861e-01] + rescale_func = np.poly1d(coefficients) + self.accumulated_rel_l1_distance += rescale_func(((modulated_inp-self.previous_modulated_input).abs().mean() / self.previous_modulated_input.abs().mean()).cpu().item()) + if self.accumulated_rel_l1_distance < self.rel_l1_thresh: + should_calc = False + else: + should_calc = True + self.accumulated_rel_l1_distance = 0 + self.previous_modulated_input = modulated_inp + self.step += 1 + if self.step == self.num_inference_steps: + self.step = 0 + if should_calc: + self.previous_hidden_states = hidden_states.clone() + return not should_calc + + def store(self, hidden_states): + self.previous_residual = hidden_states - self.previous_hidden_states + self.previous_hidden_states = None + + def update(self, hidden_states): + hidden_states = hidden_states + self.previous_residual + return hidden_states + + +def model_fn_flux_image( + dit: FluxDiT, + controlnet=None, + step1x_connector=None, + latents=None, + timestep=None, + prompt_emb=None, + pooled_prompt_emb=None, + guidance=None, + text_ids=None, + image_ids=None, + kontext_latents=None, + kontext_image_ids=None, + controlnet_inputs=None, + controlnet_conditionings=None, + tiled=False, + tile_size=128, + tile_stride=64, + entity_prompt_emb=None, + entity_masks=None, + ipadapter_kwargs_list={}, + id_emb=None, + infinityou_guidance=None, + flex_condition=None, + flex_uncondition=None, + flex_control_stop_timestep=None, + step1x_llm_embedding=None, + step1x_mask=None, + step1x_reference_latents=None, + tea_cache: TeaCache = None, + progress_id=0, + num_inference_steps=1, + use_gradient_checkpointing=False, + use_gradient_checkpointing_offload=False, + **kwargs +): + if tiled: + def flux_forward_fn(hl, hr, wl, wr): + tiled_controlnet_conditionings = [f[:, :, hl: hr, wl: wr] for f in controlnet_conditionings] if controlnet_conditionings is not None else None + return model_fn_flux_image( + dit=dit, + controlnet=controlnet, + latents=latents[:, :, hl: hr, wl: wr], + timestep=timestep, + prompt_emb=prompt_emb, + pooled_prompt_emb=pooled_prompt_emb, + guidance=guidance, + text_ids=text_ids, + image_ids=None, + controlnet_inputs=controlnet_inputs, + controlnet_conditionings=tiled_controlnet_conditionings, + tiled=False, + **kwargs + ) + return FastTileWorker().tiled_forward( + flux_forward_fn, + latents, + tile_size=tile_size, + tile_stride=tile_stride, + tile_device=latents.device, + tile_dtype=latents.dtype + ) + + hidden_states = latents + + # ControlNet + if controlnet is not None and controlnet_conditionings is not None: + controlnet_extra_kwargs = { + "hidden_states": hidden_states, + "timestep": timestep, + "prompt_emb": prompt_emb, + "pooled_prompt_emb": pooled_prompt_emb, + "guidance": guidance, + "text_ids": text_ids, + "image_ids": image_ids, + "controlnet_inputs": controlnet_inputs, + "tiled": tiled, + "tile_size": tile_size, + "tile_stride": tile_stride, + "progress_id": progress_id, + "num_inference_steps": num_inference_steps, + } + if id_emb is not None: + controlnet_text_ids = torch.zeros(id_emb.shape[0], id_emb.shape[1], 3).to(device=hidden_states.device, dtype=hidden_states.dtype) + controlnet_extra_kwargs.update({"prompt_emb": id_emb, 'text_ids': controlnet_text_ids, 'guidance': infinityou_guidance}) + controlnet_res_stack, controlnet_single_res_stack = controlnet( + controlnet_conditionings, **controlnet_extra_kwargs + ) + + # Flex + if flex_condition is not None: + if timestep.tolist()[0] >= flex_control_stop_timestep: + hidden_states = torch.concat([hidden_states, flex_condition], dim=1) + else: + hidden_states = torch.concat([hidden_states, flex_uncondition], dim=1) + + # Step1x + if step1x_llm_embedding is not None: + prompt_emb, pooled_prompt_emb = step1x_connector(step1x_llm_embedding, timestep / 1000, step1x_mask) + text_ids = torch.zeros((1, prompt_emb.shape[1], 3), dtype=prompt_emb.dtype, device=prompt_emb.device) + + if image_ids is None: + image_ids = dit.prepare_image_ids(hidden_states) + + conditioning = dit.time_embedder(timestep, hidden_states.dtype) + dit.pooled_text_embedder(pooled_prompt_emb) + if dit.guidance_embedder is not None: + guidance = guidance * 1000 + conditioning = conditioning + dit.guidance_embedder(guidance, hidden_states.dtype) + + height, width = hidden_states.shape[-2:] + hidden_states = dit.patchify(hidden_states) + + # Kontext + if kontext_latents is not None: + image_ids = torch.concat([image_ids, kontext_image_ids], dim=-2) + hidden_states = torch.concat([hidden_states, kontext_latents], dim=1) + + # Step1x + if step1x_reference_latents is not None: + step1x_reference_image_ids = dit.prepare_image_ids(step1x_reference_latents) + step1x_reference_latents = dit.patchify(step1x_reference_latents) + image_ids = torch.concat([image_ids, step1x_reference_image_ids], dim=-2) + hidden_states = torch.concat([hidden_states, step1x_reference_latents], dim=1) + + hidden_states = dit.x_embedder(hidden_states) + + # EliGen + if entity_prompt_emb is not None and entity_masks is not None: + prompt_emb, image_rotary_emb, attention_mask = dit.process_entity_masks(hidden_states, prompt_emb, entity_prompt_emb, entity_masks, text_ids, image_ids, latents.shape[1]) + else: + prompt_emb = dit.context_embedder(prompt_emb) + image_rotary_emb = dit.pos_embedder(torch.cat((text_ids, image_ids), dim=1)) + attention_mask = None + + # TeaCache + if tea_cache is not None: + tea_cache_update = tea_cache.check(dit, hidden_states, conditioning) + else: + tea_cache_update = False + + if tea_cache_update: + hidden_states = tea_cache.update(hidden_states) + else: + # Joint Blocks + for block_id, block in enumerate(dit.blocks): + hidden_states, prompt_emb = gradient_checkpoint_forward( + block, + use_gradient_checkpointing, + use_gradient_checkpointing_offload, + hidden_states, + prompt_emb, + conditioning, + image_rotary_emb, + attention_mask, + ipadapter_kwargs_list=ipadapter_kwargs_list.get(block_id, None), + ) + # ControlNet + if controlnet is not None and controlnet_conditionings is not None and controlnet_res_stack is not None: + if kontext_latents is None: + hidden_states = hidden_states + controlnet_res_stack[block_id] + else: + hidden_states[:, :-kontext_latents.shape[1]] = hidden_states[:, :-kontext_latents.shape[1]] + controlnet_res_stack[block_id] + + # Single Blocks + hidden_states = torch.cat([prompt_emb, hidden_states], dim=1) + num_joint_blocks = len(dit.blocks) + for block_id, block in enumerate(dit.single_blocks): + hidden_states, prompt_emb = gradient_checkpoint_forward( + block, + use_gradient_checkpointing, + use_gradient_checkpointing_offload, + hidden_states, + prompt_emb, + conditioning, + image_rotary_emb, + attention_mask, + ipadapter_kwargs_list=ipadapter_kwargs_list.get(block_id + num_joint_blocks, None), + ) + # ControlNet + if controlnet is not None and controlnet_conditionings is not None and controlnet_single_res_stack is not None: + if kontext_latents is None: + hidden_states[:, prompt_emb.shape[1]:] = hidden_states[:, prompt_emb.shape[1]:] + controlnet_single_res_stack[block_id] + else: + hidden_states[:, prompt_emb.shape[1]:-kontext_latents.shape[1]] = hidden_states[:, prompt_emb.shape[1]:-kontext_latents.shape[1]] + controlnet_single_res_stack[block_id] + hidden_states = hidden_states[:, prompt_emb.shape[1]:] + + if tea_cache is not None: + tea_cache.store(hidden_states) + + hidden_states = dit.final_norm_out(hidden_states, conditioning) + hidden_states = dit.final_proj_out(hidden_states) + + # Step1x + if step1x_reference_latents is not None: + hidden_states = hidden_states[:, :hidden_states.shape[1] // 2] + + # Kontext + if kontext_latents is not None: + hidden_states = hidden_states[:, :-kontext_latents.shape[1]] + + hidden_states = dit.unpatchify(hidden_states, height, width) + + return hidden_states diff --git a/diffsynth/pipelines/hunyuan_image.py b/diffsynth/pipelines/hunyuan_image.py new file mode 100644 index 0000000000000000000000000000000000000000..0c6f6d5dedc6aac50b06a9f10701f7f8ab33117f --- /dev/null +++ b/diffsynth/pipelines/hunyuan_image.py @@ -0,0 +1,288 @@ +from ..models.hunyuan_dit import HunyuanDiT +from ..models.hunyuan_dit_text_encoder import HunyuanDiTCLIPTextEncoder, HunyuanDiTT5TextEncoder +from ..models.sdxl_vae_encoder import SDXLVAEEncoder +from ..models.sdxl_vae_decoder import SDXLVAEDecoder +from ..models import ModelManager +from ..prompters import HunyuanDiTPrompter +from ..schedulers import EnhancedDDIMScheduler +from .base import BasePipeline +import torch +from tqdm import tqdm +import numpy as np + + + +class ImageSizeManager: + def __init__(self): + pass + + + def _to_tuple(self, x): + if isinstance(x, int): + return x, x + else: + return x + + + def get_fill_resize_and_crop(self, src, tgt): + th, tw = self._to_tuple(tgt) + h, w = self._to_tuple(src) + + tr = th / tw # base 分辨率 + r = h / w # 目标分辨率 + + # resize + if r > tr: + resize_height = th + resize_width = int(round(th / h * w)) + else: + resize_width = tw + resize_height = int(round(tw / w * h)) # 根据base分辨率，将目标分辨率resize下来 + + crop_top = int(round((th - resize_height) / 2.0)) + crop_left = int(round((tw - resize_width) / 2.0)) + + return (crop_top, crop_left), (crop_top + resize_height, crop_left + resize_width) + + + def get_meshgrid(self, start, *args): + if len(args) == 0: + # start is grid_size + num = self._to_tuple(start) + start = (0, 0) + stop = num + elif len(args) == 1: + # start is start, args[0] is stop, step is 1 + start = self._to_tuple(start) + stop = self._to_tuple(args[0]) + num = (stop[0] - start[0], stop[1] - start[1]) + elif len(args) == 2: + # start is start, args[0] is stop, args[1] is num + start = self._to_tuple(start) # 左上角 eg: 12,0 + stop = self._to_tuple(args[0]) # 右下角 eg: 20,32 + num = self._to_tuple(args[1]) # 目标大小 eg: 32,124 + else: + raise ValueError(f"len(args) should be 0, 1 or 2, but got {len(args)}") + + grid_h = np.linspace(start[0], stop[0], num[0], endpoint=False, dtype=np.float32) # 12-20 中间差值32份 0-32 中间差值124份 + grid_w = np.linspace(start[1], stop[1], num[1], endpoint=False, dtype=np.float32) + grid = np.meshgrid(grid_w, grid_h) # here w goes first + grid = np.stack(grid, axis=0) # [2, W, H] + return grid + + + def get_2d_rotary_pos_embed(self, embed_dim, start, *args, use_real=True): + grid = self.get_meshgrid(start, *args) # [2, H, w] + grid = grid.reshape([2, 1, *grid.shape[1:]]) # 返回一个采样矩阵 分辨率与目标分辨率一致 + pos_embed = self.get_2d_rotary_pos_embed_from_grid(embed_dim, grid, use_real=use_real) + return pos_embed + + + def get_2d_rotary_pos_embed_from_grid(self, embed_dim, grid, use_real=False): + assert embed_dim % 4 == 0 + + # use half of dimensions to encode grid_h + emb_h = self.get_1d_rotary_pos_embed(embed_dim // 2, grid[0].reshape(-1), use_real=use_real) # (H*W, D/4) + emb_w = self.get_1d_rotary_pos_embed(embed_dim // 2, grid[1].reshape(-1), use_real=use_real) # (H*W, D/4) + + if use_real: + cos = torch.cat([emb_h[0], emb_w[0]], dim=1) # (H*W, D/2) + sin = torch.cat([emb_h[1], emb_w[1]], dim=1) # (H*W, D/2) + return cos, sin + else: + emb = torch.cat([emb_h, emb_w], dim=1) # (H*W, D/2) + return emb + + + def get_1d_rotary_pos_embed(self, dim: int, pos, theta: float = 10000.0, use_real=False): + if isinstance(pos, int): + pos = np.arange(pos) + freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim)) # [D/2] + t = torch.from_numpy(pos).to(freqs.device) # type: ignore # [S] + freqs = torch.outer(t, freqs).float() # type: ignore # [S, D/2] + if use_real: + freqs_cos = freqs.cos().repeat_interleave(2, dim=1) # [S, D] + freqs_sin = freqs.sin().repeat_interleave(2, dim=1) # [S, D] + return freqs_cos, freqs_sin + else: + freqs_cis = torch.polar(torch.ones_like(freqs), freqs) # complex64 # [S, D/2] + return freqs_cis + + + def calc_rope(self, height, width): + patch_size = 2 + head_size = 88 + th = height // 8 // patch_size + tw = width // 8 // patch_size + base_size = 512 // 8 // patch_size + start, stop = self.get_fill_resize_and_crop((th, tw), base_size) + sub_args = [start, stop, (th, tw)] + rope = self.get_2d_rotary_pos_embed(head_size, *sub_args) + return rope + + + +class HunyuanDiTImagePipeline(BasePipeline): + + def __init__(self, device="cuda", torch_dtype=torch.float16): + super().__init__(device=device, torch_dtype=torch_dtype, height_division_factor=16, width_division_factor=16) + self.scheduler = EnhancedDDIMScheduler(prediction_type="v_prediction", beta_start=0.00085, beta_end=0.03) + self.prompter = HunyuanDiTPrompter() + self.image_size_manager = ImageSizeManager() + # models + self.text_encoder: HunyuanDiTCLIPTextEncoder = None + self.text_encoder_t5: HunyuanDiTT5TextEncoder = None + self.dit: HunyuanDiT = None + self.vae_decoder: SDXLVAEDecoder = None + self.vae_encoder: SDXLVAEEncoder = None + self.model_names = ['text_encoder', 'text_encoder_t5', 'dit', 'vae_decoder', 'vae_encoder'] + + + def denoising_model(self): + return self.dit + + + def fetch_models(self, model_manager: ModelManager, prompt_refiner_classes=[]): + # Main models + self.text_encoder = model_manager.fetch_model("hunyuan_dit_clip_text_encoder") + self.text_encoder_t5 = model_manager.fetch_model("hunyuan_dit_t5_text_encoder") + self.dit = model_manager.fetch_model("hunyuan_dit") + self.vae_decoder = model_manager.fetch_model("sdxl_vae_decoder") + self.vae_encoder = model_manager.fetch_model("sdxl_vae_encoder") + self.prompter.fetch_models(self.text_encoder, self.text_encoder_t5) + self.prompter.load_prompt_refiners(model_manager, prompt_refiner_classes) + + + @staticmethod + def from_model_manager(model_manager: ModelManager, prompt_refiner_classes=[], device=None): + pipe = HunyuanDiTImagePipeline( + device=model_manager.device if device is None else device, + torch_dtype=model_manager.torch_dtype, + ) + pipe.fetch_models(model_manager, prompt_refiner_classes) + return pipe + + + def encode_image(self, image, tiled=False, tile_size=64, tile_stride=32): + latents = self.vae_encoder(image, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) + return latents + + + def decode_image(self, latent, tiled=False, tile_size=64, tile_stride=32): + image = self.vae_decoder(latent.to(self.device), tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) + image = self.vae_output_to_image(image) + return image + + + def encode_prompt(self, prompt, clip_skip=1, clip_skip_2=1, positive=True): + text_emb, text_emb_mask, text_emb_t5, text_emb_mask_t5 = self.prompter.encode_prompt( + prompt, + clip_skip=clip_skip, + clip_skip_2=clip_skip_2, + positive=positive, + device=self.device + ) + return { + "text_emb": text_emb, + "text_emb_mask": text_emb_mask, + "text_emb_t5": text_emb_t5, + "text_emb_mask_t5": text_emb_mask_t5 + } + + + def prepare_extra_input(self, latents=None, tiled=False, tile_size=64, tile_stride=32): + batch_size, height, width = latents.shape[0], latents.shape[2] * 8, latents.shape[3] * 8 + if tiled: + height, width = tile_size * 16, tile_size * 16 + image_meta_size = torch.as_tensor([width, height, width, height, 0, 0]).to(device=self.device) + freqs_cis_img = self.image_size_manager.calc_rope(height, width) + image_meta_size = torch.stack([image_meta_size] * batch_size) + return { + "size_emb": image_meta_size, + "freq_cis_img": (freqs_cis_img[0].to(dtype=self.torch_dtype, device=self.device), freqs_cis_img[1].to(dtype=self.torch_dtype, device=self.device)), + "tiled": tiled, + "tile_size": tile_size, + "tile_stride": tile_stride + } + + + @torch.no_grad() + def __call__( + self, + prompt, + local_prompts=[], + masks=[], + mask_scales=[], + negative_prompt="", + cfg_scale=7.5, + clip_skip=1, + clip_skip_2=1, + input_image=None, + reference_strengths=[0.4], + denoising_strength=1.0, + height=1024, + width=1024, + num_inference_steps=20, + tiled=False, + tile_size=64, + tile_stride=32, + seed=None, + progress_bar_cmd=tqdm, + progress_bar_st=None, + ): + height, width = self.check_resize_height_width(height, width) + + # Prepare scheduler + self.scheduler.set_timesteps(num_inference_steps, denoising_strength) + + # Prepare latent tensors + noise = self.generate_noise((1, 4, height//8, width//8), seed=seed, device=self.device, dtype=self.torch_dtype) + if input_image is not None: + self.load_models_to_device(['vae_encoder']) + image = self.preprocess_image(input_image).to(device=self.device, dtype=torch.float32) + latents = self.vae_encoder(image, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride).to(self.torch_dtype) + latents = self.scheduler.add_noise(latents, noise, timestep=self.scheduler.timesteps[0]) + else: + latents = noise.clone() + + # Encode prompts + self.load_models_to_device(['text_encoder', 'text_encoder_t5']) + prompt_emb_posi = self.encode_prompt(prompt, clip_skip=clip_skip, clip_skip_2=clip_skip_2, positive=True) + if cfg_scale != 1.0: + prompt_emb_nega = self.encode_prompt(negative_prompt, clip_skip=clip_skip, clip_skip_2=clip_skip_2, positive=True) + prompt_emb_locals = [self.encode_prompt(prompt_local, clip_skip=clip_skip, clip_skip_2=clip_skip_2, positive=True) for prompt_local in local_prompts] + + # Prepare positional id + extra_input = self.prepare_extra_input(latents, tiled, tile_size) + + # Denoise + self.load_models_to_device(['dit']) + for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)): + timestep = torch.tensor([timestep]).to(dtype=self.torch_dtype, device=self.device) + + # Positive side + inference_callback = lambda prompt_emb_posi: self.dit(latents, timestep=timestep, **prompt_emb_posi, **extra_input) + noise_pred_posi = self.control_noise_via_local_prompts(prompt_emb_posi, prompt_emb_locals, masks, mask_scales, inference_callback) + + if cfg_scale != 1.0: + # Negative side + noise_pred_nega = self.dit( + latents, timestep=timestep, **prompt_emb_nega, **extra_input, + ) + # Classifier-free guidance + noise_pred = noise_pred_nega + cfg_scale * (noise_pred_posi - noise_pred_nega) + else: + noise_pred = noise_pred_posi + + latents = self.scheduler.step(noise_pred, self.scheduler.timesteps[progress_id], latents) + + if progress_bar_st is not None: + progress_bar_st.progress(progress_id / len(self.scheduler.timesteps)) + + # Decode image + self.load_models_to_device(['vae_decoder']) + image = self.decode_image(latents.to(torch.float32), tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) + + # Offload all models + self.load_models_to_device([]) + return image diff --git a/diffsynth/pipelines/hunyuan_video.py b/diffsynth/pipelines/hunyuan_video.py new file mode 100644 index 0000000000000000000000000000000000000000..d8a0411e155f293e86a2b64073fa8b25af3d83d5 --- /dev/null +++ b/diffsynth/pipelines/hunyuan_video.py @@ -0,0 +1,395 @@ +from ..models import ModelManager, SD3TextEncoder1, HunyuanVideoVAEDecoder, HunyuanVideoVAEEncoder +from ..models.hunyuan_video_dit import HunyuanVideoDiT +from ..models.hunyuan_video_text_encoder import HunyuanVideoLLMEncoder +from ..schedulers.flow_match import FlowMatchScheduler +from .base import BasePipeline +from ..prompters import HunyuanVideoPrompter +import torch +import torchvision.transforms as transforms +from einops import rearrange +import numpy as np +from PIL import Image +from tqdm import tqdm + + +class HunyuanVideoPipeline(BasePipeline): + + def __init__(self, device="cuda", torch_dtype=torch.float16): + super().__init__(device=device, torch_dtype=torch_dtype) + self.scheduler = FlowMatchScheduler(shift=7.0, sigma_min=0.0, extra_one_step=True) + self.prompter = HunyuanVideoPrompter() + self.text_encoder_1: SD3TextEncoder1 = None + self.text_encoder_2: HunyuanVideoLLMEncoder = None + self.dit: HunyuanVideoDiT = None + self.vae_decoder: HunyuanVideoVAEDecoder = None + self.vae_encoder: HunyuanVideoVAEEncoder = None + self.model_names = ['text_encoder_1', 'text_encoder_2', 'dit', 'vae_decoder', 'vae_encoder'] + self.vram_management = False + + + def enable_vram_management(self): + self.vram_management = True + self.enable_cpu_offload() + self.text_encoder_2.enable_auto_offload(dtype=self.torch_dtype, device=self.device) + self.dit.enable_auto_offload(dtype=self.torch_dtype, device=self.device) + + + def fetch_models(self, model_manager: ModelManager): + self.text_encoder_1 = model_manager.fetch_model("sd3_text_encoder_1") + self.text_encoder_2 = model_manager.fetch_model("hunyuan_video_text_encoder_2") + self.dit = model_manager.fetch_model("hunyuan_video_dit") + self.vae_decoder = model_manager.fetch_model("hunyuan_video_vae_decoder") + self.vae_encoder = model_manager.fetch_model("hunyuan_video_vae_encoder") + self.prompter.fetch_models(self.text_encoder_1, self.text_encoder_2) + + + @staticmethod + def from_model_manager(model_manager: ModelManager, torch_dtype=None, device=None, enable_vram_management=True): + if device is None: device = model_manager.device + if torch_dtype is None: torch_dtype = model_manager.torch_dtype + pipe = HunyuanVideoPipeline(device=device, torch_dtype=torch_dtype) + pipe.fetch_models(model_manager) + if enable_vram_management: + pipe.enable_vram_management() + return pipe + + def generate_crop_size_list(self, base_size=256, patch_size=32, max_ratio=4.0): + num_patches = round((base_size / patch_size)**2) + assert max_ratio >= 1.0 + crop_size_list = [] + wp, hp = num_patches, 1 + while wp > 0: + if max(wp, hp) / min(wp, hp) <= max_ratio: + crop_size_list.append((wp * patch_size, hp * patch_size)) + if (hp + 1) * wp <= num_patches: + hp += 1 + else: + wp -= 1 + return crop_size_list + + + def get_closest_ratio(self, height: float, width: float, ratios: list, buckets: list): + aspect_ratio = float(height) / float(width) + closest_ratio_id = np.abs(ratios - aspect_ratio).argmin() + closest_ratio = min(ratios, key=lambda ratio: abs(float(ratio) - aspect_ratio)) + return buckets[closest_ratio_id], float(closest_ratio) + + + def prepare_vae_images_inputs(self, semantic_images, i2v_resolution="720p"): + if i2v_resolution == "720p": + bucket_hw_base_size = 960 + elif i2v_resolution == "540p": + bucket_hw_base_size = 720 + elif i2v_resolution == "360p": + bucket_hw_base_size = 480 + else: + raise ValueError(f"i2v_resolution: {i2v_resolution} must be in [360p, 540p, 720p]") + origin_size = semantic_images[0].size + + crop_size_list = self.generate_crop_size_list(bucket_hw_base_size, 32) + aspect_ratios = np.array([round(float(h) / float(w), 5) for h, w in crop_size_list]) + closest_size, closest_ratio = self.get_closest_ratio(origin_size[1], origin_size[0], aspect_ratios, crop_size_list) + ref_image_transform = transforms.Compose([ + transforms.Resize(closest_size), + transforms.CenterCrop(closest_size), + transforms.ToTensor(), + transforms.Normalize([0.5], [0.5]) + ]) + + semantic_image_pixel_values = [ref_image_transform(semantic_image) for semantic_image in semantic_images] + semantic_image_pixel_values = torch.cat(semantic_image_pixel_values).unsqueeze(0).unsqueeze(2).to(self.device) + target_height, target_width = closest_size + return semantic_image_pixel_values, target_height, target_width + + + def encode_prompt(self, prompt, positive=True, clip_sequence_length=77, llm_sequence_length=256, input_images=None): + prompt_emb, pooled_prompt_emb, text_mask = self.prompter.encode_prompt( + prompt, device=self.device, positive=positive, clip_sequence_length=clip_sequence_length, llm_sequence_length=llm_sequence_length, images=input_images + ) + return {"prompt_emb": prompt_emb, "pooled_prompt_emb": pooled_prompt_emb, "text_mask": text_mask} + + + def prepare_extra_input(self, latents=None, guidance=1.0): + freqs_cos, freqs_sin = self.dit.prepare_freqs(latents) + guidance = torch.Tensor([guidance] * latents.shape[0]).to(device=latents.device, dtype=latents.dtype) + return {"freqs_cos": freqs_cos, "freqs_sin": freqs_sin, "guidance": guidance} + + + def tensor2video(self, frames): + frames = rearrange(frames, "C T H W -> T H W C") + frames = ((frames.float() + 1) * 127.5).clip(0, 255).cpu().numpy().astype(np.uint8) + frames = [Image.fromarray(frame) for frame in frames] + return frames + + + def encode_video(self, frames, tile_size=(17, 30, 30), tile_stride=(12, 20, 20)): + tile_size = ((tile_size[0] - 1) * 4 + 1, tile_size[1] * 8, tile_size[2] * 8) + tile_stride = (tile_stride[0] * 4, tile_stride[1] * 8, tile_stride[2] * 8) + latents = self.vae_encoder.encode_video(frames, tile_size=tile_size, tile_stride=tile_stride) + return latents + + + @torch.no_grad() + def __call__( + self, + prompt, + negative_prompt="", + input_video=None, + input_images=None, + i2v_resolution="720p", + i2v_stability=True, + denoising_strength=1.0, + seed=None, + rand_device=None, + height=720, + width=1280, + num_frames=129, + embedded_guidance=6.0, + cfg_scale=1.0, + num_inference_steps=30, + tea_cache_l1_thresh=None, + tile_size=(17, 30, 30), + tile_stride=(12, 20, 20), + step_processor=None, + progress_bar_cmd=lambda x: x, + progress_bar_st=None, + ): + # Tiler parameters + tiler_kwargs = {"tile_size": tile_size, "tile_stride": tile_stride} + + # Scheduler + self.scheduler.set_timesteps(num_inference_steps, denoising_strength) + + # encoder input images + if input_images is not None: + self.load_models_to_device(['vae_encoder']) + image_pixel_values, height, width = self.prepare_vae_images_inputs(input_images, i2v_resolution=i2v_resolution) + with torch.autocast(device_type=self.device, dtype=torch.float16, enabled=True): + image_latents = self.vae_encoder(image_pixel_values) + + # Initialize noise + rand_device = self.device if rand_device is None else rand_device + noise = self.generate_noise((1, 16, (num_frames - 1) // 4 + 1, height//8, width//8), seed=seed, device=rand_device, dtype=self.torch_dtype).to(self.device) + if input_video is not None: + self.load_models_to_device(['vae_encoder']) + input_video = self.preprocess_images(input_video) + input_video = torch.stack(input_video, dim=2) + latents = self.encode_video(input_video, **tiler_kwargs).to(dtype=self.torch_dtype, device=self.device) + latents = self.scheduler.add_noise(latents, noise, timestep=self.scheduler.timesteps[0]) + elif input_images is not None and i2v_stability: + noise = self.generate_noise((1, 16, (num_frames - 1) // 4 + 1, height//8, width//8), seed=seed, device=rand_device, dtype=image_latents.dtype).to(self.device) + t = torch.tensor([0.999]).to(device=self.device) + latents = noise * t + image_latents.repeat(1, 1, (num_frames - 1) // 4 + 1, 1, 1) * (1 - t) + latents = latents.to(dtype=image_latents.dtype) + else: + latents = noise + + # Encode prompts + # current mllm does not support vram_management + self.load_models_to_device(["text_encoder_1"] if self.vram_management and input_images is None else ["text_encoder_1", "text_encoder_2"]) + prompt_emb_posi = self.encode_prompt(prompt, positive=True, input_images=input_images) + if cfg_scale != 1.0: + prompt_emb_nega = self.encode_prompt(negative_prompt, positive=False) + + # Extra input + extra_input = self.prepare_extra_input(latents, guidance=embedded_guidance) + + # TeaCache + tea_cache_kwargs = {"tea_cache": TeaCache(num_inference_steps, rel_l1_thresh=tea_cache_l1_thresh) if tea_cache_l1_thresh is not None else None} + + # Denoise + self.load_models_to_device([] if self.vram_management else ["dit"]) + for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)): + timestep = timestep.unsqueeze(0).to(self.device) + print(f"Step {progress_id + 1} / {len(self.scheduler.timesteps)}") + + forward_func = lets_dance_hunyuan_video + if input_images is not None: + latents = torch.concat([image_latents, latents[:, :, 1:, :, :]], dim=2) + forward_func = lets_dance_hunyuan_video_i2v + + # Inference + with torch.autocast(device_type=self.device, dtype=self.torch_dtype): + noise_pred_posi = forward_func(self.dit, latents, timestep, **prompt_emb_posi, **extra_input, **tea_cache_kwargs) + if cfg_scale != 1.0: + noise_pred_nega = forward_func(self.dit, latents, timestep, **prompt_emb_nega, **extra_input) + noise_pred = noise_pred_nega + cfg_scale * (noise_pred_posi - noise_pred_nega) + else: + noise_pred = noise_pred_posi + + # (Experimental feature, may be removed in the future) + if step_processor is not None: + self.load_models_to_device(['vae_decoder']) + rendered_frames = self.scheduler.step(noise_pred, self.scheduler.timesteps[progress_id], latents, to_final=True) + rendered_frames = self.vae_decoder.decode_video(rendered_frames, **tiler_kwargs) + rendered_frames = self.tensor2video(rendered_frames[0]) + rendered_frames = step_processor(rendered_frames, original_frames=input_video) + self.load_models_to_device(['vae_encoder']) + rendered_frames = self.preprocess_images(rendered_frames) + rendered_frames = torch.stack(rendered_frames, dim=2) + target_latents = self.encode_video(rendered_frames).to(dtype=self.torch_dtype, device=self.device) + noise_pred = self.scheduler.return_to_timestep(self.scheduler.timesteps[progress_id], latents, target_latents) + self.load_models_to_device([] if self.vram_management else ["dit"]) + + # Scheduler + if input_images is not None: + latents = self.scheduler.step(noise_pred[:, :, 1:, :, :], self.scheduler.timesteps[progress_id], latents[:, :, 1:, :, :]) + latents = torch.concat([image_latents, latents], dim=2) + else: + latents = self.scheduler.step(noise_pred, self.scheduler.timesteps[progress_id], latents) + + # Decode + self.load_models_to_device(['vae_decoder']) + frames = self.vae_decoder.decode_video(latents, **tiler_kwargs) + self.load_models_to_device([]) + frames = self.tensor2video(frames[0]) + + return frames + + + +class TeaCache: + def __init__(self, num_inference_steps, rel_l1_thresh): + self.num_inference_steps = num_inference_steps + self.step = 0 + self.accumulated_rel_l1_distance = 0 + self.previous_modulated_input = None + self.rel_l1_thresh = rel_l1_thresh + self.previous_residual = None + self.previous_hidden_states = None + + def check(self, dit: HunyuanVideoDiT, img, vec): + img_ = img.clone() + vec_ = vec.clone() + img_mod1_shift, img_mod1_scale, _, _, _, _ = dit.double_blocks[0].component_a.mod(vec_).chunk(6, dim=-1) + normed_inp = dit.double_blocks[0].component_a.norm1(img_) + modulated_inp = normed_inp * (1 + img_mod1_scale.unsqueeze(1)) + img_mod1_shift.unsqueeze(1) + if self.step == 0 or self.step == self.num_inference_steps - 1: + should_calc = True + self.accumulated_rel_l1_distance = 0 + else: + coefficients = [7.33226126e+02, -4.01131952e+02, 6.75869174e+01, -3.14987800e+00, 9.61237896e-02] + rescale_func = np.poly1d(coefficients) + self.accumulated_rel_l1_distance += rescale_func(((modulated_inp-self.previous_modulated_input).abs().mean() / self.previous_modulated_input.abs().mean()).cpu().item()) + if self.accumulated_rel_l1_distance < self.rel_l1_thresh: + should_calc = False + else: + should_calc = True + self.accumulated_rel_l1_distance = 0 + self.previous_modulated_input = modulated_inp + self.step += 1 + if self.step == self.num_inference_steps: + self.step = 0 + if should_calc: + self.previous_hidden_states = img.clone() + return not should_calc + + def store(self, hidden_states): + self.previous_residual = hidden_states - self.previous_hidden_states + self.previous_hidden_states = None + + def update(self, hidden_states): + hidden_states = hidden_states + self.previous_residual + return hidden_states + + + +def lets_dance_hunyuan_video( + dit: HunyuanVideoDiT, + x: torch.Tensor, + t: torch.Tensor, + prompt_emb: torch.Tensor = None, + text_mask: torch.Tensor = None, + pooled_prompt_emb: torch.Tensor = None, + freqs_cos: torch.Tensor = None, + freqs_sin: torch.Tensor = None, + guidance: torch.Tensor = None, + tea_cache: TeaCache = None, + **kwargs +): + B, C, T, H, W = x.shape + + vec = dit.time_in(t, dtype=torch.float32) + dit.vector_in(pooled_prompt_emb) + dit.guidance_in(guidance * 1000, dtype=torch.float32) + img = dit.img_in(x) + txt = dit.txt_in(prompt_emb, t, text_mask) + + # TeaCache + if tea_cache is not None: + tea_cache_update = tea_cache.check(dit, img, vec) + else: + tea_cache_update = False + + if tea_cache_update: + print("TeaCache skip forward.") + img = tea_cache.update(img) + else: + split_token = int(text_mask.sum(dim=1)) + txt_len = int(txt.shape[1]) + for block in tqdm(dit.double_blocks, desc="Double stream blocks"): + img, txt = block(img, txt, vec, (freqs_cos, freqs_sin), split_token=split_token) + + x = torch.concat([img, txt], dim=1) + for block in tqdm(dit.single_blocks, desc="Single stream blocks"): + x = block(x, vec, (freqs_cos, freqs_sin), txt_len=txt_len, split_token=split_token) + img = x[:, :-txt_len] + + if tea_cache is not None: + tea_cache.store(img) + img = dit.final_layer(img, vec) + img = dit.unpatchify(img, T=T//1, H=H//2, W=W//2) + return img + + +def lets_dance_hunyuan_video_i2v( + dit: HunyuanVideoDiT, + x: torch.Tensor, + t: torch.Tensor, + prompt_emb: torch.Tensor = None, + text_mask: torch.Tensor = None, + pooled_prompt_emb: torch.Tensor = None, + freqs_cos: torch.Tensor = None, + freqs_sin: torch.Tensor = None, + guidance: torch.Tensor = None, + tea_cache: TeaCache = None, + **kwargs +): + B, C, T, H, W = x.shape + # Uncomment below to keep same as official implementation + # guidance = guidance.to(dtype=torch.float32).to(torch.bfloat16) + vec = dit.time_in(t, dtype=torch.bfloat16) + vec_2 = dit.vector_in(pooled_prompt_emb) + vec = vec + vec_2 + vec = vec + dit.guidance_in(guidance * 1000., dtype=torch.bfloat16) + + token_replace_vec = dit.time_in(torch.zeros_like(t), dtype=torch.bfloat16) + tr_token = (H // 2) * (W // 2) + token_replace_vec = token_replace_vec + vec_2 + + img = dit.img_in(x) + txt = dit.txt_in(prompt_emb, t, text_mask) + + # TeaCache + if tea_cache is not None: + tea_cache_update = tea_cache.check(dit, img, vec) + else: + tea_cache_update = False + + if tea_cache_update: + print("TeaCache skip forward.") + img = tea_cache.update(img) + else: + split_token = int(text_mask.sum(dim=1)) + txt_len = int(txt.shape[1]) + for block in tqdm(dit.double_blocks, desc="Double stream blocks"): + img, txt = block(img, txt, vec, (freqs_cos, freqs_sin), token_replace_vec, tr_token, split_token) + + x = torch.concat([img, txt], dim=1) + for block in tqdm(dit.single_blocks, desc="Single stream blocks"): + x = block(x, vec, (freqs_cos, freqs_sin), txt_len, token_replace_vec, tr_token, split_token) + img = x[:, :-txt_len] + + if tea_cache is not None: + tea_cache.store(img) + img = dit.final_layer(img, vec) + img = dit.unpatchify(img, T=T//1, H=H//2, W=W//2) + return img diff --git a/diffsynth/pipelines/omnigen_image.py b/diffsynth/pipelines/omnigen_image.py new file mode 100644 index 0000000000000000000000000000000000000000..ddb2ae656639550084b7143fe690186602c0387d --- /dev/null +++ b/diffsynth/pipelines/omnigen_image.py @@ -0,0 +1,289 @@ +from ..models.omnigen import OmniGenTransformer +from ..models.sdxl_vae_encoder import SDXLVAEEncoder +from ..models.sdxl_vae_decoder import SDXLVAEDecoder +from ..models.model_manager import ModelManager +from ..prompters.omnigen_prompter import OmniGenPrompter +from ..schedulers import FlowMatchScheduler +from .base import BasePipeline +from typing import Optional, Dict, Any, Tuple, List +from transformers.cache_utils import DynamicCache +import torch, os +from tqdm import tqdm + + + +class OmniGenCache(DynamicCache): + def __init__(self, + num_tokens_for_img: int, offload_kv_cache: bool=False) -> None: + if not torch.cuda.is_available(): + print("No available GPU, offload_kv_cache will be set to False, which will result in large memory usage and time cost when input multiple images!!!") + offload_kv_cache = False + raise RuntimeError("OffloadedCache can only be used with a GPU") + super().__init__() + self.original_device = [] + self.prefetch_stream = torch.cuda.Stream() + self.num_tokens_for_img = num_tokens_for_img + self.offload_kv_cache = offload_kv_cache + + def prefetch_layer(self, layer_idx: int): + "Starts prefetching the next layer cache" + if layer_idx < len(self): + with torch.cuda.stream(self.prefetch_stream): + # Prefetch next layer tensors to GPU + device = self.original_device[layer_idx] + self.key_cache[layer_idx] = self.key_cache[layer_idx].to(device, non_blocking=True) + self.value_cache[layer_idx] = self.value_cache[layer_idx].to(device, non_blocking=True) + + + def evict_previous_layer(self, layer_idx: int): + "Moves the previous layer cache to the CPU" + if len(self) > 2: + # We do it on the default stream so it occurs after all earlier computations on these tensors are done + if layer_idx == 0: + prev_layer_idx = -1 + else: + prev_layer_idx = (layer_idx - 1) % len(self) + self.key_cache[prev_layer_idx] = self.key_cache[prev_layer_idx].to("cpu", non_blocking=True) + self.value_cache[prev_layer_idx] = self.value_cache[prev_layer_idx].to("cpu", non_blocking=True) + + + def __getitem__(self, layer_idx: int) -> List[Tuple[torch.Tensor]]: + "Gets the cache for this layer to the device. Prefetches the next and evicts the previous layer." + if layer_idx < len(self): + if self.offload_kv_cache: + # Evict the previous layer if necessary + torch.cuda.current_stream().synchronize() + self.evict_previous_layer(layer_idx) + # Load current layer cache to its original device if not already there + original_device = self.original_device[layer_idx] + # self.prefetch_stream.synchronize(original_device) + torch.cuda.synchronize(self.prefetch_stream) + key_tensor = self.key_cache[layer_idx] + value_tensor = self.value_cache[layer_idx] + + # Prefetch the next layer + self.prefetch_layer((layer_idx + 1) % len(self)) + else: + key_tensor = self.key_cache[layer_idx] + value_tensor = self.value_cache[layer_idx] + return (key_tensor, value_tensor) + else: + raise KeyError(f"Cache only has {len(self)} layers, attempted to access layer with index {layer_idx}") + + + def update( + self, + key_states: torch.Tensor, + value_states: torch.Tensor, + layer_idx: int, + cache_kwargs: Optional[Dict[str, Any]] = None, + ) -> Tuple[torch.Tensor, torch.Tensor]: + """ + Updates the cache with the new `key_states` and `value_states` for the layer `layer_idx`. + Parameters: + key_states (`torch.Tensor`): + The new key states to cache. + value_states (`torch.Tensor`): + The new value states to cache. + layer_idx (`int`): + The index of the layer to cache the states for. + cache_kwargs (`Dict[str, Any]`, `optional`): + Additional arguments for the cache subclass. No additional arguments are used in `OffloadedCache`. + Return: + A tuple containing the updated key and value states. + """ + # Update the cache + if len(self.key_cache) < layer_idx: + raise ValueError("OffloadedCache does not support model usage where layers are skipped. Use DynamicCache.") + elif len(self.key_cache) == layer_idx: + # only cache the states for condition tokens + key_states = key_states[..., :-(self.num_tokens_for_img+1), :] + value_states = value_states[..., :-(self.num_tokens_for_img+1), :] + + # Update the number of seen tokens + if layer_idx == 0: + self._seen_tokens += key_states.shape[-2] + + self.key_cache.append(key_states) + self.value_cache.append(value_states) + self.original_device.append(key_states.device) + if self.offload_kv_cache: + self.evict_previous_layer(layer_idx) + return self.key_cache[layer_idx], self.value_cache[layer_idx] + else: + # only cache the states for condition tokens + key_tensor, value_tensor = self[layer_idx] + k = torch.cat([key_tensor, key_states], dim=-2) + v = torch.cat([value_tensor, value_states], dim=-2) + return k, v + + + +class OmnigenImagePipeline(BasePipeline): + + def __init__(self, device="cuda", torch_dtype=torch.float16): + super().__init__(device=device, torch_dtype=torch_dtype) + self.scheduler = FlowMatchScheduler(num_train_timesteps=1, shift=1, inverse_timesteps=True, sigma_min=0, sigma_max=1) + # models + self.vae_decoder: SDXLVAEDecoder = None + self.vae_encoder: SDXLVAEEncoder = None + self.transformer: OmniGenTransformer = None + self.prompter: OmniGenPrompter = None + self.model_names = ['transformer', 'vae_decoder', 'vae_encoder'] + + + def denoising_model(self): + return self.transformer + + + def fetch_models(self, model_manager: ModelManager, prompt_refiner_classes=[]): + # Main models + self.transformer, model_path = model_manager.fetch_model("omnigen_transformer", require_model_path=True) + self.vae_decoder = model_manager.fetch_model("sdxl_vae_decoder") + self.vae_encoder = model_manager.fetch_model("sdxl_vae_encoder") + self.prompter = OmniGenPrompter.from_pretrained(os.path.dirname(model_path)) + + + @staticmethod + def from_model_manager(model_manager: ModelManager, prompt_refiner_classes=[], device=None): + pipe = OmnigenImagePipeline( + device=model_manager.device if device is None else device, + torch_dtype=model_manager.torch_dtype, + ) + pipe.fetch_models(model_manager, prompt_refiner_classes=[]) + return pipe + + + def encode_image(self, image, tiled=False, tile_size=64, tile_stride=32): + latents = self.vae_encoder(image, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) + return latents + + + def encode_images(self, images, tiled=False, tile_size=64, tile_stride=32): + latents = [self.encode_image(image.to(device=self.device), tiled, tile_size, tile_stride).to(self.torch_dtype) for image in images] + return latents + + + def decode_image(self, latent, tiled=False, tile_size=64, tile_stride=32): + image = self.vae_decoder(latent.to(self.device), tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) + image = self.vae_output_to_image(image) + return image + + + def encode_prompt(self, prompt, clip_skip=1, positive=True): + prompt_emb = self.prompter.encode_prompt(prompt, clip_skip=clip_skip, device=self.device, positive=positive) + return {"encoder_hidden_states": prompt_emb} + + + def prepare_extra_input(self, latents=None): + return {} + + + def crop_position_ids_for_cache(self, position_ids, num_tokens_for_img): + if isinstance(position_ids, list): + for i in range(len(position_ids)): + position_ids[i] = position_ids[i][:, -(num_tokens_for_img+1):] + else: + position_ids = position_ids[:, -(num_tokens_for_img+1):] + return position_ids + + + def crop_attention_mask_for_cache(self, attention_mask, num_tokens_for_img): + if isinstance(attention_mask, list): + return [x[..., -(num_tokens_for_img+1):, :] for x in attention_mask] + return attention_mask[..., -(num_tokens_for_img+1):, :] + + + @torch.no_grad() + def __call__( + self, + prompt, + reference_images=[], + cfg_scale=2.0, + image_cfg_scale=2.0, + use_kv_cache=True, + offload_kv_cache=True, + input_image=None, + denoising_strength=1.0, + height=1024, + width=1024, + num_inference_steps=20, + tiled=False, + tile_size=64, + tile_stride=32, + seed=None, + progress_bar_cmd=tqdm, + progress_bar_st=None, + ): + height, width = self.check_resize_height_width(height, width) + + # Tiler parameters + tiler_kwargs = {"tiled": tiled, "tile_size": tile_size, "tile_stride": tile_stride} + + # Prepare scheduler + self.scheduler.set_timesteps(num_inference_steps, denoising_strength) + + # Prepare latent tensors + if input_image is not None: + self.load_models_to_device(['vae_encoder']) + image = self.preprocess_image(input_image).to(device=self.device, dtype=self.torch_dtype) + latents = self.encode_image(image, **tiler_kwargs) + noise = self.generate_noise((1, 4, height//8, width//8), seed=seed, device=self.device, dtype=self.torch_dtype) + latents = self.scheduler.add_noise(latents, noise, timestep=self.scheduler.timesteps[0]) + else: + latents = self.generate_noise((1, 4, height//8, width//8), seed=seed, device=self.device, dtype=self.torch_dtype) + latents = latents.repeat(3, 1, 1, 1) + + # Encode prompts + input_data = self.prompter(prompt, reference_images, height=height, width=width, use_img_cfg=True, separate_cfg_input=True, use_input_image_size_as_output=False) + + # Encode images + reference_latents = [self.encode_images(images, **tiler_kwargs) for images in input_data['input_pixel_values']] + + # Pack all parameters + model_kwargs = dict(input_ids=[input_ids.to(self.device) for input_ids in input_data['input_ids']], + input_img_latents=reference_latents, + input_image_sizes=input_data['input_image_sizes'], + attention_mask=[attention_mask.to(self.device) for attention_mask in input_data["attention_mask"]], + position_ids=[position_ids.to(self.device) for position_ids in input_data["position_ids"]], + cfg_scale=cfg_scale, + img_cfg_scale=image_cfg_scale, + use_img_cfg=True, + use_kv_cache=use_kv_cache, + offload_model=False, + ) + + # Denoise + self.load_models_to_device(['transformer']) + cache = [OmniGenCache(latents.size(-1)*latents.size(-2) // 4, offload_kv_cache) for _ in range(len(model_kwargs['input_ids']))] if use_kv_cache else None + for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)): + timestep = timestep.unsqueeze(0).repeat(latents.shape[0]).to(self.device) + + # Forward + noise_pred, cache = self.transformer.forward_with_separate_cfg(latents, timestep, past_key_values=cache, **model_kwargs) + + # Scheduler + latents = self.scheduler.step(noise_pred, self.scheduler.timesteps[progress_id], latents) + + # Update KV cache + if progress_id == 0 and use_kv_cache: + num_tokens_for_img = latents.size(-1)*latents.size(-2) // 4 + if isinstance(cache, list): + model_kwargs['input_ids'] = [None] * len(cache) + else: + model_kwargs['input_ids'] = None + model_kwargs['position_ids'] = self.crop_position_ids_for_cache(model_kwargs['position_ids'], num_tokens_for_img) + model_kwargs['attention_mask'] = self.crop_attention_mask_for_cache(model_kwargs['attention_mask'], num_tokens_for_img) + + # UI + if progress_bar_st is not None: + progress_bar_st.progress(progress_id / len(self.scheduler.timesteps)) + + # Decode image + del cache + self.load_models_to_device(['vae_decoder']) + image = self.decode_image(latents, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) + + # offload all models + self.load_models_to_device([]) + return image diff --git a/diffsynth/pipelines/pipeline_runner.py b/diffsynth/pipelines/pipeline_runner.py new file mode 100644 index 0000000000000000000000000000000000000000..1b842f9bd7b25edca1c9951e67ebe5c364deca81 --- /dev/null +++ b/diffsynth/pipelines/pipeline_runner.py @@ -0,0 +1,105 @@ +import os, torch, json +from .sd_video import ModelManager, SDVideoPipeline, ControlNetConfigUnit +from ..processors.sequencial_processor import SequencialProcessor +from ..data import VideoData, save_frames, save_video + + + +class SDVideoPipelineRunner: + def __init__(self, in_streamlit=False): + self.in_streamlit = in_streamlit + + + def load_pipeline(self, model_list, textual_inversion_folder, device, lora_alphas, controlnet_units): + # Load models + model_manager = ModelManager(torch_dtype=torch.float16, device=device) + model_manager.load_models(model_list) + pipe = SDVideoPipeline.from_model_manager( + model_manager, + [ + ControlNetConfigUnit( + processor_id=unit["processor_id"], + model_path=unit["model_path"], + scale=unit["scale"] + ) for unit in controlnet_units + ] + ) + textual_inversion_paths = [] + for file_name in os.listdir(textual_inversion_folder): + if file_name.endswith(".pt") or file_name.endswith(".bin") or file_name.endswith(".pth") or file_name.endswith(".safetensors"): + textual_inversion_paths.append(os.path.join(textual_inversion_folder, file_name)) + pipe.prompter.load_textual_inversions(textual_inversion_paths) + return model_manager, pipe + + + def load_smoother(self, model_manager, smoother_configs): + smoother = SequencialProcessor.from_model_manager(model_manager, smoother_configs) + return smoother + + + def synthesize_video(self, model_manager, pipe, seed, smoother, **pipeline_inputs): + torch.manual_seed(seed) + if self.in_streamlit: + import streamlit as st + progress_bar_st = st.progress(0.0) + output_video = pipe(**pipeline_inputs, smoother=smoother, progress_bar_st=progress_bar_st) + progress_bar_st.progress(1.0) + else: + output_video = pipe(**pipeline_inputs, smoother=smoother) + model_manager.to("cpu") + return output_video + + + def load_video(self, video_file, image_folder, height, width, start_frame_id, end_frame_id): + video = VideoData(video_file=video_file, image_folder=image_folder, height=height, width=width) + if start_frame_id is None: + start_frame_id = 0 + if end_frame_id is None: + end_frame_id = len(video) + frames = [video[i] for i in range(start_frame_id, end_frame_id)] + return frames + + + def add_data_to_pipeline_inputs(self, data, pipeline_inputs): + pipeline_inputs["input_frames"] = self.load_video(**data["input_frames"]) + pipeline_inputs["num_frames"] = len(pipeline_inputs["input_frames"]) + pipeline_inputs["width"], pipeline_inputs["height"] = pipeline_inputs["input_frames"][0].size + if len(data["controlnet_frames"]) > 0: + pipeline_inputs["controlnet_frames"] = [self.load_video(**unit) for unit in data["controlnet_frames"]] + return pipeline_inputs + + + def save_output(self, video, output_folder, fps, config): + os.makedirs(output_folder, exist_ok=True) + save_frames(video, os.path.join(output_folder, "frames")) + save_video(video, os.path.join(output_folder, "video.mp4"), fps=fps) + config["pipeline"]["pipeline_inputs"]["input_frames"] = [] + config["pipeline"]["pipeline_inputs"]["controlnet_frames"] = [] + with open(os.path.join(output_folder, "config.json"), 'w') as file: + json.dump(config, file, indent=4) + + + def run(self, config): + if self.in_streamlit: + import streamlit as st + if self.in_streamlit: st.markdown("Loading videos ...") + config["pipeline"]["pipeline_inputs"] = self.add_data_to_pipeline_inputs(config["data"], config["pipeline"]["pipeline_inputs"]) + if self.in_streamlit: st.markdown("Loading videos ... done!") + if self.in_streamlit: st.markdown("Loading models ...") + model_manager, pipe = self.load_pipeline(**config["models"]) + if self.in_streamlit: st.markdown("Loading models ... done!") + if "smoother_configs" in config: + if self.in_streamlit: st.markdown("Loading smoother ...") + smoother = self.load_smoother(model_manager, config["smoother_configs"]) + if self.in_streamlit: st.markdown("Loading smoother ... done!") + else: + smoother = None + if self.in_streamlit: st.markdown("Synthesizing videos ...") + output_video = self.synthesize_video(model_manager, pipe, config["pipeline"]["seed"], smoother, **config["pipeline"]["pipeline_inputs"]) + if self.in_streamlit: st.markdown("Synthesizing videos ... done!") + if self.in_streamlit: st.markdown("Saving videos ...") + self.save_output(output_video, config["data"]["output_folder"], config["data"]["fps"], config) + if self.in_streamlit: st.markdown("Saving videos ... done!") + if self.in_streamlit: st.markdown("Finished!") + video_file = open(os.path.join(os.path.join(config["data"]["output_folder"], "video.mp4")), 'rb') + if self.in_streamlit: st.video(video_file.read()) diff --git a/diffsynth/pipelines/qwen_image.py b/diffsynth/pipelines/qwen_image.py new file mode 100644 index 0000000000000000000000000000000000000000..e949b56e02492db9ca8b514a18c106c108c9efd0 --- /dev/null +++ b/diffsynth/pipelines/qwen_image.py @@ -0,0 +1,861 @@ +import torch +from PIL import Image +from typing import Union +from PIL import Image +from tqdm import tqdm +from einops import rearrange +import numpy as np + +from ..models import ModelManager, load_state_dict +from ..models.qwen_image_dit import QwenImageDiT +from ..models.qwen_image_text_encoder import QwenImageTextEncoder +from ..models.qwen_image_vae import QwenImageVAE +from ..models.qwen_image_controlnet import QwenImageBlockWiseControlNet +from ..schedulers import FlowMatchScheduler +from ..utils import BasePipeline, ModelConfig, PipelineUnitRunner, PipelineUnit +from ..lora import GeneralLoRALoader +from .flux_image_new import ControlNetInput + +from ..vram_management import gradient_checkpoint_forward, enable_vram_management, AutoWrappedModule, AutoWrappedLinear + + +class QwenImageBlockwiseMultiControlNet(torch.nn.Module): + def __init__(self, models: list[QwenImageBlockWiseControlNet]): + super().__init__() + if not isinstance(models, list): + models = [models] + self.models = torch.nn.ModuleList(models) + + def preprocess(self, controlnet_inputs: list[ControlNetInput], conditionings: list[torch.Tensor], **kwargs): + processed_conditionings = [] + for controlnet_input, conditioning in zip(controlnet_inputs, conditionings): + conditioning = rearrange(conditioning, "B C (H P) (W Q) -> B (H W) (C P Q)", P=2, Q=2) + model_output = self.models[controlnet_input.controlnet_id].process_controlnet_conditioning(conditioning) + processed_conditionings.append(model_output) + return processed_conditionings + + def blockwise_forward(self, image, conditionings: list[torch.Tensor], controlnet_inputs: list[ControlNetInput], progress_id, num_inference_steps, block_id, **kwargs): + res = 0 + for controlnet_input, conditioning in zip(controlnet_inputs, conditionings): + progress = (num_inference_steps - 1 - progress_id) / max(num_inference_steps - 1, 1) + if progress > controlnet_input.start + (1e-4) or progress < controlnet_input.end - (1e-4): + continue + model_output = self.models[controlnet_input.controlnet_id].blockwise_forward(image, conditioning, block_id) + res = res + model_output * controlnet_input.scale + return res + + +class QwenImagePipeline(BasePipeline): + + def __init__(self, device="cuda", torch_dtype=torch.bfloat16): + super().__init__( + device=device, torch_dtype=torch_dtype, + height_division_factor=16, width_division_factor=16, + ) + from transformers import Qwen2Tokenizer, Qwen2VLProcessor + + self.scheduler = FlowMatchScheduler(sigma_min=0, sigma_max=1, extra_one_step=True, exponential_shift=True, exponential_shift_mu=0.8, shift_terminal=0.02) + self.text_encoder: QwenImageTextEncoder = None + self.dit: QwenImageDiT = None + self.vae: QwenImageVAE = None + self.blockwise_controlnet: QwenImageBlockwiseMultiControlNet = None + self.tokenizer: Qwen2Tokenizer = None + self.processor: Qwen2VLProcessor = None + self.unit_runner = PipelineUnitRunner() + self.in_iteration_models = ("dit", "blockwise_controlnet") + self.units = [ + QwenImageUnit_ShapeChecker(), + QwenImageUnit_NoiseInitializer(), + QwenImageUnit_InputImageEmbedder(), + QwenImageUnit_Inpaint(), + QwenImageUnit_EditImageEmbedder(), + QwenImageUnit_ContextImageEmbedder(), + QwenImageUnit_PromptEmbedder(), + QwenImageUnit_EntityControl(), + QwenImageUnit_BlockwiseControlNet(), + ] + self.model_fn = model_fn_qwen_image + + + def load_lora( + self, + module: torch.nn.Module, + lora_config: Union[ModelConfig, str] = None, + alpha=1, + hotload=False, + state_dict=None, + ): + if state_dict is None: + if isinstance(lora_config, str): + lora = load_state_dict(lora_config, torch_dtype=self.torch_dtype, device=self.device) + else: + lora_config.download_if_necessary() + lora = load_state_dict(lora_config.path, torch_dtype=self.torch_dtype, device=self.device) + else: + lora = state_dict + if hotload: + for name, module in module.named_modules(): + if isinstance(module, AutoWrappedLinear): + lora_a_name = f'{name}.lora_A.default.weight' + lora_b_name = f'{name}.lora_B.default.weight' + if lora_a_name in lora and lora_b_name in lora: + module.lora_A_weights.append(lora[lora_a_name] * alpha) + module.lora_B_weights.append(lora[lora_b_name]) + else: + loader = GeneralLoRALoader(torch_dtype=self.torch_dtype, device=self.device) + loader.load(module, lora, alpha=alpha) + + + def clear_lora(self): + for name, module in self.named_modules(): + if isinstance(module, AutoWrappedLinear): + if hasattr(module, "lora_A_weights"): + module.lora_A_weights.clear() + if hasattr(module, "lora_B_weights"): + module.lora_B_weights.clear() + + + def enable_lora_magic(self): + if self.dit is not None: + if not (hasattr(self.dit, "vram_management_enabled") and self.dit.vram_management_enabled): + dtype = next(iter(self.dit.parameters())).dtype + enable_vram_management( + self.dit, + module_map = { + torch.nn.Linear: AutoWrappedLinear, + }, + module_config = dict( + offload_dtype=dtype, + offload_device=self.device, + onload_dtype=dtype, + onload_device=self.device, + computation_dtype=self.torch_dtype, + computation_device=self.device, + ), + vram_limit=None, + ) + + + def training_loss(self, **inputs): + timestep_id = torch.randint(0, self.scheduler.num_train_timesteps, (1,)) + timestep = self.scheduler.timesteps[timestep_id].to(dtype=self.torch_dtype, device=self.device) + + noise = torch.randn_like(inputs["input_latents"]) + inputs["latents"] = self.scheduler.add_noise(inputs["input_latents"], noise, timestep) + training_target = self.scheduler.training_target(inputs["input_latents"], noise, timestep) + + noise_pred = self.model_fn(**inputs, timestep=timestep) + + loss = torch.nn.functional.mse_loss(noise_pred.float(), training_target.float()) + loss = loss * self.scheduler.training_weight(timestep) + return loss + + + def direct_distill_loss(self, **inputs): + self.scheduler.set_timesteps(inputs["num_inference_steps"]) + models = {name: getattr(self, name) for name in self.in_iteration_models} + for progress_id, timestep in enumerate(self.scheduler.timesteps): + timestep = timestep.unsqueeze(0).to(dtype=self.torch_dtype, device=self.device) + noise_pred = self.model_fn(**models, **inputs, timestep=timestep, progress_id=progress_id) + inputs["latents"] = self.step(self.scheduler, progress_id=progress_id, noise_pred=noise_pred, **inputs) + loss = torch.nn.functional.mse_loss(inputs["latents"].float(), inputs["input_latents"].float()) + return loss + + + def _enable_fp8_lora_training(self, dtype): + from transformers.models.qwen2_5_vl.modeling_qwen2_5_vl import Qwen2_5_VLRotaryEmbedding, Qwen2RMSNorm, Qwen2_5_VisionPatchEmbed, Qwen2_5_VisionRotaryEmbedding + from ..models.qwen_image_dit import RMSNorm + from ..models.qwen_image_vae import QwenImageRMS_norm + module_map = { + RMSNorm: AutoWrappedModule, + torch.nn.Linear: AutoWrappedLinear, + torch.nn.Conv3d: AutoWrappedModule, + torch.nn.Conv2d: AutoWrappedModule, + torch.nn.Embedding: AutoWrappedModule, + Qwen2_5_VLRotaryEmbedding: AutoWrappedModule, + Qwen2RMSNorm: AutoWrappedModule, + Qwen2_5_VisionPatchEmbed: AutoWrappedModule, + Qwen2_5_VisionRotaryEmbedding: AutoWrappedModule, + QwenImageRMS_norm: AutoWrappedModule, + } + model_config = dict( + offload_dtype=dtype, + offload_device="cuda", + onload_dtype=dtype, + onload_device="cuda", + computation_dtype=self.torch_dtype, + computation_device="cuda", + ) + if self.text_encoder is not None: + enable_vram_management(self.text_encoder, module_map=module_map, module_config=model_config) + if self.dit is not None: + enable_vram_management(self.dit, module_map=module_map, module_config=model_config) + if self.vae is not None: + enable_vram_management(self.vae, module_map=module_map, module_config=model_config) + + + def enable_vram_management(self, num_persistent_param_in_dit=None, vram_limit=None, vram_buffer=0.5, auto_offload=True, enable_dit_fp8_computation=False): + self.vram_management_enabled = True + if vram_limit is None and auto_offload: + vram_limit = self.get_vram() + if vram_limit is not None: + vram_limit = vram_limit - vram_buffer + + if self.text_encoder is not None: + from transformers.models.qwen2_5_vl.modeling_qwen2_5_vl import Qwen2_5_VLRotaryEmbedding, Qwen2RMSNorm, Qwen2_5_VisionPatchEmbed, Qwen2_5_VisionRotaryEmbedding + dtype = next(iter(self.text_encoder.parameters())).dtype + enable_vram_management( + self.text_encoder, + module_map = { + torch.nn.Linear: AutoWrappedLinear, + torch.nn.Embedding: AutoWrappedModule, + Qwen2_5_VLRotaryEmbedding: AutoWrappedModule, + Qwen2RMSNorm: AutoWrappedModule, + Qwen2_5_VisionPatchEmbed: AutoWrappedModule, + Qwen2_5_VisionRotaryEmbedding: AutoWrappedModule, + }, + module_config = dict( + offload_dtype=dtype, + offload_device="cpu", + onload_dtype=dtype, + onload_device="cpu", + computation_dtype=self.torch_dtype, + computation_device=self.device, + ), + vram_limit=vram_limit, + ) + if self.dit is not None: + from ..models.qwen_image_dit import RMSNorm + dtype = next(iter(self.dit.parameters())).dtype + device = "cpu" if vram_limit is not None else self.device + if not enable_dit_fp8_computation: + enable_vram_management( + self.dit, + module_map = { + RMSNorm: AutoWrappedModule, + torch.nn.Linear: AutoWrappedLinear, + }, + module_config = dict( + offload_dtype=dtype, + offload_device="cpu", + onload_dtype=dtype, + onload_device=device, + computation_dtype=self.torch_dtype, + computation_device=self.device, + ), + vram_limit=vram_limit, + ) + else: + enable_vram_management( + self.dit, + module_map = { + RMSNorm: AutoWrappedModule, + }, + module_config = dict( + offload_dtype=dtype, + offload_device="cpu", + onload_dtype=dtype, + onload_device=device, + computation_dtype=self.torch_dtype, + computation_device=self.device, + ), + vram_limit=vram_limit, + ) + enable_vram_management( + self.dit, + module_map = { + torch.nn.Linear: AutoWrappedLinear, + }, + module_config = dict( + offload_dtype=dtype, + offload_device="cpu", + onload_dtype=dtype, + onload_device=device, + computation_dtype=dtype, + computation_device=self.device, + ), + vram_limit=vram_limit, + ) + if self.vae is not None: + from ..models.qwen_image_vae import QwenImageRMS_norm + dtype = next(iter(self.vae.parameters())).dtype + enable_vram_management( + self.vae, + module_map = { + torch.nn.Linear: AutoWrappedLinear, + torch.nn.Conv3d: AutoWrappedModule, + torch.nn.Conv2d: AutoWrappedModule, + QwenImageRMS_norm: AutoWrappedModule, + }, + module_config = dict( + offload_dtype=dtype, + offload_device="cpu", + onload_dtype=dtype, + onload_device="cpu", + computation_dtype=self.torch_dtype, + computation_device=self.device, + ), + vram_limit=vram_limit, + ) + if self.blockwise_controlnet is not None: + enable_vram_management( + self.blockwise_controlnet, + module_map = { + RMSNorm: AutoWrappedModule, + torch.nn.Linear: AutoWrappedLinear, + }, + module_config = dict( + offload_dtype=dtype, + offload_device="cpu", + onload_dtype=dtype, + onload_device=device, + computation_dtype=self.torch_dtype, + computation_device=self.device, + ), + vram_limit=vram_limit, + ) + + + @staticmethod + def from_pretrained( + torch_dtype: torch.dtype = torch.bfloat16, + device: Union[str, torch.device] = "cuda", + model_configs: list[ModelConfig] = [], + tokenizer_config: ModelConfig = ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), + processor_config: ModelConfig = None, + ): + # Download and load models + model_manager = ModelManager() + for model_config in model_configs: + model_config.download_if_necessary() + model_manager.load_model( + model_config.path, + device=model_config.offload_device or device, + torch_dtype=model_config.offload_dtype or torch_dtype + ) + + # Initialize pipeline + pipe = QwenImagePipeline(device=device, torch_dtype=torch_dtype) + pipe.text_encoder = model_manager.fetch_model("qwen_image_text_encoder") + pipe.dit = model_manager.fetch_model("qwen_image_dit") + pipe.vae = model_manager.fetch_model("qwen_image_vae") + pipe.blockwise_controlnet = QwenImageBlockwiseMultiControlNet(model_manager.fetch_model("qwen_image_blockwise_controlnet", index="all")) + if tokenizer_config is not None and pipe.text_encoder is not None: + tokenizer_config.download_if_necessary() + from transformers import Qwen2Tokenizer + pipe.tokenizer = Qwen2Tokenizer.from_pretrained(tokenizer_config.path) + if processor_config is not None: + processor_config.download_if_necessary() + from transformers import Qwen2VLProcessor + pipe.processor = Qwen2VLProcessor.from_pretrained(processor_config.path) + return pipe + + + @torch.no_grad() + def __call__( + self, + # Prompt + prompt: str, + negative_prompt: str = "", + cfg_scale: float = 4.0, + # Image + input_image: Image.Image = None, + denoising_strength: float = 1.0, + # Inpaint + inpaint_mask: Image.Image = None, + inpaint_blur_size: int = None, + inpaint_blur_sigma: float = None, + # Shape + height: int = 1328, + width: int = 1328, + # Randomness + seed: int = None, + rand_device: str = "cpu", + # Steps + num_inference_steps: int = 30, + exponential_shift_mu: float = None, + # Blockwise ControlNet + blockwise_controlnet_inputs: list[ControlNetInput] = None, + # EliGen + eligen_entity_prompts: list[str] = None, + eligen_entity_masks: list[Image.Image] = None, + eligen_enable_on_negative: bool = False, + # Qwen-Image-Edit + edit_image: Image.Image = None, + edit_image_auto_resize: bool = True, + edit_rope_interpolation: bool = False, + # In-context control + context_image: Image.Image = None, + # FP8 + enable_fp8_attention: bool = False, + # Tile + tiled: bool = False, + tile_size: int = 128, + tile_stride: int = 64, + # Progress bar + progress_bar_cmd = tqdm, + ): + # Scheduler + self.scheduler.set_timesteps(num_inference_steps, denoising_strength=denoising_strength, dynamic_shift_len=(height // 16) * (width // 16), exponential_shift_mu=exponential_shift_mu) + + # Parameters + inputs_posi = { + "prompt": prompt, + } + inputs_nega = { + "negative_prompt": negative_prompt, + } + inputs_shared = { + "cfg_scale": cfg_scale, + "input_image": input_image, "denoising_strength": denoising_strength, + "inpaint_mask": inpaint_mask, "inpaint_blur_size": inpaint_blur_size, "inpaint_blur_sigma": inpaint_blur_sigma, + "height": height, "width": width, + "seed": seed, "rand_device": rand_device, + "enable_fp8_attention": enable_fp8_attention, + "num_inference_steps": num_inference_steps, + "blockwise_controlnet_inputs": blockwise_controlnet_inputs, + "tiled": tiled, "tile_size": tile_size, "tile_stride": tile_stride, + "eligen_entity_prompts": eligen_entity_prompts, "eligen_entity_masks": eligen_entity_masks, "eligen_enable_on_negative": eligen_enable_on_negative, + "edit_image": edit_image, "edit_image_auto_resize": edit_image_auto_resize, "edit_rope_interpolation": edit_rope_interpolation, + "context_image": context_image, + } + for unit in self.units: + inputs_shared, inputs_posi, inputs_nega = self.unit_runner(unit, self, inputs_shared, inputs_posi, inputs_nega) + + # Denoise + self.load_models_to_device(self.in_iteration_models) + models = {name: getattr(self, name) for name in self.in_iteration_models} + for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)): + timestep = timestep.unsqueeze(0).to(dtype=self.torch_dtype, device=self.device) + + # Inference + noise_pred_posi = self.model_fn(**models, **inputs_shared, **inputs_posi, timestep=timestep, progress_id=progress_id) + if cfg_scale != 1.0: + noise_pred_nega = self.model_fn(**models, **inputs_shared, **inputs_nega, timestep=timestep, progress_id=progress_id) + noise_pred = noise_pred_nega + cfg_scale * (noise_pred_posi - noise_pred_nega) + else: + noise_pred = noise_pred_posi + + # Scheduler + inputs_shared["latents"] = self.step(self.scheduler, progress_id=progress_id, noise_pred=noise_pred, **inputs_shared) + + # Decode + self.load_models_to_device(['vae']) + image = self.vae.decode(inputs_shared["latents"], device=self.device, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) + image = self.vae_output_to_image(image) + self.load_models_to_device([]) + + return image + + + +class QwenImageUnit_ShapeChecker(PipelineUnit): + def __init__(self): + super().__init__(input_params=("height", "width")) + + def process(self, pipe: QwenImagePipeline, height, width): + height, width = pipe.check_resize_height_width(height, width) + return {"height": height, "width": width} + + + +class QwenImageUnit_NoiseInitializer(PipelineUnit): + def __init__(self): + super().__init__(input_params=("height", "width", "seed", "rand_device")) + + def process(self, pipe: QwenImagePipeline, height, width, seed, rand_device): + noise = pipe.generate_noise((1, 16, height//8, width//8), seed=seed, rand_device=rand_device, rand_torch_dtype=pipe.torch_dtype) + return {"noise": noise} + + + +class QwenImageUnit_InputImageEmbedder(PipelineUnit): + def __init__(self): + super().__init__( + input_params=("input_image", "noise", "tiled", "tile_size", "tile_stride"), + onload_model_names=("vae",) + ) + + def process(self, pipe: QwenImagePipeline, input_image, noise, tiled, tile_size, tile_stride): + if input_image is None: + return {"latents": noise, "input_latents": None} + pipe.load_models_to_device(['vae']) + image = pipe.preprocess_image(input_image).to(device=pipe.device, dtype=pipe.torch_dtype) + input_latents = pipe.vae.encode(image, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) + if pipe.scheduler.training: + return {"latents": noise, "input_latents": input_latents} + else: + latents = pipe.scheduler.add_noise(input_latents, noise, timestep=pipe.scheduler.timesteps[0]) + return {"latents": latents, "input_latents": input_latents} + + + +class QwenImageUnit_Inpaint(PipelineUnit): + def __init__(self): + super().__init__( + input_params=("inpaint_mask", "height", "width", "inpaint_blur_size", "inpaint_blur_sigma"), + ) + + def process(self, pipe: QwenImagePipeline, inpaint_mask, height, width, inpaint_blur_size, inpaint_blur_sigma): + if inpaint_mask is None: + return {} + inpaint_mask = pipe.preprocess_image(inpaint_mask.convert("RGB").resize((width // 8, height // 8)), min_value=0, max_value=1) + inpaint_mask = inpaint_mask.mean(dim=1, keepdim=True) + if inpaint_blur_size is not None and inpaint_blur_sigma is not None: + from torchvision.transforms import GaussianBlur + blur = GaussianBlur(kernel_size=inpaint_blur_size * 2 + 1, sigma=inpaint_blur_sigma) + inpaint_mask = blur(inpaint_mask) + return {"inpaint_mask": inpaint_mask} + + +class QwenImageUnit_PromptEmbedder(PipelineUnit): + def __init__(self): + super().__init__( + seperate_cfg=True, + input_params_posi={"prompt": "prompt"}, + input_params_nega={"prompt": "negative_prompt"}, + input_params=("edit_image",), + onload_model_names=("text_encoder",) + ) + + def extract_masked_hidden(self, hidden_states: torch.Tensor, mask: torch.Tensor): + bool_mask = mask.bool() + valid_lengths = bool_mask.sum(dim=1) + selected = hidden_states[bool_mask] + split_result = torch.split(selected, valid_lengths.tolist(), dim=0) + return split_result + + def calculate_dimensions(self, target_area, ratio): + import math + width = math.sqrt(target_area * ratio) + height = width / ratio + width = round(width / 32) * 32 + height = round(height / 32) * 32 + return width, height + + def resize_image(self, image, target_area=384*384): + width, height = self.calculate_dimensions(target_area, image.size[0] / image.size[1]) + return image.resize((width, height)) + + def encode_prompt(self, pipe: QwenImagePipeline, prompt): + template = "<|im_start|>system\nDescribe the image by detailing the color, shape, size, texture, quantity, text, spatial relationships of the objects and background:<|im_end|>\n<|im_start|>user\n{}<|im_end|>\n<|im_start|>assistant\n" + drop_idx = 34 + txt = [template.format(e) for e in prompt] + model_inputs = pipe.tokenizer(txt, max_length=4096+drop_idx, padding=True, truncation=True, return_tensors="pt").to(pipe.device) + if model_inputs.input_ids.shape[1] >= 1024: + print(f"Warning!!! QwenImage model was trained on prompts up to 512 tokens. Current prompt requires {model_inputs['input_ids'].shape[1] - drop_idx} tokens, which may lead to unpredictable behavior.") + hidden_states = pipe.text_encoder(input_ids=model_inputs.input_ids, attention_mask=model_inputs.attention_mask, output_hidden_states=True,)[-1] + split_hidden_states = self.extract_masked_hidden(hidden_states, model_inputs.attention_mask) + split_hidden_states = [e[drop_idx:] for e in split_hidden_states] + return split_hidden_states + + def encode_prompt_edit(self, pipe: QwenImagePipeline, prompt, edit_image): + template = "<|im_start|>system\nDescribe the key features of the input image (color, shape, size, texture, objects, background), then explain how the user's text instruction should alter or modify the image. Generate a new image that meets the user's requirements while maintaining consistency with the original input where appropriate.<|im_end|>\n<|im_start|>user\n<|vision_start|><|image_pad|><|vision_end|>{}<|im_end|>\n<|im_start|>assistant\n" + drop_idx = 64 + txt = [template.format(e) for e in prompt] + model_inputs = pipe.processor(text=txt, images=edit_image, padding=True, return_tensors="pt").to(pipe.device) + hidden_states = pipe.text_encoder(input_ids=model_inputs.input_ids, attention_mask=model_inputs.attention_mask, pixel_values=model_inputs.pixel_values, image_grid_thw=model_inputs.image_grid_thw, output_hidden_states=True,)[-1] + split_hidden_states = self.extract_masked_hidden(hidden_states, model_inputs.attention_mask) + split_hidden_states = [e[drop_idx:] for e in split_hidden_states] + return split_hidden_states + + def encode_prompt_edit_multi(self, pipe: QwenImagePipeline, prompt, edit_image): + template = "<|im_start|>system\nDescribe the key features of the input image (color, shape, size, texture, objects, background), then explain how the user's text instruction should alter or modify the image. Generate a new image that meets the user's requirements while maintaining consistency with the original input where appropriate.<|im_end|>\n<|im_start|>user\n{}<|im_end|>\n<|im_start|>assistant\n" + drop_idx = 64 + img_prompt_template = "Picture {}: <|vision_start|><|image_pad|><|vision_end|>" + base_img_prompt = "".join([img_prompt_template.format(i + 1) for i in range(len(edit_image))]) + txt = [template.format(base_img_prompt + e) for e in prompt] + edit_image = [self.resize_image(image) for image in edit_image] + model_inputs = pipe.processor(text=txt, images=edit_image, padding=True, return_tensors="pt").to(pipe.device) + hidden_states = pipe.text_encoder(input_ids=model_inputs.input_ids, attention_mask=model_inputs.attention_mask, pixel_values=model_inputs.pixel_values, image_grid_thw=model_inputs.image_grid_thw, output_hidden_states=True,)[-1] + split_hidden_states = self.extract_masked_hidden(hidden_states, model_inputs.attention_mask) + split_hidden_states = [e[drop_idx:] for e in split_hidden_states] + return split_hidden_states + + def process(self, pipe: QwenImagePipeline, prompt, edit_image=None) -> dict: + if pipe.text_encoder is not None: + prompt = [prompt] + if edit_image is None: + split_hidden_states = self.encode_prompt(pipe, prompt) + elif isinstance(edit_image, Image.Image): + split_hidden_states = self.encode_prompt_edit(pipe, prompt, edit_image) + else: + split_hidden_states = self.encode_prompt_edit_multi(pipe, prompt, edit_image) + attn_mask_list = [torch.ones(e.size(0), dtype=torch.long, device=e.device) for e in split_hidden_states] + max_seq_len = max([e.size(0) for e in split_hidden_states]) + prompt_embeds = torch.stack([torch.cat([u, u.new_zeros(max_seq_len - u.size(0), u.size(1))]) for u in split_hidden_states]) + encoder_attention_mask = torch.stack([torch.cat([u, u.new_zeros(max_seq_len - u.size(0))]) for u in attn_mask_list]) + prompt_embeds = prompt_embeds.to(dtype=pipe.torch_dtype, device=pipe.device) + return {"prompt_emb": prompt_embeds, "prompt_emb_mask": encoder_attention_mask} + else: + return {} + + +class QwenImageUnit_EntityControl(PipelineUnit): + def __init__(self): + super().__init__( + take_over=True, + onload_model_names=("text_encoder",) + ) + + def extract_masked_hidden(self, hidden_states: torch.Tensor, mask: torch.Tensor): + bool_mask = mask.bool() + valid_lengths = bool_mask.sum(dim=1) + selected = hidden_states[bool_mask] + split_result = torch.split(selected, valid_lengths.tolist(), dim=0) + return split_result + + def get_prompt_emb(self, pipe: QwenImagePipeline, prompt) -> dict: + if pipe.text_encoder is not None: + prompt = [prompt] + template = "<|im_start|>system\nDescribe the image by detailing the color, shape, size, texture, quantity, text, spatial relationships of the objects and background:<|im_end|>\n<|im_start|>user\n{}<|im_end|>\n<|im_start|>assistant\n" + drop_idx = 34 + txt = [template.format(e) for e in prompt] + txt_tokens = pipe.tokenizer(txt, max_length=1024+drop_idx, padding=True, truncation=True, return_tensors="pt").to(pipe.device) + hidden_states = pipe.text_encoder(input_ids=txt_tokens.input_ids, attention_mask=txt_tokens.attention_mask, output_hidden_states=True,)[-1] + + split_hidden_states = self.extract_masked_hidden(hidden_states, txt_tokens.attention_mask) + split_hidden_states = [e[drop_idx:] for e in split_hidden_states] + attn_mask_list = [torch.ones(e.size(0), dtype=torch.long, device=e.device) for e in split_hidden_states] + max_seq_len = max([e.size(0) for e in split_hidden_states]) + prompt_embeds = torch.stack([torch.cat([u, u.new_zeros(max_seq_len - u.size(0), u.size(1))]) for u in split_hidden_states]) + encoder_attention_mask = torch.stack([torch.cat([u, u.new_zeros(max_seq_len - u.size(0))]) for u in attn_mask_list]) + prompt_embeds = prompt_embeds.to(dtype=pipe.torch_dtype, device=pipe.device) + return {"prompt_emb": prompt_embeds, "prompt_emb_mask": encoder_attention_mask} + else: + return {} + + def preprocess_masks(self, pipe, masks, height, width, dim): + out_masks = [] + for mask in masks: + mask = pipe.preprocess_image(mask.resize((width, height), resample=Image.NEAREST)).mean(dim=1, keepdim=True) > 0 + mask = mask.repeat(1, dim, 1, 1).to(device=pipe.device, dtype=pipe.torch_dtype) + out_masks.append(mask) + return out_masks + + def prepare_entity_inputs(self, pipe, entity_prompts, entity_masks, width, height): + entity_masks = self.preprocess_masks(pipe, entity_masks, height//8, width//8, 1) + entity_masks = torch.cat(entity_masks, dim=0).unsqueeze(0) # b, n_mask, c, h, w + prompt_embs, prompt_emb_masks = [], [] + for entity_prompt in entity_prompts: + prompt_emb_dict = self.get_prompt_emb(pipe, entity_prompt) + prompt_embs.append(prompt_emb_dict['prompt_emb']) + prompt_emb_masks.append(prompt_emb_dict['prompt_emb_mask']) + return prompt_embs, prompt_emb_masks, entity_masks + + def prepare_eligen(self, pipe, prompt_emb_nega, eligen_entity_prompts, eligen_entity_masks, width, height, enable_eligen_on_negative, cfg_scale): + entity_prompt_emb_posi, entity_prompt_emb_posi_mask, entity_masks_posi = self.prepare_entity_inputs(pipe, eligen_entity_prompts, eligen_entity_masks, width, height) + if enable_eligen_on_negative and cfg_scale != 1.0: + entity_prompt_emb_nega = [prompt_emb_nega['prompt_emb']] * len(entity_prompt_emb_posi) + entity_prompt_emb_nega_mask = [prompt_emb_nega['prompt_emb_mask']] * len(entity_prompt_emb_posi) + entity_masks_nega = entity_masks_posi + else: + entity_prompt_emb_nega, entity_prompt_emb_nega_mask, entity_masks_nega = None, None, None + eligen_kwargs_posi = {"entity_prompt_emb": entity_prompt_emb_posi, "entity_masks": entity_masks_posi, "entity_prompt_emb_mask": entity_prompt_emb_posi_mask} + eligen_kwargs_nega = {"entity_prompt_emb": entity_prompt_emb_nega, "entity_masks": entity_masks_nega, "entity_prompt_emb_mask": entity_prompt_emb_nega_mask} + return eligen_kwargs_posi, eligen_kwargs_nega + + def process(self, pipe: QwenImagePipeline, inputs_shared, inputs_posi, inputs_nega): + eligen_entity_prompts, eligen_entity_masks = inputs_shared.get("eligen_entity_prompts", None), inputs_shared.get("eligen_entity_masks", None) + if eligen_entity_prompts is None or eligen_entity_masks is None or len(eligen_entity_prompts) == 0 or len(eligen_entity_masks) == 0: + return inputs_shared, inputs_posi, inputs_nega + pipe.load_models_to_device(self.onload_model_names) + eligen_enable_on_negative = inputs_shared.get("eligen_enable_on_negative", False) + eligen_kwargs_posi, eligen_kwargs_nega = self.prepare_eligen(pipe, inputs_nega, + eligen_entity_prompts, eligen_entity_masks, inputs_shared["width"], inputs_shared["height"], + eligen_enable_on_negative, inputs_shared["cfg_scale"]) + inputs_posi.update(eligen_kwargs_posi) + if inputs_shared.get("cfg_scale", 1.0) != 1.0: + inputs_nega.update(eligen_kwargs_nega) + return inputs_shared, inputs_posi, inputs_nega + + + +class QwenImageUnit_BlockwiseControlNet(PipelineUnit): + def __init__(self): + super().__init__( + input_params=("blockwise_controlnet_inputs", "tiled", "tile_size", "tile_stride"), + onload_model_names=("vae",) + ) + + def apply_controlnet_mask_on_latents(self, pipe, latents, mask): + mask = (pipe.preprocess_image(mask) + 1) / 2 + mask = mask.mean(dim=1, keepdim=True) + mask = 1 - torch.nn.functional.interpolate(mask, size=latents.shape[-2:]) + latents = torch.concat([latents, mask], dim=1) + return latents + + def apply_controlnet_mask_on_image(self, pipe, image, mask): + mask = mask.resize(image.size) + mask = pipe.preprocess_image(mask).mean(dim=[0, 1]).cpu() + image = np.array(image) + image[mask > 0] = 0 + image = Image.fromarray(image) + return image + + def process(self, pipe: QwenImagePipeline, blockwise_controlnet_inputs: list[ControlNetInput], tiled, tile_size, tile_stride): + if blockwise_controlnet_inputs is None: + return {} + pipe.load_models_to_device(self.onload_model_names) + conditionings = [] + for controlnet_input in blockwise_controlnet_inputs: + image = controlnet_input.image + if controlnet_input.inpaint_mask is not None: + image = self.apply_controlnet_mask_on_image(pipe, image, controlnet_input.inpaint_mask) + + image = pipe.preprocess_image(image).to(device=pipe.device, dtype=pipe.torch_dtype) + image = pipe.vae.encode(image, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) + + if controlnet_input.inpaint_mask is not None: + image = self.apply_controlnet_mask_on_latents(pipe, image, controlnet_input.inpaint_mask) + conditionings.append(image) + + return {"blockwise_controlnet_conditioning": conditionings} + + +class QwenImageUnit_EditImageEmbedder(PipelineUnit): + def __init__(self): + super().__init__( + input_params=("edit_image", "tiled", "tile_size", "tile_stride", "edit_image_auto_resize"), + onload_model_names=("vae",) + ) + + + def calculate_dimensions(self, target_area, ratio): + import math + width = math.sqrt(target_area * ratio) + height = width / ratio + width = round(width / 32) * 32 + height = round(height / 32) * 32 + return width, height + + + def edit_image_auto_resize(self, edit_image): + calculated_width, calculated_height = self.calculate_dimensions(1024 * 1024, edit_image.size[0] / edit_image.size[1]) + return edit_image.resize((calculated_width, calculated_height)) + + + def process(self, pipe: QwenImagePipeline, edit_image, tiled, tile_size, tile_stride, edit_image_auto_resize=False): + if edit_image is None: + return {} + pipe.load_models_to_device(['vae']) + if isinstance(edit_image, Image.Image): + resized_edit_image = self.edit_image_auto_resize(edit_image) if edit_image_auto_resize else edit_image + edit_image = pipe.preprocess_image(resized_edit_image).to(device=pipe.device, dtype=pipe.torch_dtype) + edit_latents = pipe.vae.encode(edit_image, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) + else: + resized_edit_image, edit_latents = [], [] + for image in edit_image: + if edit_image_auto_resize: + image = self.edit_image_auto_resize(image) + resized_edit_image.append(image) + image = pipe.preprocess_image(image).to(device=pipe.device, dtype=pipe.torch_dtype) + latents = pipe.vae.encode(image, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) + edit_latents.append(latents) + return {"edit_latents": edit_latents, "edit_image": resized_edit_image} + + +class QwenImageUnit_ContextImageEmbedder(PipelineUnit): + def __init__(self): + super().__init__( + input_params=("context_image", "height", "width", "tiled", "tile_size", "tile_stride"), + onload_model_names=("vae",) + ) + + def process(self, pipe: QwenImagePipeline, context_image, height, width, tiled, tile_size, tile_stride): + if context_image is None: + return {} + pipe.load_models_to_device(['vae']) + context_image = pipe.preprocess_image(context_image.resize((width, height))).to(device=pipe.device, dtype=pipe.torch_dtype) + context_latents = pipe.vae.encode(context_image, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) + return {"context_latents": context_latents} + + +def model_fn_qwen_image( + dit: QwenImageDiT = None, + blockwise_controlnet: QwenImageBlockwiseMultiControlNet = None, + latents=None, + timestep=None, + prompt_emb=None, + prompt_emb_mask=None, + height=None, + width=None, + blockwise_controlnet_conditioning=None, + blockwise_controlnet_inputs=None, + progress_id=0, + num_inference_steps=1, + entity_prompt_emb=None, + entity_prompt_emb_mask=None, + entity_masks=None, + edit_latents=None, + context_latents=None, + enable_fp8_attention=False, + use_gradient_checkpointing=False, + use_gradient_checkpointing_offload=False, + edit_rope_interpolation=False, + **kwargs +): + img_shapes = [(latents.shape[0], latents.shape[2]//2, latents.shape[3]//2)] + txt_seq_lens = prompt_emb_mask.sum(dim=1).tolist() + timestep = timestep / 1000 + + image = rearrange(latents, "B C (H P) (W Q) -> B (H W) (C P Q)", H=height//16, W=width//16, P=2, Q=2) + image_seq_len = image.shape[1] + + if context_latents is not None: + img_shapes += [(context_latents.shape[0], context_latents.shape[2]//2, context_latents.shape[3]//2)] + context_image = rearrange(context_latents, "B C (H P) (W Q) -> B (H W) (C P Q)", H=context_latents.shape[2]//2, W=context_latents.shape[3]//2, P=2, Q=2) + image = torch.cat([image, context_image], dim=1) + if edit_latents is not None: + edit_latents_list = edit_latents if isinstance(edit_latents, list) else [edit_latents] + img_shapes += [(e.shape[0], e.shape[2]//2, e.shape[3]//2) for e in edit_latents_list] + edit_image = [rearrange(e, "B C (H P) (W Q) -> B (H W) (C P Q)", H=e.shape[2]//2, W=e.shape[3]//2, P=2, Q=2) for e in edit_latents_list] + image = torch.cat([image] + edit_image, dim=1) + + image = dit.img_in(image) + conditioning = dit.time_text_embed(timestep, image.dtype) + + if entity_prompt_emb is not None: + text, image_rotary_emb, attention_mask = dit.process_entity_masks( + latents, prompt_emb, prompt_emb_mask, entity_prompt_emb, entity_prompt_emb_mask, + entity_masks, height, width, image, img_shapes, + ) + else: + text = dit.txt_in(dit.txt_norm(prompt_emb)) + if edit_rope_interpolation: + image_rotary_emb = dit.pos_embed.forward_sampling(img_shapes, txt_seq_lens, device=latents.device) + else: + image_rotary_emb = dit.pos_embed(img_shapes, txt_seq_lens, device=latents.device) + attention_mask = None + + if blockwise_controlnet_conditioning is not None: + blockwise_controlnet_conditioning = blockwise_controlnet.preprocess( + blockwise_controlnet_inputs, blockwise_controlnet_conditioning) + + for block_id, block in enumerate(dit.transformer_blocks): + text, image = gradient_checkpoint_forward( + block, + use_gradient_checkpointing, + use_gradient_checkpointing_offload, + image=image, + text=text, + temb=conditioning, + image_rotary_emb=image_rotary_emb, + attention_mask=attention_mask, + enable_fp8_attention=enable_fp8_attention, + ) + if blockwise_controlnet_conditioning is not None: + image_slice = image[:, :image_seq_len].clone() + controlnet_output = blockwise_controlnet.blockwise_forward( + image=image_slice, conditionings=blockwise_controlnet_conditioning, + controlnet_inputs=blockwise_controlnet_inputs, block_id=block_id, + progress_id=progress_id, num_inference_steps=num_inference_steps, + ) + image[:, :image_seq_len] = image_slice + controlnet_output + + image = dit.norm_out(image, conditioning) + image = dit.proj_out(image) + image = image[:, :image_seq_len] + + latents = rearrange(image, "B (H W) (C P Q) -> B C (H P) (W Q)", H=height//16, W=width//16, P=2, Q=2) + return latents diff --git a/diffsynth/pipelines/sd3_image.py b/diffsynth/pipelines/sd3_image.py new file mode 100644 index 0000000000000000000000000000000000000000..c6098739b2701d59958ef3fa85b0dc96b5ffe86a --- /dev/null +++ b/diffsynth/pipelines/sd3_image.py @@ -0,0 +1,147 @@ +from ..models import ModelManager, SD3TextEncoder1, SD3TextEncoder2, SD3TextEncoder3, SD3DiT, SD3VAEDecoder, SD3VAEEncoder +from ..prompters import SD3Prompter +from ..schedulers import FlowMatchScheduler +from .base import BasePipeline +import torch +from tqdm import tqdm + + + +class SD3ImagePipeline(BasePipeline): + + def __init__(self, device="cuda", torch_dtype=torch.float16): + super().__init__(device=device, torch_dtype=torch_dtype, height_division_factor=16, width_division_factor=16) + self.scheduler = FlowMatchScheduler() + self.prompter = SD3Prompter() + # models + self.text_encoder_1: SD3TextEncoder1 = None + self.text_encoder_2: SD3TextEncoder2 = None + self.text_encoder_3: SD3TextEncoder3 = None + self.dit: SD3DiT = None + self.vae_decoder: SD3VAEDecoder = None + self.vae_encoder: SD3VAEEncoder = None + self.model_names = ['text_encoder_1', 'text_encoder_2', 'text_encoder_3', 'dit', 'vae_decoder', 'vae_encoder'] + + + def denoising_model(self): + return self.dit + + + def fetch_models(self, model_manager: ModelManager, prompt_refiner_classes=[]): + self.text_encoder_1 = model_manager.fetch_model("sd3_text_encoder_1") + self.text_encoder_2 = model_manager.fetch_model("sd3_text_encoder_2") + self.text_encoder_3 = model_manager.fetch_model("sd3_text_encoder_3") + self.dit = model_manager.fetch_model("sd3_dit") + self.vae_decoder = model_manager.fetch_model("sd3_vae_decoder") + self.vae_encoder = model_manager.fetch_model("sd3_vae_encoder") + self.prompter.fetch_models(self.text_encoder_1, self.text_encoder_2, self.text_encoder_3) + self.prompter.load_prompt_refiners(model_manager, prompt_refiner_classes) + + + @staticmethod + def from_model_manager(model_manager: ModelManager, prompt_refiner_classes=[], device=None): + pipe = SD3ImagePipeline( + device=model_manager.device if device is None else device, + torch_dtype=model_manager.torch_dtype, + ) + pipe.fetch_models(model_manager, prompt_refiner_classes) + return pipe + + + def encode_image(self, image, tiled=False, tile_size=64, tile_stride=32): + latents = self.vae_encoder(image, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) + return latents + + + def decode_image(self, latent, tiled=False, tile_size=64, tile_stride=32): + image = self.vae_decoder(latent.to(self.device), tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) + image = self.vae_output_to_image(image) + return image + + + def encode_prompt(self, prompt, positive=True, t5_sequence_length=77): + prompt_emb, pooled_prompt_emb = self.prompter.encode_prompt( + prompt, device=self.device, positive=positive, t5_sequence_length=t5_sequence_length + ) + return {"prompt_emb": prompt_emb, "pooled_prompt_emb": pooled_prompt_emb} + + + def prepare_extra_input(self, latents=None): + return {} + + + @torch.no_grad() + def __call__( + self, + prompt, + local_prompts=[], + masks=[], + mask_scales=[], + negative_prompt="", + cfg_scale=7.5, + input_image=None, + denoising_strength=1.0, + height=1024, + width=1024, + num_inference_steps=20, + t5_sequence_length=77, + tiled=False, + tile_size=128, + tile_stride=64, + seed=None, + progress_bar_cmd=tqdm, + progress_bar_st=None, + ): + height, width = self.check_resize_height_width(height, width) + + # Tiler parameters + tiler_kwargs = {"tiled": tiled, "tile_size": tile_size, "tile_stride": tile_stride} + + # Prepare scheduler + self.scheduler.set_timesteps(num_inference_steps, denoising_strength) + + # Prepare latent tensors + if input_image is not None: + self.load_models_to_device(['vae_encoder']) + image = self.preprocess_image(input_image).to(device=self.device, dtype=self.torch_dtype) + latents = self.encode_image(image, **tiler_kwargs) + noise = self.generate_noise((1, 16, height//8, width//8), seed=seed, device=self.device, dtype=self.torch_dtype) + latents = self.scheduler.add_noise(latents, noise, timestep=self.scheduler.timesteps[0]) + else: + latents = self.generate_noise((1, 16, height//8, width//8), seed=seed, device=self.device, dtype=self.torch_dtype) + + # Encode prompts + self.load_models_to_device(['text_encoder_1', 'text_encoder_2', 'text_encoder_3']) + prompt_emb_posi = self.encode_prompt(prompt, positive=True, t5_sequence_length=t5_sequence_length) + prompt_emb_nega = self.encode_prompt(negative_prompt, positive=False, t5_sequence_length=t5_sequence_length) + prompt_emb_locals = [self.encode_prompt(prompt_local, t5_sequence_length=t5_sequence_length) for prompt_local in local_prompts] + + # Denoise + self.load_models_to_device(['dit']) + for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)): + timestep = timestep.unsqueeze(0).to(self.device) + + # Classifier-free guidance + inference_callback = lambda prompt_emb_posi: self.dit( + latents, timestep=timestep, **prompt_emb_posi, **tiler_kwargs, + ) + noise_pred_posi = self.control_noise_via_local_prompts(prompt_emb_posi, prompt_emb_locals, masks, mask_scales, inference_callback) + noise_pred_nega = self.dit( + latents, timestep=timestep, **prompt_emb_nega, **tiler_kwargs, + ) + noise_pred = noise_pred_nega + cfg_scale * (noise_pred_posi - noise_pred_nega) + + # DDIM + latents = self.scheduler.step(noise_pred, self.scheduler.timesteps[progress_id], latents) + + # UI + if progress_bar_st is not None: + progress_bar_st.progress(progress_id / len(self.scheduler.timesteps)) + + # Decode image + self.load_models_to_device(['vae_decoder']) + image = self.decode_image(latents, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) + + # offload all models + self.load_models_to_device([]) + return image diff --git a/diffsynth/pipelines/sd_image.py b/diffsynth/pipelines/sd_image.py new file mode 100644 index 0000000000000000000000000000000000000000..c22c3fe69578f28925be900036bf21afeb750f17 --- /dev/null +++ b/diffsynth/pipelines/sd_image.py @@ -0,0 +1,191 @@ +from ..models import SDTextEncoder, SDUNet, SDVAEDecoder, SDVAEEncoder, SDIpAdapter, IpAdapterCLIPImageEmbedder +from ..models.model_manager import ModelManager +from ..controlnets import MultiControlNetManager, ControlNetUnit, ControlNetConfigUnit, Annotator +from ..prompters import SDPrompter +from ..schedulers import EnhancedDDIMScheduler +from .base import BasePipeline +from .dancer import lets_dance +from typing import List +import torch +from tqdm import tqdm + + + +class SDImagePipeline(BasePipeline): + + def __init__(self, device="cuda", torch_dtype=torch.float16): + super().__init__(device=device, torch_dtype=torch_dtype) + self.scheduler = EnhancedDDIMScheduler() + self.prompter = SDPrompter() + # models + self.text_encoder: SDTextEncoder = None + self.unet: SDUNet = None + self.vae_decoder: SDVAEDecoder = None + self.vae_encoder: SDVAEEncoder = None + self.controlnet: MultiControlNetManager = None + self.ipadapter_image_encoder: IpAdapterCLIPImageEmbedder = None + self.ipadapter: SDIpAdapter = None + self.model_names = ['text_encoder', 'unet', 'vae_decoder', 'vae_encoder', 'controlnet', 'ipadapter_image_encoder', 'ipadapter'] + + + def denoising_model(self): + return self.unet + + + def fetch_models(self, model_manager: ModelManager, controlnet_config_units: List[ControlNetConfigUnit]=[], prompt_refiner_classes=[]): + # Main models + self.text_encoder = model_manager.fetch_model("sd_text_encoder") + self.unet = model_manager.fetch_model("sd_unet") + self.vae_decoder = model_manager.fetch_model("sd_vae_decoder") + self.vae_encoder = model_manager.fetch_model("sd_vae_encoder") + self.prompter.fetch_models(self.text_encoder) + self.prompter.load_prompt_refiners(model_manager, prompt_refiner_classes) + + # ControlNets + controlnet_units = [] + for config in controlnet_config_units: + controlnet_unit = ControlNetUnit( + Annotator(config.processor_id, device=self.device), + model_manager.fetch_model("sd_controlnet", config.model_path), + config.scale + ) + controlnet_units.append(controlnet_unit) + self.controlnet = MultiControlNetManager(controlnet_units) + + # IP-Adapters + self.ipadapter = model_manager.fetch_model("sd_ipadapter") + self.ipadapter_image_encoder = model_manager.fetch_model("sd_ipadapter_clip_image_encoder") + + + @staticmethod + def from_model_manager(model_manager: ModelManager, controlnet_config_units: List[ControlNetConfigUnit]=[], prompt_refiner_classes=[], device=None): + pipe = SDImagePipeline( + device=model_manager.device if device is None else device, + torch_dtype=model_manager.torch_dtype, + ) + pipe.fetch_models(model_manager, controlnet_config_units, prompt_refiner_classes=[]) + return pipe + + + def encode_image(self, image, tiled=False, tile_size=64, tile_stride=32): + latents = self.vae_encoder(image, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) + return latents + + + def decode_image(self, latent, tiled=False, tile_size=64, tile_stride=32): + image = self.vae_decoder(latent.to(self.device), tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) + image = self.vae_output_to_image(image) + return image + + + def encode_prompt(self, prompt, clip_skip=1, positive=True): + prompt_emb = self.prompter.encode_prompt(prompt, clip_skip=clip_skip, device=self.device, positive=positive) + return {"encoder_hidden_states": prompt_emb} + + + def prepare_extra_input(self, latents=None): + return {} + + + @torch.no_grad() + def __call__( + self, + prompt, + local_prompts=[], + masks=[], + mask_scales=[], + negative_prompt="", + cfg_scale=7.5, + clip_skip=1, + input_image=None, + ipadapter_images=None, + ipadapter_scale=1.0, + controlnet_image=None, + denoising_strength=1.0, + height=512, + width=512, + num_inference_steps=20, + tiled=False, + tile_size=64, + tile_stride=32, + seed=None, + progress_bar_cmd=tqdm, + progress_bar_st=None, + ): + height, width = self.check_resize_height_width(height, width) + + # Tiler parameters + tiler_kwargs = {"tiled": tiled, "tile_size": tile_size, "tile_stride": tile_stride} + + # Prepare scheduler + self.scheduler.set_timesteps(num_inference_steps, denoising_strength) + + # Prepare latent tensors + if input_image is not None: + self.load_models_to_device(['vae_encoder']) + image = self.preprocess_image(input_image).to(device=self.device, dtype=self.torch_dtype) + latents = self.encode_image(image, **tiler_kwargs) + noise = self.generate_noise((1, 4, height//8, width//8), seed=seed, device=self.device, dtype=self.torch_dtype) + latents = self.scheduler.add_noise(latents, noise, timestep=self.scheduler.timesteps[0]) + else: + latents = self.generate_noise((1, 4, height//8, width//8), seed=seed, device=self.device, dtype=self.torch_dtype) + + # Encode prompts + self.load_models_to_device(['text_encoder']) + prompt_emb_posi = self.encode_prompt(prompt, clip_skip=clip_skip, positive=True) + prompt_emb_nega = self.encode_prompt(negative_prompt, clip_skip=clip_skip, positive=False) + prompt_emb_locals = [self.encode_prompt(prompt_local, clip_skip=clip_skip, positive=True) for prompt_local in local_prompts] + + # IP-Adapter + if ipadapter_images is not None: + self.load_models_to_device(['ipadapter_image_encoder']) + ipadapter_image_encoding = self.ipadapter_image_encoder(ipadapter_images) + self.load_models_to_device(['ipadapter']) + ipadapter_kwargs_list_posi = {"ipadapter_kwargs_list": self.ipadapter(ipadapter_image_encoding, scale=ipadapter_scale)} + ipadapter_kwargs_list_nega = {"ipadapter_kwargs_list": self.ipadapter(torch.zeros_like(ipadapter_image_encoding))} + else: + ipadapter_kwargs_list_posi, ipadapter_kwargs_list_nega = {"ipadapter_kwargs_list": {}}, {"ipadapter_kwargs_list": {}} + + # Prepare ControlNets + if controlnet_image is not None: + self.load_models_to_device(['controlnet']) + controlnet_image = self.controlnet.process_image(controlnet_image).to(device=self.device, dtype=self.torch_dtype) + controlnet_image = controlnet_image.unsqueeze(1) + controlnet_kwargs = {"controlnet_frames": controlnet_image} + else: + controlnet_kwargs = {"controlnet_frames": None} + + # Denoise + self.load_models_to_device(['controlnet', 'unet']) + for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)): + timestep = timestep.unsqueeze(0).to(self.device) + + # Classifier-free guidance + inference_callback = lambda prompt_emb_posi: lets_dance( + self.unet, motion_modules=None, controlnet=self.controlnet, + sample=latents, timestep=timestep, + **prompt_emb_posi, **controlnet_kwargs, **tiler_kwargs, **ipadapter_kwargs_list_posi, + device=self.device, + ) + noise_pred_posi = self.control_noise_via_local_prompts(prompt_emb_posi, prompt_emb_locals, masks, mask_scales, inference_callback) + noise_pred_nega = lets_dance( + self.unet, motion_modules=None, controlnet=self.controlnet, + sample=latents, timestep=timestep, **prompt_emb_nega, **controlnet_kwargs, **tiler_kwargs, **ipadapter_kwargs_list_nega, + device=self.device, + ) + noise_pred = noise_pred_nega + cfg_scale * (noise_pred_posi - noise_pred_nega) + + # DDIM + latents = self.scheduler.step(noise_pred, timestep, latents) + + # UI + if progress_bar_st is not None: + progress_bar_st.progress(progress_id / len(self.scheduler.timesteps)) + + # Decode image + self.load_models_to_device(['vae_decoder']) + image = self.decode_image(latents, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) + + # offload all models + self.load_models_to_device([]) + return image diff --git a/diffsynth/pipelines/sd_video.py b/diffsynth/pipelines/sd_video.py new file mode 100644 index 0000000000000000000000000000000000000000..4337beb4f7a2d4a08c5955fdbd5f528ea328b39e --- /dev/null +++ b/diffsynth/pipelines/sd_video.py @@ -0,0 +1,269 @@ +from ..models import SDTextEncoder, SDUNet, SDVAEDecoder, SDVAEEncoder, SDIpAdapter, IpAdapterCLIPImageEmbedder, SDMotionModel +from ..models.model_manager import ModelManager +from ..controlnets import MultiControlNetManager, ControlNetUnit, ControlNetConfigUnit, Annotator +from ..prompters import SDPrompter +from ..schedulers import EnhancedDDIMScheduler +from .sd_image import SDImagePipeline +from .dancer import lets_dance +from typing import List +import torch +from tqdm import tqdm + + + +def lets_dance_with_long_video( + unet: SDUNet, + motion_modules: SDMotionModel = None, + controlnet: MultiControlNetManager = None, + sample = None, + timestep = None, + encoder_hidden_states = None, + ipadapter_kwargs_list = {}, + controlnet_frames = None, + unet_batch_size = 1, + controlnet_batch_size = 1, + cross_frame_attention = False, + tiled=False, + tile_size=64, + tile_stride=32, + device="cuda", + animatediff_batch_size=16, + animatediff_stride=8, +): + num_frames = sample.shape[0] + hidden_states_output = [(torch.zeros(sample[0].shape, dtype=sample[0].dtype), 0) for i in range(num_frames)] + + for batch_id in range(0, num_frames, animatediff_stride): + batch_id_ = min(batch_id + animatediff_batch_size, num_frames) + + # process this batch + hidden_states_batch = lets_dance( + unet, motion_modules, controlnet, + sample[batch_id: batch_id_].to(device), + timestep, + encoder_hidden_states, + ipadapter_kwargs_list=ipadapter_kwargs_list, + controlnet_frames=controlnet_frames[:, batch_id: batch_id_].to(device) if controlnet_frames is not None else None, + unet_batch_size=unet_batch_size, controlnet_batch_size=controlnet_batch_size, + cross_frame_attention=cross_frame_attention, + tiled=tiled, tile_size=tile_size, tile_stride=tile_stride, device=device + ).cpu() + + # update hidden_states + for i, hidden_states_updated in zip(range(batch_id, batch_id_), hidden_states_batch): + bias = max(1 - abs(i - (batch_id + batch_id_ - 1) / 2) / ((batch_id_ - batch_id - 1 + 1e-2) / 2), 1e-2) + hidden_states, num = hidden_states_output[i] + hidden_states = hidden_states * (num / (num + bias)) + hidden_states_updated * (bias / (num + bias)) + hidden_states_output[i] = (hidden_states, num + bias) + + if batch_id_ == num_frames: + break + + # output + hidden_states = torch.stack([h for h, _ in hidden_states_output]) + return hidden_states + + + +class SDVideoPipeline(SDImagePipeline): + + def __init__(self, device="cuda", torch_dtype=torch.float16, use_original_animatediff=True): + super().__init__(device=device, torch_dtype=torch_dtype) + self.scheduler = EnhancedDDIMScheduler(beta_schedule="linear" if use_original_animatediff else "scaled_linear") + self.prompter = SDPrompter() + # models + self.text_encoder: SDTextEncoder = None + self.unet: SDUNet = None + self.vae_decoder: SDVAEDecoder = None + self.vae_encoder: SDVAEEncoder = None + self.controlnet: MultiControlNetManager = None + self.ipadapter_image_encoder: IpAdapterCLIPImageEmbedder = None + self.ipadapter: SDIpAdapter = None + self.motion_modules: SDMotionModel = None + + + def fetch_models(self, model_manager: ModelManager, controlnet_config_units: List[ControlNetConfigUnit]=[], prompt_refiner_classes=[]): + # Main models + self.text_encoder = model_manager.fetch_model("sd_text_encoder") + self.unet = model_manager.fetch_model("sd_unet") + self.vae_decoder = model_manager.fetch_model("sd_vae_decoder") + self.vae_encoder = model_manager.fetch_model("sd_vae_encoder") + self.prompter.fetch_models(self.text_encoder) + self.prompter.load_prompt_refiners(model_manager, prompt_refiner_classes) + + # ControlNets + controlnet_units = [] + for config in controlnet_config_units: + controlnet_unit = ControlNetUnit( + Annotator(config.processor_id, device=self.device), + model_manager.fetch_model("sd_controlnet", config.model_path), + config.scale + ) + controlnet_units.append(controlnet_unit) + self.controlnet = MultiControlNetManager(controlnet_units) + + # IP-Adapters + self.ipadapter = model_manager.fetch_model("sd_ipadapter") + self.ipadapter_image_encoder = model_manager.fetch_model("sd_ipadapter_clip_image_encoder") + + # Motion Modules + self.motion_modules = model_manager.fetch_model("sd_motion_modules") + if self.motion_modules is None: + self.scheduler = EnhancedDDIMScheduler(beta_schedule="scaled_linear") + + + @staticmethod + def from_model_manager(model_manager: ModelManager, controlnet_config_units: List[ControlNetConfigUnit]=[], prompt_refiner_classes=[]): + pipe = SDVideoPipeline( + device=model_manager.device, + torch_dtype=model_manager.torch_dtype, + ) + pipe.fetch_models(model_manager, controlnet_config_units, prompt_refiner_classes) + return pipe + + + def decode_video(self, latents, tiled=False, tile_size=64, tile_stride=32): + images = [ + self.decode_image(latents[frame_id: frame_id+1], tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) + for frame_id in range(latents.shape[0]) + ] + return images + + + def encode_video(self, processed_images, tiled=False, tile_size=64, tile_stride=32): + latents = [] + for image in processed_images: + image = self.preprocess_image(image).to(device=self.device, dtype=self.torch_dtype) + latent = self.encode_image(image, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) + latents.append(latent.cpu()) + latents = torch.concat(latents, dim=0) + return latents + + + @torch.no_grad() + def __call__( + self, + prompt, + negative_prompt="", + cfg_scale=7.5, + clip_skip=1, + num_frames=None, + input_frames=None, + ipadapter_images=None, + ipadapter_scale=1.0, + controlnet_frames=None, + denoising_strength=1.0, + height=512, + width=512, + num_inference_steps=20, + animatediff_batch_size = 16, + animatediff_stride = 8, + unet_batch_size = 1, + controlnet_batch_size = 1, + cross_frame_attention = False, + smoother=None, + smoother_progress_ids=[], + tiled=False, + tile_size=64, + tile_stride=32, + seed=None, + progress_bar_cmd=tqdm, + progress_bar_st=None, + ): + height, width = self.check_resize_height_width(height, width) + + # Tiler parameters, batch size ... + tiler_kwargs = {"tiled": tiled, "tile_size": tile_size, "tile_stride": tile_stride} + other_kwargs = { + "animatediff_batch_size": animatediff_batch_size, "animatediff_stride": animatediff_stride, + "unet_batch_size": unet_batch_size, "controlnet_batch_size": controlnet_batch_size, + "cross_frame_attention": cross_frame_attention, + } + + # Prepare scheduler + self.scheduler.set_timesteps(num_inference_steps, denoising_strength) + + # Prepare latent tensors + if self.motion_modules is None: + noise = self.generate_noise((1, 4, height//8, width//8), seed=seed, device="cpu", dtype=self.torch_dtype).repeat(num_frames, 1, 1, 1) + else: + noise = self.generate_noise((num_frames, 4, height//8, width//8), seed=seed, device="cpu", dtype=self.torch_dtype) + if input_frames is None or denoising_strength == 1.0: + latents = noise + else: + latents = self.encode_video(input_frames, **tiler_kwargs) + latents = self.scheduler.add_noise(latents, noise, timestep=self.scheduler.timesteps[0]) + + # Encode prompts + prompt_emb_posi = self.encode_prompt(prompt, clip_skip=clip_skip, positive=True) + prompt_emb_nega = self.encode_prompt(negative_prompt, clip_skip=clip_skip, positive=False) + + # IP-Adapter + if ipadapter_images is not None: + ipadapter_image_encoding = self.ipadapter_image_encoder(ipadapter_images) + ipadapter_kwargs_list_posi = {"ipadapter_kwargs_list": self.ipadapter(ipadapter_image_encoding, scale=ipadapter_scale)} + ipadapter_kwargs_list_nega = {"ipadapter_kwargs_list": self.ipadapter(torch.zeros_like(ipadapter_image_encoding))} + else: + ipadapter_kwargs_list_posi, ipadapter_kwargs_list_nega = {"ipadapter_kwargs_list": {}}, {"ipadapter_kwargs_list": {}} + + # Prepare ControlNets + if controlnet_frames is not None: + if isinstance(controlnet_frames[0], list): + controlnet_frames_ = [] + for processor_id in range(len(controlnet_frames)): + controlnet_frames_.append( + torch.stack([ + self.controlnet.process_image(controlnet_frame, processor_id=processor_id).to(self.torch_dtype) + for controlnet_frame in progress_bar_cmd(controlnet_frames[processor_id]) + ], dim=1) + ) + controlnet_frames = torch.concat(controlnet_frames_, dim=0) + else: + controlnet_frames = torch.stack([ + self.controlnet.process_image(controlnet_frame).to(self.torch_dtype) + for controlnet_frame in progress_bar_cmd(controlnet_frames) + ], dim=1) + controlnet_kwargs = {"controlnet_frames": controlnet_frames} + else: + controlnet_kwargs = {"controlnet_frames": None} + + # Denoise + for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)): + timestep = timestep.unsqueeze(0).to(self.device) + + # Classifier-free guidance + noise_pred_posi = lets_dance_with_long_video( + self.unet, motion_modules=self.motion_modules, controlnet=self.controlnet, + sample=latents, timestep=timestep, + **prompt_emb_posi, **controlnet_kwargs, **ipadapter_kwargs_list_posi, **other_kwargs, **tiler_kwargs, + device=self.device, + ) + noise_pred_nega = lets_dance_with_long_video( + self.unet, motion_modules=self.motion_modules, controlnet=self.controlnet, + sample=latents, timestep=timestep, + **prompt_emb_nega, **controlnet_kwargs, **ipadapter_kwargs_list_nega, **other_kwargs, **tiler_kwargs, + device=self.device, + ) + noise_pred = noise_pred_nega + cfg_scale * (noise_pred_posi - noise_pred_nega) + + # DDIM and smoother + if smoother is not None and progress_id in smoother_progress_ids: + rendered_frames = self.scheduler.step(noise_pred, timestep, latents, to_final=True) + rendered_frames = self.decode_video(rendered_frames) + rendered_frames = smoother(rendered_frames, original_frames=input_frames) + target_latents = self.encode_video(rendered_frames) + noise_pred = self.scheduler.return_to_timestep(timestep, latents, target_latents) + latents = self.scheduler.step(noise_pred, timestep, latents) + + # UI + if progress_bar_st is not None: + progress_bar_st.progress(progress_id / len(self.scheduler.timesteps)) + + # Decode image + output_frames = self.decode_video(latents, **tiler_kwargs) + + # Post-process + if smoother is not None and (num_inference_steps in smoother_progress_ids or -1 in smoother_progress_ids): + output_frames = smoother(output_frames, original_frames=input_frames) + + return output_frames diff --git a/diffsynth/pipelines/sdxl_image.py b/diffsynth/pipelines/sdxl_image.py new file mode 100644 index 0000000000000000000000000000000000000000..499c4bbce707fa7cfd026c66af8c8dca3e554127 --- /dev/null +++ b/diffsynth/pipelines/sdxl_image.py @@ -0,0 +1,226 @@ +from ..models import SDXLTextEncoder, SDXLTextEncoder2, SDXLUNet, SDXLVAEDecoder, SDXLVAEEncoder, SDXLIpAdapter, IpAdapterXLCLIPImageEmbedder +from ..models.kolors_text_encoder import ChatGLMModel +from ..models.model_manager import ModelManager +from ..controlnets import MultiControlNetManager, ControlNetUnit, ControlNetConfigUnit, Annotator +from ..prompters import SDXLPrompter, KolorsPrompter +from ..schedulers import EnhancedDDIMScheduler +from .base import BasePipeline +from .dancer import lets_dance_xl +from typing import List +import torch +from tqdm import tqdm +from einops import repeat + + + +class SDXLImagePipeline(BasePipeline): + + def __init__(self, device="cuda", torch_dtype=torch.float16): + super().__init__(device=device, torch_dtype=torch_dtype) + self.scheduler = EnhancedDDIMScheduler() + self.prompter = SDXLPrompter() + # models + self.text_encoder: SDXLTextEncoder = None + self.text_encoder_2: SDXLTextEncoder2 = None + self.text_encoder_kolors: ChatGLMModel = None + self.unet: SDXLUNet = None + self.vae_decoder: SDXLVAEDecoder = None + self.vae_encoder: SDXLVAEEncoder = None + self.controlnet: MultiControlNetManager = None + self.ipadapter_image_encoder: IpAdapterXLCLIPImageEmbedder = None + self.ipadapter: SDXLIpAdapter = None + self.model_names = ['text_encoder', 'text_encoder_2', 'text_encoder_kolors', 'unet', 'vae_decoder', 'vae_encoder', 'controlnet', 'ipadapter_image_encoder', 'ipadapter'] + + + def denoising_model(self): + return self.unet + + + def fetch_models(self, model_manager: ModelManager, controlnet_config_units: List[ControlNetConfigUnit]=[], prompt_refiner_classes=[]): + # Main models + self.text_encoder = model_manager.fetch_model("sdxl_text_encoder") + self.text_encoder_2 = model_manager.fetch_model("sdxl_text_encoder_2") + self.text_encoder_kolors = model_manager.fetch_model("kolors_text_encoder") + self.unet = model_manager.fetch_model("sdxl_unet") + self.vae_decoder = model_manager.fetch_model("sdxl_vae_decoder") + self.vae_encoder = model_manager.fetch_model("sdxl_vae_encoder") + + # ControlNets + controlnet_units = [] + for config in controlnet_config_units: + controlnet_unit = ControlNetUnit( + Annotator(config.processor_id, device=self.device), + model_manager.fetch_model("sdxl_controlnet", config.model_path), + config.scale + ) + controlnet_units.append(controlnet_unit) + self.controlnet = MultiControlNetManager(controlnet_units) + + # IP-Adapters + self.ipadapter = model_manager.fetch_model("sdxl_ipadapter") + self.ipadapter_image_encoder = model_manager.fetch_model("sdxl_ipadapter_clip_image_encoder") + + # Kolors + if self.text_encoder_kolors is not None: + print("Switch to Kolors. The prompter and scheduler will be replaced.") + self.prompter = KolorsPrompter() + self.prompter.fetch_models(self.text_encoder_kolors) + self.scheduler = EnhancedDDIMScheduler(beta_end=0.014, num_train_timesteps=1100) + else: + self.prompter.fetch_models(self.text_encoder, self.text_encoder_2) + self.prompter.load_prompt_refiners(model_manager, prompt_refiner_classes) + + + @staticmethod + def from_model_manager(model_manager: ModelManager, controlnet_config_units: List[ControlNetConfigUnit]=[], prompt_refiner_classes=[], device=None): + pipe = SDXLImagePipeline( + device=model_manager.device if device is None else device, + torch_dtype=model_manager.torch_dtype, + ) + pipe.fetch_models(model_manager, controlnet_config_units, prompt_refiner_classes) + return pipe + + + def encode_image(self, image, tiled=False, tile_size=64, tile_stride=32): + latents = self.vae_encoder(image, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) + return latents + + + def decode_image(self, latent, tiled=False, tile_size=64, tile_stride=32): + image = self.vae_decoder(latent.to(self.device), tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) + image = self.vae_output_to_image(image) + return image + + + def encode_prompt(self, prompt, clip_skip=1, clip_skip_2=2, positive=True): + add_prompt_emb, prompt_emb = self.prompter.encode_prompt( + prompt, + clip_skip=clip_skip, clip_skip_2=clip_skip_2, + device=self.device, + positive=positive, + ) + return {"encoder_hidden_states": prompt_emb, "add_text_embeds": add_prompt_emb} + + + def prepare_extra_input(self, latents=None): + height, width = latents.shape[2] * 8, latents.shape[3] * 8 + add_time_id = torch.tensor([height, width, 0, 0, height, width], device=self.device).repeat(latents.shape[0]) + return {"add_time_id": add_time_id} + + + @torch.no_grad() + def __call__( + self, + prompt, + local_prompts=[], + masks=[], + mask_scales=[], + negative_prompt="", + cfg_scale=7.5, + clip_skip=1, + clip_skip_2=2, + input_image=None, + ipadapter_images=None, + ipadapter_scale=1.0, + ipadapter_use_instant_style=False, + controlnet_image=None, + denoising_strength=1.0, + height=1024, + width=1024, + num_inference_steps=20, + tiled=False, + tile_size=64, + tile_stride=32, + seed=None, + progress_bar_cmd=tqdm, + progress_bar_st=None, + ): + height, width = self.check_resize_height_width(height, width) + + # Tiler parameters + tiler_kwargs = {"tiled": tiled, "tile_size": tile_size, "tile_stride": tile_stride} + + # Prepare scheduler + self.scheduler.set_timesteps(num_inference_steps, denoising_strength) + + # Prepare latent tensors + if input_image is not None: + self.load_models_to_device(['vae_encoder']) + image = self.preprocess_image(input_image).to(device=self.device, dtype=self.torch_dtype) + latents = self.encode_image(image, **tiler_kwargs) + noise = self.generate_noise((1, 4, height//8, width//8), seed=seed, device=self.device, dtype=self.torch_dtype) + latents = self.scheduler.add_noise(latents, noise, timestep=self.scheduler.timesteps[0]) + else: + latents = self.generate_noise((1, 4, height//8, width//8), seed=seed, device=self.device, dtype=self.torch_dtype) + + # Encode prompts + self.load_models_to_device(['text_encoder', 'text_encoder_2', 'text_encoder_kolors']) + prompt_emb_posi = self.encode_prompt(prompt, clip_skip=clip_skip, clip_skip_2=clip_skip_2, positive=True) + prompt_emb_nega = self.encode_prompt(negative_prompt, clip_skip=clip_skip, clip_skip_2=clip_skip_2, positive=False) + prompt_emb_locals = [self.encode_prompt(prompt_local, clip_skip=clip_skip, clip_skip_2=clip_skip_2, positive=True) for prompt_local in local_prompts] + + # IP-Adapter + if ipadapter_images is not None: + if ipadapter_use_instant_style: + self.ipadapter.set_less_adapter() + else: + self.ipadapter.set_full_adapter() + self.load_models_to_device(['ipadapter_image_encoder']) + ipadapter_image_encoding = self.ipadapter_image_encoder(ipadapter_images) + self.load_models_to_device(['ipadapter']) + ipadapter_kwargs_list_posi = {"ipadapter_kwargs_list": self.ipadapter(ipadapter_image_encoding, scale=ipadapter_scale)} + ipadapter_kwargs_list_nega = {"ipadapter_kwargs_list": self.ipadapter(torch.zeros_like(ipadapter_image_encoding))} + else: + ipadapter_kwargs_list_posi, ipadapter_kwargs_list_nega = {"ipadapter_kwargs_list": {}}, {"ipadapter_kwargs_list": {}} + + # Prepare ControlNets + if controlnet_image is not None: + self.load_models_to_device(['controlnet']) + controlnet_image = self.controlnet.process_image(controlnet_image).to(device=self.device, dtype=self.torch_dtype) + controlnet_image = controlnet_image.unsqueeze(1) + controlnet_kwargs = {"controlnet_frames": controlnet_image} + else: + controlnet_kwargs = {"controlnet_frames": None} + + # Prepare extra input + extra_input = self.prepare_extra_input(latents) + + # Denoise + self.load_models_to_device(['controlnet', 'unet']) + for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)): + timestep = timestep.unsqueeze(0).to(self.device) + + # Classifier-free guidance + inference_callback = lambda prompt_emb_posi: lets_dance_xl( + self.unet, motion_modules=None, controlnet=self.controlnet, + sample=latents, timestep=timestep, **extra_input, + **prompt_emb_posi, **controlnet_kwargs, **tiler_kwargs, **ipadapter_kwargs_list_posi, + device=self.device, + ) + noise_pred_posi = self.control_noise_via_local_prompts(prompt_emb_posi, prompt_emb_locals, masks, mask_scales, inference_callback) + + if cfg_scale != 1.0: + noise_pred_nega = lets_dance_xl( + self.unet, motion_modules=None, controlnet=self.controlnet, + sample=latents, timestep=timestep, **extra_input, + **prompt_emb_nega, **controlnet_kwargs, **tiler_kwargs, **ipadapter_kwargs_list_nega, + device=self.device, + ) + noise_pred = noise_pred_nega + cfg_scale * (noise_pred_posi - noise_pred_nega) + else: + noise_pred = noise_pred_posi + + # DDIM + latents = self.scheduler.step(noise_pred, timestep, latents) + + # UI + if progress_bar_st is not None: + progress_bar_st.progress(progress_id / len(self.scheduler.timesteps)) + + # Decode image + self.load_models_to_device(['vae_decoder']) + image = self.decode_image(latents, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) + + # offload all models + self.load_models_to_device([]) + return image diff --git a/diffsynth/pipelines/sdxl_video.py b/diffsynth/pipelines/sdxl_video.py new file mode 100644 index 0000000000000000000000000000000000000000..308590ca6a874c5803da95db1d90fced26126893 --- /dev/null +++ b/diffsynth/pipelines/sdxl_video.py @@ -0,0 +1,226 @@ +from ..models import SDXLTextEncoder, SDXLTextEncoder2, SDXLUNet, SDXLVAEDecoder, SDXLVAEEncoder, SDXLIpAdapter, IpAdapterXLCLIPImageEmbedder, SDXLMotionModel +from ..models.kolors_text_encoder import ChatGLMModel +from ..models.model_manager import ModelManager +from ..controlnets import MultiControlNetManager, ControlNetUnit, ControlNetConfigUnit, Annotator +from ..prompters import SDXLPrompter, KolorsPrompter +from ..schedulers import EnhancedDDIMScheduler +from .sdxl_image import SDXLImagePipeline +from .dancer import lets_dance_xl +from typing import List +import torch +from tqdm import tqdm + + + +class SDXLVideoPipeline(SDXLImagePipeline): + + def __init__(self, device="cuda", torch_dtype=torch.float16, use_original_animatediff=True): + super().__init__(device=device, torch_dtype=torch_dtype) + self.scheduler = EnhancedDDIMScheduler(beta_schedule="linear" if use_original_animatediff else "scaled_linear") + self.prompter = SDXLPrompter() + # models + self.text_encoder: SDXLTextEncoder = None + self.text_encoder_2: SDXLTextEncoder2 = None + self.text_encoder_kolors: ChatGLMModel = None + self.unet: SDXLUNet = None + self.vae_decoder: SDXLVAEDecoder = None + self.vae_encoder: SDXLVAEEncoder = None + # self.controlnet: MultiControlNetManager = None (TODO) + self.ipadapter_image_encoder: IpAdapterXLCLIPImageEmbedder = None + self.ipadapter: SDXLIpAdapter = None + self.motion_modules: SDXLMotionModel = None + + + def fetch_models(self, model_manager: ModelManager, controlnet_config_units: List[ControlNetConfigUnit]=[], prompt_refiner_classes=[]): + # Main models + self.text_encoder = model_manager.fetch_model("sdxl_text_encoder") + self.text_encoder_2 = model_manager.fetch_model("sdxl_text_encoder_2") + self.text_encoder_kolors = model_manager.fetch_model("kolors_text_encoder") + self.unet = model_manager.fetch_model("sdxl_unet") + self.vae_decoder = model_manager.fetch_model("sdxl_vae_decoder") + self.vae_encoder = model_manager.fetch_model("sdxl_vae_encoder") + self.prompter.fetch_models(self.text_encoder) + self.prompter.load_prompt_refiners(model_manager, prompt_refiner_classes) + + # ControlNets (TODO) + + # IP-Adapters + self.ipadapter = model_manager.fetch_model("sdxl_ipadapter") + self.ipadapter_image_encoder = model_manager.fetch_model("sdxl_ipadapter_clip_image_encoder") + + # Motion Modules + self.motion_modules = model_manager.fetch_model("sdxl_motion_modules") + if self.motion_modules is None: + self.scheduler = EnhancedDDIMScheduler(beta_schedule="scaled_linear") + + # Kolors + if self.text_encoder_kolors is not None: + print("Switch to Kolors. The prompter will be replaced.") + self.prompter = KolorsPrompter() + self.prompter.fetch_models(self.text_encoder_kolors) + # The schedulers of AniamteDiff and Kolors are incompatible. We align it with AniamteDiff. + if self.motion_modules is None: + self.scheduler = EnhancedDDIMScheduler(beta_end=0.014, num_train_timesteps=1100) + else: + self.prompter.fetch_models(self.text_encoder, self.text_encoder_2) + + + @staticmethod + def from_model_manager(model_manager: ModelManager, controlnet_config_units: List[ControlNetConfigUnit]=[], prompt_refiner_classes=[]): + pipe = SDXLVideoPipeline( + device=model_manager.device, + torch_dtype=model_manager.torch_dtype, + ) + pipe.fetch_models(model_manager, controlnet_config_units, prompt_refiner_classes) + return pipe + + + def decode_video(self, latents, tiled=False, tile_size=64, tile_stride=32): + images = [ + self.decode_image(latents[frame_id: frame_id+1], tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) + for frame_id in range(latents.shape[0]) + ] + return images + + + def encode_video(self, processed_images, tiled=False, tile_size=64, tile_stride=32): + latents = [] + for image in processed_images: + image = self.preprocess_image(image).to(device=self.device, dtype=self.torch_dtype) + latent = self.encode_image(image, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) + latents.append(latent.cpu()) + latents = torch.concat(latents, dim=0) + return latents + + + @torch.no_grad() + def __call__( + self, + prompt, + negative_prompt="", + cfg_scale=7.5, + clip_skip=1, + num_frames=None, + input_frames=None, + ipadapter_images=None, + ipadapter_scale=1.0, + ipadapter_use_instant_style=False, + controlnet_frames=None, + denoising_strength=1.0, + height=512, + width=512, + num_inference_steps=20, + animatediff_batch_size = 16, + animatediff_stride = 8, + unet_batch_size = 1, + controlnet_batch_size = 1, + cross_frame_attention = False, + smoother=None, + smoother_progress_ids=[], + tiled=False, + tile_size=64, + tile_stride=32, + seed=None, + progress_bar_cmd=tqdm, + progress_bar_st=None, + ): + height, width = self.check_resize_height_width(height, width) + + # Tiler parameters, batch size ... + tiler_kwargs = {"tiled": tiled, "tile_size": tile_size, "tile_stride": tile_stride} + + # Prepare scheduler + self.scheduler.set_timesteps(num_inference_steps, denoising_strength) + + # Prepare latent tensors + if self.motion_modules is None: + noise = self.generate_noise((1, 4, height//8, width//8), seed=seed, device="cpu", dtype=self.torch_dtype).repeat(num_frames, 1, 1, 1) + else: + noise = self.generate_noise((num_frames, 4, height//8, width//8), seed=seed, device="cpu", dtype=self.torch_dtype) + if input_frames is None or denoising_strength == 1.0: + latents = noise + else: + latents = self.encode_video(input_frames, **tiler_kwargs) + latents = self.scheduler.add_noise(latents, noise, timestep=self.scheduler.timesteps[0]) + latents = latents.to(self.device) # will be deleted for supporting long videos + + # Encode prompts + prompt_emb_posi = self.encode_prompt(prompt, clip_skip=clip_skip, positive=True) + prompt_emb_nega = self.encode_prompt(negative_prompt, clip_skip=clip_skip, positive=False) + + # IP-Adapter + if ipadapter_images is not None: + if ipadapter_use_instant_style: + self.ipadapter.set_less_adapter() + else: + self.ipadapter.set_full_adapter() + ipadapter_image_encoding = self.ipadapter_image_encoder(ipadapter_images) + ipadapter_kwargs_list_posi = {"ipadapter_kwargs_list": self.ipadapter(ipadapter_image_encoding, scale=ipadapter_scale)} + ipadapter_kwargs_list_nega = {"ipadapter_kwargs_list": self.ipadapter(torch.zeros_like(ipadapter_image_encoding))} + else: + ipadapter_kwargs_list_posi, ipadapter_kwargs_list_nega = {"ipadapter_kwargs_list": {}}, {"ipadapter_kwargs_list": {}} + + # Prepare ControlNets + if controlnet_frames is not None: + if isinstance(controlnet_frames[0], list): + controlnet_frames_ = [] + for processor_id in range(len(controlnet_frames)): + controlnet_frames_.append( + torch.stack([ + self.controlnet.process_image(controlnet_frame, processor_id=processor_id).to(self.torch_dtype) + for controlnet_frame in progress_bar_cmd(controlnet_frames[processor_id]) + ], dim=1) + ) + controlnet_frames = torch.concat(controlnet_frames_, dim=0) + else: + controlnet_frames = torch.stack([ + self.controlnet.process_image(controlnet_frame).to(self.torch_dtype) + for controlnet_frame in progress_bar_cmd(controlnet_frames) + ], dim=1) + controlnet_kwargs = {"controlnet_frames": controlnet_frames} + else: + controlnet_kwargs = {"controlnet_frames": None} + + # Prepare extra input + extra_input = self.prepare_extra_input(latents) + + # Denoise + for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)): + timestep = timestep.unsqueeze(0).to(self.device) + + # Classifier-free guidance + noise_pred_posi = lets_dance_xl( + self.unet, motion_modules=self.motion_modules, controlnet=None, + sample=latents, timestep=timestep, + **prompt_emb_posi, **controlnet_kwargs, **ipadapter_kwargs_list_posi, **extra_input, **tiler_kwargs, + device=self.device, + ) + noise_pred_nega = lets_dance_xl( + self.unet, motion_modules=self.motion_modules, controlnet=None, + sample=latents, timestep=timestep, + **prompt_emb_nega, **controlnet_kwargs, **ipadapter_kwargs_list_nega, **extra_input, **tiler_kwargs, + device=self.device, + ) + noise_pred = noise_pred_nega + cfg_scale * (noise_pred_posi - noise_pred_nega) + + # DDIM and smoother + if smoother is not None and progress_id in smoother_progress_ids: + rendered_frames = self.scheduler.step(noise_pred, timestep, latents, to_final=True) + rendered_frames = self.decode_video(rendered_frames) + rendered_frames = smoother(rendered_frames, original_frames=input_frames) + target_latents = self.encode_video(rendered_frames) + noise_pred = self.scheduler.return_to_timestep(timestep, latents, target_latents) + latents = self.scheduler.step(noise_pred, timestep, latents) + + # UI + if progress_bar_st is not None: + progress_bar_st.progress(progress_id / len(self.scheduler.timesteps)) + + # Decode image + output_frames = self.decode_video(latents, **tiler_kwargs) + + # Post-process + if smoother is not None and (num_inference_steps in smoother_progress_ids or -1 in smoother_progress_ids): + output_frames = smoother(output_frames, original_frames=input_frames) + + return output_frames diff --git a/diffsynth/pipelines/step_video.py b/diffsynth/pipelines/step_video.py new file mode 100644 index 0000000000000000000000000000000000000000..56140178e9d6cdaf5efeca77ea061f8232836f11 --- /dev/null +++ b/diffsynth/pipelines/step_video.py @@ -0,0 +1,209 @@ +from ..models import ModelManager +from ..models.hunyuan_dit_text_encoder import HunyuanDiTCLIPTextEncoder +from ..models.stepvideo_text_encoder import STEP1TextEncoder +from ..models.stepvideo_dit import StepVideoModel +from ..models.stepvideo_vae import StepVideoVAE +from ..schedulers.flow_match import FlowMatchScheduler +from .base import BasePipeline +from ..prompters import StepVideoPrompter +import torch +from einops import rearrange +import numpy as np +from PIL import Image +from ..vram_management import enable_vram_management, AutoWrappedModule, AutoWrappedLinear +from transformers.models.bert.modeling_bert import BertEmbeddings +from ..models.stepvideo_dit import RMSNorm +from ..models.stepvideo_vae import CausalConv, CausalConvAfterNorm, Upsample2D, BaseGroupNorm + + + +class StepVideoPipeline(BasePipeline): + + def __init__(self, device="cuda", torch_dtype=torch.float16): + super().__init__(device=device, torch_dtype=torch_dtype) + self.scheduler = FlowMatchScheduler(sigma_min=0.0, extra_one_step=True, shift=13.0, reverse_sigmas=True, num_train_timesteps=1) + self.prompter = StepVideoPrompter() + self.text_encoder_1: HunyuanDiTCLIPTextEncoder = None + self.text_encoder_2: STEP1TextEncoder = None + self.dit: StepVideoModel = None + self.vae: StepVideoVAE = None + self.model_names = ['text_encoder_1', 'text_encoder_2', 'dit', 'vae'] + + + def enable_vram_management(self, num_persistent_param_in_dit=None): + dtype = next(iter(self.text_encoder_1.parameters())).dtype + enable_vram_management( + self.text_encoder_1, + module_map = { + torch.nn.Linear: AutoWrappedLinear, + BertEmbeddings: AutoWrappedModule, + torch.nn.LayerNorm: AutoWrappedModule, + }, + module_config = dict( + offload_dtype=dtype, + offload_device="cpu", + onload_dtype=dtype, + onload_device="cpu", + computation_dtype=torch.float32, + computation_device=self.device, + ), + ) + dtype = next(iter(self.text_encoder_2.parameters())).dtype + enable_vram_management( + self.text_encoder_2, + module_map = { + torch.nn.Linear: AutoWrappedLinear, + RMSNorm: AutoWrappedModule, + torch.nn.Embedding: AutoWrappedModule, + }, + module_config = dict( + offload_dtype=dtype, + offload_device="cpu", + onload_dtype=dtype, + onload_device="cpu", + computation_dtype=self.torch_dtype, + computation_device=self.device, + ), + ) + dtype = next(iter(self.dit.parameters())).dtype + enable_vram_management( + self.dit, + module_map = { + torch.nn.Linear: AutoWrappedLinear, + torch.nn.Conv2d: AutoWrappedModule, + torch.nn.LayerNorm: AutoWrappedModule, + RMSNorm: AutoWrappedModule, + }, + module_config = dict( + offload_dtype=dtype, + offload_device="cpu", + onload_dtype=dtype, + onload_device=self.device, + computation_dtype=self.torch_dtype, + computation_device=self.device, + ), + max_num_param=num_persistent_param_in_dit, + overflow_module_config = dict( + offload_dtype=dtype, + offload_device="cpu", + onload_dtype=dtype, + onload_device="cpu", + computation_dtype=self.torch_dtype, + computation_device=self.device, + ), + ) + dtype = next(iter(self.vae.parameters())).dtype + enable_vram_management( + self.vae, + module_map = { + torch.nn.Linear: AutoWrappedLinear, + torch.nn.Conv3d: AutoWrappedModule, + CausalConv: AutoWrappedModule, + CausalConvAfterNorm: AutoWrappedModule, + Upsample2D: AutoWrappedModule, + BaseGroupNorm: AutoWrappedModule, + }, + module_config = dict( + offload_dtype=dtype, + offload_device="cpu", + onload_dtype=dtype, + onload_device="cpu", + computation_dtype=self.torch_dtype, + computation_device=self.device, + ), + ) + self.enable_cpu_offload() + + + def fetch_models(self, model_manager: ModelManager): + self.text_encoder_1 = model_manager.fetch_model("hunyuan_dit_clip_text_encoder") + self.text_encoder_2 = model_manager.fetch_model("stepvideo_text_encoder_2") + self.dit = model_manager.fetch_model("stepvideo_dit") + self.vae = model_manager.fetch_model("stepvideo_vae") + self.prompter.fetch_models(self.text_encoder_1, self.text_encoder_2) + + + @staticmethod + def from_model_manager(model_manager: ModelManager, torch_dtype=None, device=None): + if device is None: device = model_manager.device + if torch_dtype is None: torch_dtype = model_manager.torch_dtype + pipe = StepVideoPipeline(device=device, torch_dtype=torch_dtype) + pipe.fetch_models(model_manager) + return pipe + + + def encode_prompt(self, prompt, positive=True): + clip_embeds, llm_embeds, llm_mask = self.prompter.encode_prompt(prompt, device=self.device, positive=positive) + clip_embeds = clip_embeds.to(dtype=self.torch_dtype, device=self.device) + llm_embeds = llm_embeds.to(dtype=self.torch_dtype, device=self.device) + llm_mask = llm_mask.to(dtype=self.torch_dtype, device=self.device) + return {"encoder_hidden_states_2": clip_embeds, "encoder_hidden_states": llm_embeds, "encoder_attention_mask": llm_mask} + + + def tensor2video(self, frames): + frames = rearrange(frames, "C T H W -> T H W C") + frames = ((frames.float() + 1) * 127.5).clip(0, 255).cpu().numpy().astype(np.uint8) + frames = [Image.fromarray(frame) for frame in frames] + return frames + + + @torch.no_grad() + def __call__( + self, + prompt, + negative_prompt="", + input_video=None, + denoising_strength=1.0, + seed=None, + rand_device="cpu", + height=544, + width=992, + num_frames=204, + cfg_scale=9.0, + num_inference_steps=30, + tiled=True, + tile_size=(34, 34), + tile_stride=(16, 16), + smooth_scale=0.6, + progress_bar_cmd=lambda x: x, + progress_bar_st=None, + ): + # Tiler parameters + tiler_kwargs = {"tiled": tiled, "tile_size": tile_size, "tile_stride": tile_stride} + + # Scheduler + self.scheduler.set_timesteps(num_inference_steps, denoising_strength) + + # Initialize noise + latents = self.generate_noise((1, max(num_frames//17*3, 1), 64, height//16, width//16), seed=seed, device=rand_device, dtype=self.torch_dtype).to(self.device) + + # Encode prompts + self.load_models_to_device(["text_encoder_1", "text_encoder_2"]) + prompt_emb_posi = self.encode_prompt(prompt, positive=True) + if cfg_scale != 1.0: + prompt_emb_nega = self.encode_prompt(negative_prompt, positive=False) + + # Denoise + self.load_models_to_device(["dit"]) + for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)): + timestep = timestep.unsqueeze(0).to(dtype=self.torch_dtype, device=self.device) + print(f"Step {progress_id + 1} / {len(self.scheduler.timesteps)}") + + # Inference + noise_pred_posi = self.dit(latents, timestep=timestep, **prompt_emb_posi) + if cfg_scale != 1.0: + noise_pred_nega = self.dit(latents, timestep=timestep, **prompt_emb_nega) + noise_pred = noise_pred_nega + cfg_scale * (noise_pred_posi - noise_pred_nega) + else: + noise_pred = noise_pred_posi + + # Scheduler + latents = self.scheduler.step(noise_pred, self.scheduler.timesteps[progress_id], latents) + + # Decode + self.load_models_to_device(['vae']) + frames = self.vae.decode(latents, device=self.device, smooth_scale=smooth_scale, **tiler_kwargs) + self.load_models_to_device([]) + frames = self.tensor2video(frames[0]) + + return frames diff --git a/diffsynth/pipelines/svd_video.py b/diffsynth/pipelines/svd_video.py new file mode 100644 index 0000000000000000000000000000000000000000..b71597efa73783f7e3746a2bcf6b7be5c70c360e --- /dev/null +++ b/diffsynth/pipelines/svd_video.py @@ -0,0 +1,300 @@ +from ..models import ModelManager, SVDImageEncoder, SVDUNet, SVDVAEEncoder, SVDVAEDecoder +from ..schedulers import ContinuousODEScheduler +from .base import BasePipeline +import torch +from tqdm import tqdm +from PIL import Image +import numpy as np +from einops import rearrange, repeat + + + +class SVDVideoPipeline(BasePipeline): + + def __init__(self, device="cuda", torch_dtype=torch.float16): + super().__init__(device=device, torch_dtype=torch_dtype) + self.scheduler = ContinuousODEScheduler() + # models + self.image_encoder: SVDImageEncoder = None + self.unet: SVDUNet = None + self.vae_encoder: SVDVAEEncoder = None + self.vae_decoder: SVDVAEDecoder = None + + + def fetch_models(self, model_manager: ModelManager): + self.image_encoder = model_manager.fetch_model("svd_image_encoder") + self.unet = model_manager.fetch_model("svd_unet") + self.vae_encoder = model_manager.fetch_model("svd_vae_encoder") + self.vae_decoder = model_manager.fetch_model("svd_vae_decoder") + + + @staticmethod + def from_model_manager(model_manager: ModelManager, **kwargs): + pipe = SVDVideoPipeline( + device=model_manager.device, + torch_dtype=model_manager.torch_dtype + ) + pipe.fetch_models(model_manager) + return pipe + + + def encode_image_with_clip(self, image): + image = self.preprocess_image(image).to(device=self.device, dtype=self.torch_dtype) + image = SVDCLIPImageProcessor().resize_with_antialiasing(image, (224, 224)) + image = (image + 1.0) / 2.0 + mean = torch.tensor([0.48145466, 0.4578275, 0.40821073]).reshape(1, 3, 1, 1).to(device=self.device, dtype=self.torch_dtype) + std = torch.tensor([0.26862954, 0.26130258, 0.27577711]).reshape(1, 3, 1, 1).to(device=self.device, dtype=self.torch_dtype) + image = (image - mean) / std + image_emb = self.image_encoder(image) + return image_emb + + + def encode_image_with_vae(self, image, noise_aug_strength, seed=None): + image = self.preprocess_image(image).to(device=self.device, dtype=self.torch_dtype) + noise = self.generate_noise(image.shape, seed=seed, device=self.device, dtype=self.torch_dtype) + image = image + noise_aug_strength * noise + image_emb = self.vae_encoder(image) / self.vae_encoder.scaling_factor + return image_emb + + + def encode_video_with_vae(self, video): + video = torch.concat([self.preprocess_image(frame) for frame in video], dim=0) + video = rearrange(video, "T C H W -> 1 C T H W") + video = video.to(device=self.device, dtype=self.torch_dtype) + latents = self.vae_encoder.encode_video(video) + latents = rearrange(latents[0], "C T H W -> T C H W") + return latents + + + def tensor2video(self, frames): + frames = rearrange(frames, "C T H W -> T H W C") + frames = ((frames.float() + 1) * 127.5).clip(0, 255).cpu().numpy().astype(np.uint8) + frames = [Image.fromarray(frame) for frame in frames] + return frames + + + def calculate_noise_pred( + self, + latents, + timestep, + add_time_id, + cfg_scales, + image_emb_vae_posi, image_emb_clip_posi, + image_emb_vae_nega, image_emb_clip_nega + ): + # Positive side + noise_pred_posi = self.unet( + torch.cat([latents, image_emb_vae_posi], dim=1), + timestep, image_emb_clip_posi, add_time_id + ) + # Negative side + noise_pred_nega = self.unet( + torch.cat([latents, image_emb_vae_nega], dim=1), + timestep, image_emb_clip_nega, add_time_id + ) + + # Classifier-free guidance + noise_pred = noise_pred_nega + cfg_scales * (noise_pred_posi - noise_pred_nega) + + return noise_pred + + + def post_process_latents(self, latents, post_normalize=True, contrast_enhance_scale=1.0): + if post_normalize: + mean, std = latents.mean(), latents.std() + latents = (latents - latents.mean(dim=[1, 2, 3], keepdim=True)) / latents.std(dim=[1, 2, 3], keepdim=True) * std + mean + latents = latents * contrast_enhance_scale + return latents + + + @torch.no_grad() + def __call__( + self, + input_image=None, + input_video=None, + mask_frames=[], + mask_frame_ids=[], + min_cfg_scale=1.0, + max_cfg_scale=3.0, + denoising_strength=1.0, + num_frames=25, + height=576, + width=1024, + fps=7, + motion_bucket_id=127, + noise_aug_strength=0.02, + num_inference_steps=20, + post_normalize=True, + contrast_enhance_scale=1.2, + seed=None, + progress_bar_cmd=tqdm, + progress_bar_st=None, + ): + height, width = self.check_resize_height_width(height, width) + + # Prepare scheduler + self.scheduler.set_timesteps(num_inference_steps, denoising_strength=denoising_strength) + + # Prepare latent tensors + noise = self.generate_noise((num_frames, 4, height//8, width//8), seed=seed, device=self.device, dtype=self.torch_dtype) + if denoising_strength == 1.0: + latents = noise.clone() + else: + latents = self.encode_video_with_vae(input_video) + latents = self.scheduler.add_noise(latents, noise, self.scheduler.timesteps[0]) + + # Prepare mask frames + if len(mask_frames) > 0: + mask_latents = self.encode_video_with_vae(mask_frames) + + # Encode image + image_emb_clip_posi = self.encode_image_with_clip(input_image) + image_emb_clip_nega = torch.zeros_like(image_emb_clip_posi) + image_emb_vae_posi = repeat(self.encode_image_with_vae(input_image, noise_aug_strength, seed=seed), "B C H W -> (B T) C H W", T=num_frames) + image_emb_vae_nega = torch.zeros_like(image_emb_vae_posi) + + # Prepare classifier-free guidance + cfg_scales = torch.linspace(min_cfg_scale, max_cfg_scale, num_frames) + cfg_scales = cfg_scales.reshape(num_frames, 1, 1, 1).to(device=self.device, dtype=self.torch_dtype) + + # Prepare positional id + add_time_id = torch.tensor([[fps-1, motion_bucket_id, noise_aug_strength]], device=self.device) + + # Denoise + for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)): + + # Mask frames + for frame_id, mask_frame_id in enumerate(mask_frame_ids): + latents[mask_frame_id] = self.scheduler.add_noise(mask_latents[frame_id], noise[mask_frame_id], timestep) + + # Fetch model output + noise_pred = self.calculate_noise_pred( + latents, timestep, add_time_id, cfg_scales, + image_emb_vae_posi, image_emb_clip_posi, image_emb_vae_nega, image_emb_clip_nega + ) + + # Forward Euler + latents = self.scheduler.step(noise_pred, timestep, latents) + + # Update progress bar + if progress_bar_st is not None: + progress_bar_st.progress(progress_id / len(self.scheduler.timesteps)) + + # Decode image + latents = self.post_process_latents(latents, post_normalize=post_normalize, contrast_enhance_scale=contrast_enhance_scale) + video = self.vae_decoder.decode_video(latents, progress_bar=progress_bar_cmd) + video = self.tensor2video(video) + + return video + + + +class SVDCLIPImageProcessor: + def __init__(self): + pass + + def resize_with_antialiasing(self, input, size, interpolation="bicubic", align_corners=True): + h, w = input.shape[-2:] + factors = (h / size[0], w / size[1]) + + # First, we have to determine sigma + # Taken from skimage: https://github.com/scikit-image/scikit-image/blob/v0.19.2/skimage/transform/_warps.py#L171 + sigmas = ( + max((factors[0] - 1.0) / 2.0, 0.001), + max((factors[1] - 1.0) / 2.0, 0.001), + ) + + # Now kernel size. Good results are for 3 sigma, but that is kind of slow. Pillow uses 1 sigma + # https://github.com/python-pillow/Pillow/blob/master/src/libImaging/Resample.c#L206 + # But they do it in the 2 passes, which gives better results. Let's try 2 sigmas for now + ks = int(max(2.0 * 2 * sigmas[0], 3)), int(max(2.0 * 2 * sigmas[1], 3)) + + # Make sure it is odd + if (ks[0] % 2) == 0: + ks = ks[0] + 1, ks[1] + + if (ks[1] % 2) == 0: + ks = ks[0], ks[1] + 1 + + input = self._gaussian_blur2d(input, ks, sigmas) + + output = torch.nn.functional.interpolate(input, size=size, mode=interpolation, align_corners=align_corners) + return output + + + def _compute_padding(self, kernel_size): + """Compute padding tuple.""" + # 4 or 6 ints: (padding_left, padding_right,padding_top,padding_bottom) + # https://pytorch.org/docs/stable/nn.html#torch.nn.functional.pad + if len(kernel_size) < 2: + raise AssertionError(kernel_size) + computed = [k - 1 for k in kernel_size] + + # for even kernels we need to do asymmetric padding :( + out_padding = 2 * len(kernel_size) * [0] + + for i in range(len(kernel_size)): + computed_tmp = computed[-(i + 1)] + + pad_front = computed_tmp // 2 + pad_rear = computed_tmp - pad_front + + out_padding[2 * i + 0] = pad_front + out_padding[2 * i + 1] = pad_rear + + return out_padding + + + def _filter2d(self, input, kernel): + # prepare kernel + b, c, h, w = input.shape + tmp_kernel = kernel[:, None, ...].to(device=input.device, dtype=input.dtype) + + tmp_kernel = tmp_kernel.expand(-1, c, -1, -1) + + height, width = tmp_kernel.shape[-2:] + + padding_shape: list[int] = self._compute_padding([height, width]) + input = torch.nn.functional.pad(input, padding_shape, mode="reflect") + + # kernel and input tensor reshape to align element-wise or batch-wise params + tmp_kernel = tmp_kernel.reshape(-1, 1, height, width) + input = input.view(-1, tmp_kernel.size(0), input.size(-2), input.size(-1)) + + # convolve the tensor with the kernel. + output = torch.nn.functional.conv2d(input, tmp_kernel, groups=tmp_kernel.size(0), padding=0, stride=1) + + out = output.view(b, c, h, w) + return out + + + def _gaussian(self, window_size: int, sigma): + if isinstance(sigma, float): + sigma = torch.tensor([[sigma]]) + + batch_size = sigma.shape[0] + + x = (torch.arange(window_size, device=sigma.device, dtype=sigma.dtype) - window_size // 2).expand(batch_size, -1) + + if window_size % 2 == 0: + x = x + 0.5 + + gauss = torch.exp(-x.pow(2.0) / (2 * sigma.pow(2.0))) + + return gauss / gauss.sum(-1, keepdim=True) + + + def _gaussian_blur2d(self, input, kernel_size, sigma): + if isinstance(sigma, tuple): + sigma = torch.tensor([sigma], dtype=input.dtype) + else: + sigma = sigma.to(dtype=input.dtype) + + ky, kx = int(kernel_size[0]), int(kernel_size[1]) + bs = sigma.shape[0] + kernel_x = self._gaussian(kx, sigma[:, 1].view(bs, 1)) + kernel_y = self._gaussian(ky, sigma[:, 0].view(bs, 1)) + out_x = self._filter2d(input, kernel_x[..., None, :]) + out = self._filter2d(out_x, kernel_y[..., None]) + + return out diff --git a/diffsynth/pipelines/wan_video.py b/diffsynth/pipelines/wan_video.py new file mode 100644 index 0000000000000000000000000000000000000000..e70e0cc246e2d41ffd19056fd42060144ab6230c --- /dev/null +++ b/diffsynth/pipelines/wan_video.py @@ -0,0 +1,626 @@ +import types +from ..models import ModelManager +from ..models.wan_video_dit import WanModel +from ..models.wan_video_text_encoder import WanTextEncoder +from ..models.wan_video_vae import WanVideoVAE +from ..models.wan_video_image_encoder import WanImageEncoder +from ..models.wan_video_vace import VaceWanModel +from ..schedulers.flow_match import FlowMatchScheduler +from .base import BasePipeline +from ..prompters import WanPrompter +import torch, os +from einops import rearrange +import numpy as np +from PIL import Image +from tqdm import tqdm +from typing import Optional + +from ..vram_management import enable_vram_management, AutoWrappedModule, AutoWrappedLinear +from ..models.wan_video_text_encoder import T5RelativeEmbedding, T5LayerNorm +from ..models.wan_video_dit import RMSNorm, sinusoidal_embedding_1d +from ..models.wan_video_vae import RMS_norm, CausalConv3d, Upsample +from ..models.wan_video_motion_controller import WanMotionControllerModel + + + +class WanVideoPipeline(BasePipeline): + + def __init__(self, device="cuda", torch_dtype=torch.float16, tokenizer_path=None): + super().__init__(device=device, torch_dtype=torch_dtype) + self.scheduler = FlowMatchScheduler(shift=5, sigma_min=0.0, extra_one_step=True) + self.prompter = WanPrompter(tokenizer_path=tokenizer_path) + self.text_encoder: WanTextEncoder = None + self.image_encoder: WanImageEncoder = None + self.dit: WanModel = None + self.vae: WanVideoVAE = None + self.motion_controller: WanMotionControllerModel = None + self.vace: VaceWanModel = None + self.model_names = ['text_encoder', 'dit', 'vae', 'image_encoder', 'motion_controller', 'vace'] + self.height_division_factor = 16 + self.width_division_factor = 16 + self.use_unified_sequence_parallel = False + + + def enable_vram_management(self, num_persistent_param_in_dit=None): + dtype = next(iter(self.text_encoder.parameters())).dtype + enable_vram_management( + self.text_encoder, + module_map = { + torch.nn.Linear: AutoWrappedLinear, + torch.nn.Embedding: AutoWrappedModule, + T5RelativeEmbedding: AutoWrappedModule, + T5LayerNorm: AutoWrappedModule, + }, + module_config = dict( + offload_dtype=dtype, + offload_device="cpu", + onload_dtype=dtype, + onload_device="cpu", + computation_dtype=self.torch_dtype, + computation_device=self.device, + ), + ) + dtype = next(iter(self.dit.parameters())).dtype + enable_vram_management( + self.dit, + module_map = { + torch.nn.Linear: AutoWrappedLinear, + torch.nn.Conv3d: AutoWrappedModule, + torch.nn.LayerNorm: AutoWrappedModule, + RMSNorm: AutoWrappedModule, + torch.nn.Conv2d: AutoWrappedModule, + }, + module_config = dict( + offload_dtype=dtype, + offload_device="cpu", + onload_dtype=dtype, + onload_device=self.device, + computation_dtype=self.torch_dtype, + computation_device=self.device, + ), + max_num_param=num_persistent_param_in_dit, + overflow_module_config = dict( + offload_dtype=dtype, + offload_device="cpu", + onload_dtype=dtype, + onload_device="cpu", + computation_dtype=self.torch_dtype, + computation_device=self.device, + ), + ) + dtype = next(iter(self.vae.parameters())).dtype + enable_vram_management( + self.vae, + module_map = { + torch.nn.Linear: AutoWrappedLinear, + torch.nn.Conv2d: AutoWrappedModule, + RMS_norm: AutoWrappedModule, + CausalConv3d: AutoWrappedModule, + Upsample: AutoWrappedModule, + torch.nn.SiLU: AutoWrappedModule, + torch.nn.Dropout: AutoWrappedModule, + }, + module_config = dict( + offload_dtype=dtype, + offload_device="cpu", + onload_dtype=dtype, + onload_device=self.device, + computation_dtype=self.torch_dtype, + computation_device=self.device, + ), + ) + if self.image_encoder is not None: + dtype = next(iter(self.image_encoder.parameters())).dtype + enable_vram_management( + self.image_encoder, + module_map = { + torch.nn.Linear: AutoWrappedLinear, + torch.nn.Conv2d: AutoWrappedModule, + torch.nn.LayerNorm: AutoWrappedModule, + }, + module_config = dict( + offload_dtype=dtype, + offload_device="cpu", + onload_dtype=dtype, + onload_device="cpu", + computation_dtype=dtype, + computation_device=self.device, + ), + ) + if self.motion_controller is not None: + dtype = next(iter(self.motion_controller.parameters())).dtype + enable_vram_management( + self.motion_controller, + module_map = { + torch.nn.Linear: AutoWrappedLinear, + }, + module_config = dict( + offload_dtype=dtype, + offload_device="cpu", + onload_dtype=dtype, + onload_device="cpu", + computation_dtype=dtype, + computation_device=self.device, + ), + ) + if self.vace is not None: + enable_vram_management( + self.vace, + module_map = { + torch.nn.Linear: AutoWrappedLinear, + torch.nn.Conv3d: AutoWrappedModule, + torch.nn.LayerNorm: AutoWrappedModule, + RMSNorm: AutoWrappedModule, + }, + module_config = dict( + offload_dtype=dtype, + offload_device="cpu", + onload_dtype=dtype, + onload_device=self.device, + computation_dtype=self.torch_dtype, + computation_device=self.device, + ), + ) + self.enable_cpu_offload() + + + def fetch_models(self, model_manager: ModelManager): + text_encoder_model_and_path = model_manager.fetch_model("wan_video_text_encoder", require_model_path=True) + if text_encoder_model_and_path is not None: + self.text_encoder, tokenizer_path = text_encoder_model_and_path + self.prompter.fetch_models(self.text_encoder) + self.prompter.fetch_tokenizer(os.path.join(os.path.dirname(tokenizer_path), "google/umt5-xxl")) + self.dit = model_manager.fetch_model("wan_video_dit") + self.vae = model_manager.fetch_model("wan_video_vae") + self.image_encoder = model_manager.fetch_model("wan_video_image_encoder") + self.motion_controller = model_manager.fetch_model("wan_video_motion_controller") + self.vace = model_manager.fetch_model("wan_video_vace") + + + @staticmethod + def from_model_manager(model_manager: ModelManager, torch_dtype=None, device=None, use_usp=False): + if device is None: device = model_manager.device + if torch_dtype is None: torch_dtype = model_manager.torch_dtype + pipe = WanVideoPipeline(device=device, torch_dtype=torch_dtype) + pipe.fetch_models(model_manager) + if use_usp: + from xfuser.core.distributed import get_sequence_parallel_world_size + from ..distributed.xdit_context_parallel import usp_attn_forward, usp_dit_forward + + for block in pipe.dit.blocks: + block.self_attn.forward = types.MethodType(usp_attn_forward, block.self_attn) + pipe.dit.forward = types.MethodType(usp_dit_forward, pipe.dit) + pipe.sp_size = get_sequence_parallel_world_size() + pipe.use_unified_sequence_parallel = True + return pipe + + + def denoising_model(self): + return self.dit + + + def encode_prompt(self, prompt, positive=True): + prompt_emb = self.prompter.encode_prompt(prompt, positive=positive, device=self.device) + return {"context": prompt_emb} + + + def encode_image(self, image, end_image, num_frames, height, width, tiled=False, tile_size=(34, 34), tile_stride=(18, 16)): + image = self.preprocess_image(image.resize((width, height))).to(self.device) + clip_context = self.image_encoder.encode_image([image]) + msk = torch.ones(1, num_frames, height//8, width//8, device=self.device) + msk[:, 1:] = 0 + if end_image is not None: + end_image = self.preprocess_image(end_image.resize((width, height))).to(self.device) + vae_input = torch.concat([image.transpose(0,1), torch.zeros(3, num_frames-2, height, width).to(image.device), end_image.transpose(0,1)],dim=1) + if self.dit.has_image_pos_emb: + clip_context = torch.concat([clip_context, self.image_encoder.encode_image([end_image])], dim=1) + msk[:, -1:] = 1 + else: + vae_input = torch.concat([image.transpose(0, 1), torch.zeros(3, num_frames-1, height, width).to(image.device)], dim=1) + + msk = torch.concat([torch.repeat_interleave(msk[:, 0:1], repeats=4, dim=1), msk[:, 1:]], dim=1) + msk = msk.view(1, msk.shape[1] // 4, 4, height//8, width//8) + msk = msk.transpose(1, 2)[0] + + y = self.vae.encode([vae_input.to(dtype=self.torch_dtype, device=self.device)], device=self.device, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)[0] + y = y.to(dtype=self.torch_dtype, device=self.device) + y = torch.concat([msk, y]) + y = y.unsqueeze(0) + clip_context = clip_context.to(dtype=self.torch_dtype, device=self.device) + y = y.to(dtype=self.torch_dtype, device=self.device) + return {"clip_feature": clip_context, "y": y} + + + def encode_control_video(self, control_video, tiled=True, tile_size=(34, 34), tile_stride=(18, 16)): + control_video = self.preprocess_images(control_video) + control_video = torch.stack(control_video, dim=2).to(dtype=self.torch_dtype, device=self.device) + latents = self.encode_video(control_video, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride).to(dtype=self.torch_dtype, device=self.device) + return latents + + + def prepare_reference_image(self, reference_image, height, width): + if reference_image is not None: + self.load_models_to_device(["vae"]) + reference_image = reference_image.resize((width, height)) + reference_image = self.preprocess_images([reference_image]) + reference_image = torch.stack(reference_image, dim=2).to(dtype=self.torch_dtype, device=self.device) + reference_latents = self.vae.encode(reference_image, device=self.device) + return {"reference_latents": reference_latents} + else: + return {} + + + def prepare_controlnet_kwargs(self, control_video, num_frames, height, width, clip_feature=None, y=None, tiled=True, tile_size=(34, 34), tile_stride=(18, 16)): + if control_video is not None: + control_latents = self.encode_control_video(control_video, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) + if clip_feature is None or y is None: + clip_feature = torch.zeros((1, 257, 1280), dtype=self.torch_dtype, device=self.device) + y = torch.zeros((1, 16, (num_frames - 1) // 4 + 1, height//8, width//8), dtype=self.torch_dtype, device=self.device) + else: + y = y[:, -16:] + y = torch.concat([control_latents, y], dim=1) + return {"clip_feature": clip_feature, "y": y} + + + def tensor2video(self, frames): + frames = rearrange(frames, "C T H W -> T H W C") + frames = ((frames.float() + 1) * 127.5).clip(0, 255).cpu().numpy().astype(np.uint8) + frames = [Image.fromarray(frame) for frame in frames] + return frames + + + def prepare_extra_input(self, latents=None): + return {} + + + def encode_video(self, input_video, tiled=True, tile_size=(34, 34), tile_stride=(18, 16)): + latents = self.vae.encode(input_video, device=self.device, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) + return latents + + + def decode_video(self, latents, tiled=True, tile_size=(34, 34), tile_stride=(18, 16)): + frames = self.vae.decode(latents, device=self.device, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) + return frames + + + def prepare_unified_sequence_parallel(self): + return {"use_unified_sequence_parallel": self.use_unified_sequence_parallel} + + + def prepare_motion_bucket_id(self, motion_bucket_id): + motion_bucket_id = torch.Tensor((motion_bucket_id,)).to(dtype=self.torch_dtype, device=self.device) + return {"motion_bucket_id": motion_bucket_id} + + + def prepare_vace_kwargs( + self, + latents, + vace_video=None, vace_mask=None, vace_reference_image=None, vace_scale=1.0, + height=480, width=832, num_frames=81, + seed=None, rand_device="cpu", + tiled=True, tile_size=(34, 34), tile_stride=(18, 16) + ): + if vace_video is not None or vace_mask is not None or vace_reference_image is not None: + self.load_models_to_device(["vae"]) + if vace_video is None: + vace_video = torch.zeros((1, 3, num_frames, height, width), dtype=self.torch_dtype, device=self.device) + else: + vace_video = self.preprocess_images(vace_video) + vace_video = torch.stack(vace_video, dim=2).to(dtype=self.torch_dtype, device=self.device) + + if vace_mask is None: + vace_mask = torch.ones_like(vace_video) + else: + vace_mask = self.preprocess_images(vace_mask) + vace_mask = torch.stack(vace_mask, dim=2).to(dtype=self.torch_dtype, device=self.device) + + inactive = vace_video * (1 - vace_mask) + 0 * vace_mask + reactive = vace_video * vace_mask + 0 * (1 - vace_mask) + inactive = self.encode_video(inactive, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride).to(dtype=self.torch_dtype, device=self.device) + reactive = self.encode_video(reactive, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride).to(dtype=self.torch_dtype, device=self.device) + vace_video_latents = torch.concat((inactive, reactive), dim=1) + + vace_mask_latents = rearrange(vace_mask[0,0], "T (H P) (W Q) -> 1 (P Q) T H W", P=8, Q=8) + vace_mask_latents = torch.nn.functional.interpolate(vace_mask_latents, size=((vace_mask_latents.shape[2] + 3) // 4, vace_mask_latents.shape[3], vace_mask_latents.shape[4]), mode='nearest-exact') + + if vace_reference_image is None: + pass + else: + vace_reference_image = self.preprocess_images([vace_reference_image]) + vace_reference_image = torch.stack(vace_reference_image, dim=2).to(dtype=self.torch_dtype, device=self.device) + vace_reference_latents = self.encode_video(vace_reference_image, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride).to(dtype=self.torch_dtype, device=self.device) + vace_reference_latents = torch.concat((vace_reference_latents, torch.zeros_like(vace_reference_latents)), dim=1) + vace_video_latents = torch.concat((vace_reference_latents, vace_video_latents), dim=2) + vace_mask_latents = torch.concat((torch.zeros_like(vace_mask_latents[:, :, :1]), vace_mask_latents), dim=2) + + noise = self.generate_noise((1, 16, 1, latents.shape[3], latents.shape[4]), seed=seed, device=rand_device, dtype=torch.float32) + noise = noise.to(dtype=self.torch_dtype, device=self.device) + latents = torch.concat((noise, latents), dim=2) + + vace_context = torch.concat((vace_video_latents, vace_mask_latents), dim=1) + return latents, {"vace_context": vace_context, "vace_scale": vace_scale} + else: + return latents, {"vace_context": None, "vace_scale": vace_scale} + + + @torch.no_grad() + def __call__( + self, + prompt, + negative_prompt="", + input_image=None, + end_image=None, + input_video=None, + control_video=None, + reference_image=None, + vace_video=None, + vace_video_mask=None, + vace_reference_image=None, + vace_scale=1.0, + denoising_strength=1.0, + seed=None, + rand_device="cpu", + height=480, + width=832, + num_frames=81, + cfg_scale=5.0, + num_inference_steps=50, + sigma_shift=5.0, + motion_bucket_id=None, + tiled=True, + tile_size=(30, 52), + tile_stride=(15, 26), + tea_cache_l1_thresh=None, + tea_cache_model_id="", + progress_bar_cmd=tqdm, + progress_bar_st=None, + ): + # Parameter check + height, width = self.check_resize_height_width(height, width) + if num_frames % 4 != 1: + num_frames = (num_frames + 2) // 4 * 4 + 1 + print(f"Only `num_frames % 4 == 1` is acceptable. We round it up to {num_frames}.") + + # Tiler parameters + tiler_kwargs = {"tiled": tiled, "tile_size": tile_size, "tile_stride": tile_stride} + + # Scheduler + self.scheduler.set_timesteps(num_inference_steps, denoising_strength=denoising_strength, shift=sigma_shift) + + # Initialize noise + noise = self.generate_noise((1, 16, (num_frames - 1) // 4 + 1, height//8, width//8), seed=seed, device=rand_device, dtype=torch.float32) + noise = noise.to(dtype=self.torch_dtype, device=self.device) + if input_video is not None: + self.load_models_to_device(['vae']) + input_video = self.preprocess_images(input_video) + input_video = torch.stack(input_video, dim=2).to(dtype=self.torch_dtype, device=self.device) + latents = self.encode_video(input_video, **tiler_kwargs).to(dtype=self.torch_dtype, device=self.device) + latents = self.scheduler.add_noise(latents, noise, timestep=self.scheduler.timesteps[0]) + else: + latents = noise + + # Encode prompts + self.load_models_to_device(["text_encoder"]) + prompt_emb_posi = self.encode_prompt(prompt, positive=True) + if cfg_scale != 1.0: + prompt_emb_nega = self.encode_prompt(negative_prompt, positive=False) + + # Encode image + if input_image is not None and self.image_encoder is not None: + self.load_models_to_device(["image_encoder", "vae"]) + image_emb = self.encode_image(input_image, end_image, num_frames, height, width, **tiler_kwargs) + else: + image_emb = {} + + # Reference image + reference_image_kwargs = self.prepare_reference_image(reference_image, height, width) + + # ControlNet + if control_video is not None: + self.load_models_to_device(["image_encoder", "vae"]) + image_emb = self.prepare_controlnet_kwargs(control_video, num_frames, height, width, **image_emb, **tiler_kwargs) + + # Motion Controller + if self.motion_controller is not None and motion_bucket_id is not None: + motion_kwargs = self.prepare_motion_bucket_id(motion_bucket_id) + else: + motion_kwargs = {} + + # Extra input + extra_input = self.prepare_extra_input(latents) + + # VACE + latents, vace_kwargs = self.prepare_vace_kwargs( + latents, vace_video, vace_video_mask, vace_reference_image, vace_scale, + height=height, width=width, num_frames=num_frames, seed=seed, rand_device=rand_device, **tiler_kwargs + ) + + # TeaCache + tea_cache_posi = {"tea_cache": TeaCache(num_inference_steps, rel_l1_thresh=tea_cache_l1_thresh, model_id=tea_cache_model_id) if tea_cache_l1_thresh is not None else None} + tea_cache_nega = {"tea_cache": TeaCache(num_inference_steps, rel_l1_thresh=tea_cache_l1_thresh, model_id=tea_cache_model_id) if tea_cache_l1_thresh is not None else None} + + # Unified Sequence Parallel + usp_kwargs = self.prepare_unified_sequence_parallel() + + # Denoise + self.load_models_to_device(["dit", "motion_controller", "vace"]) + for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)): + timestep = timestep.unsqueeze(0).to(dtype=self.torch_dtype, device=self.device) + + # Inference + noise_pred_posi = model_fn_wan_video( + self.dit, motion_controller=self.motion_controller, vace=self.vace, + x=latents, timestep=timestep, + **prompt_emb_posi, **image_emb, **extra_input, + **tea_cache_posi, **usp_kwargs, **motion_kwargs, **vace_kwargs, **reference_image_kwargs, + ) + if cfg_scale != 1.0: + noise_pred_nega = model_fn_wan_video( + self.dit, motion_controller=self.motion_controller, vace=self.vace, + x=latents, timestep=timestep, + **prompt_emb_nega, **image_emb, **extra_input, + **tea_cache_nega, **usp_kwargs, **motion_kwargs, **vace_kwargs, **reference_image_kwargs, + ) + noise_pred = noise_pred_nega + cfg_scale * (noise_pred_posi - noise_pred_nega) + else: + noise_pred = noise_pred_posi + + # Scheduler + latents = self.scheduler.step(noise_pred, self.scheduler.timesteps[progress_id], latents) + + if vace_reference_image is not None: + latents = latents[:, :, 1:] + + # Decode + self.load_models_to_device(['vae']) + frames = self.decode_video(latents, **tiler_kwargs) + self.load_models_to_device([]) + frames = self.tensor2video(frames[0]) + + return frames + + + +class TeaCache: + def __init__(self, num_inference_steps, rel_l1_thresh, model_id): + self.num_inference_steps = num_inference_steps + self.step = 0 + self.accumulated_rel_l1_distance = 0 + self.previous_modulated_input = None + self.rel_l1_thresh = rel_l1_thresh + self.previous_residual = None + self.previous_hidden_states = None + + self.coefficients_dict = { + "Wan2.1-T2V-1.3B": [-5.21862437e+04, 9.23041404e+03, -5.28275948e+02, 1.36987616e+01, -4.99875664e-02], + "Wan2.1-T2V-14B": [-3.03318725e+05, 4.90537029e+04, -2.65530556e+03, 5.87365115e+01, -3.15583525e-01], + "Wan2.1-I2V-14B-480P": [2.57151496e+05, -3.54229917e+04, 1.40286849e+03, -1.35890334e+01, 1.32517977e-01], + "Wan2.1-I2V-14B-720P": [ 8.10705460e+03, 2.13393892e+03, -3.72934672e+02, 1.66203073e+01, -4.17769401e-02], + } + if model_id not in self.coefficients_dict: + supported_model_ids = ", ".join([i for i in self.coefficients_dict]) + raise ValueError(f"{model_id} is not a supported TeaCache model id. Please choose a valid model id in ({supported_model_ids}).") + self.coefficients = self.coefficients_dict[model_id] + + def check(self, dit: WanModel, x, t_mod): + modulated_inp = t_mod.clone() + if self.step == 0 or self.step == self.num_inference_steps - 1: + should_calc = True + self.accumulated_rel_l1_distance = 0 + else: + coefficients = self.coefficients + rescale_func = np.poly1d(coefficients) + self.accumulated_rel_l1_distance += rescale_func(((modulated_inp-self.previous_modulated_input).abs().mean() / self.previous_modulated_input.abs().mean()).cpu().item()) + if self.accumulated_rel_l1_distance < self.rel_l1_thresh: + should_calc = False + else: + should_calc = True + self.accumulated_rel_l1_distance = 0 + self.previous_modulated_input = modulated_inp + self.step += 1 + if self.step == self.num_inference_steps: + self.step = 0 + if should_calc: + self.previous_hidden_states = x.clone() + return not should_calc + + def store(self, hidden_states): + self.previous_residual = hidden_states - self.previous_hidden_states + self.previous_hidden_states = None + + def update(self, hidden_states): + hidden_states = hidden_states + self.previous_residual + return hidden_states + + + +def model_fn_wan_video( + dit: WanModel, + motion_controller: WanMotionControllerModel = None, + vace: VaceWanModel = None, + x: torch.Tensor = None, + timestep: torch.Tensor = None, + context: torch.Tensor = None, + clip_feature: Optional[torch.Tensor] = None, + y: Optional[torch.Tensor] = None, + reference_latents = None, + vace_context = None, + vace_scale = 1.0, + tea_cache: TeaCache = None, + use_unified_sequence_parallel: bool = False, + motion_bucket_id: Optional[torch.Tensor] = None, + **kwargs, +): + if use_unified_sequence_parallel: + import torch.distributed as dist + from xfuser.core.distributed import (get_sequence_parallel_rank, + get_sequence_parallel_world_size, + get_sp_group) + + t = dit.time_embedding(sinusoidal_embedding_1d(dit.freq_dim, timestep)) + t_mod = dit.time_projection(t).unflatten(1, (6, dit.dim)) + if motion_bucket_id is not None and motion_controller is not None: + t_mod = t_mod + motion_controller(motion_bucket_id).unflatten(1, (6, dit.dim)) + context = dit.text_embedding(context) + + if dit.has_image_input: + x = torch.cat([x, y], dim=1) # (b, c_x + c_y, f, h, w) + clip_embdding = dit.img_emb(clip_feature) + context = torch.cat([clip_embdding, context], dim=1) + + x, (f, h, w) = dit.patchify(x) + + # Reference image + if reference_latents is not None: + reference_latents = dit.ref_conv(reference_latents[:, :, 0]).flatten(2).transpose(1, 2) + x = torch.concat([reference_latents, x], dim=1) + f += 1 + + freqs = torch.cat([ + dit.freqs[0][:f].view(f, 1, 1, -1).expand(f, h, w, -1), + dit.freqs[1][:h].view(1, h, 1, -1).expand(f, h, w, -1), + dit.freqs[2][:w].view(1, 1, w, -1).expand(f, h, w, -1) + ], dim=-1).reshape(f * h * w, 1, -1).to(x.device) + + # TeaCache + if tea_cache is not None: + tea_cache_update = tea_cache.check(dit, x, t_mod) + else: + tea_cache_update = False + + if vace_context is not None: + vace_hints = vace(x, vace_context, context, t_mod, freqs) + + # blocks + if use_unified_sequence_parallel: + if dist.is_initialized() and dist.get_world_size() > 1: + chunks = torch.chunk(x, get_sequence_parallel_world_size(), dim=1) + pad_shape = chunks[0].shape[1] - chunks[-1].shape[1] + chunks = [torch.nn.functional.pad(chunk, (0, 0, 0, chunks[0].shape[1]-chunk.shape[1]), value=0) for chunk in chunks] + x = chunks[get_sequence_parallel_rank()] + + if tea_cache_update: + x = tea_cache.update(x) + else: + for block_id, block in enumerate(dit.blocks): + x = block(x, context, t_mod, freqs) + if vace_context is not None and block_id in vace.vace_layers_mapping: + current_vace_hint = vace_hints[vace.vace_layers_mapping[block_id]] + if use_unified_sequence_parallel and dist.is_initialized() and dist.get_world_size() > 1: + current_vace_hint = torch.chunk(current_vace_hint, get_sequence_parallel_world_size(), dim=1)[get_sequence_parallel_rank()] + current_vace_hint = torch.nn.functional.pad(current_vace_hint, (0, 0, 0, chunks[0].shape[1] - current_vace_hint.shape[1]), value=0) + x = x + current_vace_hint * vace_scale + if tea_cache is not None: + tea_cache.store(x) + + x = dit.head(x, t) + if use_unified_sequence_parallel: + if dist.is_initialized() and dist.get_world_size() > 1: + x = get_sp_group().all_gather(x, dim=1) + x = x[:, :-pad_shape] if pad_shape > 0 else x + # Remove reference latents + if reference_latents is not None: + x = x[:, reference_latents.shape[1]:] + f -= 1 + x = dit.unpatchify(x, (f, h, w)) + return x diff --git a/diffsynth/pipelines/wan_video_new.py b/diffsynth/pipelines/wan_video_new.py new file mode 100644 index 0000000000000000000000000000000000000000..80608388ea3cdb6d65b83f51ab08af18d3bebfbe --- /dev/null +++ b/diffsynth/pipelines/wan_video_new.py @@ -0,0 +1,1758 @@ +import torch, warnings, glob, os, types +import numpy as np +from PIL import Image +from einops import repeat, reduce +from typing import Optional, Union +from dataclasses import dataclass +from modelscope import snapshot_download +from einops import rearrange +import numpy as np +from PIL import Image +from tqdm import tqdm +from typing import Optional +from typing_extensions import Literal + +from ..utils import BasePipeline, ModelConfig, PipelineUnit, PipelineUnitRunner +from ..models import ModelManager, load_state_dict +from ..models.wan_video_dit import WanModel, RMSNorm, sinusoidal_embedding_1d +from ..models.wan_video_dit_s2v import rope_precompute +from ..models.wan_video_text_encoder import WanTextEncoder, T5RelativeEmbedding, T5LayerNorm +from ..models.wan_video_vae import WanVideoVAE, RMS_norm, CausalConv3d, Upsample +from ..models.wan_video_image_encoder import WanImageEncoder +from ..models.wan_video_vace import VaceWanModel +from ..models.wan_video_motion_controller import WanMotionControllerModel +from ..models.wan_video_animate_adapter import WanAnimateAdapter +from ..models.wan_video_mot import MotWanModel +from ..models.longcat_video_dit import LongCatVideoTransformer3DModel +from ..schedulers.flow_match import FlowMatchScheduler +from ..prompters import WanPrompter +from ..vram_management import enable_vram_management, AutoWrappedModule, AutoWrappedLinear, WanAutoCastLayerNorm +from ..lora import GeneralLoRALoader + + + +class WanVideoPipeline(BasePipeline): + + def __init__(self, device="cuda", torch_dtype=torch.bfloat16, tokenizer_path=None): + super().__init__( + device=device, torch_dtype=torch_dtype, + height_division_factor=16, width_division_factor=16, time_division_factor=4, time_division_remainder=1 + ) + self.scheduler = FlowMatchScheduler(shift=5, sigma_min=0.0, extra_one_step=True) + self.prompter = WanPrompter(tokenizer_path=tokenizer_path) + self.text_encoder: WanTextEncoder = None + self.image_encoder: WanImageEncoder = None + self.dit: WanModel = None + self.dit2: WanModel = None + self.vae: WanVideoVAE = None + self.motion_controller: WanMotionControllerModel = None + self.vace: VaceWanModel = None + self.vace2: VaceWanModel = None + self.vap: MotWanModel = None + self.animate_adapter: WanAnimateAdapter = None + self.in_iteration_models = ("dit", "motion_controller", "vace", "animate_adapter", "vap") + self.in_iteration_models_2 = ("dit2", "motion_controller", "vace2", "animate_adapter", "vap") + self.unit_runner = PipelineUnitRunner() + self.units = [ + WanVideoUnit_ShapeChecker(), + WanVideoUnit_NoiseInitializer(), + WanVideoUnit_PromptEmbedder(), + WanVideoUnit_S2V(), + WanVideoUnit_InputVideoEmbedder(), + WanVideoUnit_ImageEmbedderVAE(), + WanVideoUnit_ImageEmbedderCLIP(), + WanVideoUnit_ImageEmbedderFused(), + WanVideoUnit_FunControl(), + WanVideoUnit_FunReference(), + WanVideoUnit_FunCameraControl(), + WanVideoUnit_SpeedControl(), + WanVideoUnit_VACE(), + WanVideoPostUnit_AnimateVideoSplit(), + WanVideoPostUnit_AnimatePoseLatents(), + WanVideoPostUnit_AnimateFacePixelValues(), + WanVideoPostUnit_AnimateInpaint(), + WanVideoUnit_VAP(), + WanVideoUnit_UnifiedSequenceParallel(), + WanVideoUnit_TeaCache(), + WanVideoUnit_CfgMerger(), + WanVideoUnit_LongCatVideo(), + ] + self.post_units = [ + WanVideoPostUnit_S2V(), + ] + self.model_fn = model_fn_wan_video + + def load_lora( + self, + module: torch.nn.Module, + lora_config: Union[ModelConfig, str] = None, + alpha=1, + hotload=False, + state_dict=None, + ): + if state_dict is None: + if isinstance(lora_config, str): + lora = load_state_dict(lora_config, torch_dtype=self.torch_dtype, device=self.device) + else: + lora_config.download_if_necessary() + lora = load_state_dict(lora_config.path, torch_dtype=self.torch_dtype, device=self.device) + else: + lora = state_dict + if hotload: + for name, module in module.named_modules(): + if isinstance(module, AutoWrappedLinear): + lora_a_name = f'{name}.lora_A.default.weight' + lora_b_name = f'{name}.lora_B.default.weight' + if lora_a_name in lora and lora_b_name in lora: + module.lora_A_weights.append(lora[lora_a_name] * alpha) + module.lora_B_weights.append(lora[lora_b_name]) + else: + loader = GeneralLoRALoader(torch_dtype=self.torch_dtype, device=self.device) + loader.load(module, lora, alpha=alpha) + + def training_loss(self, **inputs): + max_timestep_boundary = int(inputs.get("max_timestep_boundary", 1) * self.scheduler.num_train_timesteps) + min_timestep_boundary = int(inputs.get("min_timestep_boundary", 0) * self.scheduler.num_train_timesteps) + timestep_id = torch.randint(min_timestep_boundary, max_timestep_boundary, (1,)) + timestep = self.scheduler.timesteps[timestep_id].to(dtype=self.torch_dtype, device=self.device) + + inputs["latents"] = self.scheduler.add_noise(inputs["input_latents"], inputs["noise"], timestep) + training_target = self.scheduler.training_target(inputs["input_latents"], inputs["noise"], timestep) + + noise_pred = self.model_fn(**inputs, timestep=timestep) + + loss = torch.nn.functional.mse_loss(noise_pred.float(), training_target.float()) + loss = loss * self.scheduler.training_weight(timestep) + return loss + + + def enable_vram_management(self, num_persistent_param_in_dit=None, vram_limit=None, vram_buffer=0.5): + self.vram_management_enabled = True + if num_persistent_param_in_dit is not None: + vram_limit = None + else: + if vram_limit is None: + vram_limit = self.get_vram() + vram_limit = vram_limit - vram_buffer + if self.text_encoder is not None: + dtype = next(iter(self.text_encoder.parameters())).dtype + enable_vram_management( + self.text_encoder, + module_map = { + torch.nn.Linear: AutoWrappedLinear, + torch.nn.Embedding: AutoWrappedModule, + T5RelativeEmbedding: AutoWrappedModule, + T5LayerNorm: AutoWrappedModule, + }, + module_config = dict( + offload_dtype=dtype, + offload_device="cpu", + onload_dtype=dtype, + onload_device="cpu", + computation_dtype=self.torch_dtype, + computation_device=self.device, + ), + vram_limit=vram_limit, + ) + if self.dit is not None: + from ..models.longcat_video_dit import LayerNorm_FP32, RMSNorm_FP32 + dtype = next(iter(self.dit.parameters())).dtype + device = "cpu" if vram_limit is not None else self.device + enable_vram_management( + self.dit, + module_map = { + torch.nn.Linear: AutoWrappedLinear, + torch.nn.Conv3d: AutoWrappedModule, + torch.nn.LayerNorm: WanAutoCastLayerNorm, + RMSNorm: AutoWrappedModule, + torch.nn.Conv2d: AutoWrappedModule, + torch.nn.Conv1d: AutoWrappedModule, + torch.nn.Embedding: AutoWrappedModule, + LayerNorm_FP32: AutoWrappedModule, + RMSNorm_FP32: AutoWrappedModule, + }, + module_config = dict( + offload_dtype=dtype, + offload_device="cpu", + onload_dtype=dtype, + onload_device=device, + computation_dtype=self.torch_dtype, + computation_device=self.device, + ), + max_num_param=num_persistent_param_in_dit, + overflow_module_config = dict( + offload_dtype=dtype, + offload_device="cpu", + onload_dtype=dtype, + onload_device="cpu", + computation_dtype=self.torch_dtype, + computation_device=self.device, + ), + vram_limit=vram_limit, + ) + if self.dit2 is not None: + dtype = next(iter(self.dit2.parameters())).dtype + device = "cpu" if vram_limit is not None else self.device + enable_vram_management( + self.dit2, + module_map = { + torch.nn.Linear: AutoWrappedLinear, + torch.nn.Conv3d: AutoWrappedModule, + torch.nn.LayerNorm: WanAutoCastLayerNorm, + RMSNorm: AutoWrappedModule, + torch.nn.Conv2d: AutoWrappedModule, + }, + module_config = dict( + offload_dtype=dtype, + offload_device="cpu", + onload_dtype=dtype, + onload_device=device, + computation_dtype=self.torch_dtype, + computation_device=self.device, + ), + max_num_param=num_persistent_param_in_dit, + overflow_module_config = dict( + offload_dtype=dtype, + offload_device="cpu", + onload_dtype=dtype, + onload_device="cpu", + computation_dtype=self.torch_dtype, + computation_device=self.device, + ), + vram_limit=vram_limit, + ) + if self.vae is not None: + dtype = next(iter(self.vae.parameters())).dtype + enable_vram_management( + self.vae, + module_map = { + torch.nn.Linear: AutoWrappedLinear, + torch.nn.Conv2d: AutoWrappedModule, + RMS_norm: AutoWrappedModule, + CausalConv3d: AutoWrappedModule, + Upsample: AutoWrappedModule, + torch.nn.SiLU: AutoWrappedModule, + torch.nn.Dropout: AutoWrappedModule, + }, + module_config = dict( + offload_dtype=dtype, + offload_device="cpu", + onload_dtype=dtype, + onload_device=self.device, + computation_dtype=self.torch_dtype, + computation_device=self.device, + ), + ) + if self.image_encoder is not None: + dtype = next(iter(self.image_encoder.parameters())).dtype + enable_vram_management( + self.image_encoder, + module_map = { + torch.nn.Linear: AutoWrappedLinear, + torch.nn.Conv2d: AutoWrappedModule, + torch.nn.LayerNorm: AutoWrappedModule, + }, + module_config = dict( + offload_dtype=dtype, + offload_device="cpu", + onload_dtype=dtype, + onload_device="cpu", + computation_dtype=dtype, + computation_device=self.device, + ), + ) + if self.motion_controller is not None: + dtype = next(iter(self.motion_controller.parameters())).dtype + enable_vram_management( + self.motion_controller, + module_map = { + torch.nn.Linear: AutoWrappedLinear, + }, + module_config = dict( + offload_dtype=dtype, + offload_device="cpu", + onload_dtype=dtype, + onload_device="cpu", + computation_dtype=dtype, + computation_device=self.device, + ), + ) + if self.vace is not None: + device = "cpu" if vram_limit is not None else self.device + enable_vram_management( + self.vace, + module_map = { + torch.nn.Linear: AutoWrappedLinear, + torch.nn.Conv3d: AutoWrappedModule, + torch.nn.LayerNorm: AutoWrappedModule, + RMSNorm: AutoWrappedModule, + }, + module_config = dict( + offload_dtype=dtype, + offload_device="cpu", + onload_dtype=dtype, + onload_device=device, + computation_dtype=self.torch_dtype, + computation_device=self.device, + ), + vram_limit=vram_limit, + ) + if self.audio_encoder is not None: + # TODO: need check + dtype = next(iter(self.audio_encoder.parameters())).dtype + enable_vram_management( + self.audio_encoder, + module_map = { + torch.nn.Linear: AutoWrappedLinear, + torch.nn.LayerNorm: AutoWrappedModule, + torch.nn.Conv1d: AutoWrappedModule, + }, + module_config = dict( + offload_dtype=dtype, + offload_device="cpu", + onload_dtype=dtype, + onload_device="cpu", + computation_dtype=self.torch_dtype, + computation_device=self.device, + ), + ) + + + def initialize_usp(self): + import torch.distributed as dist + from xfuser.core.distributed import initialize_model_parallel, init_distributed_environment + dist.init_process_group(backend="nccl", init_method="env://") + init_distributed_environment(rank=dist.get_rank(), world_size=dist.get_world_size()) + initialize_model_parallel( + sequence_parallel_degree=dist.get_world_size(), + ring_degree=1, + ulysses_degree=dist.get_world_size(), + ) + torch.cuda.set_device(dist.get_rank()) + + + def enable_usp(self): + from xfuser.core.distributed import get_sequence_parallel_world_size + from ..distributed.xdit_context_parallel import usp_attn_forward, usp_dit_forward + + for block in self.dit.blocks: + block.self_attn.forward = types.MethodType(usp_attn_forward, block.self_attn) + self.dit.forward = types.MethodType(usp_dit_forward, self.dit) + if self.dit2 is not None: + for block in self.dit2.blocks: + block.self_attn.forward = types.MethodType(usp_attn_forward, block.self_attn) + self.dit2.forward = types.MethodType(usp_dit_forward, self.dit2) + self.sp_size = get_sequence_parallel_world_size() + self.use_unified_sequence_parallel = True + + + @staticmethod + def from_pretrained( + torch_dtype: torch.dtype = torch.bfloat16, + device: Union[str, torch.device] = "cuda", + model_configs: list[ModelConfig] = [], + tokenizer_config: ModelConfig = ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="google/*"), + audio_processor_config: ModelConfig = None, + redirect_common_files: bool = True, + use_usp=False, + ): + # Redirect model path + if redirect_common_files: + redirect_dict = { + "models_t5_umt5-xxl-enc-bf16.pth": "Wan-AI/Wan2.1-T2V-1.3B", + "Wan2.1_VAE.pth": "Wan-AI/Wan2.1-T2V-1.3B", + "models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth": "Wan-AI/Wan2.1-I2V-14B-480P", + } + for model_config in model_configs: + if model_config.origin_file_pattern is None or model_config.model_id is None: + continue + if model_config.origin_file_pattern in redirect_dict and model_config.model_id != redirect_dict[model_config.origin_file_pattern]: + print(f"To avoid repeatedly downloading model files, ({model_config.model_id}, {model_config.origin_file_pattern}) is redirected to ({redirect_dict[model_config.origin_file_pattern]}, {model_config.origin_file_pattern}). You can use `redirect_common_files=False` to disable file redirection.") + model_config.model_id = redirect_dict[model_config.origin_file_pattern] + + # Initialize pipeline + pipe = WanVideoPipeline(device=device, torch_dtype=torch_dtype) + if use_usp: pipe.initialize_usp() + + # Download and load models + model_manager = ModelManager() + for model_config in model_configs: + model_config.download_if_necessary(use_usp=use_usp) + model_manager.load_model( + model_config.path, + device=model_config.offload_device or device, + torch_dtype=model_config.offload_dtype or torch_dtype + ) + + # Load models + pipe.text_encoder = model_manager.fetch_model("wan_video_text_encoder") + dit = model_manager.fetch_model("wan_video_dit", index=2) + if isinstance(dit, list): + pipe.dit, pipe.dit2 = dit + else: + pipe.dit = dit + pipe.vae = model_manager.fetch_model("wan_video_vae") + pipe.image_encoder = model_manager.fetch_model("wan_video_image_encoder") + pipe.motion_controller = model_manager.fetch_model("wan_video_motion_controller") + vace = model_manager.fetch_model("wan_video_vace", index=2) + pipe.vap = model_manager.fetch_model("wan_video_vap") + if isinstance(vace, list): + pipe.vace, pipe.vace2 = vace + else: + pipe.vace = vace + pipe.audio_encoder = model_manager.fetch_model("wans2v_audio_encoder") + pipe.animate_adapter = model_manager.fetch_model("wan_video_animate_adapter") + + # Size division factor + if pipe.vae is not None: + pipe.height_division_factor = pipe.vae.upsampling_factor * 2 + pipe.width_division_factor = pipe.vae.upsampling_factor * 2 + + # Initialize tokenizer + tokenizer_config.download_if_necessary(use_usp=use_usp) + pipe.prompter.fetch_models(pipe.text_encoder) + pipe.prompter.fetch_tokenizer(tokenizer_config.path) + + if audio_processor_config is not None: + audio_processor_config.download_if_necessary(use_usp=use_usp) + from transformers import Wav2Vec2Processor + pipe.audio_processor = Wav2Vec2Processor.from_pretrained(audio_processor_config.path) + # Unified Sequence Parallel + if use_usp: pipe.enable_usp() + return pipe + + + @torch.no_grad() + def __call__( + self, + # Prompt + prompt: str, + negative_prompt: Optional[str] = "", + # Image-to-video + input_image: Optional[Image.Image] = None, + # First-last-frame-to-video + end_image: Optional[Image.Image] = None, + # Video-to-video + input_video: Optional[list[Image.Image]] = None, + denoising_strength: Optional[float] = 1.0, + # Speech-to-video + input_audio: Optional[np.array] = None, + audio_embeds: Optional[torch.Tensor] = None, + audio_sample_rate: Optional[int] = 16000, + s2v_pose_video: Optional[list[Image.Image]] = None, + s2v_pose_latents: Optional[torch.Tensor] = None, + motion_video: Optional[list[Image.Image]] = None, + # ControlNet + control_video: Optional[list[Image.Image]] = None, + reference_image: Optional[Image.Image] = None, + # Camera control + camera_control_direction: Optional[Literal["Left", "Right", "Up", "Down", "LeftUp", "LeftDown", "RightUp", "RightDown"]] = None, + camera_control_speed: Optional[float] = 1/54, + camera_control_origin: Optional[tuple] = (0, 0.532139961, 0.946026558, 0.5, 0.5, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0), + # VACE + vace_video: Optional[list[Image.Image]] = None, + vace_video_mask: Optional[Image.Image] = None, + vace_reference_image: Optional[Image.Image] = None, + vace_scale: Optional[float] = 1.0, + # Animate + animate_pose_video: Optional[list[Image.Image]] = None, + animate_face_video: Optional[list[Image.Image]] = None, + animate_inpaint_video: Optional[list[Image.Image]] = None, + animate_mask_video: Optional[list[Image.Image]] = None, + # VAP + vap_video: Optional[list[Image.Image]] = None, + vap_prompt: Optional[str] = " ", + negative_vap_prompt: Optional[str] = " ", + # Randomness + seed: Optional[int] = None, + rand_device: Optional[str] = "cpu", + # Shape + height: Optional[int] = 480, + width: Optional[int] = 832, + num_frames=81, + # Classifier-free guidance + cfg_scale: Optional[float] = 5.0, + cfg_merge: Optional[bool] = False, + # Boundary + switch_DiT_boundary: Optional[float] = 0.875, + # Scheduler + num_inference_steps: Optional[int] = 50, + sigma_shift: Optional[float] = 5.0, + # Speed control + motion_bucket_id: Optional[int] = None, + # LongCat-Video + longcat_video: Optional[list[Image.Image]] = None, + # VAE tiling + tiled: Optional[bool] = True, + tile_size: Optional[tuple[int, int]] = (30, 52), + tile_stride: Optional[tuple[int, int]] = (15, 26), + # Sliding window + sliding_window_size: Optional[int] = None, + sliding_window_stride: Optional[int] = None, + # Teacache + tea_cache_l1_thresh: Optional[float] = None, + tea_cache_model_id: Optional[str] = "", + # progress_bar + progress_bar_cmd=tqdm, + ): + # Scheduler + self.scheduler.set_timesteps(num_inference_steps, denoising_strength=denoising_strength, shift=sigma_shift) + + # Inputs + inputs_posi = { + "prompt": prompt, + "vap_prompt": vap_prompt, + "tea_cache_l1_thresh": tea_cache_l1_thresh, "tea_cache_model_id": tea_cache_model_id, "num_inference_steps": num_inference_steps, + } + inputs_nega = { + "negative_prompt": negative_prompt, + "negative_vap_prompt": negative_vap_prompt, + "tea_cache_l1_thresh": tea_cache_l1_thresh, "tea_cache_model_id": tea_cache_model_id, "num_inference_steps": num_inference_steps, + } + inputs_shared = { + "input_image": input_image, + "end_image": end_image, + "input_video": input_video, "denoising_strength": denoising_strength, + "control_video": control_video, "reference_image": reference_image, + "camera_control_direction": camera_control_direction, "camera_control_speed": camera_control_speed, "camera_control_origin": camera_control_origin, + "vace_video": vace_video, "vace_video_mask": vace_video_mask, "vace_reference_image": vace_reference_image, "vace_scale": vace_scale, + "seed": seed, "rand_device": rand_device, + "height": height, "width": width, "num_frames": num_frames, + "cfg_scale": cfg_scale, "cfg_merge": cfg_merge, + "sigma_shift": sigma_shift, + "motion_bucket_id": motion_bucket_id, + "longcat_video": longcat_video, + "tiled": tiled, "tile_size": tile_size, "tile_stride": tile_stride, + "sliding_window_size": sliding_window_size, "sliding_window_stride": sliding_window_stride, + "input_audio": input_audio, "audio_sample_rate": audio_sample_rate, "s2v_pose_video": s2v_pose_video, "audio_embeds": audio_embeds, "s2v_pose_latents": s2v_pose_latents, "motion_video": motion_video, + "animate_pose_video": animate_pose_video, "animate_face_video": animate_face_video, "animate_inpaint_video": animate_inpaint_video, "animate_mask_video": animate_mask_video, + "vap_video": vap_video, + } + for unit in self.units: + inputs_shared, inputs_posi, inputs_nega = self.unit_runner(unit, self, inputs_shared, inputs_posi, inputs_nega) + + # Denoise + self.load_models_to_device(self.in_iteration_models) + models = {name: getattr(self, name) for name in self.in_iteration_models} + for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)): + # Switch DiT if necessary + if timestep.item() < switch_DiT_boundary * self.scheduler.num_train_timesteps and self.dit2 is not None and not models["dit"] is self.dit2: + self.load_models_to_device(self.in_iteration_models_2) + models["dit"] = self.dit2 + models["vace"] = self.vace2 + + # Timestep + timestep = timestep.unsqueeze(0).to(dtype=self.torch_dtype, device=self.device) + + # Inference + noise_pred_posi = self.model_fn(**models, **inputs_shared, **inputs_posi, timestep=timestep) + if cfg_scale != 1.0: + if cfg_merge: + noise_pred_posi, noise_pred_nega = noise_pred_posi.chunk(2, dim=0) + else: + noise_pred_nega = self.model_fn(**models, **inputs_shared, **inputs_nega, timestep=timestep) + noise_pred = noise_pred_nega + cfg_scale * (noise_pred_posi - noise_pred_nega) + else: + noise_pred = noise_pred_posi + + # Scheduler + inputs_shared["latents"] = self.scheduler.step(noise_pred, self.scheduler.timesteps[progress_id], inputs_shared["latents"]) + if "first_frame_latents" in inputs_shared: + inputs_shared["latents"][:, :, 0:1] = inputs_shared["first_frame_latents"] + + # VACE (TODO: remove it) + if vace_reference_image is not None or (animate_pose_video is not None and animate_face_video is not None): + if vace_reference_image is not None and isinstance(vace_reference_image, list): + f = len(vace_reference_image) + else: + f = 1 + inputs_shared["latents"] = inputs_shared["latents"][:, :, f:] + # post-denoising, pre-decoding processing logic + for unit in self.post_units: + inputs_shared, _, _ = self.unit_runner(unit, self, inputs_shared, inputs_posi, inputs_nega) + # Decode + self.load_models_to_device(['vae']) + video = self.vae.decode(inputs_shared["latents"], device=self.device, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) + video = self.vae_output_to_video(video) + self.load_models_to_device([]) + + return video + + + +class WanVideoUnit_ShapeChecker(PipelineUnit): + def __init__(self): + super().__init__(input_params=("height", "width", "num_frames")) + + def process(self, pipe: WanVideoPipeline, height, width, num_frames): + height, width, num_frames = pipe.check_resize_height_width(height, width, num_frames) + return {"height": height, "width": width, "num_frames": num_frames} + + + +class WanVideoUnit_NoiseInitializer(PipelineUnit): + def __init__(self): + super().__init__(input_params=("height", "width", "num_frames", "seed", "rand_device", "vace_reference_image")) + + def process(self, pipe: WanVideoPipeline, height, width, num_frames, seed, rand_device, vace_reference_image): + length = (num_frames - 1) // 4 + 1 + if vace_reference_image is not None: + f = len(vace_reference_image) if isinstance(vace_reference_image, list) else 1 + length += f + shape = (1, pipe.vae.model.z_dim, length, height // pipe.vae.upsampling_factor, width // pipe.vae.upsampling_factor) + noise = pipe.generate_noise(shape, seed=seed, rand_device=rand_device) + if vace_reference_image is not None: + noise = torch.concat((noise[:, :, -f:], noise[:, :, :-f]), dim=2) + return {"noise": noise} + + + +class WanVideoUnit_InputVideoEmbedder(PipelineUnit): + def __init__(self): + super().__init__( + input_params=("input_video", "noise", "tiled", "tile_size", "tile_stride", "vace_reference_image"), + onload_model_names=("vae",) + ) + + def process(self, pipe: WanVideoPipeline, input_video, noise, tiled, tile_size, tile_stride, vace_reference_image): + if input_video is None: + return {"latents": noise} + pipe.load_models_to_device(["vae"]) + input_video = pipe.preprocess_video(input_video) + input_latents = pipe.vae.encode(input_video, device=pipe.device, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride).to(dtype=pipe.torch_dtype, device=pipe.device) + if vace_reference_image is not None: + if not isinstance(vace_reference_image, list): + vace_reference_image = [vace_reference_image] + vace_reference_image = pipe.preprocess_video(vace_reference_image) + vace_reference_latents = pipe.vae.encode(vace_reference_image, device=pipe.device).to(dtype=pipe.torch_dtype, device=pipe.device) + input_latents = torch.concat([vace_reference_latents, input_latents], dim=2) + if pipe.scheduler.training: + return {"latents": noise, "input_latents": input_latents} + else: + latents = pipe.scheduler.add_noise(input_latents, noise, timestep=pipe.scheduler.timesteps[0]) + return {"latents": latents} + + + +class WanVideoUnit_PromptEmbedder(PipelineUnit): + def __init__(self): + super().__init__( + seperate_cfg=True, + input_params_posi={"prompt": "prompt", "positive": "positive"}, + input_params_nega={"prompt": "negative_prompt", "positive": "positive"}, + onload_model_names=("text_encoder",) + ) + + def process(self, pipe: WanVideoPipeline, prompt, positive) -> dict: + pipe.load_models_to_device(self.onload_model_names) + prompt_emb = pipe.prompter.encode_prompt(prompt, positive=positive, device=pipe.device) + return {"context": prompt_emb} + + + +class WanVideoUnit_ImageEmbedder(PipelineUnit): + """ + Deprecated + """ + def __init__(self): + super().__init__( + input_params=("input_image", "end_image", "num_frames", "height", "width", "tiled", "tile_size", "tile_stride"), + onload_model_names=("image_encoder", "vae") + ) + + def process(self, pipe: WanVideoPipeline, input_image, end_image, num_frames, height, width, tiled, tile_size, tile_stride): + if input_image is None or pipe.image_encoder is None: + return {} + pipe.load_models_to_device(self.onload_model_names) + image = pipe.preprocess_image(input_image.resize((width, height))).to(pipe.device) + clip_context = pipe.image_encoder.encode_image([image]) + msk = torch.ones(1, num_frames, height//8, width//8, device=pipe.device) + msk[:, 1:] = 0 + if end_image is not None: + end_image = pipe.preprocess_image(end_image.resize((width, height))).to(pipe.device) + vae_input = torch.concat([image.transpose(0,1), torch.zeros(3, num_frames-2, height, width).to(image.device), end_image.transpose(0,1)],dim=1) + if pipe.dit.has_image_pos_emb: + clip_context = torch.concat([clip_context, pipe.image_encoder.encode_image([end_image])], dim=1) + msk[:, -1:] = 1 + else: + vae_input = torch.concat([image.transpose(0, 1), torch.zeros(3, num_frames-1, height, width).to(image.device)], dim=1) + + msk = torch.concat([torch.repeat_interleave(msk[:, 0:1], repeats=4, dim=1), msk[:, 1:]], dim=1) + msk = msk.view(1, msk.shape[1] // 4, 4, height//8, width//8) + msk = msk.transpose(1, 2)[0] + + y = pipe.vae.encode([vae_input.to(dtype=pipe.torch_dtype, device=pipe.device)], device=pipe.device, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)[0] + y = y.to(dtype=pipe.torch_dtype, device=pipe.device) + y = torch.concat([msk, y]) + y = y.unsqueeze(0) + clip_context = clip_context.to(dtype=pipe.torch_dtype, device=pipe.device) + y = y.to(dtype=pipe.torch_dtype, device=pipe.device) + return {"clip_feature": clip_context, "y": y} + + + +class WanVideoUnit_ImageEmbedderCLIP(PipelineUnit): + def __init__(self): + super().__init__( + input_params=("input_image", "end_image", "height", "width"), + onload_model_names=("image_encoder",) + ) + + def process(self, pipe: WanVideoPipeline, input_image, end_image, height, width): + if input_image is None or pipe.image_encoder is None or not pipe.dit.require_clip_embedding: + return {} + pipe.load_models_to_device(self.onload_model_names) + image = pipe.preprocess_image(input_image.resize((width, height))).to(pipe.device) + clip_context = pipe.image_encoder.encode_image([image]) + if end_image is not None: + end_image = pipe.preprocess_image(end_image.resize((width, height))).to(pipe.device) + if pipe.dit.has_image_pos_emb: + clip_context = torch.concat([clip_context, pipe.image_encoder.encode_image([end_image])], dim=1) + clip_context = clip_context.to(dtype=pipe.torch_dtype, device=pipe.device) + return {"clip_feature": clip_context} + + + +class WanVideoUnit_ImageEmbedderVAE(PipelineUnit): + def __init__(self): + super().__init__( + input_params=("input_image", "end_image", "num_frames", "height", "width", "tiled", "tile_size", "tile_stride"), + onload_model_names=("vae",) + ) + + def process(self, pipe: WanVideoPipeline, input_image, end_image, num_frames, height, width, tiled, tile_size, tile_stride): + if input_image is None or not pipe.dit.require_vae_embedding: + return {} + pipe.load_models_to_device(self.onload_model_names) + image = pipe.preprocess_image(input_image.resize((width, height))).to(pipe.device) + msk = torch.ones(1, num_frames, height//8, width//8, device=pipe.device) + msk[:, 1:] = 0 + if end_image is not None: + end_image = pipe.preprocess_image(end_image.resize((width, height))).to(pipe.device) + vae_input = torch.concat([image.transpose(0,1), torch.zeros(3, num_frames-2, height, width).to(image.device), end_image.transpose(0,1)],dim=1) + msk[:, -1:] = 1 + else: + vae_input = torch.concat([image.transpose(0, 1), torch.zeros(3, num_frames-1, height, width).to(image.device)], dim=1) + + msk = torch.concat([torch.repeat_interleave(msk[:, 0:1], repeats=4, dim=1), msk[:, 1:]], dim=1) + msk = msk.view(1, msk.shape[1] // 4, 4, height//8, width//8) + msk = msk.transpose(1, 2)[0] + + y = pipe.vae.encode([vae_input.to(dtype=pipe.torch_dtype, device=pipe.device)], device=pipe.device, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)[0] + y = y.to(dtype=pipe.torch_dtype, device=pipe.device) + y = torch.concat([msk, y]) + y = y.unsqueeze(0) + y = y.to(dtype=pipe.torch_dtype, device=pipe.device) + return {"y": y} + + + +class WanVideoUnit_ImageEmbedderFused(PipelineUnit): + """ + Encode input image to latents using VAE. This unit is for Wan-AI/Wan2.2-TI2V-5B. + """ + def __init__(self): + super().__init__( + input_params=("input_image", "latents", "height", "width", "tiled", "tile_size", "tile_stride"), + onload_model_names=("vae",) + ) + + def process(self, pipe: WanVideoPipeline, input_image, latents, height, width, tiled, tile_size, tile_stride): + if input_image is None or not pipe.dit.fuse_vae_embedding_in_latents: + return {} + pipe.load_models_to_device(self.onload_model_names) + image = pipe.preprocess_image(input_image.resize((width, height))).transpose(0, 1) + z = pipe.vae.encode([image], device=pipe.device, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) + latents[:, :, 0: 1] = z + return {"latents": latents, "fuse_vae_embedding_in_latents": True, "first_frame_latents": z} + + + +class WanVideoUnit_FunControl(PipelineUnit): + def __init__(self): + super().__init__( + input_params=("control_video", "num_frames", "height", "width", "tiled", "tile_size", "tile_stride", "clip_feature", "y", "latents"), + onload_model_names=("vae",) + ) + + def process(self, pipe: WanVideoPipeline, control_video, num_frames, height, width, tiled, tile_size, tile_stride, clip_feature, y, latents): + if control_video is None: + return {} + pipe.load_models_to_device(self.onload_model_names) + control_video = pipe.preprocess_video(control_video) + control_latents = pipe.vae.encode(control_video, device=pipe.device, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride).to(dtype=pipe.torch_dtype, device=pipe.device) + control_latents = control_latents.to(dtype=pipe.torch_dtype, device=pipe.device) + y_dim = pipe.dit.in_dim-control_latents.shape[1]-latents.shape[1] + if clip_feature is None or y is None: + clip_feature = torch.zeros((1, 257, 1280), dtype=pipe.torch_dtype, device=pipe.device) + y = torch.zeros((1, y_dim, (num_frames - 1) // 4 + 1, height//8, width//8), dtype=pipe.torch_dtype, device=pipe.device) + else: + y = y[:, -y_dim:] + y = torch.concat([control_latents, y], dim=1) + return {"clip_feature": clip_feature, "y": y} + + + +class WanVideoUnit_FunReference(PipelineUnit): + def __init__(self): + super().__init__( + input_params=("reference_image", "height", "width", "reference_image"), + onload_model_names=("vae",) + ) + + def process(self, pipe: WanVideoPipeline, reference_image, height, width): + if reference_image is None: + return {} + pipe.load_models_to_device(["vae"]) + reference_image = reference_image.resize((width, height)) + reference_latents = pipe.preprocess_video([reference_image]) + reference_latents = pipe.vae.encode(reference_latents, device=pipe.device) + if pipe.image_encoder is None: + return {"reference_latents": reference_latents} + clip_feature = pipe.preprocess_image(reference_image) + clip_feature = pipe.image_encoder.encode_image([clip_feature]) + return {"reference_latents": reference_latents, "clip_feature": clip_feature} + + + +class WanVideoUnit_FunCameraControl(PipelineUnit): + def __init__(self): + super().__init__( + input_params=("height", "width", "num_frames", "camera_control_direction", "camera_control_speed", "camera_control_origin", "latents", "input_image", "tiled", "tile_size", "tile_stride"), + onload_model_names=("vae",) + ) + + def process(self, pipe: WanVideoPipeline, height, width, num_frames, camera_control_direction, camera_control_speed, camera_control_origin, latents, input_image, tiled, tile_size, tile_stride): + if camera_control_direction is None: + return {} + pipe.load_models_to_device(self.onload_model_names) + camera_control_plucker_embedding = pipe.dit.control_adapter.process_camera_coordinates( + camera_control_direction, num_frames, height, width, camera_control_speed, camera_control_origin) + + control_camera_video = camera_control_plucker_embedding[:num_frames].permute([3, 0, 1, 2]).unsqueeze(0) + control_camera_latents = torch.concat( + [ + torch.repeat_interleave(control_camera_video[:, :, 0:1], repeats=4, dim=2), + control_camera_video[:, :, 1:] + ], dim=2 + ).transpose(1, 2) + b, f, c, h, w = control_camera_latents.shape + control_camera_latents = control_camera_latents.contiguous().view(b, f // 4, 4, c, h, w).transpose(2, 3) + control_camera_latents = control_camera_latents.contiguous().view(b, f // 4, c * 4, h, w).transpose(1, 2) + control_camera_latents_input = control_camera_latents.to(device=pipe.device, dtype=pipe.torch_dtype) + + input_image = input_image.resize((width, height)) + input_latents = pipe.preprocess_video([input_image]) + input_latents = pipe.vae.encode(input_latents, device=pipe.device) + y = torch.zeros_like(latents).to(pipe.device) + y[:, :, :1] = input_latents + y = y.to(dtype=pipe.torch_dtype, device=pipe.device) + + if y.shape[1] != pipe.dit.in_dim - latents.shape[1]: + image = pipe.preprocess_image(input_image.resize((width, height))).to(pipe.device) + vae_input = torch.concat([image.transpose(0, 1), torch.zeros(3, num_frames-1, height, width).to(image.device)], dim=1) + y = pipe.vae.encode([vae_input.to(dtype=pipe.torch_dtype, device=pipe.device)], device=pipe.device, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)[0] + y = y.to(dtype=pipe.torch_dtype, device=pipe.device) + msk = torch.ones(1, num_frames, height//8, width//8, device=pipe.device) + msk[:, 1:] = 0 + msk = torch.concat([torch.repeat_interleave(msk[:, 0:1], repeats=4, dim=1), msk[:, 1:]], dim=1) + msk = msk.view(1, msk.shape[1] // 4, 4, height//8, width//8) + msk = msk.transpose(1, 2)[0] + y = torch.cat([msk,y]) + y = y.unsqueeze(0) + y = y.to(dtype=pipe.torch_dtype, device=pipe.device) + return {"control_camera_latents_input": control_camera_latents_input, "y": y} + + + +class WanVideoUnit_SpeedControl(PipelineUnit): + def __init__(self): + super().__init__(input_params=("motion_bucket_id",)) + + def process(self, pipe: WanVideoPipeline, motion_bucket_id): + if motion_bucket_id is None: + return {} + motion_bucket_id = torch.Tensor((motion_bucket_id,)).to(dtype=pipe.torch_dtype, device=pipe.device) + return {"motion_bucket_id": motion_bucket_id} + + + +class WanVideoUnit_VACE(PipelineUnit): + def __init__(self): + super().__init__( + input_params=("vace_video", "vace_video_mask", "vace_reference_image", "vace_scale", "height", "width", "num_frames", "tiled", "tile_size", "tile_stride"), + onload_model_names=("vae",) + ) + + def process( + self, + pipe: WanVideoPipeline, + vace_video, vace_video_mask, vace_reference_image, vace_scale, + height, width, num_frames, + tiled, tile_size, tile_stride + ): + if vace_video is not None or vace_video_mask is not None or vace_reference_image is not None: + pipe.load_models_to_device(["vae"]) + if vace_video is None: + vace_video = torch.zeros((1, 3, num_frames, height, width), dtype=pipe.torch_dtype, device=pipe.device) + else: + vace_video = pipe.preprocess_video(vace_video) + + if vace_video_mask is None: + vace_video_mask = torch.ones_like(vace_video) + else: + vace_video_mask = pipe.preprocess_video(vace_video_mask, min_value=0, max_value=1) + + inactive = vace_video * (1 - vace_video_mask) + 0 * vace_video_mask + reactive = vace_video * vace_video_mask + 0 * (1 - vace_video_mask) + inactive = pipe.vae.encode(inactive, device=pipe.device, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride).to(dtype=pipe.torch_dtype, device=pipe.device) + reactive = pipe.vae.encode(reactive, device=pipe.device, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride).to(dtype=pipe.torch_dtype, device=pipe.device) + vace_video_latents = torch.concat((inactive, reactive), dim=1) + + vace_mask_latents = rearrange(vace_video_mask[0,0], "T (H P) (W Q) -> 1 (P Q) T H W", P=8, Q=8) + vace_mask_latents = torch.nn.functional.interpolate(vace_mask_latents, size=((vace_mask_latents.shape[2] + 3) // 4, vace_mask_latents.shape[3], vace_mask_latents.shape[4]), mode='nearest-exact') + + if vace_reference_image is None: + pass + else: + if not isinstance(vace_reference_image,list): + vace_reference_image = [vace_reference_image] + + vace_reference_image = pipe.preprocess_video(vace_reference_image) + + bs, c, f, h, w = vace_reference_image.shape + new_vace_ref_images = [] + for j in range(f): + new_vace_ref_images.append(vace_reference_image[0, :, j:j+1]) + vace_reference_image = new_vace_ref_images + + vace_reference_latents = pipe.vae.encode(vace_reference_image, device=pipe.device, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride).to(dtype=pipe.torch_dtype, device=pipe.device) + vace_reference_latents = torch.concat((vace_reference_latents, torch.zeros_like(vace_reference_latents)), dim=1) + vace_reference_latents = [u.unsqueeze(0) for u in vace_reference_latents] + + vace_video_latents = torch.concat((*vace_reference_latents, vace_video_latents), dim=2) + vace_mask_latents = torch.concat((torch.zeros_like(vace_mask_latents[:, :, :f]), vace_mask_latents), dim=2) + + vace_context = torch.concat((vace_video_latents, vace_mask_latents), dim=1) + return {"vace_context": vace_context, "vace_scale": vace_scale} + else: + return {"vace_context": None, "vace_scale": vace_scale} + +class WanVideoUnit_VAP(PipelineUnit): + def __init__(self): + super().__init__( + take_over=True, + onload_model_names=("text_encoder", "vae", "image_encoder") + ) + + def process(self, pipe: WanVideoPipeline, inputs_shared, inputs_posi, inputs_nega): + if inputs_shared.get("vap_video") is None: + return inputs_shared, inputs_posi, inputs_nega + else: + # 1. encode vap prompt + pipe.load_models_to_device(["text_encoder"]) + vap_prompt, negative_vap_prompt = inputs_posi.get("vap_prompt", ""), inputs_nega.get("negative_vap_prompt", "") + vap_prompt_emb = pipe.prompter.encode_prompt(vap_prompt, positive=inputs_posi.get('positive',None), device=pipe.device) + negative_vap_prompt_emb = pipe.prompter.encode_prompt(negative_vap_prompt, positive=inputs_nega.get('positive',None), device=pipe.device) + inputs_posi.update({"context_vap":vap_prompt_emb}) + inputs_nega.update({"context_vap":negative_vap_prompt_emb}) + # 2. prepare vap image clip embedding + pipe.load_models_to_device(["vae", "image_encoder"]) + vap_video, end_image = inputs_shared.get("vap_video"), inputs_shared.get("end_image") + + num_frames, height, width, mot_num = inputs_shared.get("num_frames"),inputs_shared.get("height"), inputs_shared.get("width"), inputs_shared.get("mot_num",1) + + image_vap = pipe.preprocess_image(vap_video[0].resize((width, height))).to(pipe.device) + + vap_clip_context = pipe.image_encoder.encode_image([image_vap]) + if end_image is not None: + vap_end_image = pipe.preprocess_image(vap_video[-1].resize((width, height))).to(pipe.device) + if pipe.dit.has_image_pos_emb: + vap_clip_context = torch.concat([vap_clip_context, pipe.image_encoder.encode_image([vap_end_image])], dim=1) + vap_clip_context = vap_clip_context.to(dtype=pipe.torch_dtype, device=pipe.device) + inputs_shared.update({"vap_clip_feature":vap_clip_context}) + + # 3. prepare vap latents + msk = torch.ones(1, num_frames, height//8, width//8, device=pipe.device) + msk[:, 1:] = 0 + if end_image is not None: + msk[:, -1:] = 1 + last_image_vap = pipe.preprocess_image(vap_video[-1].resize((width, height))).to(pipe.device) + vae_input = torch.concat([image_vap.transpose(0,1), torch.zeros(3, num_frames-2, height, width).to(image_vap.device), last_image_vap.transpose(0,1)],dim=1) + else: + vae_input = torch.concat([image_vap.transpose(0, 1), torch.zeros(3, num_frames-1, height, width).to(image_vap.device)], dim=1) + + msk = torch.concat([torch.repeat_interleave(msk[:, 0:1], repeats=4, dim=1), msk[:, 1:]], dim=1) + msk = msk.view(1, msk.shape[1] // 4, 4, height//8, width//8) + msk = msk.transpose(1, 2)[0] + + tiled,tile_size,tile_stride = inputs_shared.get("tiled"), inputs_shared.get("tile_size"), inputs_shared.get("tile_stride") + + y = pipe.vae.encode([vae_input.to(dtype=pipe.torch_dtype, device=pipe.device)], device=pipe.device, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)[0] + y = y.to(dtype=pipe.torch_dtype, device=pipe.device) + y = torch.concat([msk, y]) + y = y.unsqueeze(0) + y = y.to(dtype=pipe.torch_dtype, device=pipe.device) + + vap_video = pipe.preprocess_video(vap_video) + vap_latent = pipe.vae.encode(vap_video, device=pipe.device, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride).to(dtype=pipe.torch_dtype, device=pipe.device) + + vap_latent = torch.concat([vap_latent,y], dim=1).to(dtype=pipe.torch_dtype, device=pipe.device) + inputs_shared.update({"vap_hidden_state":vap_latent}) + pipe.load_models_to_device([]) + + return inputs_shared, inputs_posi, inputs_nega + + + +class WanVideoUnit_UnifiedSequenceParallel(PipelineUnit): + def __init__(self): + super().__init__(input_params=()) + + def process(self, pipe: WanVideoPipeline): + if hasattr(pipe, "use_unified_sequence_parallel"): + if pipe.use_unified_sequence_parallel: + return {"use_unified_sequence_parallel": True} + return {} + + + +class WanVideoUnit_TeaCache(PipelineUnit): + def __init__(self): + super().__init__( + seperate_cfg=True, + input_params_posi={"num_inference_steps": "num_inference_steps", "tea_cache_l1_thresh": "tea_cache_l1_thresh", "tea_cache_model_id": "tea_cache_model_id"}, + input_params_nega={"num_inference_steps": "num_inference_steps", "tea_cache_l1_thresh": "tea_cache_l1_thresh", "tea_cache_model_id": "tea_cache_model_id"}, + ) + + def process(self, pipe: WanVideoPipeline, num_inference_steps, tea_cache_l1_thresh, tea_cache_model_id): + if tea_cache_l1_thresh is None: + return {} + return {"tea_cache": TeaCache(num_inference_steps, rel_l1_thresh=tea_cache_l1_thresh, model_id=tea_cache_model_id)} + + + +class WanVideoUnit_CfgMerger(PipelineUnit): + def __init__(self): + super().__init__(take_over=True) + self.concat_tensor_names = ["context", "clip_feature", "y", "reference_latents"] + + def process(self, pipe: WanVideoPipeline, inputs_shared, inputs_posi, inputs_nega): + if not inputs_shared["cfg_merge"]: + return inputs_shared, inputs_posi, inputs_nega + for name in self.concat_tensor_names: + tensor_posi = inputs_posi.get(name) + tensor_nega = inputs_nega.get(name) + tensor_shared = inputs_shared.get(name) + if tensor_posi is not None and tensor_nega is not None: + inputs_shared[name] = torch.concat((tensor_posi, tensor_nega), dim=0) + elif tensor_shared is not None: + inputs_shared[name] = torch.concat((tensor_shared, tensor_shared), dim=0) + inputs_posi.clear() + inputs_nega.clear() + return inputs_shared, inputs_posi, inputs_nega + + +class WanVideoUnit_S2V(PipelineUnit): + def __init__(self): + super().__init__( + take_over=True, + onload_model_names=("audio_encoder", "vae",) + ) + + def process_audio(self, pipe: WanVideoPipeline, input_audio, audio_sample_rate, num_frames, fps=16, audio_embeds=None, return_all=False): + if audio_embeds is not None: + return {"audio_embeds": audio_embeds} + pipe.load_models_to_device(["audio_encoder"]) + audio_embeds = pipe.audio_encoder.get_audio_feats_per_inference(input_audio, audio_sample_rate, pipe.audio_processor, fps=fps, batch_frames=num_frames-1, dtype=pipe.torch_dtype, device=pipe.device) + if return_all: + return audio_embeds + else: + return {"audio_embeds": audio_embeds[0]} + + def process_motion_latents(self, pipe: WanVideoPipeline, height, width, tiled, tile_size, tile_stride, motion_video=None): + pipe.load_models_to_device(["vae"]) + motion_frames = 73 + kwargs = {} + if motion_video is not None and len(motion_video) > 0: + assert len(motion_video) == motion_frames, f"motion video must have {motion_frames} frames, but got {len(motion_video)}" + motion_latents = pipe.preprocess_video(motion_video) + kwargs["drop_motion_frames"] = False + else: + motion_latents = torch.zeros([1, 3, motion_frames, height, width], dtype=pipe.torch_dtype, device=pipe.device) + kwargs["drop_motion_frames"] = True + motion_latents = pipe.vae.encode(motion_latents, device=pipe.device, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride).to(dtype=pipe.torch_dtype, device=pipe.device) + kwargs.update({"motion_latents": motion_latents}) + return kwargs + + def process_pose_cond(self, pipe: WanVideoPipeline, s2v_pose_video, num_frames, height, width, tiled, tile_size, tile_stride, s2v_pose_latents=None, num_repeats=1, return_all=False): + if s2v_pose_latents is not None: + return {"s2v_pose_latents": s2v_pose_latents} + if s2v_pose_video is None: + return {"s2v_pose_latents": None} + pipe.load_models_to_device(["vae"]) + infer_frames = num_frames - 1 + input_video = pipe.preprocess_video(s2v_pose_video)[:, :, :infer_frames * num_repeats] + # pad if not enough frames + padding_frames = infer_frames * num_repeats - input_video.shape[2] + input_video = torch.cat([input_video, -torch.ones(1, 3, padding_frames, height, width, device=input_video.device, dtype=input_video.dtype)], dim=2) + input_videos = input_video.chunk(num_repeats, dim=2) + pose_conds = [] + for r in range(num_repeats): + cond = input_videos[r] + cond = torch.cat([cond[:, :, 0:1].repeat(1, 1, 1, 1, 1), cond], dim=2) + cond_latents = pipe.vae.encode(cond, device=pipe.device, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride).to(dtype=pipe.torch_dtype, device=pipe.device) + pose_conds.append(cond_latents[:,:,1:]) + if return_all: + return pose_conds + else: + return {"s2v_pose_latents": pose_conds[0]} + + def process(self, pipe: WanVideoPipeline, inputs_shared, inputs_posi, inputs_nega): + if (inputs_shared.get("input_audio") is None and inputs_shared.get("audio_embeds") is None) or pipe.audio_encoder is None or pipe.audio_processor is None: + return inputs_shared, inputs_posi, inputs_nega + num_frames, height, width, tiled, tile_size, tile_stride = inputs_shared.get("num_frames"), inputs_shared.get("height"), inputs_shared.get("width"), inputs_shared.get("tiled"), inputs_shared.get("tile_size"), inputs_shared.get("tile_stride") + input_audio, audio_embeds, audio_sample_rate = inputs_shared.pop("input_audio", None), inputs_shared.pop("audio_embeds", None), inputs_shared.get("audio_sample_rate", 16000) + s2v_pose_video, s2v_pose_latents, motion_video = inputs_shared.pop("s2v_pose_video", None), inputs_shared.pop("s2v_pose_latents", None), inputs_shared.pop("motion_video", None) + + audio_input_positive = self.process_audio(pipe, input_audio, audio_sample_rate, num_frames, audio_embeds=audio_embeds) + inputs_posi.update(audio_input_positive) + inputs_nega.update({"audio_embeds": 0.0 * audio_input_positive["audio_embeds"]}) + + inputs_shared.update(self.process_motion_latents(pipe, height, width, tiled, tile_size, tile_stride, motion_video)) + inputs_shared.update(self.process_pose_cond(pipe, s2v_pose_video, num_frames, height, width, tiled, tile_size, tile_stride, s2v_pose_latents=s2v_pose_latents)) + return inputs_shared, inputs_posi, inputs_nega + + @staticmethod + def pre_calculate_audio_pose(pipe: WanVideoPipeline, input_audio=None, audio_sample_rate=16000, s2v_pose_video=None, num_frames=81, height=448, width=832, fps=16, tiled=True, tile_size=(30, 52), tile_stride=(15, 26)): + assert pipe.audio_encoder is not None and pipe.audio_processor is not None, "Please load audio encoder and audio processor first." + shapes = WanVideoUnit_ShapeChecker().process(pipe, height, width, num_frames) + height, width, num_frames = shapes["height"], shapes["width"], shapes["num_frames"] + unit = WanVideoUnit_S2V() + audio_embeds = unit.process_audio(pipe, input_audio, audio_sample_rate, num_frames, fps, return_all=True) + pose_latents = unit.process_pose_cond(pipe, s2v_pose_video, num_frames, height, width, num_repeats=len(audio_embeds), return_all=True, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) + pose_latents = None if s2v_pose_video is None else pose_latents + return audio_embeds, pose_latents, len(audio_embeds) + + +class WanVideoPostUnit_S2V(PipelineUnit): + def __init__(self): + super().__init__(input_params=("latents", "motion_latents", "drop_motion_frames")) + + def process(self, pipe: WanVideoPipeline, latents, motion_latents, drop_motion_frames): + if pipe.audio_encoder is None or motion_latents is None or drop_motion_frames: + return {} + latents = torch.cat([motion_latents, latents[:,:,1:]], dim=2) + return {"latents": latents} + + +class WanVideoPostUnit_AnimateVideoSplit(PipelineUnit): + def __init__(self): + super().__init__(input_params=("input_video", "animate_pose_video", "animate_face_video", "animate_inpaint_video", "animate_mask_video")) + + def process(self, pipe: WanVideoPipeline, input_video, animate_pose_video, animate_face_video, animate_inpaint_video, animate_mask_video): + if input_video is None: + return {} + if animate_pose_video is not None: + animate_pose_video = animate_pose_video[:len(input_video) - 4] + if animate_face_video is not None: + animate_face_video = animate_face_video[:len(input_video) - 4] + if animate_inpaint_video is not None: + animate_inpaint_video = animate_inpaint_video[:len(input_video) - 4] + if animate_mask_video is not None: + animate_mask_video = animate_mask_video[:len(input_video) - 4] + return {"animate_pose_video": animate_pose_video, "animate_face_video": animate_face_video, "animate_inpaint_video": animate_inpaint_video, "animate_mask_video": animate_mask_video} + + +class WanVideoPostUnit_AnimatePoseLatents(PipelineUnit): + def __init__(self): + super().__init__( + input_params=("animate_pose_video", "tiled", "tile_size", "tile_stride"), + onload_model_names=("vae",) + ) + + def process(self, pipe: WanVideoPipeline, animate_pose_video, tiled, tile_size, tile_stride): + if animate_pose_video is None: + return {} + pipe.load_models_to_device(self.onload_model_names) + animate_pose_video = pipe.preprocess_video(animate_pose_video) + pose_latents = pipe.vae.encode(animate_pose_video, device=pipe.device, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride).to(dtype=pipe.torch_dtype, device=pipe.device) + return {"pose_latents": pose_latents} + + +class WanVideoPostUnit_AnimateFacePixelValues(PipelineUnit): + def __init__(self): + super().__init__(take_over=True) + + def process(self, pipe: WanVideoPipeline, inputs_shared, inputs_posi, inputs_nega): + if inputs_shared.get("animate_face_video", None) is None: + return inputs_shared, inputs_posi, inputs_nega + inputs_posi["face_pixel_values"] = pipe.preprocess_video(inputs_shared["animate_face_video"]) + inputs_nega["face_pixel_values"] = torch.zeros_like(inputs_posi["face_pixel_values"]) - 1 + return inputs_shared, inputs_posi, inputs_nega + + +class WanVideoPostUnit_AnimateInpaint(PipelineUnit): + def __init__(self): + super().__init__( + input_params=("animate_inpaint_video", "animate_mask_video", "input_image", "tiled", "tile_size", "tile_stride"), + onload_model_names=("vae",) + ) + + def get_i2v_mask(self, lat_t, lat_h, lat_w, mask_len=1, mask_pixel_values=None, device="cuda"): + if mask_pixel_values is None: + msk = torch.zeros(1, (lat_t-1) * 4 + 1, lat_h, lat_w, device=device) + else: + msk = mask_pixel_values.clone() + msk[:, :mask_len] = 1 + msk = torch.concat([torch.repeat_interleave(msk[:, 0:1], repeats=4, dim=1), msk[:, 1:]], dim=1) + msk = msk.view(1, msk.shape[1] // 4, 4, lat_h, lat_w) + msk = msk.transpose(1, 2)[0] + return msk + + def process(self, pipe: WanVideoPipeline, animate_inpaint_video, animate_mask_video, input_image, tiled, tile_size, tile_stride): + if animate_inpaint_video is None or animate_mask_video is None: + return {} + pipe.load_models_to_device(self.onload_model_names) + + bg_pixel_values = pipe.preprocess_video(animate_inpaint_video) + y_reft = pipe.vae.encode(bg_pixel_values, device=pipe.device, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)[0].to(dtype=pipe.torch_dtype, device=pipe.device) + _, lat_t, lat_h, lat_w = y_reft.shape + + ref_pixel_values = pipe.preprocess_video([input_image]) + ref_latents = pipe.vae.encode(ref_pixel_values, device=pipe.device, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride).to(dtype=pipe.torch_dtype, device=pipe.device) + mask_ref = self.get_i2v_mask(1, lat_h, lat_w, 1, device=pipe.device) + y_ref = torch.concat([mask_ref, ref_latents[0]]).to(dtype=torch.bfloat16, device=pipe.device) + + mask_pixel_values = 1 - pipe.preprocess_video(animate_mask_video, max_value=1, min_value=0) + mask_pixel_values = rearrange(mask_pixel_values, "b c t h w -> (b t) c h w") + mask_pixel_values = torch.nn.functional.interpolate(mask_pixel_values, size=(lat_h, lat_w), mode='nearest') + mask_pixel_values = rearrange(mask_pixel_values, "(b t) c h w -> b t c h w", b=1)[:,:,0] + msk_reft = self.get_i2v_mask(lat_t, lat_h, lat_w, 0, mask_pixel_values=mask_pixel_values, device=pipe.device) + + y_reft = torch.concat([msk_reft, y_reft]).to(dtype=torch.bfloat16, device=pipe.device) + y = torch.concat([y_ref, y_reft], dim=1).unsqueeze(0) + return {"y": y} + + +class WanVideoUnit_LongCatVideo(PipelineUnit): + def __init__(self): + super().__init__( + input_params=("longcat_video",), + onload_model_names=("vae",) + ) + + def process(self, pipe: WanVideoPipeline, longcat_video): + if longcat_video is None: + return {} + pipe.load_models_to_device(self.onload_model_names) + longcat_video = pipe.preprocess_video(longcat_video) + longcat_latents = pipe.vae.encode(longcat_video, device=pipe.device).to(dtype=pipe.torch_dtype, device=pipe.device) + return {"longcat_latents": longcat_latents} + + +class TeaCache: + def __init__(self, num_inference_steps, rel_l1_thresh, model_id): + self.num_inference_steps = num_inference_steps + self.step = 0 + self.accumulated_rel_l1_distance = 0 + self.previous_modulated_input = None + self.rel_l1_thresh = rel_l1_thresh + self.previous_residual = None + self.previous_hidden_states = None + + self.coefficients_dict = { + "Wan2.1-T2V-1.3B": [-5.21862437e+04, 9.23041404e+03, -5.28275948e+02, 1.36987616e+01, -4.99875664e-02], + "Wan2.1-T2V-14B": [-3.03318725e+05, 4.90537029e+04, -2.65530556e+03, 5.87365115e+01, -3.15583525e-01], + "Wan2.1-I2V-14B-480P": [2.57151496e+05, -3.54229917e+04, 1.40286849e+03, -1.35890334e+01, 1.32517977e-01], + "Wan2.1-I2V-14B-720P": [ 8.10705460e+03, 2.13393892e+03, -3.72934672e+02, 1.66203073e+01, -4.17769401e-02], + } + if model_id not in self.coefficients_dict: + supported_model_ids = ", ".join([i for i in self.coefficients_dict]) + raise ValueError(f"{model_id} is not a supported TeaCache model id. Please choose a valid model id in ({supported_model_ids}).") + self.coefficients = self.coefficients_dict[model_id] + + def check(self, dit: WanModel, x, t_mod): + modulated_inp = t_mod.clone() + if self.step == 0 or self.step == self.num_inference_steps - 1: + should_calc = True + self.accumulated_rel_l1_distance = 0 + else: + coefficients = self.coefficients + rescale_func = np.poly1d(coefficients) + self.accumulated_rel_l1_distance += rescale_func(((modulated_inp-self.previous_modulated_input).abs().mean() / self.previous_modulated_input.abs().mean()).cpu().item()) + if self.accumulated_rel_l1_distance < self.rel_l1_thresh: + should_calc = False + else: + should_calc = True + self.accumulated_rel_l1_distance = 0 + self.previous_modulated_input = modulated_inp + self.step += 1 + if self.step == self.num_inference_steps: + self.step = 0 + if should_calc: + self.previous_hidden_states = x.clone() + return not should_calc + + def store(self, hidden_states): + self.previous_residual = hidden_states - self.previous_hidden_states + self.previous_hidden_states = None + + def update(self, hidden_states): + hidden_states = hidden_states + self.previous_residual + return hidden_states + + + +class TemporalTiler_BCTHW: + def __init__(self): + pass + + def build_1d_mask(self, length, left_bound, right_bound, border_width): + x = torch.ones((length,)) + if border_width == 0: + return x + + shift = 0.5 + if not left_bound: + x[:border_width] = (torch.arange(border_width) + shift) / border_width + if not right_bound: + x[-border_width:] = torch.flip((torch.arange(border_width) + shift) / border_width, dims=(0,)) + return x + + def build_mask(self, data, is_bound, border_width): + _, _, T, _, _ = data.shape + t = self.build_1d_mask(T, is_bound[0], is_bound[1], border_width[0]) + mask = repeat(t, "T -> 1 1 T 1 1") + return mask + + def run(self, model_fn, sliding_window_size, sliding_window_stride, computation_device, computation_dtype, model_kwargs, tensor_names, batch_size=None): + tensor_names = [tensor_name for tensor_name in tensor_names if model_kwargs.get(tensor_name) is not None] + tensor_dict = {tensor_name: model_kwargs[tensor_name] for tensor_name in tensor_names} + B, C, T, H, W = tensor_dict[tensor_names[0]].shape + if batch_size is not None: + B *= batch_size + data_device, data_dtype = tensor_dict[tensor_names[0]].device, tensor_dict[tensor_names[0]].dtype + value = torch.zeros((B, C, T, H, W), device=data_device, dtype=data_dtype) + weight = torch.zeros((1, 1, T, 1, 1), device=data_device, dtype=data_dtype) + for t in range(0, T, sliding_window_stride): + if t - sliding_window_stride >= 0 and t - sliding_window_stride + sliding_window_size >= T: + continue + t_ = min(t + sliding_window_size, T) + model_kwargs.update({ + tensor_name: tensor_dict[tensor_name][:, :, t: t_:, :].to(device=computation_device, dtype=computation_dtype) \ + for tensor_name in tensor_names + }) + model_output = model_fn(**model_kwargs).to(device=data_device, dtype=data_dtype) + mask = self.build_mask( + model_output, + is_bound=(t == 0, t_ == T), + border_width=(sliding_window_size - sliding_window_stride,) + ).to(device=data_device, dtype=data_dtype) + value[:, :, t: t_, :, :] += model_output * mask + weight[:, :, t: t_, :, :] += mask + value /= weight + model_kwargs.update(tensor_dict) + return value + + + +def model_fn_wan_video( + dit: WanModel, + motion_controller: WanMotionControllerModel = None, + vace: VaceWanModel = None, + vap: MotWanModel = None, + animate_adapter: WanAnimateAdapter = None, + latents: torch.Tensor = None, + timestep: torch.Tensor = None, + context: torch.Tensor = None, + clip_feature: Optional[torch.Tensor] = None, + y: Optional[torch.Tensor] = None, + reference_latents = None, + vace_context = None, + vace_scale = 1.0, + audio_embeds: Optional[torch.Tensor] = None, + motion_latents: Optional[torch.Tensor] = None, + s2v_pose_latents: Optional[torch.Tensor] = None, + vap_hidden_state = None, + vap_clip_feature = None, + context_vap = None, + drop_motion_frames: bool = True, + tea_cache: TeaCache = None, + use_unified_sequence_parallel: bool = False, + motion_bucket_id: Optional[torch.Tensor] = None, + pose_latents=None, + face_pixel_values=None, + longcat_latents=None, + sliding_window_size: Optional[int] = None, + sliding_window_stride: Optional[int] = None, + cfg_merge: bool = False, + use_gradient_checkpointing: bool = False, + use_gradient_checkpointing_offload: bool = False, + control_camera_latents_input = None, + fuse_vae_embedding_in_latents: bool = False, + **kwargs, +): + if sliding_window_size is not None and sliding_window_stride is not None: + model_kwargs = dict( + dit=dit, + motion_controller=motion_controller, + vace=vace, + latents=latents, + timestep=timestep, + context=context, + clip_feature=clip_feature, + y=y, + reference_latents=reference_latents, + vace_context=vace_context, + vace_scale=vace_scale, + tea_cache=tea_cache, + use_unified_sequence_parallel=use_unified_sequence_parallel, + motion_bucket_id=motion_bucket_id, + ) + return TemporalTiler_BCTHW().run( + model_fn_wan_video, + sliding_window_size, sliding_window_stride, + latents.device, latents.dtype, + model_kwargs=model_kwargs, + tensor_names=["latents", "y"], + batch_size=2 if cfg_merge else 1 + ) + # LongCat-Video + if isinstance(dit, LongCatVideoTransformer3DModel): + return model_fn_longcat_video( + dit=dit, + latents=latents, + timestep=timestep, + context=context, + longcat_latents=longcat_latents, + use_gradient_checkpointing=use_gradient_checkpointing, + use_gradient_checkpointing_offload=use_gradient_checkpointing_offload, + ) + + # wan2.2 s2v + if audio_embeds is not None: + return model_fn_wans2v( + dit=dit, + latents=latents, + timestep=timestep, + context=context, + audio_embeds=audio_embeds, + motion_latents=motion_latents, + s2v_pose_latents=s2v_pose_latents, + drop_motion_frames=drop_motion_frames, + use_gradient_checkpointing_offload=use_gradient_checkpointing_offload, + use_gradient_checkpointing=use_gradient_checkpointing, + use_unified_sequence_parallel=use_unified_sequence_parallel, + ) + + if use_unified_sequence_parallel: + import torch.distributed as dist + from xfuser.core.distributed import (get_sequence_parallel_rank, + get_sequence_parallel_world_size, + get_sp_group) + + # Timestep + if dit.seperated_timestep and fuse_vae_embedding_in_latents: + timestep = torch.concat([ + torch.zeros((1, latents.shape[3] * latents.shape[4] // 4), dtype=latents.dtype, device=latents.device), + torch.ones((latents.shape[2] - 1, latents.shape[3] * latents.shape[4] // 4), dtype=latents.dtype, device=latents.device) * timestep + ]).flatten() + t = dit.time_embedding(sinusoidal_embedding_1d(dit.freq_dim, timestep).unsqueeze(0)) + if use_unified_sequence_parallel and dist.is_initialized() and dist.get_world_size() > 1: + t_chunks = torch.chunk(t, get_sequence_parallel_world_size(), dim=1) + t_chunks = [torch.nn.functional.pad(chunk, (0, 0, 0, t_chunks[0].shape[1]-chunk.shape[1]), value=0) for chunk in t_chunks] + t = t_chunks[get_sequence_parallel_rank()] + t_mod = dit.time_projection(t).unflatten(2, (6, dit.dim)) + else: + t = dit.time_embedding(sinusoidal_embedding_1d(dit.freq_dim, timestep)) + t_mod = dit.time_projection(t).unflatten(1, (6, dit.dim)) + + # Motion Controller + if motion_bucket_id is not None and motion_controller is not None: + t_mod = t_mod + motion_controller(motion_bucket_id).unflatten(1, (6, dit.dim)) + context = dit.text_embedding(context) + + x = latents + # Merged cfg + if x.shape[0] != context.shape[0]: + x = torch.concat([x] * context.shape[0], dim=0) + if timestep.shape[0] != context.shape[0]: + timestep = torch.concat([timestep] * context.shape[0], dim=0) + + # Image Embedding + if y is not None and dit.require_vae_embedding: + x = torch.cat([x, y], dim=1) + if clip_feature is not None and dit.require_clip_embedding: + clip_embdding = dit.img_emb(clip_feature) + context = torch.cat([clip_embdding, context], dim=1) + + # Camera control + x = dit.patchify(x, control_camera_latents_input) + + # Animate + if pose_latents is not None and face_pixel_values is not None: + x, motion_vec = animate_adapter.after_patch_embedding(x, pose_latents, face_pixel_values) + + # Patchify + f, h, w = x.shape[2:] + x = rearrange(x, 'b c f h w -> b (f h w) c').contiguous() + + # Reference image + if reference_latents is not None: + if len(reference_latents.shape) == 5: + reference_latents = reference_latents[:, :, 0] + reference_latents = dit.ref_conv(reference_latents).flatten(2).transpose(1, 2) + x = torch.concat([reference_latents, x], dim=1) + f += 1 + + freqs = torch.cat([ + dit.freqs[0][:f].view(f, 1, 1, -1).expand(f, h, w, -1), + dit.freqs[1][:h].view(1, h, 1, -1).expand(f, h, w, -1), + dit.freqs[2][:w].view(1, 1, w, -1).expand(f, h, w, -1) + ], dim=-1).reshape(f * h * w, 1, -1).to(x.device) + + # VAP + if vap is not None: + # hidden state + x_vap = vap_hidden_state + x_vap = vap.patchify(x_vap) + x_vap = rearrange(x_vap, 'b c f h w -> b (f h w) c').contiguous() + # Timestep + clean_timestep = torch.ones(timestep.shape, device=timestep.device).to(timestep.dtype) + t = vap.time_embedding(sinusoidal_embedding_1d(vap.freq_dim, clean_timestep)) + t_mod_vap = vap.time_projection(t).unflatten(1, (6, vap.dim)) + + # rope + freqs_vap = vap.compute_freqs_mot(f,h,w).to(x.device) + + # context + vap_clip_embedding = vap.img_emb(vap_clip_feature) + context_vap = vap.text_embedding(context_vap) + context_vap = torch.cat([vap_clip_embedding, context_vap], dim=1) + + # TeaCache + if tea_cache is not None: + tea_cache_update = tea_cache.check(dit, x, t_mod) + else: + tea_cache_update = False + + if vace_context is not None: + vace_hints = vace( + x, vace_context, context, t_mod, freqs, + use_gradient_checkpointing=use_gradient_checkpointing, + use_gradient_checkpointing_offload=use_gradient_checkpointing_offload + ) + + # blocks + if use_unified_sequence_parallel: + if dist.is_initialized() and dist.get_world_size() > 1: + chunks = torch.chunk(x, get_sequence_parallel_world_size(), dim=1) + pad_shape = chunks[0].shape[1] - chunks[-1].shape[1] + chunks = [torch.nn.functional.pad(chunk, (0, 0, 0, chunks[0].shape[1]-chunk.shape[1]), value=0) for chunk in chunks] + x = chunks[get_sequence_parallel_rank()] + if tea_cache_update: + x = tea_cache.update(x) + else: + def create_custom_forward(module): + def custom_forward(*inputs): + return module(*inputs) + return custom_forward + + def create_custom_forward_vap(block, vap): + def custom_forward(*inputs): + return vap(block, *inputs) + return custom_forward + + for block_id, block in enumerate(dit.blocks): + # Block + if vap is not None and block_id in vap.mot_layers_mapping: + if use_gradient_checkpointing_offload: + with torch.autograd.graph.save_on_cpu(): + x, x_vap = torch.utils.checkpoint.checkpoint( + create_custom_forward_vap(block, vap), + x, context, t_mod, freqs, x_vap, context_vap, t_mod_vap, freqs_vap, block_id, + use_reentrant=False, + ) + elif use_gradient_checkpointing: + x, x_vap = torch.utils.checkpoint.checkpoint( + create_custom_forward_vap(block, vap), + x, context, t_mod, freqs, x_vap, context_vap, t_mod_vap, freqs_vap, block_id, + use_reentrant=False, + ) + else: + x, x_vap = vap(block, x, context, t_mod, freqs, x_vap, context_vap, t_mod_vap, freqs_vap, block_id) + else: + if use_gradient_checkpointing_offload: + with torch.autograd.graph.save_on_cpu(): + x = torch.utils.checkpoint.checkpoint( + create_custom_forward(block), + x, context, t_mod, freqs, + use_reentrant=False, + ) + elif use_gradient_checkpointing: + x = torch.utils.checkpoint.checkpoint( + create_custom_forward(block), + x, context, t_mod, freqs, + use_reentrant=False, + ) + else: + x = block(x, context, t_mod, freqs) + + # VACE + if vace_context is not None and block_id in vace.vace_layers_mapping: + current_vace_hint = vace_hints[vace.vace_layers_mapping[block_id]] + if use_unified_sequence_parallel and dist.is_initialized() and dist.get_world_size() > 1: + current_vace_hint = torch.chunk(current_vace_hint, get_sequence_parallel_world_size(), dim=1)[get_sequence_parallel_rank()] + current_vace_hint = torch.nn.functional.pad(current_vace_hint, (0, 0, 0, chunks[0].shape[1] - current_vace_hint.shape[1]), value=0) + x = x + current_vace_hint * vace_scale + + # Animate + if pose_latents is not None and face_pixel_values is not None: + x = animate_adapter.after_transformer_block(block_id, x, motion_vec) + if tea_cache is not None: + tea_cache.store(x) + + x = dit.head(x, t) + if use_unified_sequence_parallel: + if dist.is_initialized() and dist.get_world_size() > 1: + x = get_sp_group().all_gather(x, dim=1) + x = x[:, :-pad_shape] if pad_shape > 0 else x + # Remove reference latents + if reference_latents is not None: + x = x[:, reference_latents.shape[1]:] + f -= 1 + x = dit.unpatchify(x, (f, h, w)) + return x + + +def model_fn_longcat_video( + dit: LongCatVideoTransformer3DModel, + latents: torch.Tensor = None, + timestep: torch.Tensor = None, + context: torch.Tensor = None, + longcat_latents: torch.Tensor = None, + use_gradient_checkpointing=False, + use_gradient_checkpointing_offload=False, +): + if longcat_latents is not None: + latents[:, :, :longcat_latents.shape[2]] = longcat_latents + num_cond_latents = longcat_latents.shape[2] + else: + num_cond_latents = 0 + context = context.unsqueeze(0) + encoder_attention_mask = torch.any(context != 0, dim=-1)[:, 0].to(torch.int64) + output = dit( + latents, + timestep, + context, + encoder_attention_mask, + num_cond_latents=num_cond_latents, + use_gradient_checkpointing=use_gradient_checkpointing, + use_gradient_checkpointing_offload=use_gradient_checkpointing_offload, + ) + output = -output + output = output.to(latents.dtype) + return output + + +def model_fn_wans2v( + dit, + latents, + timestep, + context, + audio_embeds, + motion_latents, + s2v_pose_latents, + drop_motion_frames=True, + use_gradient_checkpointing_offload=False, + use_gradient_checkpointing=False, + use_unified_sequence_parallel=False, +): + if use_unified_sequence_parallel: + import torch.distributed as dist + from xfuser.core.distributed import (get_sequence_parallel_rank, + get_sequence_parallel_world_size, + get_sp_group) + origin_ref_latents = latents[:, :, 0:1] + x = latents[:, :, 1:] + + # context embedding + context = dit.text_embedding(context) + + # audio encode + audio_emb_global, merged_audio_emb = dit.cal_audio_emb(audio_embeds) + + # x and s2v_pose_latents + s2v_pose_latents = torch.zeros_like(x) if s2v_pose_latents is None else s2v_pose_latents + x, (f, h, w) = dit.patchify(dit.patch_embedding(x) + dit.cond_encoder(s2v_pose_latents)) + seq_len_x = seq_len_x_global = x.shape[1] # global used for unified sequence parallel + + # reference image + ref_latents, (rf, rh, rw) = dit.patchify(dit.patch_embedding(origin_ref_latents)) + grid_sizes = dit.get_grid_sizes((f, h, w), (rf, rh, rw)) + x = torch.cat([x, ref_latents], dim=1) + # mask + mask = torch.cat([torch.zeros([1, seq_len_x]), torch.ones([1, ref_latents.shape[1]])], dim=1).to(torch.long).to(x.device) + # freqs + pre_compute_freqs = rope_precompute(x.detach().view(1, x.size(1), dit.num_heads, dit.dim // dit.num_heads), grid_sizes, dit.freqs, start=None) + # motion + x, pre_compute_freqs, mask = dit.inject_motion(x, pre_compute_freqs, mask, motion_latents, drop_motion_frames=drop_motion_frames, add_last_motion=2) + + x = x + dit.trainable_cond_mask(mask).to(x.dtype) + + # tmod + timestep = torch.cat([timestep, torch.zeros([1], dtype=timestep.dtype, device=timestep.device)]) + t = dit.time_embedding(sinusoidal_embedding_1d(dit.freq_dim, timestep)) + t_mod = dit.time_projection(t).unflatten(1, (6, dit.dim)).unsqueeze(2).transpose(0, 2) + + if use_unified_sequence_parallel and dist.is_initialized() and dist.get_world_size() > 1: + world_size, sp_rank = get_sequence_parallel_world_size(), get_sequence_parallel_rank() + assert x.shape[1] % world_size == 0, f"the dimension after chunk must be divisible by world size, but got {x.shape[1]} and {get_sequence_parallel_world_size()}" + x = torch.chunk(x, world_size, dim=1)[sp_rank] + seg_idxs = [0] + list(torch.cumsum(torch.tensor([x.shape[1]] * world_size), dim=0).cpu().numpy()) + seq_len_x_list = [min(max(0, seq_len_x - seg_idxs[i]), x.shape[1]) for i in range(len(seg_idxs)-1)] + seq_len_x = seq_len_x_list[sp_rank] + + def create_custom_forward(module): + def custom_forward(*inputs): + return module(*inputs) + return custom_forward + + for block_id, block in enumerate(dit.blocks): + if use_gradient_checkpointing_offload: + with torch.autograd.graph.save_on_cpu(): + x = torch.utils.checkpoint.checkpoint( + create_custom_forward(block), + x, context, t_mod, seq_len_x, pre_compute_freqs[0], + use_reentrant=False, + ) + x = torch.utils.checkpoint.checkpoint( + create_custom_forward(lambda x: dit.after_transformer_block(block_id, x, audio_emb_global, merged_audio_emb, seq_len_x)), + x, + use_reentrant=False, + ) + elif use_gradient_checkpointing: + x = torch.utils.checkpoint.checkpoint( + create_custom_forward(block), + x, context, t_mod, seq_len_x, pre_compute_freqs[0], + use_reentrant=False, + ) + x = torch.utils.checkpoint.checkpoint( + create_custom_forward(lambda x: dit.after_transformer_block(block_id, x, audio_emb_global, merged_audio_emb, seq_len_x)), + x, + use_reentrant=False, + ) + else: + x = block(x, context, t_mod, seq_len_x, pre_compute_freqs[0]) + x = dit.after_transformer_block(block_id, x, audio_emb_global, merged_audio_emb, seq_len_x_global, use_unified_sequence_parallel) + + if use_unified_sequence_parallel and dist.is_initialized() and dist.get_world_size() > 1: + x = get_sp_group().all_gather(x, dim=1) + + x = x[:, :seq_len_x_global] + x = dit.head(x, t[:-1]) + x = dit.unpatchify(x, (f, h, w)) + # make compatible with wan video + x = torch.cat([origin_ref_latents, x], dim=2) + return x diff --git a/diffsynth/processors/FastBlend.py b/diffsynth/processors/FastBlend.py new file mode 100644 index 0000000000000000000000000000000000000000..fed33f4fdd215c8c9dc46f3b07d9453a12cc6b98 --- /dev/null +++ b/diffsynth/processors/FastBlend.py @@ -0,0 +1,142 @@ +from PIL import Image +import cupy as cp +import numpy as np +from tqdm import tqdm +from ..extensions.FastBlend.patch_match import PyramidPatchMatcher +from ..extensions.FastBlend.runners.fast import TableManager +from .base import VideoProcessor + + +class FastBlendSmoother(VideoProcessor): + def __init__( + self, + inference_mode="fast", batch_size=8, window_size=60, + minimum_patch_size=5, threads_per_block=8, num_iter=5, gpu_id=0, guide_weight=10.0, initialize="identity", tracking_window_size=0 + ): + self.inference_mode = inference_mode + self.batch_size = batch_size + self.window_size = window_size + self.ebsynth_config = { + "minimum_patch_size": minimum_patch_size, + "threads_per_block": threads_per_block, + "num_iter": num_iter, + "gpu_id": gpu_id, + "guide_weight": guide_weight, + "initialize": initialize, + "tracking_window_size": tracking_window_size + } + + @staticmethod + def from_model_manager(model_manager, **kwargs): + # TODO: fetch GPU ID from model_manager + return FastBlendSmoother(**kwargs) + + def inference_fast(self, frames_guide, frames_style): + table_manager = TableManager() + patch_match_engine = PyramidPatchMatcher( + image_height=frames_style[0].shape[0], + image_width=frames_style[0].shape[1], + channel=3, + **self.ebsynth_config + ) + # left part + table_l = table_manager.build_remapping_table(frames_guide, frames_style, patch_match_engine, self.batch_size, desc="Fast Mode Step 1/4") + table_l = table_manager.remapping_table_to_blending_table(table_l) + table_l = table_manager.process_window_sum(frames_guide, table_l, patch_match_engine, self.window_size, self.batch_size, desc="Fast Mode Step 2/4") + # right part + table_r = table_manager.build_remapping_table(frames_guide[::-1], frames_style[::-1], patch_match_engine, self.batch_size, desc="Fast Mode Step 3/4") + table_r = table_manager.remapping_table_to_blending_table(table_r) + table_r = table_manager.process_window_sum(frames_guide[::-1], table_r, patch_match_engine, self.window_size, self.batch_size, desc="Fast Mode Step 4/4")[::-1] + # merge + frames = [] + for (frame_l, weight_l), frame_m, (frame_r, weight_r) in zip(table_l, frames_style, table_r): + weight_m = -1 + weight = weight_l + weight_m + weight_r + frame = frame_l * (weight_l / weight) + frame_m * (weight_m / weight) + frame_r * (weight_r / weight) + frames.append(frame) + frames = [frame.clip(0, 255).astype("uint8") for frame in frames] + frames = [Image.fromarray(frame) for frame in frames] + return frames + + def inference_balanced(self, frames_guide, frames_style): + patch_match_engine = PyramidPatchMatcher( + image_height=frames_style[0].shape[0], + image_width=frames_style[0].shape[1], + channel=3, + **self.ebsynth_config + ) + output_frames = [] + # tasks + n = len(frames_style) + tasks = [] + for target in range(n): + for source in range(target - self.window_size, target + self.window_size + 1): + if source >= 0 and source < n and source != target: + tasks.append((source, target)) + # run + frames = [(None, 1) for i in range(n)] + for batch_id in tqdm(range(0, len(tasks), self.batch_size), desc="Balanced Mode"): + tasks_batch = tasks[batch_id: min(batch_id+self.batch_size, len(tasks))] + source_guide = np.stack([frames_guide[source] for source, target in tasks_batch]) + target_guide = np.stack([frames_guide[target] for source, target in tasks_batch]) + source_style = np.stack([frames_style[source] for source, target in tasks_batch]) + _, target_style = patch_match_engine.estimate_nnf(source_guide, target_guide, source_style) + for (source, target), result in zip(tasks_batch, target_style): + frame, weight = frames[target] + if frame is None: + frame = frames_style[target] + frames[target] = ( + frame * (weight / (weight + 1)) + result / (weight + 1), + weight + 1 + ) + if weight + 1 == min(n, target + self.window_size + 1) - max(0, target - self.window_size): + frame = frame.clip(0, 255).astype("uint8") + output_frames.append(Image.fromarray(frame)) + frames[target] = (None, 1) + return output_frames + + def inference_accurate(self, frames_guide, frames_style): + patch_match_engine = PyramidPatchMatcher( + image_height=frames_style[0].shape[0], + image_width=frames_style[0].shape[1], + channel=3, + use_mean_target_style=True, + **self.ebsynth_config + ) + output_frames = [] + # run + n = len(frames_style) + for target in tqdm(range(n), desc="Accurate Mode"): + l, r = max(target - self.window_size, 0), min(target + self.window_size + 1, n) + remapped_frames = [] + for i in range(l, r, self.batch_size): + j = min(i + self.batch_size, r) + source_guide = np.stack([frames_guide[source] for source in range(i, j)]) + target_guide = np.stack([frames_guide[target]] * (j - i)) + source_style = np.stack([frames_style[source] for source in range(i, j)]) + _, target_style = patch_match_engine.estimate_nnf(source_guide, target_guide, source_style) + remapped_frames.append(target_style) + frame = np.concatenate(remapped_frames, axis=0).mean(axis=0) + frame = frame.clip(0, 255).astype("uint8") + output_frames.append(Image.fromarray(frame)) + return output_frames + + def release_vram(self): + mempool = cp.get_default_memory_pool() + pinned_mempool = cp.get_default_pinned_memory_pool() + mempool.free_all_blocks() + pinned_mempool.free_all_blocks() + + def __call__(self, rendered_frames, original_frames=None, **kwargs): + rendered_frames = [np.array(frame) for frame in rendered_frames] + original_frames = [np.array(frame) for frame in original_frames] + if self.inference_mode == "fast": + output_frames = self.inference_fast(original_frames, rendered_frames) + elif self.inference_mode == "balanced": + output_frames = self.inference_balanced(original_frames, rendered_frames) + elif self.inference_mode == "accurate": + output_frames = self.inference_accurate(original_frames, rendered_frames) + else: + raise ValueError("inference_mode must be fast, balanced or accurate") + self.release_vram() + return output_frames diff --git a/diffsynth/processors/PILEditor.py b/diffsynth/processors/PILEditor.py new file mode 100644 index 0000000000000000000000000000000000000000..01011d8724f61283550d503c5c20ae6fd0375ec7 --- /dev/null +++ b/diffsynth/processors/PILEditor.py @@ -0,0 +1,28 @@ +from PIL import ImageEnhance +from .base import VideoProcessor + + +class ContrastEditor(VideoProcessor): + def __init__(self, rate=1.5): + self.rate = rate + + @staticmethod + def from_model_manager(model_manager, **kwargs): + return ContrastEditor(**kwargs) + + def __call__(self, rendered_frames, **kwargs): + rendered_frames = [ImageEnhance.Contrast(i).enhance(self.rate) for i in rendered_frames] + return rendered_frames + + +class SharpnessEditor(VideoProcessor): + def __init__(self, rate=1.5): + self.rate = rate + + @staticmethod + def from_model_manager(model_manager, **kwargs): + return SharpnessEditor(**kwargs) + + def __call__(self, rendered_frames, **kwargs): + rendered_frames = [ImageEnhance.Sharpness(i).enhance(self.rate) for i in rendered_frames] + return rendered_frames diff --git a/diffsynth/processors/RIFE.py b/diffsynth/processors/RIFE.py new file mode 100644 index 0000000000000000000000000000000000000000..4186eb31496e9a1bf38df06eb64921226f07ee09 --- /dev/null +++ b/diffsynth/processors/RIFE.py @@ -0,0 +1,77 @@ +import torch +import numpy as np +from PIL import Image +from .base import VideoProcessor + + +class RIFESmoother(VideoProcessor): + def __init__(self, model, device="cuda", scale=1.0, batch_size=4, interpolate=True): + self.model = model + self.device = device + + # IFNet only does not support float16 + self.torch_dtype = torch.float32 + + # Other parameters + self.scale = scale + self.batch_size = batch_size + self.interpolate = interpolate + + @staticmethod + def from_model_manager(model_manager, **kwargs): + return RIFESmoother(model_manager.RIFE, device=model_manager.device, **kwargs) + + def process_image(self, image): + width, height = image.size + if width % 32 != 0 or height % 32 != 0: + width = (width + 31) // 32 + height = (height + 31) // 32 + image = image.resize((width, height)) + image = torch.Tensor(np.array(image, dtype=np.float32)[:, :, [2,1,0]] / 255).permute(2, 0, 1) + return image + + def process_images(self, images): + images = [self.process_image(image) for image in images] + images = torch.stack(images) + return images + + def decode_images(self, images): + images = (images[:, [2,1,0]].permute(0, 2, 3, 1) * 255).clip(0, 255).numpy().astype(np.uint8) + images = [Image.fromarray(image) for image in images] + return images + + def process_tensors(self, input_tensor, scale=1.0, batch_size=4): + output_tensor = [] + for batch_id in range(0, input_tensor.shape[0], batch_size): + batch_id_ = min(batch_id + batch_size, input_tensor.shape[0]) + batch_input_tensor = input_tensor[batch_id: batch_id_] + batch_input_tensor = batch_input_tensor.to(device=self.device, dtype=self.torch_dtype) + flow, mask, merged = self.model(batch_input_tensor, [4/scale, 2/scale, 1/scale]) + output_tensor.append(merged[2].cpu()) + output_tensor = torch.concat(output_tensor, dim=0) + return output_tensor + + @torch.no_grad() + def __call__(self, rendered_frames, **kwargs): + # Preprocess + processed_images = self.process_images(rendered_frames) + + # Input + input_tensor = torch.cat((processed_images[:-2], processed_images[2:]), dim=1) + + # Interpolate + output_tensor = self.process_tensors(input_tensor, scale=self.scale, batch_size=self.batch_size) + + if self.interpolate: + # Blend + input_tensor = torch.cat((processed_images[1:-1], output_tensor), dim=1) + output_tensor = self.process_tensors(input_tensor, scale=self.scale, batch_size=self.batch_size) + processed_images[1:-1] = output_tensor + else: + processed_images[1:-1] = (processed_images[1:-1] + output_tensor) / 2 + + # To images + output_images = self.decode_images(processed_images) + if output_images[0].size != rendered_frames[0].size: + output_images = [image.resize(rendered_frames[0].size) for image in output_images] + return output_images diff --git a/diffsynth/processors/__init__.py b/diffsynth/processors/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/diffsynth/processors/base.py b/diffsynth/processors/base.py new file mode 100644 index 0000000000000000000000000000000000000000..278a9c1b74044987cc116de35292a96de8b13737 --- /dev/null +++ b/diffsynth/processors/base.py @@ -0,0 +1,6 @@ +class VideoProcessor: + def __init__(self): + pass + + def __call__(self): + raise NotImplementedError diff --git a/diffsynth/processors/sequencial_processor.py b/diffsynth/processors/sequencial_processor.py new file mode 100644 index 0000000000000000000000000000000000000000..9b5bc9454f0b9d74f10bb4a6bff92db77f26325c --- /dev/null +++ b/diffsynth/processors/sequencial_processor.py @@ -0,0 +1,41 @@ +from .base import VideoProcessor + + +class AutoVideoProcessor(VideoProcessor): + def __init__(self): + pass + + @staticmethod + def from_model_manager(model_manager, processor_type, **kwargs): + if processor_type == "FastBlend": + from .FastBlend import FastBlendSmoother + return FastBlendSmoother.from_model_manager(model_manager, **kwargs) + elif processor_type == "Contrast": + from .PILEditor import ContrastEditor + return ContrastEditor.from_model_manager(model_manager, **kwargs) + elif processor_type == "Sharpness": + from .PILEditor import SharpnessEditor + return SharpnessEditor.from_model_manager(model_manager, **kwargs) + elif processor_type == "RIFE": + from .RIFE import RIFESmoother + return RIFESmoother.from_model_manager(model_manager, **kwargs) + else: + raise ValueError(f"invalid processor_type: {processor_type}") + + +class SequencialProcessor(VideoProcessor): + def __init__(self, processors=[]): + self.processors = processors + + @staticmethod + def from_model_manager(model_manager, configs): + processors = [ + AutoVideoProcessor.from_model_manager(model_manager, config["processor_type"], **config["config"]) + for config in configs + ] + return SequencialProcessor(processors) + + def __call__(self, rendered_frames, **kwargs): + for processor in self.processors: + rendered_frames = processor(rendered_frames, **kwargs) + return rendered_frames diff --git a/diffsynth/prompters/__init__.py b/diffsynth/prompters/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..f27c6f153b076de484c2b650e8bf16d7142d1099 --- /dev/null +++ b/diffsynth/prompters/__init__.py @@ -0,0 +1,12 @@ +from .prompt_refiners import Translator, BeautifulPrompt, QwenPrompt +from .sd_prompter import SDPrompter +from .sdxl_prompter import SDXLPrompter +from .sd3_prompter import SD3Prompter +from .hunyuan_dit_prompter import HunyuanDiTPrompter +from .kolors_prompter import KolorsPrompter +from .flux_prompter import FluxPrompter +from .omost import OmostPromter +from .cog_prompter import CogPrompter +from .hunyuan_video_prompter import HunyuanVideoPrompter +from .stepvideo_prompter import StepVideoPrompter +from .wan_prompter import WanPrompter diff --git a/diffsynth/prompters/base_prompter.py b/diffsynth/prompters/base_prompter.py new file mode 100644 index 0000000000000000000000000000000000000000..136abd18fabdb04e618f59801420c9ce5fb94634 --- /dev/null +++ b/diffsynth/prompters/base_prompter.py @@ -0,0 +1,70 @@ +from ..models.model_manager import ModelManager +import torch + + + +def tokenize_long_prompt(tokenizer, prompt, max_length=None): + # Get model_max_length from self.tokenizer + length = tokenizer.model_max_length if max_length is None else max_length + + # To avoid the warning. set self.tokenizer.model_max_length to +oo. + tokenizer.model_max_length = 99999999 + + # Tokenize it! + input_ids = tokenizer(prompt, return_tensors="pt").input_ids + + # Determine the real length. + max_length = (input_ids.shape[1] + length - 1) // length * length + + # Restore tokenizer.model_max_length + tokenizer.model_max_length = length + + # Tokenize it again with fixed length. + input_ids = tokenizer( + prompt, + return_tensors="pt", + padding="max_length", + max_length=max_length, + truncation=True + ).input_ids + + # Reshape input_ids to fit the text encoder. + num_sentence = input_ids.shape[1] // length + input_ids = input_ids.reshape((num_sentence, length)) + + return input_ids + + + +class BasePrompter: + def __init__(self): + self.refiners = [] + self.extenders = [] + + + def load_prompt_refiners(self, model_manager: ModelManager, refiner_classes=[]): + for refiner_class in refiner_classes: + refiner = refiner_class.from_model_manager(model_manager) + self.refiners.append(refiner) + + def load_prompt_extenders(self,model_manager:ModelManager,extender_classes=[]): + for extender_class in extender_classes: + extender = extender_class.from_model_manager(model_manager) + self.extenders.append(extender) + + + @torch.no_grad() + def process_prompt(self, prompt, positive=True): + if isinstance(prompt, list): + prompt = [self.process_prompt(prompt_, positive=positive) for prompt_ in prompt] + else: + for refiner in self.refiners: + prompt = refiner(prompt, positive=positive) + return prompt + + @torch.no_grad() + def extend_prompt(self, prompt:str, positive=True): + extended_prompt = dict(prompt=prompt) + for extender in self.extenders: + extended_prompt = extender(extended_prompt) + return extended_prompt \ No newline at end of file diff --git a/diffsynth/prompters/cog_prompter.py b/diffsynth/prompters/cog_prompter.py new file mode 100644 index 0000000000000000000000000000000000000000..a1ab84a69c32e681e087ba7ed0642a6177fe1f7a --- /dev/null +++ b/diffsynth/prompters/cog_prompter.py @@ -0,0 +1,46 @@ +from .base_prompter import BasePrompter +from ..models.flux_text_encoder import FluxTextEncoder2 +from transformers import T5TokenizerFast +import os + + +class CogPrompter(BasePrompter): + def __init__( + self, + tokenizer_path=None + ): + if tokenizer_path is None: + base_path = os.path.dirname(os.path.dirname(__file__)) + tokenizer_path = os.path.join(base_path, "tokenizer_configs/cog/tokenizer") + super().__init__() + self.tokenizer = T5TokenizerFast.from_pretrained(tokenizer_path) + self.text_encoder: FluxTextEncoder2 = None + + + def fetch_models(self, text_encoder: FluxTextEncoder2 = None): + self.text_encoder = text_encoder + + + def encode_prompt_using_t5(self, prompt, text_encoder, tokenizer, max_length, device): + input_ids = tokenizer( + prompt, + return_tensors="pt", + padding="max_length", + max_length=max_length, + truncation=True, + ).input_ids.to(device) + prompt_emb = text_encoder(input_ids) + prompt_emb = prompt_emb.reshape((1, prompt_emb.shape[0]*prompt_emb.shape[1], -1)) + + return prompt_emb + + + def encode_prompt( + self, + prompt, + positive=True, + device="cuda" + ): + prompt = self.process_prompt(prompt, positive=positive) + prompt_emb = self.encode_prompt_using_t5(prompt, self.text_encoder, self.tokenizer, 226, device) + return prompt_emb diff --git a/diffsynth/prompters/flux_prompter.py b/diffsynth/prompters/flux_prompter.py new file mode 100644 index 0000000000000000000000000000000000000000..a3a06ff8df29345f505873cf1b79c963229f3efb --- /dev/null +++ b/diffsynth/prompters/flux_prompter.py @@ -0,0 +1,74 @@ +from .base_prompter import BasePrompter +from ..models.flux_text_encoder import FluxTextEncoder2 +from ..models.sd3_text_encoder import SD3TextEncoder1 +from transformers import CLIPTokenizer, T5TokenizerFast +import os, torch + + +class FluxPrompter(BasePrompter): + def __init__( + self, + tokenizer_1_path=None, + tokenizer_2_path=None + ): + if tokenizer_1_path is None: + base_path = os.path.dirname(os.path.dirname(__file__)) + tokenizer_1_path = os.path.join(base_path, "tokenizer_configs/flux/tokenizer_1") + if tokenizer_2_path is None: + base_path = os.path.dirname(os.path.dirname(__file__)) + tokenizer_2_path = os.path.join(base_path, "tokenizer_configs/flux/tokenizer_2") + super().__init__() + self.tokenizer_1 = CLIPTokenizer.from_pretrained(tokenizer_1_path) + self.tokenizer_2 = T5TokenizerFast.from_pretrained(tokenizer_2_path) + self.text_encoder_1: SD3TextEncoder1 = None + self.text_encoder_2: FluxTextEncoder2 = None + + + def fetch_models(self, text_encoder_1: SD3TextEncoder1 = None, text_encoder_2: FluxTextEncoder2 = None): + self.text_encoder_1 = text_encoder_1 + self.text_encoder_2 = text_encoder_2 + + + def encode_prompt_using_clip(self, prompt, text_encoder, tokenizer, max_length, device): + input_ids = tokenizer( + prompt, + return_tensors="pt", + padding="max_length", + max_length=max_length, + truncation=True + ).input_ids.to(device) + pooled_prompt_emb, _ = text_encoder(input_ids) + return pooled_prompt_emb + + + def encode_prompt_using_t5(self, prompt, text_encoder, tokenizer, max_length, device): + input_ids = tokenizer( + prompt, + return_tensors="pt", + padding="max_length", + max_length=max_length, + truncation=True, + ).input_ids.to(device) + prompt_emb = text_encoder(input_ids) + return prompt_emb + + + def encode_prompt( + self, + prompt, + positive=True, + device="cuda", + t5_sequence_length=512, + ): + prompt = self.process_prompt(prompt, positive=positive) + + # CLIP + pooled_prompt_emb = self.encode_prompt_using_clip(prompt, self.text_encoder_1, self.tokenizer_1, 77, device) + + # T5 + prompt_emb = self.encode_prompt_using_t5(prompt, self.text_encoder_2, self.tokenizer_2, t5_sequence_length, device) + + # text_ids + text_ids = torch.zeros(prompt_emb.shape[0], prompt_emb.shape[1], 3).to(device=device, dtype=prompt_emb.dtype) + + return prompt_emb, pooled_prompt_emb, text_ids diff --git a/diffsynth/prompters/hunyuan_dit_prompter.py b/diffsynth/prompters/hunyuan_dit_prompter.py new file mode 100644 index 0000000000000000000000000000000000000000..52a22ed72ab77ef668183119fff67db3141ee561 --- /dev/null +++ b/diffsynth/prompters/hunyuan_dit_prompter.py @@ -0,0 +1,69 @@ +from .base_prompter import BasePrompter +from ..models.model_manager import ModelManager +from ..models import HunyuanDiTCLIPTextEncoder, HunyuanDiTT5TextEncoder +from transformers import BertTokenizer, AutoTokenizer +import warnings, os + + +class HunyuanDiTPrompter(BasePrompter): + def __init__( + self, + tokenizer_path=None, + tokenizer_t5_path=None + ): + if tokenizer_path is None: + base_path = os.path.dirname(os.path.dirname(__file__)) + tokenizer_path = os.path.join(base_path, "tokenizer_configs/hunyuan_dit/tokenizer") + if tokenizer_t5_path is None: + base_path = os.path.dirname(os.path.dirname(__file__)) + tokenizer_t5_path = os.path.join(base_path, "tokenizer_configs/hunyuan_dit/tokenizer_t5") + super().__init__() + self.tokenizer = BertTokenizer.from_pretrained(tokenizer_path) + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + self.tokenizer_t5 = AutoTokenizer.from_pretrained(tokenizer_t5_path) + self.text_encoder: HunyuanDiTCLIPTextEncoder = None + self.text_encoder_t5: HunyuanDiTT5TextEncoder = None + + + def fetch_models(self, text_encoder: HunyuanDiTCLIPTextEncoder = None, text_encoder_t5: HunyuanDiTT5TextEncoder = None): + self.text_encoder = text_encoder + self.text_encoder_t5 = text_encoder_t5 + + + def encode_prompt_using_signle_model(self, prompt, text_encoder, tokenizer, max_length, clip_skip, device): + text_inputs = tokenizer( + prompt, + padding="max_length", + max_length=max_length, + truncation=True, + return_attention_mask=True, + return_tensors="pt", + ) + text_input_ids = text_inputs.input_ids + attention_mask = text_inputs.attention_mask.to(device) + prompt_embeds = text_encoder( + text_input_ids.to(device), + attention_mask=attention_mask, + clip_skip=clip_skip + ) + return prompt_embeds, attention_mask + + + def encode_prompt( + self, + prompt, + clip_skip=1, + clip_skip_2=1, + positive=True, + device="cuda" + ): + prompt = self.process_prompt(prompt, positive=positive) + + # CLIP + prompt_emb, attention_mask = self.encode_prompt_using_signle_model(prompt, self.text_encoder, self.tokenizer, self.tokenizer.model_max_length, clip_skip, device) + + # T5 + prompt_emb_t5, attention_mask_t5 = self.encode_prompt_using_signle_model(prompt, self.text_encoder_t5, self.tokenizer_t5, self.tokenizer_t5.model_max_length, clip_skip_2, device) + + return prompt_emb, attention_mask, prompt_emb_t5, attention_mask_t5 diff --git a/diffsynth/prompters/hunyuan_video_prompter.py b/diffsynth/prompters/hunyuan_video_prompter.py new file mode 100644 index 0000000000000000000000000000000000000000..5b97356cacd4b9ccd9d0912b5694e1c1b4868ae9 --- /dev/null +++ b/diffsynth/prompters/hunyuan_video_prompter.py @@ -0,0 +1,275 @@ +from .base_prompter import BasePrompter +from ..models.sd3_text_encoder import SD3TextEncoder1 +from ..models.hunyuan_video_text_encoder import HunyuanVideoLLMEncoder, HunyuanVideoMLLMEncoder +from transformers import CLIPTokenizer, LlamaTokenizerFast, CLIPImageProcessor +import os, torch +from typing import Union + +PROMPT_TEMPLATE_ENCODE = ( + "<|start_header_id|>system<|end_header_id|>\n\nDescribe the image by detailing the color, shape, size, texture, " + "quantity, text, spatial relationships of the objects and background:<|eot_id|>" + "<|start_header_id|>user<|end_header_id|>\n\n{}<|eot_id|>") + +PROMPT_TEMPLATE_ENCODE_VIDEO = ( + "<|start_header_id|>system<|end_header_id|>\n\nDescribe the video by detailing the following aspects: " + "1. The main content and theme of the video." + "2. The color, shape, size, texture, quantity, text, and spatial relationships of the objects." + "3. Actions, events, behaviors temporal relationships, physical movement changes of the objects." + "4. background environment, light, style and atmosphere." + "5. camera angles, movements, and transitions used in the video:<|eot_id|>" + "<|start_header_id|>user<|end_header_id|>\n\n{}<|eot_id|>") + +PROMPT_TEMPLATE_ENCODE_I2V = ( + "<|start_header_id|>system<|end_header_id|>\n\n\nDescribe the image by detailing the color, shape, size, texture, " + "quantity, text, spatial relationships of the objects and background:<|eot_id|>" + "<|start_header_id|>user<|end_header_id|>\n\n{}<|eot_id|>" + "<|start_header_id|>assistant<|end_header_id|>\n\n" +) + +PROMPT_TEMPLATE_ENCODE_VIDEO_I2V = ( + "<|start_header_id|>system<|end_header_id|>\n\n\nDescribe the video by detailing the following aspects according to the reference image: " + "1. The main content and theme of the video." + "2. The color, shape, size, texture, quantity, text, and spatial relationships of the objects." + "3. Actions, events, behaviors temporal relationships, physical movement changes of the objects." + "4. background environment, light, style and atmosphere." + "5. camera angles, movements, and transitions used in the video:<|eot_id|>\n\n" + "<|start_header_id|>user<|end_header_id|>\n\n{}<|eot_id|>" + "<|start_header_id|>assistant<|end_header_id|>\n\n" +) + +PROMPT_TEMPLATE = { + "dit-llm-encode": { + "template": PROMPT_TEMPLATE_ENCODE, + "crop_start": 36, + }, + "dit-llm-encode-video": { + "template": PROMPT_TEMPLATE_ENCODE_VIDEO, + "crop_start": 95, + }, + "dit-llm-encode-i2v": { + "template": PROMPT_TEMPLATE_ENCODE_I2V, + "crop_start": 36, + "image_emb_start": 5, + "image_emb_end": 581, + "image_emb_len": 576, + "double_return_token_id": 271 + }, + "dit-llm-encode-video-i2v": { + "template": PROMPT_TEMPLATE_ENCODE_VIDEO_I2V, + "crop_start": 103, + "image_emb_start": 5, + "image_emb_end": 581, + "image_emb_len": 576, + "double_return_token_id": 271 + }, +} + +NEGATIVE_PROMPT = "Aerial view, aerial view, overexposed, low quality, deformation, a poor composition, bad hands, bad teeth, bad eyes, bad limbs, distortion" + + +class HunyuanVideoPrompter(BasePrompter): + + def __init__( + self, + tokenizer_1_path=None, + tokenizer_2_path=None, + ): + if tokenizer_1_path is None: + base_path = os.path.dirname(os.path.dirname(__file__)) + tokenizer_1_path = os.path.join( + base_path, "tokenizer_configs/hunyuan_video/tokenizer_1") + if tokenizer_2_path is None: + base_path = os.path.dirname(os.path.dirname(__file__)) + tokenizer_2_path = os.path.join( + base_path, "tokenizer_configs/hunyuan_video/tokenizer_2") + super().__init__() + self.tokenizer_1 = CLIPTokenizer.from_pretrained(tokenizer_1_path) + self.tokenizer_2 = LlamaTokenizerFast.from_pretrained(tokenizer_2_path, padding_side='right') + self.text_encoder_1: SD3TextEncoder1 = None + self.text_encoder_2: HunyuanVideoLLMEncoder = None + + self.prompt_template = PROMPT_TEMPLATE['dit-llm-encode'] + self.prompt_template_video = PROMPT_TEMPLATE['dit-llm-encode-video'] + + def fetch_models(self, + text_encoder_1: SD3TextEncoder1 = None, + text_encoder_2: Union[HunyuanVideoLLMEncoder, HunyuanVideoMLLMEncoder] = None): + self.text_encoder_1 = text_encoder_1 + self.text_encoder_2 = text_encoder_2 + if isinstance(text_encoder_2, HunyuanVideoMLLMEncoder): + # processor + # TODO: may need to replace processor with local implementation + base_path = os.path.dirname(os.path.dirname(__file__)) + tokenizer_2_path = os.path.join(base_path, "tokenizer_configs/hunyuan_video/tokenizer_2") + self.processor = CLIPImageProcessor.from_pretrained(tokenizer_2_path) + # template + self.prompt_template = PROMPT_TEMPLATE['dit-llm-encode-i2v'] + self.prompt_template_video = PROMPT_TEMPLATE['dit-llm-encode-video-i2v'] + + def apply_text_to_template(self, text, template): + assert isinstance(template, str) + if isinstance(text, list): + return [self.apply_text_to_template(text_) for text_ in text] + elif isinstance(text, str): + # Will send string to tokenizer. Used for llm + return template.format(text) + else: + raise TypeError(f"Unsupported prompt type: {type(text)}") + + def encode_prompt_using_clip(self, prompt, max_length, device): + tokenized_result = self.tokenizer_1( + prompt, + return_tensors="pt", + padding="max_length", + max_length=max_length, + truncation=True, + return_attention_mask=True + ) + input_ids = tokenized_result.input_ids.to(device) + attention_mask = tokenized_result.attention_mask.to(device) + return self.text_encoder_1(input_ids=input_ids, extra_mask=attention_mask)[0] + + def encode_prompt_using_llm(self, + prompt, + max_length, + device, + crop_start, + hidden_state_skip_layer=2, + use_attention_mask=True): + max_length += crop_start + inputs = self.tokenizer_2(prompt, + return_tensors="pt", + padding="max_length", + max_length=max_length, + truncation=True) + input_ids = inputs.input_ids.to(device) + attention_mask = inputs.attention_mask.to(device) + last_hidden_state = self.text_encoder_2(input_ids, attention_mask, hidden_state_skip_layer) + + # crop out + if crop_start > 0: + last_hidden_state = last_hidden_state[:, crop_start:] + attention_mask = (attention_mask[:, crop_start:] if use_attention_mask else None) + + return last_hidden_state, attention_mask + + def encode_prompt_using_mllm(self, + prompt, + images, + max_length, + device, + crop_start, + hidden_state_skip_layer=2, + use_attention_mask=True, + image_embed_interleave=4): + image_outputs = self.processor(images, return_tensors="pt")["pixel_values"].to(device) + max_length += crop_start + inputs = self.tokenizer_2(prompt, + return_tensors="pt", + padding="max_length", + max_length=max_length, + truncation=True) + input_ids = inputs.input_ids.to(device) + attention_mask = inputs.attention_mask.to(device) + last_hidden_state = self.text_encoder_2(input_ids=input_ids, + attention_mask=attention_mask, + hidden_state_skip_layer=hidden_state_skip_layer, + pixel_values=image_outputs) + + text_crop_start = (crop_start - 1 + self.prompt_template_video.get("image_emb_len", 576)) + image_crop_start = self.prompt_template_video.get("image_emb_start", 5) + image_crop_end = self.prompt_template_video.get("image_emb_end", 581) + batch_indices, last_double_return_token_indices = torch.where( + input_ids == self.prompt_template_video.get("double_return_token_id", 271)) + if last_double_return_token_indices.shape[0] == 3: + # in case the prompt is too long + last_double_return_token_indices = torch.cat(( + last_double_return_token_indices, + torch.tensor([input_ids.shape[-1]]), + )) + batch_indices = torch.cat((batch_indices, torch.tensor([0]))) + last_double_return_token_indices = (last_double_return_token_indices.reshape(input_ids.shape[0], -1)[:, -1]) + batch_indices = batch_indices.reshape(input_ids.shape[0], -1)[:, -1] + assistant_crop_start = (last_double_return_token_indices - 1 + self.prompt_template_video.get("image_emb_len", 576) - 4) + assistant_crop_end = (last_double_return_token_indices - 1 + self.prompt_template_video.get("image_emb_len", 576)) + attention_mask_assistant_crop_start = (last_double_return_token_indices - 4) + attention_mask_assistant_crop_end = last_double_return_token_indices + text_last_hidden_state = [] + text_attention_mask = [] + image_last_hidden_state = [] + image_attention_mask = [] + for i in range(input_ids.shape[0]): + text_last_hidden_state.append( + torch.cat([ + last_hidden_state[i, text_crop_start:assistant_crop_start[i].item()], + last_hidden_state[i, assistant_crop_end[i].item():], + ])) + text_attention_mask.append( + torch.cat([ + attention_mask[ + i, + crop_start:attention_mask_assistant_crop_start[i].item(), + ], + attention_mask[i, attention_mask_assistant_crop_end[i].item():], + ]) if use_attention_mask else None) + image_last_hidden_state.append(last_hidden_state[i, image_crop_start:image_crop_end]) + image_attention_mask.append( + torch.ones(image_last_hidden_state[-1].shape[0]).to(last_hidden_state.device). + to(attention_mask.dtype) if use_attention_mask else None) + + text_last_hidden_state = torch.stack(text_last_hidden_state) + text_attention_mask = torch.stack(text_attention_mask) + image_last_hidden_state = torch.stack(image_last_hidden_state) + image_attention_mask = torch.stack(image_attention_mask) + + image_last_hidden_state = image_last_hidden_state[:, ::image_embed_interleave, :] + image_attention_mask = image_attention_mask[:, ::image_embed_interleave] + + assert (text_last_hidden_state.shape[0] == text_attention_mask.shape[0] and + image_last_hidden_state.shape[0] == image_attention_mask.shape[0]) + + last_hidden_state = torch.cat([image_last_hidden_state, text_last_hidden_state], dim=1) + attention_mask = torch.cat([image_attention_mask, text_attention_mask], dim=1) + + return last_hidden_state, attention_mask + + def encode_prompt(self, + prompt, + images=None, + positive=True, + device="cuda", + clip_sequence_length=77, + llm_sequence_length=256, + data_type='video', + use_template=True, + hidden_state_skip_layer=2, + use_attention_mask=True, + image_embed_interleave=4): + + prompt = self.process_prompt(prompt, positive=positive) + + # apply template + if use_template: + template = self.prompt_template_video if data_type == 'video' else self.prompt_template + prompt_formated = self.apply_text_to_template(prompt, template['template']) + else: + prompt_formated = prompt + # Text encoder + if data_type == 'video': + crop_start = self.prompt_template_video.get("crop_start", 0) + else: + crop_start = self.prompt_template.get("crop_start", 0) + + # CLIP + pooled_prompt_emb = self.encode_prompt_using_clip(prompt, clip_sequence_length, device) + + # LLM + if images is None: + prompt_emb, attention_mask = self.encode_prompt_using_llm(prompt_formated, llm_sequence_length, device, crop_start, + hidden_state_skip_layer, use_attention_mask) + else: + prompt_emb, attention_mask = self.encode_prompt_using_mllm(prompt_formated, images, llm_sequence_length, device, + crop_start, hidden_state_skip_layer, use_attention_mask, + image_embed_interleave) + + return prompt_emb, pooled_prompt_emb, attention_mask diff --git a/diffsynth/prompters/kolors_prompter.py b/diffsynth/prompters/kolors_prompter.py new file mode 100644 index 0000000000000000000000000000000000000000..e3d5d58a9dbb816ea8c8e0e3b4f0433bd11d3306 --- /dev/null +++ b/diffsynth/prompters/kolors_prompter.py @@ -0,0 +1,354 @@ +from .base_prompter import BasePrompter +from ..models.model_manager import ModelManager +import json, os, re +from typing import List, Optional, Union, Dict +from sentencepiece import SentencePieceProcessor +from transformers import PreTrainedTokenizer +from transformers.utils import PaddingStrategy +from transformers.tokenization_utils_base import EncodedInput, BatchEncoding +from ..models.kolors_text_encoder import ChatGLMModel + + +class SPTokenizer: + def __init__(self, model_path: str): + # reload tokenizer + assert os.path.isfile(model_path), model_path + self.sp_model = SentencePieceProcessor(model_file=model_path) + + # BOS / EOS token IDs + self.n_words: int = self.sp_model.vocab_size() + self.bos_id: int = self.sp_model.bos_id() + self.eos_id: int = self.sp_model.eos_id() + self.pad_id: int = self.sp_model.unk_id() + assert self.sp_model.vocab_size() == self.sp_model.get_piece_size() + + role_special_tokens = ["<|system|>", "<|user|>", "<|assistant|>", "<|observation|>"] + special_tokens = ["[MASK]", "[gMASK]", "[sMASK]", "sop", "eop"] + role_special_tokens + self.special_tokens = {} + self.index_special_tokens = {} + for token in special_tokens: + self.special_tokens[token] = self.n_words + self.index_special_tokens[self.n_words] = token + self.n_words += 1 + self.role_special_token_expression = "|".join([re.escape(token) for token in role_special_tokens]) + + def tokenize(self, s: str, encode_special_tokens=False): + if encode_special_tokens: + last_index = 0 + t = [] + for match in re.finditer(self.role_special_token_expression, s): + if last_index < match.start(): + t.extend(self.sp_model.EncodeAsPieces(s[last_index:match.start()])) + t.append(s[match.start():match.end()]) + last_index = match.end() + if last_index < len(s): + t.extend(self.sp_model.EncodeAsPieces(s[last_index:])) + return t + else: + return self.sp_model.EncodeAsPieces(s) + + def encode(self, s: str, bos: bool = False, eos: bool = False) -> List[int]: + assert type(s) is str + t = self.sp_model.encode(s) + if bos: + t = [self.bos_id] + t + if eos: + t = t + [self.eos_id] + return t + + def decode(self, t: List[int]) -> str: + text, buffer = "", [] + for token in t: + if token in self.index_special_tokens: + if buffer: + text += self.sp_model.decode(buffer) + buffer = [] + text += self.index_special_tokens[token] + else: + buffer.append(token) + if buffer: + text += self.sp_model.decode(buffer) + return text + + def decode_tokens(self, tokens: List[str]) -> str: + text = self.sp_model.DecodePieces(tokens) + return text + + def convert_token_to_id(self, token): + """ Converts a token (str) in an id using the vocab. """ + if token in self.special_tokens: + return self.special_tokens[token] + return self.sp_model.PieceToId(token) + + def convert_id_to_token(self, index): + """Converts an index (integer) in a token (str) using the vocab.""" + if index in self.index_special_tokens: + return self.index_special_tokens[index] + if index in [self.eos_id, self.bos_id, self.pad_id] or index < 0: + return "" + return self.sp_model.IdToPiece(index) + + + +class ChatGLMTokenizer(PreTrainedTokenizer): + vocab_files_names = {"vocab_file": "tokenizer.model"} + + model_input_names = ["input_ids", "attention_mask", "position_ids"] + + def __init__(self, vocab_file, padding_side="left", clean_up_tokenization_spaces=False, encode_special_tokens=False, + **kwargs): + self.name = "GLMTokenizer" + + self.vocab_file = vocab_file + self.tokenizer = SPTokenizer(vocab_file) + self.special_tokens = { + "": self.tokenizer.bos_id, + "": self.tokenizer.eos_id, + "": self.tokenizer.pad_id + } + self.encode_special_tokens = encode_special_tokens + super().__init__(padding_side=padding_side, clean_up_tokenization_spaces=clean_up_tokenization_spaces, + encode_special_tokens=encode_special_tokens, + **kwargs) + + def get_command(self, token): + if token in self.special_tokens: + return self.special_tokens[token] + assert token in self.tokenizer.special_tokens, f"{token} is not a special token for {self.name}" + return self.tokenizer.special_tokens[token] + + @property + def unk_token(self) -> str: + return "" + + @property + def pad_token(self) -> str: + return "" + + @property + def pad_token_id(self): + return self.get_command("") + + @property + def eos_token(self) -> str: + return "" + + @property + def eos_token_id(self): + return self.get_command("") + + @property + def vocab_size(self): + return self.tokenizer.n_words + + def get_vocab(self): + """ Returns vocab as a dict """ + vocab = {self._convert_id_to_token(i): i for i in range(self.vocab_size)} + vocab.update(self.added_tokens_encoder) + return vocab + + def _tokenize(self, text, **kwargs): + return self.tokenizer.tokenize(text, encode_special_tokens=self.encode_special_tokens) + + def _convert_token_to_id(self, token): + """ Converts a token (str) in an id using the vocab. """ + return self.tokenizer.convert_token_to_id(token) + + def _convert_id_to_token(self, index): + """Converts an index (integer) in a token (str) using the vocab.""" + return self.tokenizer.convert_id_to_token(index) + + def convert_tokens_to_string(self, tokens: List[str]) -> str: + return self.tokenizer.decode_tokens(tokens) + + def save_vocabulary(self, save_directory, filename_prefix=None): + """ + Save the vocabulary and special tokens file to a directory. + + Args: + save_directory (`str`): + The directory in which to save the vocabulary. + filename_prefix (`str`, *optional*): + An optional prefix to add to the named of the saved files. + + Returns: + `Tuple(str)`: Paths to the files saved. + """ + if os.path.isdir(save_directory): + vocab_file = os.path.join( + save_directory, self.vocab_files_names["vocab_file"] + ) + else: + vocab_file = save_directory + + with open(self.vocab_file, 'rb') as fin: + proto_str = fin.read() + + with open(vocab_file, "wb") as writer: + writer.write(proto_str) + + return (vocab_file,) + + def get_prefix_tokens(self): + prefix_tokens = [self.get_command("[gMASK]"), self.get_command("sop")] + return prefix_tokens + + def build_single_message(self, role, metadata, message): + assert role in ["system", "user", "assistant", "observation"], role + role_tokens = [self.get_command(f"<|{role}|>")] + self.tokenizer.encode(f"{metadata}\n") + message_tokens = self.tokenizer.encode(message) + tokens = role_tokens + message_tokens + return tokens + + def build_chat_input(self, query, history=None, role="user"): + if history is None: + history = [] + input_ids = [] + for item in history: + content = item["content"] + if item["role"] == "system" and "tools" in item: + content = content + "\n" + json.dumps(item["tools"], indent=4, ensure_ascii=False) + input_ids.extend(self.build_single_message(item["role"], item.get("metadata", ""), content)) + input_ids.extend(self.build_single_message(role, "", query)) + input_ids.extend([self.get_command("<|assistant|>")]) + return self.batch_encode_plus([input_ids], return_tensors="pt", is_split_into_words=True) + + def build_inputs_with_special_tokens( + self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None + ) -> List[int]: + """ + Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and + adding special tokens. A BERT sequence has the following format: + + - single sequence: `[CLS] X [SEP]` + - pair of sequences: `[CLS] A [SEP] B [SEP]` + + Args: + token_ids_0 (`List[int]`): + List of IDs to which the special tokens will be added. + token_ids_1 (`List[int]`, *optional*): + Optional second list of IDs for sequence pairs. + + Returns: + `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens. + """ + prefix_tokens = self.get_prefix_tokens() + token_ids_0 = prefix_tokens + token_ids_0 + if token_ids_1 is not None: + token_ids_0 = token_ids_0 + token_ids_1 + [self.get_command("")] + return token_ids_0 + + def _pad( + self, + encoded_inputs: Union[Dict[str, EncodedInput], BatchEncoding], + max_length: Optional[int] = None, + padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD, + pad_to_multiple_of: Optional[int] = None, + return_attention_mask: Optional[bool] = None, + padding_side: Optional[str] = None, + ) -> dict: + """ + Pad encoded inputs (on left/right and up to predefined length or max length in the batch) + + Args: + encoded_inputs: + Dictionary of tokenized inputs (`List[int]`) or batch of tokenized inputs (`List[List[int]]`). + max_length: maximum length of the returned list and optionally padding length (see below). + Will truncate by taking into account the special tokens. + padding_strategy: PaddingStrategy to use for padding. + + - PaddingStrategy.LONGEST Pad to the longest sequence in the batch + - PaddingStrategy.MAX_LENGTH: Pad to the max length (default) + - PaddingStrategy.DO_NOT_PAD: Do not pad + The tokenizer padding sides are defined in self.padding_side: + + - 'left': pads on the left of the sequences + - 'right': pads on the right of the sequences + pad_to_multiple_of: (optional) Integer if set will pad the sequence to a multiple of the provided value. + This is especially useful to enable the use of Tensor Core on NVIDIA hardware with compute capability + `>= 7.5` (Volta). + return_attention_mask: + (optional) Set to False to avoid returning attention mask (default: set to model specifics) + """ + # Load from model defaults + assert self.padding_side == "left" + + required_input = encoded_inputs[self.model_input_names[0]] + seq_length = len(required_input) + + if padding_strategy == PaddingStrategy.LONGEST: + max_length = len(required_input) + + if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0): + max_length = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of + + needs_to_be_padded = padding_strategy != PaddingStrategy.DO_NOT_PAD and len(required_input) != max_length + + # Initialize attention mask if not present. + if "attention_mask" not in encoded_inputs: + encoded_inputs["attention_mask"] = [1] * seq_length + + if "position_ids" not in encoded_inputs: + encoded_inputs["position_ids"] = list(range(seq_length)) + + if needs_to_be_padded: + difference = max_length - len(required_input) + + if "attention_mask" in encoded_inputs: + encoded_inputs["attention_mask"] = [0] * difference + encoded_inputs["attention_mask"] + if "position_ids" in encoded_inputs: + encoded_inputs["position_ids"] = [0] * difference + encoded_inputs["position_ids"] + encoded_inputs[self.model_input_names[0]] = [self.pad_token_id] * difference + required_input + + return encoded_inputs + + + +class KolorsPrompter(BasePrompter): + def __init__( + self, + tokenizer_path=None + ): + if tokenizer_path is None: + base_path = os.path.dirname(os.path.dirname(__file__)) + tokenizer_path = os.path.join(base_path, "tokenizer_configs/kolors/tokenizer") + super().__init__() + self.tokenizer = ChatGLMTokenizer.from_pretrained(tokenizer_path) + self.text_encoder: ChatGLMModel = None + + + def fetch_models(self, text_encoder: ChatGLMModel = None): + self.text_encoder = text_encoder + + + def encode_prompt_using_ChatGLM(self, prompt, text_encoder, tokenizer, max_length, clip_skip, device): + text_inputs = tokenizer( + prompt, + padding="max_length", + max_length=max_length, + truncation=True, + return_tensors="pt", + ).to(device) + output = text_encoder( + input_ids=text_inputs['input_ids'] , + attention_mask=text_inputs['attention_mask'], + position_ids=text_inputs['position_ids'], + output_hidden_states=True + ) + prompt_emb = output.hidden_states[-clip_skip].permute(1, 0, 2).clone() + pooled_prompt_emb = output.hidden_states[-1][-1, :, :].clone() + return prompt_emb, pooled_prompt_emb + + + def encode_prompt( + self, + prompt, + clip_skip=1, + clip_skip_2=2, + positive=True, + device="cuda" + ): + prompt = self.process_prompt(prompt, positive=positive) + prompt_emb, pooled_prompt_emb = self.encode_prompt_using_ChatGLM(prompt, self.text_encoder, self.tokenizer, 256, clip_skip_2, device) + + return pooled_prompt_emb, prompt_emb diff --git a/diffsynth/prompters/omnigen_prompter.py b/diffsynth/prompters/omnigen_prompter.py new file mode 100644 index 0000000000000000000000000000000000000000..616efabebb7d327ecf968165dd12341ab8f83894 --- /dev/null +++ b/diffsynth/prompters/omnigen_prompter.py @@ -0,0 +1,356 @@ +import os +import re +from typing import Dict, List + +import torch +from PIL import Image +from torchvision import transforms +from transformers import AutoTokenizer +from huggingface_hub import snapshot_download +import numpy as np + + + +def crop_arr(pil_image, max_image_size): + while min(*pil_image.size) >= 2 * max_image_size: + pil_image = pil_image.resize( + tuple(x // 2 for x in pil_image.size), resample=Image.BOX + ) + + if max(*pil_image.size) > max_image_size: + scale = max_image_size / max(*pil_image.size) + pil_image = pil_image.resize( + tuple(round(x * scale) for x in pil_image.size), resample=Image.BICUBIC + ) + + if min(*pil_image.size) < 16: + scale = 16 / min(*pil_image.size) + pil_image = pil_image.resize( + tuple(round(x * scale) for x in pil_image.size), resample=Image.BICUBIC + ) + + arr = np.array(pil_image) + crop_y1 = (arr.shape[0] % 16) // 2 + crop_y2 = arr.shape[0] % 16 - crop_y1 + + crop_x1 = (arr.shape[1] % 16) // 2 + crop_x2 = arr.shape[1] % 16 - crop_x1 + + arr = arr[crop_y1:arr.shape[0]-crop_y2, crop_x1:arr.shape[1]-crop_x2] + return Image.fromarray(arr) + + + +class OmniGenPrompter: + def __init__(self, + text_tokenizer, + max_image_size: int=1024): + self.text_tokenizer = text_tokenizer + self.max_image_size = max_image_size + + self.image_transform = transforms.Compose([ + transforms.Lambda(lambda pil_image: crop_arr(pil_image, max_image_size)), + transforms.ToTensor(), + transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True) + ]) + + self.collator = OmniGenCollator() + self.separate_collator = OmniGenSeparateCollator() + + @classmethod + def from_pretrained(cls, model_name): + if not os.path.exists(model_name): + cache_folder = os.getenv('HF_HUB_CACHE') + model_name = snapshot_download(repo_id=model_name, + cache_dir=cache_folder, + allow_patterns="*.json") + text_tokenizer = AutoTokenizer.from_pretrained(model_name) + + return cls(text_tokenizer) + + + def process_image(self, image): + return self.image_transform(image) + + def process_multi_modal_prompt(self, text, input_images): + text = self.add_prefix_instruction(text) + if input_images is None or len(input_images) == 0: + model_inputs = self.text_tokenizer(text) + return {"input_ids": model_inputs.input_ids, "pixel_values": None, "image_sizes": None} + + pattern = r"<\|image_\d+\|>" + prompt_chunks = [self.text_tokenizer(chunk).input_ids for chunk in re.split(pattern, text)] + + for i in range(1, len(prompt_chunks)): + if prompt_chunks[i][0] == 1: + prompt_chunks[i] = prompt_chunks[i][1:] + + image_tags = re.findall(pattern, text) + image_ids = [int(s.split("|")[1].split("_")[-1]) for s in image_tags] + + unique_image_ids = sorted(list(set(image_ids))) + assert unique_image_ids == list(range(1, len(unique_image_ids)+1)), f"image_ids must start from 1, and must be continuous int, e.g. [1, 2, 3], cannot be {unique_image_ids}" + # total images must be the same as the number of image tags + assert len(unique_image_ids) == len(input_images), f"total images must be the same as the number of image tags, got {len(unique_image_ids)} image tags and {len(input_images)} images" + + input_images = [input_images[x-1] for x in image_ids] + + all_input_ids = [] + img_inx = [] + idx = 0 + for i in range(len(prompt_chunks)): + all_input_ids.extend(prompt_chunks[i]) + if i != len(prompt_chunks) -1: + start_inx = len(all_input_ids) + size = input_images[i].size(-2) * input_images[i].size(-1) // 16 // 16 + img_inx.append([start_inx, start_inx+size]) + all_input_ids.extend([0]*size) + + return {"input_ids": all_input_ids, "pixel_values": input_images, "image_sizes": img_inx} + + + def add_prefix_instruction(self, prompt): + user_prompt = '<|user|>\n' + generation_prompt = 'Generate an image according to the following instructions\n' + assistant_prompt = '<|assistant|>\n<|diffusion|>' + prompt_suffix = "<|end|>\n" + prompt = f"{user_prompt}{generation_prompt}{prompt}{prompt_suffix}{assistant_prompt}" + return prompt + + + def __call__(self, + instructions: List[str], + input_images: List[List[str]] = None, + height: int = 1024, + width: int = 1024, + negative_prompt: str = "low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers.", + use_img_cfg: bool = True, + separate_cfg_input: bool = False, + use_input_image_size_as_output: bool=False, + ) -> Dict: + + if input_images is None: + use_img_cfg = False + if isinstance(instructions, str): + instructions = [instructions] + input_images = [input_images] + + input_data = [] + for i in range(len(instructions)): + cur_instruction = instructions[i] + cur_input_images = None if input_images is None else input_images[i] + if cur_input_images is not None and len(cur_input_images) > 0: + cur_input_images = [self.process_image(x) for x in cur_input_images] + else: + cur_input_images = None + assert "<|image_1|>" not in cur_instruction + + mllm_input = self.process_multi_modal_prompt(cur_instruction, cur_input_images) + + + neg_mllm_input, img_cfg_mllm_input = None, None + neg_mllm_input = self.process_multi_modal_prompt(negative_prompt, None) + if use_img_cfg: + if cur_input_images is not None and len(cur_input_images) >= 1: + img_cfg_prompt = [f"<|image_{i+1}|>" for i in range(len(cur_input_images))] + img_cfg_mllm_input = self.process_multi_modal_prompt(" ".join(img_cfg_prompt), cur_input_images) + else: + img_cfg_mllm_input = neg_mllm_input + + if use_input_image_size_as_output: + input_data.append((mllm_input, neg_mllm_input, img_cfg_mllm_input, [mllm_input['pixel_values'][0].size(-2), mllm_input['pixel_values'][0].size(-1)])) + else: + input_data.append((mllm_input, neg_mllm_input, img_cfg_mllm_input, [height, width])) + + if separate_cfg_input: + return self.separate_collator(input_data) + return self.collator(input_data) + + + + +class OmniGenCollator: + def __init__(self, pad_token_id=2, hidden_size=3072): + self.pad_token_id = pad_token_id + self.hidden_size = hidden_size + + def create_position(self, attention_mask, num_tokens_for_output_images): + position_ids = [] + text_length = attention_mask.size(-1) + img_length = max(num_tokens_for_output_images) + for mask in attention_mask: + temp_l = torch.sum(mask) + temp_position = [0]*(text_length-temp_l) + [i for i in range(temp_l+img_length+1)] # we add a time embedding into the sequence, so add one more token + position_ids.append(temp_position) + return torch.LongTensor(position_ids) + + def create_mask(self, attention_mask, num_tokens_for_output_images): + extended_mask = [] + padding_images = [] + text_length = attention_mask.size(-1) + img_length = max(num_tokens_for_output_images) + seq_len = text_length + img_length + 1 # we add a time embedding into the sequence, so add one more token + inx = 0 + for mask in attention_mask: + temp_l = torch.sum(mask) + pad_l = text_length - temp_l + + temp_mask = torch.tril(torch.ones(size=(temp_l+1, temp_l+1))) + + image_mask = torch.zeros(size=(temp_l+1, img_length)) + temp_mask = torch.cat([temp_mask, image_mask], dim=-1) + + image_mask = torch.ones(size=(img_length, temp_l+img_length+1)) + temp_mask = torch.cat([temp_mask, image_mask], dim=0) + + if pad_l > 0: + pad_mask = torch.zeros(size=(temp_l+1+img_length, pad_l)) + temp_mask = torch.cat([pad_mask, temp_mask], dim=-1) + + pad_mask = torch.ones(size=(pad_l, seq_len)) + temp_mask = torch.cat([pad_mask, temp_mask], dim=0) + + true_img_length = num_tokens_for_output_images[inx] + pad_img_length = img_length - true_img_length + if pad_img_length > 0: + temp_mask[:, -pad_img_length:] = 0 + temp_padding_imgs = torch.zeros(size=(1, pad_img_length, self.hidden_size)) + else: + temp_padding_imgs = None + + extended_mask.append(temp_mask.unsqueeze(0)) + padding_images.append(temp_padding_imgs) + inx += 1 + return torch.cat(extended_mask, dim=0), padding_images + + def adjust_attention_for_input_images(self, attention_mask, image_sizes): + for b_inx in image_sizes.keys(): + for start_inx, end_inx in image_sizes[b_inx]: + attention_mask[b_inx][start_inx:end_inx, start_inx:end_inx] = 1 + + return attention_mask + + def pad_input_ids(self, input_ids, image_sizes): + max_l = max([len(x) for x in input_ids]) + padded_ids = [] + attention_mask = [] + new_image_sizes = [] + + for i in range(len(input_ids)): + temp_ids = input_ids[i] + temp_l = len(temp_ids) + pad_l = max_l - temp_l + if pad_l == 0: + attention_mask.append([1]*max_l) + padded_ids.append(temp_ids) + else: + attention_mask.append([0]*pad_l+[1]*temp_l) + padded_ids.append([self.pad_token_id]*pad_l+temp_ids) + + if i in image_sizes: + new_inx = [] + for old_inx in image_sizes[i]: + new_inx.append([x+pad_l for x in old_inx]) + image_sizes[i] = new_inx + + return torch.LongTensor(padded_ids), torch.LongTensor(attention_mask), image_sizes + + + def process_mllm_input(self, mllm_inputs, target_img_size): + num_tokens_for_output_images = [] + for img_size in target_img_size: + num_tokens_for_output_images.append(img_size[0]*img_size[1]//16//16) + + pixel_values, image_sizes = [], {} + b_inx = 0 + for x in mllm_inputs: + if x['pixel_values'] is not None: + pixel_values.extend(x['pixel_values']) + for size in x['image_sizes']: + if b_inx not in image_sizes: + image_sizes[b_inx] = [size] + else: + image_sizes[b_inx].append(size) + b_inx += 1 + pixel_values = [x.unsqueeze(0) for x in pixel_values] + + + input_ids = [x['input_ids'] for x in mllm_inputs] + padded_input_ids, attention_mask, image_sizes = self.pad_input_ids(input_ids, image_sizes) + position_ids = self.create_position(attention_mask, num_tokens_for_output_images) + attention_mask, padding_images = self.create_mask(attention_mask, num_tokens_for_output_images) + attention_mask = self.adjust_attention_for_input_images(attention_mask, image_sizes) + + return padded_input_ids, position_ids, attention_mask, padding_images, pixel_values, image_sizes + + + def __call__(self, features): + mllm_inputs = [f[0] for f in features] + cfg_mllm_inputs = [f[1] for f in features] + img_cfg_mllm_input = [f[2] for f in features] + target_img_size = [f[3] for f in features] + + + if img_cfg_mllm_input[0] is not None: + mllm_inputs = mllm_inputs + cfg_mllm_inputs + img_cfg_mllm_input + target_img_size = target_img_size + target_img_size + target_img_size + else: + mllm_inputs = mllm_inputs + cfg_mllm_inputs + target_img_size = target_img_size + target_img_size + + + all_padded_input_ids, all_position_ids, all_attention_mask, all_padding_images, all_pixel_values, all_image_sizes = self.process_mllm_input(mllm_inputs, target_img_size) + + data = {"input_ids": all_padded_input_ids, + "attention_mask": all_attention_mask, + "position_ids": all_position_ids, + "input_pixel_values": all_pixel_values, + "input_image_sizes": all_image_sizes, + "padding_images": all_padding_images, + } + return data + + +class OmniGenSeparateCollator(OmniGenCollator): + def __call__(self, features): + mllm_inputs = [f[0] for f in features] + cfg_mllm_inputs = [f[1] for f in features] + img_cfg_mllm_input = [f[2] for f in features] + target_img_size = [f[3] for f in features] + + all_padded_input_ids, all_attention_mask, all_position_ids, all_pixel_values, all_image_sizes, all_padding_images = [], [], [], [], [], [] + + + padded_input_ids, position_ids, attention_mask, padding_images, pixel_values, image_sizes = self.process_mllm_input(mllm_inputs, target_img_size) + all_padded_input_ids.append(padded_input_ids) + all_attention_mask.append(attention_mask) + all_position_ids.append(position_ids) + all_pixel_values.append(pixel_values) + all_image_sizes.append(image_sizes) + all_padding_images.append(padding_images) + + if cfg_mllm_inputs[0] is not None: + padded_input_ids, position_ids, attention_mask, padding_images, pixel_values, image_sizes = self.process_mllm_input(cfg_mllm_inputs, target_img_size) + all_padded_input_ids.append(padded_input_ids) + all_attention_mask.append(attention_mask) + all_position_ids.append(position_ids) + all_pixel_values.append(pixel_values) + all_image_sizes.append(image_sizes) + all_padding_images.append(padding_images) + if img_cfg_mllm_input[0] is not None: + padded_input_ids, position_ids, attention_mask, padding_images, pixel_values, image_sizes = self.process_mllm_input(img_cfg_mllm_input, target_img_size) + all_padded_input_ids.append(padded_input_ids) + all_attention_mask.append(attention_mask) + all_position_ids.append(position_ids) + all_pixel_values.append(pixel_values) + all_image_sizes.append(image_sizes) + all_padding_images.append(padding_images) + + data = {"input_ids": all_padded_input_ids, + "attention_mask": all_attention_mask, + "position_ids": all_position_ids, + "input_pixel_values": all_pixel_values, + "input_image_sizes": all_image_sizes, + "padding_images": all_padding_images, + } + return data diff --git a/diffsynth/prompters/omost.py b/diffsynth/prompters/omost.py new file mode 100644 index 0000000000000000000000000000000000000000..81828ad79978103eea42389d439847c0877cbd85 --- /dev/null +++ b/diffsynth/prompters/omost.py @@ -0,0 +1,323 @@ +from transformers import AutoTokenizer, TextIteratorStreamer +import difflib +import torch +import numpy as np +import re +from ..models.model_manager import ModelManager +from PIL import Image + +valid_colors = { # r, g, b + 'aliceblue': (240, 248, 255), 'antiquewhite': (250, 235, 215), 'aqua': (0, 255, 255), + 'aquamarine': (127, 255, 212), 'azure': (240, 255, 255), 'beige': (245, 245, 220), + 'bisque': (255, 228, 196), 'black': (0, 0, 0), 'blanchedalmond': (255, 235, 205), 'blue': (0, 0, 255), + 'blueviolet': (138, 43, 226), 'brown': (165, 42, 42), 'burlywood': (222, 184, 135), + 'cadetblue': (95, 158, 160), 'chartreuse': (127, 255, 0), 'chocolate': (210, 105, 30), + 'coral': (255, 127, 80), 'cornflowerblue': (100, 149, 237), 'cornsilk': (255, 248, 220), + 'crimson': (220, 20, 60), 'cyan': (0, 255, 255), 'darkblue': (0, 0, 139), 'darkcyan': (0, 139, 139), + 'darkgoldenrod': (184, 134, 11), 'darkgray': (169, 169, 169), 'darkgrey': (169, 169, 169), + 'darkgreen': (0, 100, 0), 'darkkhaki': (189, 183, 107), 'darkmagenta': (139, 0, 139), + 'darkolivegreen': (85, 107, 47), 'darkorange': (255, 140, 0), 'darkorchid': (153, 50, 204), + 'darkred': (139, 0, 0), 'darksalmon': (233, 150, 122), 'darkseagreen': (143, 188, 143), + 'darkslateblue': (72, 61, 139), 'darkslategray': (47, 79, 79), 'darkslategrey': (47, 79, 79), + 'darkturquoise': (0, 206, 209), 'darkviolet': (148, 0, 211), 'deeppink': (255, 20, 147), + 'deepskyblue': (0, 191, 255), 'dimgray': (105, 105, 105), 'dimgrey': (105, 105, 105), + 'dodgerblue': (30, 144, 255), 'firebrick': (178, 34, 34), 'floralwhite': (255, 250, 240), + 'forestgreen': (34, 139, 34), 'fuchsia': (255, 0, 255), 'gainsboro': (220, 220, 220), + 'ghostwhite': (248, 248, 255), 'gold': (255, 215, 0), 'goldenrod': (218, 165, 32), + 'gray': (128, 128, 128), 'grey': (128, 128, 128), 'green': (0, 128, 0), 'greenyellow': (173, 255, 47), + 'honeydew': (240, 255, 240), 'hotpink': (255, 105, 180), 'indianred': (205, 92, 92), + 'indigo': (75, 0, 130), 'ivory': (255, 255, 240), 'khaki': (240, 230, 140), 'lavender': (230, 230, 250), + 'lavenderblush': (255, 240, 245), 'lawngreen': (124, 252, 0), 'lemonchiffon': (255, 250, 205), + 'lightblue': (173, 216, 230), 'lightcoral': (240, 128, 128), 'lightcyan': (224, 255, 255), + 'lightgoldenrodyellow': (250, 250, 210), 'lightgray': (211, 211, 211), 'lightgrey': (211, 211, 211), + 'lightgreen': (144, 238, 144), 'lightpink': (255, 182, 193), 'lightsalmon': (255, 160, 122), + 'lightseagreen': (32, 178, 170), 'lightskyblue': (135, 206, 250), 'lightslategray': (119, 136, 153), + 'lightslategrey': (119, 136, 153), 'lightsteelblue': (176, 196, 222), 'lightyellow': (255, 255, 224), + 'lime': (0, 255, 0), 'limegreen': (50, 205, 50), 'linen': (250, 240, 230), 'magenta': (255, 0, 255), + 'maroon': (128, 0, 0), 'mediumaquamarine': (102, 205, 170), 'mediumblue': (0, 0, 205), + 'mediumorchid': (186, 85, 211), 'mediumpurple': (147, 112, 219), 'mediumseagreen': (60, 179, 113), + 'mediumslateblue': (123, 104, 238), 'mediumspringgreen': (0, 250, 154), + 'mediumturquoise': (72, 209, 204), 'mediumvioletred': (199, 21, 133), 'midnightblue': (25, 25, 112), + 'mintcream': (245, 255, 250), 'mistyrose': (255, 228, 225), 'moccasin': (255, 228, 181), + 'navajowhite': (255, 222, 173), 'navy': (0, 0, 128), 'navyblue': (0, 0, 128), + 'oldlace': (253, 245, 230), 'olive': (128, 128, 0), 'olivedrab': (107, 142, 35), + 'orange': (255, 165, 0), 'orangered': (255, 69, 0), 'orchid': (218, 112, 214), + 'palegoldenrod': (238, 232, 170), 'palegreen': (152, 251, 152), 'paleturquoise': (175, 238, 238), + 'palevioletred': (219, 112, 147), 'papayawhip': (255, 239, 213), 'peachpuff': (255, 218, 185), + 'peru': (205, 133, 63), 'pink': (255, 192, 203), 'plum': (221, 160, 221), 'powderblue': (176, 224, 230), + 'purple': (128, 0, 128), 'rebeccapurple': (102, 51, 153), 'red': (255, 0, 0), + 'rosybrown': (188, 143, 143), 'royalblue': (65, 105, 225), 'saddlebrown': (139, 69, 19), + 'salmon': (250, 128, 114), 'sandybrown': (244, 164, 96), 'seagreen': (46, 139, 87), + 'seashell': (255, 245, 238), 'sienna': (160, 82, 45), 'silver': (192, 192, 192), + 'skyblue': (135, 206, 235), 'slateblue': (106, 90, 205), 'slategray': (112, 128, 144), + 'slategrey': (112, 128, 144), 'snow': (255, 250, 250), 'springgreen': (0, 255, 127), + 'steelblue': (70, 130, 180), 'tan': (210, 180, 140), 'teal': (0, 128, 128), 'thistle': (216, 191, 216), + 'tomato': (255, 99, 71), 'turquoise': (64, 224, 208), 'violet': (238, 130, 238), + 'wheat': (245, 222, 179), 'white': (255, 255, 255), 'whitesmoke': (245, 245, 245), + 'yellow': (255, 255, 0), 'yellowgreen': (154, 205, 50) +} + +valid_locations = { # x, y in 90*90 + 'in the center': (45, 45), + 'on the left': (15, 45), + 'on the right': (75, 45), + 'on the top': (45, 15), + 'on the bottom': (45, 75), + 'on the top-left': (15, 15), + 'on the top-right': (75, 15), + 'on the bottom-left': (15, 75), + 'on the bottom-right': (75, 75) +} + +valid_offsets = { # x, y in 90*90 + 'no offset': (0, 0), + 'slightly to the left': (-10, 0), + 'slightly to the right': (10, 0), + 'slightly to the upper': (0, -10), + 'slightly to the lower': (0, 10), + 'slightly to the upper-left': (-10, -10), + 'slightly to the upper-right': (10, -10), + 'slightly to the lower-left': (-10, 10), + 'slightly to the lower-right': (10, 10)} + +valid_areas = { # w, h in 90*90 + "a small square area": (50, 50), + "a small vertical area": (40, 60), + "a small horizontal area": (60, 40), + "a medium-sized square area": (60, 60), + "a medium-sized vertical area": (50, 80), + "a medium-sized horizontal area": (80, 50), + "a large square area": (70, 70), + "a large vertical area": (60, 90), + "a large horizontal area": (90, 60) +} + +def safe_str(x): + return x.strip(',. ') + '.' + +def closest_name(input_str, options): + input_str = input_str.lower() + + closest_match = difflib.get_close_matches(input_str, list(options.keys()), n=1, cutoff=0.5) + assert isinstance(closest_match, list) and len(closest_match) > 0, f'The value [{input_str}] is not valid!' + result = closest_match[0] + + if result != input_str: + print(f'Automatically corrected [{input_str}] -> [{result}].') + + return result + +class Canvas: + @staticmethod + def from_bot_response(response: str): + + matched = re.search(r'```python\n(.*?)\n```', response, re.DOTALL) + assert matched, 'Response does not contain codes!' + code_content = matched.group(1) + assert 'canvas = Canvas()' in code_content, 'Code block must include valid canvas var!' + local_vars = {'Canvas': Canvas} + exec(code_content, {}, local_vars) + canvas = local_vars.get('canvas', None) + assert isinstance(canvas, Canvas), 'Code block must produce valid canvas var!' + return canvas + + def __init__(self): + self.components = [] + self.color = None + self.record_tags = True + self.prefixes = [] + self.suffixes = [] + return + + def set_global_description(self, description: str, detailed_descriptions: list, tags: str, + HTML_web_color_name: str): + assert isinstance(description, str), 'Global description is not valid!' + assert isinstance(detailed_descriptions, list) and all(isinstance(item, str) for item in detailed_descriptions), \ + 'Global detailed_descriptions is not valid!' + assert isinstance(tags, str), 'Global tags is not valid!' + + HTML_web_color_name = closest_name(HTML_web_color_name, valid_colors) + self.color = np.array([[valid_colors[HTML_web_color_name]]], dtype=np.uint8) + + self.prefixes = [description] + self.suffixes = detailed_descriptions + + if self.record_tags: + self.suffixes = self.suffixes + [tags] + + self.prefixes = [safe_str(x) for x in self.prefixes] + self.suffixes = [safe_str(x) for x in self.suffixes] + + return + + def add_local_description(self, location: str, offset: str, area: str, distance_to_viewer: float, description: str, + detailed_descriptions: list, tags: str, atmosphere: str, style: str, + quality_meta: str, HTML_web_color_name: str): + assert isinstance(description, str), 'Local description is wrong!' + assert isinstance(distance_to_viewer, (int, float)) and distance_to_viewer > 0, \ + f'The distance_to_viewer for [{description}] is not positive float number!' + assert isinstance(detailed_descriptions, list) and all(isinstance(item, str) for item in detailed_descriptions), \ + f'The detailed_descriptions for [{description}] is not valid!' + assert isinstance(tags, str), f'The tags for [{description}] is not valid!' + assert isinstance(atmosphere, str), f'The atmosphere for [{description}] is not valid!' + assert isinstance(style, str), f'The style for [{description}] is not valid!' + assert isinstance(quality_meta, str), f'The quality_meta for [{description}] is not valid!' + + location = closest_name(location, valid_locations) + offset = closest_name(offset, valid_offsets) + area = closest_name(area, valid_areas) + HTML_web_color_name = closest_name(HTML_web_color_name, valid_colors) + + xb, yb = valid_locations[location] + xo, yo = valid_offsets[offset] + w, h = valid_areas[area] + rect = (yb + yo - h // 2, yb + yo + h // 2, xb + xo - w // 2, xb + xo + w // 2) + rect = [max(0, min(90, i)) for i in rect] + color = np.array([[valid_colors[HTML_web_color_name]]], dtype=np.uint8) + + prefixes = self.prefixes + [description] + suffixes = detailed_descriptions + + if self.record_tags: + suffixes = suffixes + [tags, atmosphere, style, quality_meta] + + prefixes = [safe_str(x) for x in prefixes] + suffixes = [safe_str(x) for x in suffixes] + + self.components.append(dict( + rect=rect, + distance_to_viewer=distance_to_viewer, + color=color, + prefixes=prefixes, + suffixes=suffixes, + location=location, + )) + + return + + def process(self): + # sort components + self.components = sorted(self.components, key=lambda x: x['distance_to_viewer'], reverse=True) + + # compute initial latent + # print(self.color) + initial_latent = np.zeros(shape=(90, 90, 3), dtype=np.float32) + self.color + + for component in self.components: + a, b, c, d = component['rect'] + initial_latent[a:b, c:d] = 0.7 * component['color'] + 0.3 * initial_latent[a:b, c:d] + + initial_latent = initial_latent.clip(0, 255).astype(np.uint8) + + # compute conditions + + bag_of_conditions = [ + dict(mask=np.ones(shape=(90, 90), dtype=np.float32), prefixes=self.prefixes, suffixes=self.suffixes,location= "full") + ] + + for i, component in enumerate(self.components): + a, b, c, d = component['rect'] + m = np.zeros(shape=(90, 90), dtype=np.float32) + m[a:b, c:d] = 1.0 + bag_of_conditions.append(dict( + mask = m, + prefixes = component['prefixes'], + suffixes = component['suffixes'], + location = component['location'], + )) + + return dict( + initial_latent = initial_latent, + bag_of_conditions = bag_of_conditions, + ) + + +class OmostPromter(torch.nn.Module): + + def __init__(self,model = None,tokenizer = None, template = "",device="cpu"): + super().__init__() + self.model=model + self.tokenizer = tokenizer + self.device = device + if template == "": + template = r'''You are a helpful AI assistant to compose images using the below python class `Canvas`: + ```python + class Canvas: + def set_global_description(self, description: str, detailed_descriptions: list[str], tags: str, HTML_web_color_name: str): + pass + + def add_local_description(self, location: str, offset: str, area: str, distance_to_viewer: float, description: str, detailed_descriptions: list[str], tags: str, atmosphere: str, style: str, quality_meta: str, HTML_web_color_name: str): + assert location in ["in the center", "on the left", "on the right", "on the top", "on the bottom", "on the top-left", "on the top-right", "on the bottom-left", "on the bottom-right"] + assert offset in ["no offset", "slightly to the left", "slightly to the right", "slightly to the upper", "slightly to the lower", "slightly to the upper-left", "slightly to the upper-right", "slightly to the lower-left", "slightly to the lower-right"] + assert area in ["a small square area", "a small vertical area", "a small horizontal area", "a medium-sized square area", "a medium-sized vertical area", "a medium-sized horizontal area", "a large square area", "a large vertical area", "a large horizontal area"] + assert distance_to_viewer > 0 + pass + ```''' + self.template = template + + @staticmethod + def from_model_manager(model_manager: ModelManager): + model, model_path = model_manager.fetch_model("omost_prompt", require_model_path=True) + tokenizer = AutoTokenizer.from_pretrained(model_path) + omost = OmostPromter( + model= model, + tokenizer = tokenizer, + device = model_manager.device + ) + return omost + + + def __call__(self,prompt_dict:dict): + raw_prompt=prompt_dict["prompt"] + conversation = [{"role": "system", "content": self.template}] + conversation.append({"role": "user", "content": raw_prompt}) + + input_ids = self.tokenizer.apply_chat_template(conversation, return_tensors="pt", add_generation_prompt=True).to(self.device) + streamer = TextIteratorStreamer(self.tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True) + attention_mask = torch.ones(input_ids.shape, dtype=torch.bfloat16, device=self.device) + + generate_kwargs = dict( + input_ids = input_ids, + streamer = streamer, + # stopping_criteria=stopping_criteria, + # max_new_tokens=max_new_tokens, + do_sample = True, + attention_mask = attention_mask, + pad_token_id = self.tokenizer.eos_token_id, + # temperature=temperature, + # top_p=top_p, + ) + self.model.generate(**generate_kwargs) + outputs = [] + for text in streamer: + outputs.append(text) + llm_outputs = "".join(outputs) + + canvas = Canvas.from_bot_response(llm_outputs) + canvas_output = canvas.process() + + prompts = [" ".join(_["prefixes"]+_["suffixes"][:2]) for _ in canvas_output["bag_of_conditions"]] + canvas_output["prompt"] = prompts[0] + canvas_output["prompts"] = prompts[1:] + + raw_masks = [_["mask"] for _ in canvas_output["bag_of_conditions"]] + masks=[] + for mask in raw_masks: + mask[mask>0.5]=255 + mask = np.stack([mask] * 3, axis=-1).astype("uint8") + masks.append(Image.fromarray(mask)) + + canvas_output["masks"] = masks + prompt_dict.update(canvas_output) + print(f"Your prompt is extended by Omost:\n") + cnt = 0 + for component,pmt in zip(canvas_output["bag_of_conditions"],prompts): + loc = component["location"] + cnt += 1 + print(f"Component {cnt} - Location : {loc}\nPrompt:{pmt}\n") + + return prompt_dict + + + + \ No newline at end of file diff --git a/diffsynth/prompters/prompt_refiners.py b/diffsynth/prompters/prompt_refiners.py new file mode 100644 index 0000000000000000000000000000000000000000..0ac19f565b076cccb21d9e05149b604e4bb55854 --- /dev/null +++ b/diffsynth/prompters/prompt_refiners.py @@ -0,0 +1,130 @@ +from transformers import AutoTokenizer +from ..models.model_manager import ModelManager +import torch +from .omost import OmostPromter + +class BeautifulPrompt(torch.nn.Module): + def __init__(self, tokenizer_path=None, model=None, template=""): + super().__init__() + self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_path) + self.model = model + self.template = template + + + @staticmethod + def from_model_manager(model_manager: ModelManager): + model, model_path = model_manager.fetch_model("beautiful_prompt", require_model_path=True) + template = 'Instruction: Give a simple description of the image to generate a drawing prompt.\nInput: {raw_prompt}\nOutput:' + if model_path.endswith("v2"): + template = """Converts a simple image description into a prompt. \ +Prompts are formatted as multiple related tags separated by commas, plus you can use () to increase the weight, [] to decrease the weight, \ +or use a number to specify the weight. You should add appropriate words to make the images described in the prompt more aesthetically pleasing, \ +but make sure there is a correlation between the input and output.\n\ +### Input: {raw_prompt}\n### Output:""" + beautiful_prompt = BeautifulPrompt( + tokenizer_path=model_path, + model=model, + template=template + ) + return beautiful_prompt + + + def __call__(self, raw_prompt, positive=True, **kwargs): + if positive: + model_input = self.template.format(raw_prompt=raw_prompt) + input_ids = self.tokenizer.encode(model_input, return_tensors='pt').to(self.model.device) + outputs = self.model.generate( + input_ids, + max_new_tokens=384, + do_sample=True, + temperature=0.9, + top_k=50, + top_p=0.95, + repetition_penalty=1.1, + num_return_sequences=1 + ) + prompt = raw_prompt + ", " + self.tokenizer.batch_decode( + outputs[:, input_ids.size(1):], + skip_special_tokens=True + )[0].strip() + print(f"Your prompt is refined by BeautifulPrompt: {prompt}") + return prompt + else: + return raw_prompt + + + +class QwenPrompt(torch.nn.Module): + # This class leverages the open-source Qwen model to translate Chinese prompts into English, + # with an integrated optimization mechanism for enhanced translation quality. + def __init__(self, tokenizer_path=None, model=None, system_prompt=""): + super().__init__() + self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_path) + self.model = model + self.system_prompt = system_prompt + + + @staticmethod + def from_model_manager(model_nameger: ModelManager): + model, model_path = model_nameger.fetch_model("qwen_prompt", require_model_path=True) + system_prompt = """You are an English image describer. Here are some example image styles:\n\n1. Extreme close-up: Clear focus on a single object with a blurred background, highlighted under natural sunlight.\n2. Vintage: A photograph of a historical scene, using techniques such as Daguerreotype or cyanotype.\n3. Anime: A stylized cartoon image, emphasizing hyper-realistic portraits and luminous brushwork.\n4. Candid: A natural, unposed shot capturing spontaneous moments, often with cinematic qualities.\n5. Landscape: A photorealistic image of natural scenery, such as a sunrise over the sea.\n6. Design: Colorful and detailed illustrations, often in the style of 2D game art or botanical illustrations.\n7. Urban: An ultrarealistic scene in a modern setting, possibly a cityscape viewed from indoors.\n\nYour task is to translate a given Chinese image description into a concise and precise English description. Ensure that the imagery is vivid and descriptive, and include stylistic elements to enrich the description.\nPlease note the following points:\n\n1. Capture the essence and mood of the Chinese description without including direct phrases or words from the examples provided.\n2. You should add appropriate words to make the images described in the prompt more aesthetically pleasing. If the Chinese description does not specify a style, you need to add some stylistic descriptions based on the essence of the Chinese text.\n3. The generated English description should not exceed 200 words.\n\n""" + qwen_prompt = QwenPrompt( + tokenizer_path=model_path, + model=model, + system_prompt=system_prompt + ) + return qwen_prompt + + + def __call__(self, raw_prompt, positive=True, **kwargs): + if positive: + messages = [{ + 'role': 'system', + 'content': self.system_prompt + }, { + 'role': 'user', + 'content': raw_prompt + }] + text = self.tokenizer.apply_chat_template( + messages, + tokenize=False, + add_generation_prompt=True + ) + model_inputs = self.tokenizer([text], return_tensors="pt").to(self.model.device) + + generated_ids = self.model.generate( + model_inputs.input_ids, + max_new_tokens=512 + ) + generated_ids = [ + output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) + ] + + prompt = self.tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] + print(f"Your prompt is refined by Qwen: {prompt}") + return prompt + else: + return raw_prompt + + + +class Translator(torch.nn.Module): + def __init__(self, tokenizer_path=None, model=None): + super().__init__() + self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_path) + self.model = model + + + @staticmethod + def from_model_manager(model_manager: ModelManager): + model, model_path = model_manager.fetch_model("translator", require_model_path=True) + translator = Translator(tokenizer_path=model_path, model=model) + return translator + + + def __call__(self, prompt, **kwargs): + input_ids = self.tokenizer.encode(prompt, return_tensors='pt').to(self.model.device) + output_ids = self.model.generate(input_ids) + prompt = self.tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0] + print(f"Your prompt is translated: {prompt}") + return prompt diff --git a/diffsynth/prompters/sd3_prompter.py b/diffsynth/prompters/sd3_prompter.py new file mode 100644 index 0000000000000000000000000000000000000000..ecf9bca30ae53e78822d06d769a65a6c79e8b5d8 --- /dev/null +++ b/diffsynth/prompters/sd3_prompter.py @@ -0,0 +1,93 @@ +from .base_prompter import BasePrompter +from ..models.model_manager import ModelManager +from ..models import SD3TextEncoder1, SD3TextEncoder2, SD3TextEncoder3 +from transformers import CLIPTokenizer, T5TokenizerFast +import os, torch + + +class SD3Prompter(BasePrompter): + def __init__( + self, + tokenizer_1_path=None, + tokenizer_2_path=None, + tokenizer_3_path=None + ): + if tokenizer_1_path is None: + base_path = os.path.dirname(os.path.dirname(__file__)) + tokenizer_1_path = os.path.join(base_path, "tokenizer_configs/stable_diffusion_3/tokenizer_1") + if tokenizer_2_path is None: + base_path = os.path.dirname(os.path.dirname(__file__)) + tokenizer_2_path = os.path.join(base_path, "tokenizer_configs/stable_diffusion_3/tokenizer_2") + if tokenizer_3_path is None: + base_path = os.path.dirname(os.path.dirname(__file__)) + tokenizer_3_path = os.path.join(base_path, "tokenizer_configs/stable_diffusion_3/tokenizer_3") + super().__init__() + self.tokenizer_1 = CLIPTokenizer.from_pretrained(tokenizer_1_path) + self.tokenizer_2 = CLIPTokenizer.from_pretrained(tokenizer_2_path) + self.tokenizer_3 = T5TokenizerFast.from_pretrained(tokenizer_3_path) + self.text_encoder_1: SD3TextEncoder1 = None + self.text_encoder_2: SD3TextEncoder2 = None + self.text_encoder_3: SD3TextEncoder3 = None + + + def fetch_models(self, text_encoder_1: SD3TextEncoder1 = None, text_encoder_2: SD3TextEncoder2 = None, text_encoder_3: SD3TextEncoder3 = None): + self.text_encoder_1 = text_encoder_1 + self.text_encoder_2 = text_encoder_2 + self.text_encoder_3 = text_encoder_3 + + + def encode_prompt_using_clip(self, prompt, text_encoder, tokenizer, max_length, device): + input_ids = tokenizer( + prompt, + return_tensors="pt", + padding="max_length", + max_length=max_length, + truncation=True + ).input_ids.to(device) + pooled_prompt_emb, prompt_emb = text_encoder(input_ids) + return pooled_prompt_emb, prompt_emb + + + def encode_prompt_using_t5(self, prompt, text_encoder, tokenizer, max_length, device): + input_ids = tokenizer( + prompt, + return_tensors="pt", + padding="max_length", + max_length=max_length, + truncation=True, + add_special_tokens=True, + ).input_ids.to(device) + prompt_emb = text_encoder(input_ids) + prompt_emb = prompt_emb.reshape((1, prompt_emb.shape[0]*prompt_emb.shape[1], -1)) + + return prompt_emb + + + def encode_prompt( + self, + prompt, + positive=True, + device="cuda", + t5_sequence_length=77, + ): + prompt = self.process_prompt(prompt, positive=positive) + + # CLIP + pooled_prompt_emb_1, prompt_emb_1 = self.encode_prompt_using_clip(prompt, self.text_encoder_1, self.tokenizer_1, 77, device) + pooled_prompt_emb_2, prompt_emb_2 = self.encode_prompt_using_clip(prompt, self.text_encoder_2, self.tokenizer_2, 77, device) + + # T5 + if self.text_encoder_3 is None: + prompt_emb_3 = torch.zeros((prompt_emb_1.shape[0], t5_sequence_length, 4096), dtype=prompt_emb_1.dtype, device=device) + else: + prompt_emb_3 = self.encode_prompt_using_t5(prompt, self.text_encoder_3, self.tokenizer_3, t5_sequence_length, device) + prompt_emb_3 = prompt_emb_3.to(prompt_emb_1.dtype) # float32 -> float16 + + # Merge + prompt_emb = torch.cat([ + torch.nn.functional.pad(torch.cat([prompt_emb_1, prompt_emb_2], dim=-1), (0, 4096 - 768 - 1280)), + prompt_emb_3 + ], dim=-2) + pooled_prompt_emb = torch.cat([pooled_prompt_emb_1, pooled_prompt_emb_2], dim=-1) + + return prompt_emb, pooled_prompt_emb diff --git a/diffsynth/prompters/sd_prompter.py b/diffsynth/prompters/sd_prompter.py new file mode 100644 index 0000000000000000000000000000000000000000..e3b31ea2836b3b02edab37d7f610c13f2cf6cead --- /dev/null +++ b/diffsynth/prompters/sd_prompter.py @@ -0,0 +1,73 @@ +from .base_prompter import BasePrompter, tokenize_long_prompt +from ..models.utils import load_state_dict, search_for_embeddings +from ..models import SDTextEncoder +from transformers import CLIPTokenizer +import torch, os + + + +class SDPrompter(BasePrompter): + def __init__(self, tokenizer_path=None): + if tokenizer_path is None: + base_path = os.path.dirname(os.path.dirname(__file__)) + tokenizer_path = os.path.join(base_path, "tokenizer_configs/stable_diffusion/tokenizer") + super().__init__() + self.tokenizer = CLIPTokenizer.from_pretrained(tokenizer_path) + self.text_encoder: SDTextEncoder = None + self.textual_inversion_dict = {} + self.keyword_dict = {} + + + def fetch_models(self, text_encoder: SDTextEncoder = None): + self.text_encoder = text_encoder + + + def add_textual_inversions_to_model(self, textual_inversion_dict, text_encoder): + dtype = next(iter(text_encoder.parameters())).dtype + state_dict = text_encoder.token_embedding.state_dict() + token_embeddings = [state_dict["weight"]] + for keyword in textual_inversion_dict: + _, embeddings = textual_inversion_dict[keyword] + token_embeddings.append(embeddings.to(dtype=dtype, device=token_embeddings[0].device)) + token_embeddings = torch.concat(token_embeddings, dim=0) + state_dict["weight"] = token_embeddings + text_encoder.token_embedding = torch.nn.Embedding(token_embeddings.shape[0], token_embeddings.shape[1]) + text_encoder.token_embedding = text_encoder.token_embedding.to(dtype=dtype, device=token_embeddings[0].device) + text_encoder.token_embedding.load_state_dict(state_dict) + + + def add_textual_inversions_to_tokenizer(self, textual_inversion_dict, tokenizer): + additional_tokens = [] + for keyword in textual_inversion_dict: + tokens, _ = textual_inversion_dict[keyword] + additional_tokens += tokens + self.keyword_dict[keyword] = " " + " ".join(tokens) + " " + tokenizer.add_tokens(additional_tokens) + + + def load_textual_inversions(self, model_paths): + for model_path in model_paths: + keyword = os.path.splitext(os.path.split(model_path)[-1])[0] + state_dict = load_state_dict(model_path) + + # Search for embeddings + for embeddings in search_for_embeddings(state_dict): + if len(embeddings.shape) == 2 and embeddings.shape[1] == 768: + tokens = [f"{keyword}_{i}" for i in range(embeddings.shape[0])] + self.textual_inversion_dict[keyword] = (tokens, embeddings) + + self.add_textual_inversions_to_model(self.textual_inversion_dict, self.text_encoder) + self.add_textual_inversions_to_tokenizer(self.textual_inversion_dict, self.tokenizer) + + + def encode_prompt(self, prompt, clip_skip=1, device="cuda", positive=True): + prompt = self.process_prompt(prompt, positive=positive) + for keyword in self.keyword_dict: + if keyword in prompt: + print(f"Textual inversion {keyword} is enabled.") + prompt = prompt.replace(keyword, self.keyword_dict[keyword]) + input_ids = tokenize_long_prompt(self.tokenizer, prompt).to(device) + prompt_emb = self.text_encoder(input_ids, clip_skip=clip_skip) + prompt_emb = prompt_emb.reshape((1, prompt_emb.shape[0]*prompt_emb.shape[1], -1)) + + return prompt_emb \ No newline at end of file diff --git a/diffsynth/prompters/sdxl_prompter.py b/diffsynth/prompters/sdxl_prompter.py new file mode 100644 index 0000000000000000000000000000000000000000..d84145402538b89b23d39a98271cbad64c2d9fc3 --- /dev/null +++ b/diffsynth/prompters/sdxl_prompter.py @@ -0,0 +1,61 @@ +from .base_prompter import BasePrompter, tokenize_long_prompt +from ..models.model_manager import ModelManager +from ..models import SDXLTextEncoder, SDXLTextEncoder2 +from transformers import CLIPTokenizer +import torch, os + + + +class SDXLPrompter(BasePrompter): + def __init__( + self, + tokenizer_path=None, + tokenizer_2_path=None + ): + if tokenizer_path is None: + base_path = os.path.dirname(os.path.dirname(__file__)) + tokenizer_path = os.path.join(base_path, "tokenizer_configs/stable_diffusion/tokenizer") + if tokenizer_2_path is None: + base_path = os.path.dirname(os.path.dirname(__file__)) + tokenizer_2_path = os.path.join(base_path, "tokenizer_configs/stable_diffusion_xl/tokenizer_2") + super().__init__() + self.tokenizer = CLIPTokenizer.from_pretrained(tokenizer_path) + self.tokenizer_2 = CLIPTokenizer.from_pretrained(tokenizer_2_path) + self.text_encoder: SDXLTextEncoder = None + self.text_encoder_2: SDXLTextEncoder2 = None + + + def fetch_models(self, text_encoder: SDXLTextEncoder = None, text_encoder_2: SDXLTextEncoder2 = None): + self.text_encoder = text_encoder + self.text_encoder_2 = text_encoder_2 + + + def encode_prompt( + self, + prompt, + clip_skip=1, + clip_skip_2=2, + positive=True, + device="cuda" + ): + prompt = self.process_prompt(prompt, positive=positive) + + # 1 + input_ids = tokenize_long_prompt(self.tokenizer, prompt).to(device) + prompt_emb_1 = self.text_encoder(input_ids, clip_skip=clip_skip) + + # 2 + input_ids_2 = tokenize_long_prompt(self.tokenizer_2, prompt).to(device) + add_text_embeds, prompt_emb_2 = self.text_encoder_2(input_ids_2, clip_skip=clip_skip_2) + + # Merge + if prompt_emb_1.shape[0] != prompt_emb_2.shape[0]: + max_batch_size = min(prompt_emb_1.shape[0], prompt_emb_2.shape[0]) + prompt_emb_1 = prompt_emb_1[: max_batch_size] + prompt_emb_2 = prompt_emb_2[: max_batch_size] + prompt_emb = torch.concatenate([prompt_emb_1, prompt_emb_2], dim=-1) + + # For very long prompt, we only use the first 77 tokens to compute `add_text_embeds`. + add_text_embeds = add_text_embeds[0:1] + prompt_emb = prompt_emb.reshape((1, prompt_emb.shape[0]*prompt_emb.shape[1], -1)) + return add_text_embeds, prompt_emb diff --git a/diffsynth/prompters/stepvideo_prompter.py b/diffsynth/prompters/stepvideo_prompter.py new file mode 100644 index 0000000000000000000000000000000000000000..79d374b1f8a4be2a2298520fcbf87800e0ca91d9 --- /dev/null +++ b/diffsynth/prompters/stepvideo_prompter.py @@ -0,0 +1,56 @@ +from .base_prompter import BasePrompter +from ..models.hunyuan_dit_text_encoder import HunyuanDiTCLIPTextEncoder +from ..models.stepvideo_text_encoder import STEP1TextEncoder +from transformers import BertTokenizer +import os, torch + + +class StepVideoPrompter(BasePrompter): + + def __init__( + self, + tokenizer_1_path=None, + ): + if tokenizer_1_path is None: + base_path = os.path.dirname(os.path.dirname(__file__)) + tokenizer_1_path = os.path.join( + base_path, "tokenizer_configs/hunyuan_dit/tokenizer") + super().__init__() + self.tokenizer_1 = BertTokenizer.from_pretrained(tokenizer_1_path) + + def fetch_models(self, text_encoder_1: HunyuanDiTCLIPTextEncoder = None, text_encoder_2: STEP1TextEncoder = None): + self.text_encoder_1 = text_encoder_1 + self.text_encoder_2 = text_encoder_2 + + def encode_prompt_using_clip(self, prompt, max_length, device): + text_inputs = self.tokenizer_1( + prompt, + padding="max_length", + max_length=max_length, + truncation=True, + return_attention_mask=True, + return_tensors="pt", + ) + prompt_embeds = self.text_encoder_1( + text_inputs.input_ids.to(device), + attention_mask=text_inputs.attention_mask.to(device), + ) + return prompt_embeds + + def encode_prompt_using_llm(self, prompt, max_length, device): + y, y_mask = self.text_encoder_2(prompt, max_length=max_length, device=device) + return y, y_mask + + def encode_prompt(self, + prompt, + positive=True, + device="cuda"): + + prompt = self.process_prompt(prompt, positive=positive) + + clip_embeds = self.encode_prompt_using_clip(prompt, max_length=77, device=device) + llm_embeds, llm_mask = self.encode_prompt_using_llm(prompt, max_length=320, device=device) + + llm_mask = torch.nn.functional.pad(llm_mask, (clip_embeds.shape[1], 0), value=1) + + return clip_embeds, llm_embeds, llm_mask diff --git a/diffsynth/prompters/wan_prompter.py b/diffsynth/prompters/wan_prompter.py new file mode 100644 index 0000000000000000000000000000000000000000..01a765d3cb3bf2ee4d06553fd061ed7dd75443b2 --- /dev/null +++ b/diffsynth/prompters/wan_prompter.py @@ -0,0 +1,109 @@ +from .base_prompter import BasePrompter +from ..models.wan_video_text_encoder import WanTextEncoder +from transformers import AutoTokenizer +import os, torch +import ftfy +import html +import string +import regex as re + + +def basic_clean(text): + text = ftfy.fix_text(text) + text = html.unescape(html.unescape(text)) + return text.strip() + + +def whitespace_clean(text): + text = re.sub(r'\s+', ' ', text) + text = text.strip() + return text + + +def canonicalize(text, keep_punctuation_exact_string=None): + text = text.replace('_', ' ') + if keep_punctuation_exact_string: + text = keep_punctuation_exact_string.join( + part.translate(str.maketrans('', '', string.punctuation)) + for part in text.split(keep_punctuation_exact_string)) + else: + text = text.translate(str.maketrans('', '', string.punctuation)) + text = text.lower() + text = re.sub(r'\s+', ' ', text) + return text.strip() + + +class HuggingfaceTokenizer: + + def __init__(self, name, seq_len=None, clean=None, **kwargs): + assert clean in (None, 'whitespace', 'lower', 'canonicalize') + self.name = name + self.seq_len = seq_len + self.clean = clean + + # init tokenizer + self.tokenizer = AutoTokenizer.from_pretrained(name, **kwargs) + self.vocab_size = self.tokenizer.vocab_size + + def __call__(self, sequence, **kwargs): + return_mask = kwargs.pop('return_mask', False) + + # arguments + _kwargs = {'return_tensors': 'pt'} + if self.seq_len is not None: + _kwargs.update({ + 'padding': 'max_length', + 'truncation': True, + 'max_length': self.seq_len + }) + _kwargs.update(**kwargs) + + # tokenization + if isinstance(sequence, str): + sequence = [sequence] + if self.clean: + sequence = [self._clean(u) for u in sequence] + ids = self.tokenizer(sequence, **_kwargs) + + # output + if return_mask: + return ids.input_ids, ids.attention_mask + else: + return ids.input_ids + + def _clean(self, text): + if self.clean == 'whitespace': + text = whitespace_clean(basic_clean(text)) + elif self.clean == 'lower': + text = whitespace_clean(basic_clean(text)).lower() + elif self.clean == 'canonicalize': + text = canonicalize(basic_clean(text)) + return text + + +class WanPrompter(BasePrompter): + + def __init__(self, tokenizer_path=None, text_len=512): + super().__init__() + self.text_len = text_len + self.text_encoder = None + self.fetch_tokenizer(tokenizer_path) + + def fetch_tokenizer(self, tokenizer_path=None): + if tokenizer_path is not None: + self.tokenizer = HuggingfaceTokenizer(name=tokenizer_path, seq_len=self.text_len, clean='whitespace') + + def fetch_models(self, text_encoder: WanTextEncoder = None): + self.text_encoder = text_encoder + + def encode_prompt(self, prompt, positive=True, device="cuda"): + prompt = self.process_prompt(prompt, positive=positive) + + ids, mask = self.tokenizer(prompt, return_mask=True, add_special_tokens=True) + ids = ids.to(device) + mask = mask.to(device) + seq_lens = mask.gt(0).sum(dim=1).long() + prompt_emb = self.text_encoder(ids, mask) + for i, v in enumerate(seq_lens): + prompt_emb[:, v:] = 0 + return prompt_emb diff --git a/diffsynth/schedulers/__init__.py b/diffsynth/schedulers/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..0ec43257b687c9b5504e08e05763332755566ea5 --- /dev/null +++ b/diffsynth/schedulers/__init__.py @@ -0,0 +1,3 @@ +from .ddim import EnhancedDDIMScheduler +from .continuous_ode import ContinuousODEScheduler +from .flow_match import FlowMatchScheduler diff --git a/diffsynth/schedulers/continuous_ode.py b/diffsynth/schedulers/continuous_ode.py new file mode 100644 index 0000000000000000000000000000000000000000..c73b9e221aa54a8385322b42012c30c598550fcd --- /dev/null +++ b/diffsynth/schedulers/continuous_ode.py @@ -0,0 +1,59 @@ +import torch + + +class ContinuousODEScheduler(): + + def __init__(self, num_inference_steps=100, sigma_max=700.0, sigma_min=0.002, rho=7.0): + self.sigma_max = sigma_max + self.sigma_min = sigma_min + self.rho = rho + self.set_timesteps(num_inference_steps) + + + def set_timesteps(self, num_inference_steps=100, denoising_strength=1.0, **kwargs): + ramp = torch.linspace(1-denoising_strength, 1, num_inference_steps) + min_inv_rho = torch.pow(torch.tensor((self.sigma_min,)), (1 / self.rho)) + max_inv_rho = torch.pow(torch.tensor((self.sigma_max,)), (1 / self.rho)) + self.sigmas = torch.pow(max_inv_rho + ramp * (min_inv_rho - max_inv_rho), self.rho) + self.timesteps = torch.log(self.sigmas) * 0.25 + + + def step(self, model_output, timestep, sample, to_final=False): + timestep_id = torch.argmin((self.timesteps - timestep).abs()) + sigma = self.sigmas[timestep_id] + sample *= (sigma*sigma + 1).sqrt() + estimated_sample = -sigma / (sigma*sigma + 1).sqrt() * model_output + 1 / (sigma*sigma + 1) * sample + if to_final or timestep_id + 1 >= len(self.timesteps): + prev_sample = estimated_sample + else: + sigma_ = self.sigmas[timestep_id + 1] + derivative = 1 / sigma * (sample - estimated_sample) + prev_sample = sample + derivative * (sigma_ - sigma) + prev_sample /= (sigma_*sigma_ + 1).sqrt() + return prev_sample + + + def return_to_timestep(self, timestep, sample, sample_stablized): + # This scheduler doesn't support this function. + pass + + + def add_noise(self, original_samples, noise, timestep): + timestep_id = torch.argmin((self.timesteps - timestep).abs()) + sigma = self.sigmas[timestep_id] + sample = (original_samples + noise * sigma) / (sigma*sigma + 1).sqrt() + return sample + + + def training_target(self, sample, noise, timestep): + timestep_id = torch.argmin((self.timesteps - timestep).abs()) + sigma = self.sigmas[timestep_id] + target = (-(sigma*sigma + 1).sqrt() / sigma + 1 / (sigma*sigma + 1).sqrt() / sigma) * sample + 1 / (sigma*sigma + 1).sqrt() * noise + return target + + + def training_weight(self, timestep): + timestep_id = torch.argmin((self.timesteps - timestep).abs()) + sigma = self.sigmas[timestep_id] + weight = (1 + sigma*sigma).sqrt() / sigma + return weight diff --git a/diffsynth/schedulers/ddim.py b/diffsynth/schedulers/ddim.py new file mode 100644 index 0000000000000000000000000000000000000000..da524963c62f662016b1429d5047ebe7b5922604 --- /dev/null +++ b/diffsynth/schedulers/ddim.py @@ -0,0 +1,105 @@ +import torch, math + + +class EnhancedDDIMScheduler(): + + def __init__(self, num_train_timesteps=1000, beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", prediction_type="epsilon", rescale_zero_terminal_snr=False): + self.num_train_timesteps = num_train_timesteps + if beta_schedule == "scaled_linear": + betas = torch.square(torch.linspace(math.sqrt(beta_start), math.sqrt(beta_end), num_train_timesteps, dtype=torch.float32)) + elif beta_schedule == "linear": + betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32) + else: + raise NotImplementedError(f"{beta_schedule} is not implemented") + self.alphas_cumprod = torch.cumprod(1.0 - betas, dim=0) + if rescale_zero_terminal_snr: + self.alphas_cumprod = self.rescale_zero_terminal_snr(self.alphas_cumprod) + self.alphas_cumprod = self.alphas_cumprod.tolist() + self.set_timesteps(10) + self.prediction_type = prediction_type + + + def rescale_zero_terminal_snr(self, alphas_cumprod): + alphas_bar_sqrt = alphas_cumprod.sqrt() + + # Store old values. + alphas_bar_sqrt_0 = alphas_bar_sqrt[0].clone() + alphas_bar_sqrt_T = alphas_bar_sqrt[-1].clone() + + # Shift so the last timestep is zero. + alphas_bar_sqrt -= alphas_bar_sqrt_T + + # Scale so the first timestep is back to the old value. + alphas_bar_sqrt *= alphas_bar_sqrt_0 / (alphas_bar_sqrt_0 - alphas_bar_sqrt_T) + + # Convert alphas_bar_sqrt to betas + alphas_bar = alphas_bar_sqrt.square() # Revert sqrt + + return alphas_bar + + + def set_timesteps(self, num_inference_steps, denoising_strength=1.0, **kwargs): + # The timesteps are aligned to 999...0, which is different from other implementations, + # but I think this implementation is more reasonable in theory. + max_timestep = max(round(self.num_train_timesteps * denoising_strength) - 1, 0) + num_inference_steps = min(num_inference_steps, max_timestep + 1) + if num_inference_steps == 1: + self.timesteps = torch.Tensor([max_timestep]) + else: + step_length = max_timestep / (num_inference_steps - 1) + self.timesteps = torch.Tensor([round(max_timestep - i*step_length) for i in range(num_inference_steps)]) + + + def denoise(self, model_output, sample, alpha_prod_t, alpha_prod_t_prev): + if self.prediction_type == "epsilon": + weight_e = math.sqrt(1 - alpha_prod_t_prev) - math.sqrt(alpha_prod_t_prev * (1 - alpha_prod_t) / alpha_prod_t) + weight_x = math.sqrt(alpha_prod_t_prev / alpha_prod_t) + prev_sample = sample * weight_x + model_output * weight_e + elif self.prediction_type == "v_prediction": + weight_e = -math.sqrt(alpha_prod_t_prev * (1 - alpha_prod_t)) + math.sqrt(alpha_prod_t * (1 - alpha_prod_t_prev)) + weight_x = math.sqrt(alpha_prod_t * alpha_prod_t_prev) + math.sqrt((1 - alpha_prod_t) * (1 - alpha_prod_t_prev)) + prev_sample = sample * weight_x + model_output * weight_e + else: + raise NotImplementedError(f"{self.prediction_type} is not implemented") + return prev_sample + + + def step(self, model_output, timestep, sample, to_final=False): + alpha_prod_t = self.alphas_cumprod[int(timestep.flatten().tolist()[0])] + if isinstance(timestep, torch.Tensor): + timestep = timestep.cpu() + timestep_id = torch.argmin((self.timesteps - timestep).abs()) + if to_final or timestep_id + 1 >= len(self.timesteps): + alpha_prod_t_prev = 1.0 + else: + timestep_prev = int(self.timesteps[timestep_id + 1]) + alpha_prod_t_prev = self.alphas_cumprod[timestep_prev] + + return self.denoise(model_output, sample, alpha_prod_t, alpha_prod_t_prev) + + + def return_to_timestep(self, timestep, sample, sample_stablized): + alpha_prod_t = self.alphas_cumprod[int(timestep.flatten().tolist()[0])] + noise_pred = (sample - math.sqrt(alpha_prod_t) * sample_stablized) / math.sqrt(1 - alpha_prod_t) + return noise_pred + + + def add_noise(self, original_samples, noise, timestep): + sqrt_alpha_prod = math.sqrt(self.alphas_cumprod[int(timestep.flatten().tolist()[0])]) + sqrt_one_minus_alpha_prod = math.sqrt(1 - self.alphas_cumprod[int(timestep.flatten().tolist()[0])]) + noisy_samples = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise + return noisy_samples + + + def training_target(self, sample, noise, timestep): + if self.prediction_type == "epsilon": + return noise + else: + sqrt_alpha_prod = math.sqrt(self.alphas_cumprod[int(timestep.flatten().tolist()[0])]) + sqrt_one_minus_alpha_prod = math.sqrt(1 - self.alphas_cumprod[int(timestep.flatten().tolist()[0])]) + target = sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample + return target + + + def training_weight(self, timestep): + return 1.0 diff --git a/diffsynth/schedulers/flow_match.py b/diffsynth/schedulers/flow_match.py new file mode 100644 index 0000000000000000000000000000000000000000..3bb2405a0069586595a675896876ce6ad37054b3 --- /dev/null +++ b/diffsynth/schedulers/flow_match.py @@ -0,0 +1,125 @@ +import torch, math + + + +class FlowMatchScheduler(): + + def __init__( + self, + num_inference_steps=100, + num_train_timesteps=1000, + shift=3.0, + sigma_max=1.0, + sigma_min=0.003/1.002, + inverse_timesteps=False, + extra_one_step=False, + reverse_sigmas=False, + exponential_shift=False, + exponential_shift_mu=None, + shift_terminal=None, + ): + self.num_train_timesteps = num_train_timesteps + self.shift = shift + self.sigma_max = sigma_max + self.sigma_min = sigma_min + self.inverse_timesteps = inverse_timesteps + self.extra_one_step = extra_one_step + self.reverse_sigmas = reverse_sigmas + self.exponential_shift = exponential_shift + self.exponential_shift_mu = exponential_shift_mu + self.shift_terminal = shift_terminal + self.set_timesteps(num_inference_steps) + + + def set_timesteps(self, num_inference_steps=100, denoising_strength=1.0, training=False, shift=None, dynamic_shift_len=None, exponential_shift_mu=None): + if shift is not None: + self.shift = shift + sigma_start = self.sigma_min + (self.sigma_max - self.sigma_min) * denoising_strength + if self.extra_one_step: + self.sigmas = torch.linspace(sigma_start, self.sigma_min, num_inference_steps + 1)[:-1] + else: + self.sigmas = torch.linspace(sigma_start, self.sigma_min, num_inference_steps) + if self.inverse_timesteps: + self.sigmas = torch.flip(self.sigmas, dims=[0]) + if self.exponential_shift: + if exponential_shift_mu is not None: + mu = exponential_shift_mu + elif dynamic_shift_len is not None: + mu = self.calculate_shift(dynamic_shift_len) + else: + mu = self.exponential_shift_mu + self.sigmas = math.exp(mu) / (math.exp(mu) + (1 / self.sigmas - 1)) + else: + self.sigmas = self.shift * self.sigmas / (1 + (self.shift - 1) * self.sigmas) + if self.shift_terminal is not None: + one_minus_z = 1 - self.sigmas + scale_factor = one_minus_z[-1] / (1 - self.shift_terminal) + self.sigmas = 1 - (one_minus_z / scale_factor) + if self.reverse_sigmas: + self.sigmas = 1 - self.sigmas + self.timesteps = self.sigmas * self.num_train_timesteps + if training: + x = self.timesteps + y = torch.exp(-2 * ((x - num_inference_steps / 2) / num_inference_steps) ** 2) + y_shifted = y - y.min() + bsmntw_weighing = y_shifted * (num_inference_steps / y_shifted.sum()) + self.linear_timesteps_weights = bsmntw_weighing + self.training = True + else: + self.training = False + + + def step(self, model_output, timestep, sample, to_final=False, **kwargs): + if isinstance(timestep, torch.Tensor): + timestep = timestep.cpu() + timestep_id = torch.argmin((self.timesteps - timestep).abs()) + sigma = self.sigmas[timestep_id] + if to_final or timestep_id + 1 >= len(self.timesteps): + sigma_ = 1 if (self.inverse_timesteps or self.reverse_sigmas) else 0 + else: + sigma_ = self.sigmas[timestep_id + 1] + prev_sample = sample + model_output * (sigma_ - sigma) + return prev_sample + + + def return_to_timestep(self, timestep, sample, sample_stablized): + if isinstance(timestep, torch.Tensor): + timestep = timestep.cpu() + timestep_id = torch.argmin((self.timesteps - timestep).abs()) + sigma = self.sigmas[timestep_id] + model_output = (sample - sample_stablized) / sigma + return model_output + + + def add_noise(self, original_samples, noise, timestep): + if isinstance(timestep, torch.Tensor): + timestep = timestep.cpu() + timestep_id = torch.argmin((self.timesteps - timestep).abs()) + sigma = self.sigmas[timestep_id] + sample = (1 - sigma) * original_samples + sigma * noise + return sample + + + def training_target(self, sample, noise, timestep): + target = noise - sample + return target + + + def training_weight(self, timestep): + timestep_id = torch.argmin((self.timesteps - timestep.to(self.timesteps.device)).abs()) + weights = self.linear_timesteps_weights[timestep_id] + return weights + + + def calculate_shift( + self, + image_seq_len, + base_seq_len: int = 256, + max_seq_len: int = 8192, + base_shift: float = 0.5, + max_shift: float = 0.9, + ): + m = (max_shift - base_shift) / (max_seq_len - base_seq_len) + b = base_shift - m * base_seq_len + mu = image_seq_len * m + b + return mu diff --git a/diffsynth/tokenizer_configs/__init__.py b/diffsynth/tokenizer_configs/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/diffsynth/tokenizer_configs/cog/tokenizer/added_tokens.json b/diffsynth/tokenizer_configs/cog/tokenizer/added_tokens.json new file mode 100644 index 0000000000000000000000000000000000000000..3f5132007c4fcf42b75b65c8b6aa49c7098bcdf4 --- /dev/null +++ b/diffsynth/tokenizer_configs/cog/tokenizer/added_tokens.json @@ -0,0 +1,102 @@ +{ + "": 32099, + "": 32089, + "": 32088, + "": 32087, + "": 32086, + "": 32085, + "": 32084, + "": 32083, + "": 32082, + "": 32081, + "": 32080, + "": 32098, + "": 32079, + "": 32078, + "": 32077, + "": 32076, + "": 32075, + "": 32074, + "": 32073, + "": 32072, + "": 32071, + "": 32070, + "": 32097, + "": 32069, + "": 32068, + "": 32067, + "": 32066, + "": 32065, + "": 32064, + "": 32063, + "": 32062, + "": 32061, + "": 32060, + "": 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"32098": { + "content": "", + "lstrip": true, + "normalized": false, + "rstrip": true, + "single_word": false, + "special": true + }, + "32099": { + "content": "", + "lstrip": true, + "normalized": false, + "rstrip": true, + "single_word": false, + "special": true + } + }, + "additional_special_tokens": [ + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "" + ], + "clean_up_tokenization_spaces": true, + "eos_token": "", + "extra_ids": 100, + "legacy": true, + "model_max_length": 226, + "pad_token": "", + "sp_model_kwargs": {}, + "tokenizer_class": "T5Tokenizer", + "unk_token": "" +} diff --git a/diffsynth/tokenizer_configs/flux/tokenizer_1/merges.txt b/diffsynth/tokenizer_configs/flux/tokenizer_1/merges.txt new file mode 100644 index 0000000000000000000000000000000000000000..76e821f1b6f0a9709293c3b6b51ed90980b3166b --- /dev/null +++ b/diffsynth/tokenizer_configs/flux/tokenizer_1/merges.txt @@ -0,0 +1,48895 @@ +#version: 0.2 +i n +t h +a n +r e +a r +e r +th e +in g +o u +o n +s t +o r +e n +o n +a l +a t +e r +i t +i n +t o +r o +i s +l e +i c +a t +an d +e d +o f +c h +o r +e s +i l +e l +s t +a c +o m +a m +l o +a n +a y +s h +r i +l i +t i +f or +n e +ð Ł +r a +h a +d e +o l +v e +s i +u r +a l +s e +' s +u n +d i +b e +l a +w h +o o +d ay +e n +m a +n o +l e +t o +ou r +i r +g h +w it +i t +y o +a s +s p +th is +t s +at i +yo u +wit h +a d +i s +a b +l y +w e +th e +t e +a s +a g +v i +p p +s u +h o +m y +. . +b u +c 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"chat_template": "{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' %}{% if loop.index0 == 0 %}{% set content = bos_token + content %}{% endif %}{{ content }}{% endfor %}{{ '<|start_header_id|>assistant<|end_header_id|>\n\n' }}", + "clean_up_tokenization_spaces": true, + "eos_token": "<|end_of_text|>", + "legacy": true, + "model_input_names": [ + "input_ids", + "attention_mask" + ], + "model_max_length": 1000000000000000019884624838656, + "pad_token": "", + "padding_side": "right", + "processor_class": "LlavaProcessor", + "tokenizer_class": "LlamaTokenizer", + "unk_token": "", + "use_default_system_prompt": false +} diff --git a/diffsynth/tokenizer_configs/kolors/tokenizer/tokenizer.model b/diffsynth/tokenizer_configs/kolors/tokenizer/tokenizer.model new file mode 100644 index 0000000000000000000000000000000000000000..8a8007697b7cc3d3868dcffbbebf8c1f2bd690ba --- /dev/null +++ b/diffsynth/tokenizer_configs/kolors/tokenizer/tokenizer.model @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e7dc4c393423b76e4373e5157ddc34803a0189ba96b21ddbb40269d31468a6f2 +size 1018370 diff --git a/diffsynth/tokenizer_configs/kolors/tokenizer/tokenizer_config.json b/diffsynth/tokenizer_configs/kolors/tokenizer/tokenizer_config.json new file mode 100644 index 0000000000000000000000000000000000000000..f6f13c88707490cebd8023da86e8bf7a56fa21e3 --- /dev/null +++ b/diffsynth/tokenizer_configs/kolors/tokenizer/tokenizer_config.json @@ -0,0 +1,12 @@ +{ + "name_or_path": "THUDM/chatglm3-6b-base", + "remove_space": false, + "do_lower_case": false, + "tokenizer_class": "ChatGLMTokenizer", + "auto_map": { + "AutoTokenizer": [ + "tokenization_chatglm.ChatGLMTokenizer", + null + ] + } +} diff --git a/diffsynth/tokenizer_configs/kolors/tokenizer/vocab.txt b/diffsynth/tokenizer_configs/kolors/tokenizer/vocab.txt new file mode 100644 index 0000000000000000000000000000000000000000..8a8007697b7cc3d3868dcffbbebf8c1f2bd690ba --- /dev/null +++ b/diffsynth/tokenizer_configs/kolors/tokenizer/vocab.txt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e7dc4c393423b76e4373e5157ddc34803a0189ba96b21ddbb40269d31468a6f2 +size 1018370 diff --git a/diffsynth/tokenizer_configs/stable_diffusion/tokenizer/merges.txt b/diffsynth/tokenizer_configs/stable_diffusion/tokenizer/merges.txt new file mode 100644 index 0000000000000000000000000000000000000000..76e821f1b6f0a9709293c3b6b51ed90980b3166b --- /dev/null +++ b/diffsynth/tokenizer_configs/stable_diffusion/tokenizer/merges.txt @@ -0,0 +1,48895 @@ +#version: 0.2 +i n +t h +a n +r e +a r +e r +th e +in g +o u +o n +s t +o r +e n +o n +a l +a t +e r +i t +i n +t o +r o +i s +l e +i c +a t +an d +e d +o f +c h +o r +e s +i l +e l +s t +a c +o m +a m +l o +a n +a y +s h +r i +l i +t i +f or 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+ "ļ": 248, + "ļ": 504, + "Ľ": 249, + "Ľ": 505, + "ľ": 250, + "ľ": 506, + "ľë": 39810, + "Ŀ": 251, + "Ŀ": 507, + "ŀ": 252, + "ŀ": 508, + "Ł": 253, + "Ł": 509, + "ŁãģĦ": 46023, + "ł": 254, + "ł": 510, + "łï¸ı": 27899, + "łï¸ı": 12715, + "łĪ": 43364, + "Ń": 255, + "Ń": 511 +} + \ No newline at end of file diff --git a/diffsynth/trainers/__init__.py b/diffsynth/trainers/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/diffsynth/trainers/text_to_image.py b/diffsynth/trainers/text_to_image.py new file mode 100644 index 0000000000000000000000000000000000000000..a76a7912da12f1ca665758542d92e50fea02713c --- /dev/null +++ b/diffsynth/trainers/text_to_image.py @@ -0,0 +1,318 @@ +import lightning as pl +from peft import LoraConfig, inject_adapter_in_model +import torch, os +from ..data.simple_text_image import TextImageDataset +from modelscope.hub.api import HubApi +from ..models.utils import load_state_dict + + + +class LightningModelForT2ILoRA(pl.LightningModule): + def __init__( + self, + learning_rate=1e-4, + use_gradient_checkpointing=True, + state_dict_converter=None, + ): + super().__init__() + # Set parameters + self.learning_rate = learning_rate + self.use_gradient_checkpointing = use_gradient_checkpointing + self.state_dict_converter = state_dict_converter + self.lora_alpha = None + + + def load_models(self): + # This function is implemented in other modules + self.pipe = None + + + def freeze_parameters(self): + # Freeze parameters + self.pipe.requires_grad_(False) + self.pipe.eval() + self.pipe.denoising_model().train() + + + def add_lora_to_model(self, model, lora_rank=4, lora_alpha=4, lora_target_modules="to_q,to_k,to_v,to_out", init_lora_weights="gaussian", pretrained_lora_path=None, state_dict_converter=None): + # Add LoRA to UNet + self.lora_alpha = lora_alpha + if init_lora_weights == "kaiming": + init_lora_weights = True + + lora_config = LoraConfig( + r=lora_rank, + lora_alpha=lora_alpha, + init_lora_weights=init_lora_weights, + target_modules=lora_target_modules.split(","), + ) + model = inject_adapter_in_model(lora_config, model) + for param in model.parameters(): + # Upcast LoRA parameters into fp32 + if param.requires_grad: + param.data = param.to(torch.float32) + + # Lora pretrained lora weights + if pretrained_lora_path is not None: + state_dict = load_state_dict(pretrained_lora_path) + if state_dict_converter is not None: + state_dict = state_dict_converter(state_dict) + missing_keys, unexpected_keys = model.load_state_dict(state_dict, strict=False) + all_keys = [i for i, _ in model.named_parameters()] + num_updated_keys = len(all_keys) - len(missing_keys) + num_unexpected_keys = len(unexpected_keys) + print(f"{num_updated_keys} parameters are loaded from {pretrained_lora_path}. {num_unexpected_keys} parameters are unexpected.") + + + def training_step(self, batch, batch_idx): + # Data + text, image = batch["text"], batch["image"] + + # Prepare input parameters + self.pipe.device = self.device + prompt_emb = self.pipe.encode_prompt(text, positive=True) + if "latents" in batch: + latents = batch["latents"].to(dtype=self.pipe.torch_dtype, device=self.device) + else: + latents = self.pipe.vae_encoder(image.to(dtype=self.pipe.torch_dtype, device=self.device)) + noise = torch.randn_like(latents) + timestep_id = torch.randint(0, self.pipe.scheduler.num_train_timesteps, (1,)) + timestep = self.pipe.scheduler.timesteps[timestep_id].to(self.device) + extra_input = self.pipe.prepare_extra_input(latents) + noisy_latents = self.pipe.scheduler.add_noise(latents, noise, timestep) + training_target = self.pipe.scheduler.training_target(latents, noise, timestep) + + # Compute loss + noise_pred = self.pipe.denoising_model()( + noisy_latents, timestep=timestep, **prompt_emb, **extra_input, + use_gradient_checkpointing=self.use_gradient_checkpointing + ) + loss = torch.nn.functional.mse_loss(noise_pred.float(), training_target.float()) + loss = loss * self.pipe.scheduler.training_weight(timestep) + + # Record log + self.log("train_loss", loss, prog_bar=True) + return loss + + + def configure_optimizers(self): + trainable_modules = filter(lambda p: p.requires_grad, self.pipe.denoising_model().parameters()) + optimizer = torch.optim.AdamW(trainable_modules, lr=self.learning_rate) + return optimizer + + + def on_save_checkpoint(self, checkpoint): + checkpoint.clear() + trainable_param_names = list(filter(lambda named_param: named_param[1].requires_grad, self.pipe.denoising_model().named_parameters())) + trainable_param_names = set([named_param[0] for named_param in trainable_param_names]) + state_dict = self.pipe.denoising_model().state_dict() + lora_state_dict = {} + for name, param in state_dict.items(): + if name in trainable_param_names: + lora_state_dict[name] = param + if self.state_dict_converter is not None: + lora_state_dict = self.state_dict_converter(lora_state_dict, alpha=self.lora_alpha) + checkpoint.update(lora_state_dict) + + + +def add_general_parsers(parser): + parser.add_argument( + "--dataset_path", + type=str, + default=None, + required=True, + help="The path of the Dataset.", + ) + parser.add_argument( + "--output_path", + type=str, + default="./", + help="Path to save the model.", + ) + parser.add_argument( + "--steps_per_epoch", + type=int, + default=500, + help="Number of steps per epoch.", + ) + parser.add_argument( + "--height", + type=int, + default=1024, + help="Image height.", + ) + parser.add_argument( + "--width", + type=int, + default=1024, + help="Image width.", + ) + parser.add_argument( + "--center_crop", + default=False, + action="store_true", + help=( + "Whether to center crop the input images to the resolution. If not set, the images will be randomly" + " cropped. The images will be resized to the resolution first before cropping." + ), + ) + parser.add_argument( + "--random_flip", + default=False, + action="store_true", + help="Whether to randomly flip images horizontally", + ) + parser.add_argument( + "--batch_size", + type=int, + default=1, + help="Batch size (per device) for the training dataloader.", + ) + parser.add_argument( + "--dataloader_num_workers", + type=int, + default=0, + help="Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.", + ) + parser.add_argument( + "--precision", + type=str, + default="16-mixed", + choices=["32", "16", "16-mixed", "bf16"], + help="Training precision", + ) + parser.add_argument( + "--learning_rate", + type=float, + default=1e-4, + help="Learning rate.", + ) + parser.add_argument( + "--lora_rank", + type=int, + default=4, + help="The dimension of the LoRA update matrices.", + ) + parser.add_argument( + "--lora_alpha", + type=float, + default=4.0, + help="The weight of the LoRA update matrices.", + ) + parser.add_argument( + "--init_lora_weights", + type=str, + default="kaiming", + choices=["gaussian", "kaiming"], + help="The initializing method of LoRA weight.", + ) + parser.add_argument( + "--use_gradient_checkpointing", + default=False, + action="store_true", + help="Whether to use gradient checkpointing.", + ) + parser.add_argument( + "--accumulate_grad_batches", + type=int, + default=1, + help="The number of batches in gradient accumulation.", + ) + parser.add_argument( + "--training_strategy", + type=str, + default="auto", + choices=["auto", "deepspeed_stage_1", "deepspeed_stage_2", "deepspeed_stage_3"], + help="Training strategy", + ) + parser.add_argument( + "--max_epochs", + type=int, + default=1, + help="Number of epochs.", + ) + parser.add_argument( + "--modelscope_model_id", + type=str, + default=None, + help="Model ID on ModelScope (https://www.modelscope.cn/). The model will be uploaded to ModelScope automatically if you provide a Model ID.", + ) + parser.add_argument( + "--modelscope_access_token", + type=str, + default=None, + help="Access key on ModelScope (https://www.modelscope.cn/). Required if you want to upload the model to ModelScope.", + ) + parser.add_argument( + "--pretrained_lora_path", + type=str, + default=None, + help="Pretrained LoRA path. Required if the training is resumed.", + ) + parser.add_argument( + "--use_swanlab", + default=False, + action="store_true", + help="Whether to use SwanLab logger.", + ) + parser.add_argument( + "--swanlab_mode", + default=None, + help="SwanLab mode (cloud or local).", + ) + return parser + + +def launch_training_task(model, args): + # dataset and data loader + dataset = TextImageDataset( + args.dataset_path, + steps_per_epoch=args.steps_per_epoch * args.batch_size, + height=args.height, + width=args.width, + center_crop=args.center_crop, + random_flip=args.random_flip + ) + train_loader = torch.utils.data.DataLoader( + dataset, + shuffle=True, + batch_size=args.batch_size, + num_workers=args.dataloader_num_workers + ) + # train + if args.use_swanlab: + from swanlab.integration.pytorch_lightning import SwanLabLogger + swanlab_config = {"UPPERFRAMEWORK": "DiffSynth-Studio"} + swanlab_config.update(vars(args)) + swanlab_logger = SwanLabLogger( + project="diffsynth_studio", + name="diffsynth_studio", + config=swanlab_config, + mode=args.swanlab_mode, + logdir=os.path.join(args.output_path, "swanlog"), + ) + logger = [swanlab_logger] + else: + logger = None + trainer = pl.Trainer( + max_epochs=args.max_epochs, + accelerator="gpu", + devices="auto", + precision=args.precision, + strategy=args.training_strategy, + default_root_dir=args.output_path, + accumulate_grad_batches=args.accumulate_grad_batches, + callbacks=[pl.pytorch.callbacks.ModelCheckpoint(save_top_k=-1)], + logger=logger, + ) + trainer.fit(model=model, train_dataloaders=train_loader) + + # Upload models + if args.modelscope_model_id is not None and args.modelscope_access_token is not None: + print(f"Uploading models to modelscope. model_id: {args.modelscope_model_id} local_path: {trainer.log_dir}") + with open(os.path.join(trainer.log_dir, "configuration.json"), "w", encoding="utf-8") as f: + f.write('{"framework":"Pytorch","task":"text-to-image-synthesis"}\n') + api = HubApi() + api.login(args.modelscope_access_token) + api.push_model(model_id=args.modelscope_model_id, model_dir=trainer.log_dir) diff --git a/diffsynth/trainers/unified_dataset.py b/diffsynth/trainers/unified_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..ac5ba4a91a806935546d4a4fb8a342b3bef3818b --- /dev/null +++ b/diffsynth/trainers/unified_dataset.py @@ -0,0 +1,362 @@ +import torch, torchvision, imageio, os, json, pandas +import imageio.v3 as iio +from PIL import Image + + + +class DataProcessingPipeline: + def __init__(self, operators=None): + self.operators: list[DataProcessingOperator] = [] if operators is None else operators + + def __call__(self, data): + for operator in self.operators: + data = operator(data) + return data + + def __rshift__(self, pipe): + if isinstance(pipe, DataProcessingOperator): + pipe = DataProcessingPipeline([pipe]) + return DataProcessingPipeline(self.operators + pipe.operators) + + + +class DataProcessingOperator: + def __call__(self, data): + raise NotImplementedError("DataProcessingOperator cannot be called directly.") + + def __rshift__(self, pipe): + if isinstance(pipe, DataProcessingOperator): + pipe = DataProcessingPipeline([pipe]) + return DataProcessingPipeline([self]).__rshift__(pipe) + + + +class DataProcessingOperatorRaw(DataProcessingOperator): + def __call__(self, data): + return data + + + +class ToInt(DataProcessingOperator): + def __call__(self, data): + return int(data) + + + +class ToFloat(DataProcessingOperator): + def __call__(self, data): + return float(data) + + + +class ToStr(DataProcessingOperator): + def __init__(self, none_value=""): + self.none_value = none_value + + def __call__(self, data): + if data is None: data = self.none_value + return str(data) + + + +class LoadImage(DataProcessingOperator): + def __init__(self, convert_RGB=True): + self.convert_RGB = convert_RGB + + def __call__(self, data: str): + image = Image.open(data) + if self.convert_RGB: image = image.convert("RGB") + return image + + + +class ImageCropAndResize(DataProcessingOperator): + def __init__(self, height, width, max_pixels, height_division_factor, width_division_factor): + self.height = height + self.width = width + self.max_pixels = max_pixels + self.height_division_factor = height_division_factor + self.width_division_factor = width_division_factor + + def crop_and_resize(self, image, target_height, target_width): + width, height = image.size + scale = max(target_width / width, target_height / height) + image = torchvision.transforms.functional.resize( + image, + (round(height*scale), round(width*scale)), + interpolation=torchvision.transforms.InterpolationMode.BILINEAR + ) + image = torchvision.transforms.functional.center_crop(image, (target_height, target_width)) + return image + + def get_height_width(self, image): + if self.height is None or self.width is None: + width, height = image.size + if width * height > self.max_pixels: + scale = (width * height / self.max_pixels) ** 0.5 + height, width = int(height / scale), int(width / scale) + height = height // self.height_division_factor * self.height_division_factor + width = width // self.width_division_factor * self.width_division_factor + else: + height, width = self.height, self.width + return height, width + + + def __call__(self, data: Image.Image): + image = self.crop_and_resize(data, *self.get_height_width(data)) + return image + + + +class ToList(DataProcessingOperator): + def __call__(self, data): + return [data] + + + +class LoadVideo(DataProcessingOperator): + def __init__(self, num_frames=81, time_division_factor=4, time_division_remainder=1, frame_processor=lambda x: x): + self.num_frames = num_frames + self.time_division_factor = time_division_factor + self.time_division_remainder = time_division_remainder + # frame_processor is build in the video loader for high efficiency. + self.frame_processor = frame_processor + + def get_num_frames(self, reader): + num_frames = self.num_frames + if int(reader.count_frames()) < num_frames: + num_frames = int(reader.count_frames()) + while num_frames > 1 and num_frames % self.time_division_factor != self.time_division_remainder: + num_frames -= 1 + return num_frames + + def __call__(self, data: str): + reader = imageio.get_reader(data) + num_frames = self.get_num_frames(reader) + frames = [] + for frame_id in range(num_frames): + frame = reader.get_data(frame_id) + frame = Image.fromarray(frame) + frame = self.frame_processor(frame) + frames.append(frame) + reader.close() + return frames + + + +class SequencialProcess(DataProcessingOperator): + def __init__(self, operator=lambda x: x): + self.operator = operator + + def __call__(self, data): + return [self.operator(i) for i in data] + + + +class LoadGIF(DataProcessingOperator): + def __init__(self, num_frames=81, time_division_factor=4, time_division_remainder=1, frame_processor=lambda x: x): + self.num_frames = num_frames + self.time_division_factor = time_division_factor + self.time_division_remainder = time_division_remainder + # frame_processor is build in the video loader for high efficiency. + self.frame_processor = frame_processor + + def get_num_frames(self, path): + num_frames = self.num_frames + images = iio.imread(path, mode="RGB") + if len(images) < num_frames: + num_frames = len(images) + while num_frames > 1 and num_frames % self.time_division_factor != self.time_division_remainder: + num_frames -= 1 + return num_frames + + def __call__(self, data: str): + num_frames = self.get_num_frames(data) + frames = [] + images = iio.imread(data, mode="RGB") + for img in images: + frame = Image.fromarray(img) + frame = self.frame_processor(frame) + frames.append(frame) + if len(frames) >= num_frames: + break + return frames + + + +class RouteByExtensionName(DataProcessingOperator): + def __init__(self, operator_map): + self.operator_map = operator_map + + def __call__(self, data: str): + file_ext_name = data.split(".")[-1].lower() + for ext_names, operator in self.operator_map: + if ext_names is None or file_ext_name in ext_names: + return operator(data) + raise ValueError(f"Unsupported file: {data}") + + + +class RouteByType(DataProcessingOperator): + def __init__(self, operator_map): + self.operator_map = operator_map + + def __call__(self, data): + for dtype, operator in self.operator_map: + if dtype is None or isinstance(data, dtype): + return operator(data) + raise ValueError(f"Unsupported data: {data}") + + + +class LoadTorchPickle(DataProcessingOperator): + def __init__(self, map_location="cpu"): + self.map_location = map_location + + def __call__(self, data): + return torch.load(data, map_location=self.map_location, weights_only=False) + + + +class ToAbsolutePath(DataProcessingOperator): + def __init__(self, base_path=""): + self.base_path = base_path + + def __call__(self, data): + return os.path.join(self.base_path, data) + +class LoadAudio(DataProcessingOperator): + def __init__(self, sr=16000): + self.sr = sr + def __call__(self, data: str): + import librosa + input_audio, sample_rate = librosa.load(data, sr=self.sr) + return input_audio + + +class UnifiedDataset(torch.utils.data.Dataset): + def __init__( + self, + base_path=None, metadata_path=None, + repeat=1, + data_file_keys=tuple(), + main_data_operator=lambda x: x, + special_operator_map=None, + default_caption=None,): + self.base_path = base_path + self.default_caption = default_caption + self.metadata_path = metadata_path + self.repeat = repeat + self.data_file_keys = data_file_keys + self.main_data_operator = main_data_operator + self.cached_data_operator = LoadTorchPickle() + self.special_operator_map = {} if special_operator_map is None else special_operator_map + self.data = [] + self.cached_data = [] + self.load_from_cache = metadata_path is None + self.load_metadata(metadata_path) + + @staticmethod + def default_image_operator( + base_path="", + max_pixels=1920*1080, height=None, width=None, + height_division_factor=16, width_division_factor=16, + ): + return RouteByType(operator_map=[ + (str, ToAbsolutePath(base_path) >> LoadImage() >> ImageCropAndResize(height, width, max_pixels, height_division_factor, width_division_factor)), + (list, SequencialProcess(ToAbsolutePath(base_path) >> LoadImage() >> ImageCropAndResize(height, width, max_pixels, height_division_factor, width_division_factor))), + ]) + + @staticmethod + def default_video_operator( + base_path="", + max_pixels=1920*1080, height=None, width=None, + height_division_factor=16, width_division_factor=16, + num_frames=81, time_division_factor=4, time_division_remainder=1, + ): + return RouteByType(operator_map=[ + (str, ToAbsolutePath(base_path) >> RouteByExtensionName(operator_map=[ + (("jpg", "jpeg", "png", "webp"), LoadImage() >> ImageCropAndResize(height, width, max_pixels, height_division_factor, width_division_factor) >> ToList()), + (("gif",), LoadGIF( + num_frames, time_division_factor, time_division_remainder, + frame_processor=ImageCropAndResize(height, width, max_pixels, height_division_factor, width_division_factor), + )), + (("mp4", "avi", "mov", "wmv", "mkv", "flv", "webm"), LoadVideo( + num_frames, time_division_factor, time_division_remainder, + frame_processor=ImageCropAndResize(height, width, max_pixels, height_division_factor, width_division_factor), + )), + ])), + ]) + + def search_for_cached_data_files(self, path): + for file_name in os.listdir(path): + subpath = os.path.join(path, file_name) + if os.path.isdir(subpath): + self.search_for_cached_data_files(subpath) + elif subpath.endswith(".pth"): + self.cached_data.append(subpath) + + def load_metadata(self, metadata_path): + if metadata_path is None: + print("No metadata_path. Searching for cached data files.") + self.search_for_cached_data_files(self.base_path) + print(f"{len(self.cached_data)} cached data files found.") + elif metadata_path.endswith(".json"): + with open(metadata_path, "r") as f: + metadata = json.load(f) + self.data = metadata + elif metadata_path.endswith(".jsonl"): + metadata = [] + with open(metadata_path, 'r') as f: + for line in f: + metadata.append(json.loads(line.strip())) + self.data = metadata + elif metadata_path.endswith(".txt"): + with open(metadata_path, "r") as f: + lines = f.readlines() + # self.data_file_keys: image, kontext_images 1x2 + # lines nx2 + self.data = [] + for line in lines: + items = line.strip().split("\t") + data_entry = {} + for key, item in zip(self.data_file_keys, items): + data_entry[key] = item + data_entry["prompt"] = self.default_caption + + self.data.append(data_entry) + + else: + metadata = pandas.read_csv(metadata_path) + self.data = [metadata.iloc[i].to_dict() for i in range(len(metadata))] + + def __getitem__(self, data_id): + if self.load_from_cache: + data = self.cached_data[data_id % len(self.cached_data)] + data = self.cached_data_operator(data) + else: + data = self.data[data_id % len(self.data)].copy() + for key in self.data_file_keys: + if key in data: + if key in self.special_operator_map: + data[key] = self.special_operator_map[key](data[key]) + elif key == "prompt": + pass + elif key in self.data_file_keys: + data[key] = self.main_data_operator(data[key]) + return data + + def __len__(self): + if self.load_from_cache: + return len(self.cached_data) * self.repeat + else: + return len(self.data) * self.repeat + + def check_data_equal(self, data1, data2): + # Debug only + if len(data1) != len(data2): + return False + for k in data1: + if data1[k] != data2[k]: + return False + return True diff --git a/diffsynth/trainers/utils.py b/diffsynth/trainers/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..566d393445e6f63a9df26575aac34f4287c42dc2 --- /dev/null +++ b/diffsynth/trainers/utils.py @@ -0,0 +1,696 @@ +import imageio, os, torch, warnings, torchvision, argparse, json +from ..utils import ModelConfig +from ..models.utils import load_state_dict +from peft import LoraConfig, inject_adapter_in_model +from PIL import Image +import pandas as pd +from tqdm import tqdm +from accelerate import Accelerator +from accelerate.utils import DistributedDataParallelKwargs + + + +class ImageDataset(torch.utils.data.Dataset): + def __init__( + self, + base_path=None, metadata_path=None, + max_pixels=1920*1080, height=None, width=None, + height_division_factor=16, width_division_factor=16, + data_file_keys=("image",), + image_file_extension=("jpg", "jpeg", "png", "webp"), + repeat=1, + args=None, + ): + if args is not None: + base_path = args.dataset_base_path + metadata_path = args.dataset_metadata_path + height = args.height + width = args.width + max_pixels = args.max_pixels + data_file_keys = args.data_file_keys.split(",") + repeat = args.dataset_repeat + + self.base_path = base_path + self.max_pixels = max_pixels + self.height = height + self.width = width + self.height_division_factor = height_division_factor + self.width_division_factor = width_division_factor + self.data_file_keys = data_file_keys + self.image_file_extension = image_file_extension + self.repeat = repeat + + if height is not None and width is not None: + print("Height and width are fixed. Setting `dynamic_resolution` to False.") + self.dynamic_resolution = False + elif height is None and width is None: + print("Height and width are none. Setting `dynamic_resolution` to True.") + self.dynamic_resolution = True + + if metadata_path is None: + print("No metadata. Trying to generate it.") + metadata = self.generate_metadata(base_path) + print(f"{len(metadata)} lines in metadata.") + self.data = [metadata.iloc[i].to_dict() for i in range(len(metadata))] + elif metadata_path.endswith(".json"): + with open(metadata_path, "r") as f: + metadata = json.load(f) + self.data = metadata + elif metadata_path.endswith(".jsonl"): + metadata = [] + with open(metadata_path, 'r') as f: + for line in tqdm(f): + metadata.append(json.loads(line.strip())) + self.data = metadata + else: + metadata = pd.read_csv(metadata_path) + self.data = [metadata.iloc[i].to_dict() for i in range(len(metadata))] + + + def generate_metadata(self, folder): + image_list, prompt_list = [], [] + file_set = set(os.listdir(folder)) + for file_name in file_set: + if "." not in file_name: + continue + file_ext_name = file_name.split(".")[-1].lower() + file_base_name = file_name[:-len(file_ext_name)-1] + if file_ext_name not in self.image_file_extension: + continue + prompt_file_name = file_base_name + ".txt" + if prompt_file_name not in file_set: + continue + with open(os.path.join(folder, prompt_file_name), "r", encoding="utf-8") as f: + prompt = f.read().strip() + image_list.append(file_name) + prompt_list.append(prompt) + metadata = pd.DataFrame() + metadata["image"] = image_list + metadata["prompt"] = prompt_list + return metadata + + + def crop_and_resize(self, image, target_height, target_width): + width, height = image.size + scale = max(target_width / width, target_height / height) + image = torchvision.transforms.functional.resize( + image, + (round(height*scale), round(width*scale)), + interpolation=torchvision.transforms.InterpolationMode.BILINEAR + ) + image = torchvision.transforms.functional.center_crop(image, (target_height, target_width)) + return image + + + def get_height_width(self, image): + if self.dynamic_resolution: + width, height = image.size + if width * height > self.max_pixels: + scale = (width * height / self.max_pixels) ** 0.5 + height, width = int(height / scale), int(width / scale) + height = height // self.height_division_factor * self.height_division_factor + width = width // self.width_division_factor * self.width_division_factor + else: + height, width = self.height, self.width + return height, width + + + def load_image(self, file_path): + image = Image.open(file_path).convert("RGB") + image = self.crop_and_resize(image, *self.get_height_width(image)) + return image + + + def load_data(self, file_path): + return self.load_image(file_path) + + + def __getitem__(self, data_id): + data = self.data[data_id % len(self.data)].copy() + for key in self.data_file_keys: + if key in data: + if isinstance(data[key], list): + path = [os.path.join(self.base_path, p) for p in data[key]] + data[key] = [self.load_data(p) for p in path] + else: + path = os.path.join(self.base_path, data[key]) + data[key] = self.load_data(path) + if data[key] is None: + warnings.warn(f"cannot load file {data[key]}.") + return None + return data + + + def __len__(self): + return len(self.data) * self.repeat + + + +class VideoDataset(torch.utils.data.Dataset): + def __init__( + self, + base_path=None, metadata_path=None, + num_frames=81, + time_division_factor=4, time_division_remainder=1, + max_pixels=1920*1080, height=None, width=None, + height_division_factor=16, width_division_factor=16, + data_file_keys=("video",), + image_file_extension=("jpg", "jpeg", "png", "webp"), + video_file_extension=("mp4", "avi", "mov", "wmv", "mkv", "flv", "webm", "gif"), + repeat=1, + args=None, + ): + if args is not None: + base_path = args.dataset_base_path + metadata_path = args.dataset_metadata_path + height = args.height + width = args.width + max_pixels = args.max_pixels + num_frames = args.num_frames + data_file_keys = args.data_file_keys.split(",") + repeat = args.dataset_repeat + + self.base_path = base_path + self.num_frames = num_frames + self.time_division_factor = time_division_factor + self.time_division_remainder = time_division_remainder + self.max_pixels = max_pixels + self.height = height + self.width = width + self.height_division_factor = height_division_factor + self.width_division_factor = width_division_factor + self.data_file_keys = data_file_keys + self.image_file_extension = image_file_extension + self.video_file_extension = video_file_extension + self.repeat = repeat + + if height is not None and width is not None: + print("Height and width are fixed. Setting `dynamic_resolution` to False.") + self.dynamic_resolution = False + elif height is None and width is None: + print("Height and width are none. Setting `dynamic_resolution` to True.") + self.dynamic_resolution = True + + if metadata_path is None: + print("No metadata. Trying to generate it.") + metadata = self.generate_metadata(base_path) + print(f"{len(metadata)} lines in metadata.") + self.data = [metadata.iloc[i].to_dict() for i in range(len(metadata))] + elif metadata_path.endswith(".json"): + with open(metadata_path, "r") as f: + metadata = json.load(f) + self.data = metadata + else: + metadata = pd.read_csv(metadata_path) + self.data = [metadata.iloc[i].to_dict() for i in range(len(metadata))] + + + def generate_metadata(self, folder): + video_list, prompt_list = [], [] + file_set = set(os.listdir(folder)) + for file_name in file_set: + if "." not in file_name: + continue + file_ext_name = file_name.split(".")[-1].lower() + file_base_name = file_name[:-len(file_ext_name)-1] + if file_ext_name not in self.image_file_extension and file_ext_name not in self.video_file_extension: + continue + prompt_file_name = file_base_name + ".txt" + if prompt_file_name not in file_set: + continue + with open(os.path.join(folder, prompt_file_name), "r", encoding="utf-8") as f: + prompt = f.read().strip() + video_list.append(file_name) + prompt_list.append(prompt) + metadata = pd.DataFrame() + metadata["video"] = video_list + metadata["prompt"] = prompt_list + return metadata + + + def crop_and_resize(self, image, target_height, target_width): + width, height = image.size + scale = max(target_width / width, target_height / height) + image = torchvision.transforms.functional.resize( + image, + (round(height*scale), round(width*scale)), + interpolation=torchvision.transforms.InterpolationMode.BILINEAR + ) + image = torchvision.transforms.functional.center_crop(image, (target_height, target_width)) + return image + + + def get_height_width(self, image): + if self.dynamic_resolution: + width, height = image.size + if width * height > self.max_pixels: + scale = (width * height / self.max_pixels) ** 0.5 + height, width = int(height / scale), int(width / scale) + height = height // self.height_division_factor * self.height_division_factor + width = width // self.width_division_factor * self.width_division_factor + else: + height, width = self.height, self.width + return height, width + + + def get_num_frames(self, reader): + num_frames = self.num_frames + if int(reader.count_frames()) < num_frames: + num_frames = int(reader.count_frames()) + while num_frames > 1 and num_frames % self.time_division_factor != self.time_division_remainder: + num_frames -= 1 + return num_frames + + def _load_gif(self, file_path): + gif_img = Image.open(file_path) + frame_count = 0 + delays, frames = [], [] + while True: + delay = gif_img.info.get('duration', 100) # ms + delays.append(delay) + rgb_frame = gif_img.convert("RGB") + croped_frame = self.crop_and_resize(rgb_frame, *self.get_height_width(rgb_frame)) + frames.append(croped_frame) + frame_count += 1 + try: + gif_img.seek(frame_count) + except: + break + # delays canbe used to calculate framerates + # i guess it is better to sample images with stable interval, + # and using minimal_interval as the interval, + # and framerate = 1000 / minimal_interval + if any((delays[0] != i) for i in delays): + minimal_interval = min([i for i in delays if i > 0]) + # make a ((start,end),frameid) struct + start_end_idx_map = [((sum(delays[:i]), sum(delays[:i+1])), i) for i in range(len(delays))] + _frames = [] + # according gemini-code-assist, make it more efficient to locate + # where to sample the frame + last_match = 0 + for i in range(sum(delays) // minimal_interval): + current_time = minimal_interval * i + for idx, ((start, end), frame_idx) in enumerate(start_end_idx_map[last_match:]): + if start <= current_time < end: + _frames.append(frames[frame_idx]) + last_match = idx + last_match + break + frames = _frames + num_frames = len(frames) + if num_frames > self.num_frames: + num_frames = self.num_frames + else: + while num_frames > 1 and num_frames % self.time_division_factor != self.time_division_remainder: + num_frames -= 1 + frames = frames[:num_frames] + return frames + + def load_video(self, file_path): + if file_path.lower().endswith(".gif"): + return self._load_gif(file_path) + reader = imageio.get_reader(file_path) + num_frames = self.get_num_frames(reader) + frames = [] + for frame_id in range(num_frames): + frame = reader.get_data(frame_id) + frame = Image.fromarray(frame) + frame = self.crop_and_resize(frame, *self.get_height_width(frame)) + frames.append(frame) + reader.close() + return frames + + + def load_image(self, file_path): + image = Image.open(file_path).convert("RGB") + image = self.crop_and_resize(image, *self.get_height_width(image)) + frames = [image] + return frames + + + def is_image(self, file_path): + file_ext_name = file_path.split(".")[-1] + return file_ext_name.lower() in self.image_file_extension + + + def is_video(self, file_path): + file_ext_name = file_path.split(".")[-1] + return file_ext_name.lower() in self.video_file_extension + + + def load_data(self, file_path): + if self.is_image(file_path): + return self.load_image(file_path) + elif self.is_video(file_path): + return self.load_video(file_path) + else: + return None + + + def __getitem__(self, data_id): + data = self.data[data_id % len(self.data)].copy() + for key in self.data_file_keys: + if key in data: + path = os.path.join(self.base_path, data[key]) + data[key] = self.load_data(path) + if data[key] is None: + warnings.warn(f"cannot load file {data[key]}.") + return None + return data + + + def __len__(self): + return len(self.data) * self.repeat + + + +class DiffusionTrainingModule(torch.nn.Module): + def __init__(self): + super().__init__() + + + def to(self, *args, **kwargs): + for name, model in self.named_children(): + model.to(*args, **kwargs) + return self + + + def trainable_modules(self): + trainable_modules = filter(lambda p: p.requires_grad, self.parameters()) + return trainable_modules + + + def trainable_param_names(self): + trainable_param_names = list(filter(lambda named_param: named_param[1].requires_grad, self.named_parameters())) + trainable_param_names = set([named_param[0] for named_param in trainable_param_names]) + return trainable_param_names + + + def add_lora_to_model(self, model, target_modules, lora_rank, lora_alpha=None, upcast_dtype=None): + if lora_alpha is None: + lora_alpha = lora_rank + lora_config = LoraConfig(r=lora_rank, lora_alpha=lora_alpha, target_modules=target_modules) + model = inject_adapter_in_model(lora_config, model) + if upcast_dtype is not None: + for param in model.parameters(): + if param.requires_grad: + param.data = param.to(upcast_dtype) + return model + + + def mapping_lora_state_dict(self, state_dict): + new_state_dict = {} + for key, value in state_dict.items(): + if "lora_A.weight" in key or "lora_B.weight" in key: + new_key = key.replace("lora_A.weight", "lora_A.default.weight").replace("lora_B.weight", "lora_B.default.weight") + new_state_dict[new_key] = value + elif "lora_A.default.weight" in key or "lora_B.default.weight" in key: + new_state_dict[key] = value + return new_state_dict + + + def export_trainable_state_dict(self, state_dict, remove_prefix=None): + trainable_param_names = self.trainable_param_names() + state_dict = {name: param for name, param in state_dict.items() if name in trainable_param_names} + if remove_prefix is not None: + state_dict_ = {} + for name, param in state_dict.items(): + if name.startswith(remove_prefix): + name = name[len(remove_prefix):] + state_dict_[name] = param + state_dict = state_dict_ + return state_dict + + + def transfer_data_to_device(self, data, device, torch_float_dtype=None): + for key in data: + if isinstance(data[key], torch.Tensor): + data[key] = data[key].to(device) + if torch_float_dtype is not None and data[key].dtype in [torch.float, torch.float16, torch.bfloat16]: + data[key] = data[key].to(torch_float_dtype) + return data + + + def parse_model_configs(self, model_paths, model_id_with_origin_paths, enable_fp8_training=False): + offload_dtype = torch.float8_e4m3fn if enable_fp8_training else None + model_configs = [] + if model_paths is not None: + model_paths = json.loads(model_paths) + model_configs += [ModelConfig(path=path, offload_dtype=offload_dtype) for path in model_paths] + if model_id_with_origin_paths is not None: + model_id_with_origin_paths = model_id_with_origin_paths.split(",") + model_configs += [ModelConfig(model_id=i.split(":")[0], origin_file_pattern=i.split(":")[1], offload_dtype=offload_dtype) for i in model_id_with_origin_paths] + return model_configs + + + def switch_pipe_to_training_mode( + self, + pipe, + trainable_models, + lora_base_model, lora_target_modules, lora_rank, lora_checkpoint=None, + enable_fp8_training=False, + ): + # Scheduler + pipe.scheduler.set_timesteps(1000, training=True) + + # Freeze untrainable models + pipe.freeze_except([] if trainable_models is None else trainable_models.split(",")) + + # Enable FP8 if pipeline supports + if enable_fp8_training and hasattr(pipe, "_enable_fp8_lora_training"): + pipe._enable_fp8_lora_training(torch.float8_e4m3fn) + + # Add LoRA to the base models + if lora_base_model is not None: + model = self.add_lora_to_model( + getattr(pipe, lora_base_model), + target_modules=lora_target_modules.split(","), + lora_rank=lora_rank, + upcast_dtype=pipe.torch_dtype, + ) + if lora_checkpoint is not None: + state_dict = load_state_dict(lora_checkpoint) + state_dict = self.mapping_lora_state_dict(state_dict) + load_result = model.load_state_dict(state_dict, strict=False) + print(f"LoRA checkpoint loaded: {lora_checkpoint}, total {len(state_dict)} keys") + if len(load_result[1]) > 0: + print(f"Warning, LoRA key mismatch! Unexpected keys in LoRA checkpoint: {load_result[1]}") + setattr(pipe, lora_base_model, model) + + +class ModelLogger: + def __init__(self, output_path, remove_prefix_in_ckpt=None, state_dict_converter=lambda x:x): + self.output_path = output_path + self.remove_prefix_in_ckpt = remove_prefix_in_ckpt + self.state_dict_converter = state_dict_converter + self.num_steps = 0 + + + def on_step_end(self, accelerator, model, save_steps=None): + self.num_steps += 1 + if save_steps is not None and self.num_steps % save_steps == 0: + self.save_model(accelerator, model, f"step-{self.num_steps}.safetensors") + + + def on_epoch_end(self, accelerator, model, epoch_id): + accelerator.wait_for_everyone() + if accelerator.is_main_process: + state_dict = accelerator.get_state_dict(model) + state_dict = accelerator.unwrap_model(model).export_trainable_state_dict(state_dict, remove_prefix=self.remove_prefix_in_ckpt) + state_dict = self.state_dict_converter(state_dict) + os.makedirs(self.output_path, exist_ok=True) + path = os.path.join(self.output_path, f"epoch-{epoch_id}.safetensors") + accelerator.save(state_dict, path, safe_serialization=True) + + + def on_training_end(self, accelerator, model, save_steps=None): + if save_steps is not None and self.num_steps % save_steps != 0: + self.save_model(accelerator, model, f"step-{self.num_steps}.safetensors") + + + def save_model(self, accelerator, model, file_name): + accelerator.wait_for_everyone() + if accelerator.is_main_process: + state_dict = accelerator.get_state_dict(model) + state_dict = accelerator.unwrap_model(model).export_trainable_state_dict(state_dict, remove_prefix=self.remove_prefix_in_ckpt) + state_dict = self.state_dict_converter(state_dict) + os.makedirs(self.output_path, exist_ok=True) + path = os.path.join(self.output_path, file_name) + accelerator.save(state_dict, path, safe_serialization=True) + + +def launch_training_task( + dataset: torch.utils.data.Dataset, + model: DiffusionTrainingModule, + model_logger: ModelLogger, + learning_rate: float = 1e-5, + weight_decay: float = 1e-2, + num_workers: int = 8, + save_steps: int = None, + num_epochs: int = 1, + gradient_accumulation_steps: int = 1, + find_unused_parameters: bool = False, + args = None, +): + if args is not None: + learning_rate = args.learning_rate + weight_decay = args.weight_decay + num_workers = args.dataset_num_workers + save_steps = args.save_steps + num_epochs = args.num_epochs + gradient_accumulation_steps = args.gradient_accumulation_steps + find_unused_parameters = args.find_unused_parameters + + optimizer = torch.optim.AdamW(model.trainable_modules(), lr=learning_rate, weight_decay=weight_decay) + scheduler = torch.optim.lr_scheduler.ConstantLR(optimizer) + dataloader = torch.utils.data.DataLoader(dataset, shuffle=True, collate_fn=lambda x: x[0], num_workers=num_workers) + accelerator = Accelerator( + gradient_accumulation_steps=gradient_accumulation_steps, + kwargs_handlers=[DistributedDataParallelKwargs(find_unused_parameters=find_unused_parameters)], + ) + model, optimizer, dataloader, scheduler = accelerator.prepare(model, optimizer, dataloader, scheduler) + for epoch_id in range(num_epochs): + progress_bar = tqdm(dataloader, desc="loss: N/A") + for data in progress_bar: + with accelerator.accumulate(model): + optimizer.zero_grad() + if dataset.load_from_cache: + loss = model({}, inputs=data) + else: + loss = model(data) + accelerator.backward(loss) + optimizer.step() + model_logger.on_step_end(accelerator, model, save_steps) + scheduler.step() + progress_bar.set_description(f"loss: {loss.item():.4f}") + if save_steps is None: + model_logger.on_epoch_end(accelerator, model, epoch_id) + model_logger.on_training_end(accelerator, model, save_steps) + + +def launch_data_process_task( + dataset: torch.utils.data.Dataset, + model: DiffusionTrainingModule, + model_logger: ModelLogger, + num_workers: int = 8, + args = None, +): + if args is not None: + num_workers = args.dataset_num_workers + + dataloader = torch.utils.data.DataLoader(dataset, shuffle=False, collate_fn=lambda x: x[0], num_workers=num_workers) + accelerator = Accelerator() + model, dataloader = accelerator.prepare(model, dataloader) + + for data_id, data in tqdm(enumerate(dataloader)): + with accelerator.accumulate(model): + with torch.no_grad(): + folder = os.path.join(model_logger.output_path, str(accelerator.process_index)) + os.makedirs(folder, exist_ok=True) + save_path = os.path.join(model_logger.output_path, str(accelerator.process_index), f"{data_id}.pth") + data = model(data, return_inputs=True) + torch.save(data, save_path) + + + +def wan_parser(): + parser = argparse.ArgumentParser(description="Simple example of a training script.") + parser.add_argument("--dataset_base_path", type=str, default="", required=True, help="Base path of the dataset.") + parser.add_argument("--dataset_metadata_path", type=str, default=None, help="Path to the metadata file of the dataset.") + parser.add_argument("--max_pixels", type=int, default=1280*720, help="Maximum number of pixels per frame, used for dynamic resolution..") + parser.add_argument("--height", type=int, default=None, help="Height of images or videos. Leave `height` and `width` empty to enable dynamic resolution.") + parser.add_argument("--width", type=int, default=None, help="Width of images or videos. Leave `height` and `width` empty to enable dynamic resolution.") + parser.add_argument("--num_frames", type=int, default=81, help="Number of frames per video. Frames are sampled from the video prefix.") + parser.add_argument("--data_file_keys", type=str, default="image,video", help="Data file keys in the metadata. Comma-separated.") + parser.add_argument("--dataset_repeat", type=int, default=1, help="Number of times to repeat the dataset per epoch.") + parser.add_argument("--model_paths", type=str, default=None, help="Paths to load models. In JSON format.") + parser.add_argument("--model_id_with_origin_paths", type=str, default=None, help="Model ID with origin paths, e.g., Wan-AI/Wan2.1-T2V-1.3B:diffusion_pytorch_model*.safetensors. Comma-separated.") + parser.add_argument("--audio_processor_config", type=str, default=None, help="Model ID with origin paths to the audio processor config, e.g., Wan-AI/Wan2.2-S2V-14B:wav2vec2-large-xlsr-53-english/") + parser.add_argument("--learning_rate", type=float, default=1e-4, help="Learning rate.") + parser.add_argument("--num_epochs", type=int, default=1, help="Number of epochs.") + parser.add_argument("--output_path", type=str, default="./models", help="Output save path.") + parser.add_argument("--remove_prefix_in_ckpt", type=str, default="pipe.dit.", help="Remove prefix in ckpt.") + parser.add_argument("--trainable_models", type=str, default=None, help="Models to train, e.g., dit, vae, text_encoder.") + parser.add_argument("--lora_base_model", type=str, default=None, help="Which model LoRA is added to.") + parser.add_argument("--lora_target_modules", type=str, default="q,k,v,o,ffn.0,ffn.2", help="Which layers LoRA is added to.") + parser.add_argument("--lora_rank", type=int, default=32, help="Rank of LoRA.") + parser.add_argument("--lora_checkpoint", type=str, default=None, help="Path to the LoRA checkpoint. If provided, LoRA will be loaded from this checkpoint.") + parser.add_argument("--extra_inputs", default=None, help="Additional model inputs, comma-separated.") + parser.add_argument("--use_gradient_checkpointing_offload", default=False, action="store_true", help="Whether to offload gradient checkpointing to CPU memory.") + parser.add_argument("--gradient_accumulation_steps", type=int, default=1, help="Gradient accumulation steps.") + parser.add_argument("--max_timestep_boundary", type=float, default=1.0, help="Max timestep boundary (for mixed models, e.g., Wan-AI/Wan2.2-I2V-A14B).") + parser.add_argument("--min_timestep_boundary", type=float, default=0.0, help="Min timestep boundary (for mixed models, e.g., Wan-AI/Wan2.2-I2V-A14B).") + parser.add_argument("--find_unused_parameters", default=False, action="store_true", help="Whether to find unused parameters in DDP.") + parser.add_argument("--save_steps", type=int, default=None, help="Number of checkpoint saving invervals. If None, checkpoints will be saved every epoch.") + parser.add_argument("--dataset_num_workers", type=int, default=0, help="Number of workers for data loading.") + parser.add_argument("--weight_decay", type=float, default=0.01, help="Weight decay.") + return parser + + + +def flux_parser(): + parser = argparse.ArgumentParser(description="Simple example of a training script.") + parser.add_argument("--dataset_base_path", type=str, default="", required=True, help="Base path of the dataset.") + parser.add_argument("--dataset_metadata_path", type=str, default=None, help="Path to the metadata file of the dataset.") + parser.add_argument("--max_pixels", type=int, default=1024*1024, help="Maximum number of pixels per frame, used for dynamic resolution..") + parser.add_argument("--height", type=int, default=None, help="Height of images. Leave `height` and `width` empty to enable dynamic resolution.") + parser.add_argument("--width", type=int, default=None, help="Width of images. Leave `height` and `width` empty to enable dynamic resolution.") + parser.add_argument("--data_file_keys", type=str, default="image", help="Data file keys in the metadata. Comma-separated.") + parser.add_argument("--dataset_repeat", type=int, default=1, help="Number of times to repeat the dataset per epoch.") + parser.add_argument("--model_paths", type=str, default=None, help="Paths to load models. In JSON format.") + parser.add_argument("--model_id_with_origin_paths", type=str, default=None, help="Model ID with origin paths, e.g., Wan-AI/Wan2.1-T2V-1.3B:diffusion_pytorch_model*.safetensors. Comma-separated.") + parser.add_argument("--learning_rate", type=float, default=1e-4, help="Learning rate.") + parser.add_argument("--num_epochs", type=int, default=1, help="Number of epochs.") + parser.add_argument("--output_path", type=str, default="./models", help="Output save path.") + parser.add_argument("--remove_prefix_in_ckpt", type=str, default="pipe.dit.", help="Remove prefix in ckpt.") + parser.add_argument("--trainable_models", type=str, default=None, help="Models to train, e.g., dit, vae, text_encoder.") + parser.add_argument("--lora_base_model", type=str, default=None, help="Which model LoRA is added to.") + parser.add_argument("--lora_target_modules", type=str, default="q,k,v,o,ffn.0,ffn.2", help="Which layers LoRA is added to.") + parser.add_argument("--lora_rank", type=int, default=32, help="Rank of LoRA.") + parser.add_argument("--lora_checkpoint", type=str, default=None, help="Path to the LoRA checkpoint. If provided, LoRA will be loaded from this checkpoint.") + parser.add_argument("--extra_inputs", default=None, help="Additional model inputs, comma-separated.") + parser.add_argument("--align_to_opensource_format", default=False, action="store_true", help="Whether to align the lora format to opensource format. Only for DiT's LoRA.") + parser.add_argument("--use_gradient_checkpointing", default=False, action="store_true", help="Whether to use gradient checkpointing.") + parser.add_argument("--use_gradient_checkpointing_offload", default=False, action="store_true", help="Whether to offload gradient checkpointing to CPU memory.") + parser.add_argument("--gradient_accumulation_steps", type=int, default=1, help="Gradient accumulation steps.") + parser.add_argument("--find_unused_parameters", default=False, action="store_true", help="Whether to find unused parameters in DDP.") + parser.add_argument("--save_steps", type=int, default=None, help="Number of checkpoint saving invervals. If None, checkpoints will be saved every epoch.") + parser.add_argument("--dataset_num_workers", type=int, default=0, help="Number of workers for data loading.") + parser.add_argument("--weight_decay", type=float, default=0.01, help="Weight decay.") + parser.add_argument("--default_caption", type=str, default="Convert this image into a line art comic style. Keep the scenes and characters unchanged, present it as a black-and-white sketch, and use it for storyboard design.With tough lines and rich details, it focuses on shaping structures and textures with simple lines, and the style tends to be a realistic sketch. Cross-hatching is used to create simple light and shadow.", help="Default caption for images without captions in the dataset.") + return parser + + + +def qwen_image_parser(): + parser = argparse.ArgumentParser(description="Simple example of a training script.") + parser.add_argument("--dataset_base_path", type=str, default="", required=True, help="Base path of the dataset.") + parser.add_argument("--dataset_metadata_path", type=str, default=None, help="Path to the metadata file of the dataset.") + parser.add_argument("--max_pixels", type=int, default=1024*1024, help="Maximum number of pixels per frame, used for dynamic resolution..") + parser.add_argument("--height", type=int, default=None, help="Height of images. Leave `height` and `width` empty to enable dynamic resolution.") + parser.add_argument("--width", type=int, default=None, help="Width of images. Leave `height` and `width` empty to enable dynamic resolution.") + parser.add_argument("--data_file_keys", type=str, default="image", help="Data file keys in the metadata. Comma-separated.") + parser.add_argument("--dataset_repeat", type=int, default=1, help="Number of times to repeat the dataset per epoch.") + parser.add_argument("--model_paths", type=str, default=None, help="Paths to load models. In JSON format.") + parser.add_argument("--model_id_with_origin_paths", type=str, default=None, help="Model ID with origin paths, e.g., Wan-AI/Wan2.1-T2V-1.3B:diffusion_pytorch_model*.safetensors. Comma-separated.") + parser.add_argument("--tokenizer_path", type=str, default=None, help="Paths to tokenizer.") + parser.add_argument("--learning_rate", type=float, default=1e-4, help="Learning rate.") + parser.add_argument("--num_epochs", type=int, default=1, help="Number of epochs.") + parser.add_argument("--output_path", type=str, default="./models", help="Output save path.") + parser.add_argument("--remove_prefix_in_ckpt", type=str, default="pipe.dit.", help="Remove prefix in ckpt.") + parser.add_argument("--trainable_models", type=str, default=None, help="Models to train, e.g., dit, vae, text_encoder.") + parser.add_argument("--lora_base_model", type=str, default=None, help="Which model LoRA is added to.") + parser.add_argument("--lora_target_modules", type=str, default="q,k,v,o,ffn.0,ffn.2", help="Which layers LoRA is added to.") + parser.add_argument("--lora_rank", type=int, default=32, help="Rank of LoRA.") + parser.add_argument("--lora_checkpoint", type=str, default=None, help="Path to the LoRA checkpoint. If provided, LoRA will be loaded from this checkpoint.") + parser.add_argument("--extra_inputs", default=None, help="Additional model inputs, comma-separated.") + parser.add_argument("--use_gradient_checkpointing", default=False, action="store_true", help="Whether to use gradient checkpointing.") + parser.add_argument("--use_gradient_checkpointing_offload", default=False, action="store_true", help="Whether to offload gradient checkpointing to CPU memory.") + parser.add_argument("--gradient_accumulation_steps", type=int, default=1, help="Gradient accumulation steps.") + parser.add_argument("--find_unused_parameters", default=False, action="store_true", help="Whether to find unused parameters in DDP.") + parser.add_argument("--save_steps", type=int, default=None, help="Number of checkpoint saving invervals. If None, checkpoints will be saved every epoch.") + parser.add_argument("--dataset_num_workers", type=int, default=0, help="Number of workers for data loading.") + parser.add_argument("--weight_decay", type=float, default=0.01, help="Weight decay.") + parser.add_argument("--processor_path", type=str, default=None, help="Path to the processor. If provided, the processor will be used for image editing.") + parser.add_argument("--enable_fp8_training", default=False, action="store_true", help="Whether to enable FP8 training. Only available for LoRA training on a single GPU.") + parser.add_argument("--task", type=str, default="sft", required=False, help="Task type.") + return parser diff --git a/diffsynth/utils/__init__.py b/diffsynth/utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..133f8992c4830ff0a70fa44846d7a8b1a185da6a --- /dev/null +++ b/diffsynth/utils/__init__.py @@ -0,0 +1,287 @@ +import torch, warnings, glob, os +import numpy as np +from PIL import Image +from einops import repeat, reduce +from typing import Optional, Union +from dataclasses import dataclass +from huggingface_hub import snapshot_download as hf_snapshot_download +from modelscope import snapshot_download +import numpy as np +from PIL import Image +from typing import Optional + + +class BasePipeline(torch.nn.Module): + + def __init__( + self, + device="cuda", torch_dtype=torch.float16, + height_division_factor=64, width_division_factor=64, + time_division_factor=None, time_division_remainder=None, + ): + super().__init__() + # The device and torch_dtype is used for the storage of intermediate variables, not models. + self.device = device + self.torch_dtype = torch_dtype + # The following parameters are used for shape check. + self.height_division_factor = height_division_factor + self.width_division_factor = width_division_factor + self.time_division_factor = time_division_factor + self.time_division_remainder = time_division_remainder + self.vram_management_enabled = False + + + def to(self, *args, **kwargs): + device, dtype, non_blocking, convert_to_format = torch._C._nn._parse_to(*args, **kwargs) + if device is not None: + self.device = device + if dtype is not None: + self.torch_dtype = dtype + super().to(*args, **kwargs) + return self + + + def check_resize_height_width(self, height, width, num_frames=None): + # Shape check + if height % self.height_division_factor != 0: + height = (height + self.height_division_factor - 1) // self.height_division_factor * self.height_division_factor + print(f"height % {self.height_division_factor} != 0. We round it up to {height}.") + if width % self.width_division_factor != 0: + width = (width + self.width_division_factor - 1) // self.width_division_factor * self.width_division_factor + print(f"width % {self.width_division_factor} != 0. We round it up to {width}.") + if num_frames is None: + return height, width + else: + if num_frames % self.time_division_factor != self.time_division_remainder: + num_frames = (num_frames + self.time_division_factor - 1) // self.time_division_factor * self.time_division_factor + self.time_division_remainder + print(f"num_frames % {self.time_division_factor} != {self.time_division_remainder}. We round it up to {num_frames}.") + return height, width, num_frames + + + def preprocess_image(self, image, torch_dtype=None, device=None, pattern="B C H W", min_value=-1, max_value=1): + # Transform a PIL.Image to torch.Tensor + image = torch.Tensor(np.array(image, dtype=np.float32)) + image = image.to(dtype=torch_dtype or self.torch_dtype, device=device or self.device) + image = image * ((max_value - min_value) / 255) + min_value + image = repeat(image, f"H W C -> {pattern}", **({"B": 1} if "B" in pattern else {})) + return image + + + def preprocess_video(self, video, torch_dtype=None, device=None, pattern="B C T H W", min_value=-1, max_value=1): + # Transform a list of PIL.Image to torch.Tensor + video = [self.preprocess_image(image, torch_dtype=torch_dtype, device=device, min_value=min_value, max_value=max_value) for image in video] + video = torch.stack(video, dim=pattern.index("T") // 2) + return video + + + def vae_output_to_image(self, vae_output, pattern="B C H W", min_value=-1, max_value=1): + # Transform a torch.Tensor to PIL.Image + if pattern != "H W C": + vae_output = reduce(vae_output, f"{pattern} -> H W C", reduction="mean") + image = ((vae_output - min_value) * (255 / (max_value - min_value))).clip(0, 255) + image = image.to(device="cpu", dtype=torch.uint8) + image = Image.fromarray(image.numpy()) + return image + + + def vae_output_to_video(self, vae_output, pattern="B C T H W", min_value=-1, max_value=1): + # Transform a torch.Tensor to list of PIL.Image + if pattern != "T H W C": + vae_output = reduce(vae_output, f"{pattern} -> T H W C", reduction="mean") + video = [self.vae_output_to_image(image, pattern="H W C", min_value=min_value, max_value=max_value) for image in vae_output] + return video + + + def load_models_to_device(self, model_names=[]): + if self.vram_management_enabled: + # offload models + for name, model in self.named_children(): + if name not in model_names: + if hasattr(model, "vram_management_enabled") and model.vram_management_enabled: + for module in model.modules(): + if hasattr(module, "offload"): + module.offload() + else: + model.cpu() + torch.cuda.empty_cache() + # onload models + for name, model in self.named_children(): + if name in model_names: + if hasattr(model, "vram_management_enabled") and model.vram_management_enabled: + for module in model.modules(): + if hasattr(module, "onload"): + module.onload() + else: + model.to(self.device) + + + def generate_noise(self, shape, seed=None, rand_device="cpu", rand_torch_dtype=torch.float32, device=None, torch_dtype=None): + # Initialize Gaussian noise + generator = None if seed is None else torch.Generator(rand_device).manual_seed(seed) + noise = torch.randn(shape, generator=generator, device=rand_device, dtype=rand_torch_dtype) + noise = noise.to(dtype=torch_dtype or self.torch_dtype, device=device or self.device) + return noise + + + def enable_cpu_offload(self): + warnings.warn("`enable_cpu_offload` will be deprecated. Please use `enable_vram_management`.") + self.vram_management_enabled = True + + + def get_vram(self): + return torch.cuda.mem_get_info(self.device)[1] / (1024 ** 3) + + + def freeze_except(self, model_names): + for name, model in self.named_children(): + if name in model_names: + model.train() + model.requires_grad_(True) + else: + model.eval() + model.requires_grad_(False) + + + def blend_with_mask(self, base, addition, mask): + return base * (1 - mask) + addition * mask + + + def step(self, scheduler, latents, progress_id, noise_pred, input_latents=None, inpaint_mask=None, **kwargs): + timestep = scheduler.timesteps[progress_id] + if inpaint_mask is not None: + noise_pred_expected = scheduler.return_to_timestep(scheduler.timesteps[progress_id], latents, input_latents) + noise_pred = self.blend_with_mask(noise_pred_expected, noise_pred, inpaint_mask) + latents_next = scheduler.step(noise_pred, timestep, latents) + return latents_next + + + +@dataclass +class ModelConfig: + path: Union[str, list[str]] = None + model_id: str = None + origin_file_pattern: Union[str, list[str]] = None + download_resource: str = "ModelScope" + offload_device: Optional[Union[str, torch.device]] = None + offload_dtype: Optional[torch.dtype] = None + local_model_path: str = None + skip_download: bool = False + + def download_if_necessary(self, use_usp=False): + if self.path is None: + # Check model_id and origin_file_pattern + if self.model_id is None: + raise ValueError(f"""No valid model files. Please use `ModelConfig(path="xxx")` or `ModelConfig(model_id="xxx/yyy", origin_file_pattern="zzz")`.""") + + # Skip if not in rank 0 + if use_usp: + import torch.distributed as dist + skip_download = self.skip_download or dist.get_rank() != 0 + else: + skip_download = self.skip_download + + # Check whether the origin path is a folder + if self.origin_file_pattern is None or self.origin_file_pattern == "": + self.origin_file_pattern = "" + allow_file_pattern = None + is_folder = True + elif isinstance(self.origin_file_pattern, str) and self.origin_file_pattern.endswith("/"): + allow_file_pattern = self.origin_file_pattern + "*" + is_folder = True + else: + allow_file_pattern = self.origin_file_pattern + is_folder = False + + # Download + if self.local_model_path is None: + self.local_model_path = "./models" + if not skip_download: + downloaded_files = glob.glob(self.origin_file_pattern, root_dir=os.path.join(self.local_model_path, self.model_id)) + if self.download_resource.lower() == "modelscope": + snapshot_download( + self.model_id, + local_dir=os.path.join(self.local_model_path, self.model_id), + allow_file_pattern=allow_file_pattern, + ignore_file_pattern=downloaded_files, + local_files_only=False + ) + elif self.download_resource.lower() == "huggingface": + hf_snapshot_download( + self.model_id, + local_dir=os.path.join(self.local_model_path, self.model_id), + allow_patterns=allow_file_pattern, + ignore_patterns=downloaded_files, + local_files_only=False + ) + else: + raise ValueError("`download_resource` should be `modelscope` or `huggingface`.") + + # Let rank 1, 2, ... wait for rank 0 + if use_usp: + import torch.distributed as dist + dist.barrier(device_ids=[dist.get_rank()]) + + # Return downloaded files + if is_folder: + self.path = os.path.join(self.local_model_path, self.model_id, self.origin_file_pattern) + else: + self.path = glob.glob(os.path.join(self.local_model_path, self.model_id, self.origin_file_pattern)) + if isinstance(self.path, list) and len(self.path) == 1: + self.path = self.path[0] + + + +class PipelineUnit: + def __init__( + self, + seperate_cfg: bool = False, + take_over: bool = False, + input_params: tuple[str] = None, + input_params_posi: dict[str, str] = None, + input_params_nega: dict[str, str] = None, + onload_model_names: tuple[str] = None + ): + self.seperate_cfg = seperate_cfg + self.take_over = take_over + self.input_params = input_params + self.input_params_posi = input_params_posi + self.input_params_nega = input_params_nega + self.onload_model_names = onload_model_names + + + def process(self, pipe: BasePipeline, inputs: dict, positive=True, **kwargs) -> dict: + raise NotImplementedError("`process` is not implemented.") + + + +class PipelineUnitRunner: + def __init__(self): + pass + + def __call__(self, unit: PipelineUnit, pipe: BasePipeline, inputs_shared: dict, inputs_posi: dict, inputs_nega: dict) -> tuple[dict, dict]: + if unit.take_over: + # Let the pipeline unit take over this function. + inputs_shared, inputs_posi, inputs_nega = unit.process(pipe, inputs_shared=inputs_shared, inputs_posi=inputs_posi, inputs_nega=inputs_nega) + elif unit.seperate_cfg: + # Positive side + processor_inputs = {name: inputs_posi.get(name_) for name, name_ in unit.input_params_posi.items()} + if unit.input_params is not None: + for name in unit.input_params: + processor_inputs[name] = inputs_shared.get(name) + processor_outputs = unit.process(pipe, **processor_inputs) + inputs_posi.update(processor_outputs) + # Negative side + if inputs_shared["cfg_scale"] != 1: + processor_inputs = {name: inputs_nega.get(name_) for name, name_ in unit.input_params_nega.items()} + if unit.input_params is not None: + for name in unit.input_params: + processor_inputs[name] = inputs_shared.get(name) + processor_outputs = unit.process(pipe, **processor_inputs) + inputs_nega.update(processor_outputs) + else: + inputs_nega.update(processor_outputs) + else: + processor_inputs = {name: inputs_shared.get(name) for name in unit.input_params} + processor_outputs = unit.process(pipe, **processor_inputs) + inputs_shared.update(processor_outputs) + return inputs_shared, inputs_posi, inputs_nega diff --git a/diffsynth/vram_management/__init__.py b/diffsynth/vram_management/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..5b075800b6fd5b1702eeb7ff9c619aef1093ed46 --- /dev/null +++ b/diffsynth/vram_management/__init__.py @@ -0,0 +1,2 @@ +from .layers import * +from .gradient_checkpointing import * diff --git a/diffsynth/vram_management/gradient_checkpointing.py b/diffsynth/vram_management/gradient_checkpointing.py new file mode 100644 index 0000000000000000000000000000000000000000..b356415a004f3d74afdd45840f1fc4caf6659e16 --- /dev/null +++ b/diffsynth/vram_management/gradient_checkpointing.py @@ -0,0 +1,34 @@ +import torch + + +def create_custom_forward(module): + def custom_forward(*inputs, **kwargs): + return module(*inputs, **kwargs) + return custom_forward + + +def gradient_checkpoint_forward( + model, + use_gradient_checkpointing, + use_gradient_checkpointing_offload, + *args, + **kwargs, +): + if use_gradient_checkpointing_offload: + with torch.autograd.graph.save_on_cpu(): + model_output = torch.utils.checkpoint.checkpoint( + create_custom_forward(model), + *args, + **kwargs, + use_reentrant=False, + ) + elif use_gradient_checkpointing: + model_output = torch.utils.checkpoint.checkpoint( + create_custom_forward(model), + *args, + **kwargs, + use_reentrant=False, + ) + else: + model_output = model(*args, **kwargs) + return model_output diff --git a/diffsynth/vram_management/layers.py b/diffsynth/vram_management/layers.py new file mode 100644 index 0000000000000000000000000000000000000000..3e15442081e82cb998a2abc7d18b53e00fb5c17b --- /dev/null +++ b/diffsynth/vram_management/layers.py @@ -0,0 +1,213 @@ +import torch, copy +from ..models.utils import init_weights_on_device + + +def cast_to(weight, dtype, device): + r = torch.empty_like(weight, dtype=dtype, device=device) + r.copy_(weight) + return r + + +class AutoTorchModule(torch.nn.Module): + def __init__(self): + super().__init__() + + def check_free_vram(self): + gpu_mem_state = torch.cuda.mem_get_info(self.computation_device) + used_memory = (gpu_mem_state[1] - gpu_mem_state[0]) / (1024 ** 3) + return used_memory < self.vram_limit + + def offload(self): + if self.state != 0: + self.to(dtype=self.offload_dtype, device=self.offload_device) + self.state = 0 + + def onload(self): + if self.state != 1: + self.to(dtype=self.onload_dtype, device=self.onload_device) + self.state = 1 + + def keep(self): + if self.state != 2: + self.to(dtype=self.computation_dtype, device=self.computation_device) + self.state = 2 + + +class AutoWrappedModule(AutoTorchModule): + def __init__(self, module: torch.nn.Module, offload_dtype, offload_device, onload_dtype, onload_device, computation_dtype, computation_device, vram_limit, **kwargs): + super().__init__() + self.module = module.to(dtype=offload_dtype, device=offload_device) + self.offload_dtype = offload_dtype + self.offload_device = offload_device + self.onload_dtype = onload_dtype + self.onload_device = onload_device + self.computation_dtype = computation_dtype + self.computation_device = computation_device + self.vram_limit = vram_limit + self.state = 0 + + def forward(self, *args, **kwargs): + if self.state == 2: + module = self.module + else: + if self.onload_dtype == self.computation_dtype and self.onload_device == self.computation_device: + module = self.module + elif self.vram_limit is not None and self.check_free_vram(): + self.keep() + module = self.module + else: + module = copy.deepcopy(self.module).to(dtype=self.computation_dtype, device=self.computation_device) + return module(*args, **kwargs) + + +class WanAutoCastLayerNorm(torch.nn.LayerNorm, AutoTorchModule): + def __init__(self, module: torch.nn.LayerNorm, offload_dtype, offload_device, onload_dtype, onload_device, computation_dtype, computation_device, vram_limit, **kwargs): + with init_weights_on_device(device=torch.device("meta")): + super().__init__(module.normalized_shape, eps=module.eps, elementwise_affine=module.elementwise_affine, bias=module.bias is not None, dtype=offload_dtype, device=offload_device) + self.weight = module.weight + self.bias = module.bias + self.offload_dtype = offload_dtype + self.offload_device = offload_device + self.onload_dtype = onload_dtype + self.onload_device = onload_device + self.computation_dtype = computation_dtype + self.computation_device = computation_device + self.vram_limit = vram_limit + self.state = 0 + + def forward(self, x, *args, **kwargs): + if self.state == 2: + weight, bias = self.weight, self.bias + else: + if self.onload_dtype == self.computation_dtype and self.onload_device == self.computation_device: + weight, bias = self.weight, self.bias + elif self.vram_limit is not None and self.check_free_vram(): + self.keep() + weight, bias = self.weight, self.bias + else: + weight = None if self.weight is None else cast_to(self.weight, self.computation_dtype, self.computation_device) + bias = None if self.bias is None else cast_to(self.bias, self.computation_dtype, self.computation_device) + with torch.amp.autocast(device_type=x.device.type): + x = torch.nn.functional.layer_norm(x.float(), self.normalized_shape, weight, bias, self.eps).type_as(x) + return x + + +class AutoWrappedLinear(torch.nn.Linear, AutoTorchModule): + def __init__(self, module: torch.nn.Linear, offload_dtype, offload_device, onload_dtype, onload_device, computation_dtype, computation_device, vram_limit, name="", **kwargs): + with init_weights_on_device(device=torch.device("meta")): + super().__init__(in_features=module.in_features, out_features=module.out_features, bias=module.bias is not None, dtype=offload_dtype, device=offload_device) + self.weight = module.weight + self.bias = module.bias + self.offload_dtype = offload_dtype + self.offload_device = offload_device + self.onload_dtype = onload_dtype + self.onload_device = onload_device + self.computation_dtype = computation_dtype + self.computation_device = computation_device + self.vram_limit = vram_limit + self.state = 0 + self.name = name + self.lora_A_weights = [] + self.lora_B_weights = [] + self.lora_merger = None + self.enable_fp8 = computation_dtype in [torch.float8_e4m3fn, torch.float8_e4m3fnuz] + + def fp8_linear( + self, + input: torch.Tensor, + weight: torch.Tensor, + bias: torch.Tensor = None, + ) -> torch.Tensor: + device = input.device + origin_dtype = input.dtype + origin_shape = input.shape + input = input.reshape(-1, origin_shape[-1]) + + x_max = torch.max(torch.abs(input), dim=-1, keepdim=True).values + fp8_max = 448.0 + # For float8_e4m3fnuz, the maximum representable value is half of that of e4m3fn. + # To avoid overflow and ensure numerical compatibility during FP8 computation, + # we scale down the input by 2.0 in advance. + # This scaling will be compensated later during the final result scaling. + if self.computation_dtype == torch.float8_e4m3fnuz: + fp8_max = fp8_max / 2.0 + scale_a = torch.clamp(x_max / fp8_max, min=1.0).float().to(device=device) + scale_b = torch.ones((weight.shape[0], 1)).to(device=device) + input = input / (scale_a + 1e-8) + input = input.to(self.computation_dtype) + weight = weight.to(self.computation_dtype) + bias = bias.to(torch.bfloat16) + + result = torch._scaled_mm( + input, + weight.T, + scale_a=scale_a, + scale_b=scale_b.T, + bias=bias, + out_dtype=origin_dtype, + ) + new_shape = origin_shape[:-1] + result.shape[-1:] + result = result.reshape(new_shape) + return result + + def forward(self, x, *args, **kwargs): + # VRAM management + if self.state == 2: + weight, bias = self.weight, self.bias + else: + if self.onload_dtype == self.computation_dtype and self.onload_device == self.computation_device: + weight, bias = self.weight, self.bias + elif self.vram_limit is not None and self.check_free_vram(): + self.keep() + weight, bias = self.weight, self.bias + else: + weight = cast_to(self.weight, self.computation_dtype, self.computation_device) + bias = None if self.bias is None else cast_to(self.bias, self.computation_dtype, self.computation_device) + + # Linear forward + if self.enable_fp8: + out = self.fp8_linear(x, weight, bias) + else: + out = torch.nn.functional.linear(x, weight, bias) + + # LoRA + if len(self.lora_A_weights) == 0: + # No LoRA + return out + elif self.lora_merger is None: + # Native LoRA inference + for lora_A, lora_B in zip(self.lora_A_weights, self.lora_B_weights): + out = out + x @ lora_A.T @ lora_B.T + else: + # LoRA fusion + lora_output = [] + for lora_A, lora_B in zip(self.lora_A_weights, self.lora_B_weights): + lora_output.append(x @ lora_A.T @ lora_B.T) + lora_output = torch.stack(lora_output) + out = self.lora_merger(out, lora_output) + return out + + +def enable_vram_management_recursively(model: torch.nn.Module, module_map: dict, module_config: dict, max_num_param=None, overflow_module_config: dict = None, total_num_param=0, vram_limit=None, name_prefix=""): + for name, module in model.named_children(): + layer_name = name if name_prefix == "" else name_prefix + "." + name + for source_module, target_module in module_map.items(): + if isinstance(module, source_module): + num_param = sum(p.numel() for p in module.parameters()) + if max_num_param is not None and total_num_param + num_param > max_num_param: + module_config_ = overflow_module_config + else: + module_config_ = module_config + module_ = target_module(module, **module_config_, vram_limit=vram_limit, name=layer_name) + setattr(model, name, module_) + total_num_param += num_param + break + else: + total_num_param = enable_vram_management_recursively(module, module_map, module_config, max_num_param, overflow_module_config, total_num_param, vram_limit=vram_limit, name_prefix=layer_name) + return total_num_param + + +def enable_vram_management(model: torch.nn.Module, module_map: dict, module_config: dict, max_num_param=None, overflow_module_config: dict = None, vram_limit=None): + enable_vram_management_recursively(model, module_map, module_config, max_num_param, overflow_module_config, total_num_param=0, vram_limit=vram_limit) + model.vram_management_enabled = True + diff --git a/examples/ArtAug/README.md b/examples/ArtAug/README.md new file mode 100644 index 0000000000000000000000000000000000000000..21939f95af73254d1eb6d00314f39f06bce54dab --- /dev/null +++ b/examples/ArtAug/README.md @@ -0,0 +1,43 @@ +# FLUX Aesthetics Enhancement LoRA + +## Introduction + +This is a LoRA model trained for FLUX.1-dev, which enhances the aesthetic quality of images generated by the model. The improvements include, but are not limited to: rich details, beautiful lighting and shadows, aesthetic composition, and clear visuals. This model does not require any trigger words. + +* Paper: https://arxiv.org/abs/2412.12888 +* Github: https://github.com/modelscope/DiffSynth-Studio +* Model: [ModelScope](https://www.modelscope.cn/models/DiffSynth-Studio/ArtAug-lora-FLUX.1dev-v1), [HuggingFace](https://huggingface.co/ECNU-CILab/ArtAug-lora-FLUX.1dev-v1) +* Demo: [ModelScope](https://modelscope.cn/aigc/imageGeneration?tab=advanced&versionId=7228&modelType=LoRA&sdVersion=FLUX_1&modelUrl=modelscope%3A%2F%2FDiffSynth-Studio%2FArtAug-lora-FLUX.1dev-v1%3Frevision%3Dv1.0), HuggingFace (Coming soon) + +## Methodology + +![workflow](https://github.com/user-attachments/assets/cee969af-d49f-4480-911c-bedc1c095f9b) + +The ArtAug project is inspired by reasoning approaches like GPT-o1, which rely on model interaction and self-correction. We developed a framework aimed at enhancing the capabilities of image generation models through interaction with image understanding models. The training process of ArtAug consists of the following steps: + +1. **Synthesis-Understanding Interaction**: After generating an image using the image generation model, we employ a multimodal large language model (Qwen2-VL-72B) to analyze the image content and provide suggestions for modifications, which then lead to the regeneration of a higher quality image. + +2. **Data Generation and Filtering**: Interactive generation involves long inference times and sometimes produce poor image content. Therefore, we generate a large batch of image pairs offline, filter them, and use them for subsequent training. + +3. **Differential Training**: We apply differential training techniques to train a LoRA model, enabling it to learn the differences between images before and after enhancement, rather than directly training on the dataset of enhanced images. + +4. **Iterative Enhancement**: The trained LoRA model is fused into the base model, and the entire process is repeated multiple times with the fused model until the interaction algorithm no longer provides significant enhancements. The LoRA models produced in each iteration are combined to produce this final model. + +This model integrates the aesthetic understanding of Qwen2-VL-72B into FLUX.1[dev], leading to an improvement in the quality of generated images. + +## Usage + +Please see [./artaug_flux.py](./artaug_flux.py) for more details. + +Since this model is encapsulated in the universal FLUX LoRA format, it can be loaded by most LoRA loaders, allowing you to integrate this LoRA model into your own workflow. + +## Examples + +|FLUX.1-dev|FLUX.1-dev + ArtAug LoRA| +|-|-| +|![image_1_base](https://github.com/user-attachments/assets/e1d5c505-b423-45fe-be01-25c2758f5417)|![image_1_enhance](https://github.com/user-attachments/assets/335908e3-d0bd-41c2-9d99-d10528a2d719)| +|![image_2_base](https://github.com/user-attachments/assets/7f38e8d4-3c62-492e-bd96-be60f0855037)|![image_2_enhance](https://github.com/user-attachments/assets/ae3a1daf-7a7c-44fd-bdbc-1d2a83bc3de3)| +|![image_3_base](https://github.com/user-attachments/assets/e2ae4879-9202-45d6-9df7-fbcbd2093d19)|![image_3_enhance](https://github.com/user-attachments/assets/4df6e5b9-65de-408b-88c6-51db39aad801)| +|![image_4_base](https://github.com/user-attachments/assets/dbc65387-60df-4a18-b1bb-45eaa5be5c1d)|![image_4_enhance](https://github.com/user-attachments/assets/fc19860d-3e28-468b-b013-8745255ac6db)| +|![image_5_base](https://github.com/user-attachments/assets/bb65c1ba-c0c6-4d3b-b3ef-bdbbb5f03a48)|![image_5_enhance](https://github.com/user-attachments/assets/03570c62-9a0b-428f-8c86-6e01c1421202)| +|![image_6_base](https://github.com/user-attachments/assets/18e9a4e7-2afd-4ca9-bc49-7736042c25dc)|![image_6_enhance](https://github.com/user-attachments/assets/aa73571f-098a-4e65-9eda-b9729ba379cd)| diff --git a/examples/ArtAug/artaug_flux.py b/examples/ArtAug/artaug_flux.py new file mode 100644 index 0000000000000000000000000000000000000000..07043f02c044f2a62026f7150d431925ef84acfb --- /dev/null +++ b/examples/ArtAug/artaug_flux.py @@ -0,0 +1,14 @@ +import torch +from diffsynth import ModelManager, FluxImagePipeline, download_customized_models + +lora_path = download_customized_models( + model_id="DiffSynth-Studio/ArtAug-lora-FLUX.1dev-v1", + origin_file_path="merged_lora.safetensors", + local_dir="models/lora" +)[0] +model_manager = ModelManager(torch_dtype=torch.bfloat16, device="cuda", model_id_list=["FLUX.1-dev"]) +model_manager.load_lora(lora_path, lora_alpha=1.0) +pipe = FluxImagePipeline.from_model_manager(model_manager) + +image = pipe(prompt="a house", seed=0) +image.save("image_artaug.jpg") diff --git a/examples/CogVideoX/README.md b/examples/CogVideoX/README.md new file mode 100644 index 0000000000000000000000000000000000000000..95d8daa348ae1c2e2b3a39a75f502c89d61c8fcd --- /dev/null +++ b/examples/CogVideoX/README.md @@ -0,0 +1,39 @@ +# CogVideoX + +### Example: Text-to-Video using CogVideoX-5B (Experimental) + +See [cogvideo_text_to_video.py](cogvideo_text_to_video.py). + +First, we generate a video using prompt "an astronaut riding a horse on Mars". + +https://github.com/user-attachments/assets/4c91c1cd-e4a0-471a-bd8d-24d761262941 + +Then, we convert the astronaut to a robot. + +https://github.com/user-attachments/assets/225a00a4-2bc8-4740-8e86-a64b460a29ec + +Upscale the video using the model itself. + +https://github.com/user-attachments/assets/c02cb30c-de60-473c-8242-32c67b3155ad + +Make the video look smoother by interpolating frames. + +https://github.com/user-attachments/assets/f0e465b4-45df-4435-ab10-7a084ca2b0a0 + +Here is another example. + +First, we generate a video using prompt "a dog is running". + +https://github.com/user-attachments/assets/e3696297-99f5-4d0c-a5ca-1d1566db85b4 + +Then, we add a blue collar to the dog. + +https://github.com/user-attachments/assets/7ff22be7-4390-4d33-ae6c-53f6f056e18d + +Upscale the video using the model itself. + +https://github.com/user-attachments/assets/a909c32c-0b7d-495c-a53c-d23a99a3d3e9 + +Make the video look smoother by interpolating frames. + +https://github.com/user-attachments/assets/ea37c150-97a0-4858-8003-0c2e5eef3331 diff --git a/examples/CogVideoX/cogvideo_text_to_video.py b/examples/CogVideoX/cogvideo_text_to_video.py new file mode 100644 index 0000000000000000000000000000000000000000..22e4f36aa8d9d4961ccedec58a6f05181875e7dd --- /dev/null +++ b/examples/CogVideoX/cogvideo_text_to_video.py @@ -0,0 +1,73 @@ +from diffsynth import ModelManager, save_video, VideoData, download_models, CogVideoPipeline +from diffsynth.extensions.RIFE import RIFEInterpolater +import torch, os +os.environ["TOKENIZERS_PARALLELISM"] = "True" + + + +def text_to_video(model_manager, prompt, seed, output_path): + pipe = CogVideoPipeline.from_model_manager(model_manager) + torch.manual_seed(seed) + video = pipe( + prompt=prompt, + height=480, width=720, + cfg_scale=7.0, num_inference_steps=200 + ) + save_video(video, output_path, fps=8, quality=5) + + +def edit_video(model_manager, prompt, seed, input_path, output_path): + pipe = CogVideoPipeline.from_model_manager(model_manager) + input_video = VideoData(video_file=input_path) + torch.manual_seed(seed) + video = pipe( + prompt=prompt, + height=480, width=720, + cfg_scale=7.0, num_inference_steps=200, + input_video=input_video, denoising_strength=0.7 + ) + save_video(video, output_path, fps=8, quality=5) + + +def self_upscale(model_manager, prompt, seed, input_path, output_path): + pipe = CogVideoPipeline.from_model_manager(model_manager) + input_video = VideoData(video_file=input_path, height=480*2, width=720*2).raw_data() + torch.manual_seed(seed) + video = pipe( + prompt=prompt, + height=480*2, width=720*2, + cfg_scale=7.0, num_inference_steps=30, + input_video=input_video, denoising_strength=0.4, tiled=True + ) + save_video(video, output_path, fps=8, quality=7) + + +def interpolate_video(model_manager, input_path, output_path): + rife = RIFEInterpolater.from_model_manager(model_manager) + video = VideoData(video_file=input_path).raw_data() + video = rife.interpolate(video, num_iter=2) + save_video(video, output_path, fps=32, quality=5) + + + +download_models(["CogVideoX-5B", "RIFE"]) + +model_manager = ModelManager(torch_dtype=torch.bfloat16) +model_manager.load_models([ + "models/CogVideo/CogVideoX-5b/text_encoder", + "models/CogVideo/CogVideoX-5b/transformer", + "models/CogVideo/CogVideoX-5b/vae/diffusion_pytorch_model.safetensors", + "models/RIFE/flownet.pkl", +]) + +# Example 1 +text_to_video(model_manager, "an astronaut riding a horse on Mars.", 0, "1_video_1.mp4") +edit_video(model_manager, "a white robot riding a horse on Mars.", 1, "1_video_1.mp4", "1_video_2.mp4") +self_upscale(model_manager, "a white robot riding a horse on Mars.", 2, "1_video_2.mp4", "1_video_3.mp4") +interpolate_video(model_manager, "1_video_3.mp4", "1_video_4.mp4") + +# Example 2 +text_to_video(model_manager, "a dog is running.", 1, "2_video_1.mp4") +edit_video(model_manager, "a dog with blue collar.", 2, "2_video_1.mp4", "2_video_2.mp4") +self_upscale(model_manager, "a dog with blue collar.", 3, "2_video_2.mp4", "2_video_3.mp4") +interpolate_video(model_manager, "2_video_3.mp4", "2_video_4.mp4") diff --git a/examples/ControlNet/README.md b/examples/ControlNet/README.md new file mode 100644 index 0000000000000000000000000000000000000000..9bd28c277327d954dc022b1689b3a69e1af2751e --- /dev/null +++ b/examples/ControlNet/README.md @@ -0,0 +1,91 @@ +# ControlNet + +We provide extensive ControlNet support. Taking the FLUX model as an example, we support many different ControlNet models that can be freely combined, even if their structures differ. Additionally, ControlNet models are compatible with high-resolution refinement and partition control techniques, enabling very powerful controllable image generation. + +These examples are in [`flux_controlnet.py`](./flux_controlnet.py). + +## Canny/Depth/Normal: Structure Control + +Structural control is the most fundamental capability of the ControlNet model. By using Canny to extract edge information, or by utilizing depth maps and normal maps, we can extract the structure of an image, which can then serve as control information during the image generation process. + +Model link: https://modelscope.cn/models/InstantX/FLUX.1-dev-Controlnet-Union-alpha + +For example, if we generate an image of a cat and use a model like InstantX/FLUX.1-dev-Controlnet-Union-alpha that supports multiple control conditions, we can simultaneously enable both Canny and Depth controls to transform the environment into a twilight setting. + +|![image_5](https://github.com/user-attachments/assets/19d2abc4-36ae-4163-a8da-df5732d1a737)|![image_6](https://github.com/user-attachments/assets/28378271-3782-484c-bd51-3d3311dd85c6)| +|-|-| + +The control strength of ControlNet for structure can be adjusted. For example, in the case below, when we move the girl from summer to winter, we can appropriately lower the control strength of ControlNet so that the model will adapt to the content of the image and change her into warm clothes. + +|![image_7](https://github.com/user-attachments/assets/a7b8555b-bfd9-4e92-aa77-16bca81b07e3)|![image_8](https://github.com/user-attachments/assets/a1bab36b-6cce-4f29-8233-4cb824b524a8)| +|-|-| + +## Upscaler/Tile/Blur: High-Resolution Image Synthesis + +There are many ControlNet models that support high definition, such as: + +Model link: https://modelscope.cn/models/jasperai/Flux.1-dev-Controlnet-Upscaler, https://modelscope.cn/models/InstantX/FLUX.1-dev-Controlnet-Union-alpha, https://modelscope.cn/models/Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro + +These models can transform blurry, noisy low-quality images into clear ones. In DiffSynth-Studio, the native high-resolution patch processing technology supported by the framework can overcome the resolution limitations of the models, enabling image generation at resolutions of 2048 or even higher, significantly enhancing the capabilities of these models. In the example below, we can see that in the high-definition image enlarged to 2048 resolution, the cat's fur is rendered in exquisite detail, and the skin texture of the characters is delicate and realistic. + +|![image_1](https://github.com/user-attachments/assets/9038158a-118c-4ad7-ab01-22865f6a06fc)|![image_2](https://github.com/user-attachments/assets/88583a33-cd74-4cb9-8fd4-c6e14c0ada0c)| +|-|-| + +|![image_3](https://github.com/user-attachments/assets/13061ecf-bb57-448a-82c6-7e4655c9cd85)|![image_4](https://github.com/user-attachments/assets/0b7ae80f-de58-4d1d-a49c-ad17e7631bdc)| +|-|-| + +## Inpaint: Image Restoration + +The Inpaint ControlNet model can repaint specific areas in an image. For example, we can put sunglasses on a cat. + +Model link: https://modelscope.cn/models/alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta + +|![image_9](https://github.com/user-attachments/assets/babddad0-2d67-4624-b77a-c953250ebdab)|![mask_9](https://github.com/user-attachments/assets/d5bc2878-1817-457a-bdfa-200f955233d3)|![image_10](https://github.com/user-attachments/assets/e3197f2c-190b-4522-83ab-a2e0451b39f6)| +|-|-|-| + +However, we noticed that the head movements of the cat have changed. If we want to preserve the original structural features, we can use the Canny, Depth, and Normal models. DiffSynth-Studio provides seamless support for ControlNet of different structures. By using a Normal ControlNet, we can ensure that the structure of the image remains unchanged during local redrawing. + +Model link: https://modelscope.cn/models/jasperai/Flux.1-dev-Controlnet-Surface-Normals + +|![image_11](https://github.com/user-attachments/assets/c028e6fc-5125-4cba-b35a-b6211c2e6600)|![mask_11](https://github.com/user-attachments/assets/1928ee9a-7594-4c6e-9c71-5bd0b043d8f4)|![image_12](https://github.com/user-attachments/assets/97b3b9e1-f821-405e-971b-9e1c31a209aa)| +|-|-|-| + +## MultiControlNet+MultiDiffusion: Fine-Grained Control + +DiffSynth-Studio not only supports the simultaneous activation of multiple ControlNet structures, but also allows for the partitioned control of content within an image using different prompts. Additionally, it supports the chunk processing of ultra-high-resolution large images, enabling us to achieve extremely detailed high-level control. Next, we will showcase the creative process behind a beautiful image. + +First, use the prompt "a beautiful Asian woman and a cat on a bed. The woman wears a dress" to generate a cat and a young girl. + +![image_13](https://github.com/user-attachments/assets/8da006e4-0e68-4fa5-b407-31ef5dbe8e5a) + +Then, enable Inpaint ControlNet and Canny ControlNet. + +Model link: https://modelscope.cn/models/alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta, https://modelscope.cn/models/InstantX/FLUX.1-dev-Controlnet-Union-alpha + +We control the image using two component. + +|Prompt: an orange cat, highly detailed|Prompt: a girl wearing a red camisole| +|-|-| +|![mask_13_1](https://github.com/user-attachments/assets/188530a0-913c-48db-a7f1-62f0384bfdc3)|![mask_13_2](https://github.com/user-attachments/assets/99c4d0d5-8cc3-47a0-8e56-ceb37db4dfdc)| + +Generate! + +![image_14](https://github.com/user-attachments/assets/f5b9d3dd-a690-4597-91a8-a019c6fc2523) + +The background is a bit blurry, so we use deblurring LoRA for image-to-image generation. + +Model link: https://modelscope.cn/models/LiblibAI/FLUX.1-dev-LoRA-AntiBlur + +![image_15](https://github.com/user-attachments/assets/32ed2667-2260-4d80-aaa9-4435d6920a2a) + +The entire image is much clearer now. Next, let's use the high-definition model to increase the resolution to 4096*4096! + +Model link: https://modelscope.cn/models/jasperai/Flux.1-dev-Controlnet-Upscaler + +![image_17](https://github.com/user-attachments/assets/1a688a12-1544-4973-8aca-aa3a23cb34c1) + +Zoom in to see details. + +![image_17_cropped](https://github.com/user-attachments/assets/461a1fbc-9ffa-4da5-80fd-e1af9667c804) + +Enjoy! diff --git a/examples/ControlNet/flux_controlnet.py b/examples/ControlNet/flux_controlnet.py new file mode 100644 index 0000000000000000000000000000000000000000..4576939aaa2d0ce8abf2b7d88f49b77190e10819 --- /dev/null +++ b/examples/ControlNet/flux_controlnet.py @@ -0,0 +1,299 @@ +from diffsynth import ModelManager, FluxImagePipeline, ControlNetConfigUnit, download_models, download_customized_models +import torch +from PIL import Image +import numpy as np + + + +def example_1(): + model_manager = ModelManager(torch_dtype=torch.bfloat16, model_id_list=["FLUX.1-dev", "jasperai/Flux.1-dev-Controlnet-Upscaler"]) + pipe = FluxImagePipeline.from_model_manager(model_manager, controlnet_config_units=[ + ControlNetConfigUnit( + processor_id="tile", + model_path="models/ControlNet/jasperai/Flux.1-dev-Controlnet-Upscaler/diffusion_pytorch_model.safetensors", + scale=0.7 + ), + ]) + + image_1 = pipe( + prompt="a photo of a cat, highly detailed", + height=768, width=768, + seed=0 + ) + image_1.save("image_1.jpg") + + image_2 = pipe( + prompt="a photo of a cat, highly detailed", + controlnet_image=image_1.resize((2048, 2048)), + input_image=image_1.resize((2048, 2048)), denoising_strength=0.99, + height=2048, width=2048, tiled=True, + seed=1 + ) + image_2.save("image_2.jpg") + + + +def example_2(): + model_manager = ModelManager(torch_dtype=torch.bfloat16, model_id_list=["FLUX.1-dev", "jasperai/Flux.1-dev-Controlnet-Upscaler"]) + pipe = FluxImagePipeline.from_model_manager(model_manager, controlnet_config_units=[ + ControlNetConfigUnit( + processor_id="tile", + model_path="models/ControlNet/jasperai/Flux.1-dev-Controlnet-Upscaler/diffusion_pytorch_model.safetensors", + scale=0.7 + ), + ]) + + image_1 = pipe( + prompt="a beautiful Chinese girl, delicate skin texture", + height=768, width=768, + seed=2 + ) + image_1.save("image_3.jpg") + + image_2 = pipe( + prompt="a beautiful Chinese girl, delicate skin texture", + controlnet_image=image_1.resize((2048, 2048)), + input_image=image_1.resize((2048, 2048)), denoising_strength=0.99, + height=2048, width=2048, tiled=True, + seed=3 + ) + image_2.save("image_4.jpg") + + +def example_3(): + model_manager = ModelManager(torch_dtype=torch.bfloat16, model_id_list=["FLUX.1-dev", "InstantX/FLUX.1-dev-Controlnet-Union-alpha"]) + pipe = FluxImagePipeline.from_model_manager(model_manager, controlnet_config_units=[ + ControlNetConfigUnit( + processor_id="canny", + model_path="models/ControlNet/InstantX/FLUX.1-dev-Controlnet-Union-alpha/diffusion_pytorch_model.safetensors", + scale=0.3 + ), + ControlNetConfigUnit( + processor_id="depth", + model_path="models/ControlNet/InstantX/FLUX.1-dev-Controlnet-Union-alpha/diffusion_pytorch_model.safetensors", + scale=0.3 + ), + ]) + + image_1 = pipe( + prompt="a cat is running", + height=1024, width=1024, + seed=4 + ) + image_1.save("image_5.jpg") + + image_2 = pipe( + prompt="sunshine, a cat is running", + controlnet_image=image_1, + height=1024, width=1024, + seed=5 + ) + image_2.save("image_6.jpg") + + +def example_4(): + model_manager = ModelManager(torch_dtype=torch.bfloat16, model_id_list=["FLUX.1-dev", "InstantX/FLUX.1-dev-Controlnet-Union-alpha"]) + pipe = FluxImagePipeline.from_model_manager(model_manager, controlnet_config_units=[ + ControlNetConfigUnit( + processor_id="canny", + model_path="models/ControlNet/InstantX/FLUX.1-dev-Controlnet-Union-alpha/diffusion_pytorch_model.safetensors", + scale=0.3 + ), + ControlNetConfigUnit( + processor_id="depth", + model_path="models/ControlNet/InstantX/FLUX.1-dev-Controlnet-Union-alpha/diffusion_pytorch_model.safetensors", + scale=0.3 + ), + ]) + + image_1 = pipe( + prompt="a beautiful Asian girl, full body, red dress, summer", + height=1024, width=1024, + seed=6 + ) + image_1.save("image_7.jpg") + + image_2 = pipe( + prompt="a beautiful Asian girl, full body, red dress, winter", + controlnet_image=image_1, + height=1024, width=1024, + seed=7 + ) + image_2.save("image_8.jpg") + + + +def example_5(): + model_manager = ModelManager(torch_dtype=torch.bfloat16, model_id_list=["FLUX.1-dev", "alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta"]) + pipe = FluxImagePipeline.from_model_manager(model_manager, controlnet_config_units=[ + ControlNetConfigUnit( + processor_id="inpaint", + model_path="models/ControlNet/alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta/diffusion_pytorch_model.safetensors", + scale=0.9 + ), + ]) + + image_1 = pipe( + prompt="a cat sitting on a chair", + height=1024, width=1024, + seed=8 + ) + image_1.save("image_9.jpg") + + mask = np.zeros((1024, 1024, 3), dtype=np.uint8) + mask[100:350, 350: -300] = 255 + mask = Image.fromarray(mask) + mask.save("mask_9.jpg") + + image_2 = pipe( + prompt="a cat sitting on a chair, wearing sunglasses", + controlnet_image=image_1, controlnet_inpaint_mask=mask, + height=1024, width=1024, + seed=9 + ) + image_2.save("image_10.jpg") + + + +def example_6(): + model_manager = ModelManager(torch_dtype=torch.bfloat16, model_id_list=[ + "FLUX.1-dev", + "jasperai/Flux.1-dev-Controlnet-Surface-Normals", + "alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta" + ]) + pipe = FluxImagePipeline.from_model_manager(model_manager, controlnet_config_units=[ + ControlNetConfigUnit( + processor_id="inpaint", + model_path="models/ControlNet/alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta/diffusion_pytorch_model.safetensors", + scale=0.9 + ), + ControlNetConfigUnit( + processor_id="normal", + model_path="models/ControlNet/jasperai/Flux.1-dev-Controlnet-Surface-Normals/diffusion_pytorch_model.safetensors", + scale=0.6 + ), + ]) + + image_1 = pipe( + prompt="a beautiful Asian woman looking at the sky, wearing a blue t-shirt.", + height=1024, width=1024, + seed=10 + ) + image_1.save("image_11.jpg") + + mask = np.zeros((1024, 1024, 3), dtype=np.uint8) + mask[-400:, 10:-40] = 255 + mask = Image.fromarray(mask) + mask.save("mask_11.jpg") + + image_2 = pipe( + prompt="a beautiful Asian woman looking at the sky, wearing a yellow t-shirt.", + controlnet_image=image_1, controlnet_inpaint_mask=mask, + height=1024, width=1024, + seed=11 + ) + image_2.save("image_12.jpg") + + +def example_7(): + model_manager = ModelManager(torch_dtype=torch.bfloat16, model_id_list=[ + "FLUX.1-dev", + "InstantX/FLUX.1-dev-Controlnet-Union-alpha", + "alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta", + "jasperai/Flux.1-dev-Controlnet-Upscaler", + ]) + pipe = FluxImagePipeline.from_model_manager(model_manager, controlnet_config_units=[ + ControlNetConfigUnit( + processor_id="inpaint", + model_path="models/ControlNet/alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta/diffusion_pytorch_model.safetensors", + scale=0.9 + ), + ControlNetConfigUnit( + processor_id="canny", + model_path="models/ControlNet/InstantX/FLUX.1-dev-Controlnet-Union-alpha/diffusion_pytorch_model.safetensors", + scale=0.5 + ), + ]) + + image_1 = pipe( + prompt="a beautiful Asian woman and a cat on a bed. The woman wears a dress.", + height=1024, width=1024, + seed=100 + ) + image_1.save("image_13.jpg") + + mask_global = np.zeros((1024, 1024, 3), dtype=np.uint8) + mask_global = Image.fromarray(mask_global) + mask_global.save("mask_13_global.jpg") + + mask_1 = np.zeros((1024, 1024, 3), dtype=np.uint8) + mask_1[300:-100, 30: 450] = 255 + mask_1 = Image.fromarray(mask_1) + mask_1.save("mask_13_1.jpg") + + mask_2 = np.zeros((1024, 1024, 3), dtype=np.uint8) + mask_2[500:-100, -400:] = 255 + mask_2[-200:-100, -500:-400] = 255 + mask_2 = Image.fromarray(mask_2) + mask_2.save("mask_13_2.jpg") + + image_2 = pipe( + prompt="a beautiful Asian woman and a cat on a bed. The woman wears a dress.", + controlnet_image=image_1, controlnet_inpaint_mask=mask_global, + local_prompts=["an orange cat, highly detailed", "a girl wearing a red camisole"], masks=[mask_1, mask_2], mask_scales=[10.0, 10.0], + height=1024, width=1024, + seed=101 + ) + image_2.save("image_14.jpg") + + model_manager.load_lora("models/lora/FLUX-dev-lora-AntiBlur.safetensors", lora_alpha=2) + image_3 = pipe( + prompt="a beautiful Asian woman wearing a red camisole and an orange cat on a bed. clear background.", + negative_prompt="blur, blurry", + input_image=image_2, denoising_strength=0.7, + height=1024, width=1024, + cfg_scale=2.0, num_inference_steps=50, + seed=102 + ) + image_3.save("image_15.jpg") + + pipe = FluxImagePipeline.from_model_manager(model_manager, controlnet_config_units=[ + ControlNetConfigUnit( + processor_id="tile", + model_path="models/ControlNet/jasperai/Flux.1-dev-Controlnet-Upscaler/diffusion_pytorch_model.safetensors", + scale=0.7 + ), + ]) + image_4 = pipe( + prompt="a beautiful Asian woman wearing a red camisole and an orange cat on a bed. highly detailed, delicate skin texture, clear background.", + controlnet_image=image_3.resize((2048, 2048)), + input_image=image_3.resize((2048, 2048)), denoising_strength=0.99, + height=2048, width=2048, tiled=True, + seed=103 + ) + image_4.save("image_16.jpg") + + image_5 = pipe( + prompt="a beautiful Asian woman wearing a red camisole and an orange cat on a bed. highly detailed, delicate skin texture, clear background.", + controlnet_image=image_4.resize((4096, 4096)), + input_image=image_4.resize((4096, 4096)), denoising_strength=0.99, + height=4096, width=4096, tiled=True, + seed=104 + ) + image_5.save("image_17.jpg") + + + +download_models(["Annotators:Depth", "Annotators:Normal"]) +download_customized_models( + model_id="LiblibAI/FLUX.1-dev-LoRA-AntiBlur", + origin_file_path="FLUX-dev-lora-AntiBlur.safetensors", + local_dir="models/lora" +) +example_1() +example_2() +example_3() +example_4() +example_5() +example_6() +example_7() diff --git a/examples/ControlNet/flux_controlnet_quantization.py b/examples/ControlNet/flux_controlnet_quantization.py new file mode 100644 index 0000000000000000000000000000000000000000..c4aa3c1697750cffff8611652c5dd71ff876fb11 --- /dev/null +++ b/examples/ControlNet/flux_controlnet_quantization.py @@ -0,0 +1,447 @@ +from diffsynth import ModelManager, FluxImagePipeline, ControlNetConfigUnit, download_models, download_customized_models +import torch +from PIL import Image +import numpy as np + + + +def example_1(): + download_models(["FLUX.1-dev", "jasperai/Flux.1-dev-Controlnet-Upscaler"]) + model_manager = ModelManager( + torch_dtype=torch.bfloat16, + device="cpu" + ) + model_manager.load_models([ + "models/FLUX/FLUX.1-dev/text_encoder/model.safetensors", + "models/FLUX/FLUX.1-dev/text_encoder_2", + "models/FLUX/FLUX.1-dev/ae.safetensors", + ]) + model_manager.load_models( + ["models/FLUX/FLUX.1-dev/flux1-dev.safetensors"], + torch_dtype=torch.float8_e4m3fn + ) + model_manager.load_models( + ["models/ControlNet/jasperai/Flux.1-dev-Controlnet-Upscaler/diffusion_pytorch_model.safetensors"], + torch_dtype=torch.float8_e4m3fn + ) + pipe = FluxImagePipeline.from_model_manager(model_manager, controlnet_config_units=[ + ControlNetConfigUnit( + processor_id="tile", + model_path="models/ControlNet/jasperai/Flux.1-dev-Controlnet-Upscaler/diffusion_pytorch_model.safetensors", + scale=0.7 + ), + ],device="cuda") + pipe.enable_cpu_offload() + pipe.dit.quantize() + for model in pipe.controlnet.models: + model.quantize() + + image_1 = pipe( + prompt="a photo of a cat, highly detailed", + height=768, width=768, + seed=0 + ) + image_1.save("image_1.jpg") + + image_2 = pipe( + prompt="a photo of a cat, highly detailed", + controlnet_image=image_1.resize((2048, 2048)), + input_image=image_1.resize((2048, 2048)), denoising_strength=0.99, + height=2048, width=2048, tiled=True, + seed=1 + ) + image_2.save("image_2.jpg") + + + +def example_2(): + download_models(["FLUX.1-dev", "jasperai/Flux.1-dev-Controlnet-Upscaler"]) + model_manager = ModelManager( + torch_dtype=torch.bfloat16, + device="cpu" + ) + model_manager.load_models([ + "models/FLUX/FLUX.1-dev/text_encoder/model.safetensors", + "models/FLUX/FLUX.1-dev/text_encoder_2", + "models/FLUX/FLUX.1-dev/ae.safetensors", + ]) + model_manager.load_models( + ["models/FLUX/FLUX.1-dev/flux1-dev.safetensors"], + torch_dtype=torch.float8_e4m3fn + ) + model_manager.load_models( + ["models/ControlNet/jasperai/Flux.1-dev-Controlnet-Upscaler/diffusion_pytorch_model.safetensors"], + torch_dtype=torch.float8_e4m3fn + ) + pipe = FluxImagePipeline.from_model_manager(model_manager, controlnet_config_units=[ + ControlNetConfigUnit( + processor_id="tile", + model_path="models/ControlNet/jasperai/Flux.1-dev-Controlnet-Upscaler/diffusion_pytorch_model.safetensors", + scale=0.7 + ), + ],device="cuda") + pipe.enable_cpu_offload() + pipe.dit.quantize() + for model in pipe.controlnet.models: + model.quantize() + image_1 = pipe( + prompt="a beautiful Chinese girl, delicate skin texture", + height=768, width=768, + seed=2 + ) + image_1.save("image_3.jpg") + + image_2 = pipe( + prompt="a beautiful Chinese girl, delicate skin texture", + controlnet_image=image_1.resize((2048, 2048)), + input_image=image_1.resize((2048, 2048)), denoising_strength=0.99, + height=2048, width=2048, tiled=True, + seed=3 + ) + image_2.save("image_4.jpg") + + +def example_3(): + download_models(["FLUX.1-dev", "InstantX/FLUX.1-dev-Controlnet-Union-alpha"]) + model_manager = ModelManager( + torch_dtype=torch.bfloat16, + device="cpu" + ) + model_manager.load_models([ + "models/FLUX/FLUX.1-dev/text_encoder/model.safetensors", + "models/FLUX/FLUX.1-dev/text_encoder_2", + "models/FLUX/FLUX.1-dev/ae.safetensors", + ]) + model_manager.load_models( + ["models/FLUX/FLUX.1-dev/flux1-dev.safetensors"], + torch_dtype=torch.float8_e4m3fn + ) + model_manager.load_models( + ["models/ControlNet/InstantX/FLUX.1-dev-Controlnet-Union-alpha/diffusion_pytorch_model.safetensors"], + torch_dtype=torch.float8_e4m3fn + ) + pipe = FluxImagePipeline.from_model_manager(model_manager, controlnet_config_units=[ + ControlNetConfigUnit( + processor_id="canny", + model_path="models/ControlNet/InstantX/FLUX.1-dev-Controlnet-Union-alpha/diffusion_pytorch_model.safetensors", + scale=0.3 + ), + ControlNetConfigUnit( + processor_id="depth", + model_path="models/ControlNet/InstantX/FLUX.1-dev-Controlnet-Union-alpha/diffusion_pytorch_model.safetensors", + scale=0.3 + ), + ],device="cuda") + pipe.enable_cpu_offload() + pipe.dit.quantize() + for model in pipe.controlnet.models: + model.quantize() + image_1 = pipe( + prompt="a cat is running", + height=1024, width=1024, + seed=4 + ) + image_1.save("image_5.jpg") + + image_2 = pipe( + prompt="sunshine, a cat is running", + controlnet_image=image_1, + height=1024, width=1024, + seed=5 + ) + image_2.save("image_6.jpg") + + +def example_4(): + download_models(["FLUX.1-dev", "InstantX/FLUX.1-dev-Controlnet-Union-alpha"]) + model_manager = ModelManager( + torch_dtype=torch.bfloat16, + device="cpu" + ) + model_manager.load_models([ + "models/FLUX/FLUX.1-dev/text_encoder/model.safetensors", + "models/FLUX/FLUX.1-dev/text_encoder_2", + "models/FLUX/FLUX.1-dev/ae.safetensors", + ]) + model_manager.load_models( + ["models/FLUX/FLUX.1-dev/flux1-dev.safetensors"], + torch_dtype=torch.float8_e4m3fn + ) + model_manager.load_models( + ["models/ControlNet/InstantX/FLUX.1-dev-Controlnet-Union-alpha/diffusion_pytorch_model.safetensors"], + torch_dtype=torch.float8_e4m3fn + ) + pipe = FluxImagePipeline.from_model_manager(model_manager, controlnet_config_units=[ + ControlNetConfigUnit( + processor_id="canny", + model_path="models/ControlNet/InstantX/FLUX.1-dev-Controlnet-Union-alpha/diffusion_pytorch_model.safetensors", + scale=0.3 + ), + ControlNetConfigUnit( + processor_id="depth", + model_path="models/ControlNet/InstantX/FLUX.1-dev-Controlnet-Union-alpha/diffusion_pytorch_model.safetensors", + scale=0.3 + ), + ],device="cuda") + pipe.enable_cpu_offload() + pipe.dit.quantize() + for model in pipe.controlnet.models: + model.quantize() + image_1 = pipe( + prompt="a beautiful Asian girl, full body, red dress, summer", + height=1024, width=1024, + seed=6 + ) + image_1.save("image_7.jpg") + + image_2 = pipe( + prompt="a beautiful Asian girl, full body, red dress, winter", + controlnet_image=image_1, + height=1024, width=1024, + seed=7 + ) + image_2.save("image_8.jpg") + + + +def example_5(): + download_models(["FLUX.1-dev", "alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta"]) + model_manager = ModelManager( + torch_dtype=torch.bfloat16, + device="cpu" + ) + model_manager.load_models([ + "models/FLUX/FLUX.1-dev/text_encoder/model.safetensors", + "models/FLUX/FLUX.1-dev/text_encoder_2", + "models/FLUX/FLUX.1-dev/ae.safetensors", + ]) + model_manager.load_models( + ["models/FLUX/FLUX.1-dev/flux1-dev.safetensors"], + torch_dtype=torch.float8_e4m3fn + ) + model_manager.load_models( + ["models/ControlNet/alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta/diffusion_pytorch_model.safetensors"], + torch_dtype=torch.float8_e4m3fn + ) + pipe = FluxImagePipeline.from_model_manager(model_manager, controlnet_config_units=[ + ControlNetConfigUnit( + processor_id="inpaint", + model_path="models/ControlNet/alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta/diffusion_pytorch_model.safetensors", + scale=0.9 + ), + ],device="cuda") + pipe.enable_cpu_offload() + pipe.dit.quantize() + for model in pipe.controlnet.models: + model.quantize() + image_1 = pipe( + prompt="a cat sitting on a chair", + height=1024, width=1024, + seed=8 + ) + image_1.save("image_9.jpg") + + mask = np.zeros((1024, 1024, 3), dtype=np.uint8) + mask[100:350, 350: -300] = 255 + mask = Image.fromarray(mask) + mask.save("mask_9.jpg") + + image_2 = pipe( + prompt="a cat sitting on a chair, wearing sunglasses", + controlnet_image=image_1, controlnet_inpaint_mask=mask, + height=1024, width=1024, + seed=9 + ) + image_2.save("image_10.jpg") + + + +def example_6(): + download_models([ + "FLUX.1-dev", + "jasperai/Flux.1-dev-Controlnet-Surface-Normals", + "alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta" + ]) + model_manager = ModelManager( + torch_dtype=torch.bfloat16, + device="cpu" + ) + model_manager.load_models([ + "models/FLUX/FLUX.1-dev/text_encoder/model.safetensors", + "models/FLUX/FLUX.1-dev/text_encoder_2", + "models/FLUX/FLUX.1-dev/ae.safetensors", + ]) + model_manager.load_models( + ["models/FLUX/FLUX.1-dev/flux1-dev.safetensors"], + torch_dtype=torch.float8_e4m3fn + ) + model_manager.load_models( + ["models/ControlNet/alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta/diffusion_pytorch_model.safetensors", + "models/ControlNet/jasperai/Flux.1-dev-Controlnet-Surface-Normals/diffusion_pytorch_model.safetensors"], + torch_dtype=torch.float8_e4m3fn + ) + pipe = FluxImagePipeline.from_model_manager(model_manager, controlnet_config_units=[ + ControlNetConfigUnit( + processor_id="inpaint", + model_path="models/ControlNet/alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta/diffusion_pytorch_model.safetensors", + scale=0.9 + ), + ControlNetConfigUnit( + processor_id="normal", + model_path="models/ControlNet/jasperai/Flux.1-dev-Controlnet-Surface-Normals/diffusion_pytorch_model.safetensors", + scale=0.6 + ), + ],device="cuda") + pipe.enable_cpu_offload() + pipe.dit.quantize() + for model in pipe.controlnet.models: + model.quantize() + image_1 = pipe( + prompt="a beautiful Asian woman looking at the sky, wearing a blue t-shirt.", + height=1024, width=1024, + seed=10 + ) + image_1.save("image_11.jpg") + + mask = np.zeros((1024, 1024, 3), dtype=np.uint8) + mask[-400:, 10:-40] = 255 + mask = Image.fromarray(mask) + mask.save("mask_11.jpg") + + image_2 = pipe( + prompt="a beautiful Asian woman looking at the sky, wearing a yellow t-shirt.", + controlnet_image=image_1, controlnet_inpaint_mask=mask, + height=1024, width=1024, + seed=11 + ) + image_2.save("image_12.jpg") + + +def example_7(): + download_models([ + "FLUX.1-dev", + "InstantX/FLUX.1-dev-Controlnet-Union-alpha", + "alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta", + "jasperai/Flux.1-dev-Controlnet-Upscaler", + ]) + model_manager = ModelManager( + torch_dtype=torch.bfloat16, + device="cpu" + ) + model_manager.load_models([ + "models/FLUX/FLUX.1-dev/text_encoder/model.safetensors", + "models/FLUX/FLUX.1-dev/text_encoder_2", + "models/FLUX/FLUX.1-dev/ae.safetensors", + ]) + model_manager.load_models( + ["models/FLUX/FLUX.1-dev/flux1-dev.safetensors"], + torch_dtype=torch.float8_e4m3fn + ) + model_manager.load_models( + ["models/ControlNet/alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta/diffusion_pytorch_model.safetensors", + "models/ControlNet/InstantX/FLUX.1-dev-Controlnet-Union-alpha/diffusion_pytorch_model.safetensors", + "models/ControlNet/jasperai/Flux.1-dev-Controlnet-Upscaler/diffusion_pytorch_model.safetensors"], + torch_dtype=torch.float8_e4m3fn + ) + pipe = FluxImagePipeline.from_model_manager(model_manager, controlnet_config_units=[ + ControlNetConfigUnit( + processor_id="inpaint", + model_path="models/ControlNet/alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta/diffusion_pytorch_model.safetensors", + scale=0.9 + ), + ControlNetConfigUnit( + processor_id="canny", + model_path="models/ControlNet/InstantX/FLUX.1-dev-Controlnet-Union-alpha/diffusion_pytorch_model.safetensors", + scale=0.5 + ), + ],device="cuda") + pipe.enable_cpu_offload() + pipe.dit.quantize() + for model in pipe.controlnet.models: + model.quantize() + image_1 = pipe( + prompt="a beautiful Asian woman and a cat on a bed. The woman wears a dress.", + height=1024, width=1024, + seed=100 + ) + image_1.save("image_13.jpg") + + mask_global = np.zeros((1024, 1024, 3), dtype=np.uint8) + mask_global = Image.fromarray(mask_global) + mask_global.save("mask_13_global.jpg") + + mask_1 = np.zeros((1024, 1024, 3), dtype=np.uint8) + mask_1[300:-100, 30: 450] = 255 + mask_1 = Image.fromarray(mask_1) + mask_1.save("mask_13_1.jpg") + + mask_2 = np.zeros((1024, 1024, 3), dtype=np.uint8) + mask_2[500:-100, -400:] = 255 + mask_2[-200:-100, -500:-400] = 255 + mask_2 = Image.fromarray(mask_2) + mask_2.save("mask_13_2.jpg") + + image_2 = pipe( + prompt="a beautiful Asian woman and a cat on a bed. The woman wears a dress.", + controlnet_image=image_1, controlnet_inpaint_mask=mask_global, + local_prompts=["an orange cat, highly detailed", "a girl wearing a red camisole"], masks=[mask_1, mask_2], mask_scales=[10.0, 10.0], + height=1024, width=1024, + seed=101 + ) + image_2.save("image_14.jpg") + + model_manager.load_lora("models/lora/FLUX-dev-lora-AntiBlur.safetensors", lora_alpha=2) + image_3 = pipe( + prompt="a beautiful Asian woman wearing a red camisole and an orange cat on a bed. clear background.", + negative_prompt="blur, blurry", + input_image=image_2, denoising_strength=0.7, + height=1024, width=1024, + cfg_scale=2.0, num_inference_steps=50, + seed=102 + ) + image_3.save("image_15.jpg") + + pipe = FluxImagePipeline.from_model_manager(model_manager, controlnet_config_units=[ + ControlNetConfigUnit( + processor_id="tile", + model_path="models/ControlNet/jasperai/Flux.1-dev-Controlnet-Upscaler/diffusion_pytorch_model.safetensors", + scale=0.7 + ), + ],device="cuda") + pipe.enable_cpu_offload() + pipe.dit.quantize() + for model in pipe.controlnet.models: + model.quantize() + image_4 = pipe( + prompt="a beautiful Asian woman wearing a red camisole and an orange cat on a bed. highly detailed, delicate skin texture, clear background.", + controlnet_image=image_3.resize((2048, 2048)), + input_image=image_3.resize((2048, 2048)), denoising_strength=0.99, + height=2048, width=2048, tiled=True, + seed=103 + ) + image_4.save("image_16.jpg") + + image_5 = pipe( + prompt="a beautiful Asian woman wearing a red camisole and an orange cat on a bed. highly detailed, delicate skin texture, clear background.", + controlnet_image=image_4.resize((4096, 4096)), + input_image=image_4.resize((4096, 4096)), denoising_strength=0.99, + height=4096, width=4096, tiled=True, + seed=104 + ) + image_5.save("image_17.jpg") + + + +download_models(["Annotators:Depth", "Annotators:Normal"]) +download_customized_models( + model_id="LiblibAI/FLUX.1-dev-LoRA-AntiBlur", + origin_file_path="FLUX-dev-lora-AntiBlur.safetensors", + local_dir="models/lora" +) +example_1() +example_2() +example_3() +example_4() +example_5() +example_6() +example_7() diff --git a/examples/Diffutoon/Diffutoon.ipynb b/examples/Diffutoon/Diffutoon.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..302e03d75789a54fc04b85dda43b259cdc045a4d --- /dev/null +++ b/examples/Diffutoon/Diffutoon.ipynb @@ -0,0 +1,512 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "8ObdI5jCB8xy" + }, + "source": [ + "# DiffSynth Studio\n", + "\n", + "Welcome to DiffSynth Studio! This is an example of Diffutoon." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "XSkKX7O2BwuM" + }, + "source": [ + "## Install" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "msCpt0pLnT8W", + "outputId": "35d93b35-451b-4760-d1ee-ef7ff190916e" + }, + "outputs": [], + "source": [ + "!git clone https://github.com/Artiprocher/DiffSynth-Studio.git\n", + "!pip install -q transformers controlnet-aux==0.0.7 streamlit streamlit-drawable-canvas imageio imageio[ffmpeg] safetensors einops cupy-cuda12x\n", + "%cd /content/DiffSynth-Studio" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "5eCu_rlKB3kK" + }, + "source": [ + "## Download Models" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "9znMkpVj3qZ1" + }, + "outputs": [], + "source": [ + "import requests\n", + "\n", + "\n", + "def download_model(url, file_path):\n", + " model_file = requests.get(url, allow_redirects=True)\n", + " with open(file_path, \"wb\") as f:\n", + " f.write(model_file.content)\n", + "\n", + "download_model(\"https://civitai.com/api/download/models/229575\", \"models/stable_diffusion/aingdiffusion_v12.safetensors\")\n", + "download_model(\"https://huggingface.co/guoyww/animatediff/resolve/main/mm_sd_v15_v2.ckpt\", \"models/AnimateDiff/mm_sd_v15_v2.ckpt\")\n", + "download_model(\"https://huggingface.co/lllyasviel/ControlNet-v1-1/resolve/main/control_v11p_sd15_lineart.pth\", \"models/ControlNet/control_v11p_sd15_lineart.pth\")\n", + "download_model(\"https://huggingface.co/lllyasviel/ControlNet-v1-1/resolve/main/control_v11f1e_sd15_tile.pth\", \"models/ControlNet/control_v11f1e_sd15_tile.pth\")\n", + "download_model(\"https://huggingface.co/lllyasviel/ControlNet-v1-1/resolve/main/control_v11f1p_sd15_depth.pth\", \"models/ControlNet/control_v11f1p_sd15_depth.pth\")\n", + "download_model(\"https://huggingface.co/lllyasviel/ControlNet-v1-1/resolve/main/control_v11p_sd15_softedge.pth\", \"models/ControlNet/control_v11p_sd15_softedge.pth\")\n", + "download_model(\"https://huggingface.co/lllyasviel/Annotators/resolve/main/dpt_hybrid-midas-501f0c75.pt\", \"models/Annotators/dpt_hybrid-midas-501f0c75.pt\")\n", + "download_model(\"https://huggingface.co/lllyasviel/Annotators/resolve/main/ControlNetHED.pth\", \"models/Annotators/ControlNetHED.pth\")\n", + "download_model(\"https://huggingface.co/lllyasviel/Annotators/resolve/main/sk_model.pth\", \"models/Annotators/sk_model.pth\")\n", + "download_model(\"https://huggingface.co/lllyasviel/Annotators/resolve/main/sk_model2.pth\", \"models/Annotators/sk_model2.pth\")\n", + "download_model(\"https://civitai.com/api/download/models/25820?type=Model&format=PickleTensor&size=full&fp=fp16\", \"models/textual_inversion/verybadimagenegative_v1.3.pt\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "iwOq2lWtKVYS" + }, + "source": [ + "## Run Diffutoon" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "tII_XRY-PJeo" + }, + "source": [ + "### Config Template" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "vsd2alA3PrGe" + }, + "outputs": [], + "source": [ + "config_stage_1_template = {\n", + " \"models\": {\n", + " \"model_list\": [\n", + " \"models/stable_diffusion/aingdiffusion_v12.safetensors\",\n", + " \"models/ControlNet/control_v11p_sd15_softedge.pth\",\n", + " \"models/ControlNet/control_v11f1p_sd15_depth.pth\"\n", + " ],\n", + " \"textual_inversion_folder\": \"models/textual_inversion\",\n", + " \"device\": \"cuda\",\n", + " \"lora_alphas\": [],\n", + " \"controlnet_units\": [\n", + " {\n", + " \"processor_id\": \"softedge\",\n", + " \"model_path\": \"models/ControlNet/control_v11p_sd15_softedge.pth\",\n", + " \"scale\": 0.5\n", + " },\n", + " {\n", + " \"processor_id\": \"depth\",\n", + " \"model_path\": \"models/ControlNet/control_v11f1p_sd15_depth.pth\",\n", + " \"scale\": 0.5\n", + " }\n", + " ]\n", + " },\n", + " \"data\": {\n", + " \"input_frames\": {\n", + " \"video_file\": \"/content/input_video.mp4\",\n", + " \"image_folder\": None,\n", + " \"height\": 512,\n", + " \"width\": 512,\n", + " \"start_frame_id\": 0,\n", + " \"end_frame_id\": 30\n", + " },\n", + " \"controlnet_frames\": [\n", + " {\n", + " \"video_file\": \"/content/input_video.mp4\",\n", + " \"image_folder\": None,\n", + " \"height\": 512,\n", + " \"width\": 512,\n", + " \"start_frame_id\": 0,\n", + " \"end_frame_id\": 30\n", + " },\n", + " {\n", + " \"video_file\": \"/content/input_video.mp4\",\n", + " \"image_folder\": None,\n", + " \"height\": 512,\n", + " \"width\": 512,\n", + " \"start_frame_id\": 0,\n", + " \"end_frame_id\": 30\n", + " }\n", + " ],\n", + " \"output_folder\": \"data/examples/diffutoon_edit/color_video\",\n", + " \"fps\": 25\n", + " },\n", + " \"smoother_configs\": [\n", + " {\n", + " \"processor_type\": \"FastBlend\",\n", + " \"config\": {}\n", + " }\n", + " ],\n", + " \"pipeline\": {\n", + " \"seed\": 0,\n", + " \"pipeline_inputs\": {\n", + " \"prompt\": \"best quality, perfect anime illustration, orange clothes, night, a girl is dancing, smile, solo, black silk stockings\",\n", + " \"negative_prompt\": \"verybadimagenegative_v1.3\",\n", + " \"cfg_scale\": 7.0,\n", + " \"clip_skip\": 1,\n", + " \"denoising_strength\": 0.9,\n", + " \"num_inference_steps\": 20,\n", + " \"animatediff_batch_size\": 8,\n", + " \"animatediff_stride\": 4,\n", + " \"unet_batch_size\": 8,\n", + " \"controlnet_batch_size\": 8,\n", + " \"cross_frame_attention\": True,\n", + " \"smoother_progress_ids\": [-1],\n", + " # The following parameters will be overwritten. You don't need to modify them.\n", + " \"input_frames\": [],\n", + " \"num_frames\": 30,\n", + " \"width\": 512,\n", + " \"height\": 512,\n", + " \"controlnet_frames\": []\n", + " }\n", + " }\n", + "}\n", + "\n", + "config_stage_2_template = {\n", + " \"models\": {\n", + " \"model_list\": [\n", + " \"models/stable_diffusion/aingdiffusion_v12.safetensors\",\n", + " \"models/AnimateDiff/mm_sd_v15_v2.ckpt\",\n", + " \"models/ControlNet/control_v11f1e_sd15_tile.pth\",\n", + " \"models/ControlNet/control_v11p_sd15_lineart.pth\"\n", + " ],\n", + " \"textual_inversion_folder\": \"models/textual_inversion\",\n", + " \"device\": \"cuda\",\n", + " \"lora_alphas\": [],\n", + " \"controlnet_units\": [\n", + " {\n", + " \"processor_id\": \"tile\",\n", + " \"model_path\": \"models/ControlNet/control_v11f1e_sd15_tile.pth\",\n", + " \"scale\": 0.5\n", + " },\n", + " {\n", + " \"processor_id\": \"lineart\",\n", + " \"model_path\": \"models/ControlNet/control_v11p_sd15_lineart.pth\",\n", + " \"scale\": 0.5\n", + " }\n", + " ]\n", + " },\n", + " \"data\": {\n", + " \"input_frames\": {\n", + " \"video_file\": \"/content/input_video.mp4\",\n", + " \"image_folder\": None,\n", + " \"height\": 1024,\n", + " \"width\": 1024,\n", + " \"start_frame_id\": 0,\n", + " \"end_frame_id\": 30\n", + " },\n", + " \"controlnet_frames\": [\n", + " {\n", + " \"video_file\": \"/content/input_video.mp4\",\n", + " \"image_folder\": None,\n", + " \"height\": 1024,\n", + " \"width\": 1024,\n", + " \"start_frame_id\": 0,\n", + " \"end_frame_id\": 30\n", + " },\n", + " {\n", + " \"video_file\": \"/content/input_video.mp4\",\n", + " \"image_folder\": None,\n", + " \"height\": 1024,\n", + " \"width\": 1024,\n", + " \"start_frame_id\": 0,\n", + " \"end_frame_id\": 30\n", + " }\n", + " ],\n", + " \"output_folder\": \"/content/output\",\n", + " \"fps\": 25\n", + " },\n", + " \"pipeline\": {\n", + " \"seed\": 0,\n", + " \"pipeline_inputs\": {\n", + " \"prompt\": \"best quality, perfect anime illustration, light, a girl is dancing, smile, solo\",\n", + " \"negative_prompt\": \"verybadimagenegative_v1.3\",\n", + " \"cfg_scale\": 7.0,\n", + " \"clip_skip\": 2,\n", + " \"denoising_strength\": 1.0,\n", + " \"num_inference_steps\": 10,\n", + " \"animatediff_batch_size\": 16,\n", + " \"animatediff_stride\": 8,\n", + " \"unet_batch_size\": 1,\n", + " \"controlnet_batch_size\": 1,\n", + " \"cross_frame_attention\": False,\n", + " # The following parameters will be overwritten. You don't need to modify them.\n", + " \"input_frames\": [],\n", + " \"num_frames\": 30,\n", + " \"width\": 1536,\n", + " \"height\": 1536,\n", + " \"controlnet_frames\": []\n", + " }\n", + " }\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "113QAmNHP6T_" + }, + "source": [ + "### Upload Input Video\n", + "\n", + "Before you run the following code, please upload your input video to `/content/input_video.mp4`." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "CyqAsj1o5U9B" + }, + "source": [ + "### Toon Shading\n", + "\n", + "Render your video in an anime style.\n", + "\n", + "We highly recommend you to use a higher resolution for better visual quality. The default resolution of Diffutoon is 1536x1536, which requires 22GB VRAM. If you don't have enough VRAM, 1024x1024 is also acceptable.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "761nbrgeKMvj", + "outputId": "c0d47d5f-16e9-4a65-e664-9bd5fc491111" + }, + "outputs": [], + "source": [ + "from diffsynth import SDVideoPipelineRunner\n", + "\n", + "\n", + "config = config_stage_2_template.copy()\n", + "config[\"data\"][\"input_frames\"] = {\n", + " \"video_file\": \"/content/input_video.mp4\",\n", + " \"image_folder\": None,\n", + " \"height\": 1024,\n", + " \"width\": 1024,\n", + " \"start_frame_id\": 0,\n", + " \"end_frame_id\": 30\n", + "}\n", + "config[\"data\"][\"controlnet_frames\"] = [config[\"data\"][\"input_frames\"], config[\"data\"][\"input_frames\"]]\n", + "config[\"data\"][\"output_folder\"] = \"/content/toon_video\"\n", + "config[\"data\"][\"fps\"] = 25\n", + "\n", + "runner = SDVideoPipelineRunner()\n", + "runner.run(config)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "9wujhGUmDIwY" + }, + "source": [ + "Let's see the video!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 420 + }, + "id": "TBNAigacAq6h", + "outputId": "8f57c3b4-982b-4643-f3dc-53c51bd85a4b" + }, + "outputs": [], + "source": [ + "from IPython.display import HTML\n", + "from base64 import b64encode\n", + "\n", + "mp4 = open(\"/content/toon_video/video.mp4\", \"rb\").read()\n", + "data_url = \"data:video/mp4;base64,\" + b64encode(mp4).decode()\n", + "HTML(\"\"\"\n", + "\n", + "\"\"\" % data_url)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "48hQfX--5YGi" + }, + "source": [ + "### Toon Shading with Editing Signals" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "bAQ9Zq-3-MH6" + }, + "source": [ + "In stage 1, input your prompt, and diffutoon will generate the editing signals in the format of low-resolution color video." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "BtDzYgIq5bgg", + "outputId": "bb27b7b9-7979-4409-f476-f25f0a164ef4" + }, + "outputs": [], + "source": [ + "from diffsynth import SDVideoPipelineRunner\n", + "\n", + "\n", + "config_stage_1 = config_stage_1_template.copy()\n", + "config_stage_1[\"data\"][\"input_frames\"] = {\n", + " \"video_file\": \"/content/input_video.mp4\",\n", + " \"image_folder\": None,\n", + " \"height\": 512,\n", + " \"width\": 512,\n", + " \"start_frame_id\": 0,\n", + " \"end_frame_id\": 30\n", + "}\n", + "config_stage_1[\"data\"][\"controlnet_frames\"] = [config_stage_1[\"data\"][\"input_frames\"], config_stage_1[\"data\"][\"input_frames\"]]\n", + "config_stage_1[\"data\"][\"output_folder\"] = \"/content/color_video\"\n", + "config_stage_1[\"data\"][\"fps\"] = 25\n", + "config_stage_1[\"pipeline\"][\"pipeline_inputs\"][\"prompt\"] = \"best quality, perfect anime illustration, orange clothes, night, a girl is dancing, smile, solo, black silk stockings\"\n", + "\n", + "runner = SDVideoPipelineRunner()\n", + "runner.run(config_stage_1)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "D9_AWwhi-pA9" + }, + "source": [ + "In stage 2, diffutoon will rerender the whole video according to the editing signals." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "JFysCk7y51i_", + "outputId": "475050d3-c72e-4e08-b55c-d59ed86b5497" + }, + "outputs": [], + "source": [ + "from diffsynth import SDVideoPipelineRunner\n", + "\n", + "\n", + "config_stage_2 = config_stage_2_template.copy()\n", + "config_stage_2[\"data\"][\"input_frames\"] = {\n", + " \"video_file\": \"/content/input_video.mp4\",\n", + " \"image_folder\": None,\n", + " \"height\": 1024,\n", + " \"width\": 1024,\n", + " \"start_frame_id\": 0,\n", + " \"end_frame_id\": 30\n", + "}\n", + "config_stage_2[\"data\"][\"controlnet_frames\"][0] = {\n", + " \"video_file\": \"/content/color_video/video.mp4\",\n", + " \"image_folder\": None,\n", + " \"height\": config_stage_2[\"data\"][\"input_frames\"][\"height\"],\n", + " \"width\": config_stage_2[\"data\"][\"input_frames\"][\"width\"],\n", + " \"start_frame_id\": None,\n", + " \"end_frame_id\": None\n", + "}\n", + "config_stage_2[\"data\"][\"controlnet_frames\"][1] = config[\"data\"][\"input_frames\"]\n", + "config_stage_2[\"data\"][\"output_folder\"] = \"/content/edit_video\"\n", + "config_stage_2[\"data\"][\"fps\"] = 25\n", + "\n", + "runner = SDVideoPipelineRunner()\n", + "runner.run(config)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "HIPrCAIS_Im0" + }, + "source": [ + "Let's see the video!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 420 + }, + "id": "Y2nz7rew-7VI", + "outputId": "fbcbadc6-4045-4aac-dfb0-80bacec003bf" + }, + "outputs": [], + "source": [ + "from IPython.display import HTML\n", + "from base64 import b64encode\n", + "\n", + "mp4 = open(\"/content/edit_video/video.mp4\", \"rb\").read()\n", + "data_url = \"data:video/mp4;base64,\" + b64encode(mp4).decode()\n", + "HTML(\"\"\"\n", + "\n", + "\"\"\" % data_url)" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "collapsed_sections": [ + "tII_XRY-PJeo" + ], + "gpuType": "T4", + "provenance": [], + "toc_visible": true + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/examples/Diffutoon/README.md b/examples/Diffutoon/README.md new file mode 100644 index 0000000000000000000000000000000000000000..8b2caf0600f96cadcf7014648a464ba1886a65eb --- /dev/null +++ b/examples/Diffutoon/README.md @@ -0,0 +1,21 @@ +# Diffutoon + +[Diffutoon](https://arxiv.org/abs/2401.16224) is a toon shading approach. This approach is adept for rendering high-resoluton videos with rapid motion. + +## Example: Toon Shading (Diffutoon) + +Directly render realistic videos in a flatten style. In this example, you can easily modify the parameters in the config dict. See [`diffutoon_toon_shading.py`](./diffutoon_toon_shading.py). We also provide [an example on Colab](https://colab.research.google.com/github/Artiprocher/DiffSynth-Studio/blob/main/examples/Diffutoon.ipynb). + +https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/b54c05c5-d747-4709-be5e-b39af82404dd + +## Example: Toon Shading with Editing Signals (Diffutoon) + +This example supports video editing signals. See [`diffutoon_toon_shading_with_editing_signals.py`](./diffutoon_toon_shading_with_editing_signals.py). The editing feature is also supported in the [Colab example](https://colab.research.google.com/github/Artiprocher/DiffSynth-Studio/blob/main/examples/Diffutoon/Diffutoon.ipynb). + +https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/20528af5-5100-474a-8cdc-440b9efdd86c + +## Example: Toon Shading (in native Python code) + +This example is provided for developers. If you don't want to use the config to manage parameters, you can see [`sd_toon_shading.py`](./sd_toon_shading.py) to learn how to use it in native Python code. + +https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/607c199b-6140-410b-a111-3e4ffb01142c diff --git a/examples/Diffutoon/diffutoon_toon_shading.py b/examples/Diffutoon/diffutoon_toon_shading.py new file mode 100644 index 0000000000000000000000000000000000000000..2406a807acf528fa9e8a9c55e688008ab170330c --- /dev/null +++ b/examples/Diffutoon/diffutoon_toon_shading.py @@ -0,0 +1,100 @@ +from diffsynth import SDVideoPipelineRunner, download_models + + +# Download models (automatically) +# `models/stable_diffusion/aingdiffusion_v12.safetensors`: [link](https://civitai.com/api/download/models/229575) +# `models/AnimateDiff/mm_sd_v15_v2.ckpt`: [link](https://huggingface.co/guoyww/animatediff/resolve/main/mm_sd_v15_v2.ckpt) +# `models/ControlNet/control_v11p_sd15_lineart.pth`: [link](https://huggingface.co/lllyasviel/ControlNet-v1-1/resolve/main/control_v11p_sd15_lineart.pth) +# `models/ControlNet/control_v11f1e_sd15_tile.pth`: [link](https://huggingface.co/lllyasviel/ControlNet-v1-1/resolve/main/control_v11f1e_sd15_tile.pth) +# `models/Annotators/sk_model.pth`: [link](https://huggingface.co/lllyasviel/Annotators/resolve/main/sk_model.pth) +# `models/Annotators/sk_model2.pth`: [link](https://huggingface.co/lllyasviel/Annotators/resolve/main/sk_model2.pth) +# `models/textual_inversion/verybadimagenegative_v1.3.pt`: [link](https://civitai.com/api/download/models/25820?type=Model&format=PickleTensor&size=full&fp=fp16) +download_models([ + "AingDiffusion_v12", + "AnimateDiff_v2", + "ControlNet_v11p_sd15_lineart", + "ControlNet_v11f1e_sd15_tile", + "TextualInversion_VeryBadImageNegative_v1.3" +]) +# The original video in the example is https://www.bilibili.com/video/BV1iG411a7sQ/. + +config = { + "models": { + "model_list": [ + "models/stable_diffusion/aingdiffusion_v12.safetensors", + "models/AnimateDiff/mm_sd_v15_v2.ckpt", + "models/ControlNet/control_v11f1e_sd15_tile.pth", + "models/ControlNet/control_v11p_sd15_lineart.pth" + ], + "textual_inversion_folder": "models/textual_inversion", + "device": "cuda", + "lora_alphas": [], + "controlnet_units": [ + { + "processor_id": "tile", + "model_path": "models/ControlNet/control_v11f1e_sd15_tile.pth", + "scale": 0.5 + }, + { + "processor_id": "lineart", + "model_path": "models/ControlNet/control_v11p_sd15_lineart.pth", + "scale": 0.5 + } + ] + }, + "data": { + "input_frames": { + "video_file": "data/examples/diffutoon/input_video.mp4", + "image_folder": None, + "height": 1536, + "width": 1536, + "start_frame_id": 0, + "end_frame_id": 30 + }, + "controlnet_frames": [ + { + "video_file": "data/examples/diffutoon/input_video.mp4", + "image_folder": None, + "height": 1536, + "width": 1536, + "start_frame_id": 0, + "end_frame_id": 30 + }, + { + "video_file": "data/examples/diffutoon/input_video.mp4", + "image_folder": None, + "height": 1536, + "width": 1536, + "start_frame_id": 0, + "end_frame_id": 30 + } + ], + "output_folder": "output", + "fps": 30 + }, + "pipeline": { + "seed": 0, + "pipeline_inputs": { + "prompt": "best quality, perfect anime illustration, light, a girl is dancing, smile, solo", + "negative_prompt": "verybadimagenegative_v1.3", + "cfg_scale": 7.0, + "clip_skip": 2, + "denoising_strength": 1.0, + "num_inference_steps": 10, + "animatediff_batch_size": 16, + "animatediff_stride": 8, + "unet_batch_size": 1, + "controlnet_batch_size": 1, + "cross_frame_attention": False, + # The following parameters will be overwritten. You don't need to modify them. + "input_frames": [], + "num_frames": 30, + "width": 1536, + "height": 1536, + "controlnet_frames": [] + } + } +} + +runner = SDVideoPipelineRunner() +runner.run(config) diff --git a/examples/Diffutoon/diffutoon_toon_shading_with_editing_signals.py b/examples/Diffutoon/diffutoon_toon_shading_with_editing_signals.py new file mode 100644 index 0000000000000000000000000000000000000000..d07236cf1e37d1b222e924577b5329dad2abf608 --- /dev/null +++ b/examples/Diffutoon/diffutoon_toon_shading_with_editing_signals.py @@ -0,0 +1,204 @@ +from diffsynth import SDVideoPipelineRunner, download_models +import os + + +# Download models (automatically) +# `models/stable_diffusion/aingdiffusion_v12.safetensors`: [link](https://civitai.com/api/download/models/229575) +# `models/AnimateDiff/mm_sd_v15_v2.ckpt`: [link](https://huggingface.co/guoyww/animatediff/resolve/main/mm_sd_v15_v2.ckpt) +# `models/ControlNet/control_v11p_sd15_lineart.pth`: [link](https://huggingface.co/lllyasviel/ControlNet-v1-1/resolve/main/control_v11p_sd15_lineart.pth) +# `models/ControlNet/control_v11f1e_sd15_tile.pth`: [link](https://huggingface.co/lllyasviel/ControlNet-v1-1/resolve/main/control_v11f1e_sd15_tile.pth) +# `models/ControlNet/control_v11f1p_sd15_depth.pth`: [link](https://huggingface.co/lllyasviel/ControlNet-v1-1/resolve/main/control_v11f1p_sd15_depth.pth) +# `models/ControlNet/control_v11p_sd15_softedge.pth`: [link](https://huggingface.co/lllyasviel/ControlNet-v1-1/resolve/main/control_v11p_sd15_softedge.pth) +# `models/Annotators/dpt_hybrid-midas-501f0c75.pt`: [link](https://huggingface.co/lllyasviel/Annotators/resolve/main/dpt_hybrid-midas-501f0c75.pt) +# `models/Annotators/ControlNetHED.pth`: [link](https://huggingface.co/lllyasviel/Annotators/resolve/main/ControlNetHED.pth) +# `models/Annotators/sk_model.pth`: [link](https://huggingface.co/lllyasviel/Annotators/resolve/main/sk_model.pth) +# `models/Annotators/sk_model2.pth`: [link](https://huggingface.co/lllyasviel/Annotators/resolve/main/sk_model2.pth) +# `models/textual_inversion/verybadimagenegative_v1.3.pt`: [link](https://civitai.com/api/download/models/25820?type=Model&format=PickleTensor&size=full&fp=fp16) +download_models([ + "AingDiffusion_v12", + "AnimateDiff_v2", + "ControlNet_v11p_sd15_lineart", + "ControlNet_v11f1e_sd15_tile", + "ControlNet_v11f1p_sd15_depth", + "ControlNet_v11p_sd15_softedge", + "TextualInversion_VeryBadImageNegative_v1.3" +]) +# The original video in the example is https://www.bilibili.com/video/BV1zu4y1s7Ec/. + +config_stage_1 = { + "models": { + "model_list": [ + "models/stable_diffusion/aingdiffusion_v12.safetensors", + "models/ControlNet/control_v11p_sd15_softedge.pth", + "models/ControlNet/control_v11f1p_sd15_depth.pth" + ], + "textual_inversion_folder": "models/textual_inversion", + "device": "cuda", + "lora_alphas": [], + "controlnet_units": [ + { + "processor_id": "softedge", + "model_path": "models/ControlNet/control_v11p_sd15_softedge.pth", + "scale": 0.5 + }, + { + "processor_id": "depth", + "model_path": "models/ControlNet/control_v11f1p_sd15_depth.pth", + "scale": 0.5 + } + ] + }, + "data": { + "input_frames": { + "video_file": "data/examples/diffutoon_edit/input_video.mp4", + "image_folder": None, + "height": 512, + "width": 512, + "start_frame_id": 0, + "end_frame_id": 30 + }, + "controlnet_frames": [ + { + "video_file": "data/examples/diffutoon_edit/input_video.mp4", + "image_folder": None, + "height": 512, + "width": 512, + "start_frame_id": 0, + "end_frame_id": 30 + }, + { + "video_file": "data/examples/diffutoon_edit/input_video.mp4", + "image_folder": None, + "height": 512, + "width": 512, + "start_frame_id": 0, + "end_frame_id": 30 + } + ], + "output_folder": "output/color_video", + "fps": 25 + }, + "smoother_configs": [ + { + "processor_type": "FastBlend", + "config": {} + } + ], + "pipeline": { + "seed": 0, + "pipeline_inputs": { + "prompt": "best quality, perfect anime illustration, orange clothes, night, a girl is dancing, smile, solo, black silk stockings", + "negative_prompt": "verybadimagenegative_v1.3", + "cfg_scale": 7.0, + "clip_skip": 1, + "denoising_strength": 0.9, + "num_inference_steps": 20, + "animatediff_batch_size": 8, + "animatediff_stride": 4, + "unet_batch_size": 8, + "controlnet_batch_size": 8, + "cross_frame_attention": True, + "smoother_progress_ids": [-1], + # The following parameters will be overwritten. You don't need to modify them. + "input_frames": [], + "num_frames": 30, + "width": 512, + "height": 512, + "controlnet_frames": [] + } + } +} + + +config_stage_2 = { + "models": { + "model_list": [ + "models/stable_diffusion/aingdiffusion_v12.safetensors", + "models/AnimateDiff/mm_sd_v15_v2.ckpt", + "models/ControlNet/control_v11f1e_sd15_tile.pth", + "models/ControlNet/control_v11p_sd15_lineart.pth" + ], + "textual_inversion_folder": "models/textual_inversion", + "device": "cuda", + "lora_alphas": [], + "controlnet_units": [ + { + "processor_id": "tile", + "model_path": "models/ControlNet/control_v11f1e_sd15_tile.pth", + "scale": 0.5 + }, + { + "processor_id": "lineart", + "model_path": "models/ControlNet/control_v11p_sd15_lineart.pth", + "scale": 0.5 + } + ] + }, + "data": { + "input_frames": { + "video_file": "data/examples/diffutoon_edit/input_video.mp4", + "image_folder": None, + "height": 1536, + "width": 1536, + "start_frame_id": 0, + "end_frame_id": 30 + }, + "controlnet_frames": [ + { + "video_file": "data/examples/diffutoon_edit/input_video.mp4", + "image_folder": None, + "height": 1536, + "width": 1536, + "start_frame_id": 0, + "end_frame_id": 30 + }, + { + "video_file": "data/examples/diffutoon_edit/input_video.mp4", + "image_folder": None, + "height": 1536, + "width": 1536, + "start_frame_id": 0, + "end_frame_id": 30 + } + ], + "output_folder": "output/edited_video", + "fps": 30 + }, + "pipeline": { + "seed": 0, + "pipeline_inputs": { + "prompt": "best quality, perfect anime illustration, light, a girl is dancing, smile, solo", + "negative_prompt": "verybadimagenegative_v1.3", + "cfg_scale": 7.0, + "clip_skip": 2, + "denoising_strength": 1.0, + "num_inference_steps": 10, + "animatediff_batch_size": 16, + "animatediff_stride": 8, + "unet_batch_size": 1, + "controlnet_batch_size": 1, + "cross_frame_attention": False, + # The following parameters will be overwritten. You don't need to modify them. + "input_frames": [], + "num_frames": 30, + "width": 1536, + "height": 1536, + "controlnet_frames": [] + } + } +} + + +runner = SDVideoPipelineRunner() +runner.run(config_stage_1) + +# Replace the color video with the synthesized video +config_stage_2["data"]["controlnet_frames"][0] = { + "video_file": os.path.join(config_stage_1["data"]["output_folder"], "video.mp4"), + "image_folder": None, + "height": config_stage_2["data"]["input_frames"]["height"], + "width": config_stage_2["data"]["input_frames"]["width"], + "start_frame_id": None, + "end_frame_id": None +} +runner.run(config_stage_2) diff --git a/examples/Diffutoon/sd_toon_shading.py b/examples/Diffutoon/sd_toon_shading.py new file mode 100644 index 0000000000000000000000000000000000000000..20264b1d20e1a4a8df1414bb2f20d4539fb3dfee --- /dev/null +++ b/examples/Diffutoon/sd_toon_shading.py @@ -0,0 +1,65 @@ +from diffsynth import ModelManager, SDVideoPipeline, ControlNetConfigUnit, VideoData, save_video, download_models +import torch + + +# Download models (automatically) +# `models/stable_diffusion/flat2DAnimerge_v45Sharp.safetensors`: [link](https://civitai.com/api/download/models/266360?type=Model&format=SafeTensor&size=pruned&fp=fp16) +# `models/AnimateDiff/mm_sd_v15_v2.ckpt`: [link](https://huggingface.co/guoyww/animatediff/resolve/main/mm_sd_v15_v2.ckpt) +# `models/ControlNet/control_v11p_sd15_lineart.pth`: [link](https://huggingface.co/lllyasviel/ControlNet-v1-1/resolve/main/control_v11p_sd15_lineart.pth) +# `models/ControlNet/control_v11f1e_sd15_tile.pth`: [link](https://huggingface.co/lllyasviel/ControlNet-v1-1/resolve/main/control_v11f1e_sd15_tile.pth) +# `models/Annotators/sk_model.pth`: [link](https://huggingface.co/lllyasviel/Annotators/resolve/main/sk_model.pth) +# `models/Annotators/sk_model2.pth`: [link](https://huggingface.co/lllyasviel/Annotators/resolve/main/sk_model2.pth) +# `models/textual_inversion/verybadimagenegative_v1.3.pt`: [link](https://civitai.com/api/download/models/25820?type=Model&format=PickleTensor&size=full&fp=fp16) +download_models([ + "Flat2DAnimerge_v45Sharp", + "AnimateDiff_v2", + "ControlNet_v11p_sd15_lineart", + "ControlNet_v11f1e_sd15_tile", + "TextualInversion_VeryBadImageNegative_v1.3" +]) + +# Load models +model_manager = ModelManager(torch_dtype=torch.float16, device="cuda") +model_manager.load_models([ + "models/stable_diffusion/flat2DAnimerge_v45Sharp.safetensors", + "models/AnimateDiff/mm_sd_v15_v2.ckpt", + "models/ControlNet/control_v11p_sd15_lineart.pth", + "models/ControlNet/control_v11f1e_sd15_tile.pth", +]) +pipe = SDVideoPipeline.from_model_manager( + model_manager, + [ + ControlNetConfigUnit( + processor_id="lineart", + model_path="models/ControlNet/control_v11p_sd15_lineart.pth", + scale=0.5 + ), + ControlNetConfigUnit( + processor_id="tile", + model_path="models/ControlNet/control_v11f1e_sd15_tile.pth", + scale=0.5 + ) + ] +) +pipe.prompter.load_textual_inversions(["models/textual_inversion/verybadimagenegative_v1.3.pt"]) + +# Load video (we only use 60 frames for quick testing) +# The original video is here: https://www.bilibili.com/video/BV19w411A7YJ/ +video = VideoData( + video_file="data/examples/bilibili/BV19w411A7YJ.mp4", + height=1024, width=1024) +input_video = [video[i] for i in range(40*60, 41*60)] + +# Toon shading (20G VRAM) +torch.manual_seed(0) +output_video = pipe( + prompt="best quality, perfect anime illustration, light, a girl is dancing, smile, solo", + negative_prompt="verybadimagenegative_v1.3", + cfg_scale=3, clip_skip=2, + controlnet_frames=input_video, num_frames=len(input_video), + num_inference_steps=10, height=1024, width=1024, + animatediff_batch_size=32, animatediff_stride=16, +) + +# Save video +save_video(output_video, "output_video.mp4", fps=60) diff --git a/examples/EntityControl/README.md b/examples/EntityControl/README.md new file mode 100644 index 0000000000000000000000000000000000000000..79e5d56228309c4499c2f5e721458c2b6c2bb25f --- /dev/null +++ b/examples/EntityControl/README.md @@ -0,0 +1,90 @@ +# EliGen: Entity-Level Controlled Image Generation + +## Introduction + +We propose EliGen, a novel approach that leverages fine-grained entity-level information to enable precise and controllable text-to-image generation. EliGen excels in tasks such as entity-level controlled image generation and image inpainting, while its applicability is not limited to these areas. Additionally, it can be seamlessly integrated with existing community models, such as the IP-Adpater and In-Cotext LoRA. + +* Paper: [EliGen: Entity-Level Controlled Image Generation with Regional Attention](https://arxiv.org/abs/2501.01097) +* Github: [DiffSynth-Studio](https://github.com/modelscope/DiffSynth-Studio) +* Model: [ModelScope](https://www.modelscope.cn/models/DiffSynth-Studio/Eligen), [HuggingFace](https://huggingface.co/modelscope/EliGen) +* Online Demo: [ModelScope EliGen Studio](https://www.modelscope.cn/studios/DiffSynth-Studio/EliGen) +* Training Dataset: [EliGen Train Set](https://www.modelscope.cn/datasets/DiffSynth-Studio/EliGenTrainSet) + + +## Methodology + +![regional-attention](https://github.com/user-attachments/assets/bef5ae2b-cc03-404e-b9c8-0c037ac66190) + +We introduce a regional attention mechanism within the DiT framework to effectively process the conditions of each entity. This mechanism enables the local prompt associated with each entity to semantically influence specific regions through regional attention. To further enhance the layout control capabilities of EliGen, we meticulously contribute an entity-annotated dataset and fine-tune the model using the LoRA framework. + +1. **Regional Attention**: Regional attention is shown in above figure, which can be easily applied to other text-to-image models. Its core principle involves transforming the positional information of each entity into an attention mask, ensuring that the mechanism only affects the designated regions. + +2. **Dataset with Entity Annotation**: To construct a dedicated entity control dataset, we start by randomly selecting captions from DiffusionDB and generating the corresponding source image using Flux. Next, we employ Qwen2-VL 72B, recognized for its advanced grounding capabilities among MLLMs, to randomly identify entities within the image. These entities are annotated with local prompts and bounding boxes for precise localization, forming the foundation of our dataset for further training. + +3. **Training**: We utilize LoRA (Low-Rank Adaptation) and DeepSpeed to fine-tune regional attention mechanisms using a curated dataset, enabling our EliGen model to achieve effective entity-level control. + +## Usage +1. **Entity-Level Controlled Image Generation** + EliGen achieves effective entity-level control results. See [./entity_control.py](./entity_control.py) for usage. +2. **Image Inpainting** + To apply EliGen to image inpainting task, we propose a inpainting fusion pipeline to preserve the non-painting areas while enabling precise, entity-level modifications over inpaining regions. + See [./entity_inpaint.py](./entity_inpaint.py) for usage. +3. **Styled Entity Control** + EliGen can be seamlessly integrated with existing community models. We have provided an example of how to integrate it with the IP-Adpater. See [./entity_control_ipadapter.py](./entity_control_ipadapter.py) for usage. +4. **Entity Transfer** + We have provided an example of how to integrate EliGen with In-Cotext LoRA, which achieves interesting entity transfer results. See [./entity_transfer.py](./entity_transfer.py) for usage. +5. **Play with EliGen using UI** + Run the following command to try interactive UI: + ```bash + python apps/gradio/entity_level_control.py + ``` +## Examples +### Entity-Level Controlled Image Generation + +1. The effect of generating images with continuously changing entity positions. + +https://github.com/user-attachments/assets/54a048c8-b663-4262-8c40-43c87c266d4b + +2. The image generation effect of complex Entity combinations, demonstrating the strong generalization of EliGen. See [./entity_control.py](./entity_control.py) `example_1-6` for generation prompts. + +|Entity Conditions|Generated Image| +|-|-| +|![eligen_example_1_mask_0](https://github.com/user-attachments/assets/68cbedc0-32aa-4a8e-99d2-306dbb4620de)|![eligen_example_1_0](https://github.com/user-attachments/assets/c678c4b1-aa19-41df-b612-adc01b8b2009)| +|![eligen_example_2_mask_0](https://github.com/user-attachments/assets/1c6d9445-5022-4d91-ad2e-dc05321883d1)|![eligen_example_2_0](https://github.com/user-attachments/assets/86739945-cb07-4a49-b3b3-3bb65c90d14f)| +|![eligen_example_3_mask_27](https://github.com/user-attachments/assets/5ca4440d-d1db-45dd-b03c-0affefbd9ac3)|![eligen_example_3_27](https://github.com/user-attachments/assets/9160c22a-89ac-4d52-be1d-17ba2d8a67eb)| +|![eligen_example_4_mask_21](https://github.com/user-attachments/assets/26dfde2b-cc9a-4cb3-806a-7f7436d971a7)|![eligen_example_4_21](https://github.com/user-attachments/assets/1fff7346-6a8c-4eb6-986f-4ea848c6b363)| +|![eligen_example_5_mask_0](https://github.com/user-attachments/assets/8ca94e5f-f896-451d-a700-bcdc23689adb)|![eligen_example_5_0](https://github.com/user-attachments/assets/881a9395-6cc2-43e9-89b4-30b8f5437e6d)| +|![eligen_example_6_mask_8](https://github.com/user-attachments/assets/26c95abf-f2b1-44db-92c1-75d02c714c74)|![eligen_example_6_8](https://github.com/user-attachments/assets/8883abde-3fad-4a8b-ade0-ca5b977a290f)| + +1. Demonstration of the robustness of EliGen. The following examples are generated using the same prompt but different seeds. Refer to [./entity_control.py](./entity_control.py) `example_7` for the prompts. + +|Entity Conditions|Generated Image| +|-|-| +|![eligen_example_7_mask_5](https://github.com/user-attachments/assets/85630237-9d8b-41ea-9bd5-506652c61776)|![eligen_example_7_5](https://github.com/user-attachments/assets/d34b54d2-c59c-4c39-8ab4-c22f155283f1)| +|![eligen_example_7_mask_5](https://github.com/user-attachments/assets/85630237-9d8b-41ea-9bd5-506652c61776)|![eligen_example_7_6](https://github.com/user-attachments/assets/4050a3bf-a089-4f4f-81e0-e3b391cf7ceb)| +![eligen_example_7_mask_5](https://github.com/user-attachments/assets/85630237-9d8b-41ea-9bd5-506652c61776)|![eligen_example_7_7](https://github.com/user-attachments/assets/682feb5e-a27a-4ae4-a800-018b4e0e504c)| +|![eligen_example_7_mask_5](https://github.com/user-attachments/assets/85630237-9d8b-41ea-9bd5-506652c61776)|![eligen_example_7_8](https://github.com/user-attachments/assets/50266950-24b3-426a-ae74-c3ebadb853d9)| + +### Image Inpainting +Demonstration of the inpainting mode of EliGen, see [./entity_inpaint.py](./entity_inpaint.py) for generation prompts. +|Inpainting Input|Inpainting Output| +|-|-| +|![inpaint_i1](https://github.com/user-attachments/assets/5ef499f3-3d8a-49cc-8ceb-86af7f5cb9f8)|![inpaint_o1](https://github.com/user-attachments/assets/88fc3bde-0984-4b3c-8ca9-d63de660855b)| +|![inpaint_i2](https://github.com/user-attachments/assets/5f74c710-bf30-4db1-ae40-a1e1995ccef6)|![inpaint_o2](https://github.com/user-attachments/assets/7c3b4857-b774-47ea-b163-34d49e7c976d)| +### Styled Entity Control +Demonstration of the styled entity control results with EliGen and IP-Adapter, see [./entity_control_ipadapter.py](./entity_control_ipadapter.py) for generation prompts. +|Style Reference|Entity Control Variance 1|Entity Control Variance 2|Entity Control Variance 3| +|-|-|-|-| +|![image_1_base](https://github.com/user-attachments/assets/5e2dd3ab-37d3-4f58-8e02-ee2f9b238604)|![result1](https://github.com/user-attachments/assets/0f6711a2-572a-41b3-938a-95deff6d732d)|![result2](https://github.com/user-attachments/assets/ce2e66e5-1fdf-44e8-bca7-555d805a50b1)|![result3](https://github.com/user-attachments/assets/ad2da233-2f7c-4065-ab57-b2d84dc2c0e2)| + +We also provide a demo of the styled entity control results with EliGen and specific styled lora, see [./styled_entity_control.py](./styled_entity_control.py) for details. Here is the visualization of EliGen with [Lego dreambooth lora](https://huggingface.co/merve/flux-lego-lora-dreambooth). +|![image_1_base](https://github.com/user-attachments/assets/35fb60f5-48ef-4f22-95d8-f9e732a5f63f)|![result1](https://github.com/user-attachments/assets/441d700f-f0b1-40e0-8848-4db23520972c)|![result2](https://github.com/user-attachments/assets/c8fd4498-3c55-48ab-9abf-3a092a90c878)|![result3](https://github.com/user-attachments/assets/181ba2bb-62cf-41a8-9e3a-20ed8a7a672f)| +|-|-|-|-| +|![image_1_base](https://github.com/user-attachments/assets/70a3f578-8c7e-4b40-954d-8fc94d4f3ae9)|![result1](https://github.com/user-attachments/assets/65670717-6136-4594-84e5-2307fc20753d)|![result2](https://github.com/user-attachments/assets/5ec7a5bd-f2c9-4b2e-8a4e-d2655ec8036c)|![result3](https://github.com/user-attachments/assets/56f00192-9553-45a6-a971-511b9f5b1480)| + +### Entity Transfer +Demonstration of the entity transfer results with EliGen and In-Context LoRA, see [./entity_transfer.py](./entity_transfer.py) for generation prompts. + +|Entity to Transfer|Transfer Target Image|Transfer Example 1|Transfer Example 2| +|-|-|-|-| +|![source](https://github.com/user-attachments/assets/0d40ef22-0a09-420d-bd5a-bfb93120b60d)|![targe](https://github.com/user-attachments/assets/f6c58ef2-54c1-4d86-8429-dad2eb0e0685)|![result1](https://github.com/user-attachments/assets/05eed2e3-097d-40af-8aae-1e0c75051f32)|![result2](https://github.com/user-attachments/assets/54314d16-244b-411e-8a91-96c500efa5f5)| \ No newline at end of file diff --git a/examples/EntityControl/entity_control.py b/examples/EntityControl/entity_control.py new file mode 100644 index 0000000000000000000000000000000000000000..a3ab08b2c8fc723bc675c47c73f225d006ee0aa5 --- /dev/null +++ b/examples/EntityControl/entity_control.py @@ -0,0 +1,83 @@ +from diffsynth import ModelManager, FluxImagePipeline, download_customized_models +from modelscope import dataset_snapshot_download +from examples.EntityControl.utils import visualize_masks +from PIL import Image +import torch + +def example(pipe, seeds, example_id, global_prompt, entity_prompts): + dataset_snapshot_download(dataset_id="DiffSynth-Studio/examples_in_diffsynth", local_dir="./", allow_file_pattern=f"data/examples/eligen/entity_control/example_{example_id}/*.png") + masks = [Image.open(f"./data/examples/eligen/entity_control/example_{example_id}/{i}.png").convert('RGB') for i in range(len(entity_prompts))] + negative_prompt = "worst quality, low quality, monochrome, zombie, interlocked fingers, Aissist, cleavage, nsfw," + for seed in seeds: + # generate image + image = pipe( + prompt=global_prompt, + cfg_scale=3.0, + negative_prompt=negative_prompt, + num_inference_steps=50, + embedded_guidance=3.5, + seed=seed, + height=1024, + width=1024, + eligen_entity_prompts=entity_prompts, + eligen_entity_masks=masks, + ) + image.save(f"eligen_example_{example_id}_{seed}.png") + visualize_masks(image, masks, entity_prompts, f"eligen_example_{example_id}_mask_{seed}.png") + +# download and load model +model_manager = ModelManager(torch_dtype=torch.bfloat16, device="cuda", model_id_list=["FLUX.1-dev"]) +# set download_from_modelscope = False if you want to download model from huggingface +download_from_modelscope = True +if download_from_modelscope: + model_id = "DiffSynth-Studio/Eligen" + downloading_priority = ["ModelScope"] +else: + model_id = "modelscope/EliGen" + downloading_priority = ["HuggingFace"] +model_manager.load_lora( + download_customized_models( + model_id=model_id, + origin_file_path="model_bf16.safetensors", + local_dir="models/lora/entity_control", + downloading_priority=downloading_priority + ), + lora_alpha=1 +) +pipe = FluxImagePipeline.from_model_manager(model_manager) + +# example 1 +global_prompt = "A breathtaking beauty of Raja Ampat by the late-night moonlight , one beautiful woman from behind wearing a pale blue long dress with soft glow, sitting at the top of a cliff looking towards the beach,pastell light colors, a group of small distant birds flying in far sky, a boat sailing on the sea, best quality, realistic, whimsical, fantastic, splash art, intricate detailed, hyperdetailed, maximalist style, photorealistic, concept art, sharp focus, harmony, serenity, tranquility, soft pastell colors,ambient occlusion, cozy ambient lighting, masterpiece, liiv1, linquivera, metix, mentixis, masterpiece, award winning, view from above\n" +entity_prompts = ["cliff", "sea", "moon", "sailing boat", "a seated beautiful woman", "pale blue long dress with soft glow"] +example(pipe, [0], 1, global_prompt, entity_prompts) + +# example 2 +global_prompt = "samurai girl wearing a kimono, she's holding a sword glowing with red flame, her long hair is flowing in the wind, she is looking at a small bird perched on the back of her hand. ultra realist style. maximum image detail. maximum realistic render." +entity_prompts = ["flowing hair", "sword glowing with red flame", "A cute bird", "blue belt"] +example(pipe, [0], 2, global_prompt, entity_prompts) + +# example 3 +global_prompt = "Image of a neverending staircase up to a mysterious palace in the sky, The ancient palace stood majestically atop a mist-shrouded mountain, sunrise, two traditional monk walk in the stair looking at the sunrise, fog,see-through, best quality, whimsical, fantastic, splash art, intricate detailed, hyperdetailed, photorealistic, concept art, harmony, serenity, tranquility, ambient occlusion, halation, cozy ambient lighting, dynamic lighting,masterpiece, liiv1, linquivera, metix, mentixis, masterpiece, award winning," +entity_prompts = ["ancient palace", "stone staircase with railings", "a traditional monk", "a traditional monk"] +example(pipe, [27], 3, global_prompt, entity_prompts) + +# example 4 +global_prompt = "A beautiful girl wearing shirt and shorts in the street, holding a sign 'Entity Control'" +entity_prompts = ["A beautiful girl", "sign 'Entity Control'", "shorts", "shirt"] +example(pipe, [21], 4, global_prompt, entity_prompts) + +# example 5 +global_prompt = "A captivating, dramatic scene in a painting that exudes mystery and foreboding. A white sky, swirling blue clouds, and a crescent yellow moon illuminate a solitary woman standing near the water's edge. Her long dress flows in the wind, silhouetted against the eerie glow. The water mirrors the fiery sky and moonlight, amplifying the uneasy atmosphere." +entity_prompts = ["crescent yellow moon", "a solitary woman", "water", "swirling blue clouds"] +example(pipe, [0], 5, global_prompt, entity_prompts) + +# example 6 +global_prompt = "Snow White and the 6 Dwarfs." +entity_prompts = ["Dwarf 1", "Dwarf 2", "Dwarf 3", "Snow White", "Dwarf 4", "Dwarf 5", "Dwarf 6"] +example(pipe, [8], 6, global_prompt, entity_prompts) + +# example 7, same prompt with different seeds +seeds = range(5, 9) +global_prompt = "A beautiful woman wearing white dress, holding a mirror, with a warm light background;" +entity_prompts = ["A beautiful woman", "mirror", "necklace", "glasses", "earring", "white dress", "jewelry headpiece"] +example(pipe, seeds, 7, global_prompt, entity_prompts) diff --git a/examples/EntityControl/entity_control_ipadapter.py b/examples/EntityControl/entity_control_ipadapter.py new file mode 100644 index 0000000000000000000000000000000000000000..8afae514d5f853464d9d54f0804bfdee808d01f0 --- /dev/null +++ b/examples/EntityControl/entity_control_ipadapter.py @@ -0,0 +1,46 @@ +from diffsynth import ModelManager, FluxImagePipeline, download_customized_models +from modelscope import dataset_snapshot_download +from examples.EntityControl.utils import visualize_masks +from PIL import Image +import torch + + +# download and load model +model_manager = ModelManager(torch_dtype=torch.bfloat16, device="cuda", model_id_list=["FLUX.1-dev", "InstantX/FLUX.1-dev-IP-Adapter"]) +model_manager.load_lora( + download_customized_models( + model_id="DiffSynth-Studio/Eligen", + origin_file_path="model_bf16.safetensors", + local_dir="models/lora/entity_control" + ), + lora_alpha=1 +) +pipe = FluxImagePipeline.from_model_manager(model_manager) + +# download and load mask images +dataset_snapshot_download(dataset_id="DiffSynth-Studio/examples_in_diffsynth", local_dir="./", allow_file_pattern="data/examples/eligen/ipadapter/*") +masks = [Image.open(f"./data/examples/eligen/ipadapter/ipadapter_mask_{i}.png") for i in range(1, 4)] + +entity_prompts = ['A girl', 'hat', 'sunset'] +global_prompt = "A girl wearing a hat, looking at the sunset" +negative_prompt = "worst quality, low quality, monochrome, zombie, interlocked fingers, Aissist, cleavage, nsfw" +reference_img = Image.open("./data/examples/eligen/ipadapter/ipadapter_image.png") + +# generate image +image = pipe( + prompt=global_prompt, + cfg_scale=3.0, + negative_prompt=negative_prompt, + num_inference_steps=50, + embedded_guidance=3.5, + seed=4, + height=1024, + width=1024, + eligen_entity_prompts=entity_prompts, + eligen_entity_masks=masks, + enable_eligen_on_negative=False, + ipadapter_images=[reference_img], + ipadapter_scale=0.7 +) +image.save(f"styled_entity_control.png") +visualize_masks(image, masks, entity_prompts, f"styled_entity_control_with_mask.png") diff --git a/examples/EntityControl/entity_inpaint.py b/examples/EntityControl/entity_inpaint.py new file mode 100644 index 0000000000000000000000000000000000000000..a9a0e066f21d4041f513e5fc23a45d6d58da07c9 --- /dev/null +++ b/examples/EntityControl/entity_inpaint.py @@ -0,0 +1,45 @@ +from diffsynth import ModelManager, FluxImagePipeline, download_customized_models +from modelscope import dataset_snapshot_download +from examples.EntityControl.utils import visualize_masks +from PIL import Image +import torch + +# download and load model +model_manager = ModelManager(torch_dtype=torch.bfloat16, device="cuda", model_id_list=["FLUX.1-dev"]) +model_manager.load_lora( + download_customized_models( + model_id="DiffSynth-Studio/Eligen", + origin_file_path="model_bf16.safetensors", + local_dir="models/lora/entity_control" + ), + lora_alpha=1 +) +pipe = FluxImagePipeline.from_model_manager(model_manager) + +# download and load mask images +dataset_snapshot_download(dataset_id="DiffSynth-Studio/examples_in_diffsynth", local_dir="./", allow_file_pattern="data/examples/eligen/inpaint/*") +masks = [Image.open(f"./data/examples/eligen/inpaint/inpaint_mask_{i}.png") for i in range(1, 3)] +input_image = Image.open("./data/examples/eligen/inpaint/inpaint_image.jpg") + +entity_prompts = ["A person wear red shirt", "Airplane"] +global_prompt = "A person walking on the path in front of a house; An airplane in the sky" +negative_prompt = "worst quality, low quality, monochrome, zombie, interlocked fingers, Aissist, cleavage, nsfw, blur" + +# generate image +image = pipe( + prompt=global_prompt, + input_image=input_image, + cfg_scale=3.0, + negative_prompt=negative_prompt, + num_inference_steps=50, + embedded_guidance=3.5, + seed=0, + height=1024, + width=1024, + eligen_entity_prompts=entity_prompts, + eligen_entity_masks=masks, + enable_eligen_on_negative=False, + enable_eligen_inpaint=True, +) +image.save(f"entity_inpaint.png") +visualize_masks(image, masks, entity_prompts, f"entity_inpaint_with_mask.png") diff --git a/examples/EntityControl/entity_transfer.py b/examples/EntityControl/entity_transfer.py new file mode 100644 index 0000000000000000000000000000000000000000..bb92c2422c50e351a1d65e5368be004961425dfb --- /dev/null +++ b/examples/EntityControl/entity_transfer.py @@ -0,0 +1,84 @@ +from diffsynth import ModelManager, FluxImagePipeline, download_customized_models +from modelscope import dataset_snapshot_download +from examples.EntityControl.utils import visualize_masks +from PIL import Image +import torch + + +def build_pipeline(): + model_manager = ModelManager(torch_dtype=torch.bfloat16, device="cuda", model_id_list=["FLUX.1-dev"]) + model_manager.load_lora( + download_customized_models( + model_id="DiffSynth-Studio/Eligen", + origin_file_path="model_bf16.safetensors", + local_dir="models/lora/entity_control" + ), + lora_alpha=1 + ) + model_manager.load_lora( + download_customized_models( + model_id="iic/In-Context-LoRA", + origin_file_path="visual-identity-design.safetensors", + local_dir="models/lora/In-Context-LoRA" + ), + lora_alpha=1 + ) + pipe = FluxImagePipeline.from_model_manager(model_manager) + return pipe + + +def generate(pipe: FluxImagePipeline, source_image, target_image, mask, height, width, prompt, entity_prompt, image_save_path, mask_save_path, seed=0): + input_mask = Image.new('RGB', (width * 2, height)) + input_mask.paste(mask.resize((width, height), resample=Image.NEAREST).convert('RGB'), (width, 0)) + + input_image = Image.new('RGB', (width * 2, height)) + input_image.paste(source_image.resize((width, height)).convert('RGB'), (0, 0)) + input_image.paste(target_image.resize((width, height)).convert('RGB'), (width, 0)) + + image = pipe( + prompt=prompt, + input_image=input_image, + cfg_scale=3.0, + negative_prompt="", + num_inference_steps=50, + embedded_guidance=3.5, + seed=seed, + height=height, + width=width * 2, + eligen_entity_prompts=[entity_prompt], + eligen_entity_masks=[input_mask], + enable_eligen_on_negative=False, + enable_eligen_inpaint=True, + ) + target_image = image.crop((width, 0, 2 * width, height)) + target_image.save(image_save_path) + visualize_masks(target_image, [mask], [entity_prompt], mask_save_path) + return target_image + + +pipe = build_pipeline() + +dataset_snapshot_download(dataset_id="DiffSynth-Studio/examples_in_diffsynth", local_dir="./", allow_file_pattern="data/examples/eligen/logo_transfer/*") + +prompt="The two-panel image showcases the joyful identity, with the left panel showing a rabbit graphic; [LEFT] while the right panel translates the design onto a shopping tote with the rabbit logo in black, held by a person in a market setting, emphasizing the brand's approachable and eco-friendly vibe." +logo_prompt="a rabbit logo" + +logo_image = Image.open("data/examples/eligen/logo_transfer/source_image.png") +target_image = Image.open("data/examples/eligen/logo_transfer/target_image.png") +mask = Image.open("data/examples/eligen/logo_transfer/mask_1.png") +generate( + pipe, logo_image, target_image, mask, + height=1024, width=1024, + prompt=prompt, entity_prompt=logo_prompt, + image_save_path="entity_transfer_1.png", + mask_save_path="entity_transfer_with_mask_1.png" +) + +mask = Image.open("data/examples/eligen/logo_transfer/mask_2.png") +generate( + pipe, logo_image, target_image, mask, + height=1024, width=1024, + prompt=prompt, entity_prompt=logo_prompt, + image_save_path="entity_transfer_2.png", + mask_save_path="entity_transfer_with_mask_2.png" +) diff --git a/examples/EntityControl/styled_entity_control.py b/examples/EntityControl/styled_entity_control.py new file mode 100644 index 0000000000000000000000000000000000000000..e478d049392770f4a57b680752f2ed86d32ff2ee --- /dev/null +++ b/examples/EntityControl/styled_entity_control.py @@ -0,0 +1,90 @@ +from diffsynth import ModelManager, FluxImagePipeline, download_customized_models +from modelscope import dataset_snapshot_download +from examples.EntityControl.utils import visualize_masks +from PIL import Image +import torch + +def example(pipe, seeds, example_id, global_prompt, entity_prompts): + dataset_snapshot_download(dataset_id="DiffSynth-Studio/examples_in_diffsynth", local_dir="./", allow_file_pattern=f"data/examples/eligen/entity_control/example_{example_id}/*.png") + masks = [Image.open(f"./data/examples/eligen/entity_control/example_{example_id}/{i}.png").convert('RGB') for i in range(len(entity_prompts))] + negative_prompt = "worst quality, low quality, monochrome, zombie, interlocked fingers, Aissist, cleavage, nsfw," + for seed in seeds: + # generate image + image = pipe( + prompt=global_prompt, + cfg_scale=3.0, + negative_prompt=negative_prompt, + num_inference_steps=50, + embedded_guidance=3.5, + seed=seed, + height=1024, + width=1024, + eligen_entity_prompts=entity_prompts, + eligen_entity_masks=masks, + ) + image.save(f"styled_eligen_example_{example_id}_{seed}.png") + visualize_masks(image, masks, entity_prompts, f"styled_entity_control_example_{example_id}_mask_{seed}.png") + +# download and load model +model_manager = ModelManager(torch_dtype=torch.bfloat16, device="cuda", model_id_list=["FLUX.1-dev"]) +model_manager.load_lora( + download_customized_models( + model_id="FluxLora/merve-flux-lego-lora-dreambooth", + origin_file_path="pytorch_lora_weights.safetensors", + local_dir="models/lora/merve-flux-lego-lora-dreambooth" + ), + lora_alpha=1 +) +model_manager.load_lora( + download_customized_models( + model_id="DiffSynth-Studio/Eligen", + origin_file_path="model_bf16.safetensors", + local_dir="models/lora/entity_control" + ), + lora_alpha=1 +) +pipe = FluxImagePipeline.from_model_manager(model_manager) + +# example 1 +trigger_word = "lego set in style of TOK, " +global_prompt = "A breathtaking beauty of Raja Ampat by the late-night moonlight , one beautiful woman from behind wearing a pale blue long dress with soft glow, sitting at the top of a cliff looking towards the beach,pastell light colors, a group of small distant birds flying in far sky, a boat sailing on the sea, best quality, realistic, whimsical, fantastic, splash art, intricate detailed, hyperdetailed, maximalist style, photorealistic, concept art, sharp focus, harmony, serenity, tranquility, soft pastell colors,ambient occlusion, cozy ambient lighting, masterpiece, liiv1, linquivera, metix, mentixis, masterpiece, award winning, view from above\n" +global_prompt = trigger_word + global_prompt +entity_prompts = ["cliff", "sea", "moon", "sailing boat", "a seated beautiful woman", "pale blue long dress with soft glow"] +example(pipe, [0], 1, global_prompt, entity_prompts) + +# example 2 +global_prompt = "samurai girl wearing a kimono, she's holding a sword glowing with red flame, her long hair is flowing in the wind, she is looking at a small bird perched on the back of her hand. ultra realist style. maximum image detail. maximum realistic render." +global_prompt = trigger_word + global_prompt +entity_prompts = ["flowing hair", "sword glowing with red flame", "A cute bird", "blue belt"] +example(pipe, [0], 2, global_prompt, entity_prompts) + +# example 3 +global_prompt = "Image of a neverending staircase up to a mysterious palace in the sky, The ancient palace stood majestically atop a mist-shrouded mountain, sunrise, two traditional monk walk in the stair looking at the sunrise, fog,see-through, best quality, whimsical, fantastic, splash art, intricate detailed, hyperdetailed, photorealistic, concept art, harmony, serenity, tranquility, ambient occlusion, halation, cozy ambient lighting, dynamic lighting,masterpiece, liiv1, linquivera, metix, mentixis, masterpiece, award winning," +global_prompt = trigger_word + global_prompt +entity_prompts = ["ancient palace", "stone staircase with railings", "a traditional monk", "a traditional monk"] +example(pipe, [27], 3, global_prompt, entity_prompts) + +# example 4 +global_prompt = "A beautiful girl wearing shirt and shorts in the street, holding a sign 'Entity Control'" +global_prompt = trigger_word + global_prompt +entity_prompts = ["A beautiful girl", "sign 'Entity Control'", "shorts", "shirt"] +example(pipe, [21], 4, global_prompt, entity_prompts) + +# example 5 +global_prompt = "A captivating, dramatic scene in a painting that exudes mystery and foreboding. A white sky, swirling blue clouds, and a crescent yellow moon illuminate a solitary woman standing near the water's edge. Her long dress flows in the wind, silhouetted against the eerie glow. The water mirrors the fiery sky and moonlight, amplifying the uneasy atmosphere." +global_prompt = trigger_word + global_prompt +entity_prompts = ["crescent yellow moon", "a solitary woman", "water", "swirling blue clouds"] +example(pipe, [0], 5, global_prompt, entity_prompts) + +# example 6 +global_prompt = "Snow White and the 6 Dwarfs." +global_prompt = trigger_word + global_prompt +entity_prompts = ["Dwarf 1", "Dwarf 2", "Dwarf 3", "Snow White", "Dwarf 4", "Dwarf 5", "Dwarf 6"] +example(pipe, [8], 6, global_prompt, entity_prompts) + +# example 7, same prompt with different seeds +seeds = range(5, 9) +global_prompt = "A beautiful woman wearing white dress, holding a mirror, with a warm light background;" +global_prompt = trigger_word + global_prompt +entity_prompts = ["A beautiful woman", "mirror", "necklace", "glasses", "earring", "white dress", "jewelry headpiece"] +example(pipe, seeds, 7, global_prompt, entity_prompts) diff --git a/examples/EntityControl/utils.py b/examples/EntityControl/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..573c8a616e638fc8fddc3304dd5cdc73f5f489a4 --- /dev/null +++ b/examples/EntityControl/utils.py @@ -0,0 +1,59 @@ +from PIL import Image, ImageDraw, ImageFont +import random + +def visualize_masks(image, masks, mask_prompts, output_path, font_size=35, use_random_colors=False): + # Create a blank image for overlays + overlay = Image.new('RGBA', image.size, (0, 0, 0, 0)) + + colors = [ + (165, 238, 173, 80), + (76, 102, 221, 80), + (221, 160, 77, 80), + (204, 93, 71, 80), + (145, 187, 149, 80), + (134, 141, 172, 80), + (157, 137, 109, 80), + (153, 104, 95, 80), + (165, 238, 173, 80), + (76, 102, 221, 80), + (221, 160, 77, 80), + (204, 93, 71, 80), + (145, 187, 149, 80), + (134, 141, 172, 80), + (157, 137, 109, 80), + (153, 104, 95, 80), + ] + # Generate random colors for each mask + if use_random_colors: + colors = [(random.randint(0, 255), random.randint(0, 255), random.randint(0, 255), 80) for _ in range(len(masks))] + + # Font settings + try: + font = ImageFont.truetype("arial", font_size) # Adjust as needed + except IOError: + font = ImageFont.load_default(font_size) + + # Overlay each mask onto the overlay image + for mask, mask_prompt, color in zip(masks, mask_prompts, colors): + # Convert mask to RGBA mode + mask_rgba = mask.convert('RGBA') + mask_data = mask_rgba.getdata() + new_data = [(color if item[:3] == (255, 255, 255) else (0, 0, 0, 0)) for item in mask_data] + mask_rgba.putdata(new_data) + + # Draw the mask prompt text on the mask + draw = ImageDraw.Draw(mask_rgba) + mask_bbox = mask.getbbox() # Get the bounding box of the mask + text_position = (mask_bbox[0] + 10, mask_bbox[1] + 10) # Adjust text position based on mask position + draw.text(text_position, mask_prompt, fill=(255, 255, 255, 255), font=font) + + # Alpha composite the overlay with this mask + overlay = Image.alpha_composite(overlay, mask_rgba) + + # Composite the overlay onto the original image + result = Image.alpha_composite(image.convert('RGBA'), overlay) + + # Save or display the resulting image + result.save(output_path) + + return result diff --git a/examples/ExVideo/ExVideo_cogvideox_test.py b/examples/ExVideo/ExVideo_cogvideox_test.py new file mode 100644 index 0000000000000000000000000000000000000000..4d0fd3ad5c1fbddabcd27d7173088baad870cd0e --- /dev/null +++ b/examples/ExVideo/ExVideo_cogvideox_test.py @@ -0,0 +1,21 @@ +from diffsynth import ModelManager, CogVideoPipeline, save_video, download_models +import torch + + +download_models(["CogVideoX-5B", "ExVideo-CogVideoX-LoRA-129f-v1"]) +model_manager = ModelManager(torch_dtype=torch.bfloat16) +model_manager.load_models([ + "models/CogVideo/CogVideoX-5b/text_encoder", + "models/CogVideo/CogVideoX-5b/transformer", + "models/CogVideo/CogVideoX-5b/vae/diffusion_pytorch_model.safetensors", +]) +model_manager.load_lora("models/lora/ExVideo-CogVideoX-LoRA-129f-v1.safetensors") +pipe = CogVideoPipeline.from_model_manager(model_manager) + +torch.manual_seed(6) +video = pipe( + prompt="an astronaut riding a horse on Mars.", + height=480, width=720, num_frames=129, + cfg_scale=7.0, num_inference_steps=100, +) +save_video(video, "video_with_lora.mp4", fps=8, quality=5) diff --git a/examples/ExVideo/ExVideo_ema.py b/examples/ExVideo/ExVideo_ema.py new file mode 100644 index 0000000000000000000000000000000000000000..81d4c399ca6e252c0b4e799d61900b854b66ad6e --- /dev/null +++ b/examples/ExVideo/ExVideo_ema.py @@ -0,0 +1,64 @@ +import torch, os, argparse +from safetensors.torch import save_file + + +def load_pl_state_dict(file_path): + print(f"loading {file_path}") + state_dict = torch.load(file_path, map_location="cpu") + trainable_param_names = set(state_dict["trainable_param_names"]) + if "module" in state_dict: + state_dict = state_dict["module"] + if "state_dict" in state_dict: + state_dict = state_dict["state_dict"] + state_dict_ = {} + for name, param in state_dict.items(): + if name.startswith("_forward_module."): + name = name[len("_forward_module."):] + if name.startswith("unet."): + name = name[len("unet."):] + if name in trainable_param_names: + state_dict_[name] = param + return state_dict_ + + +def ckpt_to_epochs(ckpt_name): + return int(ckpt_name.split("=")[1].split("-")[0]) + + +def parse_args(): + parser = argparse.ArgumentParser(description="Simple example of a training script.") + parser.add_argument( + "--output_path", + type=str, + default="./", + help="Path to save the model.", + ) + parser.add_argument( + "--gamma", + type=float, + default=0.9, + help="Gamma in EMA.", + ) + args = parser.parse_args() + return args + + +if __name__ == '__main__': + # args + args = parse_args() + folder = args.output_path + gamma = args.gamma + + # EMA + ckpt_list = sorted([(ckpt_to_epochs(ckpt_name), ckpt_name) for ckpt_name in os.listdir(folder) if os.path.isdir(f"{folder}/{ckpt_name}")]) + state_dict_ema = None + for epochs, ckpt_name in ckpt_list: + state_dict = load_pl_state_dict(f"{folder}/{ckpt_name}/checkpoint/mp_rank_00_model_states.pt") + if state_dict_ema is None: + state_dict_ema = {name: param.float() for name, param in state_dict.items()} + else: + for name, param in state_dict.items(): + state_dict_ema[name] = state_dict_ema[name] * gamma + param.float() * (1 - gamma) + save_path = ckpt_name.replace(".ckpt", "-ema.safetensors") + print(f"save to {folder}/{save_path}") + save_file(state_dict_ema, f"{folder}/{save_path}") diff --git a/examples/ExVideo/ExVideo_svd_test.py b/examples/ExVideo/ExVideo_svd_test.py new file mode 100644 index 0000000000000000000000000000000000000000..29a1910a44ad1dbb493e38653a6a54785cd1c576 --- /dev/null +++ b/examples/ExVideo/ExVideo_svd_test.py @@ -0,0 +1,114 @@ +from diffsynth import save_video, ModelManager, SVDVideoPipeline, HunyuanDiTImagePipeline, download_models +from diffsynth import ModelManager +import torch, os + +# The models will be downloaded automatically. +# You can also use the following urls to download them manually. + +# Download models (from Huggingface) +# Text-to-image model: +# `models/HunyuanDiT/t2i/clip_text_encoder/pytorch_model.bin`: [link](https://huggingface.co/Tencent-Hunyuan/HunyuanDiT/resolve/main/t2i/clip_text_encoder/pytorch_model.bin) +# `models/HunyuanDiT/t2i/mt5/pytorch_model.bin`: [link](https://huggingface.co/Tencent-Hunyuan/HunyuanDiT/resolve/main/t2i/mt5/pytorch_model.bin) +# `models/HunyuanDiT/t2i/model/pytorch_model_ema.pt`: [link](https://huggingface.co/Tencent-Hunyuan/HunyuanDiT/resolve/main/t2i/model/pytorch_model_ema.pt) +# `models/HunyuanDiT/t2i/sdxl-vae-fp16-fix/diffusion_pytorch_model.bin`: [link](https://huggingface.co/Tencent-Hunyuan/HunyuanDiT/resolve/main/t2i/sdxl-vae-fp16-fix/diffusion_pytorch_model.bin) +# Stable Video Diffusion model: +# `models/stable_video_diffusion/svd_xt.safetensors`: [link](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid-xt/resolve/main/svd_xt.safetensors) +# ExVideo extension blocks: +# `models/stable_video_diffusion/model.fp16.safetensors`: [link](https://huggingface.co/ECNU-CILab/ExVideo-SVD-128f-v1/resolve/main/model.fp16.safetensors) + +# Download models (from Modelscope) +# Text-to-image model: +# `models/HunyuanDiT/t2i/clip_text_encoder/pytorch_model.bin`: [link](https://www.modelscope.cn/api/v1/models/modelscope/HunyuanDiT/repo?Revision=master&FilePath=t2i%2Fclip_text_encoder%2Fpytorch_model.bin) +# `models/HunyuanDiT/t2i/mt5/pytorch_model.bin`: [link](https://www.modelscope.cn/api/v1/models/modelscope/HunyuanDiT/repo?Revision=master&FilePath=t2i%2Fmt5%2Fpytorch_model.bin) +# `models/HunyuanDiT/t2i/model/pytorch_model_ema.pt`: [link](https://www.modelscope.cn/api/v1/models/modelscope/HunyuanDiT/repo?Revision=master&FilePath=t2i%2Fmodel%2Fpytorch_model_ema.pt) +# `models/HunyuanDiT/t2i/sdxl-vae-fp16-fix/diffusion_pytorch_model.bin`: [link](https://www.modelscope.cn/api/v1/models/modelscope/HunyuanDiT/repo?Revision=master&FilePath=t2i%2Fsdxl-vae-fp16-fix%2Fdiffusion_pytorch_model.bin) +# Stable Video Diffusion model: +# `models/stable_video_diffusion/svd_xt.safetensors`: [link](https://www.modelscope.cn/api/v1/models/AI-ModelScope/stable-video-diffusion-img2vid-xt/repo?Revision=master&FilePath=svd_xt.safetensors) +# ExVideo extension blocks: +# `models/stable_video_diffusion/model.fp16.safetensors`: [link](https://modelscope.cn/api/v1/models/ECNU-CILab/ExVideo-SVD-128f-v1/repo?Revision=master&FilePath=model.fp16.safetensors) + + +def generate_image(): + # Load models + os.environ["TOKENIZERS_PARALLELISM"] = "True" + download_models(["HunyuanDiT"]) + model_manager = ModelManager(torch_dtype=torch.float16, device="cuda", + file_path_list=[ + "models/HunyuanDiT/t2i/clip_text_encoder/pytorch_model.bin", + "models/HunyuanDiT/t2i/mt5/pytorch_model.bin", + "models/HunyuanDiT/t2i/model/pytorch_model_ema.pt", + "models/HunyuanDiT/t2i/sdxl-vae-fp16-fix/diffusion_pytorch_model.bin", + ]) + pipe = HunyuanDiTImagePipeline.from_model_manager(model_manager) + + # Generate an image + torch.manual_seed(0) + image = pipe( + prompt="bonfire, on the stone", + negative_prompt="错误的眼睛，糟糕的人脸，毁容，糟糕的艺术，变形，多余的肢体，模糊的颜色，模糊，重复，病态，残缺，", + num_inference_steps=50, height=1024, width=1024, + ) + model_manager.to("cpu") + return image + + +def generate_video(image): + # Load models + download_models(["stable-video-diffusion-img2vid-xt", "ExVideo-SVD-128f-v1"]) + model_manager = ModelManager(torch_dtype=torch.float16, device="cuda", + file_path_list=[ + "models/stable_video_diffusion/svd_xt.safetensors", + "models/stable_video_diffusion/model.fp16.safetensors", + ]) + pipe = SVDVideoPipeline.from_model_manager(model_manager) + + # Generate a video + torch.manual_seed(1) + video = pipe( + input_image=image.resize((512, 512)), + num_frames=128, fps=30, height=512, width=512, + motion_bucket_id=127, + num_inference_steps=50, + min_cfg_scale=2, max_cfg_scale=2, contrast_enhance_scale=1.2 + ) + model_manager.to("cpu") + return video + + +def upscale_video(image, video): + # Load models + download_models(["stable-video-diffusion-img2vid-xt", "ExVideo-SVD-128f-v1"]) + model_manager = ModelManager(torch_dtype=torch.float16, device="cuda", + file_path_list=[ + "models/stable_video_diffusion/svd_xt.safetensors", + "models/stable_video_diffusion/model.fp16.safetensors", + ]) + pipe = SVDVideoPipeline.from_model_manager(model_manager) + + # Generate a video + torch.manual_seed(2) + video = pipe( + input_image=image.resize((1024, 1024)), + input_video=[frame.resize((1024, 1024)) for frame in video], denoising_strength=0.5, + num_frames=128, fps=30, height=1024, width=1024, + motion_bucket_id=127, + num_inference_steps=25, + min_cfg_scale=2, max_cfg_scale=2, contrast_enhance_scale=1.2 + ) + model_manager.to("cpu") + return video + + +# We use Hunyuan DiT to generate the first frame. 10GB VRAM is required. +# If you want to use your own image, +# please use `image = Image.open("your_image_file.png")` to replace the following code. +image = generate_image() +image.save("image.png") + +# Now, generate a video with resolution of 512. 20GB VRAM is required. +video = generate_video(image) +save_video(video, "video_512.mp4", fps=30) + +# Upscale the video. 52GB VRAM is required. +video = upscale_video(image, video) +save_video(video, "video_1024.mp4", fps=30) diff --git a/examples/ExVideo/ExVideo_svd_train.py b/examples/ExVideo/ExVideo_svd_train.py new file mode 100644 index 0000000000000000000000000000000000000000..6a6221eb989e358764fb4fa7bdd336ac19b14ece --- /dev/null +++ b/examples/ExVideo/ExVideo_svd_train.py @@ -0,0 +1,364 @@ +import torch, json, os, imageio, argparse +from torchvision.transforms import v2 +import numpy as np +from einops import rearrange, repeat +import lightning as pl +from diffsynth import ModelManager, SVDImageEncoder, SVDUNet, SVDVAEEncoder, ContinuousODEScheduler, load_state_dict +from diffsynth.pipelines.svd_video import SVDCLIPImageProcessor +from diffsynth.models.svd_unet import TemporalAttentionBlock + + + +class TextVideoDataset(torch.utils.data.Dataset): + def __init__(self, base_path, metadata_path, steps_per_epoch=10000, training_shapes=[(128, 1, 128, 512, 512)]): + with open(metadata_path, "r") as f: + metadata = json.load(f) + self.path = [os.path.join(base_path, i["path"]) for i in metadata] + self.steps_per_epoch = steps_per_epoch + self.training_shapes = training_shapes + + self.frame_process = [] + for max_num_frames, interval, num_frames, height, width in training_shapes: + self.frame_process.append(v2.Compose([ + v2.Resize(size=max(height, width), antialias=True), + v2.CenterCrop(size=(height, width)), + v2.Normalize(mean=[127.5, 127.5, 127.5], std=[127.5, 127.5, 127.5]), + ])) + + + def load_frames_using_imageio(self, file_path, max_num_frames, start_frame_id, interval, num_frames, frame_process): + reader = imageio.get_reader(file_path) + if reader.count_frames() < max_num_frames or reader.count_frames() - 1 < start_frame_id + (num_frames - 1) * interval: + reader.close() + return None + + frames = [] + for frame_id in range(num_frames): + frame = reader.get_data(start_frame_id + frame_id * interval) + frame = torch.tensor(frame, dtype=torch.float32) + frame = rearrange(frame, "H W C -> 1 C H W") + frame = frame_process(frame) + frames.append(frame) + reader.close() + + frames = torch.concat(frames, dim=0) + frames = rearrange(frames, "T C H W -> C T H W") + + return frames + + + def load_video(self, file_path, training_shape_id): + data = {} + max_num_frames, interval, num_frames, height, width = self.training_shapes[training_shape_id] + frame_process = self.frame_process[training_shape_id] + start_frame_id = torch.randint(0, max_num_frames - (num_frames - 1) * interval, (1,))[0] + frames = self.load_frames_using_imageio(file_path, max_num_frames, start_frame_id, interval, num_frames, frame_process) + if frames is None: + return None + else: + data[f"frames_{training_shape_id}"] = frames + return data + + + def __getitem__(self, index): + video_data = {} + for training_shape_id in range(len(self.training_shapes)): + while True: + data_id = torch.randint(0, len(self.path), (1,))[0] + data_id = (data_id + index) % len(self.path) # For fixed seed. + video_file = self.path[data_id] + try: + data = self.load_video(video_file, training_shape_id) + except: + data = None + if data is not None: + break + video_data.update(data) + return video_data + + + def __len__(self): + return self.steps_per_epoch + + + +class MotionBucketManager: + def __init__(self): + self.thresholds = [ + 0.000000000, 0.012205946, 0.015117834, 0.018080613, 0.020614484, 0.021959992, 0.024088068, 0.026323952, + 0.028277775, 0.029968588, 0.031836554, 0.033596724, 0.035121530, 0.037200287, 0.038914755, 0.040696491, + 0.042368013, 0.044265781, 0.046311017, 0.048243891, 0.050294187, 0.052142400, 0.053634230, 0.055612389, + 0.057594258, 0.059410289, 0.061283995, 0.063603796, 0.065192916, 0.067146860, 0.069066539, 0.070390493, + 0.072588451, 0.073959745, 0.075889029, 0.077695683, 0.079783581, 0.082162730, 0.084092639, 0.085958421, + 0.087700523, 0.089684933, 0.091688842, 0.093335517, 0.094987206, 0.096664011, 0.098314710, 0.100262381, + 0.101984538, 0.103404313, 0.105280340, 0.106974818, 0.109028399, 0.111164779, 0.113065213, 0.114362158, + 0.116407216, 0.118063427, 0.119524263, 0.121835820, 0.124242283, 0.126202747, 0.128989249, 0.131672353, + 0.133417681, 0.135567948, 0.137313649, 0.139189199, 0.140912935, 0.143525436, 0.145718485, 0.148315132, + 0.151039496, 0.153218940, 0.155252382, 0.157651082, 0.159966752, 0.162195817, 0.164811596, 0.167341709, + 0.170251891, 0.172651157, 0.175550997, 0.178372145, 0.181039348, 0.183565900, 0.186599866, 0.190071866, + 0.192574754, 0.195026234, 0.198099136, 0.200210452, 0.202522039, 0.205410406, 0.208610669, 0.211623028, + 0.214723110, 0.218520239, 0.222194016, 0.225363150, 0.229384825, 0.233422622, 0.237012610, 0.240735114, + 0.243622541, 0.247465774, 0.252190471, 0.257356376, 0.261856794, 0.266556412, 0.271076709, 0.277361482, + 0.281250387, 0.286582440, 0.291158527, 0.296712339, 0.303008437, 0.311793238, 0.318485111, 0.326999635, + 0.332138240, 0.341770738, 0.354188830, 0.365194678, 0.379234344, 0.401538879, 0.416078776, 0.440871328, + ] + + def get_motion_score(self, frames): + score = frames.std(dim=2).mean(dim=[1, 2, 3]).tolist() + return score + + def get_bucket_id(self, motion_score): + for bucket_id in range(len(self.thresholds) - 1): + if self.thresholds[bucket_id + 1] > motion_score: + return bucket_id + return len(self.thresholds) - 1 + + def __call__(self, frames): + scores = self.get_motion_score(frames) + bucket_ids = [self.get_bucket_id(score) for score in scores] + return bucket_ids + + + +class LightningModel(pl.LightningModule): + def __init__(self, learning_rate=1e-5, svd_ckpt_path=None, add_positional_conv=128, contrast_enhance_scale=1.01): + super().__init__() + state_dict = load_state_dict(svd_ckpt_path) + + self.image_encoder = SVDImageEncoder().to(dtype=torch.float16, device=self.device) + self.image_encoder.load_state_dict(SVDImageEncoder.state_dict_converter().from_civitai(state_dict)) + self.image_encoder.eval() + self.image_encoder.requires_grad_(False) + + self.unet = SVDUNet(add_positional_conv=add_positional_conv).to(dtype=torch.float16, device=self.device) + self.unet.load_state_dict(SVDUNet.state_dict_converter().from_civitai(state_dict, add_positional_conv=add_positional_conv), strict=False) + self.unet.train() + self.unet.requires_grad_(False) + for block in self.unet.blocks: + if isinstance(block, TemporalAttentionBlock): + block.requires_grad_(True) + + self.vae_encoder = SVDVAEEncoder().to(dtype=torch.float16, device=self.device) + self.vae_encoder.load_state_dict(SVDVAEEncoder.state_dict_converter().from_civitai(state_dict)) + self.vae_encoder.eval() + self.vae_encoder.requires_grad_(False) + + self.noise_scheduler = ContinuousODEScheduler(num_inference_steps=1000) + self.learning_rate = learning_rate + + self.motion_bucket_manager = MotionBucketManager() + self.contrast_enhance_scale = contrast_enhance_scale + + + def encode_image_with_clip(self, image): + image = SVDCLIPImageProcessor().resize_with_antialiasing(image, (224, 224)) + image = (image + 1.0) / 2.0 + mean = torch.tensor([0.48145466, 0.4578275, 0.40821073]).reshape(1, 3, 1, 1).to(device=self.device, dtype=self.dtype) + std = torch.tensor([0.26862954, 0.26130258, 0.27577711]).reshape(1, 3, 1, 1).to(device=self.device, dtype=self.dtype) + image = (image - mean) / std + image_emb = self.image_encoder(image) + return image_emb + + + def encode_video_with_vae(self, video): + video = video.to(device=self.device, dtype=self.dtype) + video = video.unsqueeze(0) + latents = self.vae_encoder.encode_video(video) + latents = rearrange(latents[0], "C T H W -> T C H W") + return latents + + + def tensor2video(self, frames): + frames = rearrange(frames, "C T H W -> T H W C") + frames = ((frames.float() + 1) * 127.5).clip(0, 255).cpu().numpy().astype(np.uint8) + return frames + + + def calculate_loss(self, frames): + with torch.no_grad(): + # Call video encoder + latents = self.encode_video_with_vae(frames) + image_emb_vae = repeat(latents[0] / self.vae_encoder.scaling_factor, "C H W -> T C H W", T=frames.shape[1]) + image_emb_clip = self.encode_image_with_clip(frames[:,0].unsqueeze(0)) + + # Call scheduler + timestep = torch.randint(0, len(self.noise_scheduler.timesteps), (1,))[0] + timestep = self.noise_scheduler.timesteps[timestep] + noise = torch.randn_like(latents) + noisy_latents = self.noise_scheduler.add_noise(latents, noise, timestep) + + # Prepare positional id + fps = 30 + motion_bucket_id = self.motion_bucket_manager(frames.unsqueeze(0))[0] + noise_aug_strength = 0 + add_time_id = torch.tensor([[fps-1, motion_bucket_id, noise_aug_strength]], device=self.device) + + # Calculate loss + latents_input = torch.cat([noisy_latents, image_emb_vae], dim=1) + model_pred = self.unet(latents_input, timestep, image_emb_clip, add_time_id, use_gradient_checkpointing=True) + latents_output = self.noise_scheduler.step(model_pred.float(), timestep, noisy_latents.float(), to_final=True) + loss = torch.nn.functional.mse_loss(latents_output, latents.float() * self.contrast_enhance_scale, reduction="mean") + + # Re-weighting + reweighted_loss = loss * self.noise_scheduler.training_weight(timestep) + return loss, reweighted_loss + + + def training_step(self, batch, batch_idx): + # Loss + frames = batch["frames_0"][0] + loss, reweighted_loss = self.calculate_loss(frames) + + # Record log + self.log("train_loss", loss, prog_bar=True) + self.log("reweighted_train_loss", reweighted_loss, prog_bar=True) + return reweighted_loss + + + def configure_optimizers(self): + trainable_modules = [] + for block in self.unet.blocks: + if isinstance(block, TemporalAttentionBlock): + trainable_modules += block.parameters() + optimizer = torch.optim.AdamW(trainable_modules, lr=self.learning_rate) + return optimizer + + + def on_save_checkpoint(self, checkpoint): + trainable_param_names = list(filter(lambda named_param: named_param[1].requires_grad, self.unet.named_parameters())) + trainable_param_names = [named_param[0] for named_param in trainable_param_names] + checkpoint["trainable_param_names"] = trainable_param_names + + + +def parse_args(): + parser = argparse.ArgumentParser(description="Simple example of a training script.") + parser.add_argument( + "--pretrained_path", + type=str, + default=None, + required=True, + help="Path to pretrained model. For example, `models/stable_video_diffusion/svd_xt.safetensors`.", + ) + parser.add_argument( + "--resume_from_checkpoint", + type=str, + default=None, + required=False, + help="Path to checkpoint, in case your training program is stopped unexpectedly and you want to resume.", + ) + parser.add_argument( + "--dataset_path", + type=str, + default=None, + required=True, + help="The path of the Dataset.", + ) + parser.add_argument( + "--output_path", + type=str, + default="./", + help="Path to save the model.", + ) + parser.add_argument( + "--steps_per_epoch", + type=int, + default=500, + help="Number of steps per epoch.", + ) + parser.add_argument( + "--num_frames", + type=int, + default=128, + help="Number of frames.", + ) + parser.add_argument( + "--height", + type=int, + default=512, + help="Image height.", + ) + parser.add_argument( + "--width", + type=int, + default=512, + help="Image width.", + ) + parser.add_argument( + "--dataloader_num_workers", + type=int, + default=2, + help="Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.", + ) + parser.add_argument( + "--learning_rate", + type=float, + default=1e-5, + help="Learning rate.", + ) + parser.add_argument( + "--accumulate_grad_batches", + type=int, + default=1, + help="The number of batches in gradient accumulation.", + ) + parser.add_argument( + "--max_epochs", + type=int, + default=1, + help="Number of epochs.", + ) + parser.add_argument( + "--contrast_enhance_scale", + type=float, + default=1.01, + help="Avoid generating gray videos.", + ) + args = parser.parse_args() + return args + + +if __name__ == '__main__': + # args + args = parse_args() + + # dataset and data loader + dataset = TextVideoDataset( + args.dataset_path, + os.path.join(args.dataset_path, "metadata.json"), + training_shapes=[(args.num_frames, 1, args.num_frames, args.height, args.width)], + steps_per_epoch=args.steps_per_epoch, + ) + train_loader = torch.utils.data.DataLoader( + dataset, + shuffle=True, + # We don't support batch_size > 1, + # because sometimes our GPU cannot process even one video. + batch_size=1, + num_workers=args.dataloader_num_workers + ) + + # model + model = LightningModel( + learning_rate=args.learning_rate, + svd_ckpt_path=args.pretrained_path, + add_positional_conv=args.num_frames, + contrast_enhance_scale=args.contrast_enhance_scale + ) + + # train + trainer = pl.Trainer( + max_epochs=args.max_epochs, + accelerator="gpu", + devices="auto", + strategy="deepspeed_stage_2", + precision="16-mixed", + default_root_dir=args.output_path, + accumulate_grad_batches=args.accumulate_grad_batches, + callbacks=[pl.pytorch.callbacks.ModelCheckpoint(save_top_k=-1)] + ) + trainer.fit( + model=model, + train_dataloaders=train_loader, + ckpt_path=args.resume_from_checkpoint + ) diff --git a/examples/ExVideo/README.md b/examples/ExVideo/README.md new file mode 100644 index 0000000000000000000000000000000000000000..32f8d802a4c87af1981ec70d991f5e9687ade088 --- /dev/null +++ b/examples/ExVideo/README.md @@ -0,0 +1,89 @@ +# ExVideo + +ExVideo is a post-tuning technique aimed at enhancing the capability of video generation models. We have extended Stable Video Diffusion to achieve the generation of long videos up to 128 frames. + +* [Project Page](https://ecnu-cilab.github.io/ExVideoProjectPage/) +* [Technical report](https://arxiv.org/abs/2406.14130) +* **[New]** Extended models (ExVideo-CogVideoX) + * [HuggingFace](https://huggingface.co/ECNU-CILab/ExVideo-CogVideoX-LoRA-129f-v1) + * [ModelScope](https://modelscope.cn/models/ECNU-CILab/ExVideo-CogVideoX-LoRA-129f-v1) +* Extended models (ExVideo-SVD) + * [HuggingFace](https://huggingface.co/ECNU-CILab/ExVideo-SVD-128f-v1) + * [ModelScope](https://modelscope.cn/models/ECNU-CILab/ExVideo-SVD-128f-v1) + +## Example: Text-to-video via extended CogVideoX-5B + +Generate a video using CogVideoX-5B and our extension module. See [ExVideo_cogvideox_test.py](./ExVideo_cogvideox_test.py). + +https://github.com/user-attachments/assets/321ee04b-8c17-479e-8a95-8cbcf21f8d7e + +## Example: Text-to-video via extended Stable Video Diffusion + +Generate a video using a text-to-image model and our image-to-video model. See [ExVideo_svd_test.py](./ExVideo_svd_test.py). + +https://github.com/modelscope/DiffSynth-Studio/assets/35051019/d97f6aa9-8064-4b5b-9d49-ed6001bb9acc + +## Train + +* Step 1: Install additional packages + +``` +pip install lightning deepspeed +``` + +* Step 2: Download base model (from [HuggingFace](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid-xt/resolve/main/svd_xt.safetensors) or [ModelScope](https://www.modelscope.cn/api/v1/models/AI-ModelScope/stable-video-diffusion-img2vid-xt/repo?Revision=master&FilePath=svd_xt.safetensors)) to `models/stable_video_diffusion/svd_xt.safetensors`. + +* Step 3: Prepare datasets + +``` +path/to/your/dataset +├── metadata.json +└── videos + ├── video_1.mp4 + ├── video_2.mp4 + └── video_3.mp4 +``` + +where the `metadata.json` is + +``` +[ + { + "path": "videos/video_1.mp4" + }, + { + "path": "videos/video_2.mp4" + }, + { + "path": "videos/video_3.mp4" + } +] +``` + +* Step 4: Run + +``` +CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7" python -u ExVideo_svd_train.py \ + --pretrained_path "models/stable_video_diffusion/svd_xt.safetensors" \ + --dataset_path "path/to/your/dataset" \ + --output_path "path/to/save/models" \ + --steps_per_epoch 8000 \ + --num_frames 128 \ + --height 512 \ + --width 512 \ + --dataloader_num_workers 2 \ + --learning_rate 1e-5 \ + --max_epochs 100 +``` + +* Step 5: Post-process checkpoints + +Calculate Exponential Moving Average (EMA) and package it using `safetensors`. + +``` +python ExVideo_ema.py --output_path "path/to/save/models/lightning_logs/version_xx" --gamma 0.9 +``` + +* Step 6: Enjoy your model + +The EMA model is at `path/to/save/models/lightning_logs/version_xx/checkpoints/epoch=xx-step=yyy-ema.safetensors`. Load it in [ExVideo_svd_test.py](./ExVideo_svd_test.py) and then enjoy your model. diff --git a/examples/HunyuanVideo/README.md b/examples/HunyuanVideo/README.md new file mode 100644 index 0000000000000000000000000000000000000000..c1359b57fd88711487ab925bb9bc0d8fd6ec32f8 --- /dev/null +++ b/examples/HunyuanVideo/README.md @@ -0,0 +1,33 @@ +# HunyuanVideo + +[HunyuanVideo](https://github.com/Tencent/HunyuanVideo) is a video generation model trained by Tencent. We provide advanced VRAM management for this model, including three stages: + +|VRAM required|Example script|Frames|Resolution|Note| +|-|-|-|-|-| +|80G|[hunyuanvideo_80G.py](hunyuanvideo_80G.py)|129|720*1280|No VRAM management.| +|24G|[hunyuanvideo_24G.py](hunyuanvideo_24G.py)|129|720*1280|The video is consistent with the original implementation, but it requires 5%~10% more time than [hunyuanvideo_80G.py](hunyuanvideo_80G.py)| +|6G|[hunyuanvideo_6G.py](hunyuanvideo_6G.py)|129|512*384|The base model doesn't support low resolutions. We recommend users to use some LoRA ([example](https://civitai.com/models/1032126/walking-animation-hunyuan-video)) trained using low resolutions.| + +[HunyuanVideo-I2V](https://github.com/Tencent/HunyuanVideo-I2V) is the image-to-video generation version of HunyuanVideo. We also provide advanced VRAM management for this model. +|VRAM required|Example script|Frames|Resolution|Note| +|-|-|-|-|-| +|80G|[hunyuanvideo_i2v_80G.py](hunyuanvideo_i2v_80G.py)|129|720p|No VRAM management.| +|24G|[hunyuanvideo_i2v_24G.py](hunyuanvideo_i2v_24G.py)|129|720p|The video is consistent with the original implementation, but it requires 5%~10% more time than [hunyuanvideo_80G.py](hunyuanvideo_80G.py)| + +## Gallery + +Video generated by [hunyuanvideo_80G.py](hunyuanvideo_80G.py) and [hunyuanvideo_24G.py](hunyuanvideo_24G.py): + +https://github.com/user-attachments/assets/48dd24bb-0cc6-40d2-88c3-10feed3267e9 + +Video generated by [hunyuanvideo_6G.py](hunyuanvideo_6G.py) using [this LoRA](https://civitai.com/models/1032126/walking-animation-hunyuan-video): + +https://github.com/user-attachments/assets/2997f107-d02d-4ecb-89bb-5ce1a7f93817 + +Video to video generated by [hunyuanvideo_v2v_6G.py](./hunyuanvideo_v2v_6G.py) using [this LoRA](https://civitai.com/models/1032126/walking-animation-hunyuan-video): + +https://github.com/user-attachments/assets/4b89e52e-ce42-434e-aa57-08f09dfa2b10 + +Video generated by [hunyuanvideo_i2v_80G.py](hunyuanvideo_i2v_80G.py) and [hunyuanvideo_i2v_24G.py](hunyuanvideo_i2v_24G.py): + +https://github.com/user-attachments/assets/494f252a-c9af-440d-84ba-a8ddcdcc538a diff --git a/examples/HunyuanVideo/hunyuanvideo_24G.py b/examples/HunyuanVideo/hunyuanvideo_24G.py new file mode 100644 index 0000000000000000000000000000000000000000..e4f8b17339d2284b7dd1a092eb3f8489d2bd0287 --- /dev/null +++ b/examples/HunyuanVideo/hunyuanvideo_24G.py @@ -0,0 +1,42 @@ +import torch +torch.cuda.set_per_process_memory_fraction(1.0, 0) +from diffsynth import ModelManager, HunyuanVideoPipeline, download_models, save_video + + +download_models(["HunyuanVideo"]) +model_manager = ModelManager() + +# The DiT model is loaded in bfloat16. +model_manager.load_models( + [ + "models/HunyuanVideo/transformers/mp_rank_00_model_states.pt" + ], + torch_dtype=torch.bfloat16, # you can use torch_dtype=torch.float8_e4m3fn to enable quantization. + device="cpu" +) + +# The other modules are loaded in float16. +model_manager.load_models( + [ + "models/HunyuanVideo/text_encoder/model.safetensors", + "models/HunyuanVideo/text_encoder_2", + "models/HunyuanVideo/vae/pytorch_model.pt", + ], + torch_dtype=torch.float16, + device="cpu" +) + +# We support LoRA inference. You can use the following code to load your LoRA model. +# model_manager.load_lora("models/lora/xxx.safetensors", lora_alpha=1.0) + +# The computation device is "cuda". +pipe = HunyuanVideoPipeline.from_model_manager( + model_manager, + torch_dtype=torch.bfloat16, + device="cuda" +) + +# Enjoy! +prompt = "CG, masterpiece, best quality, solo, long hair, wavy hair, silver hair, blue eyes, blue dress, medium breasts, dress, underwater, air bubble, floating hair, refraction, portrait. The girl's flowing silver hair shimmers with every color of the rainbow and cascades down, merging with the floating flora around her." +video = pipe(prompt, seed=0) +save_video(video, "video_girl.mp4", fps=30, quality=6) diff --git a/examples/HunyuanVideo/hunyuanvideo_6G.py b/examples/HunyuanVideo/hunyuanvideo_6G.py new file mode 100644 index 0000000000000000000000000000000000000000..507ab510723997d57721cfa1dff5c728e338ad95 --- /dev/null +++ b/examples/HunyuanVideo/hunyuanvideo_6G.py @@ -0,0 +1,52 @@ +import torch +torch.cuda.set_per_process_memory_fraction(1.0, 0) +from diffsynth import ModelManager, HunyuanVideoPipeline, download_models, save_video, FlowMatchScheduler, download_customized_models + + +download_models(["HunyuanVideo"]) +model_manager = ModelManager() + +# The DiT model is loaded in bfloat16. +model_manager.load_models( + [ + "models/HunyuanVideo/transformers/mp_rank_00_model_states.pt" + ], + torch_dtype=torch.bfloat16, # you can use torch_dtype=torch.float8_e4m3fn to enable quantization. + device="cpu" +) + +# The other modules are loaded in float16. +model_manager.load_models( + [ + "models/HunyuanVideo/text_encoder/model.safetensors", + "models/HunyuanVideo/text_encoder_2", + "models/HunyuanVideo/vae/pytorch_model.pt", + ], + torch_dtype=torch.float16, + device="cpu" +) + +# We support LoRA inference. You can use the following code to load your LoRA model. +# Example LoRA: https://civitai.com/models/1032126/walking-animation-hunyuan-video +download_customized_models( + model_id="AI-ModelScope/walking_animation_hunyuan_video", + origin_file_path="kxsr_walking_anim_v1-5.safetensors", + local_dir="models/lora" +)[0] +model_manager.load_lora("models/lora/kxsr_walking_anim_v1-5.safetensors", lora_alpha=1.0) + +# The computation device is "cuda". +pipe = HunyuanVideoPipeline.from_model_manager( + model_manager, + torch_dtype=torch.bfloat16, + device="cuda" +) +# This LoRA requires shift=9.0. +pipe.scheduler = FlowMatchScheduler(shift=9.0, sigma_min=0.0, extra_one_step=True) + +# Enjoy! +for clothes_up in ["white t-shirt", "black t-shirt", "orange t-shirt"]: + for clothes_down in ["blue sports skirt", "red sports skirt", "white sports skirt"]: + prompt = f"kxsr, full body, no crop, A 3D-rendered CG animation video featuring a Gorgeous, mature, curvaceous, fair-skinned female girl with long silver hair and blue eyes. She wears a {clothes_up} and a {clothes_down}, walking offering a sense of fluid movement and vivid animation." + video = pipe(prompt, seed=0, height=512, width=384, num_frames=129, num_inference_steps=18, tile_size=(17, 16, 16), tile_stride=(12, 12, 12)) + save_video(video, f"video-{clothes_up}-{clothes_down}.mp4", fps=30, quality=6) diff --git a/examples/HunyuanVideo/hunyuanvideo_80G.py b/examples/HunyuanVideo/hunyuanvideo_80G.py new file mode 100644 index 0000000000000000000000000000000000000000..073ee570c66e4bbdf422983946aced02c2e804a5 --- /dev/null +++ b/examples/HunyuanVideo/hunyuanvideo_80G.py @@ -0,0 +1,45 @@ +import torch +torch.cuda.set_per_process_memory_fraction(1.0, 0) +from diffsynth import ModelManager, HunyuanVideoPipeline, download_models, save_video + + +download_models(["HunyuanVideo"]) +model_manager = ModelManager() + +# The DiT model is loaded in bfloat16. +model_manager.load_models( + [ + "models/HunyuanVideo/transformers/mp_rank_00_model_states.pt" + ], + torch_dtype=torch.bfloat16, # you can use torch_dtype=torch.float8_e4m3fn to enable quantization. + device="cuda" +) + +# The other modules are loaded in float16. +model_manager.load_models( + [ + "models/HunyuanVideo/text_encoder/model.safetensors", + "models/HunyuanVideo/text_encoder_2", + "models/HunyuanVideo/vae/pytorch_model.pt", + ], + torch_dtype=torch.float16, + device="cuda" +) + +# We support LoRA inference. You can use the following code to load your LoRA model. +# model_manager.load_lora("models/lora/xxx.safetensors", lora_alpha=1.0) + +# The computation device is "cuda". +pipe = HunyuanVideoPipeline.from_model_manager( + model_manager, + torch_dtype=torch.bfloat16, + device="cuda", + enable_vram_management=False +) +# Although you have enough VRAM, we still recommend you to enable offload. +pipe.enable_cpu_offload() + +# Enjoy! +prompt = "CG, masterpiece, best quality, solo, long hair, wavy hair, silver hair, blue eyes, blue dress, medium breasts, dress, underwater, air bubble, floating hair, refraction, portrait. The girl's flowing silver hair shimmers with every color of the rainbow and cascades down, merging with the floating flora around her." +video = pipe(prompt, seed=0) +save_video(video, "video.mp4", fps=30, quality=6) diff --git a/examples/HunyuanVideo/hunyuanvideo_i2v_24G.py b/examples/HunyuanVideo/hunyuanvideo_i2v_24G.py new file mode 100644 index 0000000000000000000000000000000000000000..91ae0e4c81e6069f14d2296581b4fef202e02d1a --- /dev/null +++ b/examples/HunyuanVideo/hunyuanvideo_i2v_24G.py @@ -0,0 +1,43 @@ +import torch +from diffsynth import ModelManager, HunyuanVideoPipeline, download_models, save_video +from modelscope import dataset_snapshot_download +from PIL import Image + + +download_models(["HunyuanVideoI2V"]) +model_manager = ModelManager() + +# The DiT model is loaded in bfloat16. +model_manager.load_models( + [ + "models/HunyuanVideoI2V/transformers/mp_rank_00_model_states.pt" + ], + torch_dtype=torch.bfloat16, + device="cpu" +) + +# The other modules are loaded in float16. +model_manager.load_models( + [ + "models/HunyuanVideoI2V/text_encoder/model.safetensors", + "models/HunyuanVideoI2V/text_encoder_2", + 'models/HunyuanVideoI2V/vae/pytorch_model.pt' + ], + torch_dtype=torch.float16, + device="cpu" +) +# The computation device is "cuda". +pipe = HunyuanVideoPipeline.from_model_manager(model_manager, + torch_dtype=torch.bfloat16, + device="cuda", + enable_vram_management=True) + +dataset_snapshot_download(dataset_id="DiffSynth-Studio/examples_in_diffsynth", + local_dir="./", + allow_file_pattern=f"data/examples/hunyuanvideo/*") + +i2v_resolution = "720p" +prompt = "An Asian man with short hair in black tactical uniform and white clothes waves a firework stick." +images = [Image.open("data/examples/hunyuanvideo/0.jpg").convert('RGB')] +video = pipe(prompt, input_images=images, num_inference_steps=50, seed=0, i2v_resolution=i2v_resolution) +save_video(video, f"video_{i2v_resolution}_low_vram.mp4", fps=30, quality=6) diff --git a/examples/HunyuanVideo/hunyuanvideo_i2v_80G.py b/examples/HunyuanVideo/hunyuanvideo_i2v_80G.py new file mode 100644 index 0000000000000000000000000000000000000000..fcc9f622eb2601a72f79e05d653653c57ce8a206 --- /dev/null +++ b/examples/HunyuanVideo/hunyuanvideo_i2v_80G.py @@ -0,0 +1,45 @@ +import torch +from diffsynth import ModelManager, HunyuanVideoPipeline, download_models, save_video +from modelscope import dataset_snapshot_download +from PIL import Image + + +download_models(["HunyuanVideoI2V"]) +model_manager = ModelManager() + +# The DiT model is loaded in bfloat16. +model_manager.load_models( + [ + "models/HunyuanVideoI2V/transformers/mp_rank_00_model_states.pt" + ], + torch_dtype=torch.bfloat16, + device="cuda" +) + +# The other modules are loaded in float16. +model_manager.load_models( + [ + "models/HunyuanVideoI2V/text_encoder/model.safetensors", + "models/HunyuanVideoI2V/text_encoder_2", + 'models/HunyuanVideoI2V/vae/pytorch_model.pt' + ], + torch_dtype=torch.float16, + device="cuda" +) +# The computation device is "cuda". +pipe = HunyuanVideoPipeline.from_model_manager(model_manager, + torch_dtype=torch.bfloat16, + device="cuda", + enable_vram_management=False) +# Although you have enough VRAM, we still recommend you to enable offload. +pipe.enable_cpu_offload() + +dataset_snapshot_download(dataset_id="DiffSynth-Studio/examples_in_diffsynth", + local_dir="./", + allow_file_pattern=f"data/examples/hunyuanvideo/*") + +i2v_resolution = "720p" +prompt = "An Asian man with short hair in black tactical uniform and white clothes waves a firework stick." +images = [Image.open("data/examples/hunyuanvideo/0.jpg").convert('RGB')] +video = pipe(prompt, input_images=images, num_inference_steps=50, seed=0, i2v_resolution=i2v_resolution) +save_video(video, f"video_{i2v_resolution}.mp4", fps=30, quality=6) diff --git a/examples/HunyuanVideo/hunyuanvideo_v2v_6G.py b/examples/HunyuanVideo/hunyuanvideo_v2v_6G.py new file mode 100644 index 0000000000000000000000000000000000000000..9b9234a363558e5d54e254a0bf4cc181d2a8ca08 --- /dev/null +++ b/examples/HunyuanVideo/hunyuanvideo_v2v_6G.py @@ -0,0 +1,55 @@ +import torch +torch.cuda.set_per_process_memory_fraction(1.0, 0) +from diffsynth import ModelManager, HunyuanVideoPipeline, download_models, save_video, FlowMatchScheduler, download_customized_models + + +download_models(["HunyuanVideo"]) +model_manager = ModelManager() + +# The DiT model is loaded in bfloat16. +model_manager.load_models( + [ + "models/HunyuanVideo/transformers/mp_rank_00_model_states.pt" + ], + torch_dtype=torch.bfloat16, # you can use torch_dtype=torch.float8_e4m3fn to enable quantization. + device="cpu" +) + +# The other modules are loaded in float16. +model_manager.load_models( + [ + "models/HunyuanVideo/text_encoder/model.safetensors", + "models/HunyuanVideo/text_encoder_2", + "models/HunyuanVideo/vae/pytorch_model.pt", + ], + torch_dtype=torch.float16, + device="cpu" +) + +# We support LoRA inference. You can use the following code to load your LoRA model. +# Example LoRA: https://civitai.com/models/1032126/walking-animation-hunyuan-video +download_customized_models( + model_id="AI-ModelScope/walking_animation_hunyuan_video", + origin_file_path="kxsr_walking_anim_v1-5.safetensors", + local_dir="models/lora" +)[0] +model_manager.load_lora("models/lora/kxsr_walking_anim_v1-5.safetensors", lora_alpha=1.0) + +# The computation device is "cuda". +pipe = HunyuanVideoPipeline.from_model_manager( + model_manager, + torch_dtype=torch.bfloat16, + device="cuda" +) +# This LoRA requires shift=9.0. +pipe.scheduler = FlowMatchScheduler(shift=9.0, sigma_min=0.0, extra_one_step=True) + +# Text-to-video +prompt = f"kxsr, full body, no crop. A girl is walking. CG, masterpiece, best quality, solo, long hair, wavy hair, silver hair, blue eyes, blue dress, medium breasts, dress, underwater, air bubble, floating hair, refraction, portrait. The girl's flowing silver hair shimmers with every color of the rainbow and cascades down, merging with the floating flora around her." +video = pipe(prompt, seed=1, height=512, width=384, num_frames=129, num_inference_steps=18, tile_size=(17, 16, 16), tile_stride=(12, 12, 12)) +save_video(video, f"video.mp4", fps=30, quality=6) + +# Video-to-video +prompt = f"kxsr, full body, no crop. A girl is walking. CG, masterpiece, best quality, solo, long hair, wavy hair, silver hair, blue eyes, purple dress, medium breasts, dress, underwater, air bubble, floating hair, refraction, portrait. The girl's flowing silver hair shimmers with every color of the rainbow and cascades down, merging with the floating flora around her." +video = pipe(prompt, seed=1, height=512, width=384, num_frames=129, num_inference_steps=18, tile_size=(17, 16, 16), tile_stride=(12, 12, 12), input_video=video, denoising_strength=0.85) +save_video(video, f"video_edited.mp4", fps=30, quality=6) diff --git a/examples/InfiniteYou/README.md b/examples/InfiniteYou/README.md new file mode 100644 index 0000000000000000000000000000000000000000..5ce4946942765c5fe5dd7648ec943c40a0e387be --- /dev/null +++ b/examples/InfiniteYou/README.md @@ -0,0 +1,7 @@ +# InfiniteYou: Flexible Photo Recrafting While Preserving Your Identity +We support the identity preserving feature of InfiniteYou. See [./infiniteyou.py](./infiniteyou.py) for example. The visualization of the result is shown below. + +|Identity Image|Generated Image| +|-|-| +|![man_id](https://github.com/user-attachments/assets/bbc38a91-966e-49e8-a0d7-c5467582ad1f)|![man](https://github.com/user-attachments/assets/0decd5e1-5f65-437c-98fa-90991b6f23c1)| +|![woman_id](https://github.com/user-attachments/assets/b2894695-690e-465b-929c-61e5dc57feeb)|![woman](https://github.com/user-attachments/assets/67cc7496-c4d3-4de1-a8f1-9eb4991d95e8)| diff --git a/examples/InfiniteYou/infiniteyou.py b/examples/InfiniteYou/infiniteyou.py new file mode 100644 index 0000000000000000000000000000000000000000..eef53cd416fb8cb79c93239a1276d8155774695c --- /dev/null +++ b/examples/InfiniteYou/infiniteyou.py @@ -0,0 +1,58 @@ +import importlib +import torch +from diffsynth import ModelManager, FluxImagePipeline, download_models, ControlNetConfigUnit +from modelscope import dataset_snapshot_download +from PIL import Image + +if importlib.util.find_spec("facexlib") is None: + raise ImportError("You are using InifiniteYou. It depends on facexlib, which is not installed. Please install it with `pip install facexlib`.") +if importlib.util.find_spec("insightface") is None: + raise ImportError("You are using InifiniteYou. It depends on insightface, which is not installed. Please install it with `pip install insightface`.") + +download_models(["InfiniteYou"]) +model_manager = ModelManager(torch_dtype=torch.bfloat16, device="cuda", model_id_list=["FLUX.1-dev"]) +model_manager.load_models([ + [ + "models/InfiniteYou/InfuseNetModel/diffusion_pytorch_model-00001-of-00002.safetensors", + "models/InfiniteYou/InfuseNetModel/diffusion_pytorch_model-00002-of-00002.safetensors" + ], + "models/InfiniteYou/image_proj_model.bin", +]) + + +pipe = FluxImagePipeline.from_model_manager( + model_manager, + controlnet_config_units=[ + ControlNetConfigUnit( + processor_id="none", + model_path=[ + 'models/InfiniteYou/InfuseNetModel/diffusion_pytorch_model-00001-of-00002.safetensors', + 'models/InfiniteYou/InfuseNetModel/diffusion_pytorch_model-00002-of-00002.safetensors' + ], + scale=1.0 + ) + ] +) +dataset_snapshot_download(dataset_id="DiffSynth-Studio/examples_in_diffsynth", local_dir="./", allow_file_pattern=f"data/examples/infiniteyou/*") + +prompt = "A man, portrait, cinematic" +id_image = "data/examples/infiniteyou/man.jpg" +id_image = Image.open(id_image).convert('RGB') +image = pipe( + prompt=prompt, seed=1, + infinityou_id_image=id_image, infinityou_guidance=1.0, + num_inference_steps=50, embedded_guidance=3.5, + height=1024, width=1024, +) +image.save("man.jpg") + +prompt = "A woman, portrait, cinematic" +id_image = "data/examples/infiniteyou/woman.jpg" +id_image = Image.open(id_image).convert('RGB') +image = pipe( + prompt=prompt, seed=1, + infinityou_id_image=id_image, infinityou_guidance=1.0, + num_inference_steps=50, embedded_guidance=3.5, + height=1024, width=1024, +) +image.save("woman.jpg") diff --git a/examples/Ip-Adapter/README.md b/examples/Ip-Adapter/README.md new file mode 100644 index 0000000000000000000000000000000000000000..05f28bec070345ee9b0729b2124ee9084a562ec0 --- /dev/null +++ b/examples/Ip-Adapter/README.md @@ -0,0 +1,44 @@ +# IP-Adapter + +IP-Adapter is a interesting model, which can adopt the content or style of another image to generate a new image. + +## Example: Content Controlling in Stable Diffusion + +Based on Stable Diffusion, we can transfer the object to another scene. See [`sd_ipadapter.py`](./sd_ipadapter.py). + +|First, we generate a car. The prompt is "masterpiece, best quality, a car".|Next, utilizing IP-Adapter, we move the car to the road. The prompt is "masterpiece, best quality, a car running on the road".| +|-|-| +|![car](https://github.com/modelscope/DiffSynth-Studio/assets/35051019/8530a2f0-f610-4269-a22c-ac6c2f21fc18)|![car_on_the_road](https://github.com/modelscope/DiffSynth-Studio/assets/35051019/b8ccddb2-c423-46d8-bd1a-327fcc074a36)| + +## Example: Content and Style Controlling in Stable Diffusion XL + +The IP-Adapter model based on Stable Diffusion XL is more powerful. You have the option to use the content or style. See [`sdxl_ipadapter.py`](./sdxl_ipadapter.py). + +* Content controlling (original usage of IP-Adapter) + +|First, we generate a rabbit.|Next, enable IP-Adapter and let the rabbit jump.|For comparison, disable IP-Adapter to see the generated image.| +|-|-|-| +|![rabbit](https://github.com/modelscope/DiffSynth-Studio/assets/35051019/4b452634-ec57-414f-897a-f8c50c74a650)|![rabbit_to_jumping_rabbit](https://github.com/modelscope/DiffSynth-Studio/assets/35051019/b93c5495-0b77-4d97-bcd3-3942858288f2)|![rabbit_to_jumping_rabbit_without_ipa](https://github.com/modelscope/DiffSynth-Studio/assets/35051019/52f37195-65b3-4a38-8d9b-73df37311c15)| + + +* Style controlling (InstantStyle) + +|First, we generate a rabbit.|Next, enable InstantStyle and convert the rabbit to a cat.|For comparison, disable IP-Adapter to see the generated image.| +|-|-|-| +|![rabbit](https://github.com/modelscope/DiffSynth-Studio/assets/35051019/4b452634-ec57-414f-897a-f8c50c74a650)|![rabbit_to_cat](https://github.com/modelscope/DiffSynth-Studio/assets/35051019/a006b281-f643-4ea9-b0da-712289c96059)|![rabbit_to_cat_without_ipa](https://github.com/modelscope/DiffSynth-Studio/assets/35051019/189bd11e-7a10-4c09-8554-0eebde9150fd)| + +## Example: Image Fusing (Experimental) + +Since IP-Adapter can control the content based on more than one image, we can do something interesting. See [`sdxl_ipadapter_multi_reference.py`](sdxl_ipadapter_multi_reference.py). + +We have two pokemons here: + +|Charizard|Pikachu| +|-|-| +|![](https://media.52poke.com/wiki/7/7e/006Charizard.png)|![](https://media.52poke.com/wiki/0/0d/025Pikachu.png)| + +Fuse! + +|Pikazard ???| +|-| +|![Pikazard](https://github.com/modelscope/DiffSynth-Studio/assets/35051019/807cdb31-94f5-4cc2-a978-3c6a7ffedc5b)| diff --git a/examples/Ip-Adapter/flux_ipadapter.py b/examples/Ip-Adapter/flux_ipadapter.py new file mode 100644 index 0000000000000000000000000000000000000000..6d96ebf170c2001c6410ed22bff5a8abff67ddf5 --- /dev/null +++ b/examples/Ip-Adapter/flux_ipadapter.py @@ -0,0 +1,38 @@ +from diffsynth import ModelManager, download_models, FluxImagePipeline +import torch + +# Download models (automatically) +# `models/IpAdapter/InstantX/FLUX.1-dev-IP-Adapter/ip-adapter.bin`: [link](https://huggingface.co/InstantX/FLUX.1-dev-IP-Adapter/blob/main/ip-adapter.bin) +# `models/IpAdapter/InstantX/FLUX.1-dev-IP-Adapter/image_encoder`: [link](https://huggingface.co/google/siglip-so400m-patch14-384) +download_models(["InstantX/FLUX.1-dev-IP-Adapter", "FLUX.1-dev"]) + +# Load models +model_manager = ModelManager(torch_dtype=torch.bfloat16, device="cuda") +model_manager.load_models([ + "models/IpAdapter/InstantX/FLUX.1-dev-IP-Adapter/ip-adapter.bin", + "models/IpAdapter/InstantX/FLUX.1-dev-IP-Adapter/image_encoder", + "models/FLUX/FLUX.1-dev/text_encoder/model.safetensors", + "models/FLUX/FLUX.1-dev/text_encoder_2", + "models/FLUX/FLUX.1-dev/ae.safetensors", + "models/FLUX/FLUX.1-dev/flux1-dev.safetensors", +]) +seed = 42 +pipe = FluxImagePipeline.from_model_manager(model_manager) +torch.manual_seed(seed) +origin_prompt = "a rabbit in a garden, colorful flowers" +image = pipe( + prompt=origin_prompt, + cfg_scale=1.0, embedded_guidance=3.5, + height=1280, width=960, num_inference_steps=30 +) +image.save("style image.jpg") + +torch.manual_seed(seed) +image = pipe( + prompt="A piggy", + cfg_scale=1.0, embedded_guidance=3.5, + height=1280, width=960, num_inference_steps=30, + ipadapter_images=[image], ipadapter_scale=0.7 +) +image.save("A piggy.jpg") + diff --git a/examples/Ip-Adapter/sd_ipadapter.py b/examples/Ip-Adapter/sd_ipadapter.py new file mode 100644 index 0000000000000000000000000000000000000000..413a5538e924cee7a87d0cdea4fa57946098b5b8 --- /dev/null +++ b/examples/Ip-Adapter/sd_ipadapter.py @@ -0,0 +1,38 @@ +from diffsynth import ModelManager, SDImagePipeline, download_models +import torch + + +# Download models (automatically) +# `models/stable_diffusion/aingdiffusion_v12.safetensors`: [link](https://civitai.com/api/download/models/229575?type=Model&format=SafeTensor&size=full&fp=fp16) +# `models/IpAdapter/stable_diffusion/image_encoder/model.safetensors`: [link](https://huggingface.co/h94/IP-Adapter/resolve/main/models/image_encoder/model.safetensors) +# `models/IpAdapter/stable_diffusion/ip-adapter_sd15.bin`: [link](https://huggingface.co/h94/IP-Adapter/resolve/main/models/ip-adapter_sd15.bin) +# `models/textual_inversion/verybadimagenegative_v1.3.pt`: [link](https://civitai.com/api/download/models/25820?type=Model&format=PickleTensor&size=full&fp=fp16) +download_models(["AingDiffusion_v12", "IP-Adapter-SD", "TextualInversion_VeryBadImageNegative_v1.3"]) + +# Load models +model_manager = ModelManager(torch_dtype=torch.float16, device="cuda") +model_manager.load_models([ + "models/stable_diffusion/aingdiffusion_v12.safetensors", + "models/IpAdapter/stable_diffusion/image_encoder/model.safetensors", + "models/IpAdapter/stable_diffusion/ip-adapter_sd15.bin" +]) +pipe = SDImagePipeline.from_model_manager(model_manager) +pipe.prompter.load_textual_inversions(["models/textual_inversion/verybadimagenegative_v1.3.pt"]) + +torch.manual_seed(1) +style_image = pipe( + prompt="masterpiece, best quality, a car", + negative_prompt="verybadimagenegative_v1.3", + cfg_scale=7, clip_skip=2, + height=512, width=512, num_inference_steps=50, +) +style_image.save("car.jpg") + +image = pipe( + prompt="masterpiece, best quality, a car running on the road", + negative_prompt="verybadimagenegative_v1.3", + cfg_scale=7, clip_skip=2, + height=512, width=512, num_inference_steps=50, + ipadapter_images=[style_image], ipadapter_scale=1.0 +) +image.save("car_on_the_road.jpg") diff --git a/examples/Ip-Adapter/sdxl_ipadapter.py b/examples/Ip-Adapter/sdxl_ipadapter.py new file mode 100644 index 0000000000000000000000000000000000000000..29f2da8134f7358c595f1ea7df555ecc463dd6b0 --- /dev/null +++ b/examples/Ip-Adapter/sdxl_ipadapter.py @@ -0,0 +1,61 @@ +from diffsynth import ModelManager, SDXLImagePipeline, download_models +import torch + + +# Download models (automatically) +# `models/stable_diffusion_xl/sd_xl_base_1.0.safetensors`: [link](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/resolve/main/sd_xl_base_1.0.safetensors) +# `models/IpAdapter/stable_diffusion_xl/image_encoder/model.safetensors`: [link](https://huggingface.co/h94/IP-Adapter/resolve/main/sdxl_models/image_encoder/model.safetensors) +# `models/IpAdapter/stable_diffusion_xl/ip-adapter_sdxl.bin`: [link](https://huggingface.co/h94/IP-Adapter/resolve/main/sdxl_models/ip-adapter_sdxl.safetensors) +download_models(["StableDiffusionXL_v1", "IP-Adapter-SDXL"]) + +# Load models +model_manager = ModelManager(torch_dtype=torch.float16, device="cuda") +model_manager.load_models([ + "models/stable_diffusion_xl/sd_xl_base_1.0.safetensors", + "models/IpAdapter/stable_diffusion_xl/image_encoder/model.safetensors", + "models/IpAdapter/stable_diffusion_xl/ip-adapter_sdxl.bin" +]) +pipe = SDXLImagePipeline.from_model_manager(model_manager) + +torch.manual_seed(123456) +style_image = pipe( + prompt="a rabbit in a garden, colorful flowers", + negative_prompt="anime, cartoon, graphic, text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, disfigured", + cfg_scale=5, + height=1024, width=1024, num_inference_steps=50, +) +style_image.save("rabbit.jpg") + +image = pipe( + prompt="a cat", + negative_prompt="", + cfg_scale=5, + height=1024, width=1024, num_inference_steps=50, + ipadapter_images=[style_image], ipadapter_use_instant_style=True +) +image.save("rabbit_to_cat.jpg") + +image = pipe( + prompt="a rabbit is jumping", + negative_prompt="", + cfg_scale=5, + height=1024, width=1024, num_inference_steps=50, + ipadapter_images=[style_image], ipadapter_use_instant_style=False, ipadapter_scale=0.5 +) +image.save("rabbit_to_jumping_rabbit.jpg") + +image = pipe( + prompt="a cat", + negative_prompt="", + cfg_scale=5, + height=1024, width=1024, num_inference_steps=50, +) +image.save("rabbit_to_cat_without_ipa.jpg") + +image = pipe( + prompt="a rabbit is jumping", + negative_prompt="", + cfg_scale=5, + height=1024, width=1024, num_inference_steps=50, +) +image.save("rabbit_to_jumping_rabbit_without_ipa.jpg") \ No newline at end of file diff --git a/examples/Ip-Adapter/sdxl_ipadapter_multi_reference.py b/examples/Ip-Adapter/sdxl_ipadapter_multi_reference.py new file mode 100644 index 0000000000000000000000000000000000000000..4094a70358b9bc5bd3f58d3466661ef31d93d74d --- /dev/null +++ b/examples/Ip-Adapter/sdxl_ipadapter_multi_reference.py @@ -0,0 +1,34 @@ +from diffsynth import ModelManager, SDXLImagePipeline, download_models +import torch, requests +from PIL import Image + + +# Download models (automatically) +# `models/stable_diffusion_xl/bluePencilXL_v200.safetensors`: [link](https://civitai.com/api/download/models/245614?type=Model&format=SafeTensor&size=pruned&fp=fp16) +# `models/IpAdapter/stable_diffusion_xl/image_encoder/model.safetensors`: [link](https://huggingface.co/h94/IP-Adapter/resolve/main/sdxl_models/image_encoder/model.safetensors) +# `models/IpAdapter/stable_diffusion_xl/ip-adapter_sdxl.bin`: [link](https://huggingface.co/h94/IP-Adapter/resolve/main/sdxl_models/ip-adapter_sdxl.safetensors) +download_models(["BluePencilXL_v200", "IP-Adapter-SDXL"]) + +# Load models +model_manager = ModelManager(torch_dtype=torch.float16, device="cuda") +model_manager.load_models([ + "models/stable_diffusion_xl/bluePencilXL_v200.safetensors", + "models/IpAdapter/stable_diffusion_xl/image_encoder/model.safetensors", + "models/IpAdapter/stable_diffusion_xl/ip-adapter_sdxl.bin" +]) +pipe = SDXLImagePipeline.from_model_manager(model_manager) + +image_1 = Image.open(requests.get("https://media.52poke.com/wiki/7/7e/006Charizard.png", stream=True).raw).convert("RGB").resize((1024, 1024)) +image_1.save("Charizard.jpg") +image_2 = Image.open(requests.get("https://media.52poke.com/wiki/0/0d/025Pikachu.png", stream=True).raw).convert("RGB").resize((1024, 1024)) +image_2.save("Pikachu.jpg") + +torch.manual_seed(0) +image = pipe( + prompt="a pokemon, maybe Charizard, maybe Pikachu", + negative_prompt="text, watermark, lowres, low quality, worst quality, deformed, glitch, low contrast, noisy, saturation, blurry", + cfg_scale=5, + height=1024, width=1024, num_inference_steps=50, + ipadapter_images=[image_1, image_2], ipadapter_use_instant_style=False, ipadapter_scale=0.7 +) +image.save(f"Pikazard.jpg") diff --git a/examples/TeaCache/README.md b/examples/TeaCache/README.md new file mode 100644 index 0000000000000000000000000000000000000000..2757d3ba91fb02e136270ee3883b86b48eaa3ce5 --- /dev/null +++ b/examples/TeaCache/README.md @@ -0,0 +1,34 @@ +# TeaCache + +TeaCache ([Timestep Embedding Aware Cache](https://github.com/ali-vilab/TeaCache)) is a training-free caching approach that estimates and leverages the fluctuating differences among model outputs across timesteps, thereby accelerating the inference. + +## Examples + +### FLUX + +Script: [./flux_teacache.py](./flux_teacache.py) + +Model: FLUX.1-dev + +Steps: 50 + +GPU: A100 + +|TeaCache is disabled|tea_cache_l1_thresh=0.2|tea_cache_l1_thresh=0.8| +|-|-|-| +|23s|13s|5s| +|![image_None](https://github.com/user-attachments/assets/2bf5187a-9693-44d3-9ebb-6c33cd15443f)|![image_0 2](https://github.com/user-attachments/assets/5532ba94-c7e2-446e-a9ba-1c68c0f63350)|![image_0 8](https://github.com/user-attachments/assets/d8cfdd74-8b45-4048-b1b7-ce480aa23fa1) + +### Hunyuan Video + +Script: [./hunyuanvideo_teacache.py](./hunyuanvideo_teacache.py) + +Model: Hunyuan Video + +Steps: 30 + +GPU: A100 + +The following video was generated using TeaCache. It is nearly identical to [the video without TeaCache enabled](https://github.com/user-attachments/assets/48dd24bb-0cc6-40d2-88c3-10feed3267e9), but with double the speed. + +https://github.com/user-attachments/assets/cd9801c5-88ce-4efc-b055-2c7737166f34 diff --git a/examples/TeaCache/flux_teacache.py b/examples/TeaCache/flux_teacache.py new file mode 100644 index 0000000000000000000000000000000000000000..b9006543de9a9796ad15de3b1d1c0c2e75c14e3f --- /dev/null +++ b/examples/TeaCache/flux_teacache.py @@ -0,0 +1,15 @@ +import torch +from diffsynth import ModelManager, FluxImagePipeline + + +model_manager = ModelManager(torch_dtype=torch.bfloat16, device="cuda", model_id_list=["FLUX.1-dev"]) +pipe = FluxImagePipeline.from_model_manager(model_manager) + +prompt = "CG, masterpiece, best quality, solo, long hair, wavy hair, silver hair, blue eyes, blue dress, medium breasts, dress, underwater, air bubble, floating hair, refraction, portrait. The girl's flowing silver hair shimmers with every color of the rainbow and cascades down, merging with the floating flora around her." + +for tea_cache_l1_thresh in [None, 0.2, 0.4, 0.6, 0.8]: + image = pipe( + prompt=prompt, embedded_guidance=3.5, seed=0, + num_inference_steps=50, tea_cache_l1_thresh=tea_cache_l1_thresh + ) + image.save(f"image_{tea_cache_l1_thresh}.png") diff --git a/examples/TeaCache/hunyuanvideo_teacache.py b/examples/TeaCache/hunyuanvideo_teacache.py new file mode 100644 index 0000000000000000000000000000000000000000..e29602d6196807aa48b6831001d81f9c8f79237d --- /dev/null +++ b/examples/TeaCache/hunyuanvideo_teacache.py @@ -0,0 +1,42 @@ +import torch +torch.cuda.set_per_process_memory_fraction(1.0, 0) +from diffsynth import ModelManager, HunyuanVideoPipeline, download_models, save_video + + +download_models(["HunyuanVideo"]) +model_manager = ModelManager() + +# The DiT model is loaded in bfloat16. +model_manager.load_models( + [ + "models/HunyuanVideo/transformers/mp_rank_00_model_states.pt" + ], + torch_dtype=torch.bfloat16, # you can use torch_dtype=torch.float8_e4m3fn to enable quantization. + device="cpu" +) + +# The other modules are loaded in float16. +model_manager.load_models( + [ + "models/HunyuanVideo/text_encoder/model.safetensors", + "models/HunyuanVideo/text_encoder_2", + "models/HunyuanVideo/vae/pytorch_model.pt", + ], + torch_dtype=torch.float16, + device="cpu" +) + +# We support LoRA inference. You can use the following code to load your LoRA model. +# model_manager.load_lora("models/lora/xxx.safetensors", lora_alpha=1.0) + +# The computation device is "cuda". +pipe = HunyuanVideoPipeline.from_model_manager( + model_manager, + torch_dtype=torch.bfloat16, + device="cuda" +) + +# Enjoy! +prompt = "CG, masterpiece, best quality, solo, long hair, wavy hair, silver hair, blue eyes, blue dress, medium breasts, dress, underwater, air bubble, floating hair, refraction, portrait. The girl's flowing silver hair shimmers with every color of the rainbow and cascades down, merging with the floating flora around her." +video = pipe(prompt, seed=0, tea_cache_l1_thresh=0.15) +save_video(video, "video_girl.mp4", fps=30, quality=6) diff --git a/examples/diffsynth/README.md b/examples/diffsynth/README.md new file mode 100644 index 0000000000000000000000000000000000000000..761ef8065aae230573937c79474c9ba0421b52f2 --- /dev/null +++ b/examples/diffsynth/README.md @@ -0,0 +1,7 @@ +# DiffSynth + +DiffSynth is the initial version of our video synthesis framework. In this framework, you can apply video deflickering algorithms to the latent space of diffusion models. You can refer to the [original repo](https://github.com/alibaba/EasyNLP/tree/master/diffusion/DiffSynth) for more details. + +We provide an example for video stylization. In this pipeline, the rendered video is completely different from the original video, thus we need a powerful deflickering algorithm. We use FastBlend to implement the deflickering module. Please see [`sd_video_rerender.py`](./sd_video_rerender.py). + +https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/59fb2f7b-8de0-4481-b79f-0c3a7361a1ea diff --git a/examples/diffsynth/sd_video_rerender.py b/examples/diffsynth/sd_video_rerender.py new file mode 100644 index 0000000000000000000000000000000000000000..dc0095486a19d015c18c3e99f21c0c68d1b3bb2c --- /dev/null +++ b/examples/diffsynth/sd_video_rerender.py @@ -0,0 +1,64 @@ +from diffsynth import ModelManager, SDVideoPipeline, ControlNetConfigUnit, VideoData, save_video, download_models +from diffsynth.processors.FastBlend import FastBlendSmoother +from diffsynth.processors.PILEditor import ContrastEditor, SharpnessEditor +from diffsynth.processors.sequencial_processor import SequencialProcessor +import torch + + +# Download models (automatically) +# `models/stable_diffusion/dreamshaper_8.safetensors`: [link](https://civitai.com/api/download/models/128713?type=Model&format=SafeTensor&size=pruned&fp=fp16) +# `models/ControlNet/control_v11f1p_sd15_depth.pth`: [link](https://huggingface.co/lllyasviel/ControlNet-v1-1/resolve/main/control_v11f1p_sd15_depth.pth) +# `models/ControlNet/control_v11p_sd15_softedge.pth`: [link](https://huggingface.co/lllyasviel/ControlNet-v1-1/resolve/main/control_v11p_sd15_softedge.pth) +# `models/Annotators/dpt_hybrid-midas-501f0c75.pt`: [link](https://huggingface.co/lllyasviel/Annotators/resolve/main/dpt_hybrid-midas-501f0c75.pt) +# `models/Annotators/ControlNetHED.pth`: [link](https://huggingface.co/lllyasviel/Annotators/resolve/main/ControlNetHED.pth) +download_models([ + "ControlNet_v11f1p_sd15_depth", + "ControlNet_v11p_sd15_softedge", + "DreamShaper_8" +]) + +# Load models +model_manager = ModelManager( + torch_dtype=torch.float16, device="cuda", + file_path_list=[ + "models/stable_diffusion/dreamshaper_8.safetensors", + "models/ControlNet/control_v11f1p_sd15_depth.pth", + "models/ControlNet/control_v11p_sd15_softedge.pth", + ] +) +pipe = SDVideoPipeline.from_model_manager( + model_manager, + [ + ControlNetConfigUnit( + processor_id="depth", + model_path=rf"models/ControlNet/control_v11f1p_sd15_depth.pth", + scale=0.5 + ), + ControlNetConfigUnit( + processor_id="softedge", + model_path=rf"models/ControlNet/control_v11p_sd15_softedge.pth", + scale=0.5 + ) + ] +) +smoother = SequencialProcessor([FastBlendSmoother(), ContrastEditor(rate=1.1), SharpnessEditor(rate=1.1)]) + +# Load video +# Original video: https://pixabay.com/videos/flow-rocks-water-fluent-stones-159627/ +video = VideoData(video_file="data/examples/pixabay100/159627 (1080p).mp4", height=512, width=768) +input_video = [video[i] for i in range(128)] + +# Rerender +torch.manual_seed(0) +output_video = pipe( + prompt="winter, ice, snow, water, river", + negative_prompt="", cfg_scale=7, + input_frames=input_video, controlnet_frames=input_video, num_frames=len(input_video), + num_inference_steps=20, height=512, width=768, + animatediff_batch_size=8, animatediff_stride=4, unet_batch_size=8, + cross_frame_attention=True, + smoother=smoother, smoother_progress_ids=[4, 9, 14, 19] +) + +# Save images and video +save_video(output_video, "output_video.mp4", fps=30) diff --git a/examples/flux/README.md b/examples/flux/README.md new file mode 100644 index 0000000000000000000000000000000000000000..492e0737bfd68b9705ce822a85206ca0f8e29a0d --- /dev/null +++ b/examples/flux/README.md @@ -0,0 +1,395 @@ +# FLUX + +[切换到中文](./README_zh.md) + +FLUX is a series of image generation models open-sourced by Black-Forest-Labs. + +**DiffSynth-Studio has introduced a new inference and training framework. If you need to use the old version, please click [here](https://github.com/modelscope/DiffSynth-Studio/tree/3edf3583b1f08944cee837b94d9f84d669c2729c).** + +## Installation + +Before using these models, please install DiffSynth-Studio from source code: + +```shell +git clone https://github.com/modelscope/DiffSynth-Studio.git +cd DiffSynth-Studio +pip install -e . +``` + +## Quick Start + +You can quickly load the [black-forest-labs/FLUX.1-dev](https://www.modelscope.cn/models/black-forest-labs/FLUX.1-dev ) model and run inference by executing the code below. + +```python +import torch +from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig + +pipe = FluxImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-dev.safetensors"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors"), + ], +) + +image = pipe(prompt="a cat", seed=0) +image.save("image.jpg") +``` + +## Model Overview + +|Model ID|Extra Args|Inference|Low VRAM Inference|Full Training|Validation after Full Training|LoRA Training|Validation after LoRA Training| +|-|-|-|-|-|-|-|-| +|[FLUX.1-dev](https://www.modelscope.cn/models/black-forest-labs/FLUX.1-dev)||[code](./model_inference/FLUX.1-dev.py)|[code](./model_inference_low_vram/FLUX.1-dev.py)|[code](./model_training/full/FLUX.1-dev.sh)|[code](./model_training/validate_full/FLUX.1-dev.py)|[code](./model_training/lora/FLUX.1-dev.sh)|[code](./model_training/validate_lora/FLUX.1-dev.py)| +|[FLUX.1-Krea-dev](https://www.modelscope.cn/models/black-forest-labs/FLUX.1-Krea-dev)||[code](./model_inference/FLUX.1-Krea-dev.py)|[code](./model_inference_low_vram/FLUX.1-Krea-dev.py)|[code](./model_training/full/FLUX.1-Krea-dev.sh)|[code](./model_training/validate_full/FLUX.1-Krea-dev.py)|[code](./model_training/lora/FLUX.1-Krea-dev.sh)|[code](./model_training/validate_lora/FLUX.1-Krea-dev.py)| +|[FLUX.1-Kontext-dev](https://www.modelscope.cn/models/black-forest-labs/FLUX.1-Kontext-dev)|`kontext_images`|[code](./model_inference/FLUX.1-Kontext-dev.py)|[code](./model_inference_low_vram/FLUX.1-Kontext-dev.py)|[code](./model_training/full/FLUX.1-Kontext-dev.sh)|[code](./model_training/validate_full/FLUX.1-Kontext-dev.py)|[code](./model_training/lora/FLUX.1-Kontext-dev.sh)|[code](./model_training/validate_lora/FLUX.1-Kontext-dev.py)| +|[FLUX.1-dev-Controlnet-Inpainting-Beta](https://www.modelscope.cn/models/alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta)|`controlnet_inputs`|[code](./model_inference/FLUX.1-dev-Controlnet-Inpainting-Beta.py)|[code](./model_inference_low_vram/FLUX.1-dev-Controlnet-Inpainting-Beta.py)|[code](./model_training/full/FLUX.1-dev-Controlnet-Inpainting-Beta.sh)|[code](./model_training/validate_full/FLUX.1-dev-Controlnet-Inpainting-Beta.py)|[code](./model_training/lora/FLUX.1-dev-Controlnet-Inpainting-Beta.sh)|[code](./model_training/validate_lora/FLUX.1-dev-Controlnet-Inpainting-Beta.py)| +|[FLUX.1-dev-Controlnet-Union-alpha](https://www.modelscope.cn/models/InstantX/FLUX.1-dev-Controlnet-Union-alpha)|`controlnet_inputs`|[code](./model_inference/FLUX.1-dev-Controlnet-Union-alpha.py)|[code](./model_inference_low_vram/FLUX.1-dev-Controlnet-Union-alpha.py)|[code](./model_training/full/FLUX.1-dev-Controlnet-Union-alpha.sh)|[code](./model_training/validate_full/FLUX.1-dev-Controlnet-Union-alpha.py)|[code](./model_training/lora/FLUX.1-dev-Controlnet-Union-alpha.sh)|[code](./model_training/validate_lora/FLUX.1-dev-Controlnet-Union-alpha.py)| +|[FLUX.1-dev-Controlnet-Upscaler](https://www.modelscope.cn/models/jasperai/Flux.1-dev-Controlnet-Upscaler)|`controlnet_inputs`|[code](./model_inference/FLUX.1-dev-Controlnet-Upscaler.py)|[code](./model_inference_low_vram/FLUX.1-dev-Controlnet-Upscaler.py)|[code](./model_training/full/FLUX.1-dev-Controlnet-Upscaler.sh)|[code](./model_training/validate_full/FLUX.1-dev-Controlnet-Upscaler.py)|[code](./model_training/lora/FLUX.1-dev-Controlnet-Upscaler.sh)|[code](./model_training/validate_lora/FLUX.1-dev-Controlnet-Upscaler.py)| +|[FLUX.1-dev-IP-Adapter](https://www.modelscope.cn/models/InstantX/FLUX.1-dev-IP-Adapter)|`ipadapter_images`, `ipadapter_scale`|[code](./model_inference/FLUX.1-dev-IP-Adapter.py)|[code](./model_inference_low_vram/FLUX.1-dev-IP-Adapter.py)|[code](./model_training/full/FLUX.1-dev-IP-Adapter.sh)|[code](./model_training/validate_full/FLUX.1-dev-IP-Adapter.py)|[code](./model_training/lora/FLUX.1-dev-IP-Adapter.sh)|[code](./model_training/validate_lora/FLUX.1-dev-IP-Adapter.py)| +|[FLUX.1-dev-InfiniteYou](https://www.modelscope.cn/models/ByteDance/InfiniteYou)|`infinityou_id_image`, `infinityou_guidance`, `controlnet_inputs`|[code](./model_inference/FLUX.1-dev-InfiniteYou.py)|[code](./model_inference_low_vram/FLUX.1-dev-InfiniteYou.py)|[code](./model_training/full/FLUX.1-dev-InfiniteYou.sh)|[code](./model_training/validate_full/FLUX.1-dev-InfiniteYou.py)|[code](./model_training/lora/FLUX.1-dev-InfiniteYou.sh)|[code](./model_training/validate_lora/FLUX.1-dev-InfiniteYou.py)| +|[FLUX.1-dev-EliGen](https://www.modelscope.cn/models/DiffSynth-Studio/Eligen)|`eligen_entity_prompts`, `eligen_entity_masks`, `eligen_enable_on_negative`, `eligen_enable_inpaint`|[code](./model_inference/FLUX.1-dev-EliGen.py)|[code](./model_inference_low_vram/FLUX.1-dev-EliGen.py)|-|-|[code](./model_training/lora/FLUX.1-dev-EliGen.sh)|[code](./model_training/validate_lora/FLUX.1-dev-EliGen.py)| +|[FLUX.1-dev-LoRA-Encoder](https://www.modelscope.cn/models/DiffSynth-Studio/LoRA-Encoder-FLUX.1-Dev)|`lora_encoder_inputs`, `lora_encoder_scale`|[code](./model_inference/FLUX.1-dev-LoRA-Encoder.py)|[code](./model_inference_low_vram/FLUX.1-dev-LoRA-Encoder.py)|[code](./model_training/full/FLUX.1-dev-LoRA-Encoder.sh)|[code](./model_training/validate_full/FLUX.1-dev-LoRA-Encoder.py)|-|-| +|[FLUX.1-dev-LoRA-Fusion-Preview](https://modelscope.cn/models/DiffSynth-Studio/LoRAFusion-preview-FLUX.1-dev)||[code](./model_inference/FLUX.1-dev-LoRA-Fusion.py)|-|-|-|-|-| +|[Step1X-Edit](https://www.modelscope.cn/models/stepfun-ai/Step1X-Edit)|`step1x_reference_image`|[code](./model_inference/Step1X-Edit.py)|[code](./model_inference_low_vram/Step1X-Edit.py)|[code](./model_training/full/Step1X-Edit.sh)|[code](./model_training/validate_full/Step1X-Edit.py)|[code](./model_training/lora/Step1X-Edit.sh)|[code](./model_training/validate_lora/Step1X-Edit.py)| +|[FLEX.2-preview](https://www.modelscope.cn/models/ostris/Flex.2-preview)|`flex_inpaint_image`, `flex_inpaint_mask`, `flex_control_image`, `flex_control_strength`, `flex_control_stop`|[code](./model_inference/FLEX.2-preview.py)|[code](./model_inference_low_vram/FLEX.2-preview.py)|[code](./model_training/full/FLEX.2-preview.sh)|[code](./model_training/validate_full/FLEX.2-preview.py)|[code](./model_training/lora/FLEX.2-preview.sh)|[code](./model_training/validate_lora/FLEX.2-preview.py)| +|[Nexus-Gen](https://www.modelscope.cn/models/DiffSynth-Studio/Nexus-GenV2)|`nexus_gen_reference_image`|[code](./model_inference/Nexus-Gen-Editing.py)|[code](./model_inference_low_vram/Nexus-Gen-Editing.py)|[code](./model_training/full/Nexus-Gen.sh)|[code](./model_training/validate_full/Nexus-Gen.py)|[code](./model_training/lora/Nexus-Gen.sh)|[code](./model_training/validate_lora/Nexus-Gen.py)| + +## Model Inference + +The following sections will help you understand our features and write inference code. + +
+ +Load Model + +The model is loaded using `from_pretrained`: + +```python +import torch +from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig + +pipe = FluxImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-dev.safetensors"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors"), + ], +) +``` + +Here, `torch_dtype` and `device` set the computation precision and device. The `model_configs` can be used in different ways to specify model paths: + +* Download the model from [ModelScope](https://modelscope.cn/ ) and load it. In this case, fill in `model_id` and `origin_file_pattern`, for example: + +```python +ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-dev.safetensors") +``` + +* Load the model from a local file path. In this case, fill in `path`, for example: + +```python +ModelConfig(path="models/black-forest-labs/FLUX.1-dev/flux1-dev.safetensors") +``` + +For a single model that loads from multiple files, use a list, for example: + +```python +ModelConfig(path=[ + "models/xxx/diffusion_pytorch_model-00001-of-00003.safetensors", + "models/xxx/diffusion_pytorch_model-00002-of-00003.safetensors", + "models/xxx/diffusion_pytorch_model-00003-of-00003.safetensors", +]) +``` + +The `ModelConfig` method also provides extra arguments to control model loading behavior: + +* `local_model_path`: Path to save downloaded models. Default is `"./models"`. +* `skip_download`: Whether to skip downloading. Default is `False`. If your network cannot access [ModelScope](https://modelscope.cn/ ), download the required files manually and set this to `True`. + +
+ + +
+ +VRAM Management + +DiffSynth-Studio provides fine-grained VRAM management for the FLUX model. This allows the model to run on devices with low VRAM. You can enable the offload feature using the code below. It moves some modules to CPU memory when GPU memory is limited. + +```python +pipe = FluxImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-dev.safetensors", offload_device="cpu"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors", offload_device="cpu"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/", offload_device="cpu"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors", offload_device="cpu"), + ], +) +pipe.enable_vram_management() +``` + +FP8 quantization is also supported: + +```python +pipe = FluxImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-dev.safetensors", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors", offload_dtype=torch.float8_e4m3fn), + ], +) +pipe.enable_vram_management() +``` + +You can use FP8 quantization and offload at the same time: + +```python +pipe = FluxImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-dev.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ], +) +pipe.enable_vram_management() +``` + +After enabling VRAM management, the framework will automatically decide the VRAM strategy based on available GPU memory. For most FLUX models, inference can run with as little as 8GB of VRAM. The `enable_vram_management` function has the following parameters to manually control the VRAM strategy: + +* `vram_limit`: VRAM usage limit in GB. By default, it uses all free VRAM on the device. Note that this is not an absolute limit. If the set VRAM is not enough but more VRAM is actually available, the model will run with minimal VRAM usage. Setting it to 0 achieves the theoretical minimum VRAM usage. +* `vram_buffer`: VRAM buffer size in GB. Default is 0.5GB. A buffer is needed because larger neural network layers may use more VRAM than expected during loading. The optimal value is the VRAM used by the largest layer in the model. +* `num_persistent_param_in_dit`: Number of parameters in the DiT model that stay in VRAM. Default is no limit. We plan to remove this parameter in the future. Do not rely on it. + +
+ + +
+ +Inference Acceleration + +* TeaCache: Acceleration technique [TeaCache](https://github.com/ali-vilab/TeaCache ). Please refer to the [example code](./acceleration/teacache.py). + +
+ +
+ +Input Parameters + +The pipeline supports the following input parameters during inference: + +* `prompt`: Text prompt describing what should appear in the image. +* `negative_prompt`: Negative prompt describing what should not appear in the image. Default is `""`. +* `cfg_scale`: Parameter for classifier-free guidance. Default is 1. Takes effect when set to a value greater than 1. +* `embedded_guidance`: Built-in guidance parameter for FLUX-dev. Default is 3.5. +* `t5_sequence_length`: Sequence length of text embeddings from the T5 model. Default is 512. +* `input_image`: Input image used for image-to-image generation. Used together with `denoising_strength`. +* `denoising_strength`: Denoising strength, range from 0 to 1. Default is 1. When close to 0, the output image is similar to the input. When close to 1, the output differs more from the input. Do not set it to values other than 1 if `input_image` is not provided. +* `height`: Image height. Must be a multiple of 16. +* `width`: Image width. Must be a multiple of 16. +* `seed`: Random seed. Default is `None`, meaning fully random. +* `rand_device`: Device for generating random Gaussian noise. Default is `"cpu"`. Setting it to `"cuda"` may lead to different results on different GPUs. +* `sigma_shift`: Parameter from Rectified Flow theory. Default is 3. A larger value means the model spends more steps at the start of denoising. Increasing this can improve image quality, but may cause differences between generated images and training data due to inconsistency with training. +* `num_inference_steps`: Number of inference steps. Default is 30. +* `kontext_images`: Input images for the Kontext model. +* `controlnet_inputs`: Inputs for the ControlNet model. +* `ipadapter_images`: Input images for the IP-Adapter model. +* `ipadapter_scale`: Control strength for the IP-Adapter model. +* `eligen_entity_prompts`: Local prompts for the EliGen model. +* `eligen_entity_masks`: Mask regions for local prompts in the EliGen model. Matches one-to-one with `eligen_entity_prompts`. +* `eligen_enable_on_negative`: Whether to enable EliGen on the negative prompt side. Only works when `cfg_scale > 1`. +* `eligen_enable_inpaint`: Whether to enable EliGen for local inpainting. +* `infinityou_id_image`: Face image for the InfiniteYou model. +* `infinityou_guidance`: Control strength for the InfiniteYou model. +* `flex_inpaint_image`: Image for FLEX model's inpainting. +* `flex_inpaint_mask`: Mask region for FLEX model's inpainting. +* `flex_control_image`: Image for FLEX model's structural control. +* `flex_control_strength`: Strength for FLEX model's structural control. +* `flex_control_stop`: End point for FLEX model's structural control. 1 means enabled throughout, 0.5 means enabled in the first half, 0 means disabled. +* `step1x_reference_image`: Input image for Step1x-Edit model's image editing. +* `lora_encoder_inputs`: Inputs for LoRA encoder. Can be ModelConfig or local path. +* `lora_encoder_scale`: Activation strength for LoRA encoder. Default is 1. Smaller values mean weaker LoRA activation. +* `tea_cache_l1_thresh`: Threshold for TeaCache. Larger values mean faster speed but lower image quality. Note that after enabling TeaCache, inference speed is not uniform, so the remaining time shown in the progress bar will be inaccurate. +* `tiled`: Whether to enable tiled VAE inference. Default is `False`. Setting to `True` reduces VRAM usage during VAE encoding/decoding, with slight error and slightly longer inference time. +* `tile_size`: Tile size during VAE encoding/decoding. Default is 128. Only takes effect when `tiled=True`. +* `tile_stride`: Tile stride during VAE encoding/decoding. Default is 64. Only takes effect when `tiled=True`. Must be less than or equal to `tile_size`. +* `progress_bar_cmd`: Progress bar display. Default is `tqdm.tqdm`. Set to `lambda x:x` to disable the progress bar. + +
+ + +## Model Training + +Training for the FLUX series models is done using a unified script [`./model_training/train.py`](./model_training/train.py). + +
+ +Script Parameters + +The script includes the following parameters: + +* Dataset + * `--dataset_base_path`: Root path of the dataset. + * `--dataset_metadata_path`: Path to the dataset metadata file. + * `--max_pixels`: Maximum pixel area. Default is 1024*1024. When dynamic resolution is enabled, any image with resolution higher than this will be downscaled. + * `--height`: Height of the image or video. Leave `height` and `width` empty to enable dynamic resolution. + * `--width`: Width of the image or video. Leave `height` and `width` empty to enable dynamic resolution. + * `--data_file_keys`: Data file keys in the metadata. Separate with commas. + * `--dataset_repeat`: Number of times the dataset repeats per epoch. + * `--dataset_num_workers`: Number of workers for data loading. +* Model + * `--model_paths`: Paths to load models. In JSON format. + * `--model_id_with_origin_paths`: Model ID with original paths, e.g., black-forest-labs/FLUX.1-dev:flux1-dev.safetensors. Separate with commas. +* Training + * `--learning_rate`: Learning rate. + * `--weight_decay`: Weight decay. + * `--num_epochs`: Number of epochs. + * `--output_path`: Save path. + * `--remove_prefix_in_ckpt`: Remove prefix in checkpoint. + * `--save_steps`: Number of checkpoint saving invervals. If None, checkpoints will be saved every epoch. + * `--find_unused_parameters`: Whether to find unused parameters in DDP. +* Trainable Modules + * `--trainable_models`: Models that can be trained, e.g., dit, vae, text_encoder. + * `--lora_base_model`: Which model to add LoRA to. + * `--lora_target_modules`: Which layers to add LoRA to. + * `--lora_rank`: Rank of LoRA. + * `--lora_checkpoint`: Path to the LoRA checkpoint. If provided, LoRA will be loaded from this checkpoint. +* Extra Model Inputs + * `--extra_inputs`: Extra model inputs, separated by commas. +* VRAM Management + * `--use_gradient_checkpointing`: Whether to enable gradient checkpointing. + * `--use_gradient_checkpointing_offload`: Whether to offload gradient checkpointing to CPU memory. + * `--gradient_accumulation_steps`: Number of gradient accumulation steps. +* Others + * `--align_to_opensource_format`: Whether to align the FLUX DiT LoRA format with the open-source version. Only works for LoRA training. + +In addition, the training framework is built on [`accelerate`](https://huggingface.co/docs/accelerate/index ). Run `accelerate config` before training to set GPU-related parameters. For some training scripts (e.g., full model training), we provide suggested `accelerate` config files. You can find them in the corresponding training scripts. + +
+ + +
+ +Step 1: Prepare Dataset + +A dataset contains a series of files. We suggest organizing your dataset like this: + +``` +data/example_image_dataset/ +├── metadata.csv +├── image1.jpg +└── image2.jpg +``` + +Here, `image1.jpg` and `image2.jpg` are training images, and `metadata.csv` is the metadata list, for example: + +``` +image,prompt +image1.jpg,"a cat is sleeping" +image2.jpg,"a dog is running" +``` + +We have built a sample image dataset to help you test. You can download it with the following command: + +```shell +modelscope download --dataset DiffSynth-Studio/example_image_dataset --local_dir ./data/example_image_dataset +``` + +The dataset supports multiple image formats: `"jpg", "jpeg", "png", "webp"`. + +Image size can be controlled by script arguments `--height` and `--width`. When `--height` and `--width` are left empty, dynamic resolution is enabled. The model will train using each image's actual width and height from the dataset. + +**We strongly recommend using fixed resolution for training, because there can be load balancing issues in multi-GPU training.** + +When the model needs extra inputs, for example, `kontext_images` required by controllable models like [`black-forest-labs/FLUX.1-Kontext-dev`](https://modelscope.cn/models/black-forest-labs/FLUX.1-Kontext-dev ), add the corresponding column to your dataset, for example: + +``` +image,prompt,kontext_images +image1.jpg,"a cat is sleeping",image1_reference.jpg +``` + +If an extra input includes image files, you must specify the column name in the `--data_file_keys` argument. Add column names as needed, for example `--data_file_keys "image,kontext_images"`, and also enable `--extra_inputs "kontext_images"`. + +
+ + +
+ +Step 2: Load Model + +Similar to model loading during inference, you can configure which models to load directly using model IDs. For example, during inference we load the model with this setting: + +```python +model_configs=[ + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-dev.safetensors"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors"), +] +``` + +Then, during training, use the following parameter to load the same models: + +```shell +--model_id_with_origin_paths "black-forest-labs/FLUX.1-dev:flux1-dev.safetensors,black-forest-labs/FLUX.1-dev:text_encoder/model.safetensors,black-forest-labs/FLUX.1-dev:text_encoder_2/,black-forest-labs/FLUX.1-dev:ae.safetensors" +``` + +If you want to load models from local files, for example, during inference: + +```python +model_configs=[ + ModelConfig(path="models/black-forest-labs/FLUX.1-dev/flux1-dev.safetensors"), + ModelConfig(path="models/black-forest-labs/FLUX.1-dev/text_encoder/model.safetensors"), + ModelConfig(path="models/black-forest-labs/FLUX.1-dev/text_encoder_2/"), + ModelConfig(path="models/black-forest-labs/FLUX.1-dev/ae.safetensors"), +] +``` + +Then during training, set it as: + +```shell +--model_paths '[ + "models/black-forest-labs/FLUX.1-dev/flux1-dev.safetensors", + "models/black-forest-labs/FLUX.1-dev/text_encoder/model.safetensors", + "models/black-forest-labs/FLUX.1-dev/text_encoder_2/", + "models/black-forest-labs/FLUX.1-dev/ae.safetensors" +]' \ +``` + +
+ + +
+ +Step 3: Set Trainable Modules + +The training framework supports training base models or LoRA models. Here are some examples: + +* Full training of the DiT part: `--trainable_models dit` +* Training a LoRA model on the DiT part: `--lora_base_model dit --lora_target_modules "a_to_qkv,b_to_qkv,ff_a.0,ff_a.2,ff_b.0,ff_b.2,a_to_out,b_to_out,proj_out,norm.linear,norm1_a.linear,norm1_b.linear,to_qkv_mlp" --lora_rank 32` + +Also, because the training script loads multiple modules (text encoder, dit, vae), you need to remove prefixes when saving model files. For example, when fully training the DiT part or training a LoRA model on the DiT part, set `--remove_prefix_in_ckpt pipe.dit.` + +
+ + +
+ +Step 4: Start Training + +We have written training commands for each model. Please refer to the table at the beginning of this document. + +
diff --git a/examples/flux/README_zh.md b/examples/flux/README_zh.md new file mode 100644 index 0000000000000000000000000000000000000000..65331f96440c5655d76858aab9df4d7589776c2d --- /dev/null +++ b/examples/flux/README_zh.md @@ -0,0 +1,396 @@ +# FLUX + +[Switch to English](./README.md) + +FLUX 是由 Black-Forest-Labs 开源的一系列图像生成模型。 + +**DiffSynth-Studio 启用了新的推理和训练框架，如需使用旧版本，请点击[这里](https://github.com/modelscope/DiffSynth-Studio/tree/3edf3583b1f08944cee837b94d9f84d669c2729c)。** + +## 安装 + +在使用本系列模型之前，请通过源码安装 DiffSynth-Studio。 + +```shell +git clone https://github.com/modelscope/DiffSynth-Studio.git +cd DiffSynth-Studio +pip install -e . +``` + +## 快速开始 + +通过运行以下代码可以快速加载 [black-forest-labs/FLUX.1-dev](https://www.modelscope.cn/models/black-forest-labs/FLUX.1-dev) 模型并进行推理。 + +```python +import torch +from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig + +pipe = FluxImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-dev.safetensors"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors"), + ], +) + +image = pipe(prompt="a cat", seed=0) +image.save("image.jpg") +``` + +## 模型总览 + +|模型 ID|额外参数|推理|低显存推理|全量训练|全量训练后验证|LoRA 训练|LoRA 训练后验证| +|-|-|-|-|-|-|-|-| +|[FLUX.1-dev](https://www.modelscope.cn/models/black-forest-labs/FLUX.1-dev)||[code](./model_inference/FLUX.1-dev.py)|[code](./model_inference_low_vram/FLUX.1-dev.py)|[code](./model_training/full/FLUX.1-dev.sh)|[code](./model_training/validate_full/FLUX.1-dev.py)|[code](./model_training/lora/FLUX.1-dev.sh)|[code](./model_training/validate_lora/FLUX.1-dev.py)| +|[FLUX.1-Krea-dev](https://www.modelscope.cn/models/black-forest-labs/FLUX.1-Krea-dev)||[code](./model_inference/FLUX.1-Krea-dev.py)|[code](./model_inference_low_vram/FLUX.1-Krea-dev.py)|[code](./model_training/full/FLUX.1-Krea-dev.sh)|[code](./model_training/validate_full/FLUX.1-Krea-dev.py)|[code](./model_training/lora/FLUX.1-Krea-dev.sh)|[code](./model_training/validate_lora/FLUX.1-Krea-dev.py)| +|[FLUX.1-Kontext-dev](https://www.modelscope.cn/models/black-forest-labs/FLUX.1-Kontext-dev)|`kontext_images`|[code](./model_inference/FLUX.1-Kontext-dev.py)|[code](./model_inference_low_vram/FLUX.1-Kontext-dev.py)|[code](./model_training/full/FLUX.1-Kontext-dev.sh)|[code](./model_training/validate_full/FLUX.1-Kontext-dev.py)|[code](./model_training/lora/FLUX.1-Kontext-dev.sh)|[code](./model_training/validate_lora/FLUX.1-Kontext-dev.py)| +|[FLUX.1-dev-Controlnet-Inpainting-Beta](https://www.modelscope.cn/models/alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta)|`controlnet_inputs`|[code](./model_inference/FLUX.1-dev-Controlnet-Inpainting-Beta.py)|[code](./model_inference_low_vram/FLUX.1-dev-Controlnet-Inpainting-Beta.py)|[code](./model_training/full/FLUX.1-dev-Controlnet-Inpainting-Beta.sh)|[code](./model_training/validate_full/FLUX.1-dev-Controlnet-Inpainting-Beta.py)|[code](./model_training/lora/FLUX.1-dev-Controlnet-Inpainting-Beta.sh)|[code](./model_training/validate_lora/FLUX.1-dev-Controlnet-Inpainting-Beta.py)| +|[FLUX.1-dev-Controlnet-Union-alpha](https://www.modelscope.cn/models/InstantX/FLUX.1-dev-Controlnet-Union-alpha)|`controlnet_inputs`|[code](./model_inference/FLUX.1-dev-Controlnet-Union-alpha.py)|[code](./model_inference_low_vram/FLUX.1-dev-Controlnet-Union-alpha.py)|[code](./model_training/full/FLUX.1-dev-Controlnet-Union-alpha.sh)|[code](./model_training/validate_full/FLUX.1-dev-Controlnet-Union-alpha.py)|[code](./model_training/lora/FLUX.1-dev-Controlnet-Union-alpha.sh)|[code](./model_training/validate_lora/FLUX.1-dev-Controlnet-Union-alpha.py)| +|[FLUX.1-dev-Controlnet-Upscaler](https://www.modelscope.cn/models/jasperai/Flux.1-dev-Controlnet-Upscaler)|`controlnet_inputs`|[code](./model_inference/FLUX.1-dev-Controlnet-Upscaler.py)|[code](./model_inference_low_vram/FLUX.1-dev-Controlnet-Upscaler.py)|[code](./model_training/full/FLUX.1-dev-Controlnet-Upscaler.sh)|[code](./model_training/validate_full/FLUX.1-dev-Controlnet-Upscaler.py)|[code](./model_training/lora/FLUX.1-dev-Controlnet-Upscaler.sh)|[code](./model_training/validate_lora/FLUX.1-dev-Controlnet-Upscaler.py)| +|[FLUX.1-dev-IP-Adapter](https://www.modelscope.cn/models/InstantX/FLUX.1-dev-IP-Adapter)|`ipadapter_images`, `ipadapter_scale`|[code](./model_inference/FLUX.1-dev-IP-Adapter.py)|[code](./model_inference_low_vram/FLUX.1-dev-IP-Adapter.py)|[code](./model_training/full/FLUX.1-dev-IP-Adapter.sh)|[code](./model_training/validate_full/FLUX.1-dev-IP-Adapter.py)|[code](./model_training/lora/FLUX.1-dev-IP-Adapter.sh)|[code](./model_training/validate_lora/FLUX.1-dev-IP-Adapter.py)| +|[FLUX.1-dev-InfiniteYou](https://www.modelscope.cn/models/ByteDance/InfiniteYou)|`infinityou_id_image`, `infinityou_guidance`, `controlnet_inputs`|[code](./model_inference/FLUX.1-dev-InfiniteYou.py)|[code](./model_inference_low_vram/FLUX.1-dev-InfiniteYou.py)|[code](./model_training/full/FLUX.1-dev-InfiniteYou.sh)|[code](./model_training/validate_full/FLUX.1-dev-InfiniteYou.py)|[code](./model_training/lora/FLUX.1-dev-InfiniteYou.sh)|[code](./model_training/validate_lora/FLUX.1-dev-InfiniteYou.py)| +|[FLUX.1-dev-EliGen](https://www.modelscope.cn/models/DiffSynth-Studio/Eligen)|`eligen_entity_prompts`, `eligen_entity_masks`, `eligen_enable_on_negative`, `eligen_enable_inpaint`|[code](./model_inference/FLUX.1-dev-EliGen.py)|[code](./model_inference_low_vram/FLUX.1-dev-EliGen.py)|-|-|[code](./model_training/lora/FLUX.1-dev-EliGen.sh)|[code](./model_training/validate_lora/FLUX.1-dev-EliGen.py)| +|[FLUX.1-dev-LoRA-Encoder](https://www.modelscope.cn/models/DiffSynth-Studio/LoRA-Encoder-FLUX.1-Dev)|`lora_encoder_inputs`, `lora_encoder_scale`|[code](./model_inference/FLUX.1-dev-LoRA-Encoder.py)|[code](./model_inference_low_vram/FLUX.1-dev-LoRA-Encoder.py)|[code](./model_training/full/FLUX.1-dev-LoRA-Encoder.sh)|[code](./model_training/validate_full/FLUX.1-dev-LoRA-Encoder.py)|-|-| +|[FLUX.1-dev-LoRA-Fusion-Preview](https://modelscope.cn/models/DiffSynth-Studio/LoRAFusion-preview-FLUX.1-dev)||[code](./model_inference/FLUX.1-dev-LoRA-Fusion.py)|-|-|-|-|-| +|[Step1X-Edit](https://www.modelscope.cn/models/stepfun-ai/Step1X-Edit)|`step1x_reference_image`|[code](./model_inference/Step1X-Edit.py)|[code](./model_inference_low_vram/Step1X-Edit.py)|[code](./model_training/full/Step1X-Edit.sh)|[code](./model_training/validate_full/Step1X-Edit.py)|[code](./model_training/lora/Step1X-Edit.sh)|[code](./model_training/validate_lora/Step1X-Edit.py)| +|[FLEX.2-preview](https://www.modelscope.cn/models/ostris/Flex.2-preview)|`flex_inpaint_image`, `flex_inpaint_mask`, `flex_control_image`, `flex_control_strength`, `flex_control_stop`|[code](./model_inference/FLEX.2-preview.py)|[code](./model_inference_low_vram/FLEX.2-preview.py)|[code](./model_training/full/FLEX.2-preview.sh)|[code](./model_training/validate_full/FLEX.2-preview.py)|[code](./model_training/lora/FLEX.2-preview.sh)|[code](./model_training/validate_lora/FLEX.2-preview.py)| +|[Nexus-Gen](https://www.modelscope.cn/models/DiffSynth-Studio/Nexus-GenV2)|`nexus_gen_reference_image`|[code](./model_inference/Nexus-Gen-Editing.py)|[code](./model_inference_low_vram/Nexus-Gen-Editing.py)|[code](./model_training/full/Nexus-Gen.sh)|[code](./model_training/validate_full/Nexus-Gen.py)|[code](./model_training/lora/Nexus-Gen.sh)|[code](./model_training/validate_lora/Nexus-Gen.py)| + +## 模型推理 + +以下部分将会帮助您理解我们的功能并编写推理代码。 + +
+ +加载模型 + +模型通过 `from_pretrained` 加载： + +```python +import torch +from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig + +pipe = FluxImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-dev.safetensors"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors"), + ], +) +``` + +其中 `torch_dtype` 和 `device` 是计算精度和计算设备。`model_configs` 可通过多种方式配置模型路径： + +* 从[魔搭社区](https://modelscope.cn/)下载模型并加载。此时需要填写 `model_id` 和 `origin_file_pattern`，例如 + +```python +ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-dev.safetensors") +``` + +* 从本地文件路径加载模型。此时需要填写 `path`，例如 + +```python +ModelConfig(path="models/black-forest-labs/FLUX.1-dev/flux1-dev.safetensors") +``` + +对于从多个文件加载的单一模型，使用列表即可，例如 + +```python +ModelConfig(path=[ + "models/xxx/diffusion_pytorch_model-00001-of-00003.safetensors", + "models/xxx/diffusion_pytorch_model-00002-of-00003.safetensors", + "models/xxx/diffusion_pytorch_model-00003-of-00003.safetensors", +]) +``` + +`ModelConfig` 还提供了额外的参数用于控制模型加载时的行为： + +* `local_model_path`: 用于保存下载模型的路径，默认值为 `"./models"`。 +* `skip_download`: 是否跳过下载，默认值为 `False`。当您的网络无法访问[魔搭社区](https://modelscope.cn/)时，请手动下载必要的文件，并将其设置为 `True`。 + +
+ + +
+ +显存管理 + +DiffSynth-Studio 为 FLUX 模型提供了细粒度的显存管理，让模型能够在低显存设备上进行推理，可通过以下代码开启 offload 功能，在显存有限的设备上将部分模块 offload 到内存中。 + +```python +pipe = FluxImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-dev.safetensors", offload_device="cpu"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors", offload_device="cpu"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/", offload_device="cpu"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors", offload_device="cpu"), + ], +) +pipe.enable_vram_management() +``` + +FP8 量化功能也是支持的： + +```python +pipe = FluxImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-dev.safetensors", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors", offload_dtype=torch.float8_e4m3fn), + ], +) +pipe.enable_vram_management() +``` + +FP8 量化和 offload 可同时开启： + +```python +pipe = FluxImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-dev.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ], +) +pipe.enable_vram_management() +``` + +开启显存管理后，框架会自动根据设备上的剩余显存确定显存管理策略。对于大多数 FLUX 系列模型，最低 8GB 显存即可进行推理。`enable_vram_management` 函数提供了以下参数，用于手动控制显存管理策略： + +* `vram_limit`: 显存占用量限制（GB），默认占用设备上的剩余显存。注意这不是一个绝对限制，当设置的显存不足以支持模型进行推理，但实际可用显存足够时，将会以最小化显存占用的形式进行推理。将其设置为0时，将会实现理论最小显存占用。 +* `vram_buffer`: 显存缓冲区大小（GB），默认为 0.5GB。由于部分较大的神经网络层在 onload 阶段会不可控地占用更多显存，因此一个显存缓冲区是必要的，理论上的最优值为模型中最大的层所占的显存。 +* `num_persistent_param_in_dit`: DiT 模型中常驻显存的参数数量（个），默认为无限制。我们将会在未来删除这个参数，请不要依赖这个参数。 + +
+ + +
+ +推理加速 + +* TeaCache：加速技术 [TeaCache](https://github.com/ali-vilab/TeaCache)，请参考[示例代码](./acceleration/teacache.py)。 + +
+ +
+ +输入参数 + +Pipeline 在推理阶段能够接收以下输入参数： + +* `prompt`: 提示词，描述画面中出现的内容。 +* `negative_prompt`: 负向提示词，描述画面中不应该出现的内容，默认值为 `""`。 +* `cfg_scale`: Classifier-free guidance 的参数，默认值为 1，当设置为大于1的数值时生效。 +* `embedded_guidance`: FLUX-dev 的内嵌引导参数，默认值为 3.5。 +* `t5_sequence_length`: T5 模型的文本向量序列长度，默认值为 512。 +* `input_image`: 输入图像，用于图生图，该参数与 `denoising_strength` 配合使用。 +* `denoising_strength`: 去噪强度，范围是 0～1，默认值为 1，当数值接近 0 时，生成图像与输入图像相似；当数值接近 1 时，生成图像与输入图像相差更大。在不输入 `input_image` 参数时，请不要将其设置为非 1 的数值。 +* `height`: 图像高度，需保证高度为 16 的倍数。 +* `width`: 图像宽度，需保证宽度为 16 的倍数。 +* `seed`: 随机种子。默认为 `None`，即完全随机。 +* `rand_device`: 生成随机高斯噪声矩阵的计算设备，默认为 `"cpu"`。当设置为 `cuda` 时，在不同 GPU 上会导致不同的生成结果。 +* `sigma_shift`: Rectified Flow 理论中的参数，默认为 3。数值越大，模型在去噪的开始阶段停留的步骤数越多，可适当调大这个参数来提高画面质量，但会因生成过程与训练过程不一致导致生成的图像内容与训练数据存在差异。 +* `num_inference_steps`: 推理次数，默认值为 30。 +* `kontext_images`: Kontext 模型的输入图像。 +* `controlnet_inputs`: ControlNet 模型的输入。 +* `ipadapter_images`: IP-Adapter 模型的输入图像。 +* `ipadapter_scale`: IP-Adapter 模型的控制强度。 +* `eligen_entity_prompts`: EliGen 模型的图像局部提示词。 +* `eligen_entity_masks`: EliGen 模型的局部提示词控制区域，与 `eligen_entity_prompts` 一一对应。 +* `eligen_enable_on_negative`: 是否在负向提示词一侧启用 EliGen，仅在 `cfg_scale > 1` 时生效。 +* `eligen_enable_inpaint`: 是否启用 EliGen 局部重绘。 +* `infinityou_id_image`: InfiniteYou 模型的人脸图像。 +* `infinityou_guidance`: InfiniteYou 模型的控制强度。 +* `flex_inpaint_image`: FLEX 模型用于局部重绘的图像。 +* `flex_inpaint_mask`: FLEX 模型用于局部重绘的区域。 +* `flex_control_image`: FLEX 模型用于结构控制的图像。 +* `flex_control_strength`: FLEX 模型用于结构控制的强度。 +* `flex_control_stop`: FLEX 模型结构控制的结束点，1表示全程启用，0.5表示在前半段启用，0表示不启用。 +* `step1x_reference_image`: Step1x-Edit 模型用于图像编辑的输入图像。 +* `lora_encoder_inputs`: LoRA 编码器的输入，格式为 ModelConfig 或本地路径。 +* `lora_encoder_scale`: LoRA 编码器的激活强度，默认值为1，数值越小，LoRA 激活越弱。 +* `tea_cache_l1_thresh`: TeaCache 的阈值，数值越大，速度越快，画面质量越差。请注意，开启 TeaCache 后推理速度并非均匀，因此进度条上显示的剩余时间将会变得不准确。 +* `tiled`: 是否启用 VAE 分块推理，默认为 `False`。设置为 `True` 时可显著减少 VAE 编解码阶段的显存占用，会产生少许误差，以及少量推理时间延长。 +* `tile_size`: VAE 编解码阶段的分块大小，默认为 128，仅在 `tiled=True` 时生效。 +* `tile_stride`: VAE 编解码阶段的分块步长，默认为 64，仅在 `tiled=True` 时生效，需保证其数值小于或等于 `tile_size`。 +* `progress_bar_cmd`: 进度条，默认为 `tqdm.tqdm`。可通过设置为 `lambda x:x` 来屏蔽进度条。 + +
+ + +## 模型训练 + +FLUX 系列模型训练通过统一的 [`./model_training/train.py`](./model_training/train.py) 脚本进行。 + +
+ +脚本参数 + +脚本包含以下参数： + +* 数据集 + * `--dataset_base_path`: 数据集的根路径。 + * `--dataset_metadata_path`: 数据集的元数据文件路径。 + * `--max_pixels`: 最大像素面积，默认为 1024*1024，当启用动态分辨率时，任何分辨率大于这个数值的图片都会被缩小。 + * `--height`: 图像或视频的高度。将 `height` 和 `width` 留空以启用动态分辨率。 + * `--width`: 图像或视频的宽度。将 `height` 和 `width` 留空以启用动态分辨率。 + * `--data_file_keys`: 元数据中的数据文件键。用逗号分隔。 + * `--dataset_repeat`: 每个 epoch 中数据集重复的次数。 + * `--dataset_num_workers`: 每个 Dataloder 的进程数量。 +* 模型 + * `--model_paths`: 要加载的模型路径。JSON 格式。 + * `--model_id_with_origin_paths`: 带原始路径的模型 ID，例如 black-forest-labs/FLUX.1-dev:flux1-dev.safetensors。用逗号分隔。 +* 训练 + * `--learning_rate`: 学习率。 + * `--weight_decay`：权重衰减大小。 + * `--num_epochs`: 轮数（Epoch）。 + * `--output_path`: 保存路径。 + * `--remove_prefix_in_ckpt`: 在 ckpt 中移除前缀。 + * `--save_steps`: 保存模型的间隔 step 数量，如果设置为 None ，则每个 epoch 保存一次 + * `--find_unused_parameters`: DDP 训练中是否存在未使用的参数 +* 可训练模块 + * `--trainable_models`: 可训练的模型，例如 dit、vae、text_encoder。 + * `--lora_base_model`: LoRA 添加到哪个模型上。 + * `--lora_target_modules`: LoRA 添加到哪一层上。 + * `--lora_rank`: LoRA 的秩（Rank）。 + * `--lora_checkpoint`: LoRA 检查点的路径。如果提供此路径，LoRA 将从此检查点加载。 +* 额外模型输入 + * `--extra_inputs`: 额外的模型输入，以逗号分隔。 +* 显存管理 + * `--use_gradient_checkpointing`: 是否启用 gradient checkpointing。 + * `--use_gradient_checkpointing_offload`: 是否将 gradient checkpointing 卸载到内存中。 + * `--gradient_accumulation_steps`: 梯度累积步数。 +* 其他 + * `--align_to_opensource_format`: 是否将 FLUX DiT LoRA 的格式与开源版本对齐，仅对 LoRA 训练生效。 + + +此外，训练框架基于 [`accelerate`](https://huggingface.co/docs/accelerate/index) 构建，在开始训练前运行 `accelerate config` 可配置 GPU 的相关参数。对于部分模型训练（例如模型的全量训练）脚本，我们提供了建议的 `accelerate` 配置文件，可在对应的训练脚本中查看。 + +
+ + +
+ +Step 1: 准备数据集 + +数据集包含一系列文件，我们建议您这样组织数据集文件： + +``` +data/example_image_dataset/ +├── metadata.csv +├── image1.jpg +└── image2.jpg +``` + +其中 `image1.jpg`、`image2.jpg` 为训练用图像数据，`metadata.csv` 为元数据列表，例如 + +``` +image,prompt +image1.jpg,"a cat is sleeping" +image2.jpg,"a dog is running" +``` + +我们构建了一个样例图像数据集，以方便您进行测试，通过以下命令可以下载这个数据集： + +```shell +modelscope download --dataset DiffSynth-Studio/example_image_dataset --local_dir ./data/example_image_dataset +``` + +数据集支持多种图片格式，`"jpg", "jpeg", "png", "webp"`。 + +图片的尺寸可通过脚本参数 `--height`、`--width` 控制。当 `--height` 和 `--width` 为空时将会开启动态分辨率，按照数据集中每个图像的实际宽高训练。 + +**我们强烈建议使用固定分辨率训练，因为在多卡训练中存在负载均衡问题。** + +当模型需要额外输入时，例如具备控制能力的模型 [`black-forest-labs/FLUX.1-Kontext-dev`](https://modelscope.cn/models/black-forest-labs/FLUX.1-Kontext-dev) 所需的 `kontext_images`，请在数据集中补充相应的列，例如： + +``` +image,prompt,kontext_images +image1.jpg,"a cat is sleeping",image1_reference.jpg +``` + +额外输入若包含图像文件，则需要在 `--data_file_keys` 参数中指定要解析的列名。可根据额外输入增加相应的列名，例如 `--data_file_keys "image,kontext_images"`，同时启用 `--extra_inputs "kontext_images"`。 + +
+ + +
+ +Step 2: 加载模型 + +类似于推理时的模型加载逻辑，可直接通过模型 ID 配置要加载的模型。例如，推理时我们通过以下设置加载模型 + +```python +model_configs=[ + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-dev.safetensors"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors"), +] +``` + +那么在训练时，填入以下参数即可加载对应的模型。 + +```shell +--model_id_with_origin_paths "black-forest-labs/FLUX.1-dev:flux1-dev.safetensors,black-forest-labs/FLUX.1-dev:text_encoder/model.safetensors,black-forest-labs/FLUX.1-dev:text_encoder_2/,black-forest-labs/FLUX.1-dev:ae.safetensors" +``` + +如果您希望从本地文件加载模型，例如推理时 + +```python +model_configs=[ + ModelConfig(path="models/black-forest-labs/FLUX.1-dev/flux1-dev.safetensors"), + ModelConfig(path="models/black-forest-labs/FLUX.1-dev/text_encoder/model.safetensors"), + ModelConfig(path="models/black-forest-labs/FLUX.1-dev/text_encoder_2/"), + ModelConfig(path="models/black-forest-labs/FLUX.1-dev/ae.safetensors"), +] +``` + +那么训练时需设置为 + +```shell +--model_paths '[ + "models/black-forest-labs/FLUX.1-dev/flux1-dev.safetensors", + "models/black-forest-labs/FLUX.1-dev/text_encoder/model.safetensors", + "models/black-forest-labs/FLUX.1-dev/text_encoder_2/", + "models/black-forest-labs/FLUX.1-dev/ae.safetensors" +]' \ +``` + +
+ + +
+ +Step 3: 设置可训练模块 + +训练框架支持训练基础模型，或 LoRA 模型。以下是几个例子： + +* 全量训练 DiT 部分：`--trainable_models dit` +* 训练 DiT 部分的 LoRA 模型：`--lora_base_model dit --lora_target_modules "a_to_qkv,b_to_qkv,ff_a.0,ff_a.2,ff_b.0,ff_b.2,a_to_out,b_to_out,proj_out,norm.linear,norm1_a.linear,norm1_b.linear,to_qkv_mlp" --lora_rank 32` + +此外，由于训练脚本中加载了多个模块（text encoder、dit、vae），保存模型文件时需要移除前缀，例如在全量训练 DiT 部分或者训练 DiT 部分的 LoRA 模型时，请设置 `--remove_prefix_in_ckpt pipe.dit.` + +
+ + +
+ +Step 4: 启动训练程序 + +我们为每一个模型编写了训练命令，请参考本文档开头的表格。 + +
diff --git a/examples/flux/acceleration/teacache.py b/examples/flux/acceleration/teacache.py new file mode 100644 index 0000000000000000000000000000000000000000..a3253249880d0c930456019f6f6fc95c03c7ca31 --- /dev/null +++ b/examples/flux/acceleration/teacache.py @@ -0,0 +1,24 @@ +import torch +from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig + + +pipe = FluxImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-dev.safetensors"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors"), + ], +) + + +prompt = "CG, masterpiece, best quality, solo, long hair, wavy hair, silver hair, blue eyes, blue dress, medium breasts, dress, underwater, air bubble, floating hair, refraction, portrait. The girl's flowing silver hair shimmers with every color of the rainbow and cascades down, merging with the floating flora around her." + +for tea_cache_l1_thresh in [None, 0.2, 0.4, 0.6, 0.8]: + image = pipe( + prompt=prompt, embedded_guidance=3.5, seed=0, + num_inference_steps=50, tea_cache_l1_thresh=tea_cache_l1_thresh + ) + image.save(f"image_{tea_cache_l1_thresh}.png") diff --git a/examples/flux/model_inference/FLEX.2-preview.py b/examples/flux/model_inference/FLEX.2-preview.py new file mode 100644 index 0000000000000000000000000000000000000000..aa336415c66a57dfbd9105ccd9370d67594a2ae0 --- /dev/null +++ b/examples/flux/model_inference/FLEX.2-preview.py @@ -0,0 +1,50 @@ +import torch +from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig +from diffsynth.controlnets.processors import Annotator +import numpy as np +from PIL import Image + + +pipe = FluxImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="ostris/Flex.2-preview", origin_file_pattern="Flex.2-preview.safetensors"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors"), + ], +) + +image = pipe( + prompt="portrait of a beautiful Asian girl, long hair, red t-shirt, sunshine, beach", + num_inference_steps=50, embedded_guidance=3.5, + seed=0 +) +image.save(f"image_1.jpg") + +mask = np.zeros((1024, 1024, 3), dtype=np.uint8) +mask[200:400, 400:700] = 255 +mask = Image.fromarray(mask) +mask.save(f"image_mask.jpg") + +inpaint_image = image + +image = pipe( + prompt="portrait of a beautiful Asian girl with sunglasses, long hair, red t-shirt, sunshine, beach", + num_inference_steps=50, embedded_guidance=3.5, + flex_inpaint_image=inpaint_image, flex_inpaint_mask=mask, + seed=4 +) +image.save(f"image_2_new.jpg") + +control_image = Annotator("canny")(image) +control_image.save("image_control.jpg") + +image = pipe( + prompt="portrait of a beautiful Asian girl with sunglasses, long hair, yellow t-shirt, sunshine, beach", + num_inference_steps=50, embedded_guidance=3.5, + flex_control_image=control_image, + seed=4 +) +image.save(f"image_3_new.jpg") diff --git a/examples/flux/model_inference/FLUX.1-Kontext-dev.py b/examples/flux/model_inference/FLUX.1-Kontext-dev.py new file mode 100644 index 0000000000000000000000000000000000000000..3d0e92101571cfcfd122e6d761e9a7ca302feaa0 --- /dev/null +++ b/examples/flux/model_inference/FLUX.1-Kontext-dev.py @@ -0,0 +1,54 @@ +import torch +from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig +from PIL import Image + + +pipe = FluxImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="black-forest-labs/FLUX.1-Kontext-dev", origin_file_pattern="flux1-kontext-dev.safetensors"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors"), + ], +) + +image_1 = pipe( + prompt="a beautiful Asian long-haired female college student.", + embedded_guidance=2.5, + seed=1, +) +image_1.save("image_1.jpg") + +image_2 = pipe( + prompt="transform the style to anime style.", + kontext_images=image_1, + embedded_guidance=2.5, + seed=2, +) +image_2.save("image_2.jpg") + +image_3 = pipe( + prompt="let her smile.", + kontext_images=image_1, + embedded_guidance=2.5, + seed=3, +) +image_3.save("image_3.jpg") + +image_4 = pipe( + prompt="let the girl play basketball.", + kontext_images=image_1, + embedded_guidance=2.5, + seed=4, +) +image_4.save("image_4.jpg") + +image_5 = pipe( + prompt="move the girl to a park, let her sit on a chair.", + kontext_images=image_1, + embedded_guidance=2.5, + seed=5, +) +image_5.save("image_5.jpg") \ No newline at end of file diff --git a/examples/flux/model_inference/FLUX.1-Krea-dev.py b/examples/flux/model_inference/FLUX.1-Krea-dev.py new file mode 100644 index 0000000000000000000000000000000000000000..786ab3211dc162d11dfb3525ba278fb36e0f8d42 --- /dev/null +++ b/examples/flux/model_inference/FLUX.1-Krea-dev.py @@ -0,0 +1,27 @@ +import torch +from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig + + +pipe = FluxImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="black-forest-labs/FLUX.1-Krea-dev", origin_file_pattern="flux1-krea-dev.safetensors"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors"), + ], +) + +prompt = "An beautiful woman is riding a bicycle in a park, wearing a red dress" +negative_prompt = "worst quality, low quality, monochrome, zombie, interlocked fingers, Aissist, cleavage, nsfw," + +image = pipe(prompt=prompt, seed=0, embedded_guidance=4.5) +image.save("flux_krea.jpg") + +image = pipe( + prompt=prompt, negative_prompt=negative_prompt, + seed=0, cfg_scale=2, num_inference_steps=50, + embedded_guidance=4.5 +) +image.save("flux_krea_cfg.jpg") diff --git a/examples/flux/model_inference/FLUX.1-dev-AttriCtrl.py b/examples/flux/model_inference/FLUX.1-dev-AttriCtrl.py new file mode 100644 index 0000000000000000000000000000000000000000..6c8d87019e7ba417ef031e0e3df64b0b24daaa1d --- /dev/null +++ b/examples/flux/model_inference/FLUX.1-dev-AttriCtrl.py @@ -0,0 +1,19 @@ +import torch +from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig + + +pipe = FluxImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-dev.safetensors"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors"), + ModelConfig(model_id="DiffSynth-Studio/AttriCtrl-FLUX.1-Dev", origin_file_pattern="models/brightness.safetensors") + ], +) + +for i in [0.1, 0.3, 0.5, 0.7, 0.9]: + image = pipe(prompt="a cat on the beach", seed=2, value_controller_inputs=[i]) + image.save(f"value_control_{i}.jpg") diff --git a/examples/flux/model_inference/FLUX.1-dev-Controlnet-Inpainting-Beta.py b/examples/flux/model_inference/FLUX.1-dev-Controlnet-Inpainting-Beta.py new file mode 100644 index 0000000000000000000000000000000000000000..f003cc9be556118218b7dcd01ec18c1d48c80003 --- /dev/null +++ b/examples/flux/model_inference/FLUX.1-dev-Controlnet-Inpainting-Beta.py @@ -0,0 +1,37 @@ +import torch +from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig, ControlNetInput +import numpy as np +from PIL import Image + + +pipe = FluxImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-dev.safetensors"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors"), + ModelConfig(model_id="alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta", origin_file_pattern="diffusion_pytorch_model.safetensors"), + ], +) + +image_1 = pipe( + prompt="a cat sitting on a chair", + height=1024, width=1024, + seed=8, rand_device="cuda", +) +image_1.save("image_1.jpg") + +mask = np.zeros((1024, 1024, 3), dtype=np.uint8) +mask[100:350, 350: -300] = 255 +mask = Image.fromarray(mask) +mask.save("mask.jpg") + +image_2 = pipe( + prompt="a cat sitting on a chair, wearing sunglasses", + controlnet_inputs=[ControlNetInput(image=image_1, inpaint_mask=mask, scale=0.9)], + height=1024, width=1024, + seed=9, rand_device="cuda", +) +image_2.save("image_2.jpg") \ No newline at end of file diff --git a/examples/flux/model_inference/FLUX.1-dev-Controlnet-Union-alpha.py b/examples/flux/model_inference/FLUX.1-dev-Controlnet-Union-alpha.py new file mode 100644 index 0000000000000000000000000000000000000000..4164cc7153909d461858216924410bc8107ab226 --- /dev/null +++ b/examples/flux/model_inference/FLUX.1-dev-Controlnet-Union-alpha.py @@ -0,0 +1,40 @@ +import torch +from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig, ControlNetInput +from diffsynth.controlnets.processors import Annotator +from diffsynth import download_models + + + +download_models(["Annotators:Depth"]) +pipe = FluxImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-dev.safetensors"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors"), + ModelConfig(model_id="InstantX/FLUX.1-dev-Controlnet-Union-alpha", origin_file_pattern="diffusion_pytorch_model.safetensors"), + ], +) + +image_1 = pipe( + prompt="a beautiful Asian girl, full body, red dress, summer", + height=1024, width=1024, + seed=6, rand_device="cuda", +) +image_1.save("image_1.jpg") + +image_canny = Annotator("canny")(image_1) +image_depth = Annotator("depth")(image_1) + +image_2 = pipe( + prompt="a beautiful Asian girl, full body, red dress, winter", + controlnet_inputs=[ + ControlNetInput(image=image_canny, scale=0.3, processor_id="canny"), + ControlNetInput(image=image_depth, scale=0.3, processor_id="depth"), + ], + height=1024, width=1024, + seed=7, rand_device="cuda", +) +image_2.save("image_2.jpg") diff --git a/examples/flux/model_inference/FLUX.1-dev-Controlnet-Upscaler.py b/examples/flux/model_inference/FLUX.1-dev-Controlnet-Upscaler.py new file mode 100644 index 0000000000000000000000000000000000000000..2bd47d02493f48ab6d7fccff2c77335661100011 --- /dev/null +++ b/examples/flux/model_inference/FLUX.1-dev-Controlnet-Upscaler.py @@ -0,0 +1,33 @@ +import torch +from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig, ControlNetInput + + +pipe = FluxImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-dev.safetensors"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors"), + ModelConfig(model_id="jasperai/Flux.1-dev-Controlnet-Upscaler", origin_file_pattern="diffusion_pytorch_model.safetensors"), + ], +) + +image_1 = pipe( + prompt="a photo of a cat, highly detailed", + height=768, width=768, + seed=0, rand_device="cuda", +) +image_1.save("image_1.jpg") + +image_1 = image_1.resize((2048, 2048)) +image_2 = pipe( + prompt="a photo of a cat, highly detailed", + controlnet_inputs=[ControlNetInput(image=image_1, scale=0.7)], + input_image=image_1, + denoising_strength=0.99, + height=2048, width=2048, tiled=True, + seed=1, rand_device="cuda", +) +image_2.save("image_2.jpg") \ No newline at end of file diff --git a/examples/flux/model_inference/FLUX.1-dev-EliGen.py b/examples/flux/model_inference/FLUX.1-dev-EliGen.py new file mode 100644 index 0000000000000000000000000000000000000000..380cd071564bc4f67eede250a168d3b1a71d512c --- /dev/null +++ b/examples/flux/model_inference/FLUX.1-dev-EliGen.py @@ -0,0 +1,147 @@ +import random +import torch +from PIL import Image, ImageDraw, ImageFont +from diffsynth import download_customized_models +from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +def visualize_masks(image, masks, mask_prompts, output_path, font_size=35, use_random_colors=False): + # Create a blank image for overlays + overlay = Image.new('RGBA', image.size, (0, 0, 0, 0)) + + colors = [ + (165, 238, 173, 80), + (76, 102, 221, 80), + (221, 160, 77, 80), + (204, 93, 71, 80), + (145, 187, 149, 80), + (134, 141, 172, 80), + (157, 137, 109, 80), + (153, 104, 95, 80), + (165, 238, 173, 80), + (76, 102, 221, 80), + (221, 160, 77, 80), + (204, 93, 71, 80), + (145, 187, 149, 80), + (134, 141, 172, 80), + (157, 137, 109, 80), + (153, 104, 95, 80), + ] + # Generate random colors for each mask + if use_random_colors: + colors = [(random.randint(0, 255), random.randint(0, 255), random.randint(0, 255), 80) for _ in range(len(masks))] + + # Font settings + try: + font = ImageFont.truetype("arial", font_size) # Adjust as needed + except IOError: + font = ImageFont.load_default(font_size) + + # Overlay each mask onto the overlay image + for mask, mask_prompt, color in zip(masks, mask_prompts, colors): + # Convert mask to RGBA mode + mask_rgba = mask.convert('RGBA') + mask_data = mask_rgba.getdata() + new_data = [(color if item[:3] == (255, 255, 255) else (0, 0, 0, 0)) for item in mask_data] + mask_rgba.putdata(new_data) + + # Draw the mask prompt text on the mask + draw = ImageDraw.Draw(mask_rgba) + mask_bbox = mask.getbbox() # Get the bounding box of the mask + text_position = (mask_bbox[0] + 10, mask_bbox[1] + 10) # Adjust text position based on mask position + draw.text(text_position, mask_prompt, fill=(255, 255, 255, 255), font=font) + + # Alpha composite the overlay with this mask + overlay = Image.alpha_composite(overlay, mask_rgba) + + # Composite the overlay onto the original image + result = Image.alpha_composite(image.convert('RGBA'), overlay) + + # Save or display the resulting image + result.save(output_path) + + return result + +def example(pipe, seeds, example_id, global_prompt, entity_prompts): + dataset_snapshot_download(dataset_id="DiffSynth-Studio/examples_in_diffsynth", local_dir="./", allow_file_pattern=f"data/examples/eligen/entity_control/example_{example_id}/*.png") + masks = [Image.open(f"./data/examples/eligen/entity_control/example_{example_id}/{i}.png").convert('RGB') for i in range(len(entity_prompts))] + negative_prompt = "worst quality, low quality, monochrome, zombie, interlocked fingers, Aissist, cleavage, nsfw," + for seed in seeds: + # generate image + image = pipe( + prompt=global_prompt, + cfg_scale=3.0, + negative_prompt=negative_prompt, + num_inference_steps=50, + embedded_guidance=3.5, + seed=seed, + height=1024, + width=1024, + eligen_entity_prompts=entity_prompts, + eligen_entity_masks=masks, + ) + image.save(f"eligen_example_{example_id}_{seed}.png") + visualize_masks(image, masks, entity_prompts, f"eligen_example_{example_id}_mask_{seed}.png") + + +pipe = FluxImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-dev.safetensors"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors"), + ], +) + +download_from_modelscope = True +if download_from_modelscope: + model_id = "DiffSynth-Studio/Eligen" + downloading_priority = ["ModelScope"] +else: + model_id = "modelscope/EliGen" + downloading_priority = ["HuggingFace"] +EliGen_path = download_customized_models( + model_id=model_id, + origin_file_path="model_bf16.safetensors", + local_dir="models/lora/entity_control", + downloading_priority=downloading_priority)[0] +pipe.load_lora(pipe.dit, EliGen_path, alpha=1) + +# example 1 +global_prompt = "A breathtaking beauty of Raja Ampat by the late-night moonlight , one beautiful woman from behind wearing a pale blue long dress with soft glow, sitting at the top of a cliff looking towards the beach,pastell light colors, a group of small distant birds flying in far sky, a boat sailing on the sea, best quality, realistic, whimsical, fantastic, splash art, intricate detailed, hyperdetailed, maximalist style, photorealistic, concept art, sharp focus, harmony, serenity, tranquility, soft pastell colors,ambient occlusion, cozy ambient lighting, masterpiece, liiv1, linquivera, metix, mentixis, masterpiece, award winning, view from above\n" +entity_prompts = ["cliff", "sea", "moon", "sailing boat", "a seated beautiful woman", "pale blue long dress with soft glow"] +example(pipe, [0], 1, global_prompt, entity_prompts) + +# example 2 +global_prompt = "samurai girl wearing a kimono, she's holding a sword glowing with red flame, her long hair is flowing in the wind, she is looking at a small bird perched on the back of her hand. ultra realist style. maximum image detail. maximum realistic render." +entity_prompts = ["flowing hair", "sword glowing with red flame", "A cute bird", "blue belt"] +example(pipe, [0], 2, global_prompt, entity_prompts) + +# example 3 +global_prompt = "Image of a neverending staircase up to a mysterious palace in the sky, The ancient palace stood majestically atop a mist-shrouded mountain, sunrise, two traditional monk walk in the stair looking at the sunrise, fog,see-through, best quality, whimsical, fantastic, splash art, intricate detailed, hyperdetailed, photorealistic, concept art, harmony, serenity, tranquility, ambient occlusion, halation, cozy ambient lighting, dynamic lighting,masterpiece, liiv1, linquivera, metix, mentixis, masterpiece, award winning," +entity_prompts = ["ancient palace", "stone staircase with railings", "a traditional monk", "a traditional monk"] +example(pipe, [27], 3, global_prompt, entity_prompts) + +# example 4 +global_prompt = "A beautiful girl wearing shirt and shorts in the street, holding a sign 'Entity Control'" +entity_prompts = ["A beautiful girl", "sign 'Entity Control'", "shorts", "shirt"] +example(pipe, [21], 4, global_prompt, entity_prompts) + +# example 5 +global_prompt = "A captivating, dramatic scene in a painting that exudes mystery and foreboding. A white sky, swirling blue clouds, and a crescent yellow moon illuminate a solitary woman standing near the water's edge. Her long dress flows in the wind, silhouetted against the eerie glow. The water mirrors the fiery sky and moonlight, amplifying the uneasy atmosphere." +entity_prompts = ["crescent yellow moon", "a solitary woman", "water", "swirling blue clouds"] +example(pipe, [0], 5, global_prompt, entity_prompts) + +# example 6 +global_prompt = "Snow White and the 6 Dwarfs." +entity_prompts = ["Dwarf 1", "Dwarf 2", "Dwarf 3", "Snow White", "Dwarf 4", "Dwarf 5", "Dwarf 6"] +example(pipe, [8], 6, global_prompt, entity_prompts) + +# example 7, same prompt with different seeds +seeds = range(5, 9) +global_prompt = "A beautiful woman wearing white dress, holding a mirror, with a warm light background;" +entity_prompts = ["A beautiful woman", "mirror", "necklace", "glasses", "earring", "white dress", "jewelry headpiece"] +example(pipe, seeds, 7, global_prompt, entity_prompts) diff --git a/examples/flux/model_inference/FLUX.1-dev-IP-Adapter.py b/examples/flux/model_inference/FLUX.1-dev-IP-Adapter.py new file mode 100644 index 0000000000000000000000000000000000000000..e70301fe76771bde8d1d7773f4283b1a3b5dcdd0 --- /dev/null +++ b/examples/flux/model_inference/FLUX.1-dev-IP-Adapter.py @@ -0,0 +1,24 @@ +import torch +from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig + + +pipe = FluxImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-dev.safetensors"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors"), + ModelConfig(model_id="InstantX/FLUX.1-dev-IP-Adapter", origin_file_pattern="ip-adapter.bin"), + ModelConfig(model_id="google/siglip-so400m-patch14-384"), + ], +) + +origin_prompt = "a rabbit in a garden, colorful flowers" +image = pipe(prompt=origin_prompt, height=1280, width=960, seed=42) +image.save("style image.jpg") + +image = pipe(prompt="A piggy", height=1280, width=960, seed=42, + ipadapter_images=[image], ipadapter_scale=0.7) +image.save("A piggy.jpg") diff --git a/examples/flux/model_inference/FLUX.1-dev-InfiniteYou.py b/examples/flux/model_inference/FLUX.1-dev-InfiniteYou.py new file mode 100644 index 0000000000000000000000000000000000000000..53b62b3bbb987991d3d4633e7a22d117b1cbcb53 --- /dev/null +++ b/examples/flux/model_inference/FLUX.1-dev-InfiniteYou.py @@ -0,0 +1,59 @@ +import torch +from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig, ControlNetInput +from modelscope import dataset_snapshot_download +from modelscope import snapshot_download +from PIL import Image +import numpy as np + + +snapshot_download( + "ByteDance/InfiniteYou", + allow_file_pattern="supports/insightface/models/antelopev2/*", + local_dir="models/ByteDance/InfiniteYou", +) +pipe = FluxImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-dev.safetensors"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors"), + ModelConfig(model_id="ByteDance/InfiniteYou", origin_file_pattern="infu_flux_v1.0/aes_stage2/image_proj_model.bin"), + ModelConfig(model_id="ByteDance/InfiniteYou", origin_file_pattern="infu_flux_v1.0/aes_stage2/InfuseNetModel/*.safetensors"), + ], +) + +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/examples_in_diffsynth", + local_dir="./", + allow_file_pattern=f"data/examples/infiniteyou/*", +) + +height, width = 1024, 1024 +controlnet_image = Image.fromarray(np.zeros([height, width, 3]).astype(np.uint8)) +controlnet_inputs = [ControlNetInput(image=controlnet_image, scale=1.0, processor_id="None")] + +prompt = "A man, portrait, cinematic" +id_image = "data/examples/infiniteyou/man.jpg" +id_image = Image.open(id_image).convert('RGB') +image = pipe( + prompt=prompt, seed=1, + infinityou_id_image=id_image, infinityou_guidance=1.0, + controlnet_inputs=controlnet_inputs, + num_inference_steps=50, embedded_guidance=3.5, + height=height, width=width, +) +image.save("man.jpg") + +prompt = "A woman, portrait, cinematic" +id_image = "data/examples/infiniteyou/woman.jpg" +id_image = Image.open(id_image).convert('RGB') +image = pipe( + prompt=prompt, seed=1, + infinityou_id_image=id_image, infinityou_guidance=1.0, + controlnet_inputs=controlnet_inputs, + num_inference_steps=50, embedded_guidance=3.5, + height=height, width=width, +) +image.save("woman.jpg") \ No newline at end of file diff --git a/examples/flux/model_inference/FLUX.1-dev-LoRA-Encoder.py b/examples/flux/model_inference/FLUX.1-dev-LoRA-Encoder.py new file mode 100644 index 0000000000000000000000000000000000000000..d1330249e5123793eda236c7e3cc0314e2f7543c --- /dev/null +++ b/examples/flux/model_inference/FLUX.1-dev-LoRA-Encoder.py @@ -0,0 +1,40 @@ +import torch +from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig + + +pipe = FluxImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-dev.safetensors"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors"), + ModelConfig(model_id="DiffSynth-Studio/LoRA-Encoder-FLUX.1-Dev", origin_file_pattern="model.safetensors"), + ], +) +pipe.enable_lora_magic() + +lora = ModelConfig(model_id="VoidOc/flux_animal_forest1", origin_file_pattern="20.safetensors") +pipe.load_lora(pipe.dit, lora, hotload=True) # Use `pipe.clear_lora()` to drop the loaded LoRA. + +# Empty prompt can automatically activate LoRA capabilities. +image = pipe(prompt="", seed=0, lora_encoder_inputs=lora) +image.save("image_1.jpg") + +image = pipe(prompt="", seed=0) +image.save("image_1_origin.jpg") + +# Prompt without trigger words can also activate LoRA capabilities. +image = pipe(prompt="a car", seed=0, lora_encoder_inputs=lora) +image.save("image_2.jpg") + +image = pipe(prompt="a car", seed=0,) +image.save("image_2_origin.jpg") + +# Adjust the activation intensity through the scale parameter. +image = pipe(prompt="a cat", seed=0, lora_encoder_inputs=lora, lora_encoder_scale=1.0) +image.save("image_3.jpg") + +image = pipe(prompt="a cat", seed=0, lora_encoder_inputs=lora, lora_encoder_scale=0.5) +image.save("image_3_scale.jpg") diff --git a/examples/flux/model_inference/FLUX.1-dev-LoRA-Fusion.py b/examples/flux/model_inference/FLUX.1-dev-LoRA-Fusion.py new file mode 100644 index 0000000000000000000000000000000000000000..69b20db49b1724c6b65276d6415f09b6a890124e --- /dev/null +++ b/examples/flux/model_inference/FLUX.1-dev-LoRA-Fusion.py @@ -0,0 +1,29 @@ +import torch +from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig + + +pipe = FluxImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-dev.safetensors"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors"), + ModelConfig(model_id="DiffSynth-Studio/LoRAFusion-preview-FLUX.1-dev", origin_file_pattern="model.safetensors"), + ], +) +pipe.enable_lora_magic() + +pipe.load_lora( + pipe.dit, + ModelConfig(model_id="cancel13/cxsk", origin_file_pattern="30.safetensors"), + hotload=True, +) +pipe.load_lora( + pipe.dit, + ModelConfig(model_id="DiffSynth-Studio/ArtAug-lora-FLUX.1dev-v1", origin_file_pattern="merged_lora.safetensors"), + hotload=True, +) +image = pipe(prompt="a cat", seed=0) +image.save("image_fused.jpg") diff --git a/examples/flux/model_inference/FLUX.1-dev.py b/examples/flux/model_inference/FLUX.1-dev.py new file mode 100644 index 0000000000000000000000000000000000000000..d1544600371b24fc28d4106e74fbe9b78194ecec --- /dev/null +++ b/examples/flux/model_inference/FLUX.1-dev.py @@ -0,0 +1,26 @@ +import torch +from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig + + +pipe = FluxImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-dev.safetensors"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors"), + ], +) + +prompt = "CG, masterpiece, best quality, solo, long hair, wavy hair, silver hair, blue eyes, blue dress, medium breasts, dress, underwater, air bubble, floating hair, refraction, portrait. The girl's flowing silver hair shimmers with every color of the rainbow and cascades down, merging with the floating flora around her." +negative_prompt = "worst quality, low quality, monochrome, zombie, interlocked fingers, Aissist, cleavage, nsfw," + +image = pipe(prompt=prompt, seed=0) +image.save("flux.jpg") + +image = pipe( + prompt=prompt, negative_prompt=negative_prompt, + seed=0, cfg_scale=2, num_inference_steps=50, +) +image.save("flux_cfg.jpg") diff --git a/examples/flux/model_inference/Nexus-Gen-Editing.py b/examples/flux/model_inference/Nexus-Gen-Editing.py new file mode 100644 index 0000000000000000000000000000000000000000..10351d509f2ee29d95c80598547ed270f3e32737 --- /dev/null +++ b/examples/flux/model_inference/Nexus-Gen-Editing.py @@ -0,0 +1,37 @@ +import importlib +import torch +from PIL import Image +from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +if importlib.util.find_spec("transformers") is None: + raise ImportError("You are using Nexus-GenV2. It depends on transformers, which is not installed. Please install it with `pip install transformers==4.49.0`.") +else: + import transformers + assert transformers.__version__ == "4.49.0", "Nexus-GenV2 requires transformers==4.49.0, please install it with `pip install transformers==4.49.0`." + + +pipe = FluxImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="DiffSynth-Studio/Nexus-GenV2", origin_file_pattern="model*.safetensors"), + ModelConfig(model_id="DiffSynth-Studio/Nexus-GenV2", origin_file_pattern="edit_decoder.bin"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors"), + ], + nexus_gen_processor_config=ModelConfig(model_id="DiffSynth-Studio/Nexus-GenV2", origin_file_pattern="processor/"), +) + +dataset_snapshot_download(dataset_id="DiffSynth-Studio/examples_in_diffsynth", local_dir="./", allow_file_pattern=f"data/examples/nexusgen/cat.jpg") +ref_image = Image.open("data/examples/nexusgen/cat.jpg").convert("RGB") +prompt = "Add a crown." +image = pipe( + prompt=prompt, negative_prompt="", + seed=42, cfg_scale=2.0, num_inference_steps=50, + nexus_gen_reference_image=ref_image, + height=512, width=512, +) +image.save("cat_crown.jpg") diff --git a/examples/flux/model_inference/Nexus-Gen-Generation.py b/examples/flux/model_inference/Nexus-Gen-Generation.py new file mode 100644 index 0000000000000000000000000000000000000000..dfe6880d945cc5810b01bc4d1e7606c31091fe76 --- /dev/null +++ b/examples/flux/model_inference/Nexus-Gen-Generation.py @@ -0,0 +1,32 @@ +import importlib +import torch +from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig + + +if importlib.util.find_spec("transformers") is None: + raise ImportError("You are using Nexus-GenV2. It depends on transformers, which is not installed. Please install it with `pip install transformers==4.49.0`.") +else: + import transformers + assert transformers.__version__ == "4.49.0", "Nexus-GenV2 requires transformers==4.49.0, please install it with `pip install transformers==4.49.0`." + + +pipe = FluxImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="DiffSynth-Studio/Nexus-GenV2", origin_file_pattern="model*.safetensors"), + ModelConfig(model_id="DiffSynth-Studio/Nexus-GenV2", origin_file_pattern="generation_decoder.bin"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors"), + ], + nexus_gen_processor_config=ModelConfig("DiffSynth-Studio/Nexus-GenV2", origin_file_pattern="processor"), +) + +prompt = "一只可爱的猫咪" +image = pipe( + prompt=prompt, negative_prompt="", + seed=0, cfg_scale=3, num_inference_steps=50, + height=1024, width=1024, +) +image.save("cat.jpg") diff --git a/examples/flux/model_inference/Step1X-Edit.py b/examples/flux/model_inference/Step1X-Edit.py new file mode 100644 index 0000000000000000000000000000000000000000..3767acc8530162bc168ac6cffd7d9fb6f0798028 --- /dev/null +++ b/examples/flux/model_inference/Step1X-Edit.py @@ -0,0 +1,32 @@ +import torch +from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig +from PIL import Image +import numpy as np + + +pipe = FluxImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen2.5-VL-7B-Instruct"), + ModelConfig(model_id="stepfun-ai/Step1X-Edit", origin_file_pattern="step1x-edit-i1258.safetensors"), + ModelConfig(model_id="stepfun-ai/Step1X-Edit", origin_file_pattern="vae.safetensors"), + ], +) + +image = Image.fromarray(np.zeros((1248, 832, 3), dtype=np.uint8) + 255) +image = pipe( + prompt="draw red flowers in Chinese ink painting style", + step1x_reference_image=image, + width=832, height=1248, cfg_scale=6, + seed=1, rand_device='cuda' +) +image.save("image_1.jpg") + +image = pipe( + prompt="add more flowers in Chinese ink painting style", + step1x_reference_image=image, + width=832, height=1248, cfg_scale=6, + seed=2, rand_device='cuda' +) +image.save("image_2.jpg") diff --git a/examples/flux/model_inference_low_vram/FLEX.2-preview.py b/examples/flux/model_inference_low_vram/FLEX.2-preview.py new file mode 100644 index 0000000000000000000000000000000000000000..e94cc98b15aac74961227eeeb998b6a6eea04a49 --- /dev/null +++ b/examples/flux/model_inference_low_vram/FLEX.2-preview.py @@ -0,0 +1,51 @@ +import torch +from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig +from diffsynth.controlnets.processors import Annotator +import numpy as np +from PIL import Image + + +pipe = FluxImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="ostris/Flex.2-preview", origin_file_pattern="Flex.2-preview.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ], +) +pipe.enable_vram_management() + +image = pipe( + prompt="portrait of a beautiful Asian girl, long hair, red t-shirt, sunshine, beach", + num_inference_steps=50, embedded_guidance=3.5, + seed=0 +) +image.save(f"image_1.jpg") + +mask = np.zeros((1024, 1024, 3), dtype=np.uint8) +mask[200:400, 400:700] = 255 +mask = Image.fromarray(mask) +mask.save(f"image_mask.jpg") + +inpaint_image = image + +image = pipe( + prompt="portrait of a beautiful Asian girl with sunglasses, long hair, red t-shirt, sunshine, beach", + num_inference_steps=50, embedded_guidance=3.5, + flex_inpaint_image=inpaint_image, flex_inpaint_mask=mask, + seed=4 +) +image.save(f"image_2_new.jpg") + +control_image = Annotator("canny")(image) +control_image.save("image_control.jpg") + +image = pipe( + prompt="portrait of a beautiful Asian girl with sunglasses, long hair, yellow t-shirt, sunshine, beach", + num_inference_steps=50, embedded_guidance=3.5, + flex_control_image=control_image, + seed=4 +) +image.save(f"image_3_new.jpg") diff --git a/examples/flux/model_inference_low_vram/FLUX.1-Kontext-dev.py b/examples/flux/model_inference_low_vram/FLUX.1-Kontext-dev.py new file mode 100644 index 0000000000000000000000000000000000000000..c36c0dda17a8f8fa4540758a6f66be9a2fc91cf3 --- /dev/null +++ b/examples/flux/model_inference_low_vram/FLUX.1-Kontext-dev.py @@ -0,0 +1,55 @@ +import torch +from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig +from PIL import Image + + +pipe = FluxImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="black-forest-labs/FLUX.1-Kontext-dev", origin_file_pattern="flux1-kontext-dev.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ], +) +pipe.enable_vram_management() + +image_1 = pipe( + prompt="a beautiful Asian long-haired female college student.", + embedded_guidance=2.5, + seed=1, +) +image_1.save("image_1.jpg") + +image_2 = pipe( + prompt="transform the style to anime style.", + kontext_images=image_1, + embedded_guidance=2.5, + seed=2, +) +image_2.save("image_2.jpg") + +image_3 = pipe( + prompt="let her smile.", + kontext_images=image_1, + embedded_guidance=2.5, + seed=3, +) +image_3.save("image_3.jpg") + +image_4 = pipe( + prompt="let the girl play basketball.", + kontext_images=image_1, + embedded_guidance=2.5, + seed=4, +) +image_4.save("image_4.jpg") + +image_5 = pipe( + prompt="move the girl to a park, let her sit on a chair.", + kontext_images=image_1, + embedded_guidance=2.5, + seed=5, +) +image_5.save("image_5.jpg") \ No newline at end of file diff --git a/examples/flux/model_inference_low_vram/FLUX.1-Krea-dev.py b/examples/flux/model_inference_low_vram/FLUX.1-Krea-dev.py new file mode 100644 index 0000000000000000000000000000000000000000..96f8414c509a35834fcd67c21ae27a3229a16f96 --- /dev/null +++ b/examples/flux/model_inference_low_vram/FLUX.1-Krea-dev.py @@ -0,0 +1,28 @@ +import torch +from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig + + +pipe = FluxImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="black-forest-labs/FLUX.1-Krea-dev", origin_file_pattern="flux1-krea-dev.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ], +) +pipe.enable_vram_management() + +prompt = "An beautiful woman is riding a bicycle in a park, wearing a red dress" +negative_prompt = "worst quality, low quality, monochrome, zombie, interlocked fingers, Aissist, cleavage, nsfw," + +image = pipe(prompt=prompt, seed=0, embedded_guidance=4.5) +image.save("flux_krea.jpg") + +image = pipe( + prompt=prompt, negative_prompt=negative_prompt, + seed=0, cfg_scale=2, num_inference_steps=50, + embedded_guidance=4.5 +) +image.save("flux_krea_cfg.jpg") diff --git a/examples/flux/model_inference_low_vram/FLUX.1-dev-AttriCtrl.py b/examples/flux/model_inference_low_vram/FLUX.1-dev-AttriCtrl.py new file mode 100644 index 0000000000000000000000000000000000000000..52edf2c21996bdfe82be2bad9a7aac4edcb60110 --- /dev/null +++ b/examples/flux/model_inference_low_vram/FLUX.1-dev-AttriCtrl.py @@ -0,0 +1,20 @@ +import torch +from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig + + +pipe = FluxImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-dev.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="DiffSynth-Studio/AttriCtrl-FLUX.1-Dev", origin_file_pattern="models/brightness.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn) + ], +) +pipe.enable_vram_management() + +for i in [0.1, 0.3, 0.5, 0.7, 0.9]: + image = pipe(prompt="a cat on the beach", seed=2, value_controller_inputs=[i]) + image.save(f"value_control_{i}.jpg") diff --git a/examples/flux/model_inference_low_vram/FLUX.1-dev-Controlnet-Inpainting-Beta.py b/examples/flux/model_inference_low_vram/FLUX.1-dev-Controlnet-Inpainting-Beta.py new file mode 100644 index 0000000000000000000000000000000000000000..2dcc19072b5be5f296cba3ae9d7338b6b179b36f --- /dev/null +++ b/examples/flux/model_inference_low_vram/FLUX.1-dev-Controlnet-Inpainting-Beta.py @@ -0,0 +1,38 @@ +import torch +from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig, ControlNetInput +import numpy as np +from PIL import Image + + +pipe = FluxImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-dev.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta", origin_file_pattern="diffusion_pytorch_model.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ], +) +pipe.enable_vram_management() + +image_1 = pipe( + prompt="a cat sitting on a chair", + height=1024, width=1024, + seed=8, rand_device="cuda", +) +image_1.save("image_1.jpg") + +mask = np.zeros((1024, 1024, 3), dtype=np.uint8) +mask[100:350, 350: -300] = 255 +mask = Image.fromarray(mask) +mask.save("mask.jpg") + +image_2 = pipe( + prompt="a cat sitting on a chair, wearing sunglasses", + controlnet_inputs=[ControlNetInput(image=image_1, inpaint_mask=mask, scale=0.9)], + height=1024, width=1024, + seed=9, rand_device="cuda", +) +image_2.save("image_2.jpg") \ No newline at end of file diff --git a/examples/flux/model_inference_low_vram/FLUX.1-dev-Controlnet-Union-alpha.py b/examples/flux/model_inference_low_vram/FLUX.1-dev-Controlnet-Union-alpha.py new file mode 100644 index 0000000000000000000000000000000000000000..62eeee0b29aec9ff8c6ef1d94486df4bc04c7f6d --- /dev/null +++ b/examples/flux/model_inference_low_vram/FLUX.1-dev-Controlnet-Union-alpha.py @@ -0,0 +1,41 @@ +import torch +from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig, ControlNetInput +from diffsynth.controlnets.processors import Annotator +from diffsynth import download_models + + + +download_models(["Annotators:Depth"]) +pipe = FluxImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-dev.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="InstantX/FLUX.1-dev-Controlnet-Union-alpha", origin_file_pattern="diffusion_pytorch_model.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ], +) +pipe.enable_vram_management() + +image_1 = pipe( + prompt="a beautiful Asian girl, full body, red dress, summer", + height=1024, width=1024, + seed=6, rand_device="cuda", +) +image_1.save("image_1.jpg") + +image_canny = Annotator("canny")(image_1) +image_depth = Annotator("depth")(image_1) + +image_2 = pipe( + prompt="a beautiful Asian girl, full body, red dress, winter", + controlnet_inputs=[ + ControlNetInput(image=image_canny, scale=0.3, processor_id="canny"), + ControlNetInput(image=image_depth, scale=0.3, processor_id="depth"), + ], + height=1024, width=1024, + seed=7, rand_device="cuda", +) +image_2.save("image_2.jpg") diff --git a/examples/flux/model_inference_low_vram/FLUX.1-dev-Controlnet-Upscaler.py b/examples/flux/model_inference_low_vram/FLUX.1-dev-Controlnet-Upscaler.py new file mode 100644 index 0000000000000000000000000000000000000000..58c3b9d8ed1ba8815dc17e5c00ca0af67a3b629a --- /dev/null +++ b/examples/flux/model_inference_low_vram/FLUX.1-dev-Controlnet-Upscaler.py @@ -0,0 +1,34 @@ +import torch +from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig, ControlNetInput + + +pipe = FluxImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-dev.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="jasperai/Flux.1-dev-Controlnet-Upscaler", origin_file_pattern="diffusion_pytorch_model.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ], +) +pipe.enable_vram_management() + +image_1 = pipe( + prompt="a photo of a cat, highly detailed", + height=768, width=768, + seed=0, rand_device="cuda", +) +image_1.save("image_1.jpg") + +image_1 = image_1.resize((2048, 2048)) +image_2 = pipe( + prompt="a photo of a cat, highly detailed", + controlnet_inputs=[ControlNetInput(image=image_1, scale=0.7)], + input_image=image_1, + denoising_strength=0.99, + height=2048, width=2048, tiled=True, + seed=1, rand_device="cuda", +) +image_2.save("image_2.jpg") \ No newline at end of file diff --git a/examples/flux/model_inference_low_vram/FLUX.1-dev-EliGen.py b/examples/flux/model_inference_low_vram/FLUX.1-dev-EliGen.py new file mode 100644 index 0000000000000000000000000000000000000000..55b4b27a642409fd7e498a12167804496e40d1c8 --- /dev/null +++ b/examples/flux/model_inference_low_vram/FLUX.1-dev-EliGen.py @@ -0,0 +1,148 @@ +import random +import torch +from PIL import Image, ImageDraw, ImageFont +from diffsynth import download_customized_models +from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +def visualize_masks(image, masks, mask_prompts, output_path, font_size=35, use_random_colors=False): + # Create a blank image for overlays + overlay = Image.new('RGBA', image.size, (0, 0, 0, 0)) + + colors = [ + (165, 238, 173, 80), + (76, 102, 221, 80), + (221, 160, 77, 80), + (204, 93, 71, 80), + (145, 187, 149, 80), + (134, 141, 172, 80), + (157, 137, 109, 80), + (153, 104, 95, 80), + (165, 238, 173, 80), + (76, 102, 221, 80), + (221, 160, 77, 80), + (204, 93, 71, 80), + (145, 187, 149, 80), + (134, 141, 172, 80), + (157, 137, 109, 80), + (153, 104, 95, 80), + ] + # Generate random colors for each mask + if use_random_colors: + colors = [(random.randint(0, 255), random.randint(0, 255), random.randint(0, 255), 80) for _ in range(len(masks))] + + # Font settings + try: + font = ImageFont.truetype("arial", font_size) # Adjust as needed + except IOError: + font = ImageFont.load_default(font_size) + + # Overlay each mask onto the overlay image + for mask, mask_prompt, color in zip(masks, mask_prompts, colors): + # Convert mask to RGBA mode + mask_rgba = mask.convert('RGBA') + mask_data = mask_rgba.getdata() + new_data = [(color if item[:3] == (255, 255, 255) else (0, 0, 0, 0)) for item in mask_data] + mask_rgba.putdata(new_data) + + # Draw the mask prompt text on the mask + draw = ImageDraw.Draw(mask_rgba) + mask_bbox = mask.getbbox() # Get the bounding box of the mask + text_position = (mask_bbox[0] + 10, mask_bbox[1] + 10) # Adjust text position based on mask position + draw.text(text_position, mask_prompt, fill=(255, 255, 255, 255), font=font) + + # Alpha composite the overlay with this mask + overlay = Image.alpha_composite(overlay, mask_rgba) + + # Composite the overlay onto the original image + result = Image.alpha_composite(image.convert('RGBA'), overlay) + + # Save or display the resulting image + result.save(output_path) + + return result + +def example(pipe, seeds, example_id, global_prompt, entity_prompts): + dataset_snapshot_download(dataset_id="DiffSynth-Studio/examples_in_diffsynth", local_dir="./", allow_file_pattern=f"data/examples/eligen/entity_control/example_{example_id}/*.png") + masks = [Image.open(f"./data/examples/eligen/entity_control/example_{example_id}/{i}.png").convert('RGB') for i in range(len(entity_prompts))] + negative_prompt = "worst quality, low quality, monochrome, zombie, interlocked fingers, Aissist, cleavage, nsfw," + for seed in seeds: + # generate image + image = pipe( + prompt=global_prompt, + cfg_scale=3.0, + negative_prompt=negative_prompt, + num_inference_steps=50, + embedded_guidance=3.5, + seed=seed, + height=1024, + width=1024, + eligen_entity_prompts=entity_prompts, + eligen_entity_masks=masks, + ) + image.save(f"eligen_example_{example_id}_{seed}.png") + visualize_masks(image, masks, entity_prompts, f"eligen_example_{example_id}_mask_{seed}.png") + + +pipe = FluxImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-dev.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ], +) +pipe.enable_vram_management() + +download_from_modelscope = True +if download_from_modelscope: + model_id = "DiffSynth-Studio/Eligen" + downloading_priority = ["ModelScope"] +else: + model_id = "modelscope/EliGen" + downloading_priority = ["HuggingFace"] +EliGen_path = download_customized_models( + model_id=model_id, + origin_file_path="model_bf16.safetensors", + local_dir="models/lora/entity_control", + downloading_priority=downloading_priority)[0] +pipe.load_lora(pipe.dit, EliGen_path, alpha=1) + +# example 1 +global_prompt = "A breathtaking beauty of Raja Ampat by the late-night moonlight , one beautiful woman from behind wearing a pale blue long dress with soft glow, sitting at the top of a cliff looking towards the beach,pastell light colors, a group of small distant birds flying in far sky, a boat sailing on the sea, best quality, realistic, whimsical, fantastic, splash art, intricate detailed, hyperdetailed, maximalist style, photorealistic, concept art, sharp focus, harmony, serenity, tranquility, soft pastell colors,ambient occlusion, cozy ambient lighting, masterpiece, liiv1, linquivera, metix, mentixis, masterpiece, award winning, view from above\n" +entity_prompts = ["cliff", "sea", "moon", "sailing boat", "a seated beautiful woman", "pale blue long dress with soft glow"] +example(pipe, [0], 1, global_prompt, entity_prompts) + +# example 2 +global_prompt = "samurai girl wearing a kimono, she's holding a sword glowing with red flame, her long hair is flowing in the wind, she is looking at a small bird perched on the back of her hand. ultra realist style. maximum image detail. maximum realistic render." +entity_prompts = ["flowing hair", "sword glowing with red flame", "A cute bird", "blue belt"] +example(pipe, [0], 2, global_prompt, entity_prompts) + +# example 3 +global_prompt = "Image of a neverending staircase up to a mysterious palace in the sky, The ancient palace stood majestically atop a mist-shrouded mountain, sunrise, two traditional monk walk in the stair looking at the sunrise, fog,see-through, best quality, whimsical, fantastic, splash art, intricate detailed, hyperdetailed, photorealistic, concept art, harmony, serenity, tranquility, ambient occlusion, halation, cozy ambient lighting, dynamic lighting,masterpiece, liiv1, linquivera, metix, mentixis, masterpiece, award winning," +entity_prompts = ["ancient palace", "stone staircase with railings", "a traditional monk", "a traditional monk"] +example(pipe, [27], 3, global_prompt, entity_prompts) + +# example 4 +global_prompt = "A beautiful girl wearing shirt and shorts in the street, holding a sign 'Entity Control'" +entity_prompts = ["A beautiful girl", "sign 'Entity Control'", "shorts", "shirt"] +example(pipe, [21], 4, global_prompt, entity_prompts) + +# example 5 +global_prompt = "A captivating, dramatic scene in a painting that exudes mystery and foreboding. A white sky, swirling blue clouds, and a crescent yellow moon illuminate a solitary woman standing near the water's edge. Her long dress flows in the wind, silhouetted against the eerie glow. The water mirrors the fiery sky and moonlight, amplifying the uneasy atmosphere." +entity_prompts = ["crescent yellow moon", "a solitary woman", "water", "swirling blue clouds"] +example(pipe, [0], 5, global_prompt, entity_prompts) + +# example 6 +global_prompt = "Snow White and the 6 Dwarfs." +entity_prompts = ["Dwarf 1", "Dwarf 2", "Dwarf 3", "Snow White", "Dwarf 4", "Dwarf 5", "Dwarf 6"] +example(pipe, [8], 6, global_prompt, entity_prompts) + +# example 7, same prompt with different seeds +seeds = range(5, 9) +global_prompt = "A beautiful woman wearing white dress, holding a mirror, with a warm light background;" +entity_prompts = ["A beautiful woman", "mirror", "necklace", "glasses", "earring", "white dress", "jewelry headpiece"] +example(pipe, seeds, 7, global_prompt, entity_prompts) diff --git a/examples/flux/model_inference_low_vram/FLUX.1-dev-IP-Adapter.py b/examples/flux/model_inference_low_vram/FLUX.1-dev-IP-Adapter.py new file mode 100644 index 0000000000000000000000000000000000000000..83439a9a38b134ab3258b60392512738d6496c7d --- /dev/null +++ b/examples/flux/model_inference_low_vram/FLUX.1-dev-IP-Adapter.py @@ -0,0 +1,25 @@ +import torch +from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig + + +pipe = FluxImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-dev.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="InstantX/FLUX.1-dev-IP-Adapter", origin_file_pattern="ip-adapter.bin", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="google/siglip-so400m-patch14-384", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ], +) +pipe.enable_vram_management() + +origin_prompt = "a rabbit in a garden, colorful flowers" +image = pipe(prompt=origin_prompt, height=1280, width=960, seed=42) +image.save("style image.jpg") + +image = pipe(prompt="A piggy", height=1280, width=960, seed=42, + ipadapter_images=[image], ipadapter_scale=0.7) +image.save("A piggy.jpg") diff --git a/examples/flux/model_inference_low_vram/FLUX.1-dev-InfiniteYou.py b/examples/flux/model_inference_low_vram/FLUX.1-dev-InfiniteYou.py new file mode 100644 index 0000000000000000000000000000000000000000..dbe719aa4b71cd2ef5854c5c3cdbe64cad51b8d5 --- /dev/null +++ b/examples/flux/model_inference_low_vram/FLUX.1-dev-InfiniteYou.py @@ -0,0 +1,60 @@ +import torch +from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig, ControlNetInput +from modelscope import dataset_snapshot_download +from modelscope import snapshot_download +from PIL import Image +import numpy as np + + +snapshot_download( + "ByteDance/InfiniteYou", + allow_file_pattern="supports/insightface/models/antelopev2/*", + local_dir="models/ByteDance/InfiniteYou", +) +pipe = FluxImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-dev.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="ByteDance/InfiniteYou", origin_file_pattern="infu_flux_v1.0/aes_stage2/image_proj_model.bin", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="ByteDance/InfiniteYou", origin_file_pattern="infu_flux_v1.0/aes_stage2/InfuseNetModel/*.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ], +) +pipe.enable_vram_management() + +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/examples_in_diffsynth", + local_dir="./", + allow_file_pattern=f"data/examples/infiniteyou/*", +) + +height, width = 1024, 1024 +controlnet_image = Image.fromarray(np.zeros([height, width, 3]).astype(np.uint8)) +controlnet_inputs = [ControlNetInput(image=controlnet_image, scale=1.0, processor_id="None")] + +prompt = "A man, portrait, cinematic" +id_image = "data/examples/infiniteyou/man.jpg" +id_image = Image.open(id_image).convert('RGB') +image = pipe( + prompt=prompt, seed=1, + infinityou_id_image=id_image, infinityou_guidance=1.0, + controlnet_inputs=controlnet_inputs, + num_inference_steps=50, embedded_guidance=3.5, + height=height, width=width, +) +image.save("man.jpg") + +prompt = "A woman, portrait, cinematic" +id_image = "data/examples/infiniteyou/woman.jpg" +id_image = Image.open(id_image).convert('RGB') +image = pipe( + prompt=prompt, seed=1, + infinityou_id_image=id_image, infinityou_guidance=1.0, + controlnet_inputs=controlnet_inputs, + num_inference_steps=50, embedded_guidance=3.5, + height=height, width=width, +) +image.save("woman.jpg") \ No newline at end of file diff --git a/examples/flux/model_inference_low_vram/FLUX.1-dev-LoRA-Encoder.py b/examples/flux/model_inference_low_vram/FLUX.1-dev-LoRA-Encoder.py new file mode 100644 index 0000000000000000000000000000000000000000..54322bdd4d77fb65741d923b9f0acdaa439044b1 --- /dev/null +++ b/examples/flux/model_inference_low_vram/FLUX.1-dev-LoRA-Encoder.py @@ -0,0 +1,41 @@ +import torch +from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig + + +pipe = FluxImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-dev.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="DiffSynth-Studio/LoRA-Encoder-FLUX.1-Dev", origin_file_pattern="model.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ], +) +pipe.enable_vram_management() +pipe.enable_lora_magic() + +lora = ModelConfig(model_id="VoidOc/flux_animal_forest1", origin_file_pattern="20.safetensors") +pipe.load_lora(pipe.dit, lora, hotload=True) # Use `pipe.clear_lora()` to drop the loaded LoRA. + +# Empty prompt can automatically activate LoRA capabilities. +image = pipe(prompt="", seed=0, lora_encoder_inputs=lora) +image.save("image_1.jpg") + +image = pipe(prompt="", seed=0) +image.save("image_1_origin.jpg") + +# Prompt without trigger words can also activate LoRA capabilities. +image = pipe(prompt="a car", seed=0, lora_encoder_inputs=lora) +image.save("image_2.jpg") + +image = pipe(prompt="a car", seed=0,) +image.save("image_2_origin.jpg") + +# Adjust the activation intensity through the scale parameter. +image = pipe(prompt="a cat", seed=0, lora_encoder_inputs=lora, lora_encoder_scale=1.0) +image.save("image_3.jpg") + +image = pipe(prompt="a cat", seed=0, lora_encoder_inputs=lora, lora_encoder_scale=0.5) +image.save("image_3_scale.jpg") diff --git a/examples/flux/model_inference_low_vram/FLUX.1-dev-LoRAFusion.py b/examples/flux/model_inference_low_vram/FLUX.1-dev-LoRAFusion.py new file mode 100644 index 0000000000000000000000000000000000000000..44ad3a524599d394411d33cf058e41b0193acbb2 --- /dev/null +++ b/examples/flux/model_inference_low_vram/FLUX.1-dev-LoRAFusion.py @@ -0,0 +1,35 @@ +import torch +from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig + + +pipe = FluxImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-dev.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="DiffSynth-Studio/FLUX.1-dev-LoRAFusion", origin_file_pattern="model.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn) + ], +) +pipe.enable_vram_management() +pipe.enable_lora_patcher() +pipe.load_lora( + pipe.dit, + ModelConfig(model_id="yangyufeng/fgao", origin_file_pattern="30.safetensors"), + hotload=True +) +pipe.load_lora( + pipe.dit, + ModelConfig(model_id="bobooblue/LoRA-bling-mai", origin_file_pattern="10.safetensors"), + hotload=True +) +pipe.load_lora( + pipe.dit, + ModelConfig(model_id="JIETANGAB/E", origin_file_pattern="17.safetensors"), + hotload=True +) + +image = pipe(prompt="This is a digital painting in a soft, ethereal style. a beautiful Asian girl Shine like a diamond. Everywhere is shining with bling bling luster.The background is a textured blue with visible brushstrokes, giving the image an impressionistic style reminiscent of Vincent van Gogh's work", seed=0) +image.save("flux.jpg") diff --git a/examples/flux/model_inference_low_vram/FLUX.1-dev.py b/examples/flux/model_inference_low_vram/FLUX.1-dev.py new file mode 100644 index 0000000000000000000000000000000000000000..41d05a4232e5d533ec93feb9af403e68b74e05f0 --- /dev/null +++ b/examples/flux/model_inference_low_vram/FLUX.1-dev.py @@ -0,0 +1,27 @@ +import torch +from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig + + +pipe = FluxImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-dev.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ], +) +pipe.enable_vram_management() + +prompt = "CG, masterpiece, best quality, solo, long hair, wavy hair, silver hair, blue eyes, blue dress, medium breasts, dress, underwater, air bubble, floating hair, refraction, portrait. The girl's flowing silver hair shimmers with every color of the rainbow and cascades down, merging with the floating flora around her." +negative_prompt = "worst quality, low quality, monochrome, zombie, interlocked fingers, Aissist, cleavage, nsfw," + +image = pipe(prompt=prompt, seed=0) +image.save("flux.jpg") + +image = pipe( + prompt=prompt, negative_prompt=negative_prompt, + seed=0, cfg_scale=2, num_inference_steps=50, +) +image.save("flux_cfg.jpg") diff --git a/examples/flux/model_inference_low_vram/Nexus-Gen-Editing.py b/examples/flux/model_inference_low_vram/Nexus-Gen-Editing.py new file mode 100644 index 0000000000000000000000000000000000000000..7dd3193548fa49c4a01318b114534ec97e24e15d --- /dev/null +++ b/examples/flux/model_inference_low_vram/Nexus-Gen-Editing.py @@ -0,0 +1,38 @@ +import importlib +import torch +from PIL import Image +from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +if importlib.util.find_spec("transformers") is None: + raise ImportError("You are using Nexus-GenV2. It depends on transformers, which is not installed. Please install it with `pip install transformers==4.49.0`.") +else: + import transformers + assert transformers.__version__ == "4.49.0", "Nexus-GenV2 requires transformers==4.49.0, please install it with `pip install transformers==4.49.0`." + + +pipe = FluxImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="DiffSynth-Studio/Nexus-GenV2", origin_file_pattern="model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="DiffSynth-Studio/Nexus-GenV2", origin_file_pattern="edit_decoder.bin", offload_device="cpu"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors", offload_device="cpu"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/", offload_device="cpu"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors", offload_device="cpu"), + ], + nexus_gen_processor_config=ModelConfig(model_id="DiffSynth-Studio/Nexus-GenV2", origin_file_pattern="processor/"), +) +pipe.enable_vram_management() + +dataset_snapshot_download(dataset_id="DiffSynth-Studio/examples_in_diffsynth", local_dir="./", allow_file_pattern=f"data/examples/nexusgen/cat.jpg") +ref_image = Image.open("data/examples/nexusgen/cat.jpg").convert("RGB") +prompt = "Add a crown." +image = pipe( + prompt=prompt, negative_prompt="", + seed=42, cfg_scale=2.0, num_inference_steps=50, + nexus_gen_reference_image=ref_image, + height=512, width=512, +) +image.save("cat_crown.jpg") diff --git a/examples/flux/model_inference_low_vram/Nexus-Gen-Generation.py b/examples/flux/model_inference_low_vram/Nexus-Gen-Generation.py new file mode 100644 index 0000000000000000000000000000000000000000..25feb234b49f1bfbaeeff225954055a1d137c462 --- /dev/null +++ b/examples/flux/model_inference_low_vram/Nexus-Gen-Generation.py @@ -0,0 +1,33 @@ +import importlib +import torch +from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig + + +if importlib.util.find_spec("transformers") is None: + raise ImportError("You are using Nexus-GenV2. It depends on transformers, which is not installed. Please install it with `pip install transformers==4.49.0`.") +else: + import transformers + assert transformers.__version__ == "4.49.0", "Nexus-GenV2 requires transformers==4.49.0, please install it with `pip install transformers==4.49.0`." + + +pipe = FluxImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="DiffSynth-Studio/Nexus-GenV2", origin_file_pattern="model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="DiffSynth-Studio/Nexus-GenV2", origin_file_pattern="generation_decoder.bin", offload_device="cpu"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors", offload_device="cpu"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/", offload_device="cpu"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors", offload_device="cpu"), + ], + nexus_gen_processor_config=ModelConfig(model_id="DiffSynth-Studio/Nexus-GenV2", origin_file_pattern="processor/"), +) +pipe.enable_vram_management() + +prompt = "一只可爱的猫咪" +image = pipe( + prompt=prompt, negative_prompt="", + seed=0, cfg_scale=3, num_inference_steps=50, + height=1024, width=1024, +) +image.save("cat.jpg") diff --git a/examples/flux/model_inference_low_vram/Step1X-Edit.py b/examples/flux/model_inference_low_vram/Step1X-Edit.py new file mode 100644 index 0000000000000000000000000000000000000000..aad034fcb8d460514525b4ff74bd5b69a5d417ee --- /dev/null +++ b/examples/flux/model_inference_low_vram/Step1X-Edit.py @@ -0,0 +1,33 @@ +import torch +from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig +from PIL import Image +import numpy as np + + +pipe = FluxImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen2.5-VL-7B-Instruct", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="stepfun-ai/Step1X-Edit", origin_file_pattern="step1x-edit-i1258.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="stepfun-ai/Step1X-Edit", origin_file_pattern="vae.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ], +) +pipe.enable_vram_management() + +image = Image.fromarray(np.zeros((1248, 832, 3), dtype=np.uint8) + 255) +image = pipe( + prompt="draw red flowers in Chinese ink painting style", + step1x_reference_image=image, + width=832, height=1248, cfg_scale=6, + seed=1, rand_device='cuda' +) +image.save("image_1.jpg") + +image = pipe( + prompt="add more flowers in Chinese ink painting style", + step1x_reference_image=image, + width=832, height=1248, cfg_scale=6, + seed=2, rand_device='cuda' +) +image.save("image_2.jpg") diff --git a/examples/flux/model_training/full/FLEX.2-preview.sh b/examples/flux/model_training/full/FLEX.2-preview.sh new file mode 100644 index 0000000000000000000000000000000000000000..451aeb50192b4e89d146b730f6c746d8a4ad60da --- /dev/null +++ b/examples/flux/model_training/full/FLEX.2-preview.sh @@ -0,0 +1,12 @@ +accelerate launch --config_file examples/flux/model_training/full/accelerate_config.yaml examples/flux/model_training/train.py \ + --dataset_base_path data/example_image_dataset \ + --dataset_metadata_path data/example_image_dataset/metadata.csv \ + --max_pixels 1048576 \ + --dataset_repeat 200 \ + --model_id_with_origin_paths "ostris/Flex.2-preview:Flex.2-preview.safetensors,black-forest-labs/FLUX.1-dev:text_encoder/model.safetensors,black-forest-labs/FLUX.1-dev:text_encoder_2/,black-forest-labs/FLUX.1-dev:ae.safetensors" \ + --learning_rate 1e-5 \ + --num_epochs 1 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/FLEX.2-preview_full" \ + --trainable_models "dit" \ + --use_gradient_checkpointing diff --git a/examples/flux/model_training/full/FLUX.1-Kontext-dev.sh b/examples/flux/model_training/full/FLUX.1-Kontext-dev.sh new file mode 100644 index 0000000000000000000000000000000000000000..de1fa5da150b197201e8e69c242a4f180ba8f5e2 --- /dev/null +++ b/examples/flux/model_training/full/FLUX.1-Kontext-dev.sh @@ -0,0 +1,14 @@ +accelerate launch --config_file examples/flux/model_training/full/accelerate_config.yaml examples/flux/model_training/train.py \ + --dataset_base_path data/example_image_dataset \ + --dataset_metadata_path data/example_image_dataset/metadata_kontext.csv \ + --data_file_keys "image,kontext_images" \ + --max_pixels 1048576 \ + --dataset_repeat 400 \ + --model_id_with_origin_paths "black-forest-labs/FLUX.1-Kontext-dev:flux1-kontext-dev.safetensors,black-forest-labs/FLUX.1-dev:text_encoder/model.safetensors,black-forest-labs/FLUX.1-dev:text_encoder_2/,black-forest-labs/FLUX.1-dev:ae.safetensors" \ + --learning_rate 1e-5 \ + --num_epochs 1 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/FLUX.1-Kontext-dev_full" \ + --trainable_models "dit" \ + --extra_inputs "kontext_images" \ + --use_gradient_checkpointing diff --git a/examples/flux/model_training/full/FLUX.1-Krea-dev.sh b/examples/flux/model_training/full/FLUX.1-Krea-dev.sh new file mode 100644 index 0000000000000000000000000000000000000000..76727a8590176a693f6bd2d474a3b56f4c5e3d4d --- /dev/null +++ b/examples/flux/model_training/full/FLUX.1-Krea-dev.sh @@ -0,0 +1,12 @@ +accelerate launch --config_file examples/flux/model_training/full/accelerate_config.yaml examples/flux/model_training/train.py \ + --dataset_base_path data/example_image_dataset \ + --dataset_metadata_path data/example_image_dataset/metadata.csv \ + --max_pixels 1048576 \ + --dataset_repeat 400 \ + --model_id_with_origin_paths "black-forest-labs/FLUX.1-Krea-dev:flux1-krea-dev.safetensors,black-forest-labs/FLUX.1-dev:text_encoder/model.safetensors,black-forest-labs/FLUX.1-dev:text_encoder_2/,black-forest-labs/FLUX.1-dev:ae.safetensors" \ + --learning_rate 1e-5 \ + --num_epochs 1 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/FLUX.1-Krea-dev_full" \ + --trainable_models "dit" \ + --use_gradient_checkpointing diff --git a/examples/flux/model_training/full/FLUX.1-dev-AttriCtrl.sh b/examples/flux/model_training/full/FLUX.1-dev-AttriCtrl.sh new file mode 100644 index 0000000000000000000000000000000000000000..91dc0cfe66924e9eae46fc581a1115971f49d7e4 --- /dev/null +++ b/examples/flux/model_training/full/FLUX.1-dev-AttriCtrl.sh @@ -0,0 +1,14 @@ +accelerate launch examples/flux/model_training/train.py \ + --dataset_base_path data/example_image_dataset \ + --dataset_metadata_path data/example_image_dataset/metadata_attrictrl.csv \ + --data_file_keys "image" \ + --max_pixels 1048576 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "black-forest-labs/FLUX.1-dev:flux1-dev.safetensors,black-forest-labs/FLUX.1-dev:text_encoder/model.safetensors,black-forest-labs/FLUX.1-dev:text_encoder_2/,black-forest-labs/FLUX.1-dev:ae.safetensors,DiffSynth-Studio/AttriCtrl-FLUX.1-Dev:models/brightness.safetensors" \ + --learning_rate 1e-5 \ + --num_epochs 1 \ + --remove_prefix_in_ckpt "pipe.value_controller.encoders.0." \ + --output_path "./models/train/FLUX.1-dev-AttriCtrl_full" \ + --trainable_models "value_controller" \ + --extra_inputs "value_controller_inputs" \ + --use_gradient_checkpointing diff --git a/examples/flux/model_training/full/FLUX.1-dev-Controlnet-Inpainting-Beta.sh b/examples/flux/model_training/full/FLUX.1-dev-Controlnet-Inpainting-Beta.sh new file mode 100644 index 0000000000000000000000000000000000000000..1ef6a407cf6d6e15540003ba792caf07c8111387 --- /dev/null +++ b/examples/flux/model_training/full/FLUX.1-dev-Controlnet-Inpainting-Beta.sh @@ -0,0 +1,14 @@ +accelerate launch --config_file examples/flux/model_training/full/accelerate_config.yaml examples/flux/model_training/train.py \ + --dataset_base_path data/example_image_dataset \ + --dataset_metadata_path data/example_image_dataset/metadata_controlnet_inpaint.csv \ + --data_file_keys "image,controlnet_image,controlnet_inpaint_mask" \ + --max_pixels 1048576 \ + --dataset_repeat 400 \ + --model_id_with_origin_paths "black-forest-labs/FLUX.1-dev:flux1-dev.safetensors,black-forest-labs/FLUX.1-dev:text_encoder/model.safetensors,black-forest-labs/FLUX.1-dev:text_encoder_2/,black-forest-labs/FLUX.1-dev:ae.safetensors,alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta:diffusion_pytorch_model.safetensors" \ + --learning_rate 1e-5 \ + --num_epochs 1 \ + --remove_prefix_in_ckpt "pipe.controlnet.models.0." \ + --output_path "./models/train/FLUX.1-dev-Controlnet-Inpainting-Beta_full" \ + --trainable_models "controlnet" \ + --extra_inputs "controlnet_image,controlnet_inpaint_mask" \ + --use_gradient_checkpointing diff --git a/examples/flux/model_training/full/FLUX.1-dev-Controlnet-Union-alpha.sh b/examples/flux/model_training/full/FLUX.1-dev-Controlnet-Union-alpha.sh new file mode 100644 index 0000000000000000000000000000000000000000..f905bca7a109d757b8f7b59a1080e3151e7adb2c --- /dev/null +++ b/examples/flux/model_training/full/FLUX.1-dev-Controlnet-Union-alpha.sh @@ -0,0 +1,14 @@ +accelerate launch --config_file examples/flux/model_training/full/accelerate_config.yaml examples/flux/model_training/train.py \ + --dataset_base_path data/example_image_dataset \ + --dataset_metadata_path data/example_image_dataset/metadata_controlnet_canny.csv \ + --data_file_keys "image,controlnet_image" \ + --max_pixels 1048576 \ + --dataset_repeat 400 \ + --model_id_with_origin_paths "black-forest-labs/FLUX.1-dev:flux1-dev.safetensors,black-forest-labs/FLUX.1-dev:text_encoder/model.safetensors,black-forest-labs/FLUX.1-dev:text_encoder_2/,black-forest-labs/FLUX.1-dev:ae.safetensors,InstantX/FLUX.1-dev-Controlnet-Union-alpha:diffusion_pytorch_model.safetensors" \ + --learning_rate 1e-5 \ + --num_epochs 1 \ + --remove_prefix_in_ckpt "pipe.controlnet.models.0." \ + --output_path "./models/train/FLUX.1-dev-Controlnet-Union-alpha_full" \ + --trainable_models "controlnet" \ + --extra_inputs "controlnet_image,controlnet_processor_id" \ + --use_gradient_checkpointing diff --git a/examples/flux/model_training/full/FLUX.1-dev-Controlnet-Upscaler.sh b/examples/flux/model_training/full/FLUX.1-dev-Controlnet-Upscaler.sh new file mode 100644 index 0000000000000000000000000000000000000000..e2dd5d8df19d5f5c27ddc04f632bcb22c7571afc --- /dev/null +++ b/examples/flux/model_training/full/FLUX.1-dev-Controlnet-Upscaler.sh @@ -0,0 +1,14 @@ +accelerate launch --config_file examples/flux/model_training/full/accelerate_config.yaml examples/flux/model_training/train.py \ + --dataset_base_path data/example_image_dataset \ + --dataset_metadata_path data/example_image_dataset/metadata_controlnet_upscale.csv \ + --data_file_keys "image,controlnet_image" \ + --max_pixels 1048576 \ + --dataset_repeat 400 \ + --model_id_with_origin_paths "black-forest-labs/FLUX.1-dev:flux1-dev.safetensors,black-forest-labs/FLUX.1-dev:text_encoder/model.safetensors,black-forest-labs/FLUX.1-dev:text_encoder_2/,black-forest-labs/FLUX.1-dev:ae.safetensors,jasperai/Flux.1-dev-Controlnet-Upscaler:diffusion_pytorch_model.safetensors" \ + --learning_rate 1e-5 \ + --num_epochs 1 \ + --remove_prefix_in_ckpt "pipe.controlnet.models.0." \ + --output_path "./models/train/FLUX.1-dev-Controlnet-Upscaler_full" \ + --trainable_models "controlnet" \ + --extra_inputs "controlnet_image" \ + --use_gradient_checkpointing diff --git a/examples/flux/model_training/full/FLUX.1-dev-IP-Adapter.sh b/examples/flux/model_training/full/FLUX.1-dev-IP-Adapter.sh new file mode 100644 index 0000000000000000000000000000000000000000..43bc0062f4a6f0fc61dbf9607f30af0e2ec4b3b3 --- /dev/null +++ b/examples/flux/model_training/full/FLUX.1-dev-IP-Adapter.sh @@ -0,0 +1,14 @@ +accelerate launch examples/flux/model_training/train.py \ + --dataset_base_path data/example_image_dataset \ + --dataset_metadata_path data/example_image_dataset/metadata_ipadapter.csv \ + --data_file_keys "image,ipadapter_images" \ + --max_pixels 1048576 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "black-forest-labs/FLUX.1-dev:flux1-dev.safetensors,black-forest-labs/FLUX.1-dev:text_encoder/model.safetensors,black-forest-labs/FLUX.1-dev:text_encoder_2/,black-forest-labs/FLUX.1-dev:ae.safetensors,InstantX/FLUX.1-dev-IP-Adapter:ip-adapter.bin,google/siglip-so400m-patch14-384:" \ + --learning_rate 1e-5 \ + --num_epochs 1 \ + --remove_prefix_in_ckpt "pipe.ipadapter." \ + --output_path "./models/train/FLUX.1-dev-IP-Adapter_full" \ + --trainable_models "ipadapter" \ + --extra_inputs "ipadapter_images" \ + --use_gradient_checkpointing diff --git a/examples/flux/model_training/full/FLUX.1-dev-InfiniteYou.sh b/examples/flux/model_training/full/FLUX.1-dev-InfiniteYou.sh new file mode 100644 index 0000000000000000000000000000000000000000..6040fa58a7f322c7fd21a77834300f039aa03e15 --- /dev/null +++ b/examples/flux/model_training/full/FLUX.1-dev-InfiniteYou.sh @@ -0,0 +1,14 @@ +accelerate launch --config_file examples/flux/model_training/full/accelerate_config.yaml examples/flux/model_training/train.py \ + --dataset_base_path data/example_image_dataset \ + --dataset_metadata_path data/example_image_dataset/metadata_infiniteyou.csv \ + --data_file_keys "image,controlnet_image,infinityou_id_image" \ + --max_pixels 1048576 \ + --dataset_repeat 400 \ + --model_id_with_origin_paths "black-forest-labs/FLUX.1-dev:flux1-dev.safetensors,black-forest-labs/FLUX.1-dev:text_encoder/model.safetensors,black-forest-labs/FLUX.1-dev:text_encoder_2/,black-forest-labs/FLUX.1-dev:ae.safetensors,ByteDance/InfiniteYou:infu_flux_v1.0/aes_stage2/image_proj_model.bin,ByteDance/InfiniteYou:infu_flux_v1.0/aes_stage2/InfuseNetModel/*.safetensors" \ + --learning_rate 1e-5 \ + --num_epochs 1 \ + --remove_prefix_in_ckpt "pipe." \ + --output_path "./models/train/FLUX.1-dev-InfiniteYou_full" \ + --trainable_models "controlnet,image_proj_model" \ + --extra_inputs "controlnet_image,infinityou_id_image,infinityou_guidance" \ + --use_gradient_checkpointing diff --git a/examples/flux/model_training/full/FLUX.1-dev-LoRA-Encoder.sh b/examples/flux/model_training/full/FLUX.1-dev-LoRA-Encoder.sh new file mode 100644 index 0000000000000000000000000000000000000000..f0d4f97cc1f6da5a668f852466cc19fdeae3107c --- /dev/null +++ b/examples/flux/model_training/full/FLUX.1-dev-LoRA-Encoder.sh @@ -0,0 +1,14 @@ +accelerate launch examples/flux/model_training/train.py \ + --dataset_base_path data/example_image_dataset \ + --dataset_metadata_path data/example_image_dataset/metadata_lora_encoder.csv \ + --data_file_keys "image" \ + --max_pixels 1048576 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "black-forest-labs/FLUX.1-dev:flux1-dev.safetensors,black-forest-labs/FLUX.1-dev:text_encoder/model.safetensors,black-forest-labs/FLUX.1-dev:text_encoder_2/,black-forest-labs/FLUX.1-dev:ae.safetensors,DiffSynth-Studio/LoRA-Encoder-FLUX.1-Dev:model.safetensors" \ + --learning_rate 1e-5 \ + --num_epochs 1 \ + --remove_prefix_in_ckpt "pipe.lora_encoder." \ + --output_path "./models/train/FLUX.1-dev-LoRA-Encoder_full" \ + --trainable_models "lora_encoder" \ + --extra_inputs "lora_encoder_inputs" \ + --use_gradient_checkpointing diff --git a/examples/flux/model_training/full/FLUX.1-dev.sh b/examples/flux/model_training/full/FLUX.1-dev.sh new file mode 100644 index 0000000000000000000000000000000000000000..9254957119bc97bde91f63b21261a250c37f75ef --- /dev/null +++ b/examples/flux/model_training/full/FLUX.1-dev.sh @@ -0,0 +1,12 @@ +accelerate launch --config_file examples/flux/model_training/full/accelerate_config.yaml examples/flux/model_training/train.py \ + --dataset_base_path data/example_image_dataset \ + --dataset_metadata_path data/example_image_dataset/metadata.csv \ + --max_pixels 1048576 \ + --dataset_repeat 400 \ + --model_id_with_origin_paths "black-forest-labs/FLUX.1-dev:flux1-dev.safetensors,black-forest-labs/FLUX.1-dev:text_encoder/model.safetensors,black-forest-labs/FLUX.1-dev:text_encoder_2/,black-forest-labs/FLUX.1-dev:ae.safetensors" \ + --learning_rate 1e-5 \ + --num_epochs 1 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/FLUX.1-dev_full" \ + --trainable_models "dit" \ + --use_gradient_checkpointing diff --git a/examples/flux/model_training/full/Nexus-Gen.sh b/examples/flux/model_training/full/Nexus-Gen.sh new file mode 100644 index 0000000000000000000000000000000000000000..ab1c32481c5f7aa6dbbd2a67f602e5b8aa6c92d4 --- /dev/null +++ b/examples/flux/model_training/full/Nexus-Gen.sh @@ -0,0 +1,14 @@ +accelerate launch --config_file examples/flux/model_training/full/accelerate_config_zero2offload.yaml examples/flux/model_training/train.py \ + --dataset_base_path data/example_image_dataset \ + --dataset_metadata_path data/example_image_dataset/metadata_nexusgen_edit.csv \ + --data_file_keys "image,nexus_gen_reference_image" \ + --max_pixels 262144 \ + --dataset_repeat 400 \ + --model_id_with_origin_paths "DiffSynth-Studio/Nexus-GenV2:model*.safetensors,DiffSynth-Studio/Nexus-GenV2:edit_decoder.bin,black-forest-labs/FLUX.1-dev:text_encoder/model.safetensors,black-forest-labs/FLUX.1-dev:text_encoder_2/,black-forest-labs/FLUX.1-dev:ae.safetensors" \ + --learning_rate 1e-5 \ + --num_epochs 1 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/FLUX.1-NexusGen-Edit_full" \ + --trainable_models "dit" \ + --extra_inputs "nexus_gen_reference_image" \ + --use_gradient_checkpointing_offload diff --git a/examples/flux/model_training/full/Step1X-Edit.sh b/examples/flux/model_training/full/Step1X-Edit.sh new file mode 100644 index 0000000000000000000000000000000000000000..98c45cee9e0fb1af50b16e0c92724ff45aed08df --- /dev/null +++ b/examples/flux/model_training/full/Step1X-Edit.sh @@ -0,0 +1,14 @@ +accelerate launch --config_file examples/flux/model_training/full/accelerate_config.yaml examples/flux/model_training/train.py \ + --dataset_base_path data/example_image_dataset \ + --dataset_metadata_path data/example_image_dataset/metadata_step1x.csv \ + --data_file_keys "image,step1x_reference_image" \ + --max_pixels 1048576 \ + --dataset_repeat 400 \ + --model_id_with_origin_paths "Qwen/Qwen2.5-VL-7B-Instruct:,stepfun-ai/Step1X-Edit:step1x-edit-i1258.safetensors,stepfun-ai/Step1X-Edit:vae.safetensors" \ + --learning_rate 1e-5 \ + --num_epochs 1 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Step1X-Edit_full" \ + --trainable_models "dit" \ + --extra_inputs "step1x_reference_image" \ + --use_gradient_checkpointing_offload diff --git a/examples/flux/model_training/full/accelerate_config.yaml b/examples/flux/model_training/full/accelerate_config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..0173f595fe31d639cbefc77af79eab12e1f18ae5 --- /dev/null +++ b/examples/flux/model_training/full/accelerate_config.yaml @@ -0,0 +1,23 @@ +compute_environment: LOCAL_MACHINE +debug: false +deepspeed_config: + gradient_accumulation_steps: 1 + offload_optimizer_device: none + offload_param_device: none + zero3_init_flag: false + zero_stage: 2 +distributed_type: DEEPSPEED +downcast_bf16: 'no' +enable_cpu_affinity: false +machine_rank: 0 +main_training_function: main +mixed_precision: bf16 +num_machines: 1 +num_processes: 4 +rdzv_backend: static +same_network: true +tpu_env: [] +tpu_use_cluster: false +tpu_use_sudo: false +use_cpu: false +gpu_ids: 2,3,6,7 diff --git a/examples/flux/model_training/full/accelerate_config_zero2offload.yaml b/examples/flux/model_training/full/accelerate_config_zero2offload.yaml new file mode 100644 index 0000000000000000000000000000000000000000..8a75f3d91eeae160409650b482e5383ac26b297b --- /dev/null +++ b/examples/flux/model_training/full/accelerate_config_zero2offload.yaml @@ -0,0 +1,22 @@ +compute_environment: LOCAL_MACHINE +debug: false +deepspeed_config: + gradient_accumulation_steps: 1 + offload_optimizer_device: 'cpu' + offload_param_device: 'cpu' + zero3_init_flag: false + zero_stage: 2 +distributed_type: DEEPSPEED +downcast_bf16: 'no' +enable_cpu_affinity: false +machine_rank: 0 +main_training_function: main +mixed_precision: bf16 +num_machines: 1 +num_processes: 8 +rdzv_backend: static +same_network: true +tpu_env: [] +tpu_use_cluster: false +tpu_use_sudo: false +use_cpu: false diff --git a/examples/flux/model_training/lora/FLEX.2-preview.sh b/examples/flux/model_training/lora/FLEX.2-preview.sh new file mode 100644 index 0000000000000000000000000000000000000000..6abeb57cffe0a1cb41688069cd75c2a094b31a6c --- /dev/null +++ b/examples/flux/model_training/lora/FLEX.2-preview.sh @@ -0,0 +1,15 @@ +accelerate launch examples/flux/model_training/train.py \ + --dataset_base_path data/example_image_dataset \ + --dataset_metadata_path data/example_image_dataset/metadata.csv \ + --max_pixels 1048576 \ + --dataset_repeat 50 \ + --model_id_with_origin_paths "ostris/Flex.2-preview:Flex.2-preview.safetensors,black-forest-labs/FLUX.1-dev:text_encoder/model.safetensors,black-forest-labs/FLUX.1-dev:text_encoder_2/,black-forest-labs/FLUX.1-dev:ae.safetensors" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/FLEX.2-preview_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "a_to_qkv,b_to_qkv,ff_a.0,ff_a.2,ff_b.0,ff_b.2,a_to_out,b_to_out,proj_out,norm.linear,norm1_a.linear,norm1_b.linear,to_qkv_mlp" \ + --lora_rank 32 \ + --align_to_opensource_format \ + --use_gradient_checkpointing diff --git a/examples/flux/model_training/lora/FLUX.1-Kontext-dev.sh b/examples/flux/model_training/lora/FLUX.1-Kontext-dev.sh new file mode 100644 index 0000000000000000000000000000000000000000..814d7ad06972ac4ac9b3c132fb4f71dc324d50b8 --- /dev/null +++ b/examples/flux/model_training/lora/FLUX.1-Kontext-dev.sh @@ -0,0 +1,17 @@ +accelerate launch examples/flux/model_training/train.py \ + --dataset_base_path data/example_image_dataset \ + --dataset_metadata_path data/example_image_dataset/metadata_kontext.csv \ + --data_file_keys "image,kontext_images" \ + --max_pixels 1048576 \ + --dataset_repeat 400 \ + --model_id_with_origin_paths "black-forest-labs/FLUX.1-Kontext-dev:flux1-kontext-dev.safetensors,black-forest-labs/FLUX.1-dev:text_encoder/model.safetensors,black-forest-labs/FLUX.1-dev:text_encoder_2/,black-forest-labs/FLUX.1-dev:ae.safetensors" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/FLUX.1-Kontext-dev_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "a_to_qkv,b_to_qkv,ff_a.0,ff_a.2,ff_b.0,ff_b.2,a_to_out,b_to_out,proj_out,norm.linear,norm1_a.linear,norm1_b.linear,to_qkv_mlp" \ + --lora_rank 32 \ + --align_to_opensource_format \ + --extra_inputs "kontext_images" \ + --use_gradient_checkpointing diff --git a/examples/flux/model_training/lora/FLUX.1-Krea-dev.sh b/examples/flux/model_training/lora/FLUX.1-Krea-dev.sh new file mode 100644 index 0000000000000000000000000000000000000000..1d54d434110f376c9553f452b5d5e08c3dcb950e --- /dev/null +++ b/examples/flux/model_training/lora/FLUX.1-Krea-dev.sh @@ -0,0 +1,15 @@ +accelerate launch examples/flux/model_training/train.py \ + --dataset_base_path data/example_image_dataset \ + --dataset_metadata_path data/example_image_dataset/metadata.csv \ + --max_pixels 1048576 \ + --dataset_repeat 50 \ + --model_id_with_origin_paths "black-forest-labs/FLUX.1-Krea-dev:flux1-krea-dev.safetensors,black-forest-labs/FLUX.1-dev:text_encoder/model.safetensors,black-forest-labs/FLUX.1-dev:text_encoder_2/,black-forest-labs/FLUX.1-dev:ae.safetensors" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/FLUX.1-Krea-dev_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "a_to_qkv,b_to_qkv,ff_a.0,ff_a.2,ff_b.0,ff_b.2,a_to_out,b_to_out,proj_out,norm.linear,norm1_a.linear,norm1_b.linear,to_qkv_mlp" \ + --lora_rank 32 \ + --align_to_opensource_format \ + --use_gradient_checkpointing diff --git a/examples/flux/model_training/lora/FLUX.1-dev-AttriCtrl.sh b/examples/flux/model_training/lora/FLUX.1-dev-AttriCtrl.sh new file mode 100644 index 0000000000000000000000000000000000000000..7763c5f56cc8fb587d28493fa89296391954bd1d --- /dev/null +++ b/examples/flux/model_training/lora/FLUX.1-dev-AttriCtrl.sh @@ -0,0 +1,17 @@ +accelerate launch examples/flux/model_training/train.py \ + --dataset_base_path data/example_image_dataset \ + --dataset_metadata_path data/example_image_dataset/metadata_attrictrl.csv \ + --data_file_keys "image" \ + --max_pixels 1048576 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "black-forest-labs/FLUX.1-dev:flux1-dev.safetensors,black-forest-labs/FLUX.1-dev:text_encoder/model.safetensors,black-forest-labs/FLUX.1-dev:text_encoder_2/,black-forest-labs/FLUX.1-dev:ae.safetensors,DiffSynth-Studio/AttriCtrl-FLUX.1-Dev:models/brightness.safetensors" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/FLUX.1-dev-AttriCtrl_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "a_to_qkv,b_to_qkv,ff_a.0,ff_a.2,ff_b.0,ff_b.2,a_to_out,b_to_out,proj_out,norm.linear,norm1_a.linear,norm1_b.linear,to_qkv_mlp" \ + --lora_rank 32 \ + --extra_inputs "value_controller_inputs" \ + --align_to_opensource_format \ + --use_gradient_checkpointing diff --git a/examples/flux/model_training/lora/FLUX.1-dev-Controlnet-Inpainting-Beta.sh b/examples/flux/model_training/lora/FLUX.1-dev-Controlnet-Inpainting-Beta.sh new file mode 100644 index 0000000000000000000000000000000000000000..0de6a3067569d092e7c7a5d527319f7c13f5d26f --- /dev/null +++ b/examples/flux/model_training/lora/FLUX.1-dev-Controlnet-Inpainting-Beta.sh @@ -0,0 +1,17 @@ +accelerate launch examples/flux/model_training/train.py \ + --dataset_base_path data/example_image_dataset \ + --dataset_metadata_path data/example_image_dataset/metadata_controlnet_inpaint.csv \ + --data_file_keys "image,controlnet_image,controlnet_inpaint_mask" \ + --max_pixels 1048576 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "black-forest-labs/FLUX.1-dev:flux1-dev.safetensors,black-forest-labs/FLUX.1-dev:text_encoder/model.safetensors,black-forest-labs/FLUX.1-dev:text_encoder_2/,black-forest-labs/FLUX.1-dev:ae.safetensors,alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta:diffusion_pytorch_model.safetensors" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/FLUX.1-dev-Controlnet-Inpainting-Beta_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "a_to_qkv,b_to_qkv,ff_a.0,ff_a.2,ff_b.0,ff_b.2,a_to_out,b_to_out,proj_out,norm.linear,norm1_a.linear,norm1_b.linear,to_qkv_mlp" \ + --lora_rank 32 \ + --extra_inputs "controlnet_image,controlnet_inpaint_mask" \ + --align_to_opensource_format \ + --use_gradient_checkpointing diff --git a/examples/flux/model_training/lora/FLUX.1-dev-Controlnet-Union-alpha.sh b/examples/flux/model_training/lora/FLUX.1-dev-Controlnet-Union-alpha.sh new file mode 100644 index 0000000000000000000000000000000000000000..5a65af857e67f53b6512d5c0ae495cfb7e57c3da --- /dev/null +++ b/examples/flux/model_training/lora/FLUX.1-dev-Controlnet-Union-alpha.sh @@ -0,0 +1,17 @@ +accelerate launch examples/flux/model_training/train.py \ + --dataset_base_path data/example_image_dataset \ + --dataset_metadata_path data/example_image_dataset/metadata_controlnet_canny.csv \ + --data_file_keys "image,controlnet_image" \ + --max_pixels 1048576 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "black-forest-labs/FLUX.1-dev:flux1-dev.safetensors,black-forest-labs/FLUX.1-dev:text_encoder/model.safetensors,black-forest-labs/FLUX.1-dev:text_encoder_2/,black-forest-labs/FLUX.1-dev:ae.safetensors,InstantX/FLUX.1-dev-Controlnet-Union-alpha:diffusion_pytorch_model.safetensors" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/FLUX.1-dev-Controlnet-Union-alpha_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "a_to_qkv,b_to_qkv,ff_a.0,ff_a.2,ff_b.0,ff_b.2,a_to_out,b_to_out,proj_out,norm.linear,norm1_a.linear,norm1_b.linear,to_qkv_mlp" \ + --lora_rank 32 \ + --extra_inputs "controlnet_image,controlnet_processor_id" \ + --align_to_opensource_format \ + --use_gradient_checkpointing diff --git a/examples/flux/model_training/lora/FLUX.1-dev-Controlnet-Upscaler.sh b/examples/flux/model_training/lora/FLUX.1-dev-Controlnet-Upscaler.sh new file mode 100644 index 0000000000000000000000000000000000000000..72e89714ba8fdf716df16c19ca9a06f2d818e876 --- /dev/null +++ b/examples/flux/model_training/lora/FLUX.1-dev-Controlnet-Upscaler.sh @@ -0,0 +1,17 @@ +accelerate launch examples/flux/model_training/train.py \ + --dataset_base_path data/example_image_dataset \ + --dataset_metadata_path data/example_image_dataset/metadata_controlnet_upscale.csv \ + --data_file_keys "image,controlnet_image" \ + --max_pixels 1048576 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "black-forest-labs/FLUX.1-dev:flux1-dev.safetensors,black-forest-labs/FLUX.1-dev:text_encoder/model.safetensors,black-forest-labs/FLUX.1-dev:text_encoder_2/,black-forest-labs/FLUX.1-dev:ae.safetensors,jasperai/Flux.1-dev-Controlnet-Upscaler:diffusion_pytorch_model.safetensors" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/FLUX.1-dev-Controlnet-Upscaler_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "a_to_qkv,b_to_qkv,ff_a.0,ff_a.2,ff_b.0,ff_b.2,a_to_out,b_to_out,proj_out,norm.linear,norm1_a.linear,norm1_b.linear,to_qkv_mlp" \ + --lora_rank 32 \ + --extra_inputs "controlnet_image" \ + --align_to_opensource_format \ + --use_gradient_checkpointing diff --git a/examples/flux/model_training/lora/FLUX.1-dev-EliGen.sh b/examples/flux/model_training/lora/FLUX.1-dev-EliGen.sh new file mode 100644 index 0000000000000000000000000000000000000000..10a18e0376458b8a3392626df510a500216983b3 --- /dev/null +++ b/examples/flux/model_training/lora/FLUX.1-dev-EliGen.sh @@ -0,0 +1,17 @@ +accelerate launch examples/flux/model_training/train.py \ + --dataset_base_path data/example_image_dataset \ + --dataset_metadata_path data/example_image_dataset/metadata_eligen.json \ + --data_file_keys "image,eligen_entity_masks" \ + --max_pixels 1048576 \ + --dataset_repeat 50 \ + --model_id_with_origin_paths "black-forest-labs/FLUX.1-dev:flux1-dev.safetensors,black-forest-labs/FLUX.1-dev:text_encoder/model.safetensors,black-forest-labs/FLUX.1-dev:text_encoder_2/,black-forest-labs/FLUX.1-dev:ae.safetensors" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/FLUX.1-dev-EliGen_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "a_to_qkv,b_to_qkv,ff_a.0,ff_a.2,ff_b.0,ff_b.2,a_to_out,b_to_out,proj_out,norm.linear,norm1_a.linear,norm1_b.linear,to_qkv_mlp" \ + --lora_rank 32 \ + --align_to_opensource_format \ + --extra_inputs "eligen_entity_masks,eligen_entity_prompts" \ + --use_gradient_checkpointing diff --git a/examples/flux/model_training/lora/FLUX.1-dev-IP-Adapter.sh b/examples/flux/model_training/lora/FLUX.1-dev-IP-Adapter.sh new file mode 100644 index 0000000000000000000000000000000000000000..0495c11157b72c63ce42b81d84b0e6be2ded50ad --- /dev/null +++ b/examples/flux/model_training/lora/FLUX.1-dev-IP-Adapter.sh @@ -0,0 +1,17 @@ +accelerate launch examples/flux/model_training/train.py \ + --dataset_base_path data/example_image_dataset \ + --dataset_metadata_path data/example_image_dataset/metadata_ipadapter.csv \ + --data_file_keys "image,ipadapter_images" \ + --max_pixels 1048576 \ + --dataset_repeat 50 \ + --model_id_with_origin_paths "black-forest-labs/FLUX.1-dev:flux1-dev.safetensors,black-forest-labs/FLUX.1-dev:text_encoder/model.safetensors,black-forest-labs/FLUX.1-dev:text_encoder_2/,black-forest-labs/FLUX.1-dev:ae.safetensors,InstantX/FLUX.1-dev-IP-Adapter:ip-adapter.bin,google/siglip-so400m-patch14-384:" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/FLUX.1-dev-IP-Adapter_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "a_to_qkv,b_to_qkv,ff_a.0,ff_a.2,ff_b.0,ff_b.2,a_to_out,b_to_out,proj_out,norm.linear,norm1_a.linear,norm1_b.linear,to_qkv_mlp" \ + --lora_rank 32 \ + --extra_inputs "ipadapter_images" \ + --align_to_opensource_format \ + --use_gradient_checkpointing diff --git a/examples/flux/model_training/lora/FLUX.1-dev-InfiniteYou.sh b/examples/flux/model_training/lora/FLUX.1-dev-InfiniteYou.sh new file mode 100644 index 0000000000000000000000000000000000000000..b51a5659ea5affadca5e6973534cd816dbcb9ebd --- /dev/null +++ b/examples/flux/model_training/lora/FLUX.1-dev-InfiniteYou.sh @@ -0,0 +1,17 @@ +accelerate launch examples/flux/model_training/train.py \ + --dataset_base_path data/example_image_dataset \ + --dataset_metadata_path data/example_image_dataset/metadata_infiniteyou.csv \ + --data_file_keys "image,controlnet_image,infinityou_id_image" \ + --max_pixels 1048576 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "black-forest-labs/FLUX.1-dev:flux1-dev.safetensors,black-forest-labs/FLUX.1-dev:text_encoder/model.safetensors,black-forest-labs/FLUX.1-dev:text_encoder_2/,black-forest-labs/FLUX.1-dev:ae.safetensors,ByteDance/InfiniteYou:infu_flux_v1.0/aes_stage2/image_proj_model.bin,ByteDance/InfiniteYou:infu_flux_v1.0/aes_stage2/InfuseNetModel/*.safetensors" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/FLUX.1-dev-InfiniteYou_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "a_to_qkv,b_to_qkv,ff_a.0,ff_a.2,ff_b.0,ff_b.2,a_to_out,b_to_out,proj_out,norm.linear,norm1_a.linear,norm1_b.linear,to_qkv_mlp" \ + --lora_rank 32 \ + --extra_inputs "controlnet_image,infinityou_id_image,infinityou_guidance" \ + --align_to_opensource_format \ + --use_gradient_checkpointing diff --git a/examples/flux/model_training/lora/FLUX.1-dev.sh b/examples/flux/model_training/lora/FLUX.1-dev.sh new file mode 100644 index 0000000000000000000000000000000000000000..4b207ef855d0c94c9ccde595620deb544124a43e --- /dev/null +++ b/examples/flux/model_training/lora/FLUX.1-dev.sh @@ -0,0 +1,15 @@ +accelerate launch examples/flux/model_training/train.py \ + --dataset_base_path data/example_image_dataset \ + --dataset_metadata_path data/example_image_dataset/metadata.csv \ + --max_pixels 1048576 \ + --dataset_repeat 50 \ + --model_id_with_origin_paths "black-forest-labs/FLUX.1-dev:flux1-dev.safetensors,black-forest-labs/FLUX.1-dev:text_encoder/model.safetensors,black-forest-labs/FLUX.1-dev:text_encoder_2/,black-forest-labs/FLUX.1-dev:ae.safetensors" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/FLUX.1-dev_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "a_to_qkv,b_to_qkv,ff_a.0,ff_a.2,ff_b.0,ff_b.2,a_to_out,b_to_out,proj_out,norm.linear,norm1_a.linear,norm1_b.linear,to_qkv_mlp" \ + --lora_rank 32 \ + --align_to_opensource_format \ + --use_gradient_checkpointing diff --git a/examples/flux/model_training/lora/Nexus-Gen.sh b/examples/flux/model_training/lora/Nexus-Gen.sh new file mode 100644 index 0000000000000000000000000000000000000000..3e6eac1eb7143ca1bbd8fa9996a22cc0e3ded6dc --- /dev/null +++ b/examples/flux/model_training/lora/Nexus-Gen.sh @@ -0,0 +1,17 @@ +accelerate launch examples/flux/model_training/train.py \ + --dataset_base_path data/example_image_dataset \ + --dataset_metadata_path data/example_image_dataset/metadata_nexusgen_edit.csv \ + --data_file_keys "image,nexus_gen_reference_image" \ + --max_pixels 1048576 \ + --dataset_repeat 400 \ + --model_id_with_origin_paths "DiffSynth-Studio/Nexus-GenV2:model*.safetensors,DiffSynth-Studio/Nexus-GenV2:edit_decoder.bin,black-forest-labs/FLUX.1-dev:text_encoder/model.safetensors,black-forest-labs/FLUX.1-dev:text_encoder_2/,black-forest-labs/FLUX.1-dev:ae.safetensors" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/FLUX.1-NexusGen-Edit_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "a_to_qkv,b_to_qkv,ff_a.0,ff_a.2,ff_b.0,ff_b.2,a_to_out,b_to_out,proj_out,norm.linear,norm1_a.linear,norm1_b.linear,to_qkv_mlp" \ + --lora_rank 32 \ + --align_to_opensource_format \ + --extra_inputs "nexus_gen_reference_image" \ + --use_gradient_checkpointing diff --git a/examples/flux/model_training/lora/Step1X-Edit.sh b/examples/flux/model_training/lora/Step1X-Edit.sh new file mode 100644 index 0000000000000000000000000000000000000000..01ac260419685567693e12c8c8c55fd9667f3532 --- /dev/null +++ b/examples/flux/model_training/lora/Step1X-Edit.sh @@ -0,0 +1,17 @@ +accelerate launch examples/flux/model_training/train.py \ + --dataset_base_path data/example_image_dataset \ + --dataset_metadata_path data/example_image_dataset/metadata_step1x.csv \ + --data_file_keys "image,step1x_reference_image" \ + --max_pixels 1048576 \ + --dataset_repeat 50 \ + --model_id_with_origin_paths "Qwen/Qwen2.5-VL-7B-Instruct:,stepfun-ai/Step1X-Edit:step1x-edit-i1258.safetensors,stepfun-ai/Step1X-Edit:vae.safetensors" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Step1X-Edit_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "a_to_qkv,b_to_qkv,ff_a.0,ff_a.2,ff_b.0,ff_b.2,a_to_out,b_to_out,proj_out,norm.linear,norm1_a.linear,norm1_b.linear,to_qkv_mlp" \ + --lora_rank 32 \ + --extra_inputs "step1x_reference_image" \ + --align_to_opensource_format \ + --use_gradient_checkpointing diff --git a/examples/flux/model_training/lora/train_1123_real2sketch.sh b/examples/flux/model_training/lora/train_1123_real2sketch.sh new file mode 100644 index 0000000000000000000000000000000000000000..c37742b49a75ab197dbc689cd6062adc37c97e12 --- /dev/null +++ b/examples/flux/model_training/lora/train_1123_real2sketch.sh @@ -0,0 +1,17 @@ +accelerate launch --config_file examples/flux/model_training/full/accelerate_config.yaml examples/flux/model_training/train.py \ + --dataset_base_path /fi-lib/workspace/sjx/dataset/sketch_samples \ + --dataset_metadata_path /fi-lib/workspace/sjx/DiffSynth-Studio/dataset/sketch_samples/pairs.txt \ + --data_file_keys "image,kontext_images" \ + --max_pixels 1048576 \ + --dataset_repeat 400 \ + --model_id_with_origin_paths "black-forest-labs/FLUX.1-Kontext-dev:flux1-kontext-dev.safetensors,black-forest-labs/FLUX.1-Kontext-dev:text_encoder/model.safetensors,black-forest-labs/FLUX.1-Kontext-dev:text_encoder_2/,black-forest-labs/FLUX.1-Kontext-dev:ae.safetensors" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/FLUX.1-Kontext-dev_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "a_to_qkv,b_to_qkv,ff_a.0,ff_a.2,ff_b.0,ff_b.2,a_to_out,b_to_out,proj_out,norm.linear,norm1_a.linear,norm1_b.linear,to_qkv_mlp" \ + --lora_rank 32 \ + --align_to_opensource_format \ + --extra_inputs "kontext_images" \ + --use_gradient_checkpointing \ No newline at end of file diff --git a/examples/flux/model_training/lora/train_1125_real2body.sh b/examples/flux/model_training/lora/train_1125_real2body.sh new file mode 100644 index 0000000000000000000000000000000000000000..53d4ed1dfb9a864350b220eb87cc535058165eba --- /dev/null +++ b/examples/flux/model_training/lora/train_1125_real2body.sh @@ -0,0 +1,18 @@ +accelerate launch --config_file examples/flux/model_training/full/accelerate_config.yaml examples/flux/model_training/train.py \ + --dataset_base_path /fi-lib/workspace/sjx/dataset/sketch_body \ + --dataset_metadata_path /fi-lib/workspace/sjx/DiffSynth-Studio/dataset/sketch_body/pairs.txt \ + --data_file_keys "image,kontext_images" \ + --max_pixels 1048576 \ + --dataset_repeat 400 \ + --model_id_with_origin_paths "black-forest-labs/FLUX.1-Kontext-dev:flux1-kontext-dev.safetensors,black-forest-labs/FLUX.1-Kontext-dev:text_encoder/model.safetensors,black-forest-labs/FLUX.1-Kontext-dev:text_encoder_2/,black-forest-labs/FLUX.1-Kontext-dev:ae.safetensors" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/FLUX.1_lora_1125" \ + --lora_base_model "dit" \ + --lora_target_modules "a_to_qkv,b_to_qkv,ff_a.0,ff_a.2,ff_b.0,ff_b.2,a_to_out,b_to_out,proj_out,norm.linear,norm1_a.linear,norm1_b.linear,to_qkv_mlp" \ + --lora_rank 32 \ + --align_to_opensource_format \ + --extra_inputs "kontext_images" \ + --use_gradient_checkpointing \ + --default_caption "Convert this real photo into a storyboard style + human dynamic structural sketch. Core requirements: 1. Preserve the original composition, scene elements (such as props and environmental layout), and human movements, fully restoring the narrative logic of the image; 2. Characters are depicted with concise and smooth structural sketch lines, highlighting human skeletal and muscular dynamic structures, without realistic lighting, colors, or detailed textures—only presenting a black-and-white structural feel suitable for drawing practice; 3. The overall image adopts a storyboard frame presentation, with clear and neat lines; background elements are simplified into minimalist line outlines, maintaining the original artistic tone and narrative atmosphere of the image, using only black and white color scheme, with no redundant decorations." \ No newline at end of file diff --git a/examples/flux/model_training/lora/train_1126 b/examples/flux/model_training/lora/train_1126 new file mode 100644 index 0000000000000000000000000000000000000000..5538f0dbac56ff38f958ca5eaa4edf8bf9eb4495 --- /dev/null +++ b/examples/flux/model_training/lora/train_1126 @@ -0,0 +1,17 @@ +accelerate launch --config_file examples/flux/model_training/full/accelerate_config.yaml examples/flux/model_training/train.py \ + --dataset_base_path /fi-lib/workspace/sjx/dataset/body_real_1126 \ + --dataset_metadata_path /fi-lib/workspace/sjx/DiffSynth-Studio/dataset/body_real_1126/pairs.txt \ + --data_file_keys "image,kontext_images" \ + --max_pixels 1048576 \ + --dataset_repeat 400 \ + --model_id_with_origin_paths "black-forest-labs/FLUX.1-Kontext-dev:flux1-kontext-dev.safetensors,black-forest-labs/FLUX.1-Kontext-dev:text_encoder/model.safetensors,black-forest-labs/FLUX.1-Kontext-dev:text_encoder_2/,black-forest-labs/FLUX.1-Kontext-dev:ae.safetensors" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/FLUX.1_lora_1126" \ + --lora_base_model "dit" \ + --lora_target_modules "a_to_qkv,b_to_qkv,ff_a.0,ff_a.2,ff_b.0,ff_b.2,a_to_out,b_to_out,proj_out,norm.linear,norm1_a.linear,norm1_b.linear,to_qkv_mlp" \ + --lora_rank 32 \ + --align_to_opensource_format \ + --extra_inputs "kontext_images" \ + --use_gradient_checkpointing \ No newline at end of file diff --git a/examples/flux/model_training/lora/train_1127_mbti.sh b/examples/flux/model_training/lora/train_1127_mbti.sh new file mode 100644 index 0000000000000000000000000000000000000000..eb0eb150eba95f453a57922d1c00fd0d69e2d77d --- /dev/null +++ b/examples/flux/model_training/lora/train_1127_mbti.sh @@ -0,0 +1,18 @@ +accelerate launch --config_file examples/flux/model_training/full/accelerate_config.yaml examples/flux/model_training/train.py \ + --dataset_base_path /fi-lib/workspace/sjx/DiffSynth-Studio/dataset/mbti \ + --dataset_metadata_path /fi-lib/workspace/sjx/DiffSynth-Studio/dataset/mbti/pairs.txt \ + --data_file_keys "image,kontext_images" \ + --max_pixels 1048576 \ + --dataset_repeat 400 \ + --model_id_with_origin_paths "black-forest-labs/FLUX.1-Kontext-dev:flux1-kontext-dev.safetensors,black-forest-labs/FLUX.1-Kontext-dev:text_encoder/model.safetensors,black-forest-labs/FLUX.1-Kontext-dev:text_encoder_2/,black-forest-labs/FLUX.1-Kontext-dev:ae.safetensors" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/FLUX.1_lora_1127_mbti" \ + --lora_base_model "dit" \ + --lora_target_modules "a_to_qkv,b_to_qkv,ff_a.0,ff_a.2,ff_b.0,ff_b.2,a_to_out,b_to_out,proj_out,norm.linear,norm1_a.linear,norm1_b.linear,to_qkv_mlp" \ + --lora_rank 32 \ + --align_to_opensource_format \ + --extra_inputs "kontext_images" \ + --use_gradient_checkpointing \ + --default_caption "Convert this real photo into a mbti style." > flux_train_1127.log 2>&1 \ No newline at end of file diff --git a/examples/flux/model_training/lora/train_1201.sh b/examples/flux/model_training/lora/train_1201.sh new file mode 100644 index 0000000000000000000000000000000000000000..7fa77011eeca500f14bd5499abb915f096460b7e --- /dev/null +++ b/examples/flux/model_training/lora/train_1201.sh @@ -0,0 +1,17 @@ +accelerate launch --config_file examples/flux/model_training/full/accelerate_config.yaml examples/flux/model_training/train.py \ + --dataset_base_path /fi-lib/workspace/sjx/DiffSynth-Studio/dataset/multi_frame \ + --dataset_metadata_path /fi-lib/workspace/sjx/DiffSynth-Studio/dataset/multi_frame/pairs.txt\ + --data_file_keys "image,prompt" \ + --max_pixels 1048576 \ + --dataset_repeat 40 \ + --model_id_with_origin_paths "black-forest-labs/FLUX.1-Kontext-dev:flux1-kontext-dev.safetensors,black-forest-labs/FLUX.1-Kontext-dev:text_encoder/model.safetensors,black-forest-labs/FLUX.1-Kontext-dev:text_encoder_2/,black-forest-labs/FLUX.1-Kontext-dev:ae.safetensors" \ + --learning_rate 1e-5 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/FLUX.1_lora_1201" \ + --lora_base_model "dit" \ + --lora_target_modules "a_to_qkv,b_to_qkv,ff_a.0,ff_a.2,ff_b.0,ff_b.2,a_to_out,b_to_out,proj_out,norm.linear,norm1_a.linear,norm1_b.linear,to_qkv_mlp" \ + --lora_rank 32 \ + --align_to_opensource_format \ + --use_gradient_checkpointing \ + --default_caption "Convert this real photo into a mbti style." > flux_train_1201.log 2>&1 \ No newline at end of file diff --git a/examples/flux/model_training/lora/train_1203.sh b/examples/flux/model_training/lora/train_1203.sh new file mode 100644 index 0000000000000000000000000000000000000000..bde07daa110c40d46d3df3acb61005842abe3d64 --- /dev/null +++ b/examples/flux/model_training/lora/train_1203.sh @@ -0,0 +1,16 @@ +accelerate launch --config_file examples/flux/model_training/full/accelerate_config.yaml examples/flux/model_training/train.py \ + --dataset_base_path /fi-lib/workspace/sjx/DiffSynth-Studio/dataset/spotlight_sketch_cat \ + --dataset_metadata_path /fi-lib/workspace/sjx/DiffSynth-Studio/dataset/spotlight_sketch_cat/pairs.txt \ + --data_file_keys "image,prompt" \ + --max_pixels 1048576 \ + --dataset_repeat 400 \ + --model_id_with_origin_paths "black-forest-labs/FLUX.1-Kontext-dev:flux1-kontext-dev.safetensors,black-forest-labs/FLUX.1-Kontext-dev:text_encoder/model.safetensors,black-forest-labs/FLUX.1-Kontext-dev:text_encoder_2/,black-forest-labs/FLUX.1-Kontext-dev:ae.safetensors" \ + --learning_rate 1e-5 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/FLUX.1_lora_spotlight_1203" \ + --lora_base_model "dit" \ + --lora_target_modules "a_to_qkv,b_to_qkv,ff_a.0,ff_a.2,ff_b.0,ff_b.2,a_to_out,b_to_out,proj_out,norm.linear,norm1_a.linear,norm1_b.linear,to_qkv_mlp" \ + --lora_rank 32 \ + --align_to_opensource_format \ + --use_gradient_checkpointing > flux_train_1203.log 2>&1 \ No newline at end of file diff --git a/examples/flux/model_training/lora/train_1203_i2i.sh b/examples/flux/model_training/lora/train_1203_i2i.sh new file mode 100644 index 0000000000000000000000000000000000000000..50a7621a284e9c6518dc80714a8d111567e87a88 --- /dev/null +++ b/examples/flux/model_training/lora/train_1203_i2i.sh @@ -0,0 +1,18 @@ +accelerate launch --config_file examples/flux/model_training/full/accelerate_config.yaml examples/flux/model_training/train.py \ + --dataset_base_path /fi-lib/workspace/sjx/DiffSynth-Studio/dataset/spotlight_sketch_cat \ + --dataset_metadata_path /fi-lib/workspace/sjx/DiffSynth-Studio/dataset/spotlight_sketch_cat/pairs_t2t.txt \ + --data_file_keys "image,kontext_images" \ + --max_pixels 1048576 \ + --dataset_repeat 400 \ + --model_id_with_origin_paths "black-forest-labs/FLUX.1-Kontext-dev:flux1-kontext-dev.safetensors,black-forest-labs/FLUX.1-Kontext-dev:text_encoder/model.safetensors,black-forest-labs/FLUX.1-Kontext-dev:text_encoder_2/,black-forest-labs/FLUX.1-Kontext-dev:ae.safetensors" \ + --learning_rate 1e-5 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/FLUX.1_lora_1203_i2i_fixcaption" \ + --lora_base_model "dit" \ + --lora_target_modules "a_to_qkv,b_to_qkv,ff_a.0,ff_a.2,ff_b.0,ff_b.2,a_to_out,b_to_out,proj_out,norm.linear,norm1_a.linear,norm1_b.linear,to_qkv_mlp" \ + --lora_rank 32 \ + --align_to_opensource_format \ + --extra_inputs "kontext_images" \ + --use_gradient_checkpointing \ + --default_caption "convert this sketch into real film photos, each photo are consisted of 6 small pictures" > flux_train_1203_i2i.log 2>&1 \ No newline at end of file diff --git a/examples/flux/model_training/train.py b/examples/flux/model_training/train.py new file mode 100644 index 0000000000000000000000000000000000000000..156d06be40fba88f46dd218449195efe59223cb7 --- /dev/null +++ b/examples/flux/model_training/train.py @@ -0,0 +1,122 @@ +import torch, os, json +from diffsynth import load_state_dict +from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig, ControlNetInput +from diffsynth.trainers.utils import DiffusionTrainingModule, ModelLogger, launch_training_task, flux_parser +from diffsynth.models.lora import FluxLoRAConverter +from diffsynth.trainers.unified_dataset import UnifiedDataset +os.environ["TOKENIZERS_PARALLELISM"] = "false" + + + +class FluxTrainingModule(DiffusionTrainingModule): + def __init__( + self, + model_paths=None, model_id_with_origin_paths=None, + trainable_models=None, + lora_base_model=None, lora_target_modules="a_to_qkv,b_to_qkv,ff_a.0,ff_a.2,ff_b.0,ff_b.2,a_to_out,b_to_out,proj_out,norm.linear,norm1_a.linear,norm1_b.linear,to_qkv_mlp", lora_rank=32, lora_checkpoint=None, + use_gradient_checkpointing=True, + use_gradient_checkpointing_offload=False, + extra_inputs=None, + ): + super().__init__() + # Load models + model_configs = self.parse_model_configs(model_paths, model_id_with_origin_paths, enable_fp8_training=False) + self.pipe = FluxImagePipeline.from_pretrained(torch_dtype=torch.bfloat16, device="cpu", model_configs=model_configs) + + # Training mode + self.switch_pipe_to_training_mode( + self.pipe, trainable_models, + lora_base_model, lora_target_modules, lora_rank, lora_checkpoint=lora_checkpoint, + enable_fp8_training=False, + ) + + # Store other configs + self.use_gradient_checkpointing = use_gradient_checkpointing + self.use_gradient_checkpointing_offload = use_gradient_checkpointing_offload + self.extra_inputs = extra_inputs.split(",") if extra_inputs is not None else [] + + + def forward_preprocess(self, data): + # CFG-sensitive parameters + inputs_posi = {"prompt": data["prompt"]} + inputs_nega = {"negative_prompt": ""} + resized_data = data["image"].resize((1024,1024)) + + # CFG-unsensitive parameters + inputs_shared = { + # Assume you are using this pipeline for inference, + # please fill in the input parameters. + "input_image": resized_data, + "height": resized_data.size[1], + "width": resized_data.size[0], + # Please do not modify the following parameters + # unless you clearly know what this will cause. + "cfg_scale": 1, + "embedded_guidance": 1, + "t5_sequence_length": 512, + "tiled": False, + "rand_device": self.pipe.device, + "use_gradient_checkpointing": self.use_gradient_checkpointing, + "use_gradient_checkpointing_offload": self.use_gradient_checkpointing_offload, + } + + # Extra inputs + controlnet_input = {} + for extra_input in self.extra_inputs: + if extra_input.startswith("controlnet_"): + controlnet_input[extra_input.replace("controlnet_", "")] = data[extra_input] + else: + inputs_shared[extra_input] = data[extra_input] + if len(controlnet_input) > 0: + inputs_shared["controlnet_inputs"] = [ControlNetInput(**controlnet_input)] + + # Pipeline units will automatically process the input parameters. + for unit in self.pipe.units: + inputs_shared, inputs_posi, inputs_nega = self.pipe.unit_runner(unit, self.pipe, inputs_shared, inputs_posi, inputs_nega) + return {**inputs_shared, **inputs_posi} + + + def forward(self, data, inputs=None): + if inputs is None: inputs = self.forward_preprocess(data) + models = {name: getattr(self.pipe, name) for name in self.pipe.in_iteration_models} + loss = self.pipe.training_loss(**models, **inputs) + return loss + + + +if __name__ == "__main__": + parser = flux_parser() + args = parser.parse_args() + dataset = UnifiedDataset( + base_path=args.dataset_base_path, + metadata_path=args.dataset_metadata_path, + repeat=args.dataset_repeat, + data_file_keys=args.data_file_keys.split(","), + main_data_operator=UnifiedDataset.default_image_operator( + base_path=args.dataset_base_path, + max_pixels=args.max_pixels, + height=args.height, + width=args.width, + height_division_factor=16, + width_division_factor=16, + ), + default_caption=args.default_caption + ) + model = FluxTrainingModule( + model_paths=args.model_paths, + model_id_with_origin_paths=args.model_id_with_origin_paths, + trainable_models=args.trainable_models, + lora_base_model=args.lora_base_model, + lora_target_modules=args.lora_target_modules, + lora_rank=args.lora_rank, + lora_checkpoint=args.lora_checkpoint, + use_gradient_checkpointing=args.use_gradient_checkpointing, + use_gradient_checkpointing_offload=args.use_gradient_checkpointing_offload, + extra_inputs=args.extra_inputs, + ) + model_logger = ModelLogger( + args.output_path, + remove_prefix_in_ckpt=args.remove_prefix_in_ckpt, + state_dict_converter=FluxLoRAConverter.align_to_opensource_format if args.align_to_opensource_format else lambda x:x, + ) + launch_training_task(dataset, model, model_logger, args=args) diff --git a/examples/flux/model_training/validate_full/FLEX.2-preview.py b/examples/flux/model_training/validate_full/FLEX.2-preview.py new file mode 100644 index 0000000000000000000000000000000000000000..78f64b606a7447dba5317f07e2ce8f9ea9af0bbb --- /dev/null +++ b/examples/flux/model_training/validate_full/FLEX.2-preview.py @@ -0,0 +1,20 @@ +import torch +from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig +from diffsynth import load_state_dict + + +pipe = FluxImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="ostris/Flex.2-preview", origin_file_pattern="Flex.2-preview.safetensors"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors"), + ], +) +state_dict = load_state_dict("models/train/FLEX.2-preview_full/epoch-0.safetensors") +pipe.dit.load_state_dict(state_dict) + +image = pipe(prompt="dog,white and brown dog, sitting on wall, under pink flowers", seed=0) +image.save("image_FLEX.2-preview_full.jpg") diff --git a/examples/flux/model_training/validate_full/FLUX.1-Kontext-dev.py b/examples/flux/model_training/validate_full/FLUX.1-Kontext-dev.py new file mode 100644 index 0000000000000000000000000000000000000000..af3ee3619d36cf48598cf463bbd5c0866b1b381c --- /dev/null +++ b/examples/flux/model_training/validate_full/FLUX.1-Kontext-dev.py @@ -0,0 +1,26 @@ +import torch +from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig +from diffsynth import load_state_dict +from PIL import Image + + +pipe = FluxImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="black-forest-labs/FLUX.1-Kontext-dev", origin_file_pattern="flux1-kontext-dev.safetensors"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors"), + ], +) +state_dict = load_state_dict("models/train/FLUX.1-Kontext-dev_full/epoch-0.safetensors") +pipe.dit.load_state_dict(state_dict) + +image = pipe( + prompt="Make the dog turn its head around.", + kontext_images=Image.open("data/example_image_dataset/2.jpg").resize((768, 768)), + height=768, width=768, + seed=0 +) +image.save("image_FLUX.1-Kontext-dev_full.jpg") diff --git a/examples/flux/model_training/validate_full/FLUX.1-Krea-dev.py b/examples/flux/model_training/validate_full/FLUX.1-Krea-dev.py new file mode 100644 index 0000000000000000000000000000000000000000..bceb4644d20db6a182793f982f52f511e82d7206 --- /dev/null +++ b/examples/flux/model_training/validate_full/FLUX.1-Krea-dev.py @@ -0,0 +1,20 @@ +import torch +from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig +from diffsynth import load_state_dict + + +pipe = FluxImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="black-forest-labs/FLUX.1-Krea-dev", origin_file_pattern="flux1-krea-dev.safetensors"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors"), + ], +) +state_dict = load_state_dict("models/train/FLUX.1-Krea-dev_full/epoch-0.safetensors") +pipe.dit.load_state_dict(state_dict) + +image = pipe(prompt="a dog", seed=0) +image.save("image_FLUX.1-Krea-dev_full.jpg") diff --git a/examples/flux/model_training/validate_full/FLUX.1-dev-AttriCtrl.py b/examples/flux/model_training/validate_full/FLUX.1-dev-AttriCtrl.py new file mode 100644 index 0000000000000000000000000000000000000000..17384fce08fa4fad90353ff5b8e8244eb8a23800 --- /dev/null +++ b/examples/flux/model_training/validate_full/FLUX.1-dev-AttriCtrl.py @@ -0,0 +1,21 @@ +import torch +from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig +from diffsynth import load_state_dict + + +pipe = FluxImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-dev.safetensors"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors"), + ModelConfig(model_id="DiffSynth-Studio/AttriCtrl-FLUX.1-Dev", origin_file_pattern="models/brightness.safetensors") + ], +) +state_dict = load_state_dict("models/train/FLUX.1-dev-AttriCtrl_full/epoch-0.safetensors") +pipe.value_controller.encoders[0].load_state_dict(state_dict) + +image = pipe(prompt="a cat", seed=0, value_controller_inputs=0.1, rand_device="cuda") +image.save("image_FLUX.1-dev-AttriCtrl_full.jpg") diff --git a/examples/flux/model_training/validate_full/FLUX.1-dev-Controlnet-Inpainting-Beta.py b/examples/flux/model_training/validate_full/FLUX.1-dev-Controlnet-Inpainting-Beta.py new file mode 100644 index 0000000000000000000000000000000000000000..1d35f7e33fea04b4aefdc3ee55572b7b2bd35fe3 --- /dev/null +++ b/examples/flux/model_training/validate_full/FLUX.1-dev-Controlnet-Inpainting-Beta.py @@ -0,0 +1,31 @@ +import torch +from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig, ControlNetInput +from diffsynth import load_state_dict +from PIL import Image + + +pipe = FluxImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-dev.safetensors"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors"), + ModelConfig(model_id="alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta", origin_file_pattern="diffusion_pytorch_model.safetensors"), + ], +) +state_dict = load_state_dict("models/train/FLUX.1-dev-Controlnet-Inpainting-Beta_full/epoch-0.safetensors") +pipe.controlnet.models[0].load_state_dict(state_dict) + +image = pipe( + prompt="a cat sitting on a chair, wearing sunglasses", + controlnet_inputs=[ControlNetInput( + image=Image.open("data/example_image_dataset/inpaint/image_1.jpg"), + inpaint_mask=Image.open("data/example_image_dataset/inpaint/mask.jpg"), + scale=0.9 + )], + height=1024, width=1024, + seed=0, rand_device="cuda", +) +image.save("image_FLUX.1-dev-Controlnet-Inpainting-Beta_full.jpg") diff --git a/examples/flux/model_training/validate_full/FLUX.1-dev-Controlnet-Union-alpha.py b/examples/flux/model_training/validate_full/FLUX.1-dev-Controlnet-Union-alpha.py new file mode 100644 index 0000000000000000000000000000000000000000..ceaadd8d114219a33e764a59f65f4d66c41104bb --- /dev/null +++ b/examples/flux/model_training/validate_full/FLUX.1-dev-Controlnet-Union-alpha.py @@ -0,0 +1,31 @@ +import torch +from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig, ControlNetInput +from diffsynth import load_state_dict +from PIL import Image + + +pipe = FluxImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-dev.safetensors"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors"), + ModelConfig(model_id="InstantX/FLUX.1-dev-Controlnet-Union-alpha", origin_file_pattern="diffusion_pytorch_model.safetensors"), + ], +) +state_dict = load_state_dict("models/train/FLUX.1-dev-Controlnet-Union-alpha_full/epoch-0.safetensors") +pipe.controlnet.models[0].load_state_dict(state_dict) + +image = pipe( + prompt="a dog", + controlnet_inputs=[ControlNetInput( + image=Image.open("data/example_image_dataset/canny/image_1.jpg"), + scale=0.9, + processor_id="canny", + )], + height=768, width=768, + seed=0, rand_device="cuda", +) +image.save("image_FLUX.1-dev-Controlnet-Union-alpha_full.jpg") diff --git a/examples/flux/model_training/validate_full/FLUX.1-dev-Controlnet-Upscaler.py b/examples/flux/model_training/validate_full/FLUX.1-dev-Controlnet-Upscaler.py new file mode 100644 index 0000000000000000000000000000000000000000..3ff8319e373ccf5e61026f7308d09b0d14987054 --- /dev/null +++ b/examples/flux/model_training/validate_full/FLUX.1-dev-Controlnet-Upscaler.py @@ -0,0 +1,30 @@ +import torch +from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig, ControlNetInput +from diffsynth import load_state_dict +from PIL import Image + + +pipe = FluxImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-dev.safetensors"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors"), + ModelConfig(model_id="jasperai/Flux.1-dev-Controlnet-Upscaler", origin_file_pattern="diffusion_pytorch_model.safetensors"), + ], +) +state_dict = load_state_dict("models/train/FLUX.1-dev-Controlnet-Upscaler_full/epoch-0.safetensors") +pipe.controlnet.models[0].load_state_dict(state_dict) + +image = pipe( + prompt="a dog", + controlnet_inputs=[ControlNetInput( + image=Image.open("data/example_image_dataset/upscale/image_1.jpg"), + scale=0.9 + )], + height=768, width=768, + seed=0, rand_device="cuda", +) +image.save("image_FLUX.1-dev-Controlnet-Upscaler_full.jpg") diff --git a/examples/flux/model_training/validate_full/FLUX.1-dev-IP-Adapter.py b/examples/flux/model_training/validate_full/FLUX.1-dev-IP-Adapter.py new file mode 100644 index 0000000000000000000000000000000000000000..b6bab3d8e8fd3be30814abc181ff211faab05057 --- /dev/null +++ b/examples/flux/model_training/validate_full/FLUX.1-dev-IP-Adapter.py @@ -0,0 +1,28 @@ +import torch +from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig +from diffsynth import load_state_dict +from PIL import Image + + +pipe = FluxImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-dev.safetensors"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors"), + ModelConfig(model_id="InstantX/FLUX.1-dev-IP-Adapter", origin_file_pattern="ip-adapter.bin"), + ModelConfig(model_id="google/siglip-so400m-patch14-384"), + ], +) +state_dict = load_state_dict("models/train/FLUX.1-dev-IP-Adapter_full/epoch-0.safetensors") +pipe.ipadapter.load_state_dict(state_dict) + +image = pipe( + prompt="a dog", + ipadapter_images=Image.open("data/example_image_dataset/1.jpg"), + height=768, width=768, + seed=0 +) +image.save("image_FLUX.1-dev-IP-Adapter_full.jpg") diff --git a/examples/flux/model_training/validate_full/FLUX.1-dev-InfiniteYou.py b/examples/flux/model_training/validate_full/FLUX.1-dev-InfiniteYou.py new file mode 100644 index 0000000000000000000000000000000000000000..55b7275038d25a6dbcb3d4a6ee60f7ae9c7e73b6 --- /dev/null +++ b/examples/flux/model_training/validate_full/FLUX.1-dev-InfiniteYou.py @@ -0,0 +1,33 @@ +import torch +from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig, ControlNetInput +from diffsynth import load_state_dict +from PIL import Image + + +pipe = FluxImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-dev.safetensors"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors"), + ModelConfig(model_id="ByteDance/InfiniteYou", origin_file_pattern="infu_flux_v1.0/aes_stage2/image_proj_model.bin"), + ModelConfig(model_id="ByteDance/InfiniteYou", origin_file_pattern="infu_flux_v1.0/aes_stage2/InfuseNetModel/*.safetensors"), + ], +) +state_dict = load_state_dict("models/train/FLUX.1-dev-InfiniteYou_full/epoch-0.safetensors") +state_dict_projector = {i.replace("image_proj_model.", ""): state_dict[i] for i in state_dict if i.startswith("image_proj_model.")} +pipe.image_proj_model.load_state_dict(state_dict_projector) +state_dict_controlnet = {i.replace("controlnet.models.0.", ""): state_dict[i] for i in state_dict if i.startswith("controlnet.models.0.")} +pipe.controlnet.models[0].load_state_dict(state_dict_controlnet) + +image = pipe( + prompt="a man with a red hat", + controlnet_inputs=[ControlNetInput( + image=Image.open("data/example_image_dataset/infiniteyou/image_1.jpg"), + )], + height=1024, width=1024, + seed=0, rand_device="cuda", +) +image.save("image_FLUX.1-dev-InfiniteYou_full.jpg") diff --git a/examples/flux/model_training/validate_full/FLUX.1-dev-LoRA-Encoder.py b/examples/flux/model_training/validate_full/FLUX.1-dev-LoRA-Encoder.py new file mode 100644 index 0000000000000000000000000000000000000000..166f5a4be7b231c14b2d4c3b9bcf04387b140585 --- /dev/null +++ b/examples/flux/model_training/validate_full/FLUX.1-dev-LoRA-Encoder.py @@ -0,0 +1,25 @@ +import torch +from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig +from diffsynth import load_state_dict + + +pipe = FluxImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-dev.safetensors"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors"), + ModelConfig(model_id="DiffSynth-Studio/LoRA-Encoder-FLUX.1-Dev", origin_file_pattern="model.safetensors"), + ], +) +pipe.enable_lora_magic() +state_dict = load_state_dict("models/train/FLUX.1-dev-LoRA-Encoder_full/epoch-0.safetensors") +pipe.lora_encoder.load_state_dict(state_dict) + +lora = ModelConfig(model_id="VoidOc/flux_animal_forest1", origin_file_pattern="20.safetensors") +pipe.load_lora(pipe.dit, lora, hotload=True) # Use `pipe.clear_lora()` to drop the loaded LoRA. + +image = pipe(prompt="", seed=0, lora_encoder_inputs=lora) +image.save("image_FLUX.1-dev-LoRA-Encoder_full.jpg") diff --git a/examples/flux/model_training/validate_full/FLUX.1-dev.py b/examples/flux/model_training/validate_full/FLUX.1-dev.py new file mode 100644 index 0000000000000000000000000000000000000000..d3adf7a6435f1a4abaa40bebd4445fe66278e881 --- /dev/null +++ b/examples/flux/model_training/validate_full/FLUX.1-dev.py @@ -0,0 +1,20 @@ +import torch +from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig +from diffsynth import load_state_dict + + +pipe = FluxImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-dev.safetensors"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors"), + ], +) +state_dict = load_state_dict("models/train/FLUX.1-dev_full/epoch-0.safetensors") +pipe.dit.load_state_dict(state_dict) + +image = pipe(prompt="a dog", seed=0) +image.save("image_FLUX.1-dev_full.jpg") diff --git a/examples/flux/model_training/validate_full/Nexus-Gen.py b/examples/flux/model_training/validate_full/Nexus-Gen.py new file mode 100644 index 0000000000000000000000000000000000000000..5f7a2d240a1baef2866ef1f7db34c47ca2850dcd --- /dev/null +++ b/examples/flux/model_training/validate_full/Nexus-Gen.py @@ -0,0 +1,28 @@ +import torch +from PIL import Image +from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig +from diffsynth import load_state_dict + +pipe = FluxImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="DiffSynth-Studio/Nexus-GenV2", origin_file_pattern="model*.safetensors"), + ModelConfig(model_id="DiffSynth-Studio/Nexus-GenV2", origin_file_pattern="edit_decoder.bin"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors"), + ], +) +state_dict = load_state_dict("models/train/FLUX.1-NexusGen-Edit_full/epoch-0.safetensors") +pipe.dit.load_state_dict(state_dict) + +ref_image = Image.open("data/example_image_dataset/nexus_gen/image_1.png").convert("RGB") +prompt = "Add a pair of sunglasses." +image = pipe( + prompt=prompt, negative_prompt="", + seed=42, cfg_scale=2.0, num_inference_steps=50, + nexus_gen_reference_image=ref_image, + height=512, width=512, +) +image.save("NexusGen-Edit_full.jpg") diff --git a/examples/flux/model_training/validate_full/Step1X-Edit.py b/examples/flux/model_training/validate_full/Step1X-Edit.py new file mode 100644 index 0000000000000000000000000000000000000000..ab184e7d2cde606615d5d69766990dbae6fd9c94 --- /dev/null +++ b/examples/flux/model_training/validate_full/Step1X-Edit.py @@ -0,0 +1,25 @@ +import torch +from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig +from diffsynth import load_state_dict +from PIL import Image + + +pipe = FluxImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen2.5-VL-7B-Instruct"), + ModelConfig(model_id="stepfun-ai/Step1X-Edit", origin_file_pattern="step1x-edit-i1258.safetensors"), + ModelConfig(model_id="stepfun-ai/Step1X-Edit", origin_file_pattern="vae.safetensors"), + ], +) +state_dict = load_state_dict("models/train/Step1X-Edit_full/epoch-0.safetensors") +pipe.dit.load_state_dict(state_dict) + +image = pipe( + prompt="Make the dog turn its head around.", + step1x_reference_image=Image.open("data/example_image_dataset/2.jpg").resize((768, 768)), + height=768, width=768, cfg_scale=6, + seed=0 +) +image.save("image_Step1X-Edit_full.jpg") diff --git a/examples/flux/model_training/validate_lora/FLEX.2-preview.py b/examples/flux/model_training/validate_lora/FLEX.2-preview.py new file mode 100644 index 0000000000000000000000000000000000000000..1ef01427ffb47560c96f620268c9b8b5a255677a --- /dev/null +++ b/examples/flux/model_training/validate_lora/FLEX.2-preview.py @@ -0,0 +1,18 @@ +import torch +from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig + + +pipe = FluxImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="ostris/Flex.2-preview", origin_file_pattern="Flex.2-preview.safetensors"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors"), + ], +) +pipe.load_lora(pipe.dit, "models/train/FLEX.2-preview_lora/epoch-4.safetensors", alpha=1) + +image = pipe(prompt="dog,white and brown dog, sitting on wall, under pink flowers", seed=0) +image.save("image_FLEX.2-preview_lora.jpg") diff --git a/examples/flux/model_training/validate_lora/FLUX.1-Kontext-dev.py b/examples/flux/model_training/validate_lora/FLUX.1-Kontext-dev.py new file mode 100644 index 0000000000000000000000000000000000000000..6839b7102274b7c1979cc5863295c6e6ea078ea6 --- /dev/null +++ b/examples/flux/model_training/validate_lora/FLUX.1-Kontext-dev.py @@ -0,0 +1,83 @@ +import torch +from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig +from PIL import Image +import os +import json +import shutil +from tqdm import tqdm + +for i in range(1): + pipe = FluxImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda:0",# 改1 + model_configs=[ + ModelConfig(model_id="black-forest-labs/FLUX.1-Kontext-dev", origin_file_pattern="flux1-kontext-dev.safetensors"), + ModelConfig(model_id="black-forest-labs/FLUX.1-Kontext-dev", origin_file_pattern="text_encoder/model.safetensors"), + ModelConfig(model_id="black-forest-labs/FLUX.1-Kontext-dev", origin_file_pattern="text_encoder_2/"), + ModelConfig(model_id="black-forest-labs/FLUX.1-Kontext-dev", origin_file_pattern="ae.safetensors"), + ], + ) + # pipe.load_lora(pipe.dit, f"models/train/FLUX.1_lora_1203_i2i_fixcaption/epoch-{i}.safetensors", alpha=1) # 改 2 + # print("load lora successfully") + step = 25 + input_path = "dataset/amelie_sketch_cat/epoch0" # 改3 + base_path = f"dataset/amelie_sketch_cat" # 改4 + save_path = f"{base_path}/validate_real_init" # 改5 + # save_path_GT = f"{base_path}/GT" + os.makedirs(save_path, exist_ok=True) + # os.makedirs(save_path_GT, exist_ok=True) + # for img in tqdm(os.listdir(input_path)): + # shutil.copy2(os.path.join(input_path,img),os.path.join(save_path_GT,img)) + # prompt="Convert this image into a line art style: retain the original scenes and characters unchanged, present it as a black-and-white sketch effect, and make it suitable for storyboard design. Requirements: use bold and powerful lines, highlight structures and textures with concise strokes, adopt a style close to comic sketching, roughly outline the scenes and character movements with simple lines, prohibit the depiction of details, and represent the characters' facial features with the simplest lines.", + prompt = "convert this sketch into real film photos, each photo are consisted of 6 small pictures" + # prompt = "Convert this image into a mbti style" + print("finished copy gt") + for fig in os.listdir(input_path): + # if not fig.endswith(".png") or not fig.endswith(".jpg"): + # continue + if os.path.exists(os.path.join(save_path,fig)): + continue + print("begin infer") + image = pipe( + prompt = prompt, + kontext_images=Image.open(os.path.join(input_path,fig)).resize((960, 576)), + height=960, width=576, + seed=0, + num_inference_steps=step + ) + image.save(os.path.join(save_path,fig)) + +# for i in range(1): +# pipe = FluxImagePipeline.from_pretrained( +# torch_dtype=torch.bfloat16, +# device="cuda", +# model_configs=[ +# ModelConfig(model_id="black-forest-labs/FLUX.1-Kontext-dev", origin_file_pattern="flux1-kontext-dev.safetensors"), +# ModelConfig(model_id="black-forest-labs/FLUX.1-Kontext-dev", origin_file_pattern="text_encoder/model.safetensors"), +# ModelConfig(model_id="black-forest-labs/FLUX.1-Kontext-dev", origin_file_pattern="text_encoder_2/"), +# ModelConfig(model_id="black-forest-labs/FLUX.1-Kontext-dev", origin_file_pattern="ae.safetensors"), +# ], +# ) +# pipe.load_lora(pipe.dit, f"models/train/FLUX.1_lora_spotlight_1203/epoch-{i}.safetensors", alpha=1) +# step = 5 +# base_path = f"/fi-lib/workspace/sjx/DiffSynth-Studio/validate_result/t2i_1203_STE_{step}" +# save_path = f"{base_path}/epoch{i}" +# os.makedirs(save_path, exist_ok=True) +# with open("nano_comprehension_1201.txt", "r") as f: +# prompts = f.readlines() +# for prompt in prompts: +# prompt = prompt.strip() +# if prompt == "": +# continue +# prompt_dict = json.loads(prompt) +# fig = f"{prompt_dict["Image_Name"]}.png" +# del prompt_dict["Image_Name"] +# prompt = json.dumps(prompt_dict, ensure_ascii=False) +# image = pipe( +# prompt ="draw a flower", +# height=768, width=768, +# seed=0, +# num_inference_steps=step +# ) +# image.save(os.path.join(save_path,fig)) + \ No newline at end of file diff --git a/examples/flux/model_training/validate_lora/FLUX.1-Krea-dev.py b/examples/flux/model_training/validate_lora/FLUX.1-Krea-dev.py new file mode 100644 index 0000000000000000000000000000000000000000..068ce7200892a84d00a412083fd7a14056f89d12 --- /dev/null +++ b/examples/flux/model_training/validate_lora/FLUX.1-Krea-dev.py @@ -0,0 +1,18 @@ +import torch +from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig + + +pipe = FluxImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="black-forest-labs/FLUX.1-Krea-dev", origin_file_pattern="flux1-krea-dev.safetensors"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors"), + ], +) +pipe.load_lora(pipe.dit, "models/train/FLUX.1-Krea-dev_lora/epoch-4.safetensors", alpha=1) + +image = pipe(prompt="a dog", seed=0) +image.save("image_FLUX.1-Krea-dev_lora.jpg") diff --git a/examples/flux/model_training/validate_lora/FLUX.1-dev-AttriCtrl.py b/examples/flux/model_training/validate_lora/FLUX.1-dev-AttriCtrl.py new file mode 100644 index 0000000000000000000000000000000000000000..f44df0dff307dbaab5867db429362b713d20203c --- /dev/null +++ b/examples/flux/model_training/validate_lora/FLUX.1-dev-AttriCtrl.py @@ -0,0 +1,19 @@ +import torch +from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig + + +pipe = FluxImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-dev.safetensors"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors"), + ModelConfig(model_id="DiffSynth-Studio/AttriCtrl-FLUX.1-Dev", origin_file_pattern="models/brightness.safetensors") + ], +) +pipe.load_lora(pipe.dit, "models/train/FLUX.1-dev-AttriCtrl_lora/epoch-3.safetensors", alpha=1) + +image = pipe(prompt="a cat", seed=0, value_controller_inputs=0.1, rand_device="cuda") +image.save("image_FLUX.1-dev-AttriCtrl_lora.jpg") diff --git a/examples/flux/model_training/validate_lora/FLUX.1-dev-Controlnet-Inpainting-Beta.py b/examples/flux/model_training/validate_lora/FLUX.1-dev-Controlnet-Inpainting-Beta.py new file mode 100644 index 0000000000000000000000000000000000000000..6d88b8c434efb0351f10b01498da4d1ec657ec20 --- /dev/null +++ b/examples/flux/model_training/validate_lora/FLUX.1-dev-Controlnet-Inpainting-Beta.py @@ -0,0 +1,29 @@ +import torch +from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig, ControlNetInput +from PIL import Image + + +pipe = FluxImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-dev.safetensors"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors"), + ModelConfig(model_id="alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta", origin_file_pattern="diffusion_pytorch_model.safetensors"), + ], +) +pipe.load_lora(pipe.dit, "models/train/FLUX.1-dev-Controlnet-Inpainting-Beta_lora/epoch-4.safetensors", alpha=1) + +image = pipe( + prompt="a cat sitting on a chair, wearing sunglasses", + controlnet_inputs=[ControlNetInput( + image=Image.open("data/example_image_dataset/inpaint/image_1.jpg"), + inpaint_mask=Image.open("data/example_image_dataset/inpaint/mask.jpg"), + scale=0.9 + )], + height=1024, width=1024, + seed=0, rand_device="cuda", +) +image.save("image_FLUX.1-dev-Controlnet-Inpainting-Beta_lora.jpg") diff --git a/examples/flux/model_training/validate_lora/FLUX.1-dev-Controlnet-Union-alpha.py b/examples/flux/model_training/validate_lora/FLUX.1-dev-Controlnet-Union-alpha.py new file mode 100644 index 0000000000000000000000000000000000000000..240d8b62e0dcdbb1b0fddb177b01e2ea6950a1d6 --- /dev/null +++ b/examples/flux/model_training/validate_lora/FLUX.1-dev-Controlnet-Union-alpha.py @@ -0,0 +1,29 @@ +import torch +from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig, ControlNetInput +from PIL import Image + + +pipe = FluxImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-dev.safetensors"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors"), + ModelConfig(model_id="InstantX/FLUX.1-dev-Controlnet-Union-alpha", origin_file_pattern="diffusion_pytorch_model.safetensors"), + ], +) +pipe.load_lora(pipe.dit, "models/train/FLUX.1-dev-Controlnet-Union-alpha_lora/epoch-4.safetensors", alpha=1) + +image = pipe( + prompt="a dog", + controlnet_inputs=[ControlNetInput( + image=Image.open("data/example_image_dataset/canny/image_1.jpg"), + scale=0.9, + processor_id="canny", + )], + height=768, width=768, + seed=0, rand_device="cuda", +) +image.save("image_FLUX.1-dev-Controlnet-Union-alpha_lora.jpg") diff --git a/examples/flux/model_training/validate_lora/FLUX.1-dev-Controlnet-Upscaler.py b/examples/flux/model_training/validate_lora/FLUX.1-dev-Controlnet-Upscaler.py new file mode 100644 index 0000000000000000000000000000000000000000..b27896c2bad50f630f665ba64e0bca036ab34bb1 --- /dev/null +++ b/examples/flux/model_training/validate_lora/FLUX.1-dev-Controlnet-Upscaler.py @@ -0,0 +1,28 @@ +import torch +from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig, ControlNetInput +from PIL import Image + + +pipe = FluxImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-dev.safetensors"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors"), + ModelConfig(model_id="jasperai/Flux.1-dev-Controlnet-Upscaler", origin_file_pattern="diffusion_pytorch_model.safetensors"), + ], +) +pipe.load_lora(pipe.dit, "models/train/FLUX.1-dev-Controlnet-Upscaler_lora/epoch-4.safetensors", alpha=1) + +image = pipe( + prompt="a dog", + controlnet_inputs=[ControlNetInput( + image=Image.open("data/example_image_dataset/upscale/image_1.jpg"), + scale=0.9 + )], + height=768, width=768, + seed=0, rand_device="cuda", +) +image.save("image_FLUX.1-dev-Controlnet-Upscaler_lora.jpg") diff --git a/examples/flux/model_training/validate_lora/FLUX.1-dev-EliGen.py b/examples/flux/model_training/validate_lora/FLUX.1-dev-EliGen.py new file mode 100644 index 0000000000000000000000000000000000000000..7df3db2a3414148d4a121790fa7e6dc74b4c3848 --- /dev/null +++ b/examples/flux/model_training/validate_lora/FLUX.1-dev-EliGen.py @@ -0,0 +1,33 @@ +import torch +from PIL import Image +from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig + +pipe = FluxImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-dev.safetensors"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors"), + ], +) + +pipe.load_lora(pipe.dit, "models/train/FLUX.1-dev-EliGen_lora/epoch-4.safetensors", alpha=1) + +entity_prompts = ["A beautiful girl", "sign 'Entity Control'", "shorts", "shirt"] +global_prompt = "A beautiful girl wearing shirt and shorts in the street, holding a sign 'Entity Control'" +masks = [Image.open(f"data/example_image_dataset/eligen/{i}.png").convert('RGB') for i in range(len(entity_prompts))] +# generate image +image = pipe( + prompt=global_prompt, + cfg_scale=1.0, + num_inference_steps=50, + embedded_guidance=3.5, + seed=42, + height=1024, + width=1024, + eligen_entity_prompts=entity_prompts, + eligen_entity_masks=masks, +) +image.save(f"EliGen_lora.png") diff --git a/examples/flux/model_training/validate_lora/FLUX.1-dev-IP-Adapter.py b/examples/flux/model_training/validate_lora/FLUX.1-dev-IP-Adapter.py new file mode 100644 index 0000000000000000000000000000000000000000..b085ad7193931f2679575294c02d749fc71b8ca5 --- /dev/null +++ b/examples/flux/model_training/validate_lora/FLUX.1-dev-IP-Adapter.py @@ -0,0 +1,26 @@ +import torch +from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig +from PIL import Image + + +pipe = FluxImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-dev.safetensors"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors"), + ModelConfig(model_id="InstantX/FLUX.1-dev-IP-Adapter", origin_file_pattern="ip-adapter.bin"), + ModelConfig(model_id="google/siglip-so400m-patch14-384"), + ], +) +pipe.load_lora(pipe.dit, "models/train/FLUX.1-dev-IP-Adapter_lora/epoch-4.safetensors", alpha=1) + +image = pipe( + prompt="dog,white and brown dog, sitting on wall, under pink flowers", + ipadapter_images=Image.open("data/example_image_dataset/1.jpg"), + height=768, width=768, + seed=0 +) +image.save("image_FLUX.1-dev-IP-Adapter_lora.jpg") diff --git a/examples/flux/model_training/validate_lora/FLUX.1-dev-InfiniteYou.py b/examples/flux/model_training/validate_lora/FLUX.1-dev-InfiniteYou.py new file mode 100644 index 0000000000000000000000000000000000000000..1d9d8a2e240e9f53510f0a20ab9f178643db45a2 --- /dev/null +++ b/examples/flux/model_training/validate_lora/FLUX.1-dev-InfiniteYou.py @@ -0,0 +1,28 @@ +import torch +from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig, ControlNetInput +from PIL import Image + + +pipe = FluxImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-dev.safetensors"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors"), + ModelConfig(model_id="ByteDance/InfiniteYou", origin_file_pattern="infu_flux_v1.0/aes_stage2/image_proj_model.bin"), + ModelConfig(model_id="ByteDance/InfiniteYou", origin_file_pattern="infu_flux_v1.0/aes_stage2/InfuseNetModel/*.safetensors"), + ], +) +pipe.load_lora(pipe.dit, "models/train/FLUX.1-dev-InfiniteYou_lora/epoch-4.safetensors", alpha=1) + +image = pipe( + prompt="a man with a red hat", + controlnet_inputs=[ControlNetInput( + image=Image.open("data/example_image_dataset/infiniteyou/image_1.jpg"), + )], + height=1024, width=1024, + seed=0, rand_device="cuda", +) +image.save("image_FLUX.1-dev-InfiniteYou_lora.jpg") diff --git a/examples/flux/model_training/validate_lora/FLUX.1-dev.py b/examples/flux/model_training/validate_lora/FLUX.1-dev.py new file mode 100644 index 0000000000000000000000000000000000000000..d1aebef4887d2be10c3384b7c1409af139911206 --- /dev/null +++ b/examples/flux/model_training/validate_lora/FLUX.1-dev.py @@ -0,0 +1,18 @@ +import torch +from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig + + +pipe = FluxImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-dev.safetensors"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors"), + ], +) +pipe.load_lora(pipe.dit, "models/train/FLUX.1-dev_lora/epoch-4.safetensors", alpha=1) + +image = pipe(prompt="a dog", seed=0) +image.save("image_FLUX.1-dev_lora.jpg") diff --git a/examples/flux/model_training/validate_lora/Nexus-Gen.py b/examples/flux/model_training/validate_lora/Nexus-Gen.py new file mode 100644 index 0000000000000000000000000000000000000000..21c376fbb1e8a8996a18786f44dcdc86dfaa1ff5 --- /dev/null +++ b/examples/flux/model_training/validate_lora/Nexus-Gen.py @@ -0,0 +1,26 @@ +import torch +from PIL import Image +from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig + +pipe = FluxImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="DiffSynth-Studio/Nexus-GenV2", origin_file_pattern="model*.safetensors"), + ModelConfig(model_id="DiffSynth-Studio/Nexus-GenV2", origin_file_pattern="edit_decoder.bin"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/"), + ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors"), + ], +) +pipe.load_lora(pipe.dit, "models/train/FLUX.1-NexusGen-Edit_lora/epoch-4.safetensors", alpha=1) + +ref_image = Image.open("data/example_image_dataset/nexus_gen/image_1.png").convert("RGB") +prompt = "Add a pair of sunglasses." +image = pipe( + prompt=prompt, negative_prompt="", + seed=42, cfg_scale=1.0, num_inference_steps=50, + nexus_gen_reference_image=ref_image, + height=512, width=512, +) +image.save("NexusGen-Edit_lora.jpg") diff --git a/examples/flux/model_training/validate_lora/Step1X-Edit.py b/examples/flux/model_training/validate_lora/Step1X-Edit.py new file mode 100644 index 0000000000000000000000000000000000000000..886af3f686c96424bac7585860aeb82e40100fab --- /dev/null +++ b/examples/flux/model_training/validate_lora/Step1X-Edit.py @@ -0,0 +1,23 @@ +import torch +from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig +from PIL import Image + + +pipe = FluxImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen2.5-VL-7B-Instruct"), + ModelConfig(model_id="stepfun-ai/Step1X-Edit", origin_file_pattern="step1x-edit-i1258.safetensors"), + ModelConfig(model_id="stepfun-ai/Step1X-Edit", origin_file_pattern="vae.safetensors"), + ], +) +pipe.load_lora(pipe.dit, "models/train/Step1X-Edit_lora/epoch-4.safetensors", alpha=1) + +image = pipe( + prompt="Make the dog turn its head around.", + step1x_reference_image=Image.open("data/example_image_dataset/2.jpg").resize((768, 768)), + height=768, width=768, cfg_scale=6, + seed=0 +) +image.save("image_Step1X-Edit_lora.jpg") diff --git a/examples/image_quality_metric/README.md b/examples/image_quality_metric/README.md new file mode 100644 index 0000000000000000000000000000000000000000..6d5bf8ba49a89c4d216864f2a4355ff5d4df8568 --- /dev/null +++ b/examples/image_quality_metric/README.md @@ -0,0 +1,15 @@ +# Image Quality Metric + +The image quality assessment functionality has been integrated into Diffsynth. We support the following models: + +* [ImageReward](https://github.com/THUDM/ImageReward) +* [Aesthetic](https://github.com/christophschuhmann/improved-aesthetic-predictor) +* [PickScore](https://github.com/yuvalkirstain/pickscore) +* [CLIP](https://github.com/openai/CLIP) +* [HPSv2](https://github.com/tgxs002/HPSv2) +* [HPSv2.1](https://github.com/tgxs002/HPSv2) +* [MPS](https://github.com/Kwai-Kolors/MPS) + +## Usage + +See [`./image_quality_evaluation.py`](./image_quality_evaluation.py) for more details. diff --git a/examples/image_quality_metric/image_quality_evaluation.py b/examples/image_quality_metric/image_quality_evaluation.py new file mode 100644 index 0000000000000000000000000000000000000000..9911cf8d234b4e3b2402b7c7ae24a194ea330773 --- /dev/null +++ b/examples/image_quality_metric/image_quality_evaluation.py @@ -0,0 +1,23 @@ +from diffsynth.extensions.ImageQualityMetric import download_preference_model, load_preference_model +from modelscope import dataset_snapshot_download +from PIL import Image + + +# Download example image +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/examples_in_diffsynth", + allow_file_pattern="data/examples/ImageQualityMetric/image.jpg", + local_dir="./" +) + +# Parameters +prompt = "an orange cat" +image = Image.open("data/examples/ImageQualityMetric/image.jpg") +device = "cuda" +cache_dir = "./models" + +# Run preference models +for model_name in ["ImageReward", "Aesthetic", "PickScore", "CLIP", "HPSv2", "HPSv2.1", "MPS"]: + path = download_preference_model(model_name, cache_dir=cache_dir) + preference_model = load_preference_model(model_name, device=device, path=path) + print(model_name, preference_model.score(image, prompt)) diff --git a/examples/image_synthesis/README.md b/examples/image_synthesis/README.md new file mode 100644 index 0000000000000000000000000000000000000000..ac133d28a46aa6a83e489580725850dc72c34b08 --- /dev/null +++ b/examples/image_synthesis/README.md @@ -0,0 +1,95 @@ +# Image Synthesis + +Image synthesis is the base feature of DiffSynth Studio. We can generate images with very high resolution. + +### OmniGen + +OmniGen is a text-image-to-image model, you can synthesize an image according to several given reference images. + +|Reference image 1|Reference image 2|Synthesized image| +|-|-|-| +|![image_man](https://github.com/user-attachments/assets/35d00493-625b-45d1-ad2b-b558ea09fe36)|![image_woman](https://github.com/user-attachments/assets/abebf69b-3563-4b3b-91c2-c48ff74a29ea)|![image_merged](https://github.com/user-attachments/assets/2979d5a9-f355-4bec-a91d-1e824d9fc8f6)| + +### Example: FLUX + +Example script: [`flux_text_to_image.py`](./flux_text_to_image.py) and [`flux_text_to_image_low_vram.py`](./flux_text_to_image_low_vram.py)(low VRAM). + +The original version of FLUX doesn't support classifier-free guidance; however, we believe that this guidance mechanism is an important feature for synthesizing beautiful images. You can enable it using the parameter `cfg_scale`, and the extra guidance scale introduced by FLUX is `embedded_guidance`. + +|1024*1024 (original)|1024*1024 (classifier-free guidance)|2048*2048 (highres-fix)| +|-|-|-| +|![image_1024](https://github.com/user-attachments/assets/9cbd1f6f-4ac4-4f8b-bf46-218d812a15a0)|![image_1024_cfg](https://github.com/user-attachments/assets/984561e9-553d-4952-9443-79ce144f379f)|![image_2048_highres](https://github.com/user-attachments/assets/2e92b2f8-c177-454f-84f6-f6f5d3aaeeff)| + +### Example: Stable Diffusion + +Example script: [`sd_text_to_image.py`](./sd_text_to_image.py) + +LoRA Training: [`../train/stable_diffusion/`](../train/stable_diffusion/) + +|512*512|1024*1024|2048*2048|4096*4096| +|-|-|-|-| +|![512](https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/55f679e9-7445-4605-9315-302e93d11370)|![1024](https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/6fc84611-8da6-4a1f-8fee-9a34eba3b4a5)|![2048](https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/9087a73c-9164-4c58-b2a0-effc694143fb)|![4096](https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/edee9e71-fc39-4d1c-9ca9-fa52002c67ac)| + +### Example: Stable Diffusion XL + +Example script: [`sdxl_text_to_image.py`](./sdxl_text_to_image.py) + +LoRA Training: [`../train/stable_diffusion_xl/`](../train/stable_diffusion_xl/) + +|1024*1024|2048*2048| +|-|-| +|![1024](https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/67687748-e738-438c-aee5-96096f09ac90)|![2048](https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/584186bc-9855-4140-878e-99541f9a757f)| + +### Example: Stable Diffusion 3 + +Example script: [`sd3_text_to_image.py`](./sd3_text_to_image.py) + +LoRA Training: [`../train/stable_diffusion_3/`](../train/stable_diffusion_3/) + +|1024*1024|2048*2048| +|-|-| +|![image_1024](https://github.com/modelscope/DiffSynth-Studio/assets/35051019/4df346db-6f91-420a-b4c1-26e205376098)|![image_2048](https://github.com/modelscope/DiffSynth-Studio/assets/35051019/1386c802-e580-4101-939d-f1596802df9d)| + +### Example: Kolors + +Example script: [`kolors_text_to_image.py`](./kolors_text_to_image.py) + +LoRA Training: [`../train/kolors/`](../train/kolors/) + +|1024*1024|2048*2048| +|-|-| +|![image_1024](https://github.com/modelscope/DiffSynth-Studio/assets/35051019/53ef6f41-da11-4701-8665-9f64392607bf)|![image_2048](https://github.com/modelscope/DiffSynth-Studio/assets/35051019/66bb7a75-fe31-44e5-90eb-d3140ee4686d)| + +Kolors also support the models trained for SD-XL. For example, ControlNets and LoRAs. See [`kolors_with_sdxl_models.py`](./kolors_with_sdxl_models.py) + +LoRA: https://civitai.com/models/73305/zyd232s-ink-style + +|Base model|with LoRA (alpha=0.5)|with LoRA (alpha=1.0)|with LoRA (alpha=1.5)| +|-|-|-|-| +|![image_0 0](https://github.com/user-attachments/assets/a222eae3-6e0a-4ea6-b301-99e74e2bc11a)|![image_0 5](https://github.com/user-attachments/assets/e429c501-530c-43f6-a30b-9f97996c91a2)|![image_1 0](https://github.com/user-attachments/assets/0ddeed4b-250d-4b5c-a4fa-2db50f63bf1c)|![image_1 5](https://github.com/user-attachments/assets/db35a89d-6325-4422-921e-14fb6ad66c92)| + +ControlNet: https://huggingface.co/xinsir/controlnet-union-sdxl-1.0 + +|Reference image|Depth image|with ControlNet|with ControlNet| +|-|-|-|-| +|![image_0 0](https://github.com/user-attachments/assets/a222eae3-6e0a-4ea6-b301-99e74e2bc11a)|![controlnet_input](https://github.com/user-attachments/assets/d16b2785-bc1f-4184-b170-ae90f1d704c1)|![image_depth_1](https://github.com/user-attachments/assets/90a94780-7b56-4786-8a25-aae118eda171)|![image_depth_2](https://github.com/user-attachments/assets/05eb1309-9c98-49e7-a8ee-f376ceedf18e)| + +### Example: Hunyuan-DiT + +Example script: [`hunyuan_dit_text_to_image.py`](./hunyuan_dit_text_to_image.py) + +LoRA Training: [`../train/hunyuan_dit/`](../train/hunyuan_dit/) + +|1024*1024|2048*2048| +|-|-| +|![image_1024](https://github.com/modelscope/DiffSynth-Studio/assets/35051019/60b022c8-df3f-4541-95ab-bf39f2fa8bb5)|![image_2048](https://github.com/modelscope/DiffSynth-Studio/assets/35051019/87919ea8-d428-4963-8257-da05f3901bbb)| + +### Example: Stable Diffusion XL Turbo + +Example script: [`sdxl_turbo.py`](./sdxl_turbo.py) + +We highly recommend you to use this model in the WebUI. + +|"black car"|"red car"| +|-|-| +|![black_car](https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/7fbfd803-68d4-44f3-8713-8c925fec47d0)|![black_car_to_red_car](https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/aaf886e4-c33c-4fd8-98e2-29eef117ba00)| diff --git a/examples/image_synthesis/flex_text_to_image.py b/examples/image_synthesis/flex_text_to_image.py new file mode 100644 index 0000000000000000000000000000000000000000..3770764256b25abc75e52cf17c6924252bde239b --- /dev/null +++ b/examples/image_synthesis/flex_text_to_image.py @@ -0,0 +1,49 @@ +import torch +from diffsynth import ModelManager, FluxImagePipeline, download_models +from diffsynth.controlnets.processors import Annotator +import numpy as np +from PIL import Image + + +download_models(["FLUX.1-dev"]) +model_manager = ModelManager(torch_dtype=torch.bfloat16, device="cuda") +model_manager.load_models([ + "models/FLUX/FLUX.1-dev/text_encoder/model.safetensors", + "models/FLUX/FLUX.1-dev/text_encoder_2", + "models/FLUX/FLUX.1-dev/ae.safetensors", + "models/ostris/Flex.2-preview/Flex.2-preview.safetensors" +]) +pipe = FluxImagePipeline.from_model_manager(model_manager) + +image = pipe( + prompt="portrait of a beautiful Asian girl, long hair, red t-shirt, sunshine, beach", + num_inference_steps=50, embedded_guidance=3.5, + seed=0 +) +image.save("image_1.jpg") + +mask = np.zeros((1024, 1024, 3), dtype=np.uint8) +mask[200:400, 400:700] = 255 +mask = Image.fromarray(mask) +mask.save("image_mask.jpg") + +inpaint_image = image + +image = pipe( + prompt="portrait of a beautiful Asian girl with sunglasses, long hair, red t-shirt, sunshine, beach", + num_inference_steps=50, embedded_guidance=3.5, + flex_inpaint_image=inpaint_image, flex_inpaint_mask=mask, + seed=4 +) +image.save("image_2.jpg") + +control_image = Annotator("canny")(image) +control_image.save("image_control.jpg") + +image = pipe( + prompt="portrait of a beautiful Asian girl with sunglasses, long hair, yellow t-shirt, sunshine, beach", + num_inference_steps=50, embedded_guidance=3.5, + flex_control_image=control_image, + seed=4 +) +image.save("image_3.jpg") diff --git a/examples/image_synthesis/flux_text_to_image.py b/examples/image_synthesis/flux_text_to_image.py new file mode 100644 index 0000000000000000000000000000000000000000..6a50df39bde1694c7503a2c8bbc71071a4261049 --- /dev/null +++ b/examples/image_synthesis/flux_text_to_image.py @@ -0,0 +1,41 @@ +import torch +from diffsynth import ModelManager, FluxImagePipeline, download_models + + +download_models(["FLUX.1-dev"]) +model_manager = ModelManager(torch_dtype=torch.bfloat16, device="cuda") +model_manager.load_models([ + "models/FLUX/FLUX.1-dev/text_encoder/model.safetensors", + "models/FLUX/FLUX.1-dev/text_encoder_2", + "models/FLUX/FLUX.1-dev/ae.safetensors", + "models/FLUX/FLUX.1-dev/flux1-dev.safetensors" +]) +pipe = FluxImagePipeline.from_model_manager(model_manager) + +prompt = "CG, masterpiece, best quality, solo, long hair, wavy hair, silver hair, blue eyes, blue dress, medium breasts, dress, underwater, air bubble, floating hair, refraction, portrait. The girl's flowing silver hair shimmers with every color of the rainbow and cascades down, merging with the floating flora around her." +negative_prompt = "worst quality, low quality, monochrome, zombie, interlocked fingers, Aissist, cleavage, nsfw," + +# Disable classifier-free guidance (consistent with the original implementation of FLUX.1) +torch.manual_seed(9) +image = pipe( + prompt=prompt, + num_inference_steps=50, embedded_guidance=3.5 +) +image.save("image_1024.jpg") + +# Enable classifier-free guidance +torch.manual_seed(9) +image = pipe( + prompt=prompt, negative_prompt=negative_prompt, + num_inference_steps=50, cfg_scale=2.0, embedded_guidance=3.5 +) +image.save("image_1024_cfg.jpg") + +# Highres-fix +torch.manual_seed(10) +image = pipe( + prompt=prompt, + num_inference_steps=50, embedded_guidance=3.5, + input_image=image.resize((2048, 2048)), height=2048, width=2048, denoising_strength=0.6, tiled=True +) +image.save("image_2048_highres.jpg") diff --git a/examples/image_synthesis/flux_text_to_image_low_vram.py b/examples/image_synthesis/flux_text_to_image_low_vram.py new file mode 100644 index 0000000000000000000000000000000000000000..985f009d336e7945471d26cda2b3606905273d47 --- /dev/null +++ b/examples/image_synthesis/flux_text_to_image_low_vram.py @@ -0,0 +1,51 @@ +import torch +from diffsynth import download_models, ModelManager, FluxImagePipeline + + +download_models(["FLUX.1-dev"]) + +model_manager = ModelManager( + torch_dtype=torch.bfloat16, + device="cpu" # To reduce VRAM required, we load models to RAM. +) +model_manager.load_models([ + "models/FLUX/FLUX.1-dev/text_encoder/model.safetensors", + "models/FLUX/FLUX.1-dev/text_encoder_2", + "models/FLUX/FLUX.1-dev/ae.safetensors", +]) +model_manager.load_models( + ["models/FLUX/FLUX.1-dev/flux1-dev.safetensors"], + torch_dtype=torch.float8_e4m3fn # Load the DiT model in FP8 format. +) + +pipe = FluxImagePipeline.from_model_manager(model_manager, device="cuda") +pipe.enable_cpu_offload() +pipe.dit.quantize() + +prompt = "CG, masterpiece, best quality, solo, long hair, wavy hair, silver hair, blue eyes, blue dress, medium breasts, dress, underwater, air bubble, floating hair, refraction, portrait. The girl's flowing silver hair shimmers with every color of the rainbow and cascades down, merging with the floating flora around her." +negative_prompt = "worst quality, low quality, monochrome, zombie, interlocked fingers, Aissist, cleavage, nsfw," + +# Disable classifier-free guidance (consistent with the original implementation of FLUX.1) +torch.manual_seed(9) +image = pipe( + prompt=prompt, + num_inference_steps=50, embedded_guidance=3.5 +) +image.save("image_1024.jpg") + +# Enable classifier-free guidance +torch.manual_seed(9) +image = pipe( + prompt=prompt, negative_prompt=negative_prompt, + num_inference_steps=50, cfg_scale=2.0, embedded_guidance=3.5 +) +image.save("image_1024_cfg.jpg") + +# Highres-fix +torch.manual_seed(10) +image = pipe( + prompt=prompt, + num_inference_steps=50, embedded_guidance=3.5, + input_image=image.resize((2048, 2048)), height=2048, width=2048, denoising_strength=0.6, tiled=True +) +image.save("image_2048_highres.jpg") diff --git a/examples/image_synthesis/hunyuan_dit_text_to_image.py b/examples/image_synthesis/hunyuan_dit_text_to_image.py new file mode 100644 index 0000000000000000000000000000000000000000..7e4df91a510a0f0c30eb9c955926f9ee5acbd520 --- /dev/null +++ b/examples/image_synthesis/hunyuan_dit_text_to_image.py @@ -0,0 +1,42 @@ +from diffsynth import ModelManager, HunyuanDiTImagePipeline, download_models +import torch + + +# Download models (automatically) +# `models/HunyuanDiT/t2i/clip_text_encoder/pytorch_model.bin`: [link](https://huggingface.co/Tencent-Hunyuan/HunyuanDiT/resolve/main/t2i/clip_text_encoder/pytorch_model.bin) +# `models/HunyuanDiT/t2i/mt5/pytorch_model.bin`: [link](https://huggingface.co/Tencent-Hunyuan/HunyuanDiT/resolve/main/t2i/mt5/pytorch_model.bin) +# `models/HunyuanDiT/t2i/model/pytorch_model_ema.pt`: [link](https://huggingface.co/Tencent-Hunyuan/HunyuanDiT/resolve/main/t2i/model/pytorch_model_ema.pt) +# `models/HunyuanDiT/t2i/sdxl-vae-fp16-fix/diffusion_pytorch_model.bin`: [link](https://huggingface.co/Tencent-Hunyuan/HunyuanDiT/resolve/main/t2i/sdxl-vae-fp16-fix/diffusion_pytorch_model.bin) +download_models(["HunyuanDiT"]) + +# Load models +model_manager = ModelManager(torch_dtype=torch.float16, device="cuda") +model_manager.load_models([ + "models/HunyuanDiT/t2i/clip_text_encoder/pytorch_model.bin", + "models/HunyuanDiT/t2i/mt5/pytorch_model.bin", + "models/HunyuanDiT/t2i/model/pytorch_model_ema.pt", + "models/HunyuanDiT/t2i/sdxl-vae-fp16-fix/diffusion_pytorch_model.bin" +]) +pipe = HunyuanDiTImagePipeline.from_model_manager(model_manager) + +prompt = "一幅充满诗意美感的全身肖像画，画中一位银发、蓝色眼睛、身穿蓝色连衣裙的少女漂浮在水下，周围是光彩的气泡，和煦的阳光透过水面折射进水下" +negative_prompt = "错误的眼睛，糟糕的人脸，毁容，糟糕的艺术，变形，多余的肢体，模糊的颜色，模糊，重复，病态，残缺，" + +# Enjoy! +torch.manual_seed(0) +image = pipe( + prompt=prompt, + negative_prompt=negative_prompt, + num_inference_steps=50, height=1024, width=1024, +) +image.save("image_1024.png") + +# Highres fix +image = pipe( + prompt=prompt, + negative_prompt=negative_prompt, + input_image=image.resize((2048, 2048)), + num_inference_steps=50, height=2048, width=2048, + denoising_strength=0.4, tiled=True, +) +image.save("image_2048.png") \ No newline at end of file diff --git a/examples/image_synthesis/kolors_text_to_image.py b/examples/image_synthesis/kolors_text_to_image.py new file mode 100644 index 0000000000000000000000000000000000000000..d8fefdb7e5ace6408bdc9f086dd779e9e0a133e8 --- /dev/null +++ b/examples/image_synthesis/kolors_text_to_image.py @@ -0,0 +1,38 @@ +from diffsynth import ModelManager, SDXLImagePipeline, download_models +import torch + +# Download models +# https://huggingface.co/Kwai-Kolors/Kolors +download_models(["Kolors"]) +model_manager = ModelManager(torch_dtype=torch.float16, device="cuda", + file_path_list=[ + "models/kolors/Kolors/text_encoder", + "models/kolors/Kolors/unet/diffusion_pytorch_model.safetensors", + "models/kolors/Kolors/vae/diffusion_pytorch_model.safetensors" + ]) +pipe = SDXLImagePipeline.from_model_manager(model_manager) + +# Optional (Int4 quantize) pip install cpm_kernels +# pipe.text_encoder_kolors = pipe.text_encoder_kolors.quantize(4) +# torch.cuda.empty_cache() + +prompt = "一幅充满诗意美感的全身画，泛红的肤色，画中一位银色长发、蓝色眼睛、肤色红润、身穿蓝色吊带连衣裙的少女漂浮在水下，面向镜头，周围是光彩的气泡，和煦的阳光透过水面折射进水下" +negative_prompt = "半身，苍白的肤色，蜡黄的肤色，尸体，错误的眼睛，糟糕的人脸，毁容，糟糕的艺术，变形，多余的肢体，模糊的颜色，模糊，重复，病态，残缺，错误的手指，口红，腮红" + +torch.manual_seed(7) +image = pipe( + prompt=prompt, + negative_prompt=negative_prompt, + num_inference_steps=50, + cfg_scale=4, +) +image.save(f"image_1024.jpg") + +image = pipe( + prompt=prompt, + negative_prompt=negative_prompt, + input_image=image.resize((2048, 2048)), denoising_strength=0.4, height=2048, width=2048, + num_inference_steps=50, + cfg_scale=4, +) +image.save("image_2048.jpg") diff --git a/examples/image_synthesis/kolors_with_sdxl_models.py b/examples/image_synthesis/kolors_with_sdxl_models.py new file mode 100644 index 0000000000000000000000000000000000000000..946d65ec59a88dd61b360b9e92f278f8ca5621f7 --- /dev/null +++ b/examples/image_synthesis/kolors_with_sdxl_models.py @@ -0,0 +1,68 @@ +from diffsynth import ModelManager, SDXLImagePipeline, download_models, ControlNetConfigUnit +import torch + + + +def run_kolors_with_controlnet(): + download_models(["Kolors", "ControlNet_union_sdxl_promax"]) + model_manager = ModelManager(torch_dtype=torch.float16, device="cuda", + file_path_list=[ + "models/kolors/Kolors/text_encoder", + "models/kolors/Kolors/unet/diffusion_pytorch_model.safetensors", + "models/kolors/Kolors/vae/diffusion_pytorch_model.safetensors", + "models/ControlNet/controlnet_union/diffusion_pytorch_model_promax.safetensors", + ]) + pipe = SDXLImagePipeline.from_model_manager(model_manager, controlnet_config_units=[ + ControlNetConfigUnit("depth", "models/ControlNet/controlnet_union/diffusion_pytorch_model_promax.safetensors", 0.6) + ]) + negative_prompt = "半身，苍白的肤色，蜡黄的肤色，尸体，错误的眼睛，糟糕的人脸，毁容，糟糕的艺术，变形，多余的肢体，模糊的颜色，模糊，重复，病态，残缺，错误的手指，口红，腮红" + + prompt = "一幅充满诗意美感的全身画，泛红的肤色，画中一位银色长发、蓝色眼睛、肤色红润、身穿蓝色吊带连衣裙的少女漂浮在水下，面向镜头，周围是光彩的气泡，和煦的阳光透过水面折射进水下" + torch.manual_seed(7) + image = pipe( + prompt=prompt, negative_prompt=negative_prompt, num_inference_steps=50, cfg_scale=4, + ) + image.save("image.jpg") + + prompt = "一幅充满诗意美感的全身画，泛红的肤色，画中一位银色长发、黑色眼睛、肤色红润、身穿蓝色吊带连衣裙的少女，面向镜头，周围是绚烂的火焰" + torch.manual_seed(0) + image_controlnet = pipe( + prompt=prompt, negative_prompt=negative_prompt, num_inference_steps=50, cfg_scale=4, + controlnet_image=image, + ) + image_controlnet.save("image_depth_1.jpg") + + prompt = "一幅充满诗意美感的全身画，画中一位皮肤白皙、黑色长发、黑色眼睛、身穿金色吊带连衣裙的少女，周围是闪电，画面明亮" + torch.manual_seed(1) + image_controlnet = pipe( + prompt=prompt, negative_prompt=negative_prompt, num_inference_steps=50, cfg_scale=4, + controlnet_image=image, + ) + image_controlnet.save("image_depth_2.jpg") + + + +def run_kolors_with_lora(): + download_models(["Kolors", "SDXL_lora_zyd232_ChineseInkStyle_SDXL_v1_0"]) + model_manager = ModelManager(torch_dtype=torch.float16, device="cuda", + file_path_list=[ + "models/kolors/Kolors/text_encoder", + "models/kolors/Kolors/unet/diffusion_pytorch_model.safetensors", + "models/kolors/Kolors/vae/diffusion_pytorch_model.safetensors" + ]) + model_manager.load_lora("models/lora/zyd232_ChineseInkStyle_SDXL_v1_0.safetensors", lora_alpha=1.5) + pipe = SDXLImagePipeline.from_model_manager(model_manager) + + prompt = "一幅充满诗意美感的全身画，泛红的肤色，画中一位银色长发、蓝色眼睛、肤色红润、身穿蓝色吊带连衣裙的少女漂浮在水下，面向镜头，周围是光彩的气泡，和煦的阳光透过水面折射进水下" + negative_prompt = "半身，苍白的肤色，蜡黄的肤色，尸体，错误的眼睛，糟糕的人脸，毁容，糟糕的艺术，变形，多余的肢体，模糊的颜色，模糊，重复，病态，残缺，错误的手指，口红，腮红" + + torch.manual_seed(7) + image = pipe( + prompt=prompt, negative_prompt=negative_prompt, num_inference_steps=50, cfg_scale=4, + ) + image.save("image_lora.jpg") + + + +run_kolors_with_controlnet() +run_kolors_with_lora() diff --git a/examples/image_synthesis/omnigen_text_to_image.py b/examples/image_synthesis/omnigen_text_to_image.py new file mode 100644 index 0000000000000000000000000000000000000000..42777531e4110fabff9380d8efc3b4b468e3e75c --- /dev/null +++ b/examples/image_synthesis/omnigen_text_to_image.py @@ -0,0 +1,25 @@ +import torch +from diffsynth import ModelManager, OmnigenImagePipeline + + +model_manager = ModelManager(torch_dtype=torch.bfloat16, model_id_list=["OmniGen-v1"]) +pipe = OmnigenImagePipeline.from_model_manager(model_manager) + +image_man = pipe( + prompt="A portrait of a man.", + cfg_scale=2.5, num_inference_steps=50, seed=0 +) +image_man.save("image_man.jpg") + +image_woman = pipe( + prompt="A portrait of an Asian woman with a white t-shirt.", + cfg_scale=2.5, num_inference_steps=50, seed=1 +) +image_woman.save("image_woman.jpg") + +image_merged = pipe( + prompt="a man and a woman. The man is the man in <|image_1|>. The woman is the woman in <|image_2|>.", + reference_images=[image_man, image_woman], + cfg_scale=2.5, image_cfg_scale=2.5, num_inference_steps=50, seed=2 +) +image_merged.save("image_merged.jpg") diff --git a/examples/image_synthesis/sd35_text_to_image.py b/examples/image_synthesis/sd35_text_to_image.py new file mode 100644 index 0000000000000000000000000000000000000000..94f59e0acbec5284d037bb0aae35aec4e726286e --- /dev/null +++ b/examples/image_synthesis/sd35_text_to_image.py @@ -0,0 +1,28 @@ +from diffsynth import ModelManager, SD3ImagePipeline +import torch + + +model_manager = ModelManager(torch_dtype=torch.float16, device="cuda", model_id_list=["StableDiffusion3.5-large"]) +pipe = SD3ImagePipeline.from_model_manager(model_manager) + +prompt = "a full body photo of a beautiful Asian girl. CG, masterpiece, best quality, solo, long hair, wavy hair, silver hair, blue eyes, blue dress, medium breasts, dress, underwater, air bubble, floating hair, refraction, portrait. The girl's flowing silver hair shimmers with every color of the rainbow and cascades down, merging with the floating flora around her." +negative_prompt = "worst quality, low quality, monochrome, zombie, interlocked fingers, Aissist, cleavage, nsfw," + +torch.manual_seed(1) +image = pipe( + prompt=prompt, + negative_prompt=negative_prompt, + cfg_scale=5, + num_inference_steps=100, width=1024, height=1024, +) +image.save("image_1024.jpg") + +image = pipe( + prompt=prompt, + negative_prompt=negative_prompt, + cfg_scale=5, + input_image=image.resize((2048, 2048)), denoising_strength=0.5, + num_inference_steps=50, width=2048, height=2048, + tiled=True +) +image.save("image_2048.jpg") diff --git a/examples/image_synthesis/sd3_text_to_image.py b/examples/image_synthesis/sd3_text_to_image.py new file mode 100644 index 0000000000000000000000000000000000000000..d00fcfaa65ce160cc14376db5355a0d01638e6e2 --- /dev/null +++ b/examples/image_synthesis/sd3_text_to_image.py @@ -0,0 +1,33 @@ +from diffsynth import ModelManager, SD3ImagePipeline, download_models +import torch + + +# Download models (automatically) +# `models/stable_diffusion_3/sd3_medium_incl_clips.safetensors`: [link](https://huggingface.co/stabilityai/stable-diffusion-3-medium/resolve/main/sd3_medium_incl_clips.safetensors) +download_models(["StableDiffusion3_without_T5"]) +model_manager = ModelManager(torch_dtype=torch.float16, device="cuda", + file_path_list=["models/stable_diffusion_3/sd3_medium_incl_clips.safetensors"]) +pipe = SD3ImagePipeline.from_model_manager(model_manager) + + +prompt = "masterpiece, best quality, solo, long hair, wavy hair, silver hair, blue eyes, blue dress, medium breasts, dress, underwater, air bubble, floating hair, refraction, portrait," +negative_prompt = "worst quality, low quality, monochrome, zombie, interlocked fingers, Aissist, cleavage, nsfw," + +torch.manual_seed(7) +image = pipe( + prompt=prompt, + negative_prompt=negative_prompt, + cfg_scale=7.5, + num_inference_steps=100, width=1024, height=1024, +) +image.save("image_1024.jpg") + +image = pipe( + prompt=prompt, + negative_prompt=negative_prompt, + cfg_scale=7.5, + input_image=image.resize((2048, 2048)), denoising_strength=0.5, + num_inference_steps=50, width=2048, height=2048, + tiled=True +) +image.save("image_2048.jpg") diff --git a/examples/image_synthesis/sd_text_to_image.py b/examples/image_synthesis/sd_text_to_image.py new file mode 100644 index 0000000000000000000000000000000000000000..4d553b2e6a680f312c2a0345fa3eb55ff76d7291 --- /dev/null +++ b/examples/image_synthesis/sd_text_to_image.py @@ -0,0 +1,75 @@ +from diffsynth import ModelManager, SDImagePipeline, ControlNetConfigUnit, download_models +import torch + + +# Download models (automatically) +# `models/stable_diffusion/aingdiffusion_v12.safetensors`: [link](https://civitai.com/api/download/models/229575?type=Model&format=SafeTensor&size=full&fp=fp16) +# `models/ControlNet/control_v11p_sd15_lineart.pth`: [link](https://huggingface.co/lllyasviel/ControlNet-v1-1/resolve/main/control_v11p_sd15_lineart.pth) +# `models/ControlNet/control_v11f1e_sd15_tile.pth`: [link](https://huggingface.co/lllyasviel/ControlNet-v1-1/resolve/main/control_v11f1e_sd15_tile.pth) +# `models/Annotators/sk_model.pth`: [link](https://huggingface.co/lllyasviel/Annotators/resolve/main/sk_model.pth) +# `models/Annotators/sk_model2.pth`: [link](https://huggingface.co/lllyasviel/Annotators/resolve/main/sk_model2.pth) +download_models(["AingDiffusion_v12", "ControlNet_v11p_sd15_lineart", "ControlNet_v11f1e_sd15_tile"]) + + +# Load models +model_manager = ModelManager(torch_dtype=torch.float16, device="cuda", + file_path_list=[ + "models/stable_diffusion/aingdiffusion_v12.safetensors", + "models/ControlNet/control_v11f1e_sd15_tile.pth", + "models/ControlNet/control_v11p_sd15_lineart.pth" + ]) +pipe = SDImagePipeline.from_model_manager( + model_manager, + [ + ControlNetConfigUnit( + processor_id="tile", + model_path=rf"models/ControlNet/control_v11f1e_sd15_tile.pth", + scale=0.5 + ), + ControlNetConfigUnit( + processor_id="lineart", + model_path=rf"models/ControlNet/control_v11p_sd15_lineart.pth", + scale=0.7 + ), + ] +) + +prompt = "masterpiece, best quality, solo, long hair, wavy hair, silver hair, blue eyes, blue dress, medium breasts, dress, underwater, air bubble, floating hair, refraction, portrait," +negative_prompt = "worst quality, low quality, monochrome, zombie, interlocked fingers, Aissist, cleavage, nsfw," + +torch.manual_seed(0) +image = pipe( + prompt=prompt, + negative_prompt=negative_prompt, + cfg_scale=7.5, clip_skip=1, + height=512, width=512, num_inference_steps=80, +) +image.save("512.jpg") + +image = pipe( + prompt=prompt, + negative_prompt=negative_prompt, + cfg_scale=7.5, clip_skip=1, + input_image=image.resize((1024, 1024)), controlnet_image=image.resize((1024, 1024)), + height=1024, width=1024, num_inference_steps=40, denoising_strength=0.7, +) +image.save("1024.jpg") + +image = pipe( + prompt=prompt, + negative_prompt=negative_prompt, + cfg_scale=7.5, clip_skip=1, + input_image=image.resize((2048, 2048)), controlnet_image=image.resize((2048, 2048)), + height=2048, width=2048, num_inference_steps=20, denoising_strength=0.7, +) +image.save("2048.jpg") + +image = pipe( + prompt=prompt, + negative_prompt=negative_prompt, + cfg_scale=7.5, clip_skip=1, + input_image=image.resize((4096, 4096)), controlnet_image=image.resize((4096, 4096)), + height=4096, width=4096, num_inference_steps=10, denoising_strength=0.5, + tiled=True, tile_size=128, tile_stride=64 +) +image.save("4096.jpg") diff --git a/examples/image_synthesis/sdxl_text_to_image.py b/examples/image_synthesis/sdxl_text_to_image.py new file mode 100644 index 0000000000000000000000000000000000000000..fcae3abd3ab7b9f7850eaef9fc04d5ca862664c6 --- /dev/null +++ b/examples/image_synthesis/sdxl_text_to_image.py @@ -0,0 +1,34 @@ +from diffsynth import ModelManager, SDXLImagePipeline, download_models +import torch + + +# Download models (automatically) +# `models/stable_diffusion_xl/bluePencilXL_v200.safetensors`: [link](https://civitai.com/api/download/models/245614?type=Model&format=SafeTensor&size=pruned&fp=fp16) +download_models(["BluePencilXL_v200"]) + +# Load models +model_manager = ModelManager(torch_dtype=torch.float16, device="cuda") +model_manager.load_models(["models/stable_diffusion_xl/bluePencilXL_v200.safetensors"]) +pipe = SDXLImagePipeline.from_model_manager(model_manager) + +prompt = "masterpiece, best quality, solo, long hair, wavy hair, silver hair, blue eyes, blue dress, medium breasts, dress, underwater, air bubble, floating hair, refraction, portrait," +negative_prompt = "worst quality, low quality, monochrome, zombie, interlocked fingers, Aissist, cleavage, nsfw," + +torch.manual_seed(0) +image = pipe( + prompt=prompt, + negative_prompt=negative_prompt, + cfg_scale=6, + height=1024, width=1024, num_inference_steps=60, +) +image.save("1024.jpg") + +image = pipe( + prompt=prompt, + negative_prompt=negative_prompt, + cfg_scale=6, + input_image=image.resize((2048, 2048)), + height=2048, width=2048, num_inference_steps=60, denoising_strength=0.5 +) +image.save("2048.jpg") + diff --git a/examples/image_synthesis/sdxl_turbo.py b/examples/image_synthesis/sdxl_turbo.py new file mode 100644 index 0000000000000000000000000000000000000000..c39fb088795c98860032bf7b6f5290e553809170 --- /dev/null +++ b/examples/image_synthesis/sdxl_turbo.py @@ -0,0 +1,31 @@ +from diffsynth import ModelManager, SDXLImagePipeline, download_models +import torch + + +# Download models (automatically) +# `models/stable_diffusion_xl_turbo/sd_xl_turbo_1.0_fp16.safetensors`: [link](https://huggingface.co/stabilityai/sdxl-turbo/resolve/main/sd_xl_turbo_1.0_fp16.safetensors) +download_models(["StableDiffusionXL_Turbo"]) + +# Load models +model_manager = ModelManager(torch_dtype=torch.float16, device="cuda") +model_manager.load_models(["models/stable_diffusion_xl_turbo/sd_xl_turbo_1.0_fp16.safetensors"]) +pipe = SDXLImagePipeline.from_model_manager(model_manager) + +# Text to image +torch.manual_seed(0) +image = pipe( + prompt="black car", + # Do not modify the following parameters! + cfg_scale=1, height=512, width=512, num_inference_steps=1, progress_bar_cmd=lambda x:x +) +image.save(f"black_car.jpg") + +# Image to image +torch.manual_seed(0) +image = pipe( + prompt="red car", + input_image=image, denoising_strength=0.7, + # Do not modify the following parameters! + cfg_scale=1, height=512, width=512, num_inference_steps=1, progress_bar_cmd=lambda x:x +) +image.save(f"black_car_to_red_car.jpg") diff --git a/examples/prompt_magic/README.md b/examples/prompt_magic/README.md new file mode 100644 index 0000000000000000000000000000000000000000..cbb920eb875f8490ef300073f2b0dafdce6befab --- /dev/null +++ b/examples/prompt_magic/README.md @@ -0,0 +1,34 @@ +# Prompt Refining + +Prompt refining is supported in DiffSynth-Studio. + +### Example: Qwen + +If you are not native English user, we provide LLM-based translation for you. Qwen is a typical example. See [`qwen_prompt_refining.py`](./qwen_prompt_refining.py). + +Prompt: "鹰". This prompt will be refined: + +* A majestic eagle soaring high above a vast expanse of open sky, its wings spread wide and its eyes fixed on the horizon. +* A majestic eagle soaring high above the horizon, its wingspan stretching out towards the endless sky. Its sharp beak and powerful talons stand out against the azure sky, highlighting its strength and freedom. The eagle's silhouette is silhouetted against the fading sun, casting long shadows behind it. +* A majestic eagle soaring high above a vast, open landscape, its wings spread wide and its beak pointed towards the sky. The sun casts long shadows across the ground, adding depth and texture to the image. The bird's feathers shimmer in the light, creating a sense of movement and power. +* A majestic eagle soaring high above a vast, open landscape, its sharp talons gripping a fish effortlessly in its beak. The sun casts a warm golden glow behind it, casting long shadows across the barren earth below. The eagle's wingspan stretches out towards infinity, its feathers glistening in the light. Its eyes fixate on the distant horizon, as if sensing something important about to unfold. + +|seed=0|seed=1|seed=2|seed=3| +|-|-|-|-| +|![0](https://github.com/user-attachments/assets/6f509b0d-204c-4ca9-b3f6-04925fb4b40d)|![1](https://github.com/user-attachments/assets/824f2713-134a-4cae-a155-25224d4afb9a)|![2](https://github.com/user-attachments/assets/747be52a-0b46-45fc-b0e8-a0c83f7e716b)|![3](https://github.com/user-attachments/assets/490564e6-d247-40c9-8361-5db72058c337)| + +### Example: OPUS + BeautifulPrompt + +Our prompter can translate other language to English and refine it using "BeautifulPrompt" models. Please see [`bf_prompt_refining.py`](./bf_prompt_refining.py) for more details. + +Prompt: "一个漂亮的女孩". The [translation model](https://huggingface.co/Helsinki-NLP/opus-mt-en-zh) will translate it to English. + +|seed=0|seed=1|seed=2|seed=3| +|-|-|-|-| +|![0_](https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/ebb25ca8-7ce1-4d9e-8081-59a867c70c4d)|![1_](https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/a7e79853-3c1a-471a-9c58-c209ec4b76dd)|![2_](https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/a292b959-a121-481f-b79c-61cc3346f810)|![3_](https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/1c19b54e-5a6f-4d48-960b-a7b2b149bb4c)| + +Prompt: "一个漂亮的女孩". The [translation model](https://huggingface.co/Helsinki-NLP/opus-mt-en-zh) will translate it to English. Then the [refining model](https://huggingface.co/alibaba-pai/pai-bloom-1b1-text2prompt-sd) will refine the translated prompt for better visual quality. + +|seed=0|seed=1|seed=2|seed=3| +|-|-|-|-| +|![0](https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/778b1bd9-44e0-46ac-a99c-712b3fc9aaa4)|![1](https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/c03479b8-2082-4c6e-8e1c-3582b98686f6)|![2](https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/edb33d21-3288-4a55-96ca-a4bfe1b50b00)|![3](https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/7848cfc1-cad5-4848-8373-41d24e98e584)| diff --git a/examples/prompt_magic/bf_prompt_refining.py b/examples/prompt_magic/bf_prompt_refining.py new file mode 100644 index 0000000000000000000000000000000000000000..911ed6832fba6a3e1bef6be3c74fe074dcdd0577 --- /dev/null +++ b/examples/prompt_magic/bf_prompt_refining.py @@ -0,0 +1,30 @@ +from diffsynth import ModelManager, SDXLImagePipeline, download_models, Translator, BeautifulPrompt +import torch + + +# Download models (automatically) +# `models/stable_diffusion_xl/sd_xl_base_1.0.safetensors`: [link](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/resolve/main/sd_xl_base_1.0.safetensors) +# `models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd/`: [link](https://huggingface.co/alibaba-pai/pai-bloom-1b1-text2prompt-sd) +# `models/translator/opus-mt-zh-en/`: [link](https://huggingface.co/Helsinki-NLP/opus-mt-en-zh) +download_models(["StableDiffusionXL_v1", "BeautifulPrompt", "opus-mt-zh-en"]) + +# Load models +model_manager = ModelManager(torch_dtype=torch.float16, device="cuda") +model_manager.load_models([ + "models/stable_diffusion_xl/sd_xl_base_1.0.safetensors", + "models/BeautifulPrompt/pai-bloom-1b1-text2prompt-sd", + "models/translator/opus-mt-zh-en" +]) +pipe = SDXLImagePipeline.from_model_manager(model_manager, prompt_refiner_classes=[Translator, BeautifulPrompt]) + +prompt = "一个漂亮的女孩" +negative_prompt = "" + +for seed in range(4): + torch.manual_seed(seed) + image = pipe( + prompt=prompt, negative_prompt=negative_prompt, + height=1024, width=1024, + num_inference_steps=30 + ) + image.save(f"{seed}.jpg") diff --git a/examples/prompt_magic/omost_flux_text_to_image.py b/examples/prompt_magic/omost_flux_text_to_image.py new file mode 100644 index 0000000000000000000000000000000000000000..e6e5d1df41aebf8d9a46af077d458ccd741259ab --- /dev/null +++ b/examples/prompt_magic/omost_flux_text_to_image.py @@ -0,0 +1,35 @@ +import torch +from diffsynth import download_models, ModelManager, OmostPromter, FluxImagePipeline + + +download_models(["OmostPrompt"]) +download_models(["FLUX.1-dev"]) + +model_manager = ModelManager(torch_dtype=torch.bfloat16) +model_manager.load_models([ + "models/OmostPrompt/omost-llama-3-8b-4bits", + "models/FLUX/FLUX.1-dev/text_encoder/model.safetensors", + "models/FLUX/FLUX.1-dev/text_encoder_2", + "models/FLUX/FLUX.1-dev/ae.safetensors", + "models/FLUX/FLUX.1-dev/flux1-dev.safetensors" +]) + +pipe_omost = FluxImagePipeline.from_model_manager(model_manager, prompt_extender_classes=[OmostPromter]) +pipe = FluxImagePipeline.from_model_manager(model_manager) + +prompt = "A witch uses ice magic to fight against wild beasts" +seed = 7 + +torch.manual_seed(seed) +image = pipe_omost( + prompt=prompt, + num_inference_steps=30, embedded_guidance=3.5 +) +image.save(f"image_omost.jpg") + +torch.manual_seed(seed) +image2= pipe( + prompt=prompt, + num_inference_steps=30, embedded_guidance=3.5 +) +image2.save(f"image.jpg") \ No newline at end of file diff --git a/examples/prompt_magic/qwen_prompt_refining.py b/examples/prompt_magic/qwen_prompt_refining.py new file mode 100644 index 0000000000000000000000000000000000000000..511d506995d256dd9ca52ac2e248efa8f96ba3a4 --- /dev/null +++ b/examples/prompt_magic/qwen_prompt_refining.py @@ -0,0 +1,27 @@ +from diffsynth import ModelManager, FluxImagePipeline, download_models, QwenPrompt +import torch + + +download_models(["FLUX.1-dev", "QwenPrompt"]) + +model_manager = ModelManager(torch_dtype=torch.bfloat16, device="cuda") +model_manager.load_models([ + "models/FLUX/FLUX.1-dev/text_encoder/model.safetensors", + "models/FLUX/FLUX.1-dev/text_encoder_2", + "models/FLUX/FLUX.1-dev/ae.safetensors", + "models/FLUX/FLUX.1-dev/flux1-dev.safetensors", + "models/QwenPrompt/qwen2-1.5b-instruct", +]) +pipe = FluxImagePipeline.from_model_manager(model_manager, prompt_refiner_classes=[QwenPrompt]) + +prompt = "鹰" +negative_prompt = "" + +for seed in range(4): + torch.manual_seed(seed) + image = pipe( + prompt=prompt, negative_prompt=negative_prompt, + height=1024, width=1024, + num_inference_steps=30 + ) + image.save(f"{seed}.jpg") diff --git a/examples/qwen_image/README.md b/examples/qwen_image/README.md new file mode 100644 index 0000000000000000000000000000000000000000..e5884e238b71ffc3c94092ba82a53ea1954e294f --- /dev/null +++ b/examples/qwen_image/README.md @@ -0,0 +1,404 @@ +# Qwen-Image + +[Switch to English](./README.md) + +Qwen-Image is an open-source image generation model developed by Tongyi Lab, Alibaba. + +## Installation + +Before using this model series, install DiffSynth-Studio from source code. + +```shell +git clone https://github.com/modelscope/DiffSynth-Studio.git +cd DiffSynth-Studio +pip install -e . +``` + +## Quick Start + +Run the following code to quickly load the [Qwen/Qwen-Image](https://www.modelscope.cn/models/Qwen/Qwen-Image ) model and perform inference. + +```python +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig +from PIL import Image +import torch + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), +) +prompt = "A detailed portrait of a girl underwater, wearing a blue flowing dress, hair gently floating, clear light and shadow, surrounded by bubbles, calm expression, fine details, dreamy and beautiful." +image = pipe( + prompt, seed=0, num_inference_steps=40, + # edit_image=Image.open("xxx.jpg").resize((1328, 1328)) # For Qwen-Image-Edit +) +image.save("image.jpg") +``` + +## Model Overview + +|Model ID|Inference|Low VRAM Inference|Full Training|Validation after Full Training|LoRA Training|Validation after LoRA Training| +|-|-|-|-|-|-|-| +|[Qwen/Qwen-Image](https://www.modelscope.cn/models/Qwen/Qwen-Image)|[code](./model_inference/Qwen-Image.py)|[code](./model_inference_low_vram/Qwen-Image.py)|[code](./model_training/full/Qwen-Image.sh)|[code](./model_training/validate_full/Qwen-Image.py)|[code](./model_training/lora/Qwen-Image.sh)|[code](./model_training/validate_lora/Qwen-Image.py)| +|[Qwen/Qwen-Image-Edit](https://www.modelscope.cn/models/Qwen/Qwen-Image-Edit)|[code](./model_inference/Qwen-Image-Edit.py)|[code](./model_inference_low_vram/Qwen-Image-Edit.py)|[code](./model_training/full/Qwen-Image-Edit.sh)|[code](./model_training/validate_full/Qwen-Image-Edit.py)|[code](./model_training/lora/Qwen-Image-Edit.sh)|[code](./model_training/validate_lora/Qwen-Image-Edit.py)| +|[Qwen/Qwen-Image-Edit-2509](https://www.modelscope.cn/models/Qwen/Qwen-Image-Edit-2509)|[code](./model_inference/Qwen-Image-Edit-2509.py)|[code](./model_inference_low_vram/Qwen-Image-Edit-2509.py)|[code](./model_training/full/Qwen-Image-Edit-2509.sh)|[code](./model_training/validate_full/Qwen-Image-Edit-2509.py)|[code](./model_training/lora/Qwen-Image-Edit-2509.sh)|[code](./model_training/validate_lora/Qwen-Image-Edit-2509.py)| +|[DiffSynth-Studio/Qwen-Image-Distill-Full](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Distill-Full)|[code](./model_inference/Qwen-Image-Distill-Full.py)|[code](./model_inference_low_vram/Qwen-Image-Distill-Full.py)|[code](./model_training/full/Qwen-Image-Distill-Full.sh)|[code](./model_training/validate_full/Qwen-Image-Distill-Full.py)|[code](./model_training/lora/Qwen-Image-Distill-Full.sh)|[code](./model_training/validate_lora/Qwen-Image-Distill-Full.py)| +|[DiffSynth-Studio/Qwen-Image-Distill-LoRA](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Distill-LoRA)|[code](./model_inference/Qwen-Image-Distill-LoRA.py)|[code](./model_inference_low_vram/Qwen-Image-Distill-LoRA.py)|-|-|[code](./model_training/lora/Qwen-Image-Distill-LoRA.sh)|[code](./model_training/validate_lora/Qwen-Image-Distill-LoRA.py)| +|[DiffSynth-Studio/Qwen-Image-EliGen](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-EliGen)|[code](./model_inference/Qwen-Image-EliGen.py)|[code](./model_inference_low_vram/Qwen-Image-EliGen.py)|-|-|[code](./model_training/lora/Qwen-Image-EliGen.sh)|[code](./model_training/validate_lora/Qwen-Image-EliGen.py)| +|[DiffSynth-Studio/Qwen-Image-EliGen-V2](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-EliGen-V2)|[code](./model_inference/Qwen-Image-EliGen-V2.py)|[code](./model_inference_low_vram/Qwen-Image-EliGen-V2.py)|-|-|[code](./model_training/lora/Qwen-Image-EliGen.sh)|[code](./model_training/validate_lora/Qwen-Image-EliGen.py)| +|[DiffSynth-Studio/Qwen-Image-EliGen-Poster](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-EliGen-Poster)|[code](./model_inference/Qwen-Image-EliGen-Poster.py)|[code](./model_inference_low_vram/Qwen-Image-EliGen-Poster.py)|-|-|[code](./model_training/lora/Qwen-Image-EliGen-Poster.sh)|[code](./model_training/validate_lora/Qwen-Image-EliGen-Poster.py)| +|[DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Canny](https://modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Canny)|[code](./model_inference/Qwen-Image-Blockwise-ControlNet-Canny.py)|[code](./model_inference_low_vram/Qwen-Image-Blockwise-ControlNet-Canny.py)|[code](./model_training/full/Qwen-Image-Blockwise-ControlNet-Canny.sh)|[code](./model_training/validate_full/Qwen-Image-Blockwise-ControlNet-Canny.py)|[code](./model_training/lora/Qwen-Image-Blockwise-ControlNet-Canny.sh)|[code](./model_training/validate_lora/Qwen-Image-Blockwise-ControlNet-Canny.py)| +|[DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Depth](https://modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Depth)|[code](./model_inference/Qwen-Image-Blockwise-ControlNet-Depth.py)|[code](./model_inference_low_vram/Qwen-Image-Blockwise-ControlNet-Depth.py)|[code](./model_training/full/Qwen-Image-Blockwise-ControlNet-Depth.sh)|[code](./model_training/validate_full/Qwen-Image-Blockwise-ControlNet-Depth.py)|[code](./model_training/lora/Qwen-Image-Blockwise-ControlNet-Depth.sh)|[code](./model_training/validate_lora/Qwen-Image-Blockwise-ControlNet-Depth.py)| +|[DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Inpaint](https://modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Inpaint)|[code](./model_inference/Qwen-Image-Blockwise-ControlNet-Inpaint.py)|[code](./model_inference_low_vram/Qwen-Image-Blockwise-ControlNet-Inpaint.py)|[code](./model_training/full/Qwen-Image-Blockwise-ControlNet-Inpaint.sh)|[code](./model_training/validate_full/Qwen-Image-Blockwise-ControlNet-Inpaint.py)|[code](./model_training/lora/Qwen-Image-Blockwise-ControlNet-Inpaint.sh)|[code](./model_training/validate_lora/Qwen-Image-Blockwise-ControlNet-Inpaint.py)| +|[DiffSynth-Studio/Qwen-Image-In-Context-Control-Union](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-In-Context-Control-Union)|[code](./model_inference/Qwen-Image-In-Context-Control-Union.py)|[code](./model_inference_low_vram/Qwen-Image-In-Context-Control-Union.py)|-|-|[code](./model_training/lora/Qwen-Image-In-Context-Control-Union.sh)|[code](./model_training/validate_lora/Qwen-Image-In-Context-Control-Union.py)| +|[DiffSynth-Studio/Qwen-Image-Edit-Lowres-Fix](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Edit-Lowres-Fix)|[code](./model_inference/Qwen-Image-Edit-Lowres-Fix.py)|[code](./model_inference_low_vram/Qwen-Image-Edit-Lowres-Fix.py)|-|-|-|-| + +## Model Inference + +The following section helps you understand our features and write inference code. + +
+ +Load Model + +Use `from_pretrained` to load the model: + +```python +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig +import torch + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), +) +``` + +Here, `torch_dtype` and `device` set the computation precision and device. `model_configs` can be used in different ways to specify model paths: + +* Download the model from [ModelScope](https://modelscope.cn/ ) and load it. In this case, fill in `model_id` and `origin_file_pattern`, for example: + +```python +ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), +``` + +* Load the model from a local file path. In this case, fill in `path`, for example: + +```python +ModelConfig(path="models/xxx.safetensors") +``` + +For a single model loaded from multiple files, use a list, for example: + +```python +ModelConfig(path=[ + "models/Qwen/Qwen-Image/text_encoder/model-00001-of-00004.safetensors", + "models/Qwen/Qwen-Image/text_encoder/model-00002-of-00004.safetensors", + "models/Qwen/Qwen-Image/text_encoder/model-00003-of-00004.safetensors", + "models/Qwen/Qwen-Image/text_encoder/model-00004-of-00004.safetensors", +]) +``` + +`ModelConfig` provides extra options to control model loading behavior: + +* `local_model_path`: Path to save downloaded models. Default is `"./models"`. +* `skip_download`: Whether to skip downloading. Default is `False`. If your network cannot access [ModelScope](https://modelscope.cn/ ), download the required files manually and set this to `True`. + +
+ + +
+ +VRAM Management + +DiffSynth-Studio provides fine-grained VRAM management for the Qwen-Image model. This allows the model to run on devices with low VRAM. You can enable the offload feature using the code below. It moves some model parts to CPU memory when GPU memory is limited. + +```python +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", offload_device="cpu"), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), +) +pipe.enable_vram_management() +``` + +FP8 quantization is also supported: + +```python +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", offload_dtype=torch.float8_e4m3fn), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), +) +pipe.enable_vram_management() +``` + +You can use FP8 quantization and offload at the same time: + +```python +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), +) +pipe.enable_vram_management() +``` + +FP8 quantization can greatly reduce VRAM use, but it does not speed up inference. Some models may have quality issues like blur, tearing, or distortion when using FP8. Use FP8 with care. + +After enabling VRAM management, the framework will automatically choose a memory strategy based on free VRAM. The `enable_vram_management` function has the following options to control this strategy: + +* `vram_limit`: VRAM usage limit in GB. By default, it uses all free VRAM on the device. Note that this is not a strict limit. If the set limit is too low but actual free VRAM is enough, the model will run with minimal VRAM use. Set it to 0 for the smallest possible VRAM use. +* `vram_buffer`: VRAM buffer size in GB. Default is 0.5GB. A buffer is needed because large network layers may use more VRAM than expected during loading. The best value is the VRAM size of the largest model layer. +* `num_persistent_param_in_dit`: Number of parameters to keep in VRAM in the DiT model. Default is no limit. This option will be removed in the future. Do not rely on it. +* `enable_dit_fp8_computation`: Whether to enable FP8 computation in the DiT model. This is only applicable to GPUs that support FP8 operations (e.g., H200, etc.). Disabled by default. + +
+ + +
+ +Inference Acceleration + +* FP8 Quantization: Choose the appropriate quantization method based on your hardware and requirements. + * GPUs that do not support FP8 computation (e.g., A100, 4090, etc.): FP8 quantization will only reduce VRAM usage without speeding up inference. Code: [./model_inference_low_vram/Qwen-Image.py](./model_inference_low_vram/Qwen-Image.py) + * GPUs that support FP8 operations (e.g., H200, etc.): Please install [Flash Attention 3](https://github.com/Dao-AILab/flash-attention). Otherwise, FP8 acceleration will only apply to Linear layers. + * Faster inference but higher VRAM usage: Use [./accelerate/Qwen-Image-FP8.py](./accelerate/Qwen-Image-FP8.py) + * Slightly slower inference but lower VRAM usage: Use [./accelerate/Qwen-Image-FP8-offload.py](./accelerate/Qwen-Image-FP8-offload.py) +* Distillation acceleration: We trained two distillation models for fast inference at `cfg_scale=1` and `num_inference_steps=15`. + * [DiffSynth-Studio/Qwen-Image-Distill-Full](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Distill-Full): Full distillation version. Better image quality but lower LoRA compatibility. Use [./model_inference/Qwen-Image-Distill-Full.py](./model_inference/Qwen-Image-Distill-Full.py). + * [DiffSynth-Studio/Qwen-Image-Distill-LoRA](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Distill-LoRA): LoRA distillation version. Slightly lower image quality but better LoRA compatibility. Use [./model_inference/Qwen-Image-Distill-LoRA.py](./model_inference/Qwen-Image-Distill-LoRA.py). + +
+ + +
+ +Input Parameters + +The pipeline supports the following input parameters during inference: + +* `prompt`: Text prompt that describes what should appear in the image. +* `negative_prompt`: Negative prompt that describes what should not appear in the image. Default is `""`. +* `cfg_scale`: Parameter for classifier-free guidance. Default is 1. It takes effect when set to a value greater than 1. +* `input_image`: Input image for image-to-image generation. Used with `denoising_strength`. +* `denoising_strength`: Denoising strength, range from 0 to 1. Default is 1. When close to 0, the output image is similar to the input. When close to 1, the output is more different. Do not set this to a non-1 value if `input_image` is not given. +* `height`: Image height. Must be a multiple of 16. +* `width`: Image width. Must be a multiple of 16. +* `seed`: Random seed. Default is `None`, meaning fully random. +* `rand_device`: Device for generating random noise. Default is `"cpu"`. Setting it to `"cuda"` may lead to different results on different GPUs. +* `num_inference_steps`: Number of inference steps. Default is 30. +* `tiled`: Whether to enable tiled VAE inference. Default is `False`. Set to `True` to reduce VRAM use in VAE encoding/decoding. This causes small errors and slightly longer inference time. +* `tile_size`: Tile size for VAE encoding/decoding. Default is 128. Only works when `tiled=True`. +* `tile_stride`: Tile stride for VAE encoding/decoding. Default is 64. Only works when `tiled=True`. Must be less than or equal to `tile_size`. +* `progress_bar_cmd`: Progress bar display. Default is `tqdm.tqdm`. Set to `lambda x: x` to hide the progress bar. + +
+ + +## Model Training + +The Qwen-Image series models are trained using a unified script [`./model_training/train.py`](./model_training/train.py). + +
+ +Script Parameters + +The script includes the following parameters: + +* Dataset + * `--dataset_base_path`: Root path of the dataset. + * `--dataset_metadata_path`: Path to the dataset metadata file. + * `--max_pixels`: Maximum pixel area. Default is 1024*1024. When dynamic resolution is enabled, any image with resolution higher than this will be resized down. + * `--height`: Height of image or video. Leave `height` and `width` empty to enable dynamic resolution. + * `--width`: Width of image or video. Leave `height` and `width` empty to enable dynamic resolution. + * `--data_file_keys`: Data file keys in metadata. Separate with commas. + * `--dataset_repeat`: Number of times the dataset repeats per epoch. + * `--dataset_num_workers`: Number of workers for data loading. +* Model + * `--model_paths`: Model paths to load. In JSON format. + * `--model_id_with_origin_paths`: Model ID with original paths, e.g., Qwen/Qwen-Image:transformer/diffusion_pytorch_model*.safetensors. Separate with commas. + * `--tokenizer_path`: Tokenizer path. Leave empty to auto-download. + * `--processor_path`: Path to the processor of Qwen-Image-Edit. Leave empty to auto-download. +* Training + * `--learning_rate`: Learning rate. + * `--weight_decay`: Weight decay. + * `--num_epochs`: Number of epochs. + * `--output_path`: Save path. + * `--remove_prefix_in_ckpt`: Remove prefix in checkpoint. + * `--save_steps`: Number of checkpoint saving invervals. If None, checkpoints will be saved every epoch. + * `--find_unused_parameters`: Whether to find unused parameters in DDP. +* Trainable Modules + * `--trainable_models`: Models to train, e.g., dit, vae, text_encoder. + * `--lora_base_model`: Which model to add LoRA to. + * `--lora_target_modules`: Which layers to add LoRA to. + * `--lora_rank`: Rank of LoRA. + * `--lora_checkpoint`: Path to the LoRA checkpoint. If provided, LoRA will be loaded from this checkpoint. +* Extra Model Inputs + * `--extra_inputs`: Extra model inputs, separated by commas. +* VRAM Management + * `--use_gradient_checkpointing`: Whether to enable gradient checkpointing. + * `--use_gradient_checkpointing_offload`: Whether to offload gradient checkpointing to CPU memory. + * `--gradient_accumulation_steps`: Number of gradient accumulation steps. + +In addition, the training framework is built on [`accelerate`](https://huggingface.co/docs/accelerate/index). Run `accelerate config` before training to set GPU-related settings. For some training tasks (e.g., full training of 20B model), we provide suggested `accelerate` config files. Check the corresponding training script for details. + +
+ + +
+ +Step 1: Prepare Dataset + +The dataset contains a set of files. We suggest organizing your dataset like this: + +``` +data/example_image_dataset/ +├── metadata.csv +├── image1.jpg +└── image2.jpg +``` + +Here, `image1.jpg` and `image2.jpg` are image files for training, and `metadata.csv` is a metadata list, for example: + +``` +image,prompt +image1.jpg,"a cat is sleeping" +image2.jpg,"a dog is running" +``` + +We have built a sample image dataset for your testing. Use the following command to download it: + +```shell +modelscope download --dataset DiffSynth-Studio/example_image_dataset --local_dir ./data/example_image_dataset +``` + +The dataset supports multiple image formats: `"jpg", "jpeg", "png", "webp"`. + +Image size can be controlled by script parameters `--height` and `--width`. When `--height` and `--width` are empty, dynamic resolution is enabled. Images will be trained using their original sizes. + +**We strongly recommend using fixed resolution for training, as multi-GPU training may have load balancing issues with dynamic resolution.** + +
+ + +
+ +Step 2: Load Model + +Similar to model loading during inference, you can set the model to load directly by model ID. For example, during inference we load the model like this: + +```python +model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), +] +``` + +Then during training, use the following parameter to load the same models: + +```shell +--model_id_with_origin_paths "Qwen/Qwen-Image:transformer/diffusion_pytorch_model*.safetensors,Qwen/Qwen-Image:text_encoder/model*.safetensors,Qwen/Qwen-Image:vae/diffusion_pytorch_model.safetensors" +``` + +If you want to load the model from local files, for example, during inference: + +```python +model_configs=[ + ModelConfig([ + "models/Qwen/Qwen-Image/transformer/diffusion_pytorch_model-00001-of-00009.safetensors", + "models/Qwen/Qwen-Image/transformer/diffusion_pytorch_model-00002-of-00009.safetensors", + "models/Qwen/Qwen-Image/transformer/diffusion_pytorch_model-00003-of-00009.safetensors", + "models/Qwen/Qwen-Image/transformer/diffusion_pytorch_model-00004-of-00009.safetensors", + "models/Qwen/Qwen-Image/transformer/diffusion_pytorch_model-00005-of-00009.safetensors", + "models/Qwen/Qwen-Image/transformer/diffusion_pytorch_model-00006-of-00009.safetensors", + "models/Qwen/Qwen-Image/transformer/diffusion_pytorch_model-00007-of-00009.safetensors", + "models/Qwen/Qwen-Image/transformer/diffusion_pytorch_model-00008-of-00009.safetensors", + "models/Qwen/Qwen-Image/transformer/diffusion_pytorch_model-00009-of-00009.safetensors" + ]), + ModelConfig([ + "models/Qwen/Qwen-Image/text_encoder/model-00001-of-00004.safetensors", + "models/Qwen/Qwen-Image/text_encoder/model-00002-of-00004.safetensors", + "models/Qwen/Qwen-Image/text_encoder/model-00003-of-00004.safetensors", + "models/Qwen/Qwen-Image/text_encoder/model-00004-of-00004.safetensors" + ]), + ModelConfig("models/Qwen/Qwen-Image/vae/diffusion_pytorch_model.safetensors") +] +``` + +Then during training, set it as: + +```shell +--model_paths '[ + [ + "models/Qwen/Qwen-Image/transformer/diffusion_pytorch_model-00001-of-00009.safetensors", + "models/Qwen/Qwen-Image/transformer/diffusion_pytorch_model-00002-of-00009.safetensors", + "models/Qwen/Qwen-Image/transformer/diffusion_pytorch_model-00003-of-00009.safetensors", + "models/Qwen/Qwen-Image/transformer/diffusion_pytorch_model-00004-of-00009.safetensors", + "models/Qwen/Qwen-Image/transformer/diffusion_pytorch_model-00005-of-00009.safetensors", + "models/Qwen/Qwen-Image/transformer/diffusion_pytorch_model-00006-of-00009.safetensors", + "models/Qwen/Qwen-Image/transformer/diffusion_pytorch_model-00007-of-00009.safetensors", + "models/Qwen/Qwen-Image/transformer/diffusion_pytorch_model-00008-of-00009.safetensors", + "models/Qwen/Qwen-Image/transformer/diffusion_pytorch_model-00009-of-00009.safetensors" + ], + [ + "models/Qwen/Qwen-Image/text_encoder/model-00001-of-00004.safetensors", + "models/Qwen/Qwen-Image/text_encoder/model-00002-of-00004.safetensors", + "models/Qwen/Qwen-Image/text_encoder/model-00003-of-00004.safetensors", + "models/Qwen/Qwen-Image/text_encoder/model-00004-of-00004.safetensors" + ], + "models/Qwen/Qwen-Image/vae/diffusion_pytorch_model.safetensors" +]' \ +``` + +
+ + +
+ +Step 3: Set Trainable Modules + +The training framework supports training base models or LoRA models. Here are some examples: + +* Full training of DiT part: `--trainable_models dit` +* Train LoRA on DiT part: `--lora_base_model dit --lora_target_modules "to_q,to_k,to_v,add_q_proj,add_k_proj,add_v_proj,to_out.0,to_add_out,img_mlp.net.2,img_mod.1,txt_mlp.net.2,txt_mod.1" --lora_rank 32` + +Also, since the training script loads multiple modules (text encoder, dit, vae), you need to remove prefixes when saving model files. For example, when fully training the DiT part or training LoRA on DiT, set `--remove_prefix_in_ckpt pipe.dit.` + +
+ + +
+ +Step 4: Start Training + +We have written training commands for each model. Please refer to the table at the start of this document. + +
diff --git a/examples/qwen_image/README_zh.md b/examples/qwen_image/README_zh.md new file mode 100644 index 0000000000000000000000000000000000000000..ccc34812f9f681a8c18d5d6ccf31abc0e8f4c953 --- /dev/null +++ b/examples/qwen_image/README_zh.md @@ -0,0 +1,404 @@ +# Qwen-Image + +[Switch to English](./README.md) + +Qwen-Image 是由阿里巴巴通义实验室开源的图像生成模型。 + +## 安装 + +在使用本系列模型之前，请通过源码安装 DiffSynth-Studio。 + +```shell +git clone https://github.com/modelscope/DiffSynth-Studio.git +cd DiffSynth-Studio +pip install -e . +``` + +## 快速开始 + +通过运行以下代码可以快速加载 [Qwen/Qwen-Image](https://www.modelscope.cn/models/Qwen/Qwen-Image) 模型并进行推理 + +```python +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig +from PIL import Image +import torch + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), +) +prompt = "精致肖像，水下少女，蓝裙飘逸，发丝轻扬，光影透澈，气泡环绕，面容恬静，细节精致，梦幻唯美。" +image = pipe( + prompt, seed=0, num_inference_steps=40, + # edit_image=Image.open("xxx.jpg").resize((1328, 1328)) # For Qwen-Image-Edit +) +image.save("image.jpg") +``` + +## 模型总览 + +|模型 ID|推理|低显存推理|全量训练|全量训练后验证|LoRA 训练|LoRA 训练后验证| +|-|-|-|-|-|-|-| +|[Qwen/Qwen-Image](https://www.modelscope.cn/models/Qwen/Qwen-Image)|[code](./model_inference/Qwen-Image.py)|[code](./model_inference_low_vram/Qwen-Image.py)|[code](./model_training/full/Qwen-Image.sh)|[code](./model_training/validate_full/Qwen-Image.py)|[code](./model_training/lora/Qwen-Image.sh)|[code](./model_training/validate_lora/Qwen-Image.py)| +|[Qwen/Qwen-Image-Edit](https://www.modelscope.cn/models/Qwen/Qwen-Image-Edit)|[code](./model_inference/Qwen-Image-Edit.py)|[code](./model_inference_low_vram/Qwen-Image-Edit.py)|[code](./model_training/full/Qwen-Image-Edit.sh)|[code](./model_training/validate_full/Qwen-Image-Edit.py)|[code](./model_training/lora/Qwen-Image-Edit.sh)|[code](./model_training/validate_lora/Qwen-Image-Edit.py)| +|[Qwen/Qwen-Image-Edit-2509](https://www.modelscope.cn/models/Qwen/Qwen-Image-Edit-2509)|[code](./model_inference/Qwen-Image-Edit-2509.py)|[code](./model_inference_low_vram/Qwen-Image-Edit-2509.py)|[code](./model_training/full/Qwen-Image-Edit-2509.sh)|[code](./model_training/validate_full/Qwen-Image-Edit-2509.py)|[code](./model_training/lora/Qwen-Image-Edit-2509.sh)|[code](./model_training/validate_lora/Qwen-Image-Edit-2509.py)| +|[DiffSynth-Studio/Qwen-Image-Distill-Full](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Distill-Full)|[code](./model_inference/Qwen-Image-Distill-Full.py)|[code](./model_inference_low_vram/Qwen-Image-Distill-Full.py)|[code](./model_training/full/Qwen-Image-Distill-Full.sh)|[code](./model_training/validate_full/Qwen-Image-Distill-Full.py)|[code](./model_training/lora/Qwen-Image-Distill-Full.sh)|[code](./model_training/validate_lora/Qwen-Image-Distill-Full.py)| +|[DiffSynth-Studio/Qwen-Image-Distill-LoRA](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Distill-LoRA)|[code](./model_inference/Qwen-Image-Distill-LoRA.py)|[code](./model_inference_low_vram/Qwen-Image-Distill-LoRA.py)|-|-|[code](./model_training/lora/Qwen-Image-Distill-LoRA.sh)|[code](./model_training/validate_lora/Qwen-Image-Distill-LoRA.py)| +|[DiffSynth-Studio/Qwen-Image-EliGen](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-EliGen)|[code](./model_inference/Qwen-Image-EliGen.py)|[code](./model_inference_low_vram/Qwen-Image-EliGen.py)|-|-|[code](./model_training/lora/Qwen-Image-EliGen.sh)|[code](./model_training/validate_lora/Qwen-Image-EliGen.py)| +|[DiffSynth-Studio/Qwen-Image-EliGen-V2](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-EliGen-V2)|[code](./model_inference/Qwen-Image-EliGen-V2.py)|[code](./model_inference_low_vram/Qwen-Image-EliGen-V2.py)|-|-|[code](./model_training/lora/Qwen-Image-EliGen.sh)|[code](./model_training/validate_lora/Qwen-Image-EliGen.py)| +|[DiffSynth-Studio/Qwen-Image-EliGen-Poster](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-EliGen-Poster)|[code](./model_inference/Qwen-Image-EliGen-Poster.py)|[code](./model_inference_low_vram/Qwen-Image-EliGen-Poster.py)|-|-|[code](./model_training/lora/Qwen-Image-EliGen-Poster.sh)|[code](./model_training/validate_lora/Qwen-Image-EliGen-Poster.py)| +|[DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Canny](https://modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Canny)|[code](./model_inference/Qwen-Image-Blockwise-ControlNet-Canny.py)|[code](./model_inference_low_vram/Qwen-Image-Blockwise-ControlNet-Canny.py)|[code](./model_training/full/Qwen-Image-Blockwise-ControlNet-Canny.sh)|[code](./model_training/validate_full/Qwen-Image-Blockwise-ControlNet-Canny.py)|[code](./model_training/lora/Qwen-Image-Blockwise-ControlNet-Canny.sh)|[code](./model_training/validate_lora/Qwen-Image-Blockwise-ControlNet-Canny.py)| +|[DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Depth](https://modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Depth)|[code](./model_inference/Qwen-Image-Blockwise-ControlNet-Depth.py)|[code](./model_inference_low_vram/Qwen-Image-Blockwise-ControlNet-Depth.py)|[code](./model_training/full/Qwen-Image-Blockwise-ControlNet-Depth.sh)|[code](./model_training/validate_full/Qwen-Image-Blockwise-ControlNet-Depth.py)|[code](./model_training/lora/Qwen-Image-Blockwise-ControlNet-Depth.sh)|[code](./model_training/validate_lora/Qwen-Image-Blockwise-ControlNet-Depth.py)| +|[DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Inpaint](https://modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Inpaint)|[code](./model_inference/Qwen-Image-Blockwise-ControlNet-Inpaint.py)|[code](./model_inference_low_vram/Qwen-Image-Blockwise-ControlNet-Inpaint.py)|[code](./model_training/full/Qwen-Image-Blockwise-ControlNet-Inpaint.sh)|[code](./model_training/validate_full/Qwen-Image-Blockwise-ControlNet-Inpaint.py)|[code](./model_training/lora/Qwen-Image-Blockwise-ControlNet-Inpaint.sh)|[code](./model_training/validate_lora/Qwen-Image-Blockwise-ControlNet-Inpaint.py)| +|[DiffSynth-Studio/Qwen-Image-In-Context-Control-Union](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-In-Context-Control-Union)|[code](./model_inference/Qwen-Image-In-Context-Control-Union.py)|[code](./model_inference_low_vram/Qwen-Image-In-Context-Control-Union.py)|-|-|[code](./model_training/lora/Qwen-Image-In-Context-Control-Union.sh)|[code](./model_training/validate_lora/Qwen-Image-In-Context-Control-Union.py)| +|[DiffSynth-Studio/Qwen-Image-Edit-Lowres-Fix](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Edit-Lowres-Fix)|[code](./model_inference/Qwen-Image-Edit-Lowres-Fix.py)|[code](./model_inference_low_vram/Qwen-Image-Edit-Lowres-Fix.py)|-|-|-|-| + +## 模型推理 + +以下部分将会帮助您理解我们的功能并编写推理代码。 + +
+ +加载模型 + +模型通过 `from_pretrained` 加载： + +```python +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig +import torch + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), +) +``` + +其中 `torch_dtype` 和 `device` 是计算精度和计算设备。`model_configs` 可通过多种方式配置模型路径： + +* 从[魔搭社区](https://modelscope.cn/)下载模型并加载。此时需要填写 `model_id` 和 `origin_file_pattern`，例如 + +```python +ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), +``` + +* 从本地文件路径加载模型。此时需要填写 `path`，例如 + +```python +ModelConfig(path="models/xxx.safetensors") +``` + +对于从多个文件加载的单一模型，使用列表即可，例如 + +```python +ModelConfig(path=[ + "models/Qwen/Qwen-Image/text_encoder/model-00001-of-00004.safetensors", + "models/Qwen/Qwen-Image/text_encoder/model-00002-of-00004.safetensors", + "models/Qwen/Qwen-Image/text_encoder/model-00003-of-00004.safetensors", + "models/Qwen/Qwen-Image/text_encoder/model-00004-of-00004.safetensors", +]) +``` + +`ModelConfig` 提供了额外的参数用于控制模型加载时的行为： + +* `local_model_path`: 用于保存下载模型的路径，默认值为 `"./models"`。 +* `skip_download`: 是否跳过下载，默认值为 `False`。当您的网络无法访问[魔搭社区](https://modelscope.cn/)时，请手动下载必要的文件，并将其设置为 `True`。 + +
+ + +
+ +显存管理 + +DiffSynth-Studio 为 Qwen-Image 模型提供了细粒度的显存管理，让模型能够在低显存设备上进行推理，可通过以下代码开启 offload 功能，在显存有限的设备上将部分模块 offload 到内存中。 + +```python +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", offload_device="cpu"), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), +) +pipe.enable_vram_management() +``` + +FP8 量化功能也是支持的： + +```python +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", offload_dtype=torch.float8_e4m3fn), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), +) +pipe.enable_vram_management() +``` + +FP8 量化和 offload 可同时开启： + +```python +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), +) +pipe.enable_vram_management() +``` + +FP8 量化能够大幅度减少显存占用，但不会加速，部分模型在 FP8 量化下会出现精度不足导致的画面模糊、撕裂、失真问题，请谨慎使用 FP8 量化。 + +开启显存管理后，框架会自动根据设备上的剩余显存确定显存管理策略。`enable_vram_management` 函数提供了以下参数，用于手动控制显存管理策略： + +* `vram_limit`: 显存占用量限制（GB），默认占用设备上的剩余显存。注意这不是一个绝对限制，当设置的显存不足以支持模型进行推理，但实际可用显存足够时，将会以最小化显存占用的形式进行推理。将其设置为0时，将会实现理论最小显存占用。 +* `vram_buffer`: 显存缓冲区大小（GB），默认为 0.5GB。由于部分较大的神经网络层在 onload 阶段会不可控地占用更多显存，因此一个显存缓冲区是必要的，理论上的最优值为模型中最大的层所占的显存。 +* `num_persistent_param_in_dit`: DiT 模型中常驻显存的参数数量（个），默认为无限制。我们将会在未来删除这个参数，请不要依赖这个参数。 +* `enable_dit_fp8_computation`: 是否启用 DiT 模型中的 FP8 计算，仅适用于支持 FP8 运算的 GPU（例如 H200 等），默认不启用。 + +
+ + +
+ +推理加速 + +* FP8 量化：根据您的硬件与需求，请选择合适的量化方式 + * GPU 不支持 FP8 计算（例如 A100、4090 等）：FP8 量化仅能降低显存占用，无法加速，代码：[./model_inference_low_vram/Qwen-Image.py](./model_inference_low_vram/Qwen-Image.py) + * GPU 支持 FP8 运算（例如 H200 等）：请安装 [Flash Attention 3](https://github.com/Dao-AILab/flash-attention)，否则 FP8 加速仅对 Linear 层生效 + * 更快的速度，但更大的显存：请使用 [./accelerate/Qwen-Image-FP8.py](./accelerate/Qwen-Image-FP8.py) + * 稍慢的速度，但更小的显存：请使用 [./accelerate/Qwen-Image-FP8-offload.py](./accelerate/Qwen-Image-FP8-offload.py) +* 蒸馏加速：我们训练了两个蒸馏加速模型，可以在 `cfg_scale=1` 和 `num_inference_steps=15` 设置下进行快速推理 + * [DiffSynth-Studio/Qwen-Image-Distill-Full](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Distill-Full)：全量蒸馏训练版本，更好的生成效果，稍差的 LoRA 兼容性，请使用 [./model_inference/Qwen-Image-Distill-Full.py](./model_inference/Qwen-Image-Distill-Full.py) + * [DiffSynth-Studio/Qwen-Image-Distill-LoRA](https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Distill-LoRA)：LoRA 蒸馏训练版本，稍差的生成效果，更好的 LoRA 兼容性，请使用 [./model_inference/Qwen-Image-Distill-LoRA.py](./model_inference/Qwen-Image-Distill-LoRA.py) + +
+ + +
+ +输入参数 + +Pipeline 在推理阶段能够接收以下输入参数： + +* `prompt`: 提示词，描述画面中出现的内容。 +* `negative_prompt`: 负向提示词，描述画面中不应该出现的内容，默认值为 `""`。 +* `cfg_scale`: Classifier-free guidance 的参数，默认值为 1，当设置为大于1的数值时生效。 +* `input_image`: 输入图像，用于图生图，该参数与 `denoising_strength` 配合使用。 +* `denoising_strength`: 去噪强度，范围是 0～1，默认值为 1，当数值接近 0 时，生成图像与输入图像相似；当数值接近 1 时，生成图像与输入图像相差更大。在不输入 `input_image` 参数时，请不要将其设置为非 1 的数值。 +* `height`: 图像高度，需保证高度为 16 的倍数。 +* `width`: 图像宽度，需保证宽度为 16 的倍数。 +* `seed`: 随机种子。默认为 `None`，即完全随机。 +* `rand_device`: 生成随机高斯噪声矩阵的计算设备，默认为 `"cpu"`。当设置为 `cuda` 时，在不同 GPU 上会导致不同的生成结果。 +* `num_inference_steps`: 推理次数，默认值为 30。 +* `tiled`: 是否启用 VAE 分块推理，默认为 `False`。设置为 `True` 时可显著减少 VAE 编解码阶段的显存占用，会产生少许误差，以及少量推理时间延长。 +* `tile_size`: VAE 编解码阶段的分块大小，默认为 128，仅在 `tiled=True` 时生效。 +* `tile_stride`: VAE 编解码阶段的分块步长，默认为 64，仅在 `tiled=True` 时生效，需保证其数值小于或等于 `tile_size`。 +* `progress_bar_cmd`: 进度条，默认为 `tqdm.tqdm`。可通过设置为 `lambda x:x` 来屏蔽进度条。 + +
+ + +## 模型训练 + +Qwen-Image 系列模型训练通过统一的 [`./model_training/train.py`](./model_training/train.py) 脚本进行。 + +
+ +脚本参数 + +脚本包含以下参数： + +* 数据集 + * `--dataset_base_path`: 数据集的根路径。 + * `--dataset_metadata_path`: 数据集的元数据文件路径。 + * `--max_pixels`: 最大像素面积，默认为 1024*1024，当启用动态分辨率时，任何分辨率大于这个数值的图片都会被缩小。 + * `--height`: 图像或视频的高度。将 `height` 和 `width` 留空以启用动态分辨率。 + * `--width`: 图像或视频的宽度。将 `height` 和 `width` 留空以启用动态分辨率。 + * `--data_file_keys`: 元数据中的数据文件键。用逗号分隔。 + * `--dataset_repeat`: 每个 epoch 中数据集重复的次数。 + * `--dataset_num_workers`: 每个 Dataloder 的进程数量。 +* 模型 + * `--model_paths`: 要加载的模型路径。JSON 格式。 + * `--model_id_with_origin_paths`: 带原始路径的模型 ID，例如 Qwen/Qwen-Image:transformer/diffusion_pytorch_model*.safetensors。用逗号分隔。 + * `--tokenizer_path`: tokenizer 路径，留空将会自动下载。 + * `--processor_path`：Qwen-Image-Edit 的 processor 路径。留空则自动下载。 +* 训练 + * `--learning_rate`: 学习率。 + * `--weight_decay`：权重衰减大小。 + * `--num_epochs`: 轮数（Epoch）。 + * `--output_path`: 保存路径。 + * `--remove_prefix_in_ckpt`: 在 ckpt 中移除前缀。 + * `--save_steps`: 保存模型的间隔 step 数量，如果设置为 None ，则每个 epoch 保存一次 + * `--find_unused_parameters`: DDP 训练中是否存在未使用的参数 +* 可训练模块 + * `--trainable_models`: 可训练的模型，例如 dit、vae、text_encoder。 + * `--lora_base_model`: LoRA 添加到哪个模型上。 + * `--lora_target_modules`: LoRA 添加到哪一层上。 + * `--lora_rank`: LoRA 的秩（Rank）。 + * `--lora_checkpoint`: LoRA 检查点的路径。如果提供此路径，LoRA 将从此检查点加载。 +* 额外模型输入 + * `--extra_inputs`: 额外的模型输入，以逗号分隔。 +* 显存管理 + * `--use_gradient_checkpointing`: 是否启用 gradient checkpointing。 + * `--use_gradient_checkpointing_offload`: 是否将 gradient checkpointing 卸载到内存中。 + * `--gradient_accumulation_steps`: 梯度累积步数。 + +此外，训练框架基于 [`accelerate`](https://huggingface.co/docs/accelerate/index) 构建，在开始训练前运行 `accelerate config` 可配置 GPU 的相关参数。对于部分模型训练（例如 20B 模型的全量训练）脚本，我们提供了建议的 `accelerate` 配置文件，可在对应的训练脚本中查看。 + +
+ + +
+ +Step 1: 准备数据集 + +数据集包含一系列文件，我们建议您这样组织数据集文件： + +``` +data/example_image_dataset/ +├── metadata.csv +├── image1.jpg +└── image2.jpg +``` + +其中 `image1.jpg`、`image2.jpg` 为训练用图像数据，`metadata.csv` 为元数据列表，例如 + +``` +image,prompt +image1.jpg,"a cat is sleeping" +image2.jpg,"a dog is running" +``` + +我们构建了一个样例图像数据集，以方便您进行测试，通过以下命令可以下载这个数据集： + +```shell +modelscope download --dataset DiffSynth-Studio/example_image_dataset --local_dir ./data/example_image_dataset +``` + +数据集支持多种图片格式，`"jpg", "jpeg", "png", "webp"`。 + +图片的尺寸可通过脚本参数 `--height`、`--width` 控制。当 `--height` 和 `--width` 为空时将会开启动态分辨率，按照数据集中每个图像的实际宽高训练。 + +**我们强烈建议使用固定分辨率训练，因为在多卡训练中存在负载均衡问题。** + +
+ + +
+ +Step 2: 加载模型 + +类似于推理时的模型加载逻辑，可直接通过模型 ID 配置要加载的模型。例如，推理时我们通过以下设置加载模型 + +```python +model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), +] +``` + +那么在训练时，填入以下参数即可加载对应的模型。 + +```shell +--model_id_with_origin_paths "Qwen/Qwen-Image:transformer/diffusion_pytorch_model*.safetensors,Qwen/Qwen-Image:text_encoder/model*.safetensors,Qwen/Qwen-Image:vae/diffusion_pytorch_model.safetensors" +``` + +如果您希望从本地文件加载模型，例如推理时 + +```python +model_configs=[ + ModelConfig([ + "models/Qwen/Qwen-Image/transformer/diffusion_pytorch_model-00001-of-00009.safetensors", + "models/Qwen/Qwen-Image/transformer/diffusion_pytorch_model-00002-of-00009.safetensors", + "models/Qwen/Qwen-Image/transformer/diffusion_pytorch_model-00003-of-00009.safetensors", + "models/Qwen/Qwen-Image/transformer/diffusion_pytorch_model-00004-of-00009.safetensors", + "models/Qwen/Qwen-Image/transformer/diffusion_pytorch_model-00005-of-00009.safetensors", + "models/Qwen/Qwen-Image/transformer/diffusion_pytorch_model-00006-of-00009.safetensors", + "models/Qwen/Qwen-Image/transformer/diffusion_pytorch_model-00007-of-00009.safetensors", + "models/Qwen/Qwen-Image/transformer/diffusion_pytorch_model-00008-of-00009.safetensors", + "models/Qwen/Qwen-Image/transformer/diffusion_pytorch_model-00009-of-00009.safetensors" + ]), + ModelConfig([ + "models/Qwen/Qwen-Image/text_encoder/model-00001-of-00004.safetensors", + "models/Qwen/Qwen-Image/text_encoder/model-00002-of-00004.safetensors", + "models/Qwen/Qwen-Image/text_encoder/model-00003-of-00004.safetensors", + "models/Qwen/Qwen-Image/text_encoder/model-00004-of-00004.safetensors" + ]), + ModelConfig("models/Qwen/Qwen-Image/vae/diffusion_pytorch_model.safetensors") +] +``` + +那么训练时需设置为 + +```shell +--model_paths '[ + [ + "models/Qwen/Qwen-Image/transformer/diffusion_pytorch_model-00001-of-00009.safetensors", + "models/Qwen/Qwen-Image/transformer/diffusion_pytorch_model-00002-of-00009.safetensors", + "models/Qwen/Qwen-Image/transformer/diffusion_pytorch_model-00003-of-00009.safetensors", + "models/Qwen/Qwen-Image/transformer/diffusion_pytorch_model-00004-of-00009.safetensors", + "models/Qwen/Qwen-Image/transformer/diffusion_pytorch_model-00005-of-00009.safetensors", + "models/Qwen/Qwen-Image/transformer/diffusion_pytorch_model-00006-of-00009.safetensors", + "models/Qwen/Qwen-Image/transformer/diffusion_pytorch_model-00007-of-00009.safetensors", + "models/Qwen/Qwen-Image/transformer/diffusion_pytorch_model-00008-of-00009.safetensors", + "models/Qwen/Qwen-Image/transformer/diffusion_pytorch_model-00009-of-00009.safetensors" + ], + [ + "models/Qwen/Qwen-Image/text_encoder/model-00001-of-00004.safetensors", + "models/Qwen/Qwen-Image/text_encoder/model-00002-of-00004.safetensors", + "models/Qwen/Qwen-Image/text_encoder/model-00003-of-00004.safetensors", + "models/Qwen/Qwen-Image/text_encoder/model-00004-of-00004.safetensors" + ], + "models/Qwen/Qwen-Image/vae/diffusion_pytorch_model.safetensors" +]' \ +``` + +
+ + +
+ +Step 3: 设置可训练模块 + +训练框架支持训练基础模型，或 LoRA 模型。以下是几个例子： + +* 全量训练 DiT 部分：`--trainable_models dit` +* 训练 DiT 部分的 LoRA 模型：`--lora_base_model dit --lora_target_modules "to_q,to_k,to_v,add_q_proj,add_k_proj,add_v_proj,to_out.0,to_add_out,img_mlp.net.2,img_mod.1,txt_mlp.net.2,txt_mod.1" --lora_rank 32` + +此外，由于训练脚本中加载了多个模块（text encoder、dit、vae），保存模型文件时需要移除前缀，例如在全量训练 DiT 部分或者训练 DiT 部分的 LoRA 模型时，请设置 `--remove_prefix_in_ckpt pipe.dit.` + +
+ + +
+ +Step 4: 启动训练程序 + +我们为每一个模型编写了训练命令，请参考本文档开头的表格。 + +
diff --git a/examples/qwen_image/accelerate/Qwen-Image-FP8-offload.py b/examples/qwen_image/accelerate/Qwen-Image-FP8-offload.py new file mode 100644 index 0000000000000000000000000000000000000000..2d403162fcc2846daf03600ed0b86e57e667077e --- /dev/null +++ b/examples/qwen_image/accelerate/Qwen-Image-FP8-offload.py @@ -0,0 +1,18 @@ +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig +import torch + + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), +) +pipe.enable_vram_management(enable_dit_fp8_computation=True) +prompt = "精致肖像，水下少女，蓝裙飘逸，发丝轻扬，光影透澈，气泡环绕，面容恬静，细节精致，梦幻唯美。" +image = pipe(prompt, seed=0, num_inference_steps=40, enable_fp8_attention=True) +image.save("image.jpg") diff --git a/examples/qwen_image/accelerate/Qwen-Image-FP8.py b/examples/qwen_image/accelerate/Qwen-Image-FP8.py new file mode 100644 index 0000000000000000000000000000000000000000..944140712e8b17ad2519a6563db1cfac33936525 --- /dev/null +++ b/examples/qwen_image/accelerate/Qwen-Image-FP8.py @@ -0,0 +1,51 @@ +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig +from diffsynth.models.qwen_image_dit import RMSNorm +from diffsynth.vram_management.layers import enable_vram_management, AutoWrappedLinear, AutoWrappedModule +import torch + + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), +) + +enable_vram_management( + pipe.dit, + module_map = { + RMSNorm: AutoWrappedModule, + }, + module_config = dict( + offload_dtype=torch.bfloat16, + offload_device="cuda", + onload_dtype=torch.bfloat16, + onload_device="cuda", + computation_dtype=torch.bfloat16, + computation_device="cuda", + ), + vram_limit=None, +) +enable_vram_management( + pipe.dit, + module_map = { + torch.nn.Linear: AutoWrappedLinear, + }, + module_config = dict( + offload_dtype=torch.float8_e4m3fn, + offload_device="cuda", + onload_dtype=torch.float8_e4m3fn, + onload_device="cuda", + computation_dtype=torch.float8_e4m3fn, + computation_device="cuda", + ), + vram_limit=None, +) + +prompt = "精致肖像，水下少女，蓝裙飘逸，发丝轻扬，光影透澈，气泡环绕，面容恬静，细节精致，梦幻唯美。" +image = pipe(prompt, seed=0, num_inference_steps=40, enable_fp8_attention=True) +image.save("image.jpg") diff --git a/examples/qwen_image/model_inference/Qwen-Image-Blockwise-ControlNet-Canny.py b/examples/qwen_image/model_inference/Qwen-Image-Blockwise-ControlNet-Canny.py new file mode 100644 index 0000000000000000000000000000000000000000..85b9b96886a3a48bdf30319a99557a952721ab81 --- /dev/null +++ b/examples/qwen_image/model_inference/Qwen-Image-Blockwise-ControlNet-Canny.py @@ -0,0 +1,31 @@ +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig, ControlNetInput +from PIL import Image +import torch +from modelscope import dataset_snapshot_download + + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ModelConfig(model_id="DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Canny", origin_file_pattern="model.safetensors"), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), +) + +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/example_image_dataset", + local_dir="./data/example_image_dataset", + allow_file_pattern="canny/image_1.jpg" +) +controlnet_image = Image.open("data/example_image_dataset/canny/image_1.jpg").resize((1328, 1328)) + +prompt = "一只小狗，毛发光洁柔顺，眼神灵动，背景是樱花纷飞的春日庭院，唯美温馨。" +image = pipe( + prompt, seed=0, + blockwise_controlnet_inputs=[ControlNetInput(image=controlnet_image)] +) +image.save("image.jpg") diff --git a/examples/qwen_image/model_inference/Qwen-Image-Blockwise-ControlNet-Depth.py b/examples/qwen_image/model_inference/Qwen-Image-Blockwise-ControlNet-Depth.py new file mode 100644 index 0000000000000000000000000000000000000000..6676868ad0e24d09d962211dda758f29cf7180f2 --- /dev/null +++ b/examples/qwen_image/model_inference/Qwen-Image-Blockwise-ControlNet-Depth.py @@ -0,0 +1,32 @@ +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig, ControlNetInput +from PIL import Image +import torch +from modelscope import dataset_snapshot_download + + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ModelConfig(model_id="DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Depth", origin_file_pattern="model.safetensors"), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), +) + +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/example_image_dataset", + local_dir="./data/example_image_dataset", + allow_file_pattern="depth/image_1.jpg" +) + +controlnet_image = Image.open("data/example_image_dataset/depth/image_1.jpg").resize((1328, 1328)) + +prompt = "精致肖像，水下少女，蓝裙飘逸，发丝轻扬，光影透澈，气泡环绕，面容恬静，细节精致，梦幻唯美。" +image = pipe( + prompt, seed=0, + blockwise_controlnet_inputs=[ControlNetInput(image=controlnet_image)] +) +image.save("image.jpg") diff --git a/examples/qwen_image/model_inference/Qwen-Image-Blockwise-ControlNet-Inpaint.py b/examples/qwen_image/model_inference/Qwen-Image-Blockwise-ControlNet-Inpaint.py new file mode 100644 index 0000000000000000000000000000000000000000..1cb98e0a348ba5b9e9748496c05bc8fc8aef154e --- /dev/null +++ b/examples/qwen_image/model_inference/Qwen-Image-Blockwise-ControlNet-Inpaint.py @@ -0,0 +1,33 @@ +import torch +from PIL import Image +from modelscope import dataset_snapshot_download +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig, ControlNetInput + + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ModelConfig(model_id="DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Inpaint", origin_file_pattern="model.safetensors"), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), +) + +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/example_image_dataset", + local_dir="./data/example_image_dataset", + allow_file_pattern="inpaint/*.jpg" +) +prompt = "a cat with sunglasses" +controlnet_image = Image.open("./data/example_image_dataset/inpaint/image_1.jpg").convert("RGB").resize((1328, 1328)) +inpaint_mask = Image.open("./data/example_image_dataset/inpaint/mask.jpg").convert("RGB").resize((1328, 1328)) +image = pipe( + prompt, seed=0, + input_image=controlnet_image, inpaint_mask=inpaint_mask, + blockwise_controlnet_inputs=[ControlNetInput(image=controlnet_image, inpaint_mask=inpaint_mask)], + num_inference_steps=40, +) +image.save("image.jpg") diff --git a/examples/qwen_image/model_inference/Qwen-Image-Distill-DMD2.py b/examples/qwen_image/model_inference/Qwen-Image-Distill-DMD2.py new file mode 100644 index 0000000000000000000000000000000000000000..ab7ecda6e1bb415e49c1973ee13dce6f95c9372c --- /dev/null +++ b/examples/qwen_image/model_inference/Qwen-Image-Distill-DMD2.py @@ -0,0 +1,24 @@ +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig, load_state_dict +from modelscope import snapshot_download +import torch, math + + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), +) + +snapshot_download("MusePublic/Qwen-Image-Distill", allow_file_pattern="qwen_image_distill_3step.safetensors", cache_dir="models") +lora_state_dict = load_state_dict("models/MusePublic/Qwen-Image-Distill/qwen_image_distill_3step.safetensors") +lora_state_dict = {i.replace("base_model.model.", ""): j for i, j in lora_state_dict.items()} +pipe.load_lora(pipe.dit, state_dict=lora_state_dict) + +prompt = "精致肖像，水下少女，蓝裙飘逸，发丝轻扬，光影透澈，气泡环绕，面容恬静，细节精致，梦幻唯美。" +image = pipe(prompt, seed=0, num_inference_steps=3, cfg_scale=1, exponential_shift_mu=math.log(2.5)) +image.save("image.jpg") diff --git a/examples/qwen_image/model_inference/Qwen-Image-Distill-Full.py b/examples/qwen_image/model_inference/Qwen-Image-Distill-Full.py new file mode 100644 index 0000000000000000000000000000000000000000..c13a417f5aba55820b8111b872e8cf7be26e6ed1 --- /dev/null +++ b/examples/qwen_image/model_inference/Qwen-Image-Distill-Full.py @@ -0,0 +1,17 @@ +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig +import torch + + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="DiffSynth-Studio/Qwen-Image-Distill-Full", origin_file_pattern="diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), +) +prompt = "精致肖像，水下少女，蓝裙飘逸，发丝轻扬，光影透澈，气泡环绕，面容恬静，细节精致，梦幻唯美。" +image = pipe(prompt, seed=0, num_inference_steps=15, cfg_scale=1) +image.save("image.jpg") diff --git a/examples/qwen_image/model_inference/Qwen-Image-Distill-LoRA.py b/examples/qwen_image/model_inference/Qwen-Image-Distill-LoRA.py new file mode 100644 index 0000000000000000000000000000000000000000..aad1fdd7eb222e2d87fb57180c2ffb5d1db4c3ec --- /dev/null +++ b/examples/qwen_image/model_inference/Qwen-Image-Distill-LoRA.py @@ -0,0 +1,20 @@ +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig +from modelscope import snapshot_download +import torch + +snapshot_download("DiffSynth-Studio/Qwen-Image-Distill-LoRA", local_dir="models/DiffSynth-Studio/Qwen-Image-Distill-LoRA") +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), +) +pipe.load_lora(pipe.dit, "models/DiffSynth-Studio/Qwen-Image-Distill-LoRA/model.safetensors") + +prompt = "精致肖像，水下少女，蓝裙飘逸，发丝轻扬，光影透澈，气泡环绕，面容恬静，细节精致，梦幻唯美。" +image = pipe(prompt, seed=0, num_inference_steps=15, cfg_scale=1) +image.save("image.jpg") \ No newline at end of file diff --git a/examples/qwen_image/model_inference/Qwen-Image-Edit-2509.py b/examples/qwen_image/model_inference/Qwen-Image-Edit-2509.py new file mode 100644 index 0000000000000000000000000000000000000000..45489cd065827a1c048b6225400057670cdbb6bb --- /dev/null +++ b/examples/qwen_image/model_inference/Qwen-Image-Edit-2509.py @@ -0,0 +1,25 @@ +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig +from PIL import Image +import torch + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image-Edit-2509", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ], + processor_config=ModelConfig(model_id="Qwen/Qwen-Image-Edit", origin_file_pattern="processor/"), +) + +image_1 = pipe(prompt="一位少女", seed=0, num_inference_steps=40, height=1328, width=1024) +image_1.save("image1.jpg") + +image_2 = pipe(prompt="一位老人", seed=0, num_inference_steps=40, height=1328, width=1024) +image_2.save("image2.jpg") + +prompt = "生成这两个人的合影" +edit_image = [Image.open("image1.jpg"), Image.open("image2.jpg")] +image_3 = pipe(prompt, edit_image=edit_image, seed=1, num_inference_steps=40, height=1328, width=1024, edit_image_auto_resize=True) +image_3.save("image3.jpg") diff --git a/examples/qwen_image/model_inference/Qwen-Image-Edit-Lowres-Fix.py b/examples/qwen_image/model_inference/Qwen-Image-Edit-Lowres-Fix.py new file mode 100644 index 0000000000000000000000000000000000000000..c18eaa8fd329fda7f4784fe4f3ce22309bbda2ef --- /dev/null +++ b/examples/qwen_image/model_inference/Qwen-Image-Edit-Lowres-Fix.py @@ -0,0 +1,25 @@ +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig +import torch +from modelscope import snapshot_download + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image-Edit", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ], + processor_config=ModelConfig(model_id="Qwen/Qwen-Image-Edit", origin_file_pattern="processor/"), +) +snapshot_download("DiffSynth-Studio/Qwen-Image-Edit-Lowres-Fix", local_dir="models/DiffSynth-Studio/Qwen-Image-Edit-Lowres-Fix", allow_file_pattern="model.safetensors") +pipe.load_lora(pipe.dit, "models/DiffSynth-Studio/Qwen-Image-Edit-Lowres-Fix/model.safetensors") + +prompt = "精致肖像，水下少女，蓝裙飘逸，发丝轻扬，光影透澈，气泡环绕，面容恬静，细节精致，梦幻唯美。" +image = pipe(prompt=prompt, seed=0, num_inference_steps=40, height=1024, width=768) +image.save("image.jpg") + +prompt = "将裙子变成粉色" +image = image.resize((512, 384)) +image = pipe(prompt, edit_image=image, seed=1, num_inference_steps=40, height=1024, width=768, edit_rope_interpolation=True, edit_image_auto_resize=False) +image.save(f"image2.jpg") diff --git a/examples/qwen_image/model_inference/Qwen-Image-Edit.py b/examples/qwen_image/model_inference/Qwen-Image-Edit.py new file mode 100644 index 0000000000000000000000000000000000000000..8a47756ffb71e778d204303bbb813c0fa97d987e --- /dev/null +++ b/examples/qwen_image/model_inference/Qwen-Image-Edit.py @@ -0,0 +1,25 @@ +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig +import torch + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image-Edit", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ], + processor_config=ModelConfig(model_id="Qwen/Qwen-Image-Edit", origin_file_pattern="processor/"), +) +prompt = "精致肖像，水下少女，蓝裙飘逸，发丝轻扬，光影透澈，气泡环绕，面容恬静，细节精致，梦幻唯美。" +input_image = pipe(prompt=prompt, seed=0, num_inference_steps=40, height=1328, width=1024) +input_image.save("image1.jpg") + +prompt = "将裙子改为粉色" +# edit_image_auto_resize=True: auto resize input image to match the area of 1024*1024 with the original aspect ratio +image = pipe(prompt, edit_image=input_image, seed=1, num_inference_steps=40, height=1328, width=1024, edit_image_auto_resize=True) +image.save(f"image2.jpg") + +# edit_image_auto_resize=False: do not resize input image +image = pipe(prompt, edit_image=input_image, seed=1, num_inference_steps=40, height=1328, width=1024, edit_image_auto_resize=False) +image.save(f"image3.jpg") diff --git a/examples/qwen_image/model_inference/Qwen-Image-EliGen-Poster.py b/examples/qwen_image/model_inference/Qwen-Image-EliGen-Poster.py new file mode 100644 index 0000000000000000000000000000000000000000..2cf0a12afd80cbde3d711f0942d593afc38591a0 --- /dev/null +++ b/examples/qwen_image/model_inference/Qwen-Image-EliGen-Poster.py @@ -0,0 +1,114 @@ +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig +import torch +from PIL import Image, ImageDraw, ImageFont +from modelscope import dataset_snapshot_download, snapshot_download +import random + + +def visualize_masks(image, masks, mask_prompts, output_path, font_size=35, use_random_colors=False): + # Create a blank image for overlays + overlay = Image.new('RGBA', image.size, (0, 0, 0, 0)) + + colors = [ + (165, 238, 173, 80), + (76, 102, 221, 80), + (221, 160, 77, 80), + (204, 93, 71, 80), + (145, 187, 149, 80), + (134, 141, 172, 80), + (157, 137, 109, 80), + (153, 104, 95, 80), + (165, 238, 173, 80), + (76, 102, 221, 80), + (221, 160, 77, 80), + (204, 93, 71, 80), + (145, 187, 149, 80), + (134, 141, 172, 80), + (157, 137, 109, 80), + (153, 104, 95, 80), + ] + # Generate random colors for each mask + if use_random_colors: + colors = [(random.randint(0, 255), random.randint(0, 255), random.randint(0, 255), 80) for _ in range(len(masks))] + + # Font settings + try: + font = ImageFont.truetype("wqy-zenhei.ttc", font_size) # Adjust as needed + except IOError: + font = ImageFont.load_default(font_size) + + # Overlay each mask onto the overlay image + for mask, mask_prompt, color in zip(masks, mask_prompts, colors): + # Convert mask to RGBA mode + mask_rgba = mask.convert('RGBA') + mask_data = mask_rgba.getdata() + new_data = [(color if item[:3] == (255, 255, 255) else (0, 0, 0, 0)) for item in mask_data] + mask_rgba.putdata(new_data) + + # Draw the mask prompt text on the mask + draw = ImageDraw.Draw(mask_rgba) + mask_bbox = mask.getbbox() # Get the bounding box of the mask + text_position = (mask_bbox[0] + 10, mask_bbox[1] + 10) # Adjust text position based on mask position + draw.text(text_position, mask_prompt, fill=(255, 255, 255, 255), font=font) + + # Alpha composite the overlay with this mask + overlay = Image.alpha_composite(overlay, mask_rgba) + + # Composite the overlay onto the original image + result = Image.alpha_composite(image.convert('RGBA'), overlay) + + # Save or display the resulting image + result.save(output_path) + + return result + + +def example(pipe, seeds, example_id, global_prompt, entity_prompts, height=784, width=1280): + dataset_snapshot_download( + dataset_id="DiffSynth-Studio/examples_in_diffsynth", + local_dir="./", + allow_file_pattern=f"data/examples/eligen/poster/example_{example_id}/*.png" + ) + masks = [ + Image.open(f"./data/examples/eligen/poster/example_{example_id}/{i}.png").convert('RGB').resize((width, height)) + for i in range(len(entity_prompts)) + ] + negative_prompt = "网格化，规则的网格，模糊, 低分辨率, 低质量, 变形, 畸形, 错误的解剖学, 变形的手, 变形的身体, 变形的脸, 变形的头发, 变形的眼睛, 变形的嘴巴" + for seed in seeds: + # generate image + image = pipe( + prompt=global_prompt, + cfg_scale=4.0, + negative_prompt=negative_prompt, + num_inference_steps=40, + seed=seed, + height=height, + width=width, + eligen_entity_prompts=entity_prompts, + eligen_entity_masks=masks, + ) + image.save(f"eligen_poster_example_{example_id}_{seed}.png") + image = Image.new("RGB", (width, height), (0, 0, 0)) + visualize_masks(image, masks, entity_prompts, f"eligen_poster_example_{example_id}_mask_{seed}.png") + + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), +) +snapshot_download( + "DiffSynth-Studio/Qwen-Image-EliGen-Poster", + local_dir="models/DiffSynth-Studio/Qwen-Image-EliGen-Poster", + allow_file_pattern="model.safetensors", +) +pipe.load_lora(pipe.dit, "models/DiffSynth-Studio/Qwen-Image-EliGen-Poster/model.safetensors") +global_prompt = "一张以柔粉紫为背景的海报，左侧有大号粉紫色文字“Qwen-Image EliGen-Poster”，粉紫色椭圆框内白色小字：“图像精确分区控制模型”。右侧有一只小兔子在拆礼物，旁边站着一只头顶迷你烟花发射器的小龙（卡通Q版）。背景有一些白云点缀。整体风格卡通可爱，传达节日惊喜的主题。" +entity_prompts = ["粉紫色文字“Qwen-Image EliGen-Poster”", "粉紫色椭圆框内白色小字：“图像精确分区控制模型”", "一只小兔子在拆礼物，小兔子旁边站着一只头顶迷你烟花发射器的小龙（卡通Q版）"] +seed = [42] +example(pipe, seed, 1, global_prompt, entity_prompts) diff --git a/examples/qwen_image/model_inference/Qwen-Image-EliGen-V2.py b/examples/qwen_image/model_inference/Qwen-Image-EliGen-V2.py new file mode 100644 index 0000000000000000000000000000000000000000..82bab2d0236c058216f23406be1220db3096cdfc --- /dev/null +++ b/examples/qwen_image/model_inference/Qwen-Image-EliGen-V2.py @@ -0,0 +1,106 @@ +import torch +import random +from PIL import Image, ImageDraw, ImageFont +from modelscope import dataset_snapshot_download, snapshot_download +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig + +def visualize_masks(image, masks, mask_prompts, output_path, font_size=35, use_random_colors=False): + # Create a blank image for overlays + overlay = Image.new('RGBA', image.size, (0, 0, 0, 0)) + + colors = [ + (165, 238, 173, 80), + (76, 102, 221, 80), + (221, 160, 77, 80), + (204, 93, 71, 80), + (145, 187, 149, 80), + (134, 141, 172, 80), + (157, 137, 109, 80), + (153, 104, 95, 80), + (165, 238, 173, 80), + (76, 102, 221, 80), + (221, 160, 77, 80), + (204, 93, 71, 80), + (145, 187, 149, 80), + (134, 141, 172, 80), + (157, 137, 109, 80), + (153, 104, 95, 80), + ] + # Generate random colors for each mask + if use_random_colors: + colors = [(random.randint(0, 255), random.randint(0, 255), random.randint(0, 255), 80) for _ in range(len(masks))] + + # Font settings + try: + font = ImageFont.truetype("wqy-zenhei.ttc", font_size) # Adjust as needed + except IOError: + font = ImageFont.load_default(font_size) + + # Overlay each mask onto the overlay image + for mask, mask_prompt, color in zip(masks, mask_prompts, colors): + # Convert mask to RGBA mode + mask_rgba = mask.convert('RGBA') + mask_data = mask_rgba.getdata() + new_data = [(color if item[:3] == (255, 255, 255) else (0, 0, 0, 0)) for item in mask_data] + mask_rgba.putdata(new_data) + + # Draw the mask prompt text on the mask + draw = ImageDraw.Draw(mask_rgba) + mask_bbox = mask.getbbox() # Get the bounding box of the mask + text_position = (mask_bbox[0] + 10, mask_bbox[1] + 10) # Adjust text position based on mask position + draw.text(text_position, mask_prompt, fill=(255, 255, 255, 255), font=font) + + # Alpha composite the overlay with this mask + overlay = Image.alpha_composite(overlay, mask_rgba) + + # Composite the overlay onto the original image + result = Image.alpha_composite(image.convert('RGBA'), overlay) + + # Save or display the resulting image + result.save(output_path) + + return result + +def example(pipe, seeds, example_id, global_prompt, entity_prompts): + dataset_snapshot_download(dataset_id="DiffSynth-Studio/examples_in_diffsynth", local_dir="./", allow_file_pattern=f"data/examples/eligen/qwen-image/example_{example_id}/*.png") + masks = [Image.open(f"./data/examples/eligen/qwen-image/example_{example_id}/{i}.png").convert('RGB').resize((1024, 1024)) for i in range(len(entity_prompts))] + negative_prompt = "网格化，规则的网格，模糊, 低分辨率, 低质量, 变形, 畸形, 错误的解剖学, 变形的手, 变形的身体, 变形的脸, 变形的头发, 变形的眼睛, 变形的嘴巴" + for seed in seeds: + # generate image + image = pipe( + prompt=global_prompt, + cfg_scale=4.0, + negative_prompt=negative_prompt, + num_inference_steps=40, + seed=seed, + height=1024, + width=1024, + eligen_entity_prompts=entity_prompts, + eligen_entity_masks=masks, + ) + image.save(f"eligen_example_{example_id}_{seed}.png") + visualize_masks(image, masks, entity_prompts, f"eligen_example_{example_id}_mask_{seed}.png") + + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), +) +snapshot_download("DiffSynth-Studio/Qwen-Image-EliGen-V2", local_dir="models/DiffSynth-Studio/Qwen-Image-EliGen-V2", allow_file_pattern="model.safetensors") +pipe.load_lora(pipe.dit, "models/DiffSynth-Studio/Qwen-Image-EliGen-V2/model.safetensors") + +seeds = [0] + +global_prompt = "写实摄影风格. A beautiful asia woman wearing white dress, she is holding a mirror with her right arm, with a beach background." +entity_prompts = ["A beautiful woman", "mirror", "necklace", "glasses", "earring", "white dress", "jewelry headpiece"] +example(pipe, seeds, 7, global_prompt, entity_prompts) + +global_prompt = "写实摄影风格, 细节丰富。街头一位漂亮的女孩，穿着衬衫和短裤，手持写有“实体控制”的标牌，背景是繁忙的城市街道，阳光明媚，行人匆匆。" +entity_prompts = ["一个漂亮的女孩", "标牌 '实体控制'", "短裤", "衬衫"] +example(pipe, seeds, 4, global_prompt, entity_prompts) diff --git a/examples/qwen_image/model_inference/Qwen-Image-EliGen.py b/examples/qwen_image/model_inference/Qwen-Image-EliGen.py new file mode 100644 index 0000000000000000000000000000000000000000..ab0fd148d0d03d32ca6507c85e2d95e49a70c228 --- /dev/null +++ b/examples/qwen_image/model_inference/Qwen-Image-EliGen.py @@ -0,0 +1,128 @@ +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig +import torch +from PIL import Image, ImageDraw, ImageFont +from modelscope import dataset_snapshot_download, snapshot_download +import random + + +def visualize_masks(image, masks, mask_prompts, output_path, font_size=35, use_random_colors=False): + # Create a blank image for overlays + overlay = Image.new('RGBA', image.size, (0, 0, 0, 0)) + + colors = [ + (165, 238, 173, 80), + (76, 102, 221, 80), + (221, 160, 77, 80), + (204, 93, 71, 80), + (145, 187, 149, 80), + (134, 141, 172, 80), + (157, 137, 109, 80), + (153, 104, 95, 80), + (165, 238, 173, 80), + (76, 102, 221, 80), + (221, 160, 77, 80), + (204, 93, 71, 80), + (145, 187, 149, 80), + (134, 141, 172, 80), + (157, 137, 109, 80), + (153, 104, 95, 80), + ] + # Generate random colors for each mask + if use_random_colors: + colors = [(random.randint(0, 255), random.randint(0, 255), random.randint(0, 255), 80) for _ in range(len(masks))] + + # Font settings + try: + font = ImageFont.truetype("wqy-zenhei.ttc", font_size) # Adjust as needed + except IOError: + font = ImageFont.load_default(font_size) + + # Overlay each mask onto the overlay image + for mask, mask_prompt, color in zip(masks, mask_prompts, colors): + # Convert mask to RGBA mode + mask_rgba = mask.convert('RGBA') + mask_data = mask_rgba.getdata() + new_data = [(color if item[:3] == (255, 255, 255) else (0, 0, 0, 0)) for item in mask_data] + mask_rgba.putdata(new_data) + + # Draw the mask prompt text on the mask + draw = ImageDraw.Draw(mask_rgba) + mask_bbox = mask.getbbox() # Get the bounding box of the mask + text_position = (mask_bbox[0] + 10, mask_bbox[1] + 10) # Adjust text position based on mask position + draw.text(text_position, mask_prompt, fill=(255, 255, 255, 255), font=font) + + # Alpha composite the overlay with this mask + overlay = Image.alpha_composite(overlay, mask_rgba) + + # Composite the overlay onto the original image + result = Image.alpha_composite(image.convert('RGBA'), overlay) + + # Save or display the resulting image + result.save(output_path) + + return result + +def example(pipe, seeds, example_id, global_prompt, entity_prompts): + dataset_snapshot_download(dataset_id="DiffSynth-Studio/examples_in_diffsynth", local_dir="./", allow_file_pattern=f"data/examples/eligen/qwen-image/example_{example_id}/*.png") + masks = [Image.open(f"./data/examples/eligen/qwen-image/example_{example_id}/{i}.png").convert('RGB') for i in range(len(entity_prompts))] + negative_prompt = "" + for seed in seeds: + # generate image + image = pipe( + prompt=global_prompt, + cfg_scale=4.0, + negative_prompt=negative_prompt, + num_inference_steps=30, + seed=seed, + height=1024, + width=1024, + eligen_entity_prompts=entity_prompts, + eligen_entity_masks=masks, + ) + image.save(f"eligen_example_{example_id}_{seed}.png") + visualize_masks(image, masks, entity_prompts, f"eligen_example_{example_id}_mask_{seed}.png") + + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), +) +snapshot_download("DiffSynth-Studio/Qwen-Image-EliGen", local_dir="models/DiffSynth-Studio/Qwen-Image-EliGen", allow_file_pattern="model.safetensors") +pipe.load_lora(pipe.dit, "models/DiffSynth-Studio/Qwen-Image-EliGen/model.safetensors") + +# example 1 +global_prompt = "A breathtaking beauty of Raja Ampat by the late-night moonlight , one beautiful woman from behind wearing a pale blue long dress with soft glow, sitting at the top of a cliff looking towards the beach,pastell light colors, a group of small distant birds flying in far sky, a boat sailing on the sea, best quality, realistic, whimsical, fantastic, splash art, intricate detailed, hyperdetailed, maximalist style, photorealistic, concept art, sharp focus, harmony, serenity, tranquility, soft pastell colors,ambient occlusion, cozy ambient lighting, masterpiece, liiv1, linquivera, metix, mentixis, masterpiece, award winning, view from above\n" +entity_prompts = ["cliff", "sea", "moon", "sailing boat", "a seated beautiful woman", "pale blue long dress with soft glow"] +example(pipe, [0], 1, global_prompt, entity_prompts) + +# example 2 +global_prompt = "samurai girl wearing a kimono, she's holding a sword glowing with red flame, her long hair is flowing in the wind, she is looking at a small bird perched on the back of her hand. ultra realist style. maximum image detail. maximum realistic render." +entity_prompts = ["flowing hair", "sword glowing with red flame", "A cute bird", "yellow belt"] +example(pipe, [0], 2, global_prompt, entity_prompts) + +# example 3 +global_prompt = "Image of a neverending staircase up to a mysterious palace in the sky, The ancient palace stood majestically atop a mist-shrouded mountain, sunrise, two traditional monk walk in the stair looking at the sunrise, fog,see-through, best quality, whimsical, fantastic, splash art, intricate detailed, hyperdetailed, photorealistic, concept art, harmony, serenity, tranquility, ambient occlusion, halation, cozy ambient lighting, dynamic lighting,masterpiece, liiv1, linquivera, metix, mentixis, masterpiece, award winning," +entity_prompts = ["ancient palace", "stone staircase with railings", "a traditional monk", "a traditional monk"] +example(pipe, [27], 3, global_prompt, entity_prompts) + +# example 4 +global_prompt = "A beautiful girl wearing shirt and shorts in the street, holding a sign 'Entity Control'" +entity_prompts = ["A beautiful girl", "sign 'Entity Control'", "shorts", "shirt"] +example(pipe, [21], 4, global_prompt, entity_prompts) + +# example 5 +global_prompt = "A captivating, dramatic scene in a painting that exudes mystery and foreboding. A white sky, swirling blue clouds, and a crescent yellow moon illuminate a solitary woman standing near the water's edge. Her long dress flows in the wind, silhouetted against the eerie glow. The water mirrors the fiery sky and moonlight, amplifying the uneasy atmosphere." +entity_prompts = ["crescent yellow moon", "a solitary woman", "water", "swirling blue clouds"] +example(pipe, [0], 5, global_prompt, entity_prompts) + +# example 7, same prompt with different seeds +seeds = range(5, 9) +global_prompt = "A beautiful asia woman wearing white dress, holding a mirror, with a forest background." +entity_prompts = ["A beautiful woman", "mirror", "necklace", "glasses", "earring", "white dress", "jewelry headpiece"] +example(pipe, seeds, 7, global_prompt, entity_prompts) diff --git a/examples/qwen_image/model_inference/Qwen-Image-In-Context-Control-Union.py b/examples/qwen_image/model_inference/Qwen-Image-In-Context-Control-Union.py new file mode 100644 index 0000000000000000000000000000000000000000..2a27ac019162add40de54f0b62863a5249d5c989 --- /dev/null +++ b/examples/qwen_image/model_inference/Qwen-Image-In-Context-Control-Union.py @@ -0,0 +1,35 @@ +from PIL import Image +import torch +from modelscope import dataset_snapshot_download, snapshot_download +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig +from diffsynth.controlnets.processors import Annotator + +allow_file_pattern = ["sk_model.pth", "sk_model2.pth", "dpt_hybrid-midas-501f0c75.pt", "ControlNetHED.pth", "body_pose_model.pth", "hand_pose_model.pth", "facenet.pth", "scannet.pt"] +snapshot_download("lllyasviel/Annotators", local_dir="models/Annotators", allow_file_pattern=allow_file_pattern) + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), +) +snapshot_download("DiffSynth-Studio/Qwen-Image-In-Context-Control-Union", local_dir="models/DiffSynth-Studio/Qwen-Image-In-Context-Control-Union", allow_file_pattern="model.safetensors") +pipe.load_lora(pipe.dit, "models/DiffSynth-Studio/Qwen-Image-In-Context-Control-Union/model.safetensors") + +dataset_snapshot_download(dataset_id="DiffSynth-Studio/examples_in_diffsynth", local_dir="./", allow_file_pattern=f"data/examples/qwen-image-context-control/image.jpg") +origin_image = Image.open("data/examples/qwen-image-context-control/image.jpg").resize((1024, 1024)) +annotator_ids = ['openpose', 'canny', 'depth', 'lineart', 'softedge', 'normal'] +for annotator_id in annotator_ids: + annotator = Annotator(processor_id=annotator_id, device="cuda") + control_image = annotator(origin_image) + control_image.save(f"{annotator.processor_id}.png") + + control_prompt = "Context_Control. " + prompt = f"{control_prompt}一个穿着淡蓝色的漂亮女孩正在翩翩起舞，背景是梦幻的星空，光影交错，细节精致。" + negative_prompt = "网格化，规则的网格，模糊, 低分辨率, 低质量, 变形, 畸形, 错误的解剖学, 变形的手, 变形的身体, 变形的脸, 变形的头发, 变形的眼睛, 变形的嘴巴" + image = pipe(prompt, seed=1, negative_prompt=negative_prompt, context_image=control_image, height=1024, width=1024) + image.save(f"image_{annotator.processor_id}.png") diff --git a/examples/qwen_image/model_inference/Qwen-Image.py b/examples/qwen_image/model_inference/Qwen-Image.py new file mode 100644 index 0000000000000000000000000000000000000000..275cfba8c33db6971b87493f2966b6b235744588 --- /dev/null +++ b/examples/qwen_image/model_inference/Qwen-Image.py @@ -0,0 +1,17 @@ +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig +import torch + + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), +) +prompt = "精致肖像，水下少女，蓝裙飘逸，发丝轻扬，光影透澈，气泡环绕，面容恬静，细节精致，梦幻唯美。" +image = pipe(prompt, seed=0, num_inference_steps=40) +image.save("image.jpg") diff --git a/examples/qwen_image/model_inference_low_vram/Qwen-Image-Blockwise-ControlNet-Canny.py b/examples/qwen_image/model_inference_low_vram/Qwen-Image-Blockwise-ControlNet-Canny.py new file mode 100644 index 0000000000000000000000000000000000000000..e73d62905c4eca3424a878b8c35ae9933d0942fb --- /dev/null +++ b/examples/qwen_image/model_inference_low_vram/Qwen-Image-Blockwise-ControlNet-Canny.py @@ -0,0 +1,32 @@ +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig, ControlNetInput +from PIL import Image +import torch +from modelscope import dataset_snapshot_download + + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Canny", origin_file_pattern="model.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), +) +pipe.enable_vram_management() + +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/example_image_dataset", + local_dir="./data/example_image_dataset", + allow_file_pattern="canny/image_1.jpg" +) +controlnet_image = Image.open("data/example_image_dataset/canny/image_1.jpg").resize((1328, 1328)) + +prompt = "一只小狗，毛发光洁柔顺，眼神灵动，背景是樱花纷飞的春日庭院，唯美温馨。" +image = pipe( + prompt, seed=0, + blockwise_controlnet_inputs=[ControlNetInput(image=controlnet_image)] +) +image.save("image.jpg") diff --git a/examples/qwen_image/model_inference_low_vram/Qwen-Image-Blockwise-ControlNet-Depth.py b/examples/qwen_image/model_inference_low_vram/Qwen-Image-Blockwise-ControlNet-Depth.py new file mode 100644 index 0000000000000000000000000000000000000000..601fb3e7151dd0930e30e41b1403a454ba4d8892 --- /dev/null +++ b/examples/qwen_image/model_inference_low_vram/Qwen-Image-Blockwise-ControlNet-Depth.py @@ -0,0 +1,33 @@ +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig, ControlNetInput +from PIL import Image +import torch +from modelscope import dataset_snapshot_download + + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Depth", origin_file_pattern="model.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), +) +pipe.enable_vram_management() + +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/example_image_dataset", + local_dir="./data/example_image_dataset", + allow_file_pattern="depth/image_1.jpg" +) + +controlnet_image = Image.open("data/example_image_dataset/depth/image_1.jpg").resize((1328, 1328)) + +prompt = "精致肖像，水下少女，蓝裙飘逸，发丝轻扬，光影透澈，气泡环绕，面容恬静，细节精致，梦幻唯美。" +image = pipe( + prompt, seed=0, + blockwise_controlnet_inputs=[ControlNetInput(image=controlnet_image)] +) +image.save("image.jpg") diff --git a/examples/qwen_image/model_inference_low_vram/Qwen-Image-Blockwise-ControlNet-Inpaint.py b/examples/qwen_image/model_inference_low_vram/Qwen-Image-Blockwise-ControlNet-Inpaint.py new file mode 100644 index 0000000000000000000000000000000000000000..098993238a994ef17c2de337743228ac3225e8ff --- /dev/null +++ b/examples/qwen_image/model_inference_low_vram/Qwen-Image-Blockwise-ControlNet-Inpaint.py @@ -0,0 +1,34 @@ +import torch +from PIL import Image +from modelscope import dataset_snapshot_download +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig, ControlNetInput + + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Inpaint", origin_file_pattern="model.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), +) +pipe.enable_vram_management() + +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/example_image_dataset", + local_dir="./data/example_image_dataset", + allow_file_pattern="inpaint/*.jpg" +) +prompt = "a cat with sunglasses" +controlnet_image = Image.open("./data/example_image_dataset/inpaint/image_1.jpg").convert("RGB").resize((1328, 1328)) +inpaint_mask = Image.open("./data/example_image_dataset/inpaint/mask.jpg").convert("RGB").resize((1328, 1328)) +image = pipe( + prompt, seed=0, + input_image=controlnet_image, inpaint_mask=inpaint_mask, + blockwise_controlnet_inputs=[ControlNetInput(image=controlnet_image, inpaint_mask=inpaint_mask)], + num_inference_steps=40, +) +image.save("image.jpg") diff --git a/examples/qwen_image/model_inference_low_vram/Qwen-Image-Distill-Full.py b/examples/qwen_image/model_inference_low_vram/Qwen-Image-Distill-Full.py new file mode 100644 index 0000000000000000000000000000000000000000..0839dd0c1806b4500ed3a6c5a408ae826a2ffa07 --- /dev/null +++ b/examples/qwen_image/model_inference_low_vram/Qwen-Image-Distill-Full.py @@ -0,0 +1,18 @@ +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig +import torch + + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="DiffSynth-Studio/Qwen-Image-Distill-Full", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), +) +pipe.enable_vram_management() +prompt = "精致肖像，水下少女，蓝裙飘逸，发丝轻扬，光影透澈，气泡环绕，面容恬静，细节精致，梦幻唯美。" +image = pipe(prompt, seed=0, num_inference_steps=15, cfg_scale=1) +image.save("image.jpg") diff --git a/examples/qwen_image/model_inference_low_vram/Qwen-Image-Distill-LoRA.py b/examples/qwen_image/model_inference_low_vram/Qwen-Image-Distill-LoRA.py new file mode 100644 index 0000000000000000000000000000000000000000..a058c02177d152e68c23cec82024e2bd747719c3 --- /dev/null +++ b/examples/qwen_image/model_inference_low_vram/Qwen-Image-Distill-LoRA.py @@ -0,0 +1,22 @@ +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig +from modelscope import snapshot_download +import torch + +# Please do not use float8 on this model +snapshot_download("DiffSynth-Studio/Qwen-Image-Distill-LoRA", local_dir="models/DiffSynth-Studio/Qwen-Image-Distill-LoRA") +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", offload_device="cpu"), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), +) +pipe.enable_vram_management() +pipe.load_lora(pipe.dit, "models/DiffSynth-Studio/Qwen-Image-Distill-LoRA/model.safetensors") + +prompt = "精致肖像，水下少女，蓝裙飘逸，发丝轻扬，光影透澈，气泡环绕，面容恬静，细节精致，梦幻唯美。" +image = pipe(prompt, seed=0, num_inference_steps=15, cfg_scale=1) +image.save("image.jpg") \ No newline at end of file diff --git a/examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit-2509.py b/examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit-2509.py new file mode 100644 index 0000000000000000000000000000000000000000..833cba57f47c332dbe09239ddf599da0b3b1b45e --- /dev/null +++ b/examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit-2509.py @@ -0,0 +1,26 @@ +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig +from PIL import Image +import torch + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image-Edit-2509", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ], + processor_config=ModelConfig(model_id="Qwen/Qwen-Image-Edit", origin_file_pattern="processor/"), +) +pipe.enable_vram_management() + +image_1 = pipe(prompt="一位少女", seed=0, num_inference_steps=40, height=1328, width=1024) +image_1.save("image1.jpg") + +image_2 = pipe(prompt="一位老人", seed=0, num_inference_steps=40, height=1328, width=1024) +image_2.save("image2.jpg") + +prompt = "生成这两个人的合影" +edit_image = [Image.open("image1.jpg"), Image.open("image2.jpg")] +image_3 = pipe(prompt, edit_image=edit_image, seed=1, num_inference_steps=40, height=1328, width=1024, edit_image_auto_resize=True) +image_3.save("image3.jpg") diff --git a/examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit-Lowres-Fix.py b/examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit-Lowres-Fix.py new file mode 100644 index 0000000000000000000000000000000000000000..5573fb5ea651e22df76e3b01abb36d82d0d5eb26 --- /dev/null +++ b/examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit-Lowres-Fix.py @@ -0,0 +1,27 @@ +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig +import torch +from modelscope import snapshot_download + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image-Edit", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ], + processor_config=ModelConfig(model_id="Qwen/Qwen-Image-Edit", origin_file_pattern="processor/"), +) +pipe.enable_vram_management() + +snapshot_download("DiffSynth-Studio/Qwen-Image-Edit-Lowres-Fix", local_dir="models/DiffSynth-Studio/Qwen-Image-Edit-Lowres-Fix", allow_file_pattern="model.safetensors") +pipe.load_lora(pipe.dit, "models/DiffSynth-Studio/Qwen-Image-Edit-Lowres-Fix/model.safetensors") + +prompt = "精致肖像，水下少女，蓝裙飘逸，发丝轻扬，光影透澈，气泡环绕，面容恬静，细节精致，梦幻唯美。" +image = pipe(prompt=prompt, seed=0, num_inference_steps=40, height=1024, width=768) +image.save("image.jpg") + +prompt = "将裙子变成粉色" +image = image.resize((512, 384)) +image = pipe(prompt, edit_image=image, seed=1, num_inference_steps=40, height=1024, width=768, edit_rope_interpolation=True, edit_image_auto_resize=False) +image.save(f"image2.jpg") diff --git a/examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit.py b/examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit.py new file mode 100644 index 0000000000000000000000000000000000000000..b3b9a5a1bc4b062dad376e5dd69db468b4024060 --- /dev/null +++ b/examples/qwen_image/model_inference_low_vram/Qwen-Image-Edit.py @@ -0,0 +1,22 @@ +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig +import torch + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image-Edit", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ], + processor_config=ModelConfig(model_id="Qwen/Qwen-Image-Edit", origin_file_pattern="processor/"), +) +pipe.enable_vram_management() + +prompt = "精致肖像，水下少女，蓝裙飘逸，发丝轻扬，光影透澈，气泡环绕，面容恬静，细节精致，梦幻唯美。" +image = pipe(prompt=prompt, seed=0, num_inference_steps=40, height=1024, width=1024) +image.save("image1.jpg") + +prompt = "将裙子改为粉色" +image = pipe(prompt, edit_image=image, seed=1, num_inference_steps=40, height=1024, width=1024) +image.save(f"image2.jpg") diff --git a/examples/qwen_image/model_inference_low_vram/Qwen-Image-EliGen-Poster.py b/examples/qwen_image/model_inference_low_vram/Qwen-Image-EliGen-Poster.py new file mode 100644 index 0000000000000000000000000000000000000000..d9f69c0aafde2ac20f5d3e3608d8cd39338c694d --- /dev/null +++ b/examples/qwen_image/model_inference_low_vram/Qwen-Image-EliGen-Poster.py @@ -0,0 +1,115 @@ +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig +import torch +from PIL import Image, ImageDraw, ImageFont +from modelscope import dataset_snapshot_download, snapshot_download +import random + + +def visualize_masks(image, masks, mask_prompts, output_path, font_size=35, use_random_colors=False): + # Create a blank image for overlays + overlay = Image.new('RGBA', image.size, (0, 0, 0, 0)) + + colors = [ + (165, 238, 173, 80), + (76, 102, 221, 80), + (221, 160, 77, 80), + (204, 93, 71, 80), + (145, 187, 149, 80), + (134, 141, 172, 80), + (157, 137, 109, 80), + (153, 104, 95, 80), + (165, 238, 173, 80), + (76, 102, 221, 80), + (221, 160, 77, 80), + (204, 93, 71, 80), + (145, 187, 149, 80), + (134, 141, 172, 80), + (157, 137, 109, 80), + (153, 104, 95, 80), + ] + # Generate random colors for each mask + if use_random_colors: + colors = [(random.randint(0, 255), random.randint(0, 255), random.randint(0, 255), 80) for _ in range(len(masks))] + + # Font settings + try: + font = ImageFont.truetype("wqy-zenhei.ttc", font_size) # Adjust as needed + except IOError: + font = ImageFont.load_default(font_size) + + # Overlay each mask onto the overlay image + for mask, mask_prompt, color in zip(masks, mask_prompts, colors): + # Convert mask to RGBA mode + mask_rgba = mask.convert('RGBA') + mask_data = mask_rgba.getdata() + new_data = [(color if item[:3] == (255, 255, 255) else (0, 0, 0, 0)) for item in mask_data] + mask_rgba.putdata(new_data) + + # Draw the mask prompt text on the mask + draw = ImageDraw.Draw(mask_rgba) + mask_bbox = mask.getbbox() # Get the bounding box of the mask + text_position = (mask_bbox[0] + 10, mask_bbox[1] + 10) # Adjust text position based on mask position + draw.text(text_position, mask_prompt, fill=(255, 255, 255, 255), font=font) + + # Alpha composite the overlay with this mask + overlay = Image.alpha_composite(overlay, mask_rgba) + + # Composite the overlay onto the original image + result = Image.alpha_composite(image.convert('RGBA'), overlay) + + # Save or display the resulting image + result.save(output_path) + + return result + + +def example(pipe, seeds, example_id, global_prompt, entity_prompts, height=784, width=1280): + dataset_snapshot_download( + dataset_id="DiffSynth-Studio/examples_in_diffsynth", + local_dir="./", + allow_file_pattern=f"data/examples/eligen/poster/example_{example_id}/*.png" + ) + masks = [ + Image.open(f"./data/examples/eligen/poster/example_{example_id}/{i}.png").convert('RGB').resize((width, height)) + for i in range(len(entity_prompts)) + ] + negative_prompt = "网格化，规则的网格，模糊, 低分辨率, 低质量, 变形, 畸形, 错误的解剖学, 变形的手, 变形的身体, 变形的脸, 变形的头发, 变形的眼睛, 变形的嘴巴" + for seed in seeds: + # generate image + image = pipe( + prompt=global_prompt, + cfg_scale=4.0, + negative_prompt=negative_prompt, + num_inference_steps=40, + seed=seed, + height=height, + width=width, + eligen_entity_prompts=entity_prompts, + eligen_entity_masks=masks, + ) + image.save(f"eligen_poster_example_{example_id}_{seed}.png") + image = Image.new("RGB", (width, height), (0, 0, 0)) + visualize_masks(image, masks, entity_prompts, f"eligen_poster_example_{example_id}_mask_{seed}.png") + + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), +) +pipe.enable_vram_management() +snapshot_download( + "DiffSynth-Studio/Qwen-Image-EliGen-Poster", + local_dir="models/DiffSynth-Studio/Qwen-Image-EliGen-Poster", + allow_file_pattern="model.safetensors", +) +pipe.load_lora(pipe.dit, "models/DiffSynth-Studio/Qwen-Image-EliGen-Poster/model.safetensors") +global_prompt = "一张以柔粉紫为背景的海报，左侧有大号粉紫色文字“Qwen-Image EliGen-Poster”，粉紫色椭圆框内白色小字：“图像精确分区控制模型”。右侧有一只小兔子在拆礼物，旁边站着一只头顶迷你烟花发射器的小龙（卡通Q版）。背景有一些白云点缀。整体风格卡通可爱，传达节日惊喜的主题。" +entity_prompts = ["粉紫色文字“Qwen-Image EliGen-Poster”", "粉紫色椭圆框内白色小字：“图像精确分区控制模型”", "一只小兔子在拆礼物，小兔子旁边站着一只头顶迷你烟花发射器的小龙（卡通Q版）"] +seed = [42] +example(pipe, seed, 1, global_prompt, entity_prompts) diff --git a/examples/qwen_image/model_inference_low_vram/Qwen-Image-EliGen-V2.py b/examples/qwen_image/model_inference_low_vram/Qwen-Image-EliGen-V2.py new file mode 100644 index 0000000000000000000000000000000000000000..f51fd47cb3c077ecd859b51c40bddcb42c37dc49 --- /dev/null +++ b/examples/qwen_image/model_inference_low_vram/Qwen-Image-EliGen-V2.py @@ -0,0 +1,108 @@ +import torch +import random +from PIL import Image, ImageDraw, ImageFont +from modelscope import dataset_snapshot_download, snapshot_download +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig + + +def visualize_masks(image, masks, mask_prompts, output_path, font_size=35, use_random_colors=False): + # Create a blank image for overlays + overlay = Image.new('RGBA', image.size, (0, 0, 0, 0)) + + colors = [ + (165, 238, 173, 80), + (76, 102, 221, 80), + (221, 160, 77, 80), + (204, 93, 71, 80), + (145, 187, 149, 80), + (134, 141, 172, 80), + (157, 137, 109, 80), + (153, 104, 95, 80), + (165, 238, 173, 80), + (76, 102, 221, 80), + (221, 160, 77, 80), + (204, 93, 71, 80), + (145, 187, 149, 80), + (134, 141, 172, 80), + (157, 137, 109, 80), + (153, 104, 95, 80), + ] + # Generate random colors for each mask + if use_random_colors: + colors = [(random.randint(0, 255), random.randint(0, 255), random.randint(0, 255), 80) for _ in range(len(masks))] + + # Font settings + try: + font = ImageFont.truetype("wqy-zenhei.ttc", font_size) # Adjust as needed + except IOError: + font = ImageFont.load_default(font_size) + + # Overlay each mask onto the overlay image + for mask, mask_prompt, color in zip(masks, mask_prompts, colors): + # Convert mask to RGBA mode + mask_rgba = mask.convert('RGBA') + mask_data = mask_rgba.getdata() + new_data = [(color if item[:3] == (255, 255, 255) else (0, 0, 0, 0)) for item in mask_data] + mask_rgba.putdata(new_data) + + # Draw the mask prompt text on the mask + draw = ImageDraw.Draw(mask_rgba) + mask_bbox = mask.getbbox() # Get the bounding box of the mask + text_position = (mask_bbox[0] + 10, mask_bbox[1] + 10) # Adjust text position based on mask position + draw.text(text_position, mask_prompt, fill=(255, 255, 255, 255), font=font) + + # Alpha composite the overlay with this mask + overlay = Image.alpha_composite(overlay, mask_rgba) + + # Composite the overlay onto the original image + result = Image.alpha_composite(image.convert('RGBA'), overlay) + + # Save or display the resulting image + result.save(output_path) + + return result + +def example(pipe, seeds, example_id, global_prompt, entity_prompts): + dataset_snapshot_download(dataset_id="DiffSynth-Studio/examples_in_diffsynth", local_dir="./", allow_file_pattern=f"data/examples/eligen/qwen-image/example_{example_id}/*.png") + masks = [Image.open(f"./data/examples/eligen/qwen-image/example_{example_id}/{i}.png").convert('RGB').resize((1024, 1024)) for i in range(len(entity_prompts))] + negative_prompt = "网格化，规则的网格，模糊, 低分辨率, 低质量, 变形, 畸形, 错误的解剖学, 变形的手, 变形的身体, 变形的脸, 变形的头发, 变形的眼睛, 变形的嘴巴" + for seed in seeds: + # generate image + image = pipe( + prompt=global_prompt, + cfg_scale=4.0, + negative_prompt=negative_prompt, + num_inference_steps=40, + seed=seed, + height=1024, + width=1024, + eligen_entity_prompts=entity_prompts, + eligen_entity_masks=masks, + ) + image.save(f"eligen_example_{example_id}_{seed}.png") + visualize_masks(image, masks, entity_prompts, f"eligen_example_{example_id}_mask_{seed}.png") + + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", offload_dtype=torch.float8_e4m3fn), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), +) +pipe.enable_vram_management() +snapshot_download("DiffSynth-Studio/Qwen-Image-EliGen-V2", local_dir="models/DiffSynth-Studio/Qwen-Image-EliGen-V2", allow_file_pattern="model.safetensors") +pipe.load_lora(pipe.dit, "models/DiffSynth-Studio/Qwen-Image-EliGen-V2/model.safetensors") + +seeds = [0] + +global_prompt = "写实摄影风格. A beautiful asia woman wearing white dress, she is holding a mirror with her right arm, with a beach background." +entity_prompts = ["A beautiful woman", "mirror", "necklace", "glasses", "earring", "white dress", "jewelry headpiece"] +example(pipe, seeds, 7, global_prompt, entity_prompts) + +global_prompt = "写实摄影风格, 细节丰富。街头一位漂亮的女孩，穿着衬衫和短裤，手持写有“实体控制”的标牌，背景是繁忙的城市街道，阳光明媚，行人匆匆。" +entity_prompts = ["一个漂亮的女孩", "标牌 '实体控制'", "短裤", "衬衫"] +example(pipe, seeds, 4, global_prompt, entity_prompts) diff --git a/examples/qwen_image/model_inference_low_vram/Qwen-Image-EliGen.py b/examples/qwen_image/model_inference_low_vram/Qwen-Image-EliGen.py new file mode 100644 index 0000000000000000000000000000000000000000..b13c57dfd6fd7a719da11aafb424b4d6817ba6ab --- /dev/null +++ b/examples/qwen_image/model_inference_low_vram/Qwen-Image-EliGen.py @@ -0,0 +1,129 @@ +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig +import torch +from PIL import Image, ImageDraw, ImageFont +from modelscope import dataset_snapshot_download, snapshot_download +import random + + +def visualize_masks(image, masks, mask_prompts, output_path, font_size=35, use_random_colors=False): + # Create a blank image for overlays + overlay = Image.new('RGBA', image.size, (0, 0, 0, 0)) + + colors = [ + (165, 238, 173, 80), + (76, 102, 221, 80), + (221, 160, 77, 80), + (204, 93, 71, 80), + (145, 187, 149, 80), + (134, 141, 172, 80), + (157, 137, 109, 80), + (153, 104, 95, 80), + (165, 238, 173, 80), + (76, 102, 221, 80), + (221, 160, 77, 80), + (204, 93, 71, 80), + (145, 187, 149, 80), + (134, 141, 172, 80), + (157, 137, 109, 80), + (153, 104, 95, 80), + ] + # Generate random colors for each mask + if use_random_colors: + colors = [(random.randint(0, 255), random.randint(0, 255), random.randint(0, 255), 80) for _ in range(len(masks))] + + # Font settings + try: + font = ImageFont.truetype("wqy-zenhei.ttc", font_size) # Adjust as needed + except IOError: + font = ImageFont.load_default(font_size) + + # Overlay each mask onto the overlay image + for mask, mask_prompt, color in zip(masks, mask_prompts, colors): + # Convert mask to RGBA mode + mask_rgba = mask.convert('RGBA') + mask_data = mask_rgba.getdata() + new_data = [(color if item[:3] == (255, 255, 255) else (0, 0, 0, 0)) for item in mask_data] + mask_rgba.putdata(new_data) + + # Draw the mask prompt text on the mask + draw = ImageDraw.Draw(mask_rgba) + mask_bbox = mask.getbbox() # Get the bounding box of the mask + text_position = (mask_bbox[0] + 10, mask_bbox[1] + 10) # Adjust text position based on mask position + draw.text(text_position, mask_prompt, fill=(255, 255, 255, 255), font=font) + + # Alpha composite the overlay with this mask + overlay = Image.alpha_composite(overlay, mask_rgba) + + # Composite the overlay onto the original image + result = Image.alpha_composite(image.convert('RGBA'), overlay) + + # Save or display the resulting image + result.save(output_path) + + return result + +def example(pipe, seeds, example_id, global_prompt, entity_prompts): + dataset_snapshot_download(dataset_id="DiffSynth-Studio/examples_in_diffsynth", local_dir="./", allow_file_pattern=f"data/examples/eligen/qwen-image/example_{example_id}/*.png") + masks = [Image.open(f"./data/examples/eligen/qwen-image/example_{example_id}/{i}.png").convert('RGB') for i in range(len(entity_prompts))] + negative_prompt = "" + for seed in seeds: + # generate image + image = pipe( + prompt=global_prompt, + cfg_scale=4.0, + negative_prompt=negative_prompt, + num_inference_steps=30, + seed=seed, + height=1024, + width=1024, + eligen_entity_prompts=entity_prompts, + eligen_entity_masks=masks, + ) + image.save(f"eligen_example_{example_id}_{seed}.png") + visualize_masks(image, masks, entity_prompts, f"eligen_example_{example_id}_mask_{seed}.png") + + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), +) +pipe.enable_vram_management() +snapshot_download("DiffSynth-Studio/Qwen-Image-EliGen", local_dir="models/DiffSynth-Studio/Qwen-Image-EliGen", allow_file_pattern="model.safetensors") +pipe.load_lora(pipe.dit, "models/DiffSynth-Studio/Qwen-Image-EliGen/model.safetensors") + +# example 1 +global_prompt = "A breathtaking beauty of Raja Ampat by the late-night moonlight , one beautiful woman from behind wearing a pale blue long dress with soft glow, sitting at the top of a cliff looking towards the beach,pastell light colors, a group of small distant birds flying in far sky, a boat sailing on the sea, best quality, realistic, whimsical, fantastic, splash art, intricate detailed, hyperdetailed, maximalist style, photorealistic, concept art, sharp focus, harmony, serenity, tranquility, soft pastell colors,ambient occlusion, cozy ambient lighting, masterpiece, liiv1, linquivera, metix, mentixis, masterpiece, award winning, view from above\n" +entity_prompts = ["cliff", "sea", "moon", "sailing boat", "a seated beautiful woman", "pale blue long dress with soft glow"] +example(pipe, [0], 1, global_prompt, entity_prompts) + +# example 2 +global_prompt = "samurai girl wearing a kimono, she's holding a sword glowing with red flame, her long hair is flowing in the wind, she is looking at a small bird perched on the back of her hand. ultra realist style. maximum image detail. maximum realistic render." +entity_prompts = ["flowing hair", "sword glowing with red flame", "A cute bird", "yellow belt"] +example(pipe, [0], 2, global_prompt, entity_prompts) + +# example 3 +global_prompt = "Image of a neverending staircase up to a mysterious palace in the sky, The ancient palace stood majestically atop a mist-shrouded mountain, sunrise, two traditional monk walk in the stair looking at the sunrise, fog,see-through, best quality, whimsical, fantastic, splash art, intricate detailed, hyperdetailed, photorealistic, concept art, harmony, serenity, tranquility, ambient occlusion, halation, cozy ambient lighting, dynamic lighting,masterpiece, liiv1, linquivera, metix, mentixis, masterpiece, award winning," +entity_prompts = ["ancient palace", "stone staircase with railings", "a traditional monk", "a traditional monk"] +example(pipe, [27], 3, global_prompt, entity_prompts) + +# example 4 +global_prompt = "A beautiful girl wearing shirt and shorts in the street, holding a sign 'Entity Control'" +entity_prompts = ["A beautiful girl", "sign 'Entity Control'", "shorts", "shirt"] +example(pipe, [21], 4, global_prompt, entity_prompts) + +# example 5 +global_prompt = "A captivating, dramatic scene in a painting that exudes mystery and foreboding. A white sky, swirling blue clouds, and a crescent yellow moon illuminate a solitary woman standing near the water's edge. Her long dress flows in the wind, silhouetted against the eerie glow. The water mirrors the fiery sky and moonlight, amplifying the uneasy atmosphere." +entity_prompts = ["crescent yellow moon", "a solitary woman", "water", "swirling blue clouds"] +example(pipe, [0], 5, global_prompt, entity_prompts) + +# example 7, same prompt with different seeds +seeds = range(5, 9) +global_prompt = "A beautiful asia woman wearing white dress, holding a mirror, with a forest background." +entity_prompts = ["A beautiful woman", "mirror", "necklace", "glasses", "earring", "white dress", "jewelry headpiece"] +example(pipe, seeds, 7, global_prompt, entity_prompts) diff --git a/examples/qwen_image/model_inference_low_vram/Qwen-Image-In-Context-Control-Union.py b/examples/qwen_image/model_inference_low_vram/Qwen-Image-In-Context-Control-Union.py new file mode 100644 index 0000000000000000000000000000000000000000..9faced855a0a2250f06a8bb4073733445e7ddac4 --- /dev/null +++ b/examples/qwen_image/model_inference_low_vram/Qwen-Image-In-Context-Control-Union.py @@ -0,0 +1,36 @@ +from PIL import Image +import torch +from modelscope import dataset_snapshot_download, snapshot_download +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig +from diffsynth.controlnets.processors import Annotator + +allow_file_pattern = ["sk_model.pth", "sk_model2.pth", "dpt_hybrid-midas-501f0c75.pt", "ControlNetHED.pth", "body_pose_model.pth", "hand_pose_model.pth", "facenet.pth", "scannet.pt"] +snapshot_download("lllyasviel/Annotators", local_dir="models/Annotators", allow_file_pattern=allow_file_pattern) + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", offload_dtype=torch.float8_e4m3fn), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), +) +pipe.enable_vram_management() +snapshot_download("DiffSynth-Studio/Qwen-Image-In-Context-Control-Union", local_dir="models/DiffSynth-Studio/Qwen-Image-In-Context-Control-Union", allow_file_pattern="model.safetensors") +pipe.load_lora(pipe.dit, "models/DiffSynth-Studio/Qwen-Image-In-Context-Control-Union/model.safetensors") + +dataset_snapshot_download(dataset_id="DiffSynth-Studio/examples_in_diffsynth", local_dir="./", allow_file_pattern=f"data/examples/qwen-image-context-control/image.jpg") +origin_image = Image.open("data/examples/qwen-image-context-control/image.jpg").resize((1024, 1024)) +annotator_ids = ['openpose', 'canny', 'depth', 'lineart', 'softedge', 'normal'] +for annotator_id in annotator_ids: + annotator = Annotator(processor_id=annotator_id, device="cuda") + control_image = annotator(origin_image) + control_image.save(f"{annotator.processor_id}.png") + + control_prompt = "Context_Control. " + prompt = f"{control_prompt}一个穿着淡蓝色的漂亮女孩正在翩翩起舞，背景是梦幻的星空，光影交错，细节精致。" + negative_prompt = "网格化，规则的网格，模糊, 低分辨率, 低质量, 变形, 畸形, 错误的解剖学, 变形的手, 变形的身体, 变形的脸, 变形的头发, 变形的眼睛, 变形的嘴巴" + image = pipe(prompt, seed=1, negative_prompt=negative_prompt, context_image=control_image, height=1024, width=1024) + image.save(f"image_{annotator.processor_id}.png") diff --git a/examples/qwen_image/model_inference_low_vram/Qwen-Image.py b/examples/qwen_image/model_inference_low_vram/Qwen-Image.py new file mode 100644 index 0000000000000000000000000000000000000000..6ecc11f9fb06100eed63f919ede1c8c5b0640c56 --- /dev/null +++ b/examples/qwen_image/model_inference_low_vram/Qwen-Image.py @@ -0,0 +1,18 @@ +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig +import torch + + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), +) +pipe.enable_vram_management() +prompt = "精致肖像，水下少女，蓝裙飘逸，发丝轻扬，光影透澈，气泡环绕，面容恬静，细节精致，梦幻唯美。" +image = pipe(prompt, seed=0, num_inference_steps=40) +image.save("image.jpg") diff --git a/examples/qwen_image/model_training/full/Qwen-Image-Blockwise-ControlNet-Canny.sh b/examples/qwen_image/model_training/full/Qwen-Image-Blockwise-ControlNet-Canny.sh new file mode 100644 index 0000000000000000000000000000000000000000..f563fd155b2a9f71bb0f11e4657acd9dec0219bb --- /dev/null +++ b/examples/qwen_image/model_training/full/Qwen-Image-Blockwise-ControlNet-Canny.sh @@ -0,0 +1,38 @@ +accelerate launch examples/qwen_image/model_training/train.py \ + --dataset_base_path data/example_image_dataset \ + --dataset_metadata_path data/example_image_dataset/metadata_blockwise_controlnet_canny.csv \ + --data_file_keys "image,blockwise_controlnet_image" \ + --max_pixels 1048576 \ + --dataset_repeat 50 \ + --model_id_with_origin_paths "Qwen/Qwen-Image:transformer/diffusion_pytorch_model*.safetensors,Qwen/Qwen-Image:text_encoder/model*.safetensors,Qwen/Qwen-Image:vae/diffusion_pytorch_model.safetensors,DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Canny:model.safetensors" \ + --learning_rate 1e-4 \ + --num_epochs 2 \ + --remove_prefix_in_ckpt "pipe.blockwise_controlnet.models.0." \ + --output_path "./models/train/Qwen-Image-Blockwise-ControlNet-Canny_full" \ + --trainable_models "blockwise_controlnet" \ + --extra_inputs "blockwise_controlnet_image" \ + --use_gradient_checkpointing \ + --find_unused_parameters + +# If you want to pre-train a Blockwise ControlNet from scratch, +# please run the following script to first generate the initialized model weights file, +# and then start training with a high learning rate (1e-3). + +# python examples/qwen_image/model_training/scripts/Qwen-Image-Blockwise-ControlNet-Initialize.py + +# accelerate launch examples/qwen_image/model_training/train.py \ +# --dataset_base_path data/example_image_dataset \ +# --dataset_metadata_path data/example_image_dataset/metadata_blockwise_controlnet_canny.csv \ +# --data_file_keys "image,blockwise_controlnet_image" \ +# --max_pixels 1048576 \ +# --dataset_repeat 50 \ +# --model_id_with_origin_paths "Qwen/Qwen-Image:transformer/diffusion_pytorch_model*.safetensors,Qwen/Qwen-Image:text_encoder/model*.safetensors,Qwen/Qwen-Image:vae/diffusion_pytorch_model.safetensors" \ +# --model_paths '["models/blockwise_controlnet.safetensors"]' \ +# --learning_rate 1e-3 \ +# --num_epochs 2 \ +# --remove_prefix_in_ckpt "pipe.blockwise_controlnet.models.0." \ +# --output_path "./models/train/Qwen-Image-Blockwise-ControlNet-Canny_full" \ +# --trainable_models "blockwise_controlnet" \ +# --extra_inputs "blockwise_controlnet_image" \ +# --use_gradient_checkpointing \ +# --find_unused_parameters \ No newline at end of file diff --git a/examples/qwen_image/model_training/full/Qwen-Image-Blockwise-ControlNet-Depth.sh b/examples/qwen_image/model_training/full/Qwen-Image-Blockwise-ControlNet-Depth.sh new file mode 100644 index 0000000000000000000000000000000000000000..2bd2926157aa1eb6333d3378a27613e332452f5a --- /dev/null +++ b/examples/qwen_image/model_training/full/Qwen-Image-Blockwise-ControlNet-Depth.sh @@ -0,0 +1,38 @@ +accelerate launch examples/qwen_image/model_training/train.py \ + --dataset_base_path data/example_image_dataset \ + --dataset_metadata_path data/example_image_dataset/metadata_blockwise_controlnet_depth.csv \ + --data_file_keys "image,blockwise_controlnet_image" \ + --max_pixels 1048576 \ + --dataset_repeat 50 \ + --model_id_with_origin_paths "Qwen/Qwen-Image:transformer/diffusion_pytorch_model*.safetensors,Qwen/Qwen-Image:text_encoder/model*.safetensors,Qwen/Qwen-Image:vae/diffusion_pytorch_model.safetensors,DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Depth:model.safetensors" \ + --learning_rate 1e-4 \ + --num_epochs 2 \ + --remove_prefix_in_ckpt "pipe.blockwise_controlnet.models.0." \ + --output_path "./models/train/Qwen-Image-Blockwise-ControlNet-Depth_full" \ + --trainable_models "blockwise_controlnet" \ + --extra_inputs "blockwise_controlnet_image" \ + --use_gradient_checkpointing \ + --find_unused_parameters + +# If you want to pre-train a Blockwise ControlNet from scratch, +# please run the following script to first generate the initialized model weights file, +# and then start training with a high learning rate (1e-3). + +# python examples/qwen_image/model_training/scripts/Qwen-Image-Blockwise-ControlNet-Initialize.py + +# accelerate launch examples/qwen_image/model_training/train.py \ +# --dataset_base_path data/example_image_dataset \ +# --dataset_metadata_path data/example_image_dataset/metadata_blockwise_controlnet_depth.csv \ +# --data_file_keys "image,blockwise_controlnet_image" \ +# --max_pixels 1048576 \ +# --dataset_repeat 50 \ +# --model_id_with_origin_paths "Qwen/Qwen-Image:transformer/diffusion_pytorch_model*.safetensors,Qwen/Qwen-Image:text_encoder/model*.safetensors,Qwen/Qwen-Image:vae/diffusion_pytorch_model.safetensors" \ +# --model_paths '["models/blockwise_controlnet.safetensors"]' \ +# --learning_rate 1e-3 \ +# --num_epochs 2 \ +# --remove_prefix_in_ckpt "pipe.blockwise_controlnet.models.0." \ +# --output_path "./models/train/Qwen-Image-Blockwise-ControlNet-Depth_full" \ +# --trainable_models "blockwise_controlnet" \ +# --extra_inputs "blockwise_controlnet_image" \ +# --use_gradient_checkpointing \ +# --find_unused_parameters \ No newline at end of file diff --git a/examples/qwen_image/model_training/full/Qwen-Image-Blockwise-ControlNet-Inpaint.sh b/examples/qwen_image/model_training/full/Qwen-Image-Blockwise-ControlNet-Inpaint.sh new file mode 100644 index 0000000000000000000000000000000000000000..b87552bc2fb8f9e1a3a1086a3134129bd8017de3 --- /dev/null +++ b/examples/qwen_image/model_training/full/Qwen-Image-Blockwise-ControlNet-Inpaint.sh @@ -0,0 +1,38 @@ +accelerate launch --config_file examples/qwen_image/model_training/full/accelerate_config.yaml examples/qwen_image/model_training/train.py \ + --dataset_base_path data/example_image_dataset \ + --dataset_metadata_path data/example_image_dataset/metadata_blockwise_controlnet_inpaint.csv \ + --data_file_keys "image,blockwise_controlnet_image,blockwise_controlnet_inpaint_mask" \ + --max_pixels 1048576 \ + --dataset_repeat 50 \ + --model_id_with_origin_paths "Qwen/Qwen-Image:transformer/diffusion_pytorch_model*.safetensors,Qwen/Qwen-Image:text_encoder/model*.safetensors,Qwen/Qwen-Image:vae/diffusion_pytorch_model.safetensors,DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Inpaint:model.safetensors" \ + --learning_rate 1e-4 \ + --num_epochs 2 \ + --remove_prefix_in_ckpt "pipe.blockwise_controlnet.models.0." \ + --output_path "./models/train/Qwen-Image-Blockwise-ControlNet-Inpaint_full" \ + --trainable_models "blockwise_controlnet" \ + --extra_inputs "blockwise_controlnet_image,blockwise_controlnet_inpaint_mask" \ + --use_gradient_checkpointing \ + --find_unused_parameters + +# If you want to pre-train a Inpaint Blockwise ControlNet from scratch, +# please run the following script to first generate the initialized model weights file, +# and then start training with a high learning rate (1e-3). + +# python examples/qwen_image/model_training/scripts/Qwen-Image-Blockwise-ControlNet-Inpaint-Initialize.py + +# accelerate launch --config_file examples/qwen_image/model_training/full/accelerate_config.yaml examples/qwen_image/model_training/train.py \ +# --dataset_base_path data/example_image_dataset \ +# --dataset_metadata_path data/example_image_dataset/metadata_blockwise_controlnet_inpaint.csv \ +# --data_file_keys "image,blockwise_controlnet_image,blockwise_controlnet_inpaint_mask" \ +# --max_pixels 1048576 \ +# --dataset_repeat 50 \ +# --model_id_with_origin_paths "Qwen/Qwen-Image:transformer/diffusion_pytorch_model*.safetensors,Qwen/Qwen-Image:text_encoder/model*.safetensors,Qwen/Qwen-Image:vae/diffusion_pytorch_model.safetensors" \ +# --model_paths '["models/blockwise_controlnet_inpaint.safetensors"]' \ +# --learning_rate 1e-3 \ +# --num_epochs 2 \ +# --remove_prefix_in_ckpt "pipe.blockwise_controlnet.models.0." \ +# --output_path "./models/train/Qwen-Image-Blockwise-ControlNet-Inpaint_full" \ +# --trainable_models "blockwise_controlnet" \ +# --extra_inputs "blockwise_controlnet_image,blockwise_controlnet_inpaint_mask" \ +# --use_gradient_checkpointing \ +# --find_unused_parameters diff --git a/examples/qwen_image/model_training/full/Qwen-Image-Distill-Full.sh b/examples/qwen_image/model_training/full/Qwen-Image-Distill-Full.sh new file mode 100644 index 0000000000000000000000000000000000000000..a56fe9d2b409f3dd8e80a064b2dc63f115f32dfc --- /dev/null +++ b/examples/qwen_image/model_training/full/Qwen-Image-Distill-Full.sh @@ -0,0 +1,13 @@ +accelerate launch --config_file examples/qwen_image/model_training/full/accelerate_config_zero2offload.yaml examples/qwen_image/model_training/train.py \ + --dataset_base_path data/example_image_dataset \ + --dataset_metadata_path data/example_image_dataset/metadata.csv \ + --max_pixels 1048576 \ + --dataset_repeat 50 \ + --model_id_with_origin_paths "DiffSynth-Studio/Qwen-Image-Distill-Full:diffusion_pytorch_model*.safetensors,Qwen/Qwen-Image:text_encoder/model*.safetensors,Qwen/Qwen-Image:vae/diffusion_pytorch_model.safetensors" \ + --learning_rate 1e-5 \ + --num_epochs 2 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Qwen-Image-Distill-Full_full" \ + --trainable_models "dit" \ + --use_gradient_checkpointing \ + --find_unused_parameters diff --git a/examples/qwen_image/model_training/full/Qwen-Image-Edit-2509.sh b/examples/qwen_image/model_training/full/Qwen-Image-Edit-2509.sh new file mode 100644 index 0000000000000000000000000000000000000000..7fda7b7367b5ba556a83adf645816bf2722d1292 --- /dev/null +++ b/examples/qwen_image/model_training/full/Qwen-Image-Edit-2509.sh @@ -0,0 +1,15 @@ +accelerate launch --config_file examples/qwen_image/model_training/full/accelerate_config_zero2offload.yaml examples/qwen_image/model_training/train.py \ + --dataset_base_path data/example_image_dataset \ + --dataset_metadata_path data/example_image_dataset/metadata_qwen_imgae_edit_multi.json \ + --data_file_keys "image,edit_image" \ + --extra_inputs "edit_image" \ + --max_pixels 1048576 \ + --dataset_repeat 50 \ + --model_id_with_origin_paths "Qwen/Qwen-Image-Edit-2509:transformer/diffusion_pytorch_model*.safetensors,Qwen/Qwen-Image:text_encoder/model*.safetensors,Qwen/Qwen-Image:vae/diffusion_pytorch_model.safetensors" \ + --learning_rate 1e-5 \ + --num_epochs 2 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Qwen-Image-Edit-2509_full" \ + --trainable_models "dit" \ + --use_gradient_checkpointing \ + --find_unused_parameters diff --git a/examples/qwen_image/model_training/full/Qwen-Image-Edit.sh b/examples/qwen_image/model_training/full/Qwen-Image-Edit.sh new file mode 100644 index 0000000000000000000000000000000000000000..ec257654ee3a73834e2be3c78f34907ab9218fb3 --- /dev/null +++ b/examples/qwen_image/model_training/full/Qwen-Image-Edit.sh @@ -0,0 +1,15 @@ +accelerate launch --config_file examples/qwen_image/model_training/full/accelerate_config_zero2offload.yaml examples/qwen_image/model_training/train.py \ + --dataset_base_path data/example_image_dataset \ + --dataset_metadata_path data/example_image_dataset/metadata_edit.csv \ + --data_file_keys "image,edit_image" \ + --extra_inputs "edit_image" \ + --max_pixels 1048576 \ + --dataset_repeat 50 \ + --model_id_with_origin_paths "Qwen/Qwen-Image-Edit:transformer/diffusion_pytorch_model*.safetensors,Qwen/Qwen-Image:text_encoder/model*.safetensors,Qwen/Qwen-Image:vae/diffusion_pytorch_model.safetensors" \ + --learning_rate 1e-5 \ + --num_epochs 2 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Qwen-Image-Edit_full" \ + --trainable_models "dit" \ + --use_gradient_checkpointing \ + --find_unused_parameters diff --git a/examples/qwen_image/model_training/full/Qwen-Image.sh b/examples/qwen_image/model_training/full/Qwen-Image.sh new file mode 100644 index 0000000000000000000000000000000000000000..979101e62c26e41ec408a0cbf188da5b35f3d50d --- /dev/null +++ b/examples/qwen_image/model_training/full/Qwen-Image.sh @@ -0,0 +1,13 @@ +accelerate launch --config_file examples/qwen_image/model_training/full/accelerate_config_zero2offload.yaml examples/qwen_image/model_training/train.py \ + --dataset_base_path data/example_image_dataset \ + --dataset_metadata_path data/example_image_dataset/metadata.csv \ + --max_pixels 1048576 \ + --dataset_repeat 50 \ + --model_id_with_origin_paths "Qwen/Qwen-Image:transformer/diffusion_pytorch_model*.safetensors,Qwen/Qwen-Image:text_encoder/model*.safetensors,Qwen/Qwen-Image:vae/diffusion_pytorch_model.safetensors" \ + --learning_rate 1e-5 \ + --num_epochs 2 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Qwen-Image_full" \ + --trainable_models "dit" \ + --use_gradient_checkpointing \ + --find_unused_parameters diff --git a/examples/qwen_image/model_training/full/accelerate_config.yaml b/examples/qwen_image/model_training/full/accelerate_config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..83280f73f315a32eccb065f351d66b4b2678759d --- /dev/null +++ b/examples/qwen_image/model_training/full/accelerate_config.yaml @@ -0,0 +1,22 @@ +compute_environment: LOCAL_MACHINE +debug: false +deepspeed_config: + gradient_accumulation_steps: 1 + offload_optimizer_device: none + offload_param_device: none + zero3_init_flag: false + zero_stage: 2 +distributed_type: DEEPSPEED +downcast_bf16: 'no' +enable_cpu_affinity: false +machine_rank: 0 +main_training_function: main +mixed_precision: bf16 +num_machines: 1 +num_processes: 8 +rdzv_backend: static +same_network: true +tpu_env: [] +tpu_use_cluster: false +tpu_use_sudo: false +use_cpu: false diff --git a/examples/qwen_image/model_training/full/accelerate_config_zero2offload.yaml b/examples/qwen_image/model_training/full/accelerate_config_zero2offload.yaml new file mode 100644 index 0000000000000000000000000000000000000000..8a75f3d91eeae160409650b482e5383ac26b297b --- /dev/null +++ b/examples/qwen_image/model_training/full/accelerate_config_zero2offload.yaml @@ -0,0 +1,22 @@ +compute_environment: LOCAL_MACHINE +debug: false +deepspeed_config: + gradient_accumulation_steps: 1 + offload_optimizer_device: 'cpu' + offload_param_device: 'cpu' + zero3_init_flag: false + zero_stage: 2 +distributed_type: DEEPSPEED +downcast_bf16: 'no' +enable_cpu_affinity: false +machine_rank: 0 +main_training_function: main +mixed_precision: bf16 +num_machines: 1 +num_processes: 8 +rdzv_backend: static +same_network: true +tpu_env: [] +tpu_use_cluster: false +tpu_use_sudo: false +use_cpu: false diff --git a/examples/qwen_image/model_training/lora/Qwen-Image-Blockwise-ControlNet-Canny.sh b/examples/qwen_image/model_training/lora/Qwen-Image-Blockwise-ControlNet-Canny.sh new file mode 100644 index 0000000000000000000000000000000000000000..226313466dc061481dc3e1ea427ed5f2ef8a63f1 --- /dev/null +++ b/examples/qwen_image/model_training/lora/Qwen-Image-Blockwise-ControlNet-Canny.sh @@ -0,0 +1,17 @@ +accelerate launch examples/qwen_image/model_training/train.py \ + --dataset_base_path data/example_image_dataset \ + --dataset_metadata_path data/example_image_dataset/metadata_blockwise_controlnet_canny.csv \ + --data_file_keys "image,blockwise_controlnet_image" \ + --max_pixels 1048576 \ + --dataset_repeat 50 \ + --model_id_with_origin_paths "Qwen/Qwen-Image:transformer/diffusion_pytorch_model*.safetensors,Qwen/Qwen-Image:text_encoder/model*.safetensors,Qwen/Qwen-Image:vae/diffusion_pytorch_model.safetensors,DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Canny:model.safetensors" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Qwen-Image-Blockwise-ControlNet-Canny_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "to_q,to_k,to_v,add_q_proj,add_k_proj,add_v_proj,to_out.0,to_add_out,img_mlp.net.2,img_mod.1,txt_mlp.net.2,txt_mod.1" \ + --lora_rank 32 \ + --extra_inputs "blockwise_controlnet_image" \ + --use_gradient_checkpointing \ + --find_unused_parameters diff --git a/examples/qwen_image/model_training/lora/Qwen-Image-Blockwise-ControlNet-Depth.sh b/examples/qwen_image/model_training/lora/Qwen-Image-Blockwise-ControlNet-Depth.sh new file mode 100644 index 0000000000000000000000000000000000000000..60d3ca3f8a331369092bec762f7a64ef39d5550d --- /dev/null +++ b/examples/qwen_image/model_training/lora/Qwen-Image-Blockwise-ControlNet-Depth.sh @@ -0,0 +1,17 @@ +accelerate launch examples/qwen_image/model_training/train.py \ + --dataset_base_path data/example_image_dataset \ + --dataset_metadata_path data/example_image_dataset/metadata_blockwise_controlnet_depth.csv \ + --data_file_keys "image,blockwise_controlnet_image" \ + --max_pixels 1048576 \ + --dataset_repeat 50 \ + --model_id_with_origin_paths "Qwen/Qwen-Image:transformer/diffusion_pytorch_model*.safetensors,Qwen/Qwen-Image:text_encoder/model*.safetensors,Qwen/Qwen-Image:vae/diffusion_pytorch_model.safetensors,DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Depth:model.safetensors" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Qwen-Image-Blockwise-ControlNet-Depth_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "to_q,to_k,to_v,add_q_proj,add_k_proj,add_v_proj,to_out.0,to_add_out,img_mlp.net.2,img_mod.1,txt_mlp.net.2,txt_mod.1" \ + --lora_rank 32 \ + --extra_inputs "blockwise_controlnet_image" \ + --use_gradient_checkpointing \ + --find_unused_parameters diff --git a/examples/qwen_image/model_training/lora/Qwen-Image-Blockwise-ControlNet-Inpaint.sh b/examples/qwen_image/model_training/lora/Qwen-Image-Blockwise-ControlNet-Inpaint.sh new file mode 100644 index 0000000000000000000000000000000000000000..853ffe2667421ffdbc949e3809907e71f0f8f9dc --- /dev/null +++ b/examples/qwen_image/model_training/lora/Qwen-Image-Blockwise-ControlNet-Inpaint.sh @@ -0,0 +1,17 @@ +accelerate launch examples/qwen_image/model_training/train.py \ + --dataset_base_path data/example_image_dataset \ + --dataset_metadata_path data/example_image_dataset/metadata_blockwise_controlnet_inpaint.csv \ + --data_file_keys "image,blockwise_controlnet_image,blockwise_controlnet_inpaint_mask" \ + --max_pixels 1048576 \ + --dataset_repeat 50 \ + --model_id_with_origin_paths "Qwen/Qwen-Image:transformer/diffusion_pytorch_model*.safetensors,Qwen/Qwen-Image:text_encoder/model*.safetensors,Qwen/Qwen-Image:vae/diffusion_pytorch_model.safetensors,DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Inpaint:model.safetensors" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Qwen-Image-Blockwise-ControlNet-Inpaint_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "to_q,to_k,to_v,add_q_proj,add_k_proj,add_v_proj,to_out.0,to_add_out,img_mlp.net.2,img_mod.1,txt_mlp.net.2,txt_mod.1" \ + --lora_rank 32 \ + --extra_inputs "blockwise_controlnet_image,blockwise_controlnet_inpaint_mask" \ + --use_gradient_checkpointing \ + --find_unused_parameters diff --git a/examples/qwen_image/model_training/lora/Qwen-Image-Distill-Full.sh b/examples/qwen_image/model_training/lora/Qwen-Image-Distill-Full.sh new file mode 100644 index 0000000000000000000000000000000000000000..79d7c376de52ffc9268be3eb7eb3ef64f5f9956d --- /dev/null +++ b/examples/qwen_image/model_training/lora/Qwen-Image-Distill-Full.sh @@ -0,0 +1,15 @@ +accelerate launch examples/qwen_image/model_training/train.py \ + --dataset_base_path data/example_image_dataset \ + --dataset_metadata_path data/example_image_dataset/metadata.csv \ + --max_pixels 1048576 \ + --dataset_repeat 50 \ + --model_id_with_origin_paths "DiffSynth-Studio/Qwen-Image-Distill-Full:diffusion_pytorch_model*.safetensors,Qwen/Qwen-Image:text_encoder/model*.safetensors,Qwen/Qwen-Image:vae/diffusion_pytorch_model.safetensors" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Qwen-Image-Distill-Full_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "to_q,to_k,to_v,add_q_proj,add_k_proj,add_v_proj,to_out.0,to_add_out,img_mlp.net.2,img_mod.1,txt_mlp.net.2,txt_mod.1" \ + --lora_rank 32 \ + --use_gradient_checkpointing \ + --find_unused_parameters diff --git a/examples/qwen_image/model_training/lora/Qwen-Image-Distill-LoRA.sh b/examples/qwen_image/model_training/lora/Qwen-Image-Distill-LoRA.sh new file mode 100644 index 0000000000000000000000000000000000000000..061bebb0fc63885c7bcd0e9f8c18c2c7f806a9b3 --- /dev/null +++ b/examples/qwen_image/model_training/lora/Qwen-Image-Distill-LoRA.sh @@ -0,0 +1,24 @@ +accelerate launch examples/qwen_image/model_training/train.py \ + --dataset_base_path data/example_image_dataset \ + --dataset_metadata_path data/example_image_dataset/metadata_distill_qwen_image.csv \ + --data_file_keys "image" \ + --extra_inputs "seed,rand_device,num_inference_steps,cfg_scale" \ + --height 1328 \ + --width 1328 \ + --dataset_repeat 50 \ + --model_id_with_origin_paths "Qwen/Qwen-Image:transformer/diffusion_pytorch_model*.safetensors,Qwen/Qwen-Image:text_encoder/model*.safetensors,Qwen/Qwen-Image:vae/diffusion_pytorch_model.safetensors" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Qwen-Image-Distill-LoRA_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "to_q,to_k,to_v,add_q_proj,add_k_proj,add_v_proj,to_out.0,to_add_out,img_mlp.net.2,img_mod.1,txt_mlp.net.2,txt_mod.1" \ + --lora_rank 32 \ + --use_gradient_checkpointing \ + --dataset_num_workers 8 \ + --find_unused_parameters \ + --task direct_distill + +# This is an experimental training feature designed to directly distill the model, enabling generation results with fewer steps to approximate those achieved with more steps. +# The model (https://www.modelscope.cn/models/DiffSynth-Studio/Qwen-Image-Distill-LoRA) is trained using this script. +# The sample dataset is provided solely to demonstrate the dataset format. For actual usage, please construct a larger dataset using the base model. diff --git a/examples/qwen_image/model_training/lora/Qwen-Image-Edit-2509.sh b/examples/qwen_image/model_training/lora/Qwen-Image-Edit-2509.sh new file mode 100644 index 0000000000000000000000000000000000000000..7fc0cf971569ec10102aad46db7cd5d86259d492 --- /dev/null +++ b/examples/qwen_image/model_training/lora/Qwen-Image-Edit-2509.sh @@ -0,0 +1,18 @@ +accelerate launch examples/qwen_image/model_training/train.py \ + --dataset_base_path data/example_image_dataset \ + --dataset_metadata_path data/example_image_dataset/metadata_qwen_imgae_edit_multi.json \ + --data_file_keys "image,edit_image" \ + --extra_inputs "edit_image" \ + --max_pixels 1048576 \ + --dataset_repeat 50 \ + --model_id_with_origin_paths "Qwen/Qwen-Image-Edit-2509:transformer/diffusion_pytorch_model*.safetensors,Qwen/Qwen-Image:text_encoder/model*.safetensors,Qwen/Qwen-Image:vae/diffusion_pytorch_model.safetensors" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Qwen-Image-Edit-2509_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "to_q,to_k,to_v,add_q_proj,add_k_proj,add_v_proj,to_out.0,to_add_out,img_mlp.net.2,img_mod.1,txt_mlp.net.2,txt_mod.1" \ + --lora_rank 32 \ + --use_gradient_checkpointing \ + --dataset_num_workers 8 \ + --find_unused_parameters diff --git a/examples/qwen_image/model_training/lora/Qwen-Image-Edit.sh b/examples/qwen_image/model_training/lora/Qwen-Image-Edit.sh new file mode 100644 index 0000000000000000000000000000000000000000..0662b1e38bdb078fb4ec0fa818ee7846f43dfb2d --- /dev/null +++ b/examples/qwen_image/model_training/lora/Qwen-Image-Edit.sh @@ -0,0 +1,18 @@ +accelerate launch examples/qwen_image/model_training/train.py \ + --dataset_base_path data/example_image_dataset \ + --dataset_metadata_path data/example_image_dataset/metadata_edit.csv \ + --data_file_keys "image,edit_image" \ + --extra_inputs "edit_image" \ + --max_pixels 1048576 \ + --dataset_repeat 50 \ + --model_id_with_origin_paths "Qwen/Qwen-Image-Edit:transformer/diffusion_pytorch_model*.safetensors,Qwen/Qwen-Image:text_encoder/model*.safetensors,Qwen/Qwen-Image:vae/diffusion_pytorch_model.safetensors" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Qwen-Image-Edit_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "to_q,to_k,to_v,add_q_proj,add_k_proj,add_v_proj,to_out.0,to_add_out,img_mlp.net.2,img_mod.1,txt_mlp.net.2,txt_mod.1" \ + --lora_rank 32 \ + --use_gradient_checkpointing \ + --dataset_num_workers 8 \ + --find_unused_parameters diff --git a/examples/qwen_image/model_training/lora/Qwen-Image-EliGen-Poster.sh b/examples/qwen_image/model_training/lora/Qwen-Image-EliGen-Poster.sh new file mode 100644 index 0000000000000000000000000000000000000000..b0426714a3ade7af3ad5b2c66d996e966f8f5dd1 --- /dev/null +++ b/examples/qwen_image/model_training/lora/Qwen-Image-EliGen-Poster.sh @@ -0,0 +1,18 @@ +accelerate launch examples/qwen_image/model_training/train.py \ + --dataset_base_path "data/example_image_dataset" \ + --dataset_metadata_path data/example_image_dataset/metadata_eligen.json \ + --data_file_keys "image,eligen_entity_masks" \ + --max_pixels 1048576 \ + --dataset_repeat 50 \ + --model_id_with_origin_paths "Qwen/Qwen-Image:transformer/diffusion_pytorch_model*.safetensors,Qwen/Qwen-Image:text_encoder/model*.safetensors,Qwen/Qwen-Image:vae/diffusion_pytorch_model.safetensors" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Qwen-Image-EliGen-Poster_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "to_q,to_k,to_v,add_q_proj,add_k_proj,add_v_proj,to_out.0,to_add_out,img_mlp.net.2,img_mod.1,txt_mlp.net.2,txt_mod.1" \ + --lora_rank 32 \ + --extra_inputs "eligen_entity_masks,eligen_entity_prompts" \ + --use_gradient_checkpointing \ + --find_unused_parameters \ + --lora_checkpoint "models/DiffSynth-Studio/Qwen-Image-EliGen-V2/model.safetensors" diff --git a/examples/qwen_image/model_training/lora/Qwen-Image-EliGen.sh b/examples/qwen_image/model_training/lora/Qwen-Image-EliGen.sh new file mode 100644 index 0000000000000000000000000000000000000000..af861e669116df4759a9d146198c69736b732095 --- /dev/null +++ b/examples/qwen_image/model_training/lora/Qwen-Image-EliGen.sh @@ -0,0 +1,17 @@ +accelerate launch examples/qwen_image/model_training/train.py \ + --dataset_base_path "data/example_image_dataset" \ + --dataset_metadata_path data/example_image_dataset/metadata_eligen.json \ + --data_file_keys "image,eligen_entity_masks" \ + --max_pixels 1048576 \ + --dataset_repeat 50 \ + --model_id_with_origin_paths "Qwen/Qwen-Image:transformer/diffusion_pytorch_model*.safetensors,Qwen/Qwen-Image:text_encoder/model*.safetensors,Qwen/Qwen-Image:vae/diffusion_pytorch_model.safetensors" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Qwen-Image-EliGen_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "to_q,to_k,to_v,add_q_proj,add_k_proj,add_v_proj,to_out.0,to_add_out,img_mlp.net.2,img_mod.1,txt_mlp.net.2,txt_mod.1" \ + --lora_rank 32 \ + --extra_inputs "eligen_entity_masks,eligen_entity_prompts" \ + --use_gradient_checkpointing \ + --find_unused_parameters diff --git a/examples/qwen_image/model_training/lora/Qwen-Image-In-Context-Control-Union.sh b/examples/qwen_image/model_training/lora/Qwen-Image-In-Context-Control-Union.sh new file mode 100644 index 0000000000000000000000000000000000000000..d241ad0e33e734580d23d2759e0f1050afe5e1f2 --- /dev/null +++ b/examples/qwen_image/model_training/lora/Qwen-Image-In-Context-Control-Union.sh @@ -0,0 +1,20 @@ +accelerate launch examples/qwen_image/model_training/train.py \ + --dataset_base_path "data/example_image_dataset" \ + --dataset_metadata_path data/example_image_dataset/metadata_qwenimage_context.csv \ + --data_file_keys "image,context_image" \ + --max_pixels 1048576 \ + --dataset_repeat 50 \ + --model_id_with_origin_paths "Qwen/Qwen-Image:transformer/diffusion_pytorch_model*.safetensors,Qwen/Qwen-Image:text_encoder/model*.safetensors,Qwen/Qwen-Image:vae/diffusion_pytorch_model.safetensors" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Qwen-Image-In-Context-Control-Union_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "to_q,to_k,to_v,add_q_proj,add_k_proj,add_v_proj,to_out.0,to_add_out,img_mlp.net.2,img_mod.1,txt_mlp.net.2,txt_mod.1" \ + --lora_rank 64 \ + --lora_checkpoint "models/DiffSynth-Studio/Qwen-Image-In-Context-Control-Union/model.safetensors" \ + --extra_inputs "context_image" \ + --use_gradient_checkpointing \ + --find_unused_parameters + +# if you want to train from scratch, you can remove the --lora_checkpoint argument diff --git a/examples/qwen_image/model_training/lora/Qwen-Image-Splited.sh b/examples/qwen_image/model_training/lora/Qwen-Image-Splited.sh new file mode 100644 index 0000000000000000000000000000000000000000..a5d7cdea660c6b1eb548d0adeb0c439cbaeeb651 --- /dev/null +++ b/examples/qwen_image/model_training/lora/Qwen-Image-Splited.sh @@ -0,0 +1,26 @@ +accelerate launch examples/qwen_image/model_training/train.py \ + --dataset_base_path data/example_image_dataset \ + --dataset_metadata_path data/example_image_dataset/metadata.csv \ + --max_pixels 1048576 \ + --model_id_with_origin_paths "Qwen/Qwen-Image:text_encoder/model*.safetensors,Qwen/Qwen-Image:vae/diffusion_pytorch_model.safetensors" \ + --output_path "./models/train/Qwen-Image_lora_cache" \ + --use_gradient_checkpointing \ + --dataset_num_workers 8 \ + --task data_process + +accelerate launch examples/qwen_image/model_training/train.py \ + --dataset_base_path models/train/Qwen-Image_lora_cache \ + --max_pixels 1048576 \ + --dataset_repeat 50 \ + --model_id_with_origin_paths "Qwen/Qwen-Image:transformer/diffusion_pytorch_model*.safetensors" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Qwen-Image_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "to_q,to_k,to_v,add_q_proj,add_k_proj,add_v_proj,to_out.0,to_add_out,img_mlp.net.2,img_mod.1,txt_mlp.net.2,txt_mod.1" \ + --lora_rank 32 \ + --use_gradient_checkpointing \ + --dataset_num_workers 8 \ + --find_unused_parameters \ + --enable_fp8_training diff --git a/examples/qwen_image/model_training/lora/Qwen-Image.sh b/examples/qwen_image/model_training/lora/Qwen-Image.sh new file mode 100644 index 0000000000000000000000000000000000000000..f1198a5f676f7bee45d0726e299e16c8f8a1456b --- /dev/null +++ b/examples/qwen_image/model_training/lora/Qwen-Image.sh @@ -0,0 +1,16 @@ +accelerate launch examples/qwen_image/model_training/train.py \ + --dataset_base_path data/example_image_dataset \ + --dataset_metadata_path data/example_image_dataset/metadata.csv \ + --max_pixels 1048576 \ + --dataset_repeat 50 \ + --model_id_with_origin_paths "Qwen/Qwen-Image:transformer/diffusion_pytorch_model*.safetensors,Qwen/Qwen-Image:text_encoder/model*.safetensors,Qwen/Qwen-Image:vae/diffusion_pytorch_model.safetensors" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Qwen-Image_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "to_q,to_k,to_v,add_q_proj,add_k_proj,add_v_proj,to_out.0,to_add_out,img_mlp.net.2,img_mod.1,txt_mlp.net.2,txt_mod.1" \ + --lora_rank 32 \ + --use_gradient_checkpointing \ + --dataset_num_workers 8 \ + --find_unused_parameters diff --git a/examples/qwen_image/model_training/scripts/Qwen-Image-Blockwise-ControlNet-Initialize.py b/examples/qwen_image/model_training/scripts/Qwen-Image-Blockwise-ControlNet-Initialize.py new file mode 100644 index 0000000000000000000000000000000000000000..5b0392f589d7739197ba4251bb1c6bfb0fcfe498 --- /dev/null +++ b/examples/qwen_image/model_training/scripts/Qwen-Image-Blockwise-ControlNet-Initialize.py @@ -0,0 +1,13 @@ +# This script is for initializing a Qwen-Image-Blockwise-ControlNet +from diffsynth import hash_state_dict_keys +from diffsynth.models.qwen_image_controlnet import QwenImageBlockWiseControlNet +import torch +from safetensors.torch import save_file + + +controlnet = QwenImageBlockWiseControlNet().to(dtype=torch.bfloat16, device="cuda") +controlnet.init_weight() +state_dict_controlnet = controlnet.state_dict() + +print(hash_state_dict_keys(state_dict_controlnet)) +save_file(state_dict_controlnet, "models/blockwise_controlnet.safetensors") diff --git a/examples/qwen_image/model_training/scripts/Qwen-Image-Blockwise-ControlNet-Inpaint-Initialize.py b/examples/qwen_image/model_training/scripts/Qwen-Image-Blockwise-ControlNet-Inpaint-Initialize.py new file mode 100644 index 0000000000000000000000000000000000000000..83111894709eda93fbab925ca1804e2a3fd477dd --- /dev/null +++ b/examples/qwen_image/model_training/scripts/Qwen-Image-Blockwise-ControlNet-Inpaint-Initialize.py @@ -0,0 +1,12 @@ +# This script is for initializing a Inpaint Qwen-Image-ControlNet +import torch +from diffsynth import hash_state_dict_keys +from diffsynth.models.qwen_image_controlnet import QwenImageBlockWiseControlNet +from safetensors.torch import save_file + +controlnet = QwenImageBlockWiseControlNet(additional_in_dim=4).to(dtype=torch.bfloat16, device="cuda") +controlnet.init_weight() +state_dict_controlnet = controlnet.state_dict() + +print(hash_state_dict_keys(state_dict_controlnet)) +save_file(state_dict_controlnet, "models/blockwise_controlnet_inpaint.safetensors") diff --git a/examples/qwen_image/model_training/train.py b/examples/qwen_image/model_training/train.py new file mode 100644 index 0000000000000000000000000000000000000000..f39c11cb70020455cd5486a31c15d87c435608c7 --- /dev/null +++ b/examples/qwen_image/model_training/train.py @@ -0,0 +1,147 @@ +import torch, os, json +from diffsynth import load_state_dict +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig +from diffsynth.pipelines.flux_image_new import ControlNetInput +from diffsynth.trainers.utils import DiffusionTrainingModule, ModelLogger, qwen_image_parser, launch_training_task, launch_data_process_task +from diffsynth.trainers.unified_dataset import UnifiedDataset +os.environ["TOKENIZERS_PARALLELISM"] = "false" + + + +class QwenImageTrainingModule(DiffusionTrainingModule): + def __init__( + self, + model_paths=None, model_id_with_origin_paths=None, + tokenizer_path=None, processor_path=None, + trainable_models=None, + lora_base_model=None, lora_target_modules="", lora_rank=32, lora_checkpoint=None, + use_gradient_checkpointing=True, + use_gradient_checkpointing_offload=False, + extra_inputs=None, + enable_fp8_training=False, + task="sft", + ): + super().__init__() + # Load models + model_configs = self.parse_model_configs(model_paths, model_id_with_origin_paths, enable_fp8_training=enable_fp8_training) + tokenizer_config = ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/") if tokenizer_path is None else ModelConfig(tokenizer_path) + processor_config = ModelConfig(model_id="Qwen/Qwen-Image-Edit", origin_file_pattern="processor/") if processor_path is None else ModelConfig(processor_path) + self.pipe = QwenImagePipeline.from_pretrained(torch_dtype=torch.bfloat16, device="cpu", model_configs=model_configs, tokenizer_config=tokenizer_config, processor_config=processor_config) + + # Training mode + self.switch_pipe_to_training_mode( + self.pipe, trainable_models, + lora_base_model, lora_target_modules, lora_rank, lora_checkpoint=lora_checkpoint, + enable_fp8_training=enable_fp8_training, + ) + + # Store other configs + self.use_gradient_checkpointing = use_gradient_checkpointing + self.use_gradient_checkpointing_offload = use_gradient_checkpointing_offload + self.extra_inputs = extra_inputs.split(",") if extra_inputs is not None else [] + self.task = task + + + def forward_preprocess(self, data): + # CFG-sensitive parameters + inputs_posi = {"prompt": data["prompt"]} + inputs_nega = {"negative_prompt": ""} + + # CFG-unsensitive parameters + inputs_shared = { + # Assume you are using this pipeline for inference, + # please fill in the input parameters. + "input_image": data["image"], + "height": data["image"].size[1], + "width": data["image"].size[0], + # Please do not modify the following parameters + # unless you clearly know what this will cause. + "cfg_scale": 1, + "rand_device": self.pipe.device, + "use_gradient_checkpointing": self.use_gradient_checkpointing, + "use_gradient_checkpointing_offload": self.use_gradient_checkpointing_offload, + "edit_image_auto_resize": True, + } + + # Extra inputs + controlnet_input, blockwise_controlnet_input = {}, {} + for extra_input in self.extra_inputs: + if extra_input.startswith("blockwise_controlnet_"): + blockwise_controlnet_input[extra_input.replace("blockwise_controlnet_", "")] = data[extra_input] + elif extra_input.startswith("controlnet_"): + controlnet_input[extra_input.replace("controlnet_", "")] = data[extra_input] + else: + inputs_shared[extra_input] = data[extra_input] + if len(controlnet_input) > 0: + inputs_shared["controlnet_inputs"] = [ControlNetInput(**controlnet_input)] + if len(blockwise_controlnet_input) > 0: + inputs_shared["blockwise_controlnet_inputs"] = [ControlNetInput(**blockwise_controlnet_input)] + + # Pipeline units will automatically process the input parameters. + for unit in self.pipe.units: + inputs_shared, inputs_posi, inputs_nega = self.pipe.unit_runner(unit, self.pipe, inputs_shared, inputs_posi, inputs_nega) + return {**inputs_shared, **inputs_posi} + + + def forward(self, data, inputs=None, return_inputs=False): + # Inputs + if inputs is None: + inputs = self.forward_preprocess(data) + else: + inputs = self.transfer_data_to_device(inputs, self.pipe.device, self.pipe.torch_dtype) + if return_inputs: return inputs + + # Loss + if self.task == "sft": + models = {name: getattr(self.pipe, name) for name in self.pipe.in_iteration_models} + loss = self.pipe.training_loss(**models, **inputs) + elif self.task == "data_process": + loss = inputs + elif self.task == "direct_distill": + loss = self.pipe.direct_distill_loss(**inputs) + else: + raise NotImplementedError(f"Unsupported task: {self.task}.") + return loss + + + +if __name__ == "__main__": + parser = qwen_image_parser() + args = parser.parse_args() + dataset = UnifiedDataset( + base_path=args.dataset_base_path, + metadata_path=args.dataset_metadata_path, + repeat=args.dataset_repeat, + data_file_keys=args.data_file_keys.split(","), + main_data_operator=UnifiedDataset.default_image_operator( + base_path=args.dataset_base_path, + max_pixels=args.max_pixels, + height=args.height, + width=args.width, + height_division_factor=16, + width_division_factor=16, + ) + ) + model = QwenImageTrainingModule( + model_paths=args.model_paths, + model_id_with_origin_paths=args.model_id_with_origin_paths, + tokenizer_path=args.tokenizer_path, + processor_path=args.processor_path, + trainable_models=args.trainable_models, + lora_base_model=args.lora_base_model, + lora_target_modules=args.lora_target_modules, + lora_rank=args.lora_rank, + lora_checkpoint=args.lora_checkpoint, + use_gradient_checkpointing=args.use_gradient_checkpointing, + use_gradient_checkpointing_offload=args.use_gradient_checkpointing_offload, + extra_inputs=args.extra_inputs, + enable_fp8_training=args.enable_fp8_training, + task=args.task, + ) + model_logger = ModelLogger(args.output_path, remove_prefix_in_ckpt=args.remove_prefix_in_ckpt) + launcher_map = { + "sft": launch_training_task, + "data_process": launch_data_process_task, + "direct_distill": launch_training_task, + } + launcher_map[args.task](dataset, model, model_logger, args=args) diff --git a/examples/qwen_image/model_training/validate_full/Qwen-Image-Blockwise-ControlNet-Canny.py b/examples/qwen_image/model_training/validate_full/Qwen-Image-Blockwise-ControlNet-Canny.py new file mode 100644 index 0000000000000000000000000000000000000000..6ae4d5bb72444f8d7550dc1856a2a0275b224ed9 --- /dev/null +++ b/examples/qwen_image/model_training/validate_full/Qwen-Image-Blockwise-ControlNet-Canny.py @@ -0,0 +1,31 @@ +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig, ControlNetInput +from PIL import Image +import torch +from modelscope import dataset_snapshot_download + + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ModelConfig(path="models/train/Qwen-Image-Blockwise-ControlNet-Canny_full/epoch-1.safetensors"), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), +) + +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/example_image_dataset", + local_dir="./data/example_image_dataset", + allow_file_pattern="canny/image_1.jpg" +) +controlnet_image = Image.open("data/example_image_dataset/canny/image_1.jpg").resize((1328, 1328)) + +prompt = "一只小狗，毛发光洁柔顺，眼神灵动，背景是樱花纷飞的春日庭院，唯美温馨。" +image = pipe( + prompt, seed=0, + blockwise_controlnet_inputs=[ControlNetInput(image=controlnet_image)] +) +image.save("image.jpg") diff --git a/examples/qwen_image/model_training/validate_full/Qwen-Image-Blockwise-ControlNet-Depth.py b/examples/qwen_image/model_training/validate_full/Qwen-Image-Blockwise-ControlNet-Depth.py new file mode 100644 index 0000000000000000000000000000000000000000..18b597e1fa7508127c431aa4a466c86392a83044 --- /dev/null +++ b/examples/qwen_image/model_training/validate_full/Qwen-Image-Blockwise-ControlNet-Depth.py @@ -0,0 +1,31 @@ +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig, ControlNetInput +from PIL import Image +import torch +from modelscope import dataset_snapshot_download + + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ModelConfig(path="models/train/Qwen-Image-Blockwise-ControlNet-Depth_full/epoch-1.safetensors"), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), +) + +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/example_image_dataset", + local_dir="./data/example_image_dataset", + allow_file_pattern="depth/image_1.jpg" +) +controlnet_image = Image.open("data/example_image_dataset/depth/image_1.jpg").resize((1328, 1328)) + +prompt = "精致肖像，水下少女，蓝裙飘逸，发丝轻扬，光影透澈，气泡环绕，面容恬静，细节精致，梦幻唯美。" +image = pipe( + prompt, seed=0, + blockwise_controlnet_inputs=[ControlNetInput(image=controlnet_image)] +) +image.save("image.jpg") diff --git a/examples/qwen_image/model_training/validate_full/Qwen-Image-Blockwise-ControlNet-Inpaint.py b/examples/qwen_image/model_training/validate_full/Qwen-Image-Blockwise-ControlNet-Inpaint.py new file mode 100644 index 0000000000000000000000000000000000000000..15a15b4df8bd9608f8ce6782a309d37699b923d1 --- /dev/null +++ b/examples/qwen_image/model_training/validate_full/Qwen-Image-Blockwise-ControlNet-Inpaint.py @@ -0,0 +1,32 @@ +import torch +from PIL import Image +from modelscope import dataset_snapshot_download +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig, ControlNetInput + + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ModelConfig(path="models/train/Qwen-Image-Blockwise-ControlNet-Inpaint_full/epoch-1.safetensors"), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), +) +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/example_image_dataset", + local_dir="./data/example_image_dataset", + allow_file_pattern="inpaint/*.jpg" +) +prompt = "a cat with sunglasses" +controlnet_image = Image.open("./data/example_image_dataset/inpaint/image_1.jpg").convert("RGB").resize((1024, 1024)) +inpaint_mask = Image.open("./data/example_image_dataset/inpaint/mask.jpg").convert("RGB").resize((1024, 1024)) +image = pipe( + prompt, seed=0, + blockwise_controlnet_inputs=[ControlNetInput(image=controlnet_image, inpaint_mask=inpaint_mask)], + height=1024, width=1024, + num_inference_steps=40, +) +image.save("image.jpg") diff --git a/examples/qwen_image/model_training/validate_full/Qwen-Image-Distill-Full.py b/examples/qwen_image/model_training/validate_full/Qwen-Image-Distill-Full.py new file mode 100644 index 0000000000000000000000000000000000000000..07389c524c2e142679d866257785030eb15795a2 --- /dev/null +++ b/examples/qwen_image/model_training/validate_full/Qwen-Image-Distill-Full.py @@ -0,0 +1,20 @@ +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig +from diffsynth import load_state_dict +import torch + + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="DiffSynth-Studio/Qwen-Image-Distill-Full", origin_file_pattern="diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), +) +state_dict = load_state_dict("models/train/Qwen-Image-Distill-Full_full/epoch-1.safetensors") +pipe.dit.load_state_dict(state_dict) +prompt = "a dog" +image = pipe(prompt, seed=0, num_inference_steps=15, cfg_scale=1) +image.save("image.jpg") diff --git a/examples/qwen_image/model_training/validate_full/Qwen-Image-Edit-2509.py b/examples/qwen_image/model_training/validate_full/Qwen-Image-Edit-2509.py new file mode 100644 index 0000000000000000000000000000000000000000..9295904cacc2c00bfd0eeb801f2f10343fc89f45 --- /dev/null +++ b/examples/qwen_image/model_training/validate_full/Qwen-Image-Edit-2509.py @@ -0,0 +1,26 @@ +import torch +from PIL import Image +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig +from diffsynth import load_state_dict + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image-Edit-2509", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ], + tokenizer_config=None, + processor_config=ModelConfig(model_id="Qwen/Qwen-Image-Edit", origin_file_pattern="processor/"), +) +state_dict = load_state_dict("models/train/Qwen-Image-Edit-2509_full/epoch-1.safetensors") +pipe.dit.load_state_dict(state_dict) + +prompt = "Change the color of the dress in Figure 1 to the color shown in Figure 2." +images = [ + Image.open("data/example_image_dataset/edit/image1.jpg").resize((1024, 1024)), + Image.open("data/example_image_dataset/edit/image_color.jpg").resize((1024, 1024)), +] +image = pipe(prompt, edit_image=images, seed=123, num_inference_steps=40, height=1024, width=1024) +image.save("image.jpg") diff --git a/examples/qwen_image/model_training/validate_full/Qwen-Image-Edit.py b/examples/qwen_image/model_training/validate_full/Qwen-Image-Edit.py new file mode 100644 index 0000000000000000000000000000000000000000..c08b4850c4d20925a7d008853d11b213ee3d69b6 --- /dev/null +++ b/examples/qwen_image/model_training/validate_full/Qwen-Image-Edit.py @@ -0,0 +1,23 @@ +import torch +from PIL import Image +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig +from diffsynth import load_state_dict + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image-Edit", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ], + tokenizer_config=None, + processor_config=ModelConfig(model_id="Qwen/Qwen-Image-Edit", origin_file_pattern="processor/"), +) +state_dict = load_state_dict("models/train/Qwen-Image-Edit_full/epoch-1.safetensors") +pipe.dit.load_state_dict(state_dict) + +prompt = "将裙子改为粉色" +image = Image.open("data/example_image_dataset/edit/image1.jpg").resize((1024, 1024)) +image = pipe(prompt, edit_image=image, seed=0, num_inference_steps=40, height=1024, width=1024) +image.save(f"image.jpg") diff --git a/examples/qwen_image/model_training/validate_full/Qwen-Image.py b/examples/qwen_image/model_training/validate_full/Qwen-Image.py new file mode 100644 index 0000000000000000000000000000000000000000..872321825eb4028fe18d5b00c8ee815bc6356e75 --- /dev/null +++ b/examples/qwen_image/model_training/validate_full/Qwen-Image.py @@ -0,0 +1,20 @@ +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig +from diffsynth import load_state_dict +import torch + + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), +) +state_dict = load_state_dict("models/train/Qwen-Image_full/epoch-1.safetensors") +pipe.dit.load_state_dict(state_dict) +prompt = "a dog" +image = pipe(prompt, seed=0) +image.save("image.jpg") diff --git a/examples/qwen_image/model_training/validate_lora/Qwen-Image-Blockwise-ControlNet-Canny.py b/examples/qwen_image/model_training/validate_lora/Qwen-Image-Blockwise-ControlNet-Canny.py new file mode 100644 index 0000000000000000000000000000000000000000..4a54b5ee07316dcca50e439ec1083d392305ce18 --- /dev/null +++ b/examples/qwen_image/model_training/validate_lora/Qwen-Image-Blockwise-ControlNet-Canny.py @@ -0,0 +1,32 @@ +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig, ControlNetInput +from PIL import Image +import torch +from modelscope import dataset_snapshot_download + + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ModelConfig(model_id="DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Canny", origin_file_pattern="model.safetensors"), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), +) +pipe.load_lora(pipe.dit, "models/train/Qwen-Image-Blockwise-ControlNet-Canny_lora/epoch-4.safetensors") + +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/example_image_dataset", + local_dir="./data/example_image_dataset", + allow_file_pattern="canny/image_1.jpg" +) +controlnet_image = Image.open("data/example_image_dataset/canny/image_1.jpg").resize((1328, 1328)) + +prompt = "一只小狗，毛发光洁柔顺，眼神灵动，背景是樱花纷飞的春日庭院，唯美温馨。" +image = pipe( + prompt, seed=0, + blockwise_controlnet_inputs=[ControlNetInput(image=controlnet_image)] +) +image.save("image.jpg") diff --git a/examples/qwen_image/model_training/validate_lora/Qwen-Image-Blockwise-ControlNet-Depth.py b/examples/qwen_image/model_training/validate_lora/Qwen-Image-Blockwise-ControlNet-Depth.py new file mode 100644 index 0000000000000000000000000000000000000000..626654559d1970697a2b9a12af3722581956bfbb --- /dev/null +++ b/examples/qwen_image/model_training/validate_lora/Qwen-Image-Blockwise-ControlNet-Depth.py @@ -0,0 +1,33 @@ +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig, ControlNetInput +from PIL import Image +import torch +from modelscope import dataset_snapshot_download + + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ModelConfig(model_id="DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Depth", origin_file_pattern="model.safetensors"), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), +) +pipe.load_lora(pipe.dit, "models/train/Qwen-Image-Blockwise-ControlNet-Depth_lora/epoch-4.safetensors") + +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/example_image_dataset", + local_dir="./data/example_image_dataset", + allow_file_pattern="depth/image_1.jpg" +) + +controlnet_image = Image.open("data/example_image_dataset/depth/image_1.jpg").resize((1328, 1328)) + +prompt = "精致肖像，水下少女，蓝裙飘逸，发丝轻扬，光影透澈，气泡环绕，面容恬静，细节精致，梦幻唯美。" +image = pipe( + prompt, seed=0, + blockwise_controlnet_inputs=[ControlNetInput(image=controlnet_image)] +) +image.save("image.jpg") diff --git a/examples/qwen_image/model_training/validate_lora/Qwen-Image-Blockwise-ControlNet-Inpaint.py b/examples/qwen_image/model_training/validate_lora/Qwen-Image-Blockwise-ControlNet-Inpaint.py new file mode 100644 index 0000000000000000000000000000000000000000..60bd9f2bbbefd95ab0efe1c7e07aaeb96f4a560d --- /dev/null +++ b/examples/qwen_image/model_training/validate_lora/Qwen-Image-Blockwise-ControlNet-Inpaint.py @@ -0,0 +1,34 @@ +import torch +from PIL import Image +from modelscope import dataset_snapshot_download +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig, ControlNetInput + + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ModelConfig(model_id="DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Inpaint", origin_file_pattern="model.safetensors"), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), +) +pipe.load_lora(pipe.dit, "models/train/Qwen-Image-Blockwise-ControlNet-Inpaint_lora/epoch-4.safetensors") + +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/example_image_dataset", + local_dir="./data/example_image_dataset", + allow_file_pattern="inpaint/*.jpg" +) +prompt = "a cat with sunglasses" +controlnet_image = Image.open("./data/example_image_dataset/inpaint/image_1.jpg").convert("RGB").resize((1024, 1024)) +inpaint_mask = Image.open("./data/example_image_dataset/inpaint/mask.jpg").convert("RGB").resize((1024, 1024)) +image = pipe( + prompt, seed=0, + blockwise_controlnet_inputs=[ControlNetInput(image=controlnet_image, inpaint_mask=inpaint_mask)], + height=1024, width=1024, + num_inference_steps=40, +) +image.save("image.jpg") diff --git a/examples/qwen_image/model_training/validate_lora/Qwen-Image-Distill-Full.py b/examples/qwen_image/model_training/validate_lora/Qwen-Image-Distill-Full.py new file mode 100644 index 0000000000000000000000000000000000000000..7f644aa805d47bdc04711b28d5ea0d3d3eafc36e --- /dev/null +++ b/examples/qwen_image/model_training/validate_lora/Qwen-Image-Distill-Full.py @@ -0,0 +1,18 @@ +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig +import torch + + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="DiffSynth-Studio/Qwen-Image-Distill-Full", origin_file_pattern="diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), +) +pipe.load_lora(pipe.dit, "models/train/Qwen-Image-Distill-Full_lora/epoch-4.safetensors") +prompt = "a dog" +image = pipe(prompt, seed=0, num_inference_steps=15, cfg_scale=1) +image.save("image.jpg") diff --git a/examples/qwen_image/model_training/validate_lora/Qwen-Image-Distill-LoRA.py b/examples/qwen_image/model_training/validate_lora/Qwen-Image-Distill-LoRA.py new file mode 100644 index 0000000000000000000000000000000000000000..d56b9af7f4c6e8bf6e83845c3c2511a2a9374810 --- /dev/null +++ b/examples/qwen_image/model_training/validate_lora/Qwen-Image-Distill-LoRA.py @@ -0,0 +1,23 @@ +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig +import torch + + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), +) +pipe.load_lora(pipe.dit, "models/train/Qwen-Image-Distill-LoRA_lora/epoch-4.safetensors") +prompt = "精致肖像，水下少女，蓝裙飘逸，发丝轻扬，光影透澈，气泡环绕，面容恬静，细节精致，梦幻唯美。" +image = pipe( + prompt, + seed=0, + num_inference_steps=4, + cfg_scale=1, +) +image.save("image.jpg") diff --git a/examples/qwen_image/model_training/validate_lora/Qwen-Image-Edit-2509.py b/examples/qwen_image/model_training/validate_lora/Qwen-Image-Edit-2509.py new file mode 100644 index 0000000000000000000000000000000000000000..e701b0749809f08db27dd52c37dc93445d0f4995 --- /dev/null +++ b/examples/qwen_image/model_training/validate_lora/Qwen-Image-Edit-2509.py @@ -0,0 +1,24 @@ +import torch +from PIL import Image +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image-Edit-2509", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ], + tokenizer_config=None, + processor_config=ModelConfig(model_id="Qwen/Qwen-Image-Edit", origin_file_pattern="processor/"), +) +pipe.load_lora(pipe.dit, "models/train/Qwen-Image-Edit-2509_lora/epoch-4.safetensors") + +prompt = "Change the color of the dress in Figure 1 to the color shown in Figure 2." +images = [ + Image.open("data/example_image_dataset/edit/image1.jpg").resize((1024, 1024)), + Image.open("data/example_image_dataset/edit/image_color.jpg").resize((1024, 1024)), +] +image = pipe(prompt, edit_image=images, seed=123, num_inference_steps=40, height=1024, width=1024) +image.save("image.jpg") diff --git a/examples/qwen_image/model_training/validate_lora/Qwen-Image-Edit.py b/examples/qwen_image/model_training/validate_lora/Qwen-Image-Edit.py new file mode 100644 index 0000000000000000000000000000000000000000..2576be339df5fba385c37567af0902842c306289 --- /dev/null +++ b/examples/qwen_image/model_training/validate_lora/Qwen-Image-Edit.py @@ -0,0 +1,21 @@ +import torch +from PIL import Image +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image-Edit", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ], + tokenizer_config=None, + processor_config=ModelConfig(model_id="Qwen/Qwen-Image-Edit", origin_file_pattern="processor/"), +) +pipe.load_lora(pipe.dit, "models/train/Qwen-Image-Edit_lora/epoch-4.safetensors") + +prompt = "将裙子改为粉色" +image = Image.open("data/example_image_dataset/edit/image1.jpg").resize((1024, 1024)) +image = pipe(prompt, edit_image=image, seed=0, num_inference_steps=40, height=1024, width=1024) +image.save(f"image.jpg") diff --git a/examples/qwen_image/model_training/validate_lora/Qwen-Image-EliGen-Poster.py b/examples/qwen_image/model_training/validate_lora/Qwen-Image-EliGen-Poster.py new file mode 100644 index 0000000000000000000000000000000000000000..81f697c4073b04d242761e90c5b6159a3a4e3505 --- /dev/null +++ b/examples/qwen_image/model_training/validate_lora/Qwen-Image-EliGen-Poster.py @@ -0,0 +1,29 @@ +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig +import torch +from PIL import Image + + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), +) +pipe.load_lora(pipe.dit, "models/train/Qwen-Image-EliGen-Poster_lora/epoch-4.safetensors") + + +entity_prompts = ["A beautiful girl", "sign 'Entity Control'", "shorts", "shirt"] +global_prompt = "A beautiful girl wearing shirt and shorts in the street, holding a sign 'Entity Control'" +masks = [Image.open(f"data/example_image_dataset/eligen/{i}.png").convert('RGB') for i in range(len(entity_prompts))] + +image = pipe(global_prompt, + seed=0, + height=1024, + width=1024, + eligen_entity_prompts=entity_prompts, + eligen_entity_masks=masks) +image.save("Qwen-Image-EliGen-Poster.jpg") diff --git a/examples/qwen_image/model_training/validate_lora/Qwen-Image-EliGen.py b/examples/qwen_image/model_training/validate_lora/Qwen-Image-EliGen.py new file mode 100644 index 0000000000000000000000000000000000000000..cd7904e9d844232c599b7534bc58398e1820702b --- /dev/null +++ b/examples/qwen_image/model_training/validate_lora/Qwen-Image-EliGen.py @@ -0,0 +1,29 @@ +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig +import torch +from PIL import Image + + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), +) +pipe.load_lora(pipe.dit, "models/train/Qwen-Image-EliGen_lora/epoch-4.safetensors") + + +entity_prompts = ["A beautiful girl", "sign 'Entity Control'", "shorts", "shirt"] +global_prompt = "A beautiful girl wearing shirt and shorts in the street, holding a sign 'Entity Control'" +masks = [Image.open(f"data/example_image_dataset/eligen/{i}.png").convert('RGB') for i in range(len(entity_prompts))] + +image = pipe(global_prompt, + seed=0, + height=1024, + width=1024, + eligen_entity_prompts=entity_prompts, + eligen_entity_masks=masks) +image.save("Qwen-Image_EliGen.jpg") diff --git a/examples/qwen_image/model_training/validate_lora/Qwen-Image-In-Context-Control-Union.py b/examples/qwen_image/model_training/validate_lora/Qwen-Image-In-Context-Control-Union.py new file mode 100644 index 0000000000000000000000000000000000000000..83a93a3f6bf41529c9350052e03f8f95050c8eb5 --- /dev/null +++ b/examples/qwen_image/model_training/validate_lora/Qwen-Image-In-Context-Control-Union.py @@ -0,0 +1,19 @@ +from PIL import Image +import torch +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), +) +pipe.load_lora(pipe.dit, "models/train/Qwen-Image-In-Context-Control-Union_lora/epoch-4.safetensors") +image = Image.open("data/example_image_dataset/canny/image_1.jpg").resize((1024, 1024)) +prompt = "Context_Control. a dog" +image = pipe(prompt=prompt, seed=0, context_image=image, height=1024, width=1024) +image.save("image_context.jpg") diff --git a/examples/qwen_image/model_training/validate_lora/Qwen-Image.py b/examples/qwen_image/model_training/validate_lora/Qwen-Image.py new file mode 100644 index 0000000000000000000000000000000000000000..16be2b4bda15c696c2b7bb4f3fd36176db67da0e --- /dev/null +++ b/examples/qwen_image/model_training/validate_lora/Qwen-Image.py @@ -0,0 +1,18 @@ +from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig +import torch + + +pipe = QwenImagePipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"), + ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"), + ], + tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"), +) +pipe.load_lora(pipe.dit, "models/train/Qwen-Image_lora/epoch-4.safetensors") +prompt = "a dog" +image = pipe(prompt, seed=0) +image.save("image.jpg") diff --git a/examples/step1x/step1x.py b/examples/step1x/step1x.py new file mode 100644 index 0000000000000000000000000000000000000000..80de280d5327fb4dc37d4eef0227e558d5f06281 --- /dev/null +++ b/examples/step1x/step1x.py @@ -0,0 +1,35 @@ +import torch +from diffsynth import FluxImagePipeline, ModelManager +from modelscope import snapshot_download +from PIL import Image +import numpy as np + + +snapshot_download("Qwen/Qwen2.5-VL-7B-Instruct", cache_dir="./models") +snapshot_download("stepfun-ai/Step1X-Edit", cache_dir="./models") + +model_manager = ModelManager(torch_dtype=torch.bfloat16, device="cuda") +model_manager.load_models([ + "models/Qwen/Qwen2.5-VL-7B-Instruct", + "models/stepfun-ai/Step1X-Edit/step1x-edit-i1258.safetensors", + "models/stepfun-ai/Step1X-Edit/vae.safetensors", +]) +pipe = FluxImagePipeline.from_model_manager(model_manager) +pipe.enable_vram_management() + +image = Image.fromarray(np.zeros((1248, 832, 3), dtype=np.uint8) + 255) +image = pipe( + prompt="draw red flowers in Chinese ink painting style", + step1x_reference_image=image, + width=832, height=1248, cfg_scale=6, + seed=1, +) +image.save("image_1.jpg") + +image = pipe( + prompt="add more flowers in Chinese ink painting style", + step1x_reference_image=image, + width=832, height=1248, cfg_scale=6, + seed=2, +) +image.save("image_2.jpg") diff --git a/examples/stepvideo/README.md b/examples/stepvideo/README.md new file mode 100644 index 0000000000000000000000000000000000000000..ad2f40484d9fdb94fec87b8be16f02e10aca71bb --- /dev/null +++ b/examples/stepvideo/README.md @@ -0,0 +1,19 @@ +# Stepvideo + +StepVideo is a state-of-the-art (SoTA) text-to-video pre-trained model with 30 billion parameters and the capability to generate videos up to 204 frames. + +* Model: https://modelscope.cn/models/stepfun-ai/stepvideo-t2v/summary +* GitHub: https://github.com/stepfun-ai/Step-Video-T2V +* Technical report: https://arxiv.org/abs/2502.10248 + +## Examples + +For original BF16 version, please see [`./stepvideo_text_to_video.py`](./stepvideo_text_to_video.py). 80G VRAM required. + +We also support auto-offload, which can reduce the VRAM requirement to **24GB**; however, it requires 2x time for inference. Please see [`./stepvideo_text_to_video_low_vram.py`](./stepvideo_text_to_video_low_vram.py). + +https://github.com/user-attachments/assets/5954fdaa-a3cf-45a3-bd35-886e3cc4581b + +For FP8 quantized version, please see [`./stepvideo_text_to_video_quantized.py`](./stepvideo_text_to_video_quantized.py). 40G VRAM required. + +https://github.com/user-attachments/assets/f3697f4e-bc08-47d2-b00a-32d7dfa272ad diff --git a/examples/stepvideo/stepvideo_text_to_video.py b/examples/stepvideo/stepvideo_text_to_video.py new file mode 100644 index 0000000000000000000000000000000000000000..302ed08d43fb3ab1a29406b5ff4b81f134bf072e --- /dev/null +++ b/examples/stepvideo/stepvideo_text_to_video.py @@ -0,0 +1,50 @@ +from modelscope import snapshot_download +from diffsynth import ModelManager, StepVideoPipeline, save_video +import torch + + +# Download models +snapshot_download(model_id="stepfun-ai/stepvideo-t2v", cache_dir="models") + +# Load the compiled attention for the LLM text encoder. +# If you encounter errors here. Please select other compiled file that matches your environment or delete this line. +torch.ops.load_library("models/stepfun-ai/stepvideo-t2v/lib/liboptimus_ths-torch2.5-cu124.cpython-310-x86_64-linux-gnu.so") + +# Load models +model_manager = ModelManager() +model_manager.load_models( + ["models/stepfun-ai/stepvideo-t2v/hunyuan_clip/clip_text_encoder/pytorch_model.bin"], + torch_dtype=torch.float32, device="cpu" +) +model_manager.load_models( + [ + "models/stepfun-ai/stepvideo-t2v/step_llm", + "models/stepfun-ai/stepvideo-t2v/vae/vae_v2.safetensors", + [ + "models/stepfun-ai/stepvideo-t2v/transformer/diffusion_pytorch_model-00001-of-00006.safetensors", + "models/stepfun-ai/stepvideo-t2v/transformer/diffusion_pytorch_model-00002-of-00006.safetensors", + "models/stepfun-ai/stepvideo-t2v/transformer/diffusion_pytorch_model-00003-of-00006.safetensors", + "models/stepfun-ai/stepvideo-t2v/transformer/diffusion_pytorch_model-00004-of-00006.safetensors", + "models/stepfun-ai/stepvideo-t2v/transformer/diffusion_pytorch_model-00005-of-00006.safetensors", + "models/stepfun-ai/stepvideo-t2v/transformer/diffusion_pytorch_model-00006-of-00006.safetensors", + ] + ], + torch_dtype=torch.bfloat16, device="cpu" +) +pipe = StepVideoPipeline.from_model_manager(model_manager, torch_dtype=torch.bfloat16, device="cuda") + +# Enable VRAM management +# This model requires 80G VRAM. +# In order to reduce VRAM required, please set `num_persistent_param_in_dit` to a small number. +pipe.enable_vram_management(num_persistent_param_in_dit=None) + +# Run! +video = pipe( + prompt="一名宇航员在月球上发现一块石碑，上面印有“stepfun”字样，闪闪发光。超高清、HDR 视频、环境光、杜比全景声、画面稳定、流畅动作、逼真的细节、专业级构图、超现实主义、自然、生动、超细节、清晰。", + negative_prompt="画面暗、低分辨率、不良手、文本、缺少手指、多余的手指、裁剪、低质量、颗粒状、签名、水印、用户名、模糊。", + num_inference_steps=30, cfg_scale=9, num_frames=51, seed=1 +) +save_video( + video, "video.mp4", fps=25, quality=5, + ffmpeg_params=["-vf", "atadenoise=0a=0.1:0b=0.1:1a=0.1:1b=0.1"] +) diff --git a/examples/stepvideo/stepvideo_text_to_video_low_vram.py b/examples/stepvideo/stepvideo_text_to_video_low_vram.py new file mode 100644 index 0000000000000000000000000000000000000000..f75ae1606855f062bbc36db82aa681f02d941cda --- /dev/null +++ b/examples/stepvideo/stepvideo_text_to_video_low_vram.py @@ -0,0 +1,54 @@ +from modelscope import snapshot_download +from diffsynth import ModelManager, StepVideoPipeline, save_video +import torch + + +# Download models +snapshot_download(model_id="stepfun-ai/stepvideo-t2v", cache_dir="models") + +# Load the compiled attention for the LLM text encoder. +# If you encounter errors here. Please select other compiled file that matches your environment or delete this line. +torch.ops.load_library("models/stepfun-ai/stepvideo-t2v/lib/liboptimus_ths-torch2.5-cu124.cpython-310-x86_64-linux-gnu.so") + +# Load models +model_manager = ModelManager() +model_manager.load_models( + ["models/stepfun-ai/stepvideo-t2v/hunyuan_clip/clip_text_encoder/pytorch_model.bin"], + torch_dtype=torch.float32, device="cpu" +) +model_manager.load_models( + [ + "models/stepfun-ai/stepvideo-t2v/step_llm", + [ + "models/stepfun-ai/stepvideo-t2v/transformer/diffusion_pytorch_model-00001-of-00006.safetensors", + "models/stepfun-ai/stepvideo-t2v/transformer/diffusion_pytorch_model-00002-of-00006.safetensors", + "models/stepfun-ai/stepvideo-t2v/transformer/diffusion_pytorch_model-00003-of-00006.safetensors", + "models/stepfun-ai/stepvideo-t2v/transformer/diffusion_pytorch_model-00004-of-00006.safetensors", + "models/stepfun-ai/stepvideo-t2v/transformer/diffusion_pytorch_model-00005-of-00006.safetensors", + "models/stepfun-ai/stepvideo-t2v/transformer/diffusion_pytorch_model-00006-of-00006.safetensors", + ] + ], + torch_dtype=torch.bfloat16, device="cpu" # You can set torch_dtype=torch.bfloat16 to reduce RAM (not VRAM) usage. +) +model_manager.load_models( + ["models/stepfun-ai/stepvideo-t2v/vae/vae_v2.safetensors"], + torch_dtype=torch.bfloat16, device="cpu" +) +pipe = StepVideoPipeline.from_model_manager(model_manager, torch_dtype=torch.bfloat16, device="cuda") + +# Enable VRAM management +# This model requires 24G VRAM. +# In order to speed up, please set `num_persistent_param_in_dit` to a large number or None (unlimited). +pipe.enable_vram_management(num_persistent_param_in_dit=0) + +# Run! +video = pipe( + prompt="一名宇航员在月球上发现一块石碑，上面印有“stepfun”字样，闪闪发光。超高清、HDR 视频、环境光、杜比全景声、画面稳定、流畅动作、逼真的细节、专业级构图、超现实主义、自然、生动、超细节、清晰。", + negative_prompt="画面暗、低分辨率、不良手、文本、缺少手指、多余的手指、裁剪、低质量、颗粒状、签名、水印、用户名、模糊。", + num_inference_steps=30, cfg_scale=9, num_frames=51, seed=1, + tiled=True, tile_size=(34, 34), tile_stride=(16, 16) +) +save_video( + video, "video.mp4", fps=25, quality=5, + ffmpeg_params=["-vf", "atadenoise=0a=0.1:0b=0.1:1a=0.1:1b=0.1"] +) diff --git a/examples/stepvideo/stepvideo_text_to_video_quantized.py b/examples/stepvideo/stepvideo_text_to_video_quantized.py new file mode 100644 index 0000000000000000000000000000000000000000..7868eb1b5fb7f5a71ea58ee166b39da770d8cf60 --- /dev/null +++ b/examples/stepvideo/stepvideo_text_to_video_quantized.py @@ -0,0 +1,53 @@ +from modelscope import snapshot_download +from diffsynth import ModelManager, StepVideoPipeline, save_video +import torch + + +# Download models +snapshot_download(model_id="stepfun-ai/stepvideo-t2v", cache_dir="models") + +# Load the compiled attention for the LLM text encoder. +# If you encounter errors here. Please select other compiled file that matches your environment or delete this line. +torch.ops.load_library("models/stepfun-ai/stepvideo-t2v/lib/liboptimus_ths-torch2.5-cu124.cpython-310-x86_64-linux-gnu.so") + +# Load models +model_manager = ModelManager() +model_manager.load_models( + ["models/stepfun-ai/stepvideo-t2v/hunyuan_clip/clip_text_encoder/pytorch_model.bin"], + torch_dtype=torch.float32, device="cpu" +) +model_manager.load_models( + [ + "models/stepfun-ai/stepvideo-t2v/step_llm", + [ + "models/stepfun-ai/stepvideo-t2v/transformer/diffusion_pytorch_model-00001-of-00006.safetensors", + "models/stepfun-ai/stepvideo-t2v/transformer/diffusion_pytorch_model-00002-of-00006.safetensors", + "models/stepfun-ai/stepvideo-t2v/transformer/diffusion_pytorch_model-00003-of-00006.safetensors", + "models/stepfun-ai/stepvideo-t2v/transformer/diffusion_pytorch_model-00004-of-00006.safetensors", + "models/stepfun-ai/stepvideo-t2v/transformer/diffusion_pytorch_model-00005-of-00006.safetensors", + "models/stepfun-ai/stepvideo-t2v/transformer/diffusion_pytorch_model-00006-of-00006.safetensors", + ] + ], + torch_dtype=torch.float8_e4m3fn, device="cpu" +) +model_manager.load_models( + ["models/stepfun-ai/stepvideo-t2v/vae/vae_v2.safetensors"], + torch_dtype=torch.bfloat16, device="cpu" +) +pipe = StepVideoPipeline.from_model_manager(model_manager, torch_dtype=torch.bfloat16, device="cuda") + +# Enable VRAM management +# This model requires 40G VRAM. +# In order to reduce VRAM required, please set `num_persistent_param_in_dit` to a small number. +pipe.enable_vram_management(num_persistent_param_in_dit=None) + +# Run! +video = pipe( + prompt="一名宇航员在月球上发现一块石碑，上面印有“stepfun”字样，闪闪发光。超高清、HDR 视频、环境光、杜比全景声、画面稳定、流畅动作、逼真的细节、专业级构图、超现实主义、自然、生动、超细节、清晰。", + negative_prompt="画面暗、低分辨率、不良手、文本、缺少手指、多余的手指、裁剪、低质量、颗粒状、签名、水印、用户名、模糊。", + num_inference_steps=30, cfg_scale=9, num_frames=51, seed=1 +) +save_video( + video, "video.mp4", fps=25, quality=5, + ffmpeg_params=["-vf", "atadenoise=0a=0.1:0b=0.1:1a=0.1:1b=0.1"] +) diff --git a/examples/train/README.md b/examples/train/README.md new file mode 100644 index 0000000000000000000000000000000000000000..fa99312d4518a892b4f6aca1cb206ba3edbd5632 --- /dev/null +++ b/examples/train/README.md @@ -0,0 +1,569 @@ +# DiffSynth Training Framework + +We have implemented a training framework for text-to-image Diffusion models, enabling users to easily train LoRA models using our framework. Our provided scripts come with the following advantages: + +* **Comprehensive Functionality & User-Friendliness**: Our training framework supports multi-GPU and multi-machine setups, facilitates the use of DeepSpeed for acceleration, and includes gradient checkpointing optimizations for models with excessive memory demands. +* **Code Conciseness & Researcher Accessibility**: We avoid large blocks of complicated code. General-purpose modules are implemented in `diffsynth/trainers/text_to_image.py`, while model-specific training scripts contain only minimal code pertinent to the model architecture, making it researcher-friendly. +* **Modular Design & Developer Flexibility**: Built on the universal Pytorch-Lightning framework, our training framework is decoupled in terms of functionality, allowing developers to easily introduce additional training techniques by modifying our scripts to suit their needs. + +Image Examples of fine-tuned LoRA. The prompt is "一只小狗蹦蹦跳跳，周围是姹紫嫣红的鲜花，远处是山脉" (for Chinese models) or "a dog is jumping, flowers around the dog, the background is mountains and clouds" (for English models). + +||FLUX.1-dev|Kolors|Stable Diffusion 3|Hunyuan-DiT| +|-|-|-|-|-| +|Without LoRA|![image_without_lora](https://github.com/user-attachments/assets/df62cef6-d54f-4e3d-a602-5dd290079d49)|![image_without_lora](https://github.com/modelscope/DiffSynth-Studio/assets/35051019/9d79ed7a-e8cf-4d98-800a-f182809db318)|![image_without_lora](https://github.com/modelscope/DiffSynth-Studio/assets/35051019/ddb834a5-6366-412b-93dc-6d957230d66e)|![image_without_lora](https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/1aa21de5-a992-4b66-b14f-caa44e08876e)| +|With LoRA|![image_with_lora](https://github.com/user-attachments/assets/4fd39890-0291-4d19-8a88-d70d0ae18533)|![image_with_lora](https://github.com/modelscope/DiffSynth-Studio/assets/35051019/02f62323-6ee5-4788-97a1-549732dbe4f0)|![image_with_lora](https://github.com/modelscope/DiffSynth-Studio/assets/35051019/8e7b2888-d874-4da4-a75b-11b6b214b9bf)|![image_with_lora](https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/83a0a41a-691f-4610-8e7b-d8e17c50a282)| + +## Install additional packages + +``` +pip install peft lightning pandas +``` + +## Prepare your dataset + +We provide an example dataset [here](https://modelscope.cn/datasets/buptwq/lora-stable-diffusion-finetune/files). You need to manage the training images as follows: + +``` +data/dog/ +└── train + ├── 00.jpg + ├── 01.jpg + ├── 02.jpg + ├── 03.jpg + ├── 04.jpg + └── metadata.csv +``` + +`metadata.csv`: + +``` +file_name,text +00.jpg,a dog +01.jpg,a dog +02.jpg,a dog +03.jpg,a dog +04.jpg,a dog +``` + +Note that if the model is Chinese model (for example, Hunyuan-DiT and Kolors), we recommend to use Chinese texts in the dataset. For example + +``` +file_name,text +00.jpg,一只小狗 +01.jpg,一只小狗 +02.jpg,一只小狗 +03.jpg,一只小狗 +04.jpg,一只小狗 +``` + +## Train a LoRA model + +General options: + +``` + --lora_target_modules LORA_TARGET_MODULES + Layers with LoRA modules. + --dataset_path DATASET_PATH + The path of the Dataset. + --output_path OUTPUT_PATH + Path to save the model. + --steps_per_epoch STEPS_PER_EPOCH + Number of steps per epoch. + --height HEIGHT Image height. + --width WIDTH Image width. + --center_crop Whether to center crop the input images to the resolution. If not set, the images will be randomly cropped. The images will be resized to the resolution first before cropping. + --random_flip Whether to randomly flip images horizontally + --batch_size BATCH_SIZE + Batch size (per device) for the training dataloader. + --dataloader_num_workers DATALOADER_NUM_WORKERS + Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process. + --precision {32,16,16-mixed} + Training precision + --learning_rate LEARNING_RATE + Learning rate. + --lora_rank LORA_RANK + The dimension of the LoRA update matrices. + --lora_alpha LORA_ALPHA + The weight of the LoRA update matrices. + --use_gradient_checkpointing + Whether to use gradient checkpointing. + --accumulate_grad_batches ACCUMULATE_GRAD_BATCHES + The number of batches in gradient accumulation. + --training_strategy {auto,deepspeed_stage_1,deepspeed_stage_2,deepspeed_stage_3} + Training strategy + --max_epochs MAX_EPOCHS + Number of epochs. + --modelscope_model_id MODELSCOPE_MODEL_ID + Model ID on ModelScope (https://www.modelscope.cn/). The model will be uploaded to ModelScope automatically if you provide a Model ID. + --modelscope_access_token MODELSCOPE_ACCESS_TOKEN + Access key on ModelScope (https://www.modelscope.cn/). Required if you want to upload the model to ModelScope. +``` + +### FLUX + +The following files will be used for constructing FLUX. You can download them from [huggingface](https://huggingface.co/black-forest-labs/FLUX.1-dev) or [modelscope](https://www.modelscope.cn/models/ai-modelscope/flux.1-dev). You can use the following code to download these files: + +```python +from diffsynth import download_models + +download_models(["FLUX.1-dev"]) +``` + +``` +models/FLUX/ +└── FLUX.1-dev + ├── ae.safetensors + ├── flux1-dev.safetensors + ├── text_encoder + │ └── model.safetensors + └── text_encoder_2 + ├── config.json + ├── model-00001-of-00002.safetensors + ├── model-00002-of-00002.safetensors + └── model.safetensors.index.json +``` + +Launch the training task using the following command (39G VRAM required): + +``` +CUDA_VISIBLE_DEVICES="0" python examples/train/flux/train_flux_lora.py \ + --pretrained_text_encoder_path models/FLUX/FLUX.1-dev/text_encoder/model.safetensors \ + --pretrained_text_encoder_2_path models/FLUX/FLUX.1-dev/text_encoder_2 \ + --pretrained_dit_path models/FLUX/FLUX.1-dev/flux1-dev.safetensors \ + --pretrained_vae_path models/FLUX/FLUX.1-dev/ae.safetensors \ + --dataset_path data/dog \ + --output_path ./models \ + --max_epochs 1 \ + --steps_per_epoch 100 \ + --height 1024 \ + --width 1024 \ + --center_crop \ + --precision "bf16" \ + --learning_rate 1e-4 \ + --lora_rank 16 \ + --lora_alpha 16 \ + --use_gradient_checkpointing \ + --align_to_opensource_format +``` + +By adding parameter `--quantize "float8_e4m3fn"`, you can save approximate 10G VRAM. + +**`--align_to_opensource_format` means that this script will export the LoRA weights in the opensource format. This format can be loaded in both DiffSynth-Studio and other codebases.** + +For more information about the parameters, please use `python examples/train/flux/train_flux_lora.py -h` to see the details. + +After training, use `model_manager.load_lora` to load the LoRA for inference. + +```python +from diffsynth import ModelManager, FluxImagePipeline +import torch + +model_manager = ModelManager(torch_dtype=torch.bfloat16, device="cuda", + file_path_list=[ + "models/FLUX/FLUX.1-dev/text_encoder/model.safetensors", + "models/FLUX/FLUX.1-dev/text_encoder_2", + "models/FLUX/FLUX.1-dev/ae.safetensors", + "models/FLUX/FLUX.1-dev/flux1-dev.safetensors" + ]) +model_manager.load_lora("models/lightning_logs/version_0/checkpoints/epoch=0-step=500.ckpt", lora_alpha=1.0) +pipe = FluxImagePipeline.from_model_manager(model_manager) + +torch.manual_seed(0) +image = pipe( + prompt="a dog is jumping, flowers around the dog, the background is mountains and clouds", + num_inference_steps=30, embedded_guidance=3.5 +) +image.save("image_with_lora.jpg") +``` + +### Kolors + +The following files will be used for constructing Kolors. You can download Kolors from [huggingface](https://huggingface.co/Kwai-Kolors/Kolors) or [modelscope](https://modelscope.cn/models/Kwai-Kolors/Kolors). Due to precision overflow issues, we need to download an additional VAE model (from [huggingface](https://huggingface.co/madebyollin/sdxl-vae-fp16-fix) or [modelscope](https://modelscope.cn/models/AI-ModelScope/sdxl-vae-fp16-fix)). You can use the following code to download these files: + +```python +from diffsynth import download_models + +download_models(["Kolors", "SDXL-vae-fp16-fix"]) +``` + +``` +models +├── kolors +│ └── Kolors +│ ├── text_encoder +│ │ ├── config.json +│ │ ├── pytorch_model-00001-of-00007.bin +│ │ ├── pytorch_model-00002-of-00007.bin +│ │ ├── pytorch_model-00003-of-00007.bin +│ │ ├── pytorch_model-00004-of-00007.bin +│ │ ├── pytorch_model-00005-of-00007.bin +│ │ ├── pytorch_model-00006-of-00007.bin +│ │ ├── pytorch_model-00007-of-00007.bin +│ │ └── pytorch_model.bin.index.json +│ ├── unet +│ │ └── diffusion_pytorch_model.safetensors +│ └── vae +│ └── diffusion_pytorch_model.safetensors +└── sdxl-vae-fp16-fix + └── diffusion_pytorch_model.safetensors +``` + +Launch the training task using the following command: + +``` +CUDA_VISIBLE_DEVICES="0" python examples/train/kolors/train_kolors_lora.py \ + --pretrained_unet_path models/kolors/Kolors/unet/diffusion_pytorch_model.safetensors \ + --pretrained_text_encoder_path models/kolors/Kolors/text_encoder \ + --pretrained_fp16_vae_path models/sdxl-vae-fp16-fix/diffusion_pytorch_model.safetensors \ + --dataset_path data/dog \ + --output_path ./models \ + --max_epochs 1 \ + --steps_per_epoch 500 \ + --height 1024 \ + --width 1024 \ + --center_crop \ + --precision "16-mixed" \ + --learning_rate 1e-4 \ + --lora_rank 4 \ + --lora_alpha 4 \ + --use_gradient_checkpointing +``` + +For more information about the parameters, please use `python examples/train/kolors/train_kolors_lora.py -h` to see the details. + +After training, use `model_manager.load_lora` to load the LoRA for inference. + +```python +from diffsynth import ModelManager, SDXLImagePipeline +import torch + +model_manager = ModelManager(torch_dtype=torch.float16, device="cuda", + file_path_list=[ + "models/kolors/Kolors/text_encoder", + "models/kolors/Kolors/unet/diffusion_pytorch_model.safetensors", + "models/sdxl-vae-fp16-fix/diffusion_pytorch_model.safetensors" + ]) +model_manager.load_lora("models/lightning_logs/version_0/checkpoints/epoch=0-step=500.ckpt", lora_alpha=1.0) +pipe = SDXLImagePipeline.from_model_manager(model_manager) + +torch.manual_seed(0) +image = pipe( + prompt="一只小狗蹦蹦跳跳，周围是姹紫嫣红的鲜花，远处是山脉", + negative_prompt="", + cfg_scale=7.5, + num_inference_steps=100, width=1024, height=1024, +) +image.save("image_with_lora.jpg") +``` + +### Stable Diffusion 3.5 Series + + +You need to download the text encoders and DiT model files. Please use the following code to download these files: + +```python +from diffsynth import download_models + +download_models(["StableDiffusion3.5-large"]) +``` + +``` +models/stable_diffusion_3 +├── Put Stable Diffusion 3 checkpoints here.txt +├── sd3.5_large.safetensors +└── text_encoders + ├── clip_g.safetensors + ├── clip_l.safetensors + └── t5xxl_fp16.safetensors +``` + +Launch the training task using the following command: + +``` +CUDA_VISIBLE_DEVICES="0" python examples/train/stable_diffusion_3/train_sd3_lora.py \ + --pretrained_path models/stable_diffusion_3/text_encoders/clip_g.safetensors,models/stable_diffusion_3/text_encoders/clip_l.safetensors,models/stable_diffusion_3/text_encoders/t5xxl_fp16.safetensors,models/stable_diffusion_3/sd3.5_large.safetensors \ + --dataset_path data/dog \ + --output_path ./models \ + --max_epochs 1 \ + --steps_per_epoch 500 \ + --height 1024 \ + --width 1024 \ + --center_crop \ + --precision "16" \ + --learning_rate 1e-4 \ + --lora_rank 4 \ + --lora_alpha 4 \ + --use_gradient_checkpointing +``` + +For more information about the parameters, please use `python examples/train/stable_diffusion_3/train_sd3_lora.py -h` to see the details. + +After training, use `model_manager.load_lora` to load the LoRA for inference. + +```python +from diffsynth import ModelManager, SD3ImagePipeline +import torch + +model_manager = ModelManager(torch_dtype=torch.float16, device="cuda", + file_path_list=[ + "models/stable_diffusion_3/text_encoders/clip_g.safetensors", + "models/stable_diffusion_3/text_encoders/clip_l.safetensors", + "models/stable_diffusion_3/text_encoders/t5xxl_fp16.safetensors", + "models/stable_diffusion_3/sd3.5_large.safetensors" + ]) +model_manager.load_lora("models/lightning_logs/version_0/checkpoints/epoch=0-step=500.ckpt", lora_alpha=1.0) +pipe = SD3ImagePipeline.from_model_manager(model_manager) + +torch.manual_seed(0) +image = pipe( + prompt="a dog is jumping, flowers around the dog, the background is mountains and clouds", + num_inference_steps=30, cfg_scale=7 +) +image.save("image_with_lora.jpg") +``` + +### Stable Diffusion 3 + +Only one file is required in the training script. You can use [`sd3_medium_incl_clips.safetensors`](https://huggingface.co/stabilityai/stable-diffusion-3-medium/resolve/main/sd3_medium_incl_clips.safetensors) (without T5 encoder) or [`sd3_medium_incl_clips_t5xxlfp16.safetensors`](https://huggingface.co/stabilityai/stable-diffusion-3-medium/resolve/main/sd3_medium_incl_clips_t5xxlfp16.safetensors) (with T5 encoder). Please use the following code to download these files: + +```python +from diffsynth import download_models + +download_models(["StableDiffusion3", "StableDiffusion3_without_T5"]) +``` + +``` +models/stable_diffusion_3/ +├── Put Stable Diffusion 3 checkpoints here.txt +├── sd3_medium_incl_clips.safetensors +└── sd3_medium_incl_clips_t5xxlfp16.safetensors +``` + +Launch the training task using the following command: + +``` +CUDA_VISIBLE_DEVICES="0" python examples/train/stable_diffusion_3/train_sd3_lora.py \ + --pretrained_path models/stable_diffusion_3/sd3_medium_incl_clips.safetensors \ + --dataset_path data/dog \ + --output_path ./models \ + --max_epochs 1 \ + --steps_per_epoch 500 \ + --height 1024 \ + --width 1024 \ + --center_crop \ + --precision "16" \ + --learning_rate 1e-4 \ + --lora_rank 4 \ + --lora_alpha 4 \ + --use_gradient_checkpointing +``` + +For more information about the parameters, please use `python examples/train/stable_diffusion_3/train_sd3_lora.py -h` to see the details. + +After training, use `model_manager.load_lora` to load the LoRA for inference. + +```python +from diffsynth import ModelManager, SD3ImagePipeline +import torch + +model_manager = ModelManager(torch_dtype=torch.float16, device="cuda", + file_path_list=["models/stable_diffusion_3/sd3_medium_incl_clips.safetensors"]) +model_manager.load_lora("models/lightning_logs/version_0/checkpoints/epoch=0-step=500.ckpt", lora_alpha=1.0) +pipe = SD3ImagePipeline.from_model_manager(model_manager) + +torch.manual_seed(0) +image = pipe( + prompt="a dog is jumping, flowers around the dog, the background is mountains and clouds", + negative_prompt="bad quality, poor quality, doll, disfigured, jpg, toy, bad anatomy, missing limbs, missing fingers, 3d, cgi, extra tails", + cfg_scale=7.5, + num_inference_steps=100, width=1024, height=1024, +) +image.save("image_with_lora.jpg") +``` + +### Hunyuan-DiT + +Four files will be used for constructing Hunyuan DiT. You can download them from [huggingface](https://huggingface.co/Tencent-Hunyuan/HunyuanDiT) or [modelscope](https://www.modelscope.cn/models/modelscope/HunyuanDiT/summary). You can use the following code to download these files: + +```python +from diffsynth import download_models + +download_models(["HunyuanDiT"]) +``` + +``` +models/HunyuanDiT/ +├── Put Hunyuan DiT checkpoints here.txt +└── t2i + ├── clip_text_encoder + │ └── pytorch_model.bin + ├── model + │ └── pytorch_model_ema.pt + ├── mt5 + │ └── pytorch_model.bin + └── sdxl-vae-fp16-fix + └── diffusion_pytorch_model.bin +``` + +Launch the training task using the following command: + +``` +CUDA_VISIBLE_DEVICES="0" python examples/train/hunyuan_dit/train_hunyuan_dit_lora.py \ + --pretrained_path models/HunyuanDiT/t2i \ + --dataset_path data/dog \ + --output_path ./models \ + --max_epochs 1 \ + --steps_per_epoch 500 \ + --height 1024 \ + --width 1024 \ + --center_crop \ + --precision "16-mixed" \ + --learning_rate 1e-4 \ + --lora_rank 4 \ + --lora_alpha 4 \ + --use_gradient_checkpointing +``` + +For more information about the parameters, please use `python examples/train/hunyuan_dit/train_hunyuan_dit_lora.py -h` to see the details. + +After training, use `model_manager.load_lora` to load the LoRA for inference. + +```python +from diffsynth import ModelManager, HunyuanDiTImagePipeline +import torch + +model_manager = ModelManager(torch_dtype=torch.float16, device="cuda", + file_path_list=[ + "models/HunyuanDiT/t2i/clip_text_encoder/pytorch_model.bin", + "models/HunyuanDiT/t2i/model/pytorch_model_ema.pt", + "models/HunyuanDiT/t2i/mt5/pytorch_model.bin", + "models/HunyuanDiT/t2i/sdxl-vae-fp16-fix/diffusion_pytorch_model.bin" + ]) +model_manager.load_lora("models/lightning_logs/version_0/checkpoints/epoch=0-step=500.ckpt", lora_alpha=1.0) +pipe = HunyuanDiTImagePipeline.from_model_manager(model_manager) + +torch.manual_seed(0) +image = pipe( + prompt="一只小狗蹦蹦跳跳，周围是姹紫嫣红的鲜花，远处是山脉", + negative_prompt="", + cfg_scale=7.5, + num_inference_steps=100, width=1024, height=1024, +) +image.save("image_with_lora.jpg") +``` + +### Stable Diffusion + +Only one file is required in the training script. We support the mainstream checkpoints in [CivitAI](https://civitai.com/). By default, we use the base Stable Diffusion v1.5. You can download it from [huggingface](https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.safetensors) or [modelscope](https://www.modelscope.cn/models/AI-ModelScope/stable-diffusion-v1-5/resolve/master/v1-5-pruned-emaonly.safetensors). You can use the following code to download this file: + +```python +from diffsynth import download_models + +download_models(["StableDiffusion_v15"]) +``` + +``` +models/stable_diffusion +├── Put Stable Diffusion checkpoints here.txt +└── v1-5-pruned-emaonly.safetensors +``` + +Launch the training task using the following command: + +``` +CUDA_VISIBLE_DEVICES="0" python examples/train/stable_diffusion/train_sd_lora.py \ + --pretrained_path models/stable_diffusion/v1-5-pruned-emaonly.safetensors \ + --dataset_path data/dog \ + --output_path ./models \ + --max_epochs 1 \ + --steps_per_epoch 500 \ + --height 512 \ + --width 512 \ + --center_crop \ + --precision "16-mixed" \ + --learning_rate 1e-4 \ + --lora_rank 4 \ + --lora_alpha 4 \ + --use_gradient_checkpointing +``` + +For more information about the parameters, please use `python examples/train/stable_diffusion/train_sd_lora.py -h` to see the details. + +After training, use `model_manager.load_lora` to load the LoRA for inference. + +```python +from diffsynth import ModelManager, SDImagePipeline +import torch + +model_manager = ModelManager(torch_dtype=torch.float16, device="cuda", + file_path_list=["models/stable_diffusion/v1-5-pruned-emaonly.safetensors"]) +model_manager.load_lora("models/lightning_logs/version_0/checkpoints/epoch=0-step=500.ckpt", lora_alpha=1.0) +pipe = SDImagePipeline.from_model_manager(model_manager) + +torch.manual_seed(0) +image = pipe( + prompt="a dog is jumping, flowers around the dog, the background is mountains and clouds", + negative_prompt="bad quality, poor quality, doll, disfigured, jpg, toy, bad anatomy, missing limbs, missing fingers, 3d, cgi, extra tails", + cfg_scale=7.5, + num_inference_steps=100, width=512, height=512, +) +image.save("image_with_lora.jpg") +``` + +### Stable Diffusion XL + +Only one file is required in the training script. We support the mainstream checkpoints in [CivitAI](https://civitai.com/). By default, we use the base Stable Diffusion XL. You can download it from [huggingface](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/resolve/main/sd_xl_base_1.0.safetensors) or [modelscope](https://www.modelscope.cn/models/AI-ModelScope/stable-diffusion-xl-base-1.0/resolve/master/sd_xl_base_1.0.safetensors). You can use the following code to download this file: + +```python +from diffsynth import download_models + +download_models(["StableDiffusionXL_v1"]) +``` + +``` +models/stable_diffusion_xl +├── Put Stable Diffusion XL checkpoints here.txt +└── sd_xl_base_1.0.safetensors +``` + +We observed that Stable Diffusion XL is not float16-safe, thus we recommend users to use float32. + +``` +CUDA_VISIBLE_DEVICES="0" python examples/train/stable_diffusion_xl/train_sdxl_lora.py \ + --pretrained_path models/stable_diffusion_xl/sd_xl_base_1.0.safetensors \ + --dataset_path data/dog \ + --output_path ./models \ + --max_epochs 1 \ + --steps_per_epoch 500 \ + --height 1024 \ + --width 1024 \ + --center_crop \ + --precision "32" \ + --learning_rate 1e-4 \ + --lora_rank 4 \ + --lora_alpha 4 \ + --use_gradient_checkpointing +``` + +For more information about the parameters, please use `python examples/train/stable_diffusion_xl/train_sdxl_lora.py -h` to see the details. + +After training, use `model_manager.load_lora` to load the LoRA for inference. + +```python +from diffsynth import ModelManager, SDXLImagePipeline +import torch + +model_manager = ModelManager(torch_dtype=torch.float16, device="cuda", + file_path_list=["models/stable_diffusion_xl/sd_xl_base_1.0.safetensors"]) +model_manager.load_lora("models/lightning_logs/version_0/checkpoints/epoch=0-step=500.ckpt", lora_alpha=1.0) +pipe = SDXLImagePipeline.from_model_manager(model_manager) + +torch.manual_seed(0) +image = pipe( + prompt="a dog is jumping, flowers around the dog, the background is mountains and clouds", + negative_prompt="bad quality, poor quality, doll, disfigured, jpg, toy, bad anatomy, missing limbs, missing fingers, 3d, cgi, extra tails", + cfg_scale=7.5, + num_inference_steps=100, width=1024, height=1024, +) +image.save("image_with_lora.jpg") +``` diff --git a/examples/train/flux/train_flux_lora.py b/examples/train/flux/train_flux_lora.py new file mode 100644 index 0000000000000000000000000000000000000000..eb5539a203441f9e03223dc50e3bc09badfc78c6 --- /dev/null +++ b/examples/train/flux/train_flux_lora.py @@ -0,0 +1,124 @@ +from diffsynth import ModelManager, FluxImagePipeline +from diffsynth.trainers.text_to_image import LightningModelForT2ILoRA, add_general_parsers, launch_training_task +from diffsynth.models.lora import FluxLoRAConverter +import torch, os, argparse +os.environ["TOKENIZERS_PARALLELISM"] = "True" + + +class LightningModel(LightningModelForT2ILoRA): + def __init__( + self, + torch_dtype=torch.float16, pretrained_weights=[], preset_lora_path=None, + learning_rate=1e-4, use_gradient_checkpointing=True, + lora_rank=4, lora_alpha=4, lora_target_modules="to_q,to_k,to_v,to_out", init_lora_weights="kaiming", pretrained_lora_path=None, + state_dict_converter=None, quantize = None + ): + super().__init__(learning_rate=learning_rate, use_gradient_checkpointing=use_gradient_checkpointing, state_dict_converter=state_dict_converter) + # Load models + model_manager = ModelManager(torch_dtype=torch_dtype, device=self.device) + if quantize is None: + model_manager.load_models(pretrained_weights) + else: + model_manager.load_models(pretrained_weights[1:]) + model_manager.load_model(pretrained_weights[0], torch_dtype=quantize) + if preset_lora_path is not None: + preset_lora_path = preset_lora_path.split(",") + for path in preset_lora_path: + model_manager.load_lora(path) + + self.pipe = FluxImagePipeline.from_model_manager(model_manager) + + if quantize is not None: + self.pipe.dit.quantize() + + self.pipe.scheduler.set_timesteps(1000, training=True) + + self.freeze_parameters() + self.add_lora_to_model( + self.pipe.denoising_model(), + lora_rank=lora_rank, + lora_alpha=lora_alpha, + lora_target_modules=lora_target_modules, + init_lora_weights=init_lora_weights, + pretrained_lora_path=pretrained_lora_path, + state_dict_converter=FluxLoRAConverter.align_to_diffsynth_format + ) + + +def parse_args(): + parser = argparse.ArgumentParser(description="Simple example of a training script.") + parser.add_argument( + "--pretrained_text_encoder_path", + type=str, + default=None, + required=True, + help="Path to pretrained text encoder model. For example, `models/FLUX/FLUX.1-dev/text_encoder/model.safetensors`.", + ) + parser.add_argument( + "--pretrained_text_encoder_2_path", + type=str, + default=None, + required=True, + help="Path to pretrained t5 text encoder model. For example, `models/FLUX/FLUX.1-dev/text_encoder_2`.", + ) + parser.add_argument( + "--pretrained_dit_path", + type=str, + default=None, + required=True, + help="Path to pretrained dit model. For example, `models/FLUX/FLUX.1-dev/flux1-dev.safetensors`.", + ) + parser.add_argument( + "--pretrained_vae_path", + type=str, + default=None, + required=True, + help="Path to pretrained vae model. For example, `models/FLUX/FLUX.1-dev/ae.safetensors`.", + ) + parser.add_argument( + "--lora_target_modules", + type=str, + default="a_to_qkv,b_to_qkv,ff_a.0,ff_a.2,ff_b.0,ff_b.2,a_to_out,b_to_out,proj_out,norm.linear,norm1_a.linear,norm1_b.linear,to_qkv_mlp", + help="Layers with LoRA modules.", + ) + parser.add_argument( + "--align_to_opensource_format", + default=False, + action="store_true", + help="Whether to export lora files aligned with other opensource format.", + ) + parser.add_argument( + "--quantize", + type=str, + default=None, + choices=["float8_e4m3fn"], + help="Whether to use quantization when training the model, and in which format.", + ) + parser.add_argument( + "--preset_lora_path", + type=str, + default=None, + help="Preset LoRA path.", + ) + parser = add_general_parsers(parser) + args = parser.parse_args() + return args + + +if __name__ == '__main__': + args = parse_args() + model = LightningModel( + torch_dtype={"32": torch.float32, "bf16": torch.bfloat16}.get(args.precision, torch.float16), + pretrained_weights=[args.pretrained_dit_path, args.pretrained_text_encoder_path, args.pretrained_text_encoder_2_path, args.pretrained_vae_path], + preset_lora_path=args.preset_lora_path, + learning_rate=args.learning_rate, + use_gradient_checkpointing=args.use_gradient_checkpointing, + lora_rank=args.lora_rank, + lora_alpha=args.lora_alpha, + lora_target_modules=args.lora_target_modules, + init_lora_weights=args.init_lora_weights, + pretrained_lora_path=args.pretrained_lora_path, + state_dict_converter=FluxLoRAConverter.align_to_opensource_format if args.align_to_opensource_format else None, + quantize={"float8_e4m3fn": torch.float8_e4m3fn}.get(args.quantize, None), + ) + launch_training_task(model, args) diff --git a/examples/train/hunyuan_dit/train_hunyuan_dit_lora.py b/examples/train/hunyuan_dit/train_hunyuan_dit_lora.py new file mode 100644 index 0000000000000000000000000000000000000000..7764ab5a65d8552c16c805e70b5ddfd2190b1ae3 --- /dev/null +++ b/examples/train/hunyuan_dit/train_hunyuan_dit_lora.py @@ -0,0 +1,70 @@ +from diffsynth import ModelManager, HunyuanDiTImagePipeline +from diffsynth.trainers.text_to_image import LightningModelForT2ILoRA, add_general_parsers, launch_training_task +import torch, os, argparse +os.environ["TOKENIZERS_PARALLELISM"] = "True" + + +class LightningModel(LightningModelForT2ILoRA): + def __init__( + self, + torch_dtype=torch.float16, pretrained_weights=[], + learning_rate=1e-4, use_gradient_checkpointing=True, + lora_rank=4, lora_alpha=4, lora_target_modules="to_q,to_k,to_v,to_out", init_lora_weights="gaussian", pretrained_lora_path=None, + ): + super().__init__(learning_rate=learning_rate, use_gradient_checkpointing=use_gradient_checkpointing) + # Load models + model_manager = ModelManager(torch_dtype=torch_dtype, device=self.device) + model_manager.load_models(pretrained_weights) + self.pipe = HunyuanDiTImagePipeline.from_model_manager(model_manager) + self.pipe.scheduler.set_timesteps(1000) + + self.freeze_parameters() + self.add_lora_to_model( + self.pipe.denoising_model(), + lora_rank=lora_rank, + lora_alpha=lora_alpha, + lora_target_modules=lora_target_modules, + init_lora_weights=init_lora_weights, + pretrained_lora_path=pretrained_lora_path, + ) + + +def parse_args(): + parser = argparse.ArgumentParser(description="Simple example of a training script.") + parser.add_argument( + "--pretrained_path", + type=str, + default=None, + required=True, + help="Path to pretrained model. For example, `./HunyuanDiT/t2i`.", + ) + parser.add_argument( + "--lora_target_modules", + type=str, + default="to_q,to_k,to_v,to_out", + help="Layers with LoRA modules.", + ) + parser = add_general_parsers(parser) + args = parser.parse_args() + return args + + +if __name__ == '__main__': + args = parse_args() + model = LightningModel( + torch_dtype=torch.float32 if args.precision == "32" else torch.float16, + pretrained_weights=[ + os.path.join(args.pretrained_path, "clip_text_encoder/pytorch_model.bin"), + os.path.join(args.pretrained_path, "mt5/pytorch_model.bin"), + os.path.join(args.pretrained_path, "model/pytorch_model_ema.pt"), + os.path.join(args.pretrained_path, "sdxl-vae-fp16-fix/diffusion_pytorch_model.bin"), + ], + learning_rate=args.learning_rate, + use_gradient_checkpointing=args.use_gradient_checkpointing, + lora_rank=args.lora_rank, + lora_alpha=args.lora_alpha, + init_lora_weights=args.init_lora_weights, + pretrained_lora_path=args.pretrained_lora_path, + lora_target_modules=args.lora_target_modules + ) + launch_training_task(model, args) diff --git a/examples/train/kolors/train_kolors_lora.py b/examples/train/kolors/train_kolors_lora.py new file mode 100644 index 0000000000000000000000000000000000000000..48a98929c97098c727ba9d70a68ac40094bcac62 --- /dev/null +++ b/examples/train/kolors/train_kolors_lora.py @@ -0,0 +1,86 @@ +from diffsynth import ModelManager, SDXLImagePipeline +from diffsynth.trainers.text_to_image import LightningModelForT2ILoRA, add_general_parsers, launch_training_task +import torch, os, argparse +os.environ["TOKENIZERS_PARALLELISM"] = "True" + + +class LightningModel(LightningModelForT2ILoRA): + def __init__( + self, + torch_dtype=torch.float16, pretrained_weights=[], + learning_rate=1e-4, use_gradient_checkpointing=True, + lora_rank=4, lora_alpha=4, lora_target_modules="to_q,to_k,to_v,to_out", init_lora_weights="gaussian", pretrained_lora_path=None, + ): + super().__init__(learning_rate=learning_rate, use_gradient_checkpointing=use_gradient_checkpointing) + # Load models + model_manager = ModelManager(torch_dtype=torch_dtype, device=self.device) + model_manager.load_models(pretrained_weights) + self.pipe = SDXLImagePipeline.from_model_manager(model_manager) + self.pipe.scheduler.set_timesteps(1100) + + # Convert the vae encoder to torch.float16 + self.pipe.vae_encoder.to(torch_dtype) + + self.freeze_parameters() + self.add_lora_to_model( + self.pipe.denoising_model(), + lora_rank=lora_rank, + lora_alpha=lora_alpha, + lora_target_modules=lora_target_modules, + init_lora_weights=init_lora_weights, + pretrained_lora_path=pretrained_lora_path, + ) + + +def parse_args(): + parser = argparse.ArgumentParser(description="Simple example of a training script.") + parser.add_argument( + "--pretrained_unet_path", + type=str, + default=None, + required=True, + help="Path to pretrained model (UNet). For example, `models/kolors/Kolors/unet/diffusion_pytorch_model.safetensors`.", + ) + parser.add_argument( + "--pretrained_text_encoder_path", + type=str, + default=None, + required=True, + help="Path to pretrained model (Text Encoder). For example, `models/kolors/Kolors/text_encoder`.", + ) + parser.add_argument( + "--pretrained_fp16_vae_path", + type=str, + default=None, + required=True, + help="Path to pretrained model (VAE). For example, `models/kolors/Kolors/sdxl-vae-fp16-fix/diffusion_pytorch_model.safetensors`.", + ) + parser.add_argument( + "--lora_target_modules", + type=str, + default="to_q,to_k,to_v,to_out", + help="Layers with LoRA modules.", + ) + parser = add_general_parsers(parser) + args = parser.parse_args() + return args + + +if __name__ == '__main__': + args = parse_args() + model = LightningModel( + torch_dtype=torch.float32 if args.precision == "32" else torch.float16, + pretrained_weights=[ + args.pretrained_unet_path, + args.pretrained_text_encoder_path, + args.pretrained_fp16_vae_path, + ], + learning_rate=args.learning_rate, + use_gradient_checkpointing=args.use_gradient_checkpointing, + lora_rank=args.lora_rank, + lora_alpha=args.lora_alpha, + init_lora_weights=args.init_lora_weights, + pretrained_lora_path=args.pretrained_lora_path, + lora_target_modules=args.lora_target_modules + ) + launch_training_task(model, args) diff --git a/examples/train/stable_diffusion/train_sd_lora.py b/examples/train/stable_diffusion/train_sd_lora.py new file mode 100644 index 0000000000000000000000000000000000000000..dc2452017ecf638062d93f48702730efb11cac42 --- /dev/null +++ b/examples/train/stable_diffusion/train_sd_lora.py @@ -0,0 +1,65 @@ +from diffsynth import ModelManager, SDImagePipeline +from diffsynth.trainers.text_to_image import LightningModelForT2ILoRA, add_general_parsers, launch_training_task +import torch, os, argparse +os.environ["TOKENIZERS_PARALLELISM"] = "True" + + +class LightningModel(LightningModelForT2ILoRA): + def __init__( + self, + torch_dtype=torch.float16, pretrained_weights=[], + learning_rate=1e-4, use_gradient_checkpointing=True, + lora_rank=4, lora_alpha=4, lora_target_modules="to_q,to_k,to_v,to_out", init_lora_weights="gaussian", pretrained_lora_path=None, + ): + super().__init__(learning_rate=learning_rate, use_gradient_checkpointing=use_gradient_checkpointing) + # Load models + model_manager = ModelManager(torch_dtype=torch_dtype, device=self.device) + model_manager.load_models(pretrained_weights) + self.pipe = SDImagePipeline.from_model_manager(model_manager) + self.pipe.scheduler.set_timesteps(1000) + + self.freeze_parameters() + self.add_lora_to_model( + self.pipe.denoising_model(), + lora_rank=lora_rank, + lora_alpha=lora_alpha, + lora_target_modules=lora_target_modules, + init_lora_weights=init_lora_weights, + pretrained_lora_path=pretrained_lora_path, + ) + + +def parse_args(): + parser = argparse.ArgumentParser(description="Simple example of a training script.") + parser.add_argument( + "--pretrained_path", + type=str, + default=None, + required=True, + help="Path to pretrained model. For example, `models/stable_diffusion/v1-5-pruned-emaonly.safetensors`.", + ) + parser.add_argument( + "--lora_target_modules", + type=str, + default="to_q,to_k,to_v,to_out", + help="Layers with LoRA modules.", + ) + parser = add_general_parsers(parser) + args = parser.parse_args() + return args + + +if __name__ == '__main__': + args = parse_args() + model = LightningModel( + torch_dtype=torch.float32 if args.precision == "32" else torch.float16, + pretrained_weights=[args.pretrained_path], + learning_rate=args.learning_rate, + use_gradient_checkpointing=args.use_gradient_checkpointing, + lora_rank=args.lora_rank, + lora_alpha=args.lora_alpha, + init_lora_weights=args.init_lora_weights, + pretrained_lora_path=args.pretrained_lora_path, + lora_target_modules=args.lora_target_modules + ) + launch_training_task(model, args) diff --git a/examples/train/stable_diffusion_3/train_sd3_lora.py b/examples/train/stable_diffusion_3/train_sd3_lora.py new file mode 100644 index 0000000000000000000000000000000000000000..dde1c9665f2981ae8c592b4155472b8e5f171069 --- /dev/null +++ b/examples/train/stable_diffusion_3/train_sd3_lora.py @@ -0,0 +1,83 @@ +from diffsynth import ModelManager, SD3ImagePipeline +from diffsynth.trainers.text_to_image import LightningModelForT2ILoRA, add_general_parsers, launch_training_task +import torch, os, argparse +os.environ["TOKENIZERS_PARALLELISM"] = "True" + + +class LightningModel(LightningModelForT2ILoRA): + def __init__( + self, + torch_dtype=torch.float16, pretrained_weights=[], preset_lora_path=None, + learning_rate=1e-4, use_gradient_checkpointing=True, + lora_rank=4, lora_alpha=4, lora_target_modules="to_q,to_k,to_v,to_out", init_lora_weights="gaussian", pretrained_lora_path=None, + ): + super().__init__(learning_rate=learning_rate, use_gradient_checkpointing=use_gradient_checkpointing) + # Load models + model_manager = ModelManager(torch_dtype=torch_dtype, device=self.device) + model_manager.load_models(pretrained_weights) + self.pipe = SD3ImagePipeline.from_model_manager(model_manager) + self.pipe.scheduler.set_timesteps(1000, training=True) + + if preset_lora_path is not None: + preset_lora_path = preset_lora_path.split(",") + for path in preset_lora_path: + model_manager.load_lora(path) + + self.freeze_parameters() + self.add_lora_to_model( + self.pipe.denoising_model(), + lora_rank=lora_rank, + lora_alpha=lora_alpha, + lora_target_modules=lora_target_modules, + init_lora_weights=init_lora_weights, + pretrained_lora_path=pretrained_lora_path, + ) + + +def parse_args(): + parser = argparse.ArgumentParser(description="Simple example of a training script.") + parser.add_argument( + "--pretrained_path", + type=str, + default=None, + required=True, + help="Path to pretrained models, separated by comma. For example, SD3: `models/stable_diffusion_3/sd3_medium_incl_clips_t5xxlfp16.safetensors`, SD3.5-large: `models/stable_diffusion_3/text_encoders/clip_g.safetensors,models/stable_diffusion_3/text_encoders/clip_l.safetensors,models/stable_diffusion_3/text_encoders/t5xxl_fp16.safetensors,models/stable_diffusion_3/sd3.5_large.safetensors`", + ) + parser.add_argument( + "--lora_target_modules", + type=str, + default="a_to_qkv,b_to_qkv,norm_1_a.linear,norm_1_b.linear,a_to_out,b_to_out,ff_a.0,ff_a.2,ff_b.0,ff_b.2", + help="Layers with LoRA modules.", + ) + parser.add_argument( + "--preset_lora_path", + type=str, + default=None, + help="Preset LoRA path.", + ) + parser.add_argument( + "--num_timesteps", + type=int, + default=1000, + help="Number of total timesteps. For turbo models, please set this parameter to the number of expected number of inference steps.", + ) + parser = add_general_parsers(parser) + args = parser.parse_args() + return args + + +if __name__ == '__main__': + args = parse_args() + model = LightningModel( + torch_dtype=torch.float32 if args.precision == "32" else torch.float16, + pretrained_weights=args.pretrained_path.split(","), + preset_lora_path=args.preset_lora_path, + learning_rate=args.learning_rate, + use_gradient_checkpointing=args.use_gradient_checkpointing, + lora_rank=args.lora_rank, + lora_alpha=args.lora_alpha, + init_lora_weights=args.init_lora_weights, + pretrained_lora_path=args.pretrained_lora_path, + lora_target_modules=args.lora_target_modules + ) + launch_training_task(model, args) diff --git a/examples/train/stable_diffusion_xl/train_sdxl_lora.py b/examples/train/stable_diffusion_xl/train_sdxl_lora.py new file mode 100644 index 0000000000000000000000000000000000000000..de0241d25a51d9d92c5f1ec6c543e930e9d97ec8 --- /dev/null +++ b/examples/train/stable_diffusion_xl/train_sdxl_lora.py @@ -0,0 +1,65 @@ +from diffsynth import ModelManager, SDXLImagePipeline +from diffsynth.trainers.text_to_image import LightningModelForT2ILoRA, add_general_parsers, launch_training_task +import torch, os, argparse +os.environ["TOKENIZERS_PARALLELISM"] = "True" + + +class LightningModel(LightningModelForT2ILoRA): + def __init__( + self, + torch_dtype=torch.float16, pretrained_weights=[], + learning_rate=1e-4, use_gradient_checkpointing=True, + lora_rank=4, lora_alpha=4, lora_target_modules="to_q,to_k,to_v,to_out", init_lora_weights="gaussian", pretrained_lora_path=None, + ): + super().__init__(learning_rate=learning_rate, use_gradient_checkpointing=use_gradient_checkpointing) + # Load models + model_manager = ModelManager(torch_dtype=torch_dtype, device=self.device) + model_manager.load_models(pretrained_weights) + self.pipe = SDXLImagePipeline.from_model_manager(model_manager) + self.pipe.scheduler.set_timesteps(1000) + + self.freeze_parameters() + self.add_lora_to_model( + self.pipe.denoising_model(), + lora_rank=lora_rank, + lora_alpha=lora_alpha, + lora_target_modules=lora_target_modules, + init_lora_weights=init_lora_weights, + pretrained_lora_path=pretrained_lora_path, + ) + + +def parse_args(): + parser = argparse.ArgumentParser(description="Simple example of a training script.") + parser.add_argument( + "--pretrained_path", + type=str, + default=None, + required=True, + help="Path to pretrained model. For example, `models/stable_diffusion_xl/sd_xl_base_1.0.safetensors`.", + ) + parser.add_argument( + "--lora_target_modules", + type=str, + default="to_q,to_k,to_v,to_out", + help="Layers with LoRA modules.", + ) + parser = add_general_parsers(parser) + args = parser.parse_args() + return args + + +if __name__ == '__main__': + args = parse_args() + model = LightningModel( + torch_dtype=torch.float32 if args.precision == "32" else torch.float16, + pretrained_weights=[args.pretrained_path], + learning_rate=args.learning_rate, + use_gradient_checkpointing=args.use_gradient_checkpointing, + lora_rank=args.lora_rank, + lora_alpha=args.lora_alpha, + init_lora_weights=args.init_lora_weights, + pretrained_lora_path=args.pretrained_lora_path, + lora_target_modules=args.lora_target_modules + ) + launch_training_task(model, args) diff --git a/examples/video_synthesis/README.md b/examples/video_synthesis/README.md new file mode 100644 index 0000000000000000000000000000000000000000..f6c0715b87d022c3c54ac3543e2c0dab59ef890a --- /dev/null +++ b/examples/video_synthesis/README.md @@ -0,0 +1,7 @@ +# Text to Video + +### Example: Text-to-Video using AnimateDiff + +Generate a video using a Stable Diffusion model and an AnimateDiff model. We can break the limitation of number of frames! See [sd_text_to_video.py](./sd_text_to_video.py). + +https://github.com/Artiprocher/DiffSynth-Studio/assets/35051019/8f556355-4079-4445-9b48-e9da77699437 diff --git a/examples/video_synthesis/sd_text_to_video.py b/examples/video_synthesis/sd_text_to_video.py new file mode 100644 index 0000000000000000000000000000000000000000..ac0fe892f7cfc543532c296d098406d1436bb678 --- /dev/null +++ b/examples/video_synthesis/sd_text_to_video.py @@ -0,0 +1,39 @@ +from diffsynth import ModelManager, SDImagePipeline, SDVideoPipeline, save_video, download_models +import torch + + +# Download models (automatically) +# `models/stable_diffusion/dreamshaper_8.safetensors`: [link](https://civitai.com/api/download/models/128713?type=Model&format=SafeTensor&size=pruned&fp=fp16) +# `models/AnimateDiff/mm_sd_v15_v2.ckpt`: [link](https://huggingface.co/guoyww/animatediff/resolve/main/mm_sd_v15_v2.ckpt) +download_models(["DreamShaper_8", "AnimateDiff_v2"]) + +# Load models +model_manager = ModelManager(torch_dtype=torch.float16, device="cuda") +model_manager.load_models([ + "models/stable_diffusion/dreamshaper_8.safetensors", + "models/AnimateDiff/mm_sd_v15_v2.ckpt", +]) + +# Text -> Image +pipe_image = SDImagePipeline.from_model_manager(model_manager) +torch.manual_seed(0) +image = pipe_image( + prompt = "lightning storm, sea", + negative_prompt = "", + cfg_scale=7.5, + num_inference_steps=30, height=512, width=768, +) + +# Text + Image -> Video (6GB VRAM is enough!) +pipe = SDVideoPipeline.from_model_manager(model_manager) +output_video = pipe( + prompt = "lightning storm, sea", + negative_prompt = "", + cfg_scale=7.5, + num_frames=64, + num_inference_steps=10, height=512, width=768, + animatediff_batch_size=16, animatediff_stride=1, input_frames=[image]*64, denoising_strength=0.9, +) + +# Save images and video +save_video(output_video, "output_video.mp4", fps=30) diff --git a/examples/video_synthesis/sdxl_text_to_video.py b/examples/video_synthesis/sdxl_text_to_video.py new file mode 100644 index 0000000000000000000000000000000000000000..b96498a7554335f8a7bb1c3ec8e2edea1138c287 --- /dev/null +++ b/examples/video_synthesis/sdxl_text_to_video.py @@ -0,0 +1,28 @@ +from diffsynth import ModelManager, SDXLVideoPipeline, save_video, download_models +import torch + + +# Download models (automatically) +# `models/stable_diffusion_xl/sd_xl_base_1.0.safetensors`: [link](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/resolve/main/sd_xl_base_1.0.safetensors) +# `models/AnimateDiff/mm_sdxl_v10_beta.ckpt`: [link](https://huggingface.co/guoyww/animatediff/resolve/main/mm_sdxl_v10_beta.ckpt) +download_models(["StableDiffusionXL_v1", "AnimateDiff_xl_beta"]) + +model_manager = ModelManager(torch_dtype=torch.float16, device="cuda") +model_manager.load_models([ + "models/stable_diffusion_xl/sd_xl_base_1.0.safetensors", + "models/AnimateDiff/mm_sdxl_v10_beta.ckpt" +]) +pipe = SDXLVideoPipeline.from_model_manager(model_manager) + +prompt = "A panda standing on a surfboard in the ocean in sunset, 4k, high resolution.Realistic, Cinematic, high resolution" +negative_prompt = "" + +torch.manual_seed(0) +video = pipe( + prompt=prompt, + negative_prompt=negative_prompt, + cfg_scale=8.5, + height=1024, width=1024, num_frames=16, + num_inference_steps=100, +) +save_video(video, "output_video.mp4", fps=16) diff --git a/examples/video_synthesis/svd_text_to_video.py b/examples/video_synthesis/svd_text_to_video.py new file mode 100644 index 0000000000000000000000000000000000000000..ee91c92069ffb1bbc43c19d69e0bdd0d2ced612c --- /dev/null +++ b/examples/video_synthesis/svd_text_to_video.py @@ -0,0 +1,36 @@ +from diffsynth import save_video, SDXLImagePipeline, ModelManager, SVDVideoPipeline, download_models +from diffsynth import ModelManager +import torch + + +# Download models (automatically) +# `models/stable_diffusion_xl/sd_xl_base_1.0.safetensors`: [link](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/resolve/main/sd_xl_base_1.0.safetensors) +# `models/stable_video_diffusion/svd_xt.safetensors`: [link](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid-xt/resolve/main/svd_xt.safetensors) +download_models(["StableDiffusionXL_v1", "stable-video-diffusion-img2vid-xt"]) + +prompt = "cloud, wind" +torch.manual_seed(0) + +# 1. Text-to-image using SD-XL +model_manager = ModelManager(torch_dtype=torch.float16, device="cuda") +model_manager.load_models(["models/stable_diffusion_xl/sd_xl_base_1.0.safetensors"]) +pipe = SDXLImagePipeline.from_model_manager(model_manager) +image = pipe( + prompt=prompt, + negative_prompt="", + cfg_scale=6, + height=1024, width=1024, num_inference_steps=50, +) +model_manager.to("cpu") + +# 2. Image-to-video using SVD +model_manager = ModelManager() +model_manager.load_models(["models/stable_video_diffusion/svd_xt.safetensors"]) +pipe = SVDVideoPipeline.from_model_manager(model_manager) +video = pipe( + input_image=image, + num_frames=25, fps=15, height=1024, width=1024, + motion_bucket_id=127, + num_inference_steps=50 +) +save_video(video, "output_video.mp4", fps=15) diff --git a/examples/vram_management/README.md b/examples/vram_management/README.md new file mode 100644 index 0000000000000000000000000000000000000000..98f96767eb4bb7cc90a7a1c86ea471734ef5d9cf --- /dev/null +++ b/examples/vram_management/README.md @@ -0,0 +1,3 @@ +# VRAM Management + +Experimental feature. Still under development. diff --git a/examples/vram_management/flux_text_to_image.py b/examples/vram_management/flux_text_to_image.py new file mode 100644 index 0000000000000000000000000000000000000000..ea241060bb7288d329666a7981584e263d3fe030 --- /dev/null +++ b/examples/vram_management/flux_text_to_image.py @@ -0,0 +1,25 @@ +import torch +from diffsynth import ModelManager, FluxImagePipeline + + +model_manager = ModelManager( + file_path_list=[ + "models/FLUX/FLUX.1-dev/text_encoder/model.safetensors", + "models/FLUX/FLUX.1-dev/text_encoder_2", + "models/FLUX/FLUX.1-dev/flux1-dev.safetensors", + "models/FLUX/FLUX.1-dev/ae.safetensors", + ], + torch_dtype=torch.float8_e4m3fn, + device="cpu" +) +pipe = FluxImagePipeline.from_model_manager(model_manager, torch_dtype=torch.bfloat16, device="cuda") + +# Enable VRAM management +# `num_persistent_param_in_dit` indicates the number of parameters that reside persistently in VRAM within the DiT model. +# When `num_persistent_param_in_dit=None`, it means all parameters reside persistently in memory. +# When `num_persistent_param_in_dit=7*10**9`, it indicates that 7 billion parameters reside persistently in memory. +# When `num_persistent_param_in_dit=0`, it means no parameters reside persistently in memory, and they are loaded layer by layer during inference. +pipe.enable_vram_management(num_persistent_param_in_dit=None) + +image = pipe(prompt="a beautiful orange cat", seed=0) +image.save("image.jpg") diff --git a/examples/wanvideo/README.md b/examples/wanvideo/README.md new file mode 100644 index 0000000000000000000000000000000000000000..dfd4c5e6be8dba8ca0fdc5d6e0dc0f26e3c8cfdf --- /dev/null +++ b/examples/wanvideo/README.md @@ -0,0 +1,470 @@ +# Wan + +[切换到中文](./README_zh.md) + +Wan is a collection of video synthesis models open-sourced by Alibaba. + +**DiffSynth-Studio has adopted a new inference and training framework. To use the previous version, please click [here](https://github.com/modelscope/DiffSynth-Studio/tree/3edf3583b1f08944cee837b94d9f84d669c2729c).** + +## Installation + +Before using this model, please install DiffSynth-Studio from **source code**. + +```shell +git clone https://github.com/modelscope/DiffSynth-Studio.git +cd DiffSynth-Studio +pip install -e . +``` + +## Quick Start + +You can quickly load the [Wan-AI/Wan2.1-T2V-1.3B](https://www.modelscope.cn/models/Wan-AI/Wan2.1-T2V-1.3B) model and run inference by executing the code below. + +```python +import torch +from diffsynth import save_video +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"), + ], +) +pipe.enable_vram_management() + +video = pipe( + prompt="纪实摄影风格画面，一只活泼的小狗在绿茵茵的草地上迅速奔跑。小狗毛色棕黄，两只耳朵立起，神情专注而欢快。阳光洒在它身上，使得毛发看上去格外柔软而闪亮。背景是一片开阔的草地，偶尔点缀着几朵野花，远处隐约可见蓝天和几片白云。透视感鲜明，捕捉小狗奔跑时的动感和四周草地的生机。中景侧面移动视角。", + negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + seed=0, tiled=True, +) +save_video(video, "video1.mp4", fps=15, quality=5) +``` + +## Overview + +| Model ID | Extra Parameters | Inference | Full Training | Full Training Validation | LoRA Training | LoRA Training Validation | +|-|-|-|-|-|-|-| +|[Wan-AI/Wan2.2-Animate-14B](https://www.modelscope.cn/models/Wan-AI/Wan2.2-Animate-14B)|`input_image`, `animate_pose_video`, `animate_face_video`, `animate_inpaint_video`, `animate_mask_video`|[code](./model_inference/Wan2.2-Animate-14B.py)|[code](./model_training/full/Wan2.2-Animate-14B.sh)|[code](./model_training/validate_full/Wan2.2-Animate-14B.py)|[code](./model_training/lora/Wan2.2-Animate-14B.sh)|[code](./model_training/validate_lora/Wan2.2-Animate-14B.py)| +|[Wan-AI/Wan2.2-S2V-14B](https://www.modelscope.cn/models/Wan-AI/Wan2.2-S2V-14B)|`input_image`, `input_audio`, `audio_sample_rate`, `s2v_pose_video`|[code](./model_inference/Wan2.2-S2V-14B_multi_clips.py)|[code](./model_training/full/Wan2.2-S2V-14B.sh)|[code](./model_training/validate_full/Wan2.2-S2V-14B.py)|[code](./model_training/lora/Wan2.2-S2V-14B.sh)|[code](./model_training/validate_lora/Wan2.2-S2V-14B.py)| +|[Wan-AI/Wan2.2-I2V-A14B](https://modelscope.cn/models/Wan-AI/Wan2.2-I2V-A14B)|`input_image`|[code](./model_inference/Wan2.2-I2V-A14B.py)|[code](./model_training/full/Wan2.2-I2V-A14B.sh)|[code](./model_training/validate_full/Wan2.2-I2V-A14B.py)|[code](./model_training/lora/Wan2.2-I2V-A14B.sh)|[code](./model_training/validate_lora/Wan2.2-I2V-A14B.py)| +|[Wan-AI/Wan2.2-T2V-A14B](https://modelscope.cn/models/Wan-AI/Wan2.2-T2V-A14B)||[code](./model_inference/Wan2.2-T2V-A14B.py)|[code](./model_training/full/Wan2.2-T2V-A14B.sh)|[code](./model_training/validate_full/Wan2.2-T2V-A14B.py)|[code](./model_training/lora/Wan2.2-T2V-A14B.sh)|[code](./model_training/validate_lora/Wan2.2-T2V-A14B.py)| +|[Wan-AI/Wan2.2-TI2V-5B](https://modelscope.cn/models/Wan-AI/Wan2.2-TI2V-5B)|`input_image`|[code](./model_inference/Wan2.2-TI2V-5B.py)|[code](./model_training/full/Wan2.2-TI2V-5B.sh)|[code](./model_training/validate_full/Wan2.2-TI2V-5B.py)|[code](./model_training/lora/Wan2.2-TI2V-5B.sh)|[code](./model_training/validate_lora/Wan2.2-TI2V-5B.py)| +|[Wan-AI/Wan2.2-VACE-Fun-A14B](https://www.modelscope.cn/models/PAI/Wan2.2-VACE-Fun-A14B)|`vace_control_video`, `vace_reference_image`|[code](./model_inference/Wan2.2-VACE-Fun-A14B.py)|[code](./model_training/full/Wan2.2-VACE-Fun-A14B.sh)|[code](./model_training/validate_full/Wan2.2-VACE-Fun-A14B.py)|[code](./model_training/lora/Wan2.2-VACE-Fun-A14B.sh)|[code](./model_training/validate_lora/Wan2.2-VACE-Fun-A14B.py)| +|[PAI/Wan2.2-Fun-A14B-InP](https://modelscope.cn/models/PAI/Wan2.2-Fun-A14B-InP)|`input_image`, `end_image`|[code](./model_inference/Wan2.2-Fun-A14B-InP.py)|[code](./model_training/full/Wan2.2-Fun-A14B-InP.sh)|[code](./model_training/validate_full/Wan2.2-Fun-A14B-InP.py)|[code](./model_training/lora/Wan2.2-Fun-A14B-InP.sh)|[code](./model_training/validate_lora/Wan2.2-Fun-A14B-InP.py)| +|[PAI/Wan2.2-Fun-A14B-Control](https://modelscope.cn/models/PAI/Wan2.2-Fun-A14B-Control)|`control_video`, `reference_image`|[code](./model_inference/Wan2.2-Fun-A14B-Control.py)|[code](./model_training/full/Wan2.2-Fun-A14B-Control.sh)|[code](./model_training/validate_full/Wan2.2-Fun-A14B-Control.py)|[code](./model_training/lora/Wan2.2-Fun-A14B-Control.sh)|[code](./model_training/validate_lora/Wan2.2-Fun-A14B-Control.py)| +|[PAI/Wan2.2-Fun-A14B-Control-Camera](https://modelscope.cn/models/PAI/Wan2.2-Fun-A14B-Control-Camera)|`control_camera_video`, `input_image`|[code](./model_inference/Wan2.2-Fun-A14B-Control-Camera.py)|[code](./model_training/full/Wan2.2-Fun-A14B-Control-Camera.sh)|[code](./model_training/validate_full/Wan2.2-Fun-A14B-Control-Camera.py)|[code](./model_training/lora/Wan2.2-Fun-A14B-Control-Camera.sh)|[code](./model_training/validate_lora/Wan2.2-Fun-A14B-Control-Camera.py)| +|[Wan-AI/Wan2.1-T2V-1.3B](https://modelscope.cn/models/Wan-AI/Wan2.1-T2V-1.3B)||[code](./model_inference/Wan2.1-T2V-1.3B.py)|[code](./model_training/full/Wan2.1-T2V-1.3B.sh)|[code](./model_training/validate_full/Wan2.1-T2V-1.3B.py)|[code](./model_training/lora/Wan2.1-T2V-1.3B.sh)|[code](./model_training/validate_lora/Wan2.1-T2V-1.3B.py)| +|[Wan-AI/Wan2.1-T2V-14B](https://modelscope.cn/models/Wan-AI/Wan2.1-T2V-14B)||[code](./model_inference/Wan2.1-T2V-14B.py)|[code](./model_training/full/Wan2.1-T2V-14B.sh)|[code](./model_training/validate_full/Wan2.1-T2V-14B.py)|[code](./model_training/lora/Wan2.1-T2V-14B.sh)|[code](./model_training/validate_lora/Wan2.1-T2V-14B.py)| +|[Wan-AI/Wan2.1-I2V-14B-480P](https://modelscope.cn/models/Wan-AI/Wan2.1-I2V-14B-480P)|`input_image`|[code](./model_inference/Wan2.1-I2V-14B-480P.py)|[code](./model_training/full/Wan2.1-I2V-14B-480P.sh)|[code](./model_training/validate_full/Wan2.1-I2V-14B-480P.py)|[code](./model_training/lora/Wan2.1-I2V-14B-480P.sh)|[code](./model_training/validate_lora/Wan2.1-I2V-14B-480P.py)| +|[Wan-AI/Wan2.1-I2V-14B-720P](https://modelscope.cn/models/Wan-AI/Wan2.1-I2V-14B-720P)|`input_image`|[code](./model_inference/Wan2.1-I2V-14B-720P.py)|[code](./model_training/full/Wan2.1-I2V-14B-720P.sh)|[code](./model_training/validate_full/Wan2.1-I2V-14B-720P.py)|[code](./model_training/lora/Wan2.1-I2V-14B-720P.sh)|[code](./model_training/validate_lora/Wan2.1-I2V-14B-720P.py)| +|[Wan-AI/Wan2.1-FLF2V-14B-720P](https://modelscope.cn/models/Wan-AI/Wan2.1-FLF2V-14B-720P)|`input_image`, `end_image`|[code](./model_inference/Wan2.1-FLF2V-14B-720P.py)|[code](./model_training/full/Wan2.1-FLF2V-14B-720P.sh)|[code](./model_training/validate_full/Wan2.1-FLF2V-14B-720P.py)|[code](./model_training/lora/Wan2.1-FLF2V-14B-720P.sh)|[code](./model_training/validate_lora/Wan2.1-FLF2V-14B-720P.py)| +|[PAI/Wan2.1-Fun-1.3B-InP](https://modelscope.cn/models/PAI/Wan2.1-Fun-1.3B-InP)|`input_image`, `end_image`|[code](./model_inference/Wan2.1-Fun-1.3B-InP.py)|[code](./model_training/full/Wan2.1-Fun-1.3B-InP.sh)|[code](./model_training/validate_full/Wan2.1-Fun-1.3B-InP.py)|[code](./model_training/lora/Wan2.1-Fun-1.3B-InP.sh)|[code](./model_training/validate_lora/Wan2.1-Fun-1.3B-InP.py)| +|[PAI/Wan2.1-Fun-1.3B-Control](https://modelscope.cn/models/PAI/Wan2.1-Fun-1.3B-Control)|`control_video`|[code](./model_inference/Wan2.1-Fun-1.3B-Control.py)|[code](./model_training/full/Wan2.1-Fun-1.3B-Control.sh)|[code](./model_training/validate_full/Wan2.1-Fun-1.3B-Control.py)|[code](./model_training/lora/Wan2.1-Fun-1.3B-Control.sh)|[code](./model_training/validate_lora/Wan2.1-Fun-1.3B-Control.py)| +|[PAI/Wan2.1-Fun-14B-InP](https://modelscope.cn/models/PAI/Wan2.1-Fun-14B-InP)|`input_image`, `end_image`|[code](./model_inference/Wan2.1-Fun-14B-InP.py)|[code](./model_training/full/Wan2.1-Fun-14B-InP.sh)|[code](./model_training/validate_full/Wan2.1-Fun-14B-InP.py)|[code](./model_training/lora/Wan2.1-Fun-14B-InP.sh)|[code](./model_training/validate_lora/Wan2.1-Fun-14B-InP.py)| +|[PAI/Wan2.1-Fun-14B-Control](https://modelscope.cn/models/PAI/Wan2.1-Fun-14B-Control)|`control_video`|[code](./model_inference/Wan2.1-Fun-14B-Control.py)|[code](./model_training/full/Wan2.1-Fun-14B-Control.sh)|[code](./model_training/validate_full/Wan2.1-Fun-14B-Control.py)|[code](./model_training/lora/Wan2.1-Fun-14B-Control.sh)|[code](./model_training/validate_lora/Wan2.1-Fun-14B-Control.py)| +|[PAI/Wan2.1-Fun-V1.1-1.3B-Control](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-1.3B-Control)|`control_video`, `reference_image`|[code](./model_inference/Wan2.1-Fun-V1.1-1.3B-Control.py)|[code](./model_training/full/Wan2.1-Fun-V1.1-1.3B-Control.sh)|[code](./model_training/validate_full/Wan2.1-Fun-V1.1-1.3B-Control.py)|[code](./model_training/lora/Wan2.1-Fun-V1.1-1.3B-Control.sh)|[code](./model_training/validate_lora/Wan2.1-Fun-V1.1-1.3B-Control.py)| +|[PAI/Wan2.1-Fun-V1.1-14B-Control](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-14B-Control)|`control_video`, `reference_image`|[code](./model_inference/Wan2.1-Fun-V1.1-14B-Control.py)|[code](./model_training/full/Wan2.1-Fun-V1.1-14B-Control.sh)|[code](./model_training/validate_full/Wan2.1-Fun-V1.1-14B-Control.py)|[code](./model_training/lora/Wan2.1-Fun-V1.1-14B-Control.sh)|[code](./model_training/validate_lora/Wan2.1-Fun-V1.1-14B-Control.py)| +|[PAI/Wan2.1-Fun-V1.1-1.3B-InP](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-1.3B-InP)|`input_image`, `end_image`|[code](./model_inference/Wan2.1-Fun-V1.1-1.3B-InP.py)|[code](./model_training/full/Wan2.1-Fun-V1.1-1.3B-InP.sh)|[code](./model_training/validate_full/Wan2.1-Fun-V1.1-1.3B-InP.py)|[code](./model_training/lora/Wan2.1-Fun-V1.1-1.3B-InP.sh)|[code](./model_training/validate_lora/Wan2.1-Fun-V1.1-1.3B-InP.py)| +|[PAI/Wan2.1-Fun-V1.1-14B-InP](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-14B-InP)|`input_image`, `end_image`|[code](./model_inference/Wan2.1-Fun-V1.1-14B-InP.py)|[code](./model_training/full/Wan2.1-Fun-V1.1-14B-InP.sh)|[code](./model_training/validate_full/Wan2.1-Fun-V1.1-14B-InP.py)|[code](./model_training/lora/Wan2.1-Fun-V1.1-14B-InP.sh)|[code](./model_training/validate_lora/Wan2.1-Fun-V1.1-14B-InP.py)| +|[PAI/Wan2.1-Fun-V1.1-1.3B-Control-Camera](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-1.3B-Control-Camera)|`control_camera_video`, `input_image`|[code](./model_inference/Wan2.1-Fun-V1.1-1.3B-Control-Camera.py)|[code](./model_training/full/Wan2.1-Fun-V1.1-1.3B-Control-Camera.sh)|[code](./model_training/validate_full/Wan2.1-Fun-V1.1-1.3B-Control-Camera.py)|[code](./model_training/lora/Wan2.1-Fun-V1.1-1.3B-Control-Camera.sh)|[code](./model_training/validate_lora/Wan2.1-Fun-V1.1-1.3B-Control-Camera.py)| +|[PAI/Wan2.1-Fun-V1.1-14B-Control-Camera](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-14B-Control-Camera)|`control_camera_video`, `input_image`|[code](./model_inference/Wan2.1-Fun-V1.1-14B-Control-Camera.py)|[code](./model_training/full/Wan2.1-Fun-V1.1-14B-Control-Camera.sh)|[code](./model_training/validate_full/Wan2.1-Fun-V1.1-14B-Control-Camera.py)|[code](./model_training/lora/Wan2.1-Fun-V1.1-14B-Control-Camera.sh)|[code](./model_training/validate_lora/Wan2.1-Fun-V1.1-14B-Control-Camera.py)| +|[iic/VACE-Wan2.1-1.3B-Preview](https://modelscope.cn/models/iic/VACE-Wan2.1-1.3B-Preview)|`vace_control_video`, `vace_reference_image`|[code](./model_inference/Wan2.1-VACE-1.3B-Preview.py)|[code](./model_training/full/Wan2.1-VACE-1.3B-Preview.sh)|[code](./model_training/validate_full/Wan2.1-VACE-1.3B-Preview.py)|[code](./model_training/lora/Wan2.1-VACE-1.3B-Preview.sh)|[code](./model_training/validate_lora/Wan2.1-VACE-1.3B-Preview.py)| +|[Wan-AI/Wan2.1-VACE-1.3B](https://modelscope.cn/models/Wan-AI/Wan2.1-VACE-1.3B)|`vace_control_video`, `vace_reference_image`|[code](./model_inference/Wan2.1-VACE-1.3B.py)|[code](./model_training/full/Wan2.1-VACE-1.3B.sh)|[code](./model_training/validate_full/Wan2.1-VACE-1.3B.py)|[code](./model_training/lora/Wan2.1-VACE-1.3B.sh)|[code](./model_training/validate_lora/Wan2.1-VACE-1.3B.py)| +|[Wan-AI/Wan2.1-VACE-14B](https://modelscope.cn/models/Wan-AI/Wan2.1-VACE-14B)|`vace_control_video`, `vace_reference_image`|[code](./model_inference/Wan2.1-VACE-14B.py)|[code](./model_training/full/Wan2.1-VACE-14B.sh)|[code](./model_training/validate_full/Wan2.1-VACE-14B.py)|[code](./model_training/lora/Wan2.1-VACE-14B.sh)|[code](./model_training/validate_lora/Wan2.1-VACE-14B.py)| +|[DiffSynth-Studio/Wan2.1-1.3b-speedcontrol-v1](https://modelscope.cn/models/DiffSynth-Studio/Wan2.1-1.3b-speedcontrol-v1)|`motion_bucket_id`|[code](./model_inference/Wan2.1-1.3b-speedcontrol-v1.py)|[code](./model_training/full/Wan2.1-1.3b-speedcontrol-v1.sh)|[code](./model_training/validate_full/Wan2.1-1.3b-speedcontrol-v1.py)|[code](./model_training/lora/Wan2.1-1.3b-speedcontrol-v1.sh)|[code](./model_training/validate_lora/Wan2.1-1.3b-speedcontrol-v1.py)| +|[krea/krea-realtime-video](https://www.modelscope.cn/models/krea/krea-realtime-video)||[code](./model_inference/krea-realtime-video.py)|[code](./model_training/full/krea-realtime-video.sh)|[code](./model_training/validate_full/krea-realtime-video.py)|[code](./model_training/lora/krea-realtime-video.sh)|[code](./model_training/validate_lora/krea-realtime-video.py)| +|[meituan-longcat/LongCat-Video](https://www.modelscope.cn/models/meituan-longcat/LongCat-Video)|`longcat_video`|[code](./model_inference/LongCat-Video.py)|[code](./model_training/full/LongCat-Video.sh)|[code](./model_training/validate_full/LongCat-Video.py)|[code](./model_training/lora/LongCat-Video.sh)|[code](./model_training/validate_lora/LongCat-Video.py)| +|[ByteDance/Video-As-Prompt-Wan2.1-14B](https://modelscope.cn/models/ByteDance/Video-As-Prompt-Wan2.1-14B)|`vap_video`, `vap_prompt`|[code](./model_inference/Video-As-Prompt-Wan2.1-14B.py)|[code](./model_training/full/Video-As-Prompt-Wan2.1-14B.sh)|[code](./model_training/validate_full/Video-As-Prompt-Wan2.1-14B.py)|[code](./model_training/lora/Video-As-Prompt-Wan2.1-14B.sh)|[code](./model_training/validate_lora/Video-As-Prompt-Wan2.1-14B.py)| + +## Model Inference + +The following sections will help you understand our functionalities and write inference code. + +
+ +Loading the Model + +The model is loaded using `from_pretrained`: + +```python +import torch +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="Wan2.1_VAE.pth"), + ], +) +``` + +Here, `torch_dtype` and `device` specify the computation precision and device respectively. The `model_configs` can be used to configure model paths in various ways: + +* Downloading the model from [ModelScope](https://modelscope.cn/) and loading it. In this case, both `model_id` and `origin_file_pattern` need to be specified, for example: + +```python +ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="diffusion_pytorch_model*.safetensors") +``` + +* Loading the model from a local file path. In this case, the `path` parameter needs to be specified, for example: + +```python +ModelConfig(path="models/Wan-AI/Wan2.1-T2V-1.3B/diffusion_pytorch_model.safetensors") +``` + +For models that are loaded from multiple files, simply use a list, for example: + +```python +ModelConfig(path=[ + "models/Wan-AI/Wan2.1-T2V-14B/diffusion_pytorch_model-00001-of-00006.safetensors", + "models/Wan-AI/Wan2.1-T2V-14B/diffusion_pytorch_model-00002-of-00006.safetensors", + "models/Wan-AI/Wan2.1-T2V-14B/diffusion_pytorch_model-00003-of-00006.safetensors", + "models/Wan-AI/Wan2.1-T2V-14B/diffusion_pytorch_model-00004-of-00006.safetensors", + "models/Wan-AI/Wan2.1-T2V-14B/diffusion_pytorch_model-00005-of-00006.safetensors", + "models/Wan-AI/Wan2.1-T2V-14B/diffusion_pytorch_model-00006-of-00006.safetensors", +]) +``` + +The `ModelConfig` function provides additional parameters to control the behavior during model loading: + +* `local_model_path`: Path where downloaded models are saved. Default value is `"./models"`. +* `skip_download`: Whether to skip downloading models. Default value is `False`. When your network cannot access [ModelScope](https://modelscope.cn/), manually download the necessary files and set this to `True`. + +The `from_pretrained` function provides additional parameters to control the behavior during model loading: + +* `tokenizer_config`: Path to the tokenizer of the Wan model. Default value is `ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="google/*")`. +* `redirect_common_files`: Whether to redirect duplicate model files. Default value is `True`. Since the Wan series models include multiple base models, some modules like text encoder are shared across these models. To avoid redundant downloads, we redirect the model paths. +* `use_usp`: Whether to enable Unified Sequence Parallel. Default value is `False`. Used for multi-GPU parallel inference. + +
+ +
+ +VRAM Management + +DiffSynth-Studio provides fine-grained VRAM management for the Wan model, allowing it to run on devices with limited VRAM. You can enable offloading functionality via the following code, which moves parts of the model to system memory on devices with limited VRAM: + +```python +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"), + ], +) +pipe.enable_vram_management() +``` + +FP8 quantization is also supported: + +```python +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="Wan2.1_VAE.pth", offload_dtype=torch.float8_e4m3fn), + ], +) +pipe.enable_vram_management() +``` + +Both FP8 quantization and offloading can be enabled simultaneously: + +```python +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ], +) +pipe.enable_vram_management() +``` + +FP8 quantization significantly reduces VRAM usage but does not accelerate computations. Some models may experience issues such as blurry, torn, or distorted outputs due to insufficient precision when using FP8 quantization. Use FP8 quantization with caution. + +After enabling VRAM management, the framework will automatically decide the VRAM strategy based on available GPU memory. The `enable_vram_management` function has the following parameters to manually control the VRAM strategy: + +* `vram_limit`: VRAM usage limit in GB. By default, it uses all free VRAM on the device. Note that this is not an absolute limit. If the set VRAM is not enough but more VRAM is actually available, the model will run with minimal VRAM usage. Setting it to 0 achieves the theoretical minimum VRAM usage. +* `vram_buffer`: VRAM buffer size in GB. Default is 0.5GB. A buffer is needed because larger neural network layers may use more VRAM than expected during loading. The optimal value is the VRAM used by the largest layer in the model. +* `num_persistent_param_in_dit`: Number of parameters in the DiT model that stay in VRAM. Default is no limit. We plan to remove this parameter in the future. Do not rely on it. + +
+ +
+ +Inference Acceleration + +Wan supports multiple acceleration techniques, including: + +* **Efficient attention implementations**: If any of these attention implementations are installed in your Python environment, they will be automatically enabled in the following priority: + * [Flash Attention 3](https://github.com/Dao-AILab/flash-attention) + * [Flash Attention 2](https://github.com/Dao-AILab/flash-attention) + * [Sage Attention](https://github.com/thu-ml/SageAttention) + * [torch SDPA](https://pytorch.org/docs/stable/generated/torch.nn.functional.scaled_dot_product_attention.html) (default setting; we recommend installing `torch>=2.5.0`) +* **Unified Sequence Parallel**: Sequence parallelism based on [xDiT](https://github.com/xdit-project/xDiT). Please refer to [this example](./acceleration/unified_sequence_parallel.py), and run it using the command: + +```shell +pip install "xfuser[flash-attn]>=0.4.3" +torchrun --standalone --nproc_per_node=8 examples/wanvideo/acceleration/unified_sequence_parallel.py +``` + +* **TeaCache**: Acceleration technique [TeaCache](https://github.com/ali-vilab/TeaCache). Please refer to [this example](./acceleration/teacache.py). + +
+ + +
+ +Input Parameters + +The pipeline accepts the following input parameters during inference: + +* `prompt`: Prompt describing the content to appear in the video. +* `negative_prompt`: Negative prompt describing content that should not appear in the video. Default is `""`. +* `input_image`: Input image, applicable for image-to-video models such as [`Wan-AI/Wan2.1-I2V-14B-480P`](https://modelscope.cn/models/Wan-AI/Wan2.1-I2V-14B-480P) and [`PAI/Wan2.1-Fun-1.3B-InP`](https://modelscope.cn/models/PAI/Wan2.1-Fun-1.3B-InP), as well as first-and-last-frame models like [`Wan-AI/Wan2.1-FLF2V-14B-720P`](Wan-AI/Wan2.1-FLF2V-14B-720P). +* `end_image`: End frame, applicable for first-and-last-frame models such as [`Wan-AI/Wan2.1-FLF2V-14B-720P`](Wan-AI/Wan2.1-FLF2V-14B-720P). +* `input_video`: Input video used for video-to-video generation. Applicable to any Wan series model and must be used together with `denoising_strength`. +* `denoising_strength`: Denoising strength in range [0, 1]. A smaller value results in a video closer to `input_video`. +* `control_video`: Control video, applicable to Wan models with control capabilities such as [`PAI/Wan2.1-Fun-1.3B-Control`](https://modelscope.cn/models/PAI/Wan2.1-Fun-1.3B-Control). +* `reference_image`: Reference image, applicable to Wan models supporting reference images such as [`PAI/Wan2.1-Fun-V1.1-1.3B-Control`](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-1.3B-Control). +* `camera_control_direction`: Camera control direction, optional values are "Left", "Right", "Up", "Down", "LeftUp", "LeftDown", "RightUp", "RightDown". Applicable to Camera-Control models, such as [PAI/Wan2.1-Fun-V1.1-14B-Control-Camera](https://www.modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-14B-Control-Camera). +* `camera_control_speed`: Camera control speed. Applicable to Camera-Control models, such as [PAI/Wan2.1-Fun-V1.1-14B-Control-Camera](https://www.modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-14B-Control-Camera). +* `camera_control_origin`: Origin coordinate of the camera control sequence. Please refer to the [original paper](https://arxiv.org/pdf/2404.02101) for proper configuration. Applicable to Camera-Control models, such as [PAI/Wan2.1-Fun-V1.1-14B-Control-Camera](https://www.modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-14B-Control-Camera). +* `vace_video`: Input video for VACE models, applicable to the VACE series such as [`iic/VACE-Wan2.1-1.3B-Preview`](https://modelscope.cn/models/iic/VACE-Wan2.1-1.3B-Preview). +* `vace_video_mask`: Mask video for VACE models, applicable to the VACE series such as [`iic/VACE-Wan2.1-1.3B-Preview`](https://modelscope.cn/models/iic/VACE-Wan2.1-1.3B-Preview). +* `vace_reference_image`: Reference image for VACE models, applicable to the VACE series such as [`iic/VACE-Wan2.1-1.3B-Preview`](https://modelscope.cn/models/iic/VACE-Wan2.1-1.3B-Preview). +* `vace_scale`: Influence of the VACE model on the base model, default is 1. Higher values increase control strength but may lead to visual artifacts or breakdowns. +* `seed`: Random seed. Default is `None`, meaning fully random. +* `rand_device`: Device used to generate random Gaussian noise matrix. Default is `"cpu"`. When set to `"cuda"`, different GPUs may produce different generation results. +* `height`: Frame height, default is 480. Must be a multiple of 16; if not, it will be rounded up. +* `width`: Frame width, default is 832. Must be a multiple of 16; if not, it will be rounded up. +* `num_frames`: Number of frames, default is 81. Must be a multiple of 4 plus 1; if not, it will be rounded up, minimum is 1. +* `cfg_scale`: Classifier-free guidance scale, default is 5. Higher values increase adherence to the prompt but may cause visual artifacts. +* `cfg_merge`: Whether to merge both sides of classifier-free guidance for unified inference. Default is `False`. This parameter currently only works for basic text-to-video and image-to-video models. +* `switch_DiT_boundary`: The time point for switching between DiT models. Default value is 0.875. This parameter only takes effect for mixed models with multiple DiTs, for example, [Wan-AI/Wan2.2-I2V-A14B](https://modelscope.cn/models/Wan-AI/Wan2.2-I2V-A14B). +* `num_inference_steps`: Number of inference steps, default is 50. +* `sigma_shift`: Parameter from Rectified Flow theory, default is 5. Higher values make the model stay longer at the initial denoising stage. Increasing this may improve video quality but may also cause inconsistency between generated videos and training data due to deviation from training behavior. +* `motion_bucket_id`: Motion intensity, range [0, 100], applicable to motion control modules such as [`DiffSynth-Studio/Wan2.1-1.3b-speedcontrol-v1`](https://modelscope.cn/models/DiffSynth-Studio/Wan2.1-1.3b-speedcontrol-v1). Larger values indicate more intense motion. +* `tiled`: Whether to enable tiled VAE inference, default is `False`. Setting to `True` significantly reduces VRAM usage during VAE encoding/decoding but introduces small errors and slightly increases inference time. +* `tile_size`: Tile size during VAE encoding/decoding, default is (30, 52), only effective when `tiled=True`. +* `tile_stride`: Stride of tiles during VAE encoding/decoding, default is (15, 26), only effective when `tiled=True`. Must be less than or equal to `tile_size`. +* `sliding_window_size`: Sliding window size for DiT part. Experimental feature, effects are unstable. +* `sliding_window_stride`: Sliding window stride for DiT part. Experimental feature, effects are unstable. +* `tea_cache_l1_thresh`: Threshold for TeaCache. Larger values result in faster speed but lower quality. Note that after enabling TeaCache, the inference speed is not uniform, so the remaining time shown on the progress bar becomes inaccurate. +* `tea_cache_model_id`: TeaCache parameter template, options include `"Wan2.1-T2V-1.3B"`, `"Wan2.1-T2V-14B"`, `"Wan2.1-I2V-14B-480P"`, `"Wan2.1-I2V-14B-720P"`. +* `progress_bar_cmd`: Progress bar implementation, default is `tqdm.tqdm`. You can set it to `lambda x:x` to disable the progress bar. + +
+ +## Model Training + +Wan series models are trained using a unified script located at [`./model_training/train.py`](./model_training/train.py). + +
+ +Script Parameters + +The script includes the following parameters: + +* Dataset + * `--dataset_base_path`: Base path of the dataset. + * `--dataset_metadata_path`: Path to the metadata file of the dataset. + * `--height`: Height of images or videos. Leave `height` and `width` empty to enable dynamic resolution. + * `--width`: Width of images or videos. Leave `height` and `width` empty to enable dynamic resolution. + * `--num_frames`: Number of frames per video. Frames are sampled from the video prefix. + * `--data_file_keys`: Data file keys in the metadata. Comma-separated. + * `--dataset_repeat`: Number of times to repeat the dataset per epoch. + * `--dataset_num_workers`: Number of workers for data loading. +* Models + * `--model_paths`: Paths to load models. In JSON format. + * `--model_id_with_origin_paths`: Model ID with origin paths, e.g., Wan-AI/Wan2.1-T2V-1.3B:diffusion_pytorch_model*.safetensors. Comma-separated. + * `--max_timestep_boundary`: Maximum value of the timestep interval, ranging from 0 to 1. Default is 1. This needs to be manually set only when training mixed models with multiple DiTs, for example, [Wan-AI/Wan2.2-I2V-A14B](https://modelscope.cn/models/Wan-AI/Wan2.2-I2V-A14B). + * `--min_timestep_boundary`: Minimum value of the timestep interval, ranging from 0 to 1. Default is 1. This needs to be manually set only when training mixed models with multiple DiTs, for example, [Wan-AI/Wan2.2-I2V-A14B](https://modelscope.cn/models/Wan-AI/Wan2.2-I2V-A14B). +* Training + * `--learning_rate`: Learning rate. + * `--weight_decay`: Weight decay. + * `--num_epochs`: Number of epochs. + * `--output_path`: Output save path. + * `--remove_prefix_in_ckpt`: Remove prefix in ckpt. + * `--save_steps`: Number of checkpoint saving invervals. If None, checkpoints will be saved every epoch. + * `--find_unused_parameters`: Whether to find unused parameters in DDP. +* Trainable Modules + * `--trainable_models`: Models to train, e.g., dit, vae, text_encoder. + * `--lora_base_model`: Which model LoRA is added to. + * `--lora_target_modules`: Which layers LoRA is added to. + * `--lora_rank`: Rank of LoRA. + * `--lora_checkpoint`: Path to the LoRA checkpoint. If provided, LoRA will be loaded from this checkpoint. +* Extra Inputs + * `--extra_inputs`: Additional model inputs, comma-separated. +* VRAM Management + * `--use_gradient_checkpointing_offload`: Whether to offload gradient checkpointing to CPU memory. + +Additionally, the training framework is built upon [`accelerate`](https://huggingface.co/docs/accelerate/index). Before starting training, run `accelerate config` to configure GPU-related parameters. For certain training scripts (e.g., full fine-tuning of 14B models), we provide recommended `accelerate` configuration files, which can be found in the corresponding training scripts. + +
+ + +
+ +Step 1: Prepare the Dataset + +The dataset consists of a series of files. We recommend organizing your dataset as follows: + +``` +data/example_video_dataset/ +├── metadata.csv +├── video1.mp4 +└── video2.mp4 +``` + +Here, `video1.mp4` and `video2.mp4` are training video files, and `metadata.csv` is the metadata list, for example: + +``` +video,prompt +video1.mp4,"from sunset to night, a small town, light, house, river" +video2.mp4,"a dog is running" +``` + +We have prepared a sample video dataset to help you test. You can download it using the following command: + +```shell +modelscope download --dataset DiffSynth-Studio/example_video_dataset --local_dir ./data/example_video_dataset +``` + +The dataset supports mixed training of videos and images. Supported video formats include `"mp4", "avi", "mov", "wmv", "mkv", "flv", "webm"`, and supported image formats include `"jpg", "jpeg", "png", "webp"`. + +The resolution of videos can be controlled via script parameters `--height`, `--width`, and `--num_frames`. For each video, the first `num_frames` frames will be used for training; therefore, an error will occur if the video length is less than `num_frames`. Image files will be treated as single-frame videos. When both `--height` and `--width` are left empty, dynamic resolution will be enabled, meaning training will use the actual resolution of each video or image in the dataset. + +**We strongly recommend using fixed-resolution training and avoiding mixing images and videos in the same dataset due to load balancing issues in multi-GPU training.** + +When the model requires additional inputs, such as the `control_video` needed by control-capable models like [`PAI/Wan2.1-Fun-1.3B-Control`](https://modelscope.cn/models/PAI/Wan2.1-Fun-1.3B-Control), please add corresponding columns in the metadata file, for example: + +``` +video,prompt,control_video +video1.mp4,"from sunset to night, a small town, light, house, river",video1_softedge.mp4 +``` + +If additional inputs contain video or image files, their column names need to be specified in the `--data_file_keys` parameter. The default value of this parameter is `"image,video"`, meaning it parses columns named `image` and `video`. You can extend this list based on the additional input requirements, for example: `--data_file_keys "image,video,control_video"`, and also enable `--input_contains_control_video`. + +
+ + +
+ +Step 2: Load the Model + +Similar to the model loading logic during inference, you can configure the model to be loaded directly via its model ID. For instance, during inference we load the model using: + +```python +model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-14B", origin_file_pattern="diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-14B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-14B", origin_file_pattern="Wan2.1_VAE.pth"), +] +``` + +During training, simply use the following parameter to load the corresponding model: + +```shell +--model_id_with_origin_paths "Wan-AI/Wan2.1-T2V-1.3B:diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.1-T2V-1.3B:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.1-T2V-1.3B:Wan2.1_VAE.pth" +``` + +If you want to load the model from local files, for example during inference: + +```python +model_configs=[ + ModelConfig(path=[ + "models/Wan-AI/Wan2.1-T2V-14B/diffusion_pytorch_model-00001-of-00006.safetensors", + "models/Wan-AI/Wan2.1-T2V-14B/diffusion_pytorch_model-00002-of-00006.safetensors", + "models/Wan-AI/Wan2.1-T2V-14B/diffusion_pytorch_model-00003-of-00006.safetensors", + "models/Wan-AI/Wan2.1-T2V-14B/diffusion_pytorch_model-00004-of-00006.safetensors", + "models/Wan-AI/Wan2.1-T2V-14B/diffusion_pytorch_model-00005-of-00006.safetensors", + "models/Wan-AI/Wan2.1-T2V-14B/diffusion_pytorch_model-00006-of-00006.safetensors", + ]), + ModelConfig(path="models/Wan-AI/Wan2.1-T2V-14B/models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(path="models/Wan-AI/Wan2.1-T2V-14B/Wan2.1_VAE.pth"), +] +``` + +Then during training, set the parameter as: + +```shell +--model_paths '[ + [ + "models/Wan-AI/Wan2.1-T2V-14B/diffusion_pytorch_model-00001-of-00006.safetensors", + "models/Wan-AI/Wan2.1-T2V-14B/diffusion_pytorch_model-00002-of-00006.safetensors", + "models/Wan-AI/Wan2.1-T2V-14B/diffusion_pytorch_model-00003-of-00006.safetensors", + "models/Wan-AI/Wan2.1-T2V-14B/diffusion_pytorch_model-00004-of-00006.safetensors", + "models/Wan-AI/Wan2.1-T2V-14B/diffusion_pytorch_model-00005-of-00006.safetensors", + "models/Wan-AI/Wan2.1-T2V-14B/diffusion_pytorch_model-00006-of-00006.safetensors" + ], + "models/Wan-AI/Wan2.1-T2V-14B/models_t5_umt5-xxl-enc-bf16.pth", + "models/Wan-AI/Wan2.1-T2V-14B/Wan2.1_VAE.pth" +]' \ +``` + +
+ + +
+ +Step 3: Configure Trainable Modules + +The training framework supports full fine-tuning of base models or LoRA-based training. Here are some examples: + +* Full fine-tuning of the DiT module: `--trainable_models dit` +* Training a LoRA model for the DiT module: `--lora_base_model dit --lora_target_modules "q,k,v,o,ffn.0,ffn.2" --lora_rank 32` +* Training both a LoRA model for DiT and the Motion Controller (yes, you can train such advanced structures): `--trainable_models motion_controller --lora_base_model dit --lora_target_modules "q,k,v,o,ffn.0,ffn.2" --lora_rank 32` + +Additionally, since multiple modules (text encoder, dit, vae) are loaded in the training script, you need to remove prefixes when saving model files. For example, when fully fine-tuning the DiT module or training a LoRA version of DiT, please set `--remove_prefix_in_ckpt pipe.dit.` + +
+ + +
+ +Step 4: Launch the Training Process + +We have prepared training commands for each model. Please refer to the table at the beginning of this document. + +Note that full fine-tuning of the 14B model requires 8 GPUs, each with at least 80GB VRAM. During full fine-tuning of these 14B models, you must install `deepspeed` (`pip install deepspeed`). We have provided recommended [configuration files](./model_training/full/accelerate_config_14B.yaml), which will be loaded automatically in the corresponding training scripts. These scripts have been tested on 8*A100. + +The default video resolution in the training script is `480*832*81`. Increasing the resolution may cause out-of-memory errors. To reduce VRAM usage, add the parameter `--use_gradient_checkpointing_offload`. + +
+ +## Gallery + +1.3B text-to-video: + +https://github.com/user-attachments/assets/124397be-cd6a-4f29-a87c-e4c695aaabb8 + +Put sunglasses on the dog (1.3B video-to-video): + +https://github.com/user-attachments/assets/272808d7-fbeb-4747-a6df-14a0860c75fb + +14B text-to-video: + +https://github.com/user-attachments/assets/3908bc64-d451-485a-8b61-28f6d32dd92f + +14B image-to-video: + +https://github.com/user-attachments/assets/c0bdd5ca-292f-45ed-b9bc-afe193156e75 + +LoRA training: + +https://github.com/user-attachments/assets/9bd8e30b-97e8-44f9-bb6f-da004ba376a9 diff --git a/examples/wanvideo/README_zh.md b/examples/wanvideo/README_zh.md new file mode 100644 index 0000000000000000000000000000000000000000..fe8e6f2bb5d80318a3b25ccc4f3b80f54c43d62b --- /dev/null +++ b/examples/wanvideo/README_zh.md @@ -0,0 +1,473 @@ +# 通义万相（Wan） + +[Switch to English](./README.md) + +Wan 是由阿里巴巴通义实验室开源的一系列视频生成模型。 + +**DiffSynth-Studio 启用了新的推理和训练框架，如需使用旧版本，请点击[这里](https://github.com/modelscope/DiffSynth-Studio/tree/3edf3583b1f08944cee837b94d9f84d669c2729c)。** + +## 安装 + +在使用本系列模型之前，请通过源码安装 DiffSynth-Studio。 + +```shell +git clone https://github.com/modelscope/DiffSynth-Studio.git +cd DiffSynth-Studio +pip install -e . +``` + +## 快速开始 + +通过运行以下代码可以快速加载 [Wan-AI/Wan2.1-T2V-1.3B](https://www.modelscope.cn/models/Wan-AI/Wan2.1-T2V-1.3B) 模型并进行推理 + +```python +import torch +from diffsynth import save_video +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"), + ], +) +pipe.enable_vram_management() + +video = pipe( + prompt="纪实摄影风格画面，一只活泼的小狗在绿茵茵的草地上迅速奔跑。小狗毛色棕黄，两只耳朵立起，神情专注而欢快。阳光洒在它身上，使得毛发看上去格外柔软而闪亮。背景是一片开阔的草地，偶尔点缀着几朵野花，远处隐约可见蓝天和几片白云。透视感鲜明，捕捉小狗奔跑时的动感和四周草地的生机。中景侧面移动视角。", + negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + seed=0, tiled=True, +) +save_video(video, "video1.mp4", fps=15, quality=5) +``` + +## 模型总览 + +|模型 ID|额外参数|推理|全量训练|全量训练后验证|LoRA 训练|LoRA 训练后验证| +|-|-|-|-|-|-|-| +|[Wan-AI/Wan2.2-Animate-14B](https://www.modelscope.cn/models/Wan-AI/Wan2.2-Animate-14B)|`input_image`, `animate_pose_video`, `animate_face_video`, `animate_inpaint_video`, `animate_mask_video`|[code](./model_inference/Wan2.2-Animate-14B.py)|[code](./model_training/full/Wan2.2-Animate-14B.sh)|[code](./model_training/validate_full/Wan2.2-Animate-14B.py)|[code](./model_training/lora/Wan2.2-Animate-14B.sh)|[code](./model_training/validate_lora/Wan2.2-Animate-14B.py)| +|[Wan-AI/Wan2.2-S2V-14B](https://www.modelscope.cn/models/Wan-AI/Wan2.2-S2V-14B)|`input_image`, `input_audio`, `audio_sample_rate`, `s2v_pose_video`|[code](./model_inference/Wan2.2-S2V-14B_multi_clips.py)|[code](./model_training/full/Wan2.2-S2V-14B.sh)|[code](./model_training/validate_full/Wan2.2-S2V-14B.py)|[code](./model_training/lora/Wan2.2-S2V-14B.sh)|[code](./model_training/validate_lora/Wan2.2-S2V-14B.py)| +|[Wan-AI/Wan2.2-I2V-A14B](https://modelscope.cn/models/Wan-AI/Wan2.2-I2V-A14B)|`input_image`|[code](./model_inference/Wan2.2-I2V-A14B.py)|[code](./model_training/full/Wan2.2-I2V-A14B.sh)|[code](./model_training/validate_full/Wan2.2-I2V-A14B.py)|[code](./model_training/lora/Wan2.2-I2V-A14B.sh)|[code](./model_training/validate_lora/Wan2.2-I2V-A14B.py)| +|[Wan-AI/Wan2.2-T2V-A14B](https://modelscope.cn/models/Wan-AI/Wan2.2-T2V-A14B)||[code](./model_inference/Wan2.2-T2V-A14B.py)|[code](./model_training/full/Wan2.2-T2V-A14B.sh)|[code](./model_training/validate_full/Wan2.2-T2V-A14B.py)|[code](./model_training/lora/Wan2.2-T2V-A14B.sh)|[code](./model_training/validate_lora/Wan2.2-T2V-A14B.py)| +|[Wan-AI/Wan2.2-TI2V-5B](https://modelscope.cn/models/Wan-AI/Wan2.2-TI2V-5B)|`input_image`|[code](./model_inference/Wan2.2-TI2V-5B.py)|[code](./model_training/full/Wan2.2-TI2V-5B.sh)|[code](./model_training/validate_full/Wan2.2-TI2V-5B.py)|[code](./model_training/lora/Wan2.2-TI2V-5B.sh)|[code](./model_training/validate_lora/Wan2.2-TI2V-5B.py)| +|[Wan-AI/Wan2.2-VACE-Fun-A14B](https://www.modelscope.cn/models/PAI/Wan2.2-VACE-Fun-A14B)|`vace_control_video`, `vace_reference_image`|[code](./model_inference/Wan2.2-VACE-Fun-A14B.py)|[code](./model_training/full/Wan2.2-VACE-Fun-A14B.sh)|[code](./model_training/validate_full/Wan2.2-VACE-Fun-A14B.py)|[code](./model_training/lora/Wan2.2-VACE-Fun-A14B.sh)|[code](./model_training/validate_lora/Wan2.2-VACE-Fun-A14B.py)| +|[PAI/Wan2.2-Fun-A14B-InP](https://modelscope.cn/models/PAI/Wan2.2-Fun-A14B-InP)|`input_image`, `end_image`|[code](./model_inference/Wan2.2-Fun-A14B-InP.py)|[code](./model_training/full/Wan2.2-Fun-A14B-InP.sh)|[code](./model_training/validate_full/Wan2.2-Fun-A14B-InP.py)|[code](./model_training/lora/Wan2.2-Fun-A14B-InP.sh)|[code](./model_training/validate_lora/Wan2.2-Fun-A14B-InP.py)| +|[PAI/Wan2.2-Fun-A14B-Control](https://modelscope.cn/models/PAI/Wan2.2-Fun-A14B-Control)|`control_video`, `reference_image`|[code](./model_inference/Wan2.2-Fun-A14B-Control.py)|[code](./model_training/full/Wan2.2-Fun-A14B-Control.sh)|[code](./model_training/validate_full/Wan2.2-Fun-A14B-Control.py)|[code](./model_training/lora/Wan2.2-Fun-A14B-Control.sh)|[code](./model_training/validate_lora/Wan2.2-Fun-A14B-Control.py)| +|[PAI/Wan2.2-Fun-A14B-Control-Camera](https://modelscope.cn/models/PAI/Wan2.2-Fun-A14B-Control-Camera)|`control_camera_video`, `input_image`|[code](./model_inference/Wan2.2-Fun-A14B-Control-Camera.py)|[code](./model_training/full/Wan2.2-Fun-A14B-Control-Camera.sh)|[code](./model_training/validate_full/Wan2.2-Fun-A14B-Control-Camera.py)|[code](./model_training/lora/Wan2.2-Fun-A14B-Control-Camera.sh)|[code](./model_training/validate_lora/Wan2.2-Fun-A14B-Control-Camera.py)| +|[Wan-AI/Wan2.1-T2V-1.3B](https://modelscope.cn/models/Wan-AI/Wan2.1-T2V-1.3B)||[code](./model_inference/Wan2.1-T2V-1.3B.py)|[code](./model_training/full/Wan2.1-T2V-1.3B.sh)|[code](./model_training/validate_full/Wan2.1-T2V-1.3B.py)|[code](./model_training/lora/Wan2.1-T2V-1.3B.sh)|[code](./model_training/validate_lora/Wan2.1-T2V-1.3B.py)| +|[Wan-AI/Wan2.1-T2V-14B](https://modelscope.cn/models/Wan-AI/Wan2.1-T2V-14B)||[code](./model_inference/Wan2.1-T2V-14B.py)|[code](./model_training/full/Wan2.1-T2V-14B.sh)|[code](./model_training/validate_full/Wan2.1-T2V-14B.py)|[code](./model_training/lora/Wan2.1-T2V-14B.sh)|[code](./model_training/validate_lora/Wan2.1-T2V-14B.py)| +|[Wan-AI/Wan2.1-I2V-14B-480P](https://modelscope.cn/models/Wan-AI/Wan2.1-I2V-14B-480P)|`input_image`|[code](./model_inference/Wan2.1-I2V-14B-480P.py)|[code](./model_training/full/Wan2.1-I2V-14B-480P.sh)|[code](./model_training/validate_full/Wan2.1-I2V-14B-480P.py)|[code](./model_training/lora/Wan2.1-I2V-14B-480P.sh)|[code](./model_training/validate_lora/Wan2.1-I2V-14B-480P.py)| +|[Wan-AI/Wan2.1-I2V-14B-720P](https://modelscope.cn/models/Wan-AI/Wan2.1-I2V-14B-720P)|`input_image`|[code](./model_inference/Wan2.1-I2V-14B-720P.py)|[code](./model_training/full/Wan2.1-I2V-14B-720P.sh)|[code](./model_training/validate_full/Wan2.1-I2V-14B-720P.py)|[code](./model_training/lora/Wan2.1-I2V-14B-720P.sh)|[code](./model_training/validate_lora/Wan2.1-I2V-14B-720P.py)| +|[Wan-AI/Wan2.1-FLF2V-14B-720P](https://modelscope.cn/models/Wan-AI/Wan2.1-FLF2V-14B-720P)|`input_image`, `end_image`|[code](./model_inference/Wan2.1-FLF2V-14B-720P.py)|[code](./model_training/full/Wan2.1-FLF2V-14B-720P.sh)|[code](./model_training/validate_full/Wan2.1-FLF2V-14B-720P.py)|[code](./model_training/lora/Wan2.1-FLF2V-14B-720P.sh)|[code](./model_training/validate_lora/Wan2.1-FLF2V-14B-720P.py)| +|[PAI/Wan2.1-Fun-1.3B-InP](https://modelscope.cn/models/PAI/Wan2.1-Fun-1.3B-InP)|`input_image`, `end_image`|[code](./model_inference/Wan2.1-Fun-1.3B-InP.py)|[code](./model_training/full/Wan2.1-Fun-1.3B-InP.sh)|[code](./model_training/validate_full/Wan2.1-Fun-1.3B-InP.py)|[code](./model_training/lora/Wan2.1-Fun-1.3B-InP.sh)|[code](./model_training/validate_lora/Wan2.1-Fun-1.3B-InP.py)| +|[PAI/Wan2.1-Fun-1.3B-Control](https://modelscope.cn/models/PAI/Wan2.1-Fun-1.3B-Control)|`control_video`|[code](./model_inference/Wan2.1-Fun-1.3B-Control.py)|[code](./model_training/full/Wan2.1-Fun-1.3B-Control.sh)|[code](./model_training/validate_full/Wan2.1-Fun-1.3B-Control.py)|[code](./model_training/lora/Wan2.1-Fun-1.3B-Control.sh)|[code](./model_training/validate_lora/Wan2.1-Fun-1.3B-Control.py)| +|[PAI/Wan2.1-Fun-14B-InP](https://modelscope.cn/models/PAI/Wan2.1-Fun-14B-InP)|`input_image`, `end_image`|[code](./model_inference/Wan2.1-Fun-14B-InP.py)|[code](./model_training/full/Wan2.1-Fun-14B-InP.sh)|[code](./model_training/validate_full/Wan2.1-Fun-14B-InP.py)|[code](./model_training/lora/Wan2.1-Fun-14B-InP.sh)|[code](./model_training/validate_lora/Wan2.1-Fun-14B-InP.py)| +|[PAI/Wan2.1-Fun-14B-Control](https://modelscope.cn/models/PAI/Wan2.1-Fun-14B-Control)|`control_video`|[code](./model_inference/Wan2.1-Fun-14B-Control.py)|[code](./model_training/full/Wan2.1-Fun-14B-Control.sh)|[code](./model_training/validate_full/Wan2.1-Fun-14B-Control.py)|[code](./model_training/lora/Wan2.1-Fun-14B-Control.sh)|[code](./model_training/validate_lora/Wan2.1-Fun-14B-Control.py)| +|[PAI/Wan2.1-Fun-V1.1-1.3B-Control](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-1.3B-Control)|`control_video`, `reference_image`|[code](./model_inference/Wan2.1-Fun-V1.1-1.3B-Control.py)|[code](./model_training/full/Wan2.1-Fun-V1.1-1.3B-Control.sh)|[code](./model_training/validate_full/Wan2.1-Fun-V1.1-1.3B-Control.py)|[code](./model_training/lora/Wan2.1-Fun-V1.1-1.3B-Control.sh)|[code](./model_training/validate_lora/Wan2.1-Fun-V1.1-1.3B-Control.py)| +|[PAI/Wan2.1-Fun-V1.1-14B-Control](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-14B-Control)|`control_video`, `reference_image`|[code](./model_inference/Wan2.1-Fun-V1.1-14B-Control.py)|[code](./model_training/full/Wan2.1-Fun-V1.1-14B-Control.sh)|[code](./model_training/validate_full/Wan2.1-Fun-V1.1-14B-Control.py)|[code](./model_training/lora/Wan2.1-Fun-V1.1-14B-Control.sh)|[code](./model_training/validate_lora/Wan2.1-Fun-V1.1-14B-Control.py)| +|[PAI/Wan2.1-Fun-V1.1-1.3B-InP](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-1.3B-InP)|`input_image`, `end_image`|[code](./model_inference/Wan2.1-Fun-V1.1-1.3B-InP.py)|[code](./model_training/full/Wan2.1-Fun-V1.1-1.3B-InP.sh)|[code](./model_training/validate_full/Wan2.1-Fun-V1.1-1.3B-InP.py)|[code](./model_training/lora/Wan2.1-Fun-V1.1-1.3B-InP.sh)|[code](./model_training/validate_lora/Wan2.1-Fun-V1.1-1.3B-InP.py)| +|[PAI/Wan2.1-Fun-V1.1-14B-InP](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-14B-InP)|`input_image`, `end_image`|[code](./model_inference/Wan2.1-Fun-V1.1-14B-InP.py)|[code](./model_training/full/Wan2.1-Fun-V1.1-14B-InP.sh)|[code](./model_training/validate_full/Wan2.1-Fun-V1.1-14B-InP.py)|[code](./model_training/lora/Wan2.1-Fun-V1.1-14B-InP.sh)|[code](./model_training/validate_lora/Wan2.1-Fun-V1.1-14B-InP.py)| +|[PAI/Wan2.1-Fun-V1.1-1.3B-Control-Camera](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-1.3B-Control-Camera)|`control_camera_video`, `input_image`|[code](./model_inference/Wan2.1-Fun-V1.1-1.3B-Control-Camera.py)|[code](./model_training/full/Wan2.1-Fun-V1.1-1.3B-Control-Camera.sh)|[code](./model_training/validate_full/Wan2.1-Fun-V1.1-1.3B-Control-Camera.py)|[code](./model_training/lora/Wan2.1-Fun-V1.1-1.3B-Control-Camera.sh)|[code](./model_training/validate_lora/Wan2.1-Fun-V1.1-1.3B-Control-Camera.py)| +|[PAI/Wan2.1-Fun-V1.1-14B-Control-Camera](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-14B-Control-Camera)|`control_camera_video`, `input_image`|[code](./model_inference/Wan2.1-Fun-V1.1-14B-Control-Camera.py)|[code](./model_training/full/Wan2.1-Fun-V1.1-14B-Control-Camera.sh)|[code](./model_training/validate_full/Wan2.1-Fun-V1.1-14B-Control-Camera.py)|[code](./model_training/lora/Wan2.1-Fun-V1.1-14B-Control-Camera.sh)|[code](./model_training/validate_lora/Wan2.1-Fun-V1.1-14B-Control-Camera.py)| +|[iic/VACE-Wan2.1-1.3B-Preview](https://modelscope.cn/models/iic/VACE-Wan2.1-1.3B-Preview)|`vace_control_video`, `vace_reference_image`|[code](./model_inference/Wan2.1-VACE-1.3B-Preview.py)|[code](./model_training/full/Wan2.1-VACE-1.3B-Preview.sh)|[code](./model_training/validate_full/Wan2.1-VACE-1.3B-Preview.py)|[code](./model_training/lora/Wan2.1-VACE-1.3B-Preview.sh)|[code](./model_training/validate_lora/Wan2.1-VACE-1.3B-Preview.py)| +|[Wan-AI/Wan2.1-VACE-1.3B](https://modelscope.cn/models/Wan-AI/Wan2.1-VACE-1.3B)|`vace_control_video`, `vace_reference_image`|[code](./model_inference/Wan2.1-VACE-1.3B.py)|[code](./model_training/full/Wan2.1-VACE-1.3B.sh)|[code](./model_training/validate_full/Wan2.1-VACE-1.3B.py)|[code](./model_training/lora/Wan2.1-VACE-1.3B.sh)|[code](./model_training/validate_lora/Wan2.1-VACE-1.3B.py)| +|[Wan-AI/Wan2.1-VACE-14B](https://modelscope.cn/models/Wan-AI/Wan2.1-VACE-14B)|`vace_control_video`, `vace_reference_image`|[code](./model_inference/Wan2.1-VACE-14B.py)|[code](./model_training/full/Wan2.1-VACE-14B.sh)|[code](./model_training/validate_full/Wan2.1-VACE-14B.py)|[code](./model_training/lora/Wan2.1-VACE-14B.sh)|[code](./model_training/validate_lora/Wan2.1-VACE-14B.py)| +|[DiffSynth-Studio/Wan2.1-1.3b-speedcontrol-v1](https://modelscope.cn/models/DiffSynth-Studio/Wan2.1-1.3b-speedcontrol-v1)|`motion_bucket_id`|[code](./model_inference/Wan2.1-1.3b-speedcontrol-v1.py)|[code](./model_training/full/Wan2.1-1.3b-speedcontrol-v1.sh)|[code](./model_training/validate_full/Wan2.1-1.3b-speedcontrol-v1.py)|[code](./model_training/lora/Wan2.1-1.3b-speedcontrol-v1.sh)|[code](./model_training/validate_lora/Wan2.1-1.3b-speedcontrol-v1.py)| +|[krea/krea-realtime-video](https://www.modelscope.cn/models/krea/krea-realtime-video)||[code](./model_inference/krea-realtime-video.py)|[code](./model_training/full/krea-realtime-video.sh)|[code](./model_training/validate_full/krea-realtime-video.py)|[code](./model_training/lora/krea-realtime-video.sh)|[code](./model_training/validate_lora/krea-realtime-video.py)| +|[meituan-longcat/LongCat-Video](https://www.modelscope.cn/models/meituan-longcat/LongCat-Video)|`longcat_video`|[code](./model_inference/LongCat-Video.py)|[code](./model_training/full/LongCat-Video.sh)|[code](./model_training/validate_full/LongCat-Video.py)|[code](./model_training/lora/LongCat-Video.sh)|[code](./model_training/validate_lora/LongCat-Video.py)| +|[ByteDance/Video-As-Prompt-Wan2.1-14B](https://modelscope.cn/models/ByteDance/Video-As-Prompt-Wan2.1-14B)|`vap_video`, `vap_prompt`|[code](./model_inference/Video-As-Prompt-Wan2.1-14B.py)|[code](./model_training/full/Video-As-Prompt-Wan2.1-14B.sh)|[code](./model_training/validate_full/Video-As-Prompt-Wan2.1-14B.py)|[code](./model_training/lora/Video-As-Prompt-Wan2.1-14B.sh)|[code](./model_training/validate_lora/Video-As-Prompt-Wan2.1-14B.py)| + +## 模型推理 + +以下部分将会帮助您理解我们的功能并编写推理代码。 + +
+ +加载模型 + +模型通过 `from_pretrained` 加载： + +```python +import torch +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="Wan2.1_VAE.pth"), + ], +) +``` + +其中 `torch_dtype` 和 `device` 是计算精度和计算设备。`model_configs` 可通过多种方式配置模型路径： + +* 从[魔搭社区](https://modelscope.cn/)下载模型并加载。此时需要填写 `model_id` 和 `origin_file_pattern`，例如 + +```python +ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="diffusion_pytorch_model*.safetensors") +``` + +* 从本地文件路径加载模型。此时需要填写 `path`，例如 + +```python +ModelConfig(path="models/Wan-AI/Wan2.1-T2V-1.3B/diffusion_pytorch_model.safetensors") +``` + +对于从多个文件加载的单一模型，使用列表即可，例如 + +```python +ModelConfig(path=[ + "models/Wan-AI/Wan2.1-T2V-14B/diffusion_pytorch_model-00001-of-00006.safetensors", + "models/Wan-AI/Wan2.1-T2V-14B/diffusion_pytorch_model-00002-of-00006.safetensors", + "models/Wan-AI/Wan2.1-T2V-14B/diffusion_pytorch_model-00003-of-00006.safetensors", + "models/Wan-AI/Wan2.1-T2V-14B/diffusion_pytorch_model-00004-of-00006.safetensors", + "models/Wan-AI/Wan2.1-T2V-14B/diffusion_pytorch_model-00005-of-00006.safetensors", + "models/Wan-AI/Wan2.1-T2V-14B/diffusion_pytorch_model-00006-of-00006.safetensors", +]) +``` + +`ModelConfig` 提供了额外的参数用于控制模型加载时的行为： + +* `local_model_path`: 用于保存下载模型的路径，默认值为 `"./models"`。 +* `skip_download`: 是否跳过下载，默认值为 `False`。当您的网络无法访问[魔搭社区](https://modelscope.cn/)时，请手动下载必要的文件，并将其设置为 `True`。 + +`from_pretrained` 提供了额外的参数用于控制模型加载时的行为： + +* `tokenizer_config`: Wan 模型的 tokenizer 路径，默认值为 `ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="google/*")`。 +* `redirect_common_files`: 是否重定向重复模型文件，默认值为 `True`。由于 Wan 系列模型包括多个基础模型，每个基础模型的 text encoder 等模块都是相同的，为避免重复下载，我们会对模型路径进行重定向。 +* `use_usp`: 是否启用 Unified Sequence Parallel，默认值为 `False`。用于多 GPU 并行推理。 + +
+ + +
+ +显存管理 + +DiffSynth-Studio 为 Wan 模型提供了细粒度的显存管理，让模型能够在低显存设备上进行推理，可通过以下代码开启 offload 功能，在显存有限的设备上将部分模块 offload 到内存中。 + +```python +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"), + ], +) +pipe.enable_vram_management() +``` + +FP8 量化功能也是支持的： + +```python +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="Wan2.1_VAE.pth", offload_dtype=torch.float8_e4m3fn), + ], +) +pipe.enable_vram_management() +``` + +FP8 量化和 offload 可同时开启： + +```python +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu", offload_dtype=torch.float8_e4m3fn), + ], +) +pipe.enable_vram_management() +``` + +FP8 量化能够大幅度减少显存占用，但不会加速，部分模型在 FP8 量化下会出现精度不足导致的画面模糊、撕裂、失真问题，请谨慎使用 FP8 量化。 + +开启显存管理后，框架会自动根据设备上的剩余显存确定显存管理策略。`enable_vram_management` 函数提供了以下参数，用于手动控制显存管理策略： + +* `vram_limit`: 显存占用量限制（GB），默认占用设备上的剩余显存。注意这不是一个绝对限制，当设置的显存不足以支持模型进行推理，但实际可用显存足够时，将会以最小化显存占用的形式进行推理。将其设置为0时，将会实现理论最小显存占用。 +* `vram_buffer`: 显存缓冲区大小（GB），默认为 0.5GB。由于部分较大的神经网络层在 onload 阶段会不可控地占用更多显存，因此一个显存缓冲区是必要的，理论上的最优值为模型中最大的层所占的显存。 +* `num_persistent_param_in_dit`: DiT 模型中常驻显存的参数数量（个），默认为无限制。我们将会在未来删除这个参数，请不要依赖这个参数。 + +
+ + +
+ +推理加速 + +Wan 支持多种加速方案，包括 + +* 高效注意力机制实现：当您的 Python 环境中安装过这些注意力机制实现方案时，我们将会按照以下优先级自动启用。 + * [Flash Attention 3](https://github.com/Dao-AILab/flash-attention) + * [Flash Attention 2](https://github.com/Dao-AILab/flash-attention) + * [Sage Attention](https://github.com/thu-ml/SageAttention) + * [torch SDPA](https://pytorch.org/docs/stable/generated/torch.nn.functional.scaled_dot_product_attention.html) (默认设置，建议安装 `torch>=2.5.0`) +* 统一序列并行：基于 [xDiT](https://github.com/xdit-project/xDiT) 实现的序列并行，请参考[示例代码](./acceleration/unified_sequence_parallel.py)，使用以下命令运行： + +```shell +pip install "xfuser[flash-attn]>=0.4.3" +torchrun --standalone --nproc_per_node=8 examples/wanvideo/acceleration/unified_sequence_parallel.py +``` + +* TeaCache：加速技术 [TeaCache](https://github.com/ali-vilab/TeaCache)，请参考[示例代码](./acceleration/teacache.py)。 + +
+ + +
+ +输入参数 + +Pipeline 在推理阶段能够接收以下输入参数： + +* `prompt`: 提示词，描述画面中出现的内容。 +* `negative_prompt`: 负向提示词，描述画面中不应该出现的内容，默认值为 `""`。 +* `input_image`: 输入图片，适用于图生视频模型，例如 [`Wan-AI/Wan2.1-I2V-14B-480P`](https://modelscope.cn/models/Wan-AI/Wan2.1-I2V-14B-480P)、[`PAI/Wan2.1-Fun-1.3B-InP`](https://modelscope.cn/models/PAI/Wan2.1-Fun-1.3B-InP)，以及首尾帧模型，例如 [`Wan-AI/Wan2.1-FLF2V-14B-720P`](Wan-AI/Wan2.1-FLF2V-14B-720P)。 +* `end_image`: 结尾帧，适用于首尾帧模型，例如 [`Wan-AI/Wan2.1-FLF2V-14B-720P`](Wan-AI/Wan2.1-FLF2V-14B-720P)。 +* `input_video`: 输入视频，用于视频生视频，适用于任意 Wan 系列模型，需与参数 `denoising_strength` 配合使用。 +* `denoising_strength`: 去噪强度，范围为 [0, 1]。数值越小，生成的视频越接近 `input_video`。 +* `control_video`: 控制视频，适用于带控制能力的 Wan 系列模型，例如 [`PAI/Wan2.1-Fun-1.3B-Control`](https://modelscope.cn/models/PAI/Wan2.1-Fun-1.3B-Control)。 +* `reference_image`: 参考图片，适用于带参考图能力的 Wan 系列模型，例如 [`PAI/Wan2.1-Fun-V1.1-1.3B-Control`](https://modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-1.3B-Control)。 +* `camera_control_direction`: 镜头控制方向，可选 "Left", "Right", "Up", "Down", "LeftUp", "LeftDown", "RightUp", "RightDown" 之一，适用于 Camera-Control 模型，例如 [PAI/Wan2.1-Fun-V1.1-14B-Control-Camera](https://www.modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-14B-Control-Camera)。 +* `camera_control_speed`: 镜头控制速度，适用于 Camera-Control 模型，例如 [PAI/Wan2.1-Fun-V1.1-14B-Control-Camera](https://www.modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-14B-Control-Camera)。 +* `camera_control_origin`: 镜头控制序列的原点坐标，请参考[原论文](https://arxiv.org/pdf/2404.02101)进行设置，适用于 Camera-Control 模型，例如 [PAI/Wan2.1-Fun-V1.1-14B-Control-Camera](https://www.modelscope.cn/models/PAI/Wan2.1-Fun-V1.1-14B-Control-Camera)。 +* `vace_video`: VACE 模型的输入视频，适用于 VACE 系列模型，例如 [`iic/VACE-Wan2.1-1.3B-Preview`](https://modelscope.cn/models/iic/VACE-Wan2.1-1.3B-Preview)。 +* `vace_video_mask`: VACE 模型的 mask 视频，适用于 VACE 系列模型，例如 [`iic/VACE-Wan2.1-1.3B-Preview`](https://modelscope.cn/models/iic/VACE-Wan2.1-1.3B-Preview)。 +* `vace_reference_image`: VACE 模型的参考图片，适用于 VACE 系列模型，例如 [`iic/VACE-Wan2.1-1.3B-Preview`](https://modelscope.cn/models/iic/VACE-Wan2.1-1.3B-Preview)。 +* `vace_scale`: VACE 模型对基础模型的影响程度，默认为1。数值越大，控制强度越高，但画面崩坏概率越大。 +* `seed`: 随机种子。默认为 `None`，即完全随机。 +* `rand_device`: 生成随机高斯噪声矩阵的计算设备，默认为 `"cpu"`。当设置为 `cuda` 时，在不同 GPU 上会导致不同的生成结果。 +* `height`: 帧高度，默认为 480。需设置为 16 的倍数，不满足时向上取整。 +* `width`: 帧宽度，默认为 832。需设置为 16 的倍数，不满足时向上取整。 +* `num_frames`: 帧数，默认为 81。需设置为 4 的倍数 + 1，不满足时向上取整，最小值为 1。 +* `cfg_scale`: Classifier-free guidance 机制的数值，默认为 5。数值越大，提示词的控制效果越强，但画面崩坏的概率越大。 +* `cfg_merge`: 是否合并 Classifier-free guidance 的两侧进行统一推理，默认为 `False`。该参数目前仅在基础的文生视频和图生视频模型上生效。 +* `switch_DiT_boundary`: 切换 DiT 模型的时间点，默认值为 0.875，仅对多 DiT 的混合模型生效，例如 [Wan-AI/Wan2.2-I2V-A14B](https://modelscope.cn/models/Wan-AI/Wan2.2-I2V-A14B)。 +* `num_inference_steps`: 推理次数，默认值为 50。 +* `sigma_shift`: Rectified Flow 理论中的参数，默认为 5。数值越大，模型在去噪的开始阶段停留的步骤数越多，可适当调大这个参数来提高画面质量，但会因生成过程与训练过程不一致导致生成的视频内容与训练数据存在差异。 +* `motion_bucket_id`: 运动幅度，范围为 [0, 100]。适用于速度控制模块，例如 [`DiffSynth-Studio/Wan2.1-1.3b-speedcontrol-v1`](https://modelscope.cn/models/DiffSynth-Studio/Wan2.1-1.3b-speedcontrol-v1)，数值越大，运动幅度越大。 +* `tiled`: 是否启用 VAE 分块推理，默认为 `False`。设置为 `True` 时可显著减少 VAE 编解码阶段的显存占用，会产生少许误差，以及少量推理时间延长。 +* `tile_size`: VAE 编解码阶段的分块大小，默认为 (30, 52)，仅在 `tiled=True` 时生效。 +* `tile_stride`: VAE 编解码阶段的分块步长，默认为 (15, 26)，仅在 `tiled=True` 时生效，需保证其数值小于或等于 `tile_size`。 +* `sliding_window_size`: DiT 部分的滑动窗口大小。实验性功能，效果不稳定。 +* `sliding_window_stride`: DiT 部分的滑动窗口步长。实验性功能，效果不稳定。 +* `tea_cache_l1_thresh`: TeaCache 的阈值，数值越大，速度越快，画面质量越差。请注意，开启 TeaCache 后推理速度并非均匀，因此进度条上显示的剩余时间将会变得不准确。 +* `tea_cache_model_id`: TeaCache 的参数模板，可选 `"Wan2.1-T2V-1.3B"`、`Wan2.1-T2V-14B`、`Wan2.1-I2V-14B-480P`、`Wan2.1-I2V-14B-720P` 之一。 +* `progress_bar_cmd`: 进度条，默认为 `tqdm.tqdm`。可通过设置为 `lambda x:x` 来屏蔽进度条。 + +
+ + +## 模型训练 + +Wan 系列模型训练通过统一的 [`./model_training/train.py`](./model_training/train.py) 脚本进行。 + +
+ +脚本参数 + +脚本包含以下参数： + +* 数据集 + * `--dataset_base_path`: 数据集的根路径。 + * `--dataset_metadata_path`: 数据集的元数据文件路径。 + * `--height`: 图像或视频的高度。将 `height` 和 `width` 留空以启用动态分辨率。 + * `--width`: 图像或视频的宽度。将 `height` 和 `width` 留空以启用动态分辨率。 + * `--num_frames`: 每个视频中的帧数。帧从视频前缀中采样。 + * `--data_file_keys`: 元数据中的数据文件键。用逗号分隔。 + * `--dataset_repeat`: 每个 epoch 中数据集重复的次数。 + * `--dataset_num_workers`: 每个 Dataloder 的进程数量。 +* 模型 + * `--model_paths`: 要加载的模型路径。JSON 格式。 + * `--model_id_with_origin_paths`: 带原始路径的模型 ID，例如 Wan-AI/Wan2.1-T2V-1.3B:diffusion_pytorch_model*.safetensors。用逗号分隔。 + * `--max_timestep_boundary`: Timestep 区间最大值，范围为 0～1，默认为 1，仅在多 DiT 的混合模型训练中需要手动设置，例如 [Wan-AI/Wan2.2-I2V-A14B](https://modelscope.cn/models/Wan-AI/Wan2.2-I2V-A14B)。 + * `--min_timestep_boundary`: Timestep 区间最小值，范围为 0～1，默认为 1，仅在多 DiT 的混合模型训练中需要手动设置，例如 [Wan-AI/Wan2.2-I2V-A14B](https://modelscope.cn/models/Wan-AI/Wan2.2-I2V-A14B)。 +* 训练 + * `--learning_rate`: 学习率。 + * `--weight_decay`：权重衰减大小。 + * `--num_epochs`: 轮数（Epoch）。 + * `--output_path`: 保存路径。 + * `--remove_prefix_in_ckpt`: 在 ckpt 中移除前缀。 + * `--save_steps`: 保存模型的间隔 step 数量，如果设置为 None ，则每个 epoch 保存一次 + * `--find_unused_parameters`: DDP 训练中是否存在未使用的参数 +* 可训练模块 + * `--trainable_models`: 可训练的模型，例如 dit、vae、text_encoder。 + * `--lora_base_model`: LoRA 添加到哪个模型上。 + * `--lora_target_modules`: LoRA 添加到哪一层上。 + * `--lora_rank`: LoRA 的秩（Rank）。 + * `--lora_checkpoint`: LoRA 检查点的路径。如果提供此路径，LoRA 将从此检查点加载。 +* 额外模型输入 + * `--extra_inputs`: 额外的模型输入，以逗号分隔。 +* 显存管理 + * `--use_gradient_checkpointing_offload`: 是否将 gradient checkpointing 卸载到内存中。 + +此外，训练框架基于 [`accelerate`](https://huggingface.co/docs/accelerate/index) 构建，在开始训练前运行 `accelerate config` 可配置 GPU 的相关参数。对于部分模型训练（例如 14B 模型的全量训练）脚本，我们提供了建议的 `accelerate` 配置文件，可在对应的训练脚本中查看。 + +
+ + +
+ +Step 1: 准备数据集 + +数据集包含一系列文件，我们建议您这样组织数据集文件： + +``` +data/example_video_dataset/ +├── metadata.csv +├── video1.mp4 +└── video2.mp4 +``` + +其中 `video1.mp4`、`video2.mp4` 为训练用视频数据，`metadata.csv` 为元数据列表，例如 + +``` +video,prompt +video1.mp4,"from sunset to night, a small town, light, house, river" +video2.mp4,"a dog is running" +``` + +我们构建了一个样例视频数据集，以方便您进行测试，通过以下命令可以下载这个数据集： + +```shell +modelscope download --dataset DiffSynth-Studio/example_video_dataset --local_dir ./data/example_video_dataset +``` + +数据集支持视频和图片混合训练，支持的视频文件格式包括 `"mp4", "avi", "mov", "wmv", "mkv", "flv", "webm"`，支持的图片格式包括 `"jpg", "jpeg", "png", "webp"`。 + +视频的尺寸可通过脚本参数 `--height`、`--width`、`--num_frames` 控制。在每个视频中，前 `num_frames` 帧会被用于训练，因此当视频长度不足 `num_frames` 帧时会报错，图片文件会被视为单帧视频。当 `--height` 和 `--width` 为空时将会开启动态分辨率，按照数据集中每个视频或图片的实际宽高训练。 + +**我们强烈建议使用固定分辨率训练，并避免图像和视频混合训练，因为在多卡训练中存在负载均衡问题。** + +当模型需要额外输入时，例如具备控制能力的模型 [`PAI/Wan2.1-Fun-1.3B-Control`](https://modelscope.cn/models/PAI/Wan2.1-Fun-1.3B-Control) 所需的 `control_video`，请在数据集中补充相应的列，例如： + +``` +video,prompt,control_video +video1.mp4,"from sunset to night, a small town, light, house, river",video1_softedge.mp4 +``` + +额外输入若包含视频和图像文件，则需要在 `--data_file_keys` 参数中指定要解析的列名。该参数的默认值为 `"image,video"`，即解析列名为 `image` 和 `video` 的列。可根据额外输入增加相应的列名，例如 `--data_file_keys "image,video,control_video"`，同时启用 `--input_contains_control_video`。 + +
+ + +
+ +Step 2: 加载模型 + +类似于推理时的模型加载逻辑，可直接通过模型 ID 配置要加载的模型。例如，推理时我们通过以下设置加载模型 + +```python +model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-14B", origin_file_pattern="diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-14B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-14B", origin_file_pattern="Wan2.1_VAE.pth"), +] +``` + +那么在训练时，填入以下参数即可加载对应的模型。 + +```shell +--model_id_with_origin_paths "Wan-AI/Wan2.1-T2V-1.3B:diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.1-T2V-1.3B:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.1-T2V-1.3B:Wan2.1_VAE.pth" +``` + +如果您希望从本地文件加载模型，例如推理时 + +```python +model_configs=[ + ModelConfig(path=[ + "models/Wan-AI/Wan2.1-T2V-14B/diffusion_pytorch_model-00001-of-00006.safetensors", + "models/Wan-AI/Wan2.1-T2V-14B/diffusion_pytorch_model-00002-of-00006.safetensors", + "models/Wan-AI/Wan2.1-T2V-14B/diffusion_pytorch_model-00003-of-00006.safetensors", + "models/Wan-AI/Wan2.1-T2V-14B/diffusion_pytorch_model-00004-of-00006.safetensors", + "models/Wan-AI/Wan2.1-T2V-14B/diffusion_pytorch_model-00005-of-00006.safetensors", + "models/Wan-AI/Wan2.1-T2V-14B/diffusion_pytorch_model-00006-of-00006.safetensors", + ]), + ModelConfig(path="models/Wan-AI/Wan2.1-T2V-14B/models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(path="models/Wan-AI/Wan2.1-T2V-14B/Wan2.1_VAE.pth"), +] +``` + +那么训练时需设置为 + +```shell +--model_paths '[ + [ + "models/Wan-AI/Wan2.1-T2V-14B/diffusion_pytorch_model-00001-of-00006.safetensors", + "models/Wan-AI/Wan2.1-T2V-14B/diffusion_pytorch_model-00002-of-00006.safetensors", + "models/Wan-AI/Wan2.1-T2V-14B/diffusion_pytorch_model-00003-of-00006.safetensors", + "models/Wan-AI/Wan2.1-T2V-14B/diffusion_pytorch_model-00004-of-00006.safetensors", + "models/Wan-AI/Wan2.1-T2V-14B/diffusion_pytorch_model-00005-of-00006.safetensors", + "models/Wan-AI/Wan2.1-T2V-14B/diffusion_pytorch_model-00006-of-00006.safetensors" + ], + "models/Wan-AI/Wan2.1-T2V-14B/models_t5_umt5-xxl-enc-bf16.pth", + "models/Wan-AI/Wan2.1-T2V-14B/Wan2.1_VAE.pth" +]' \ +``` + +
+ + +
+ +Step 3: 设置可训练模块 + +训练框架支持训练基础模型，或 LoRA 模型。以下是几个例子： + +* 全量训练 DiT 部分：`--trainable_models dit` +* 训练 DiT 部分的 LoRA 模型：`--lora_base_model dit --lora_target_modules "q,k,v,o,ffn.0,ffn.2" --lora_rank 32` +* 训练 DiT 部分的 LoRA 和 Motion Controller 部分（是的，可以训练这种花里胡哨的结构）：`--trainable_models motion_controller --lora_base_model dit --lora_target_modules "q,k,v,o,ffn.0,ffn.2" --lora_rank 32` + +此外，由于训练脚本中加载了多个模块（text encoder、dit、vae），保存模型文件时需要移除前缀，例如在全量训练 DiT 部分或者训练 DiT 部分的 LoRA 模型时，请设置 `--remove_prefix_in_ckpt pipe.dit.` + +
+ + +
+ +Step 4: 启动训练程序 + +我们为每一个模型编写了训练命令，请参考本文档开头的表格。 + +请注意，14B 模型全量训练需要8个GPU，每个GPU的显存至少为80G。全量训练这些14B模型时需要安装 `deepspeed`（`pip install deepspeed`），我们编写了建议的[配置文件](./model_training/full/accelerate_config_14B.yaml)，这个配置文件会在对应的训练脚本中被加载，这些脚本已在 8*A100 上测试过。 + +训练脚本的默认视频尺寸为 `480*832*81`，提升分辨率将可能导致显存不足，请添加参数 `--use_gradient_checkpointing_offload` 降低显存占用。 + +
+ +## 案例展示 + +1.3B 文生视频： + +https://github.com/user-attachments/assets/124397be-cd6a-4f29-a87c-e4c695aaabb8 + +给狗狗戴上墨镜（1.3B 视频生视频）： + +https://github.com/user-attachments/assets/272808d7-fbeb-4747-a6df-14a0860c75fb + +14B 文生视频： + +https://github.com/user-attachments/assets/3908bc64-d451-485a-8b61-28f6d32dd92f + +14B 图生视频： + +https://github.com/user-attachments/assets/c0bdd5ca-292f-45ed-b9bc-afe193156e75 + +LoRA 训练： + +https://github.com/user-attachments/assets/9bd8e30b-97e8-44f9-bb6f-da004ba376a9 diff --git a/examples/wanvideo/acceleration/teacache.py b/examples/wanvideo/acceleration/teacache.py new file mode 100644 index 0000000000000000000000000000000000000000..b88656a8f9861160d89e305dde9bccbbd651bd8e --- /dev/null +++ b/examples/wanvideo/acceleration/teacache.py @@ -0,0 +1,27 @@ +import torch +from PIL import Image +from diffsynth import save_video, VideoData +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"), + ], +) +pipe.enable_vram_management() + + +video = pipe( + prompt="纪实摄影风格画面，一只活泼的小狗在绿茵茵的草地上迅速奔跑。小狗毛色棕黄，两只耳朵立起，神情专注而欢快。阳光洒在它身上，使得毛发看上去格外柔软而闪亮。背景是一片开阔的草地，偶尔点缀着几朵野花，远处隐约可见蓝天和几片白云。透视感鲜明，捕捉小狗奔跑时的动感和四周草地的生机。中景侧面移动视角。", + negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + seed=0, tiled=True, + # TeaCache parameters + tea_cache_l1_thresh=0.05, # The larger this value is, the faster the speed, but the worse the visual quality. + tea_cache_model_id="Wan2.1-T2V-1.3B", # Choose one in (Wan2.1-T2V-1.3B, Wan2.1-T2V-14B, Wan2.1-I2V-14B-480P, Wan2.1-I2V-14B-720P). +) +save_video(video, "video1.mp4", fps=15, quality=5) diff --git a/examples/wanvideo/acceleration/unified_sequence_parallel.py b/examples/wanvideo/acceleration/unified_sequence_parallel.py new file mode 100644 index 0000000000000000000000000000000000000000..44b580b1b24f21e6e988f89f71f41050ff0f8184 --- /dev/null +++ b/examples/wanvideo/acceleration/unified_sequence_parallel.py @@ -0,0 +1,27 @@ +import torch +from PIL import Image +from diffsynth import save_video, VideoData +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig +import torch.distributed as dist + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + use_usp=True, + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-14B", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-14B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-14B", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"), + ], +) +pipe.enable_vram_management() + + +video = pipe( + prompt="一名宇航员身穿太空服，面朝镜头骑着一匹机械马在火星表面驰骋。红色的荒凉地表延伸至远方，点缀着巨大的陨石坑和奇特的岩石结构。机械马的步伐稳健，扬起微弱的尘埃，展现出未来科技与原始探索的完美结合。宇航员手持操控装置，目光坚定，仿佛正在开辟人类的新疆域。背景是深邃的宇宙和蔚蓝的地球，画面既科幻又充满希望，让人不禁畅想未来的星际生活。", + negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + seed=0, tiled=True, +) +if dist.get_rank() == 0: + save_video(video, "video1.mp4", fps=15, quality=5) diff --git a/examples/wanvideo/model_inference/LongCat-Video.py b/examples/wanvideo/model_inference/LongCat-Video.py new file mode 100644 index 0000000000000000000000000000000000000000..8df1ec8c106dc1e6bd3c690c492ccbd1f2f07b5f --- /dev/null +++ b/examples/wanvideo/model_inference/LongCat-Video.py @@ -0,0 +1,35 @@ +import torch +from diffsynth import save_video, VideoData +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="meituan-longcat/LongCat-Video", origin_file_pattern="dit/diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-14B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-14B", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"), + ], +) +pipe.enable_vram_management() + +# Text-to-video +video = pipe( + prompt="In a realistic photography style, a white boy around seven or eight years old sits on a park bench, wearing a light blue T-shirt, denim shorts, and white sneakers. He holds an ice cream cone with vanilla and chocolate flavors, and beside him is a medium-sized golden Labrador. Smiling, the boy offers the ice cream to the dog, who eagerly licks it with its tongue. The sun is shining brightly, and the background features a green lawn and several tall trees, creating a warm and loving scene.", + negative_prompt="Bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, worst quality, low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, misshapen limbs, fused fingers, still picture, messy background, three legs, many people in the background, walking backwards", + seed=0, tiled=True, num_frames=93, + cfg_scale=2, sigma_shift=1, +) +save_video(video, "video1.mp4", fps=15, quality=5) + +# Video-continuation (The number of frames in `longcat_video` should be 4n+1.) +longcat_video = video[-17:] +video = pipe( + prompt="In a realistic photography style, a white boy around seven or eight years old sits on a park bench, wearing a light blue T-shirt, denim shorts, and white sneakers. He holds an ice cream cone with vanilla and chocolate flavors, and beside him is a medium-sized golden Labrador. Smiling, the boy offers the ice cream to the dog, who eagerly licks it with its tongue. The sun is shining brightly, and the background features a green lawn and several tall trees, creating a warm and loving scene.", + negative_prompt="Bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, worst quality, low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, misshapen limbs, fused fingers, still picture, messy background, three legs, many people in the background, walking backwards", + seed=1, tiled=True, num_frames=93, + cfg_scale=2, sigma_shift=1, + longcat_video=longcat_video, +) +save_video(video, "video2.mp4", fps=15, quality=5) diff --git a/examples/wanvideo/model_inference/Video-As-Prompt-Wan2.1-14B.py b/examples/wanvideo/model_inference/Video-As-Prompt-Wan2.1-14B.py new file mode 100644 index 0000000000000000000000000000000000000000..a4c8b63d8690ce12fc749150eba1a49ff16f9a83 --- /dev/null +++ b/examples/wanvideo/model_inference/Video-As-Prompt-Wan2.1-14B.py @@ -0,0 +1,63 @@ +import torch +import PIL +from PIL import Image +from diffsynth import save_video, VideoData +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download +from typing import List + +def select_frames(video_frames: List[PIL.Image.Image], num: int, mode: str) -> List[PIL.Image.Image]: + if len(video_frames) == 0: + return [] + if mode == "first": + return video_frames[:num] + if mode == "evenly": + import torch as _torch + idx = _torch.linspace(0, len(video_frames) - 1, num).long().tolist() + return [video_frames[i] for i in idx] + if mode == "random": + if len(video_frames) <= num: + return video_frames + import random as _random + start = _random.randint(0, len(video_frames) - num) + return video_frames[start:start+num] + return video_frames + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="ByteDance/Video-As-Prompt-Wan2.1-14B", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.1-I2V-14B-720P", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.1-I2V-14B-720P", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.1-I2V-14B-720P", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth", offload_device="cpu"), + ], +) +pipe.enable_vram_management() + +dataset_snapshot_download("DiffSynth-Studio/example_video_dataset", allow_file_pattern="wanvap/*", local_dir="data/example_video_dataset") +ref_video_path = 'data/example_video_dataset/wanvap/vap_ref.mp4' +target_image_path = 'data/example_video_dataset/wanvap/input_image.jpg' + + +image = Image.open(target_image_path).convert("RGB") +ref_video = VideoData(ref_video_path, height=480, width=832) +ref_frames = select_frames(ref_video, num=49, mode="evenly") + +vap_prompt = "A man stands with his back to the camera on a dirt path overlooking sun-drenched, rolling green tea plantations. He wears a blue and green plaid shirt, dark pants, and white shoes. As he turns to face the camera and spreads his arms, a brief, magical burst of sparkling golden light particles envelops him. Through this shimmer, he seamlessly transforms into a Labubu toy character. His head morphs into the iconic large, furry-eared head of the toy, featuring a wide grin with pointed teeth and red cheek markings. The character retains the man's original plaid shirt and clothing, which now fit its stylized, cartoonish body. The camera remains static throughout the transformation, positioned low among the tea bushes, maintaining a consistent view of the subject and the expansive scenery." +prompt = "A young woman with curly hair, wearing a green hijab and a floral dress, plays a violin in front of a vintage green car on a tree-lined street. She executes a swift counter-clockwise turn to face the camera. During the turn, a brilliant shower of golden, sparkling particles erupts and momentarily obscures her figure. As the particles fade, she is revealed to have seamlessly transformed into a Labubu toy character. This new figure, now with the toy's signature large ears, big eyes, and toothy grin, maintains the original pose and continues playing the violin. The character's clothing—the green hijab, floral dress, and black overcoat—remains identical to the woman's. Throughout this transition, the camera stays static, and the street-side environment remains completely consistent." +negative_prompt = "Bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, worst quality, low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, misshapen limbs, fused fingers, still picture, messy background, three legs, many people in the background, walking backwards" + +video = pipe( + prompt=prompt, + negative_prompt=negative_prompt, + input_image=image, + seed=42, tiled=True, + height=480, width=832, + num_frames=49, + vap_video=ref_frames, + vap_prompt=vap_prompt, + negative_vap_prompt=negative_prompt, +) + +save_video(video, "video.mp4", fps=15, quality=5) diff --git a/examples/wanvideo/model_inference/Wan2.1-1.3b-speedcontrol-v1.py b/examples/wanvideo/model_inference/Wan2.1-1.3b-speedcontrol-v1.py new file mode 100644 index 0000000000000000000000000000000000000000..6efdc65c371ed426d40b30c14f6118fb0c3ce831 --- /dev/null +++ b/examples/wanvideo/model_inference/Wan2.1-1.3b-speedcontrol-v1.py @@ -0,0 +1,34 @@ +import torch +from PIL import Image +from diffsynth import save_video, VideoData +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"), + ModelConfig(model_id="DiffSynth-Studio/Wan2.1-1.3b-speedcontrol-v1", origin_file_pattern="model.safetensors", offload_device="cpu"), + ], +) +pipe.enable_vram_management() + +# Text-to-video +video = pipe( + prompt="纪实摄影风格画面，一只活泼的小狗在绿茵茵的草地上迅速奔跑。小狗毛色棕黄，两只耳朵立起，神情专注而欢快。阳光洒在它身上，使得毛发看上去格外柔软而闪亮。背景是一片开阔的草地，偶尔点缀着几朵野花，远处隐约可见蓝天和几片白云。透视感鲜明，捕捉小狗奔跑时的动感和四周草地的生机。中景侧面移动视角。", + negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + seed=1, tiled=True, + motion_bucket_id=0 +) +save_video(video, "video_slow.mp4", fps=15, quality=5) + +video = pipe( + prompt="纪实摄影风格画面，一只活泼的小狗在绿茵茵的草地上迅速奔跑。小狗毛色棕黄，两只耳朵立起，神情专注而欢快。阳光洒在它身上，使得毛发看上去格外柔软而闪亮。背景是一片开阔的草地，偶尔点缀着几朵野花，远处隐约可见蓝天和几片白云。透视感鲜明，捕捉小狗奔跑时的动感和四周草地的生机。中景侧面移动视角。", + negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + seed=1, tiled=True, + motion_bucket_id=100 +) +save_video(video, "video_fast.mp4", fps=15, quality=5) diff --git a/examples/wanvideo/model_inference/Wan2.1-FLF2V-14B-720P.py b/examples/wanvideo/model_inference/Wan2.1-FLF2V-14B-720P.py new file mode 100644 index 0000000000000000000000000000000000000000..30613982e8a208617728064a11291284dfb91309 --- /dev/null +++ b/examples/wanvideo/model_inference/Wan2.1-FLF2V-14B-720P.py @@ -0,0 +1,36 @@ +import torch +from PIL import Image +from diffsynth import save_video, VideoData +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.1-FLF2V-14B-720P", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.1-FLF2V-14B-720P", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.1-FLF2V-14B-720P", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.1-FLF2V-14B-720P", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth", offload_device="cpu"), + ], +) +pipe.enable_vram_management() + +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/examples_in_diffsynth", + local_dir="./", + allow_file_pattern=["data/examples/wan/first_frame.jpeg", "data/examples/wan/last_frame.jpeg"] +) + +# First and last frame to video +video = pipe( + prompt="写实风格，一个女生手持枯萎的花站在花园中，镜头逐渐拉远，记录下花园的全貌。", + negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + input_image=Image.open("data/examples/wan/first_frame.jpeg").resize((960, 960)), + end_image=Image.open("data/examples/wan/last_frame.jpeg").resize((960, 960)), + seed=0, tiled=True, + height=960, width=960, num_frames=33, + sigma_shift=16, +) +save_video(video, "video.mp4", fps=15, quality=5) diff --git a/examples/wanvideo/model_inference/Wan2.1-Fun-1.3B-Control.py b/examples/wanvideo/model_inference/Wan2.1-Fun-1.3B-Control.py new file mode 100644 index 0000000000000000000000000000000000000000..43374d28863613d925e75836037dd79d60184523 --- /dev/null +++ b/examples/wanvideo/model_inference/Wan2.1-Fun-1.3B-Control.py @@ -0,0 +1,34 @@ +import torch +from PIL import Image +from diffsynth import save_video, VideoData +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="PAI/Wan2.1-Fun-1.3B-Control", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.1-Fun-1.3B-Control", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.1-Fun-1.3B-Control", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.1-Fun-1.3B-Control", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth", offload_device="cpu"), + ], +) +pipe.enable_vram_management() + +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/examples_in_diffsynth", + local_dir="./", + allow_file_pattern=f"data/examples/wan/control_video.mp4" +) + +# Control video +control_video = VideoData("data/examples/wan/control_video.mp4", height=832, width=576) +video = pipe( + prompt="扁平风格动漫，一位长发少女优雅起舞。她五官精致，大眼睛明亮有神，黑色长发柔顺光泽。身穿淡蓝色T恤和深蓝色牛仔短裤。背景是粉色。", + negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + control_video=control_video, height=832, width=576, num_frames=49, + seed=1, tiled=True +) +save_video(video, "video.mp4", fps=15, quality=5) diff --git a/examples/wanvideo/model_inference/Wan2.1-Fun-1.3B-InP.py b/examples/wanvideo/model_inference/Wan2.1-Fun-1.3B-InP.py new file mode 100644 index 0000000000000000000000000000000000000000..d921c0cb75e75083fba5ee44a07f2c70798ea09d --- /dev/null +++ b/examples/wanvideo/model_inference/Wan2.1-Fun-1.3B-InP.py @@ -0,0 +1,36 @@ +import torch +from PIL import Image +from diffsynth import save_video, VideoData +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="PAI/Wan2.1-Fun-1.3B-InP", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.1-Fun-1.3B-InP", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.1-Fun-1.3B-InP", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.1-Fun-1.3B-InP", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth", offload_device="cpu"), + ], +) +pipe.enable_vram_management() + +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/examples_in_diffsynth", + local_dir="./", + allow_file_pattern=f"data/examples/wan/input_image.jpg" +) +image = Image.open("data/examples/wan/input_image.jpg") + +# First and last frame to video +video = pipe( + prompt="一艘小船正勇敢地乘风破浪前行。蔚蓝的大海波涛汹涌，白色的浪花拍打着船身，但小船毫不畏惧，坚定地驶向远方。阳光洒在水面上，闪烁着金色的光芒，为这壮丽的场景增添了一抹温暖。镜头拉近，可以看到船上的旗帜迎风飘扬，象征着不屈的精神与冒险的勇气。这段画面充满力量，激励人心，展现了面对挑战时的无畏与执着。", + negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + input_image=image, + seed=0, tiled=True + # You can input `end_image=xxx` to control the last frame of the video. + # The model will automatically generate the dynamic content between `input_image` and `end_image`. +) +save_video(video, "video.mp4", fps=15, quality=5) diff --git a/examples/wanvideo/model_inference/Wan2.1-Fun-14B-Control.py b/examples/wanvideo/model_inference/Wan2.1-Fun-14B-Control.py new file mode 100644 index 0000000000000000000000000000000000000000..db9e5c8a8aca923c93a681c53dcdaf44ad11f29f --- /dev/null +++ b/examples/wanvideo/model_inference/Wan2.1-Fun-14B-Control.py @@ -0,0 +1,34 @@ +import torch +from PIL import Image +from diffsynth import save_video, VideoData +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="PAI/Wan2.1-Fun-14B-Control", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.1-Fun-14B-Control", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.1-Fun-14B-Control", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.1-Fun-14B-Control", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth", offload_device="cpu"), + ], +) +pipe.enable_vram_management() + +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/examples_in_diffsynth", + local_dir="./", + allow_file_pattern=f"data/examples/wan/control_video.mp4" +) + +# Control video +control_video = VideoData("data/examples/wan/control_video.mp4", height=832, width=576) +video = pipe( + prompt="扁平风格动漫，一位长发少女优雅起舞。她五官精致，大眼睛明亮有神，黑色长发柔顺光泽。身穿淡蓝色T恤和深蓝色牛仔短裤。背景是粉色。", + negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + control_video=control_video, height=832, width=576, num_frames=49, + seed=1, tiled=True +) +save_video(video, "video.mp4", fps=15, quality=5) diff --git a/examples/wanvideo/model_inference/Wan2.1-Fun-14B-InP.py b/examples/wanvideo/model_inference/Wan2.1-Fun-14B-InP.py new file mode 100644 index 0000000000000000000000000000000000000000..af227cbc73fbc92811a0fcffcdea527a26eee6cd --- /dev/null +++ b/examples/wanvideo/model_inference/Wan2.1-Fun-14B-InP.py @@ -0,0 +1,36 @@ +import torch +from PIL import Image +from diffsynth import save_video, VideoData +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="PAI/Wan2.1-Fun-14B-InP", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.1-Fun-14B-InP", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.1-Fun-14B-InP", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.1-Fun-14B-InP", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth", offload_device="cpu"), + ], +) +pipe.enable_vram_management() + +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/examples_in_diffsynth", + local_dir="./", + allow_file_pattern=f"data/examples/wan/input_image.jpg" +) +image = Image.open("data/examples/wan/input_image.jpg") + +# First and last frame to video +video = pipe( + prompt="一艘小船正勇敢地乘风破浪前行。蔚蓝的大海波涛汹涌，白色的浪花拍打着船身，但小船毫不畏惧，坚定地驶向远方。阳光洒在水面上，闪烁着金色的光芒，为这壮丽的场景增添了一抹温暖。镜头拉近，可以看到船上的旗帜迎风飘扬，象征着不屈的精神与冒险的勇气。这段画面充满力量，激励人心，展现了面对挑战时的无畏与执着。", + negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + input_image=image, + seed=0, tiled=True + # You can input `end_image=xxx` to control the last frame of the video. + # The model will automatically generate the dynamic content between `input_image` and `end_image`. +) +save_video(video, "video.mp4", fps=15, quality=5) diff --git a/examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-1.3B-Control-Camera.py b/examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-1.3B-Control-Camera.py new file mode 100644 index 0000000000000000000000000000000000000000..002c34c00c755941a6301467e0e1530d7f9889ee --- /dev/null +++ b/examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-1.3B-Control-Camera.py @@ -0,0 +1,44 @@ +import torch +from PIL import Image +from diffsynth import save_video, VideoData +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-Control-Camera", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-Control-Camera", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-Control-Camera", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-Control-Camera", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth", offload_device="cpu"), + ], +) +pipe.enable_vram_management() + + +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/examples_in_diffsynth", + local_dir="./", + allow_file_pattern=f"data/examples/wan/input_image.jpg" +) +input_image = Image.open("data/examples/wan/input_image.jpg") + +video = pipe( + prompt="一艘小船正勇敢地乘风破浪前行。蔚蓝的大海波涛汹涌，白色的浪花拍打着船身，但小船毫不畏惧，坚定地驶向远方。阳光洒在水面上，闪烁着金色的光芒，为这壮丽的场景增添了一抹温暖。镜头拉近，可以看到船上的旗帜迎风飘扬，象征着不屈的精神与冒险的勇气。这段画面充满力量，激励人心，展现了面对挑战时的无畏与执着。", + negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + seed=0, tiled=True, + input_image=input_image, + camera_control_direction="Left", camera_control_speed=0.01, +) +save_video(video, "video_left.mp4", fps=15, quality=5) + +video = pipe( + prompt="一艘小船正勇敢地乘风破浪前行。蔚蓝的大海波涛汹涌，白色的浪花拍打着船身，但小船毫不畏惧，坚定地驶向远方。阳光洒在水面上，闪烁着金色的光芒，为这壮丽的场景增添了一抹温暖。镜头拉近，可以看到船上的旗帜迎风飘扬，象征着不屈的精神与冒险的勇气。这段画面充满力量，激励人心，展现了面对挑战时的无畏与执着。", + negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + seed=0, tiled=True, + input_image=input_image, + camera_control_direction="Up", camera_control_speed=0.01, +) +save_video(video, "video_up.mp4", fps=15, quality=5) diff --git a/examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-1.3B-Control.py b/examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-1.3B-Control.py new file mode 100644 index 0000000000000000000000000000000000000000..0f7e4c82517bb387a21f32e775f928b740cd966e --- /dev/null +++ b/examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-1.3B-Control.py @@ -0,0 +1,36 @@ +import torch +from PIL import Image +from diffsynth import save_video, VideoData +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-Control", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-Control", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-Control", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-Control", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth", offload_device="cpu"), + ], +) +pipe.enable_vram_management() + +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/examples_in_diffsynth", + local_dir="./", + allow_file_pattern=["data/examples/wan/control_video.mp4", "data/examples/wan/reference_image_girl.png"] +) + +# Control video +control_video = VideoData("data/examples/wan/control_video.mp4", height=832, width=576) +reference_image = Image.open("data/examples/wan/reference_image_girl.png").resize((576, 832)) +video = pipe( + prompt="扁平风格动漫，一位长发少女优雅起舞。她五官精致，大眼睛明亮有神，黑色长发柔顺光泽。身穿淡蓝色T恤和深蓝色牛仔短裤。背景是粉色。", + negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + control_video=control_video, reference_image=reference_image, + height=832, width=576, num_frames=49, + seed=1, tiled=True +) +save_video(video, "video.mp4", fps=15, quality=5) diff --git a/examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-1.3B-InP.py b/examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-1.3B-InP.py new file mode 100644 index 0000000000000000000000000000000000000000..f2fc5604292b91263969589ad4dc3f395421b16d --- /dev/null +++ b/examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-1.3B-InP.py @@ -0,0 +1,36 @@ +import torch +from PIL import Image +from diffsynth import save_video, VideoData +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-InP", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-InP", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-InP", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-InP", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth", offload_device="cpu"), + ], +) +pipe.enable_vram_management() + +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/examples_in_diffsynth", + local_dir="./", + allow_file_pattern=f"data/examples/wan/input_image.jpg" +) +image = Image.open("data/examples/wan/input_image.jpg") + +# First and last frame to video +video = pipe( + prompt="一艘小船正勇敢地乘风破浪前行。蔚蓝的大海波涛汹涌，白色的浪花拍打着船身，但小船毫不畏惧，坚定地驶向远方。阳光洒在水面上，闪烁着金色的光芒，为这壮丽的场景增添了一抹温暖。镜头拉近，可以看到船上的旗帜迎风飘扬，象征着不屈的精神与冒险的勇气。这段画面充满力量，激励人心，展现了面对挑战时的无畏与执着。", + negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + input_image=image, + seed=0, tiled=True + # You can input `end_image=xxx` to control the last frame of the video. + # The model will automatically generate the dynamic content between `input_image` and `end_image`. +) +save_video(video, "video.mp4", fps=15, quality=5) diff --git a/examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-14B-Control-Camera.py b/examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-14B-Control-Camera.py new file mode 100644 index 0000000000000000000000000000000000000000..0e0c9a8883c1001796fdf105f4e8e958a86f5550 --- /dev/null +++ b/examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-14B-Control-Camera.py @@ -0,0 +1,44 @@ +import torch +from PIL import Image +from diffsynth import save_video, VideoData +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-14B-Control-Camera", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-14B-Control-Camera", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-14B-Control-Camera", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-14B-Control-Camera", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth", offload_device="cpu"), + ], +) +pipe.enable_vram_management() + + +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/examples_in_diffsynth", + local_dir="./", + allow_file_pattern=f"data/examples/wan/input_image.jpg" +) +input_image = Image.open("data/examples/wan/input_image.jpg") + +video = pipe( + prompt="一艘小船正勇敢地乘风破浪前行。蔚蓝的大海波涛汹涌，白色的浪花拍打着船身，但小船毫不畏惧，坚定地驶向远方。阳光洒在水面上，闪烁着金色的光芒，为这壮丽的场景增添了一抹温暖。镜头拉近，可以看到船上的旗帜迎风飘扬，象征着不屈的精神与冒险的勇气。这段画面充满力量，激励人心，展现了面对挑战时的无畏与执着。", + negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + seed=0, tiled=True, + input_image=input_image, + camera_control_direction="Left", camera_control_speed=0.01, +) +save_video(video, "video_left.mp4", fps=15, quality=5) + +video = pipe( + prompt="一艘小船正勇敢地乘风破浪前行。蔚蓝的大海波涛汹涌，白色的浪花拍打着船身，但小船毫不畏惧，坚定地驶向远方。阳光洒在水面上，闪烁着金色的光芒，为这壮丽的场景增添了一抹温暖。镜头拉近，可以看到船上的旗帜迎风飘扬，象征着不屈的精神与冒险的勇气。这段画面充满力量，激励人心，展现了面对挑战时的无畏与执着。", + negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + seed=0, tiled=True, + input_image=input_image, + camera_control_direction="Up", camera_control_speed=0.01, +) +save_video(video, "video_up.mp4", fps=15, quality=5) diff --git a/examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-14B-Control.py b/examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-14B-Control.py new file mode 100644 index 0000000000000000000000000000000000000000..78635ff70b84e9ddb5c18d9959a57df96287af56 --- /dev/null +++ b/examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-14B-Control.py @@ -0,0 +1,36 @@ +import torch +from PIL import Image +from diffsynth import save_video, VideoData +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-14B-Control", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-14B-Control", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-14B-Control", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-14B-Control", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth", offload_device="cpu"), + ], +) +pipe.enable_vram_management() + +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/examples_in_diffsynth", + local_dir="./", + allow_file_pattern=["data/examples/wan/control_video.mp4", "data/examples/wan/reference_image_girl.png"] +) + +# Control video +control_video = VideoData("data/examples/wan/control_video.mp4", height=832, width=576) +reference_image = Image.open("data/examples/wan/reference_image_girl.png").resize((576, 832)) +video = pipe( + prompt="扁平风格动漫，一位长发少女优雅起舞。她五官精致，大眼睛明亮有神，黑色长发柔顺光泽。身穿淡蓝色T恤和深蓝色牛仔短裤。背景是粉色。", + negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + control_video=control_video, reference_image=reference_image, + height=832, width=576, num_frames=49, + seed=1, tiled=True +) +save_video(video, "video1.mp4", fps=15, quality=5) diff --git a/examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-14B-InP.py b/examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-14B-InP.py new file mode 100644 index 0000000000000000000000000000000000000000..334e981f7135c04bbe5826171f8e0a667a581be5 --- /dev/null +++ b/examples/wanvideo/model_inference/Wan2.1-Fun-V1.1-14B-InP.py @@ -0,0 +1,36 @@ +import torch +from PIL import Image +from diffsynth import save_video, VideoData +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-14B-InP", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-14B-InP", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-14B-InP", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-14B-InP", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth", offload_device="cpu"), + ], +) +pipe.enable_vram_management() + +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/examples_in_diffsynth", + local_dir="./", + allow_file_pattern=f"data/examples/wan/input_image.jpg" +) +image = Image.open("data/examples/wan/input_image.jpg") + +# First and last frame to video +video = pipe( + prompt="一艘小船正勇敢地乘风破浪前行。蔚蓝的大海波涛汹涌，白色的浪花拍打着船身，但小船毫不畏惧，坚定地驶向远方。阳光洒在水面上，闪烁着金色的光芒，为这壮丽的场景增添了一抹温暖。镜头拉近，可以看到船上的旗帜迎风飘扬，象征着不屈的精神与冒险的勇气。这段画面充满力量，激励人心，展现了面对挑战时的无畏与执着。", + negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + input_image=image, + seed=0, tiled=True + # You can input `end_image=xxx` to control the last frame of the video. + # The model will automatically generate the dynamic content between `input_image` and `end_image`. +) +save_video(video, "video.mp4", fps=15, quality=5) diff --git a/examples/wanvideo/model_inference/Wan2.1-I2V-14B-480P.py b/examples/wanvideo/model_inference/Wan2.1-I2V-14B-480P.py new file mode 100644 index 0000000000000000000000000000000000000000..eb2e5b06d52e404a7db6b1b0f3de4e8351e4c942 --- /dev/null +++ b/examples/wanvideo/model_inference/Wan2.1-I2V-14B-480P.py @@ -0,0 +1,34 @@ +import torch +from PIL import Image +from diffsynth import save_video, VideoData +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.1-I2V-14B-480P", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.1-I2V-14B-480P", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.1-I2V-14B-480P", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.1-I2V-14B-480P", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth", offload_device="cpu"), + ], +) +pipe.enable_vram_management() + +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/examples_in_diffsynth", + local_dir="./", + allow_file_pattern=f"data/examples/wan/input_image.jpg" +) +image = Image.open("data/examples/wan/input_image.jpg") + +# Image-to-video +video = pipe( + prompt="一艘小船正勇敢地乘风破浪前行。蔚蓝的大海波涛汹涌，白色的浪花拍打着船身，但小船毫不畏惧，坚定地驶向远方。阳光洒在水面上，闪烁着金色的光芒，为这壮丽的场景增添了一抹温暖。镜头拉近，可以看到船上的旗帜迎风飘扬，象征着不屈的精神与冒险的勇气。这段画面充满力量，激励人心，展现了面对挑战时的无畏与执着。", + negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + input_image=image, + seed=0, tiled=True +) +save_video(video, "video.mp4", fps=15, quality=5) diff --git a/examples/wanvideo/model_inference/Wan2.1-I2V-14B-720P.py b/examples/wanvideo/model_inference/Wan2.1-I2V-14B-720P.py new file mode 100644 index 0000000000000000000000000000000000000000..fb14d24f00b7c1e8d1af7fcc83f5cefb629463c5 --- /dev/null +++ b/examples/wanvideo/model_inference/Wan2.1-I2V-14B-720P.py @@ -0,0 +1,35 @@ +import torch +from PIL import Image +from diffsynth import save_video, VideoData +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.1-I2V-14B-720P", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.1-I2V-14B-720P", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.1-I2V-14B-720P", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.1-I2V-14B-720P", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth", offload_device="cpu"), + ], +) +pipe.enable_vram_management() + +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/examples_in_diffsynth", + local_dir="./", + allow_file_pattern=f"data/examples/wan/input_image.jpg" +) +image = Image.open("data/examples/wan/input_image.jpg") + +# Image-to-video +video = pipe( + prompt="一艘小船正勇敢地乘风破浪前行。蔚蓝的大海波涛汹涌，白色的浪花拍打着船身，但小船毫不畏惧，坚定地驶向远方。阳光洒在水面上，闪烁着金色的光芒，为这壮丽的场景增添了一抹温暖。镜头拉近，可以看到船上的旗帜迎风飘扬，象征着不屈的精神与冒险的勇气。这段画面充满力量，激励人心，展现了面对挑战时的无畏与执着。", + negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + input_image=image, + seed=0, tiled=True, + height=720, width=1280, +) +save_video(video, "video.mp4", fps=15, quality=5) diff --git a/examples/wanvideo/model_inference/Wan2.1-T2V-1.3B.py b/examples/wanvideo/model_inference/Wan2.1-T2V-1.3B.py new file mode 100644 index 0000000000000000000000000000000000000000..83e300b9b820530b243e13e996fe841a6f0e7dfb --- /dev/null +++ b/examples/wanvideo/model_inference/Wan2.1-T2V-1.3B.py @@ -0,0 +1,34 @@ +import torch +from PIL import Image +from diffsynth import save_video, VideoData +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"), + ], +) +pipe.enable_vram_management() + +# Text-to-video +video = pipe( + prompt="纪实摄影风格画面，一只活泼的小狗在绿茵茵的草地上迅速奔跑。小狗毛色棕黄，两只耳朵立起，神情专注而欢快。阳光洒在它身上，使得毛发看上去格外柔软而闪亮。背景是一片开阔的草地，偶尔点缀着几朵野花，远处隐约可见蓝天和几片白云。透视感鲜明，捕捉小狗奔跑时的动感和四周草地的生机。中景侧面移动视角。", + negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + seed=0, tiled=True, +) +save_video(video, "video1.mp4", fps=15, quality=5) + +# Video-to-video +video = VideoData("video1.mp4", height=480, width=832) +video = pipe( + prompt="纪实摄影风格画面，一只活泼的小狗戴着黑色墨镜在绿茵茵的草地上迅速奔跑。小狗毛色棕黄，戴着黑色墨镜，两只耳朵立起，神情专注而欢快。阳光洒在它身上，使得毛发看上去格外柔软而闪亮。背景是一片开阔的草地，偶尔点缀着几朵野花，远处隐约可见蓝天和几片白云。透视感鲜明，捕捉小狗奔跑时的动感和四周草地的生机。中景侧面移动视角。", + negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + input_video=video, denoising_strength=0.7, + seed=1, tiled=True +) +save_video(video, "video2.mp4", fps=15, quality=5) diff --git a/examples/wanvideo/model_inference/Wan2.1-T2V-14B.py b/examples/wanvideo/model_inference/Wan2.1-T2V-14B.py new file mode 100644 index 0000000000000000000000000000000000000000..40cb02d2214f12d4795ad67fb531fd5953d421bf --- /dev/null +++ b/examples/wanvideo/model_inference/Wan2.1-T2V-14B.py @@ -0,0 +1,24 @@ +import torch +from PIL import Image +from diffsynth import save_video, VideoData +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-14B", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-14B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-14B", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"), + ], +) +pipe.enable_vram_management() + +# Text-to-video +video = pipe( + prompt="一名宇航员身穿太空服，面朝镜头骑着一匹机械马在火星表面驰骋。红色的荒凉地表延伸至远方，点缀着巨大的陨石坑和奇特的岩石结构。机械马的步伐稳健，扬起微弱的尘埃，展现出未来科技与原始探索的完美结合。宇航员手持操控装置，目光坚定，仿佛正在开辟人类的新疆域。背景是深邃的宇宙和蔚蓝的地球，画面既科幻又充满希望，让人不禁畅想未来的星际生活。", + negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + seed=0, tiled=True, +) +save_video(video, "video1.mp4", fps=15, quality=5) diff --git a/examples/wanvideo/model_inference/Wan2.1-VACE-1.3B-Preview.py b/examples/wanvideo/model_inference/Wan2.1-VACE-1.3B-Preview.py new file mode 100644 index 0000000000000000000000000000000000000000..99c0242328e1c1557015dc420107b2671ca81c6a --- /dev/null +++ b/examples/wanvideo/model_inference/Wan2.1-VACE-1.3B-Preview.py @@ -0,0 +1,52 @@ +import torch +from PIL import Image +from diffsynth import save_video, VideoData +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="iic/VACE-Wan2.1-1.3B-Preview", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="iic/VACE-Wan2.1-1.3B-Preview", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"), + ModelConfig(model_id="iic/VACE-Wan2.1-1.3B-Preview", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"), + ], +) +pipe.enable_vram_management() + +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/examples_in_diffsynth", + local_dir="./", + allow_file_pattern=["data/examples/wan/depth_video.mp4", "data/examples/wan/cat_fightning.jpg"] +) + +# Depth video -> Video +control_video = VideoData("data/examples/wan/depth_video.mp4", height=480, width=832) +video = pipe( + prompt="两只可爱的橘猫戴上拳击手套，站在一个拳击台上搏斗。", + negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + vace_video=control_video, + seed=1, tiled=True +) +save_video(video, "video1.mp4", fps=15, quality=5) + +# Reference image -> Video +video = pipe( + prompt="两只可爱的橘猫戴上拳击手套，站在一个拳击台上搏斗。", + negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + vace_reference_image=Image.open("data/examples/wan/cat_fightning.jpg").resize((832, 480)), + seed=1, tiled=True +) +save_video(video, "video2.mp4", fps=15, quality=5) + +# Depth video + Reference image -> Video +video = pipe( + prompt="两只可爱的橘猫戴上拳击手套，站在一个拳击台上搏斗。", + negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + vace_video=control_video, + vace_reference_image=Image.open("data/examples/wan/cat_fightning.jpg").resize((832, 480)), + seed=1, tiled=True +) +save_video(video, "video3.mp4", fps=15, quality=5) diff --git a/examples/wanvideo/model_inference/Wan2.1-VACE-1.3B.py b/examples/wanvideo/model_inference/Wan2.1-VACE-1.3B.py new file mode 100644 index 0000000000000000000000000000000000000000..59ca2d4dcbee69cfb35be3de744aea323347d3fa --- /dev/null +++ b/examples/wanvideo/model_inference/Wan2.1-VACE-1.3B.py @@ -0,0 +1,53 @@ +import torch +from PIL import Image +from diffsynth import save_video, VideoData +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.1-VACE-1.3B", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.1-VACE-1.3B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.1-VACE-1.3B", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"), + ], +) + +pipe.enable_vram_management() + +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/examples_in_diffsynth", + local_dir="./", + allow_file_pattern=["data/examples/wan/depth_video.mp4", "data/examples/wan/cat_fightning.jpg"] +) + +# Depth video -> Video +control_video = VideoData("data/examples/wan/depth_video.mp4", height=480, width=832) +video = pipe( + prompt="两只可爱的橘猫戴上拳击手套，站在一个拳击台上搏斗。", + negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + vace_video=control_video, + seed=1, tiled=True +) +save_video(video, "video1.mp4", fps=15, quality=5) + +# Reference image -> Video +video = pipe( + prompt="两只可爱的橘猫戴上拳击手套，站在一个拳击台上搏斗。", + negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + vace_reference_image=Image.open("data/examples/wan/cat_fightning.jpg").resize((832, 480)), + seed=1, tiled=True +) +save_video(video, "video2.mp4", fps=15, quality=5) + +# Depth video + Reference image -> Video +video = pipe( + prompt="两只可爱的橘猫戴上拳击手套，站在一个拳击台上搏斗。", + negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + vace_video=control_video, + vace_reference_image=Image.open("data/examples/wan/cat_fightning.jpg").resize((832, 480)), + seed=1, tiled=True +) +save_video(video, "video3.mp4", fps=15, quality=5) diff --git a/examples/wanvideo/model_inference/Wan2.1-VACE-14B.py b/examples/wanvideo/model_inference/Wan2.1-VACE-14B.py new file mode 100644 index 0000000000000000000000000000000000000000..e504b2c9e7db78ebd67912b7cb5c324b94f05711 --- /dev/null +++ b/examples/wanvideo/model_inference/Wan2.1-VACE-14B.py @@ -0,0 +1,54 @@ +import torch +from PIL import Image +from diffsynth import save_video, VideoData +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.1-VACE-14B", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.1-VACE-14B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.1-VACE-14B", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"), + ], +) + + +pipe.enable_vram_management() + +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/examples_in_diffsynth", + local_dir="./", + allow_file_pattern=["data/examples/wan/depth_video.mp4", "data/examples/wan/cat_fightning.jpg"] +) + +# Depth video -> Video +control_video = VideoData("data/examples/wan/depth_video.mp4", height=480, width=832) +video = pipe( + prompt="两只可爱的橘猫戴上拳击手套，站在一个拳击台上搏斗。", + negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + vace_video=control_video, + seed=1, tiled=True +) +save_video(video, "video1_14b.mp4", fps=15, quality=5) + +# Reference image -> Video +video = pipe( + prompt="两只可爱的橘猫戴上拳击手套，站在一个拳击台上搏斗。", + negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + vace_reference_image=Image.open("data/examples/wan/cat_fightning.jpg").resize((832, 480)), + seed=1, tiled=True +) +save_video(video, "video2_14b.mp4", fps=15, quality=5) + +# Depth video + Reference image -> Video +video = pipe( + prompt="两只可爱的橘猫戴上拳击手套，站在一个拳击台上搏斗。", + negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + vace_video=control_video, + vace_reference_image=Image.open("data/examples/wan/cat_fightning.jpg").resize((832, 480)), + seed=1, tiled=True +) +save_video(video, "video3_14b.mp4", fps=15, quality=5) diff --git a/examples/wanvideo/model_inference/Wan2.2-Animate-14B.py b/examples/wanvideo/model_inference/Wan2.2-Animate-14B.py new file mode 100644 index 0000000000000000000000000000000000000000..436eb1757b9d90280ee830fd1178205d794935d3 --- /dev/null +++ b/examples/wanvideo/model_inference/Wan2.2-Animate-14B.py @@ -0,0 +1,62 @@ +import torch +from PIL import Image +from diffsynth import save_video, VideoData, load_state_dict +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download, snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.2-Animate-14B", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.2-Animate-14B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.2-Animate-14B", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.2-Animate-14B", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth", offload_device="cpu"), + ], +) +pipe.enable_vram_management() + +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/examples_in_diffsynth", + local_dir="./", + allow_file_pattern="data/examples/wan/animate/*", +) + +# Animate +input_image = Image.open("data/examples/wan/animate/animate_input_image.png") +animate_pose_video = VideoData("data/examples/wan/animate/animate_pose_video.mp4").raw_data()[:81-4] +animate_face_video = VideoData("data/examples/wan/animate/animate_face_video.mp4").raw_data()[:81-4] +video = pipe( + prompt="视频中的人在做动作", + seed=0, tiled=True, + input_image=input_image, + animate_pose_video=animate_pose_video, + animate_face_video=animate_face_video, + num_frames=81, height=720, width=1280, + num_inference_steps=20, cfg_scale=1, +) +save_video(video, "video1.mp4", fps=15, quality=5) + +# Replace +snapshot_download("Wan-AI/Wan2.2-Animate-14B", allow_file_pattern="relighting_lora.ckpt", local_dir="models/Wan-AI/Wan2.2-Animate-14B") +lora_state_dict = load_state_dict("models/Wan-AI/Wan2.2-Animate-14B/relighting_lora.ckpt", torch_dtype=torch.float32, device="cuda")["state_dict"] +pipe.load_lora(pipe.dit, state_dict=lora_state_dict) +input_image = Image.open("data/examples/wan/animate/replace_input_image.png") +animate_pose_video = VideoData("data/examples/wan/animate/replace_pose_video.mp4").raw_data()[:81-4] +animate_face_video = VideoData("data/examples/wan/animate/replace_face_video.mp4").raw_data()[:81-4] +animate_inpaint_video = VideoData("data/examples/wan/animate/replace_inpaint_video.mp4").raw_data()[:81-4] +animate_mask_video = VideoData("data/examples/wan/animate/replace_mask_video.mp4").raw_data()[:81-4] +video = pipe( + prompt="视频中的人在做动作", + seed=0, tiled=True, + input_image=input_image, + animate_pose_video=animate_pose_video, + animate_face_video=animate_face_video, + animate_inpaint_video=animate_inpaint_video, + animate_mask_video=animate_mask_video, + num_frames=81, height=720, width=1280, + num_inference_steps=20, cfg_scale=1, +) +save_video(video, "video2.mp4", fps=15, quality=5) + diff --git a/examples/wanvideo/model_inference/Wan2.2-Fun-A14B-Control-Camera.py b/examples/wanvideo/model_inference/Wan2.2-Fun-A14B-Control-Camera.py new file mode 100644 index 0000000000000000000000000000000000000000..27cda27a319a004e328b50af646d1223744cd725 --- /dev/null +++ b/examples/wanvideo/model_inference/Wan2.2-Fun-A14B-Control-Camera.py @@ -0,0 +1,43 @@ +import torch +from diffsynth import save_video,VideoData +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig +from PIL import Image +from modelscope import dataset_snapshot_download + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-Control-Camera", origin_file_pattern="high_noise_model/diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-Control-Camera", origin_file_pattern="low_noise_model/diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-Control-Camera", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-Control-Camera", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"), + ], +) +pipe.enable_vram_management() + + +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/examples_in_diffsynth", + local_dir="./", + allow_file_pattern=f"data/examples/wan/input_image.jpg" +) +input_image = Image.open("data/examples/wan/input_image.jpg") + +video = pipe( + prompt="一艘小船正勇敢地乘风破浪前行。蔚蓝的大海波涛汹涌，白色的浪花拍打着船身，但小船毫不畏惧，坚定地驶向远方。阳光洒在水面上，闪烁着金色的光芒，为这壮丽的场景增添了一抹温暖。镜头拉近，可以看到船上的旗帜迎风飘扬，象征着不屈的精神与冒险的勇气。这段画面充满力量，激励人心，展现了面对挑战时的无畏与执着。", + negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + seed=0, tiled=True, + input_image=input_image, + camera_control_direction="Left", camera_control_speed=0.01, +) +save_video(video, "video_left.mp4", fps=15, quality=5) + +video = pipe( + prompt="一艘小船正勇敢地乘风破浪前行。蔚蓝的大海波涛汹涌，白色的浪花拍打着船身，但小船毫不畏惧，坚定地驶向远方。阳光洒在水面上，闪烁着金色的光芒，为这壮丽的场景增添了一抹温暖。镜头拉近，可以看到船上的旗帜迎风飘扬，象征着不屈的精神与冒险的勇气。这段画面充满力量，激励人心，展现了面对挑战时的无畏与执着。", + negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + seed=0, tiled=True, + input_image=input_image, + camera_control_direction="Up", camera_control_speed=0.01, +) +save_video(video, "video_up.mp4", fps=15, quality=5) \ No newline at end of file diff --git a/examples/wanvideo/model_inference/Wan2.2-Fun-A14B-Control.py b/examples/wanvideo/model_inference/Wan2.2-Fun-A14B-Control.py new file mode 100644 index 0000000000000000000000000000000000000000..29414225505422dd535dfe26aceb9d1e06faf41d --- /dev/null +++ b/examples/wanvideo/model_inference/Wan2.2-Fun-A14B-Control.py @@ -0,0 +1,35 @@ +import torch +from diffsynth import save_video,VideoData +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig +from PIL import Image +from modelscope import dataset_snapshot_download + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-Control", origin_file_pattern="high_noise_model/diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-Control", origin_file_pattern="low_noise_model/diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-Control", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-Control", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"), + ], +) +pipe.enable_vram_management() + +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/examples_in_diffsynth", + local_dir="./", + allow_file_pattern=["data/examples/wan/control_video.mp4", "data/examples/wan/reference_image_girl.png"] +) + +# Control video +control_video = VideoData("data/examples/wan/control_video.mp4", height=832, width=576) +reference_image = Image.open("data/examples/wan/reference_image_girl.png").resize((576, 832)) +video = pipe( + prompt="扁平风格动漫，一位长发少女优雅起舞。她五官精致，大眼睛明亮有神，黑色长发柔顺光泽。身穿淡蓝色T恤和深蓝色牛仔短裤。背景是粉色。", + negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + control_video=control_video, reference_image=reference_image, + height=832, width=576, num_frames=49, + seed=1, tiled=True +) +save_video(video, "video.mp4", fps=15, quality=5) \ No newline at end of file diff --git a/examples/wanvideo/model_inference/Wan2.2-Fun-A14B-InP.py b/examples/wanvideo/model_inference/Wan2.2-Fun-A14B-InP.py new file mode 100644 index 0000000000000000000000000000000000000000..c63e522951a28caa66bd1141f2d133f51015e380 --- /dev/null +++ b/examples/wanvideo/model_inference/Wan2.2-Fun-A14B-InP.py @@ -0,0 +1,35 @@ +import torch +from diffsynth import save_video +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig +from PIL import Image +from modelscope import dataset_snapshot_download + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-InP", origin_file_pattern="high_noise_model/diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-InP", origin_file_pattern="low_noise_model/diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-InP", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-InP", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"), + ], +) +pipe.enable_vram_management() + +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/examples_in_diffsynth", + local_dir="./", + allow_file_pattern=f"data/examples/wan/input_image.jpg" +) +image = Image.open("data/examples/wan/input_image.jpg") + +# First and last frame to video +video = pipe( + prompt="一艘小船正勇敢地乘风破浪前行。蔚蓝的大海波涛汹涌，白色的浪花拍打着船身，但小船毫不畏惧，坚定地驶向远方。阳光洒在水面上，闪烁着金色的光芒，为这壮丽的场景增添了一抹温暖。镜头拉近，可以看到船上的旗帜迎风飘扬，象征着不屈的精神与冒险的勇气。这段画面充满力量，激励人心，展现了面对挑战时的无畏与执着。", + negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + input_image=image, + seed=0, tiled=True, + # You can input `end_image=xxx` to control the last frame of the video. + # The model will automatically generate the dynamic content between `input_image` and `end_image`. +) +save_video(video, "video.mp4", fps=15, quality=5) diff --git a/examples/wanvideo/model_inference/Wan2.2-I2V-A14B.py b/examples/wanvideo/model_inference/Wan2.2-I2V-A14B.py new file mode 100644 index 0000000000000000000000000000000000000000..1d795f515f2542ca27e527dcc4ab1413db09135d --- /dev/null +++ b/examples/wanvideo/model_inference/Wan2.2-I2V-A14B.py @@ -0,0 +1,33 @@ +import torch +from PIL import Image +from diffsynth import save_video +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.2-I2V-A14B", origin_file_pattern="high_noise_model/diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.2-I2V-A14B", origin_file_pattern="low_noise_model/diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.2-I2V-A14B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.2-I2V-A14B", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"), + ], +) +pipe.enable_vram_management() + +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/examples_in_diffsynth", + local_dir="./", + allow_file_pattern=["data/examples/wan/cat_fightning.jpg"] +) +input_image = Image.open("data/examples/wan/cat_fightning.jpg").resize((832, 480)) + +video = pipe( + prompt="Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage.", + negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + seed=0, tiled=True, + input_image=input_image, + switch_DiT_boundary=0.9, +) +save_video(video, "video1.mp4", fps=15, quality=5) diff --git a/examples/wanvideo/model_inference/Wan2.2-S2V-14B.py b/examples/wanvideo/model_inference/Wan2.2-S2V-14B.py new file mode 100644 index 0000000000000000000000000000000000000000..bb93871a29ec0030623ac47814f1d016a3500ba5 --- /dev/null +++ b/examples/wanvideo/model_inference/Wan2.2-S2V-14B.py @@ -0,0 +1,69 @@ +import torch +from PIL import Image +import librosa +from diffsynth import VideoData, save_video_with_audio +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.2-S2V-14B", origin_file_pattern="diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Wan-AI/Wan2.2-S2V-14B", origin_file_pattern="wav2vec2-large-xlsr-53-english/model.safetensors"), + ModelConfig(model_id="Wan-AI/Wan2.2-S2V-14B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="Wan-AI/Wan2.2-S2V-14B", origin_file_pattern="Wan2.1_VAE.pth"), + ], + audio_processor_config=ModelConfig(model_id="Wan-AI/Wan2.2-S2V-14B", origin_file_pattern="wav2vec2-large-xlsr-53-english/"), +) +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/example_video_dataset", + local_dir="./data/example_video_dataset", + allow_file_pattern=f"wans2v/*" +) + +num_frames = 81 # 4n+1 +height = 448 +width = 832 + +prompt = "a person is singing" +negative_prompt = "画面模糊，最差质量，画面模糊，细节模糊不清，情绪激动剧烈，手快速抖动，字幕，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" +input_image = Image.open("data/example_video_dataset/wans2v/pose.png").convert("RGB").resize((width, height)) +# s2v audio input, recommend 16kHz sampling rate +audio_path = 'data/example_video_dataset/wans2v/sing.MP3' +input_audio, sample_rate = librosa.load(audio_path, sr=16000) + +# Speech-to-video +video = pipe( + prompt=prompt, + input_image=input_image, + negative_prompt=negative_prompt, + seed=0, + num_frames=num_frames, + height=height, + width=width, + audio_sample_rate=sample_rate, + input_audio=input_audio, + num_inference_steps=40, +) +save_video_with_audio(video[1:], "video_with_audio.mp4", audio_path, fps=16, quality=5) + +# s2v will use the first (num_frames) frames as reference. height and width must be the same as input_image. And fps should be 16, the same as output video fps. +pose_video_path = 'data/example_video_dataset/wans2v/pose.mp4' +pose_video = VideoData(pose_video_path, height=height, width=width) + +# Speech-to-video with pose +video = pipe( + prompt=prompt, + input_image=input_image, + negative_prompt=negative_prompt, + seed=0, + num_frames=num_frames, + height=height, + width=width, + audio_sample_rate=sample_rate, + input_audio=input_audio, + s2v_pose_video=pose_video, + num_inference_steps=40, +) +save_video_with_audio(video[1:], "video_pose_with_audio.mp4", audio_path, fps=16, quality=5) diff --git a/examples/wanvideo/model_inference/Wan2.2-S2V-14B_multi_clips.py b/examples/wanvideo/model_inference/Wan2.2-S2V-14B_multi_clips.py new file mode 100644 index 0000000000000000000000000000000000000000..6ffff6d8779c89d7a259dd36df39b25349d7fe86 --- /dev/null +++ b/examples/wanvideo/model_inference/Wan2.2-S2V-14B_multi_clips.py @@ -0,0 +1,116 @@ +import torch +from PIL import Image +import librosa +from diffsynth import VideoData, save_video_with_audio +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig, WanVideoUnit_S2V +from modelscope import dataset_snapshot_download + + +def speech_to_video( + prompt, + input_image, + audio_path, + negative_prompt="", + num_clip=None, + audio_sample_rate=16000, + pose_video_path=None, + infer_frames=80, + height=448, + width=832, + num_inference_steps=40, + fps=16, # recommend fixing fps as 16 for s2v + motion_frames=73, # hyperparameter of wan2.2-s2v + save_path=None, +): + # s2v audio input, recommend 16kHz sampling rate + input_audio, sample_rate = librosa.load(audio_path, sr=audio_sample_rate) + # s2v will use the first (num_frames) frames as reference. height and width must be the same as input_image. And fps should be 16, the same as output video fps. + pose_video = VideoData(pose_video_path, height=height, width=width) if pose_video_path is not None else None + + audio_embeds, pose_latents, num_repeat = WanVideoUnit_S2V.pre_calculate_audio_pose( + pipe=pipe, + input_audio=input_audio, + audio_sample_rate=sample_rate, + s2v_pose_video=pose_video, + num_frames=infer_frames + 1, + height=height, + width=width, + fps=fps, + ) + num_repeat = min(num_repeat, num_clip) if num_clip is not None else num_repeat + print(f"Generating {num_repeat} video clips...") + motion_videos = [] + video = [] + for r in range(num_repeat): + s2v_pose_latents = pose_latents[r] if pose_latents is not None else None + current_clip = pipe( + prompt=prompt, + input_image=input_image, + negative_prompt=negative_prompt, + seed=0, + num_frames=infer_frames + 1, + height=height, + width=width, + audio_embeds=audio_embeds[r], + s2v_pose_latents=s2v_pose_latents, + motion_video=motion_videos, + num_inference_steps=num_inference_steps, + ) + current_clip = current_clip[-infer_frames:] + if r == 0: + current_clip = current_clip[3:] + overlap_frames_num = min(motion_frames, len(current_clip)) + motion_videos = motion_videos[overlap_frames_num:] + current_clip[-overlap_frames_num:] + video.extend(current_clip) + save_video_with_audio(video, save_path, audio_path, fps=16, quality=5) + print(f"processed the {r+1}th clip of total {num_repeat} clips.") + return video + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.2-S2V-14B", origin_file_pattern="diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Wan-AI/Wan2.2-S2V-14B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="Wan-AI/Wan2.2-S2V-14B", origin_file_pattern="wav2vec2-large-xlsr-53-english/model.safetensors"), + ModelConfig(model_id="Wan-AI/Wan2.2-S2V-14B", origin_file_pattern="Wan2.1_VAE.pth"), + ], + audio_processor_config=ModelConfig(model_id="Wan-AI/Wan2.2-S2V-14B", origin_file_pattern="wav2vec2-large-xlsr-53-english/"), +) + +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/example_video_dataset", + local_dir="./data/example_video_dataset", + allow_file_pattern=f"wans2v/*", +) + +infer_frames = 80 # 4n +height = 448 +width = 832 + +prompt = "a person is singing" +negative_prompt = "画面模糊，最差质量，画面模糊，细节模糊不清，情绪激动剧烈，手快速抖动，字幕，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" +input_image = Image.open("data/example_video_dataset/wans2v/pose.png").convert("RGB").resize((width, height)) + +video_with_audio = speech_to_video( + prompt=prompt, + input_image=input_image, + audio_path='data/example_video_dataset/wans2v/sing.MP3', + negative_prompt=negative_prompt, + pose_video_path='data/example_video_dataset/wans2v/pose.mp4', + save_path="video_with_audio_full.mp4", + infer_frames=infer_frames, + height=height, + width=width, +) +# num_clip means generating only the first n clips with n * infer_frames frames. +video_with_audio_pose = speech_to_video( + prompt=prompt, + input_image=input_image, + audio_path='data/example_video_dataset/wans2v/sing.MP3', + negative_prompt=negative_prompt, + pose_video_path='data/example_video_dataset/wans2v/pose.mp4', + save_path="video_with_audio_pose_clip_2.mp4", + num_clip=2 +) diff --git a/examples/wanvideo/model_inference/Wan2.2-T2V-A14B.py b/examples/wanvideo/model_inference/Wan2.2-T2V-A14B.py new file mode 100644 index 0000000000000000000000000000000000000000..27b10d0a8314cbfd85b802230828f33287f30f31 --- /dev/null +++ b/examples/wanvideo/model_inference/Wan2.2-T2V-A14B.py @@ -0,0 +1,24 @@ +import torch +from diffsynth import save_video +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.2-T2V-A14B", origin_file_pattern="high_noise_model/diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.2-T2V-A14B", origin_file_pattern="low_noise_model/diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.2-T2V-A14B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.2-T2V-A14B", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"), + ], +) +pipe.enable_vram_management() + +# Text-to-video +video = pipe( + prompt="Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage.", + negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + seed=0, tiled=True, +) +save_video(video, "video1.mp4", fps=15, quality=5) diff --git a/examples/wanvideo/model_inference/Wan2.2-TI2V-5B.py b/examples/wanvideo/model_inference/Wan2.2-TI2V-5B.py new file mode 100644 index 0000000000000000000000000000000000000000..fa919657a22b8701e1d393331c7b16dd17d9c6fb --- /dev/null +++ b/examples/wanvideo/model_inference/Wan2.2-TI2V-5B.py @@ -0,0 +1,43 @@ +import torch +from PIL import Image +from diffsynth import save_video +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.2-TI2V-5B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.2-TI2V-5B", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.2-TI2V-5B", origin_file_pattern="Wan2.2_VAE.pth", offload_device="cpu"), + ], +) +pipe.enable_vram_management() + +# Text-to-video +video = pipe( + prompt="两只可爱的橘猫戴上拳击手套，站在一个拳击台上搏斗。", + negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + seed=0, tiled=True, + height=704, width=1248, + num_frames=121, +) +save_video(video, "video1.mp4", fps=15, quality=5) + +# Image-to-video +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/examples_in_diffsynth", + local_dir="./", + allow_file_pattern=["data/examples/wan/cat_fightning.jpg"] +) +input_image = Image.open("data/examples/wan/cat_fightning.jpg").resize((1248, 704)) +video = pipe( + prompt="两只可爱的橘猫戴上拳击手套，站在一个拳击台上搏斗。", + negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + seed=0, tiled=True, + height=704, width=1248, + input_image=input_image, + num_frames=121, +) +save_video(video, "video2.mp4", fps=15, quality=5) diff --git a/examples/wanvideo/model_inference/Wan2.2-VACE-Fun-A14B.py b/examples/wanvideo/model_inference/Wan2.2-VACE-Fun-A14B.py new file mode 100644 index 0000000000000000000000000000000000000000..a7681924c802ec09043167c6f405981c790e73be --- /dev/null +++ b/examples/wanvideo/model_inference/Wan2.2-VACE-Fun-A14B.py @@ -0,0 +1,54 @@ +import torch +from PIL import Image +from diffsynth import save_video, VideoData +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="PAI/Wan2.2-VACE-Fun-A14B", origin_file_pattern="high_noise_model/diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.2-VACE-Fun-A14B", origin_file_pattern="low_noise_model/diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.2-VACE-Fun-A14B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.2-VACE-Fun-A14B", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"), + ], +) +pipe.enable_vram_management() + + +dataset_snapshot_download( + dataset_id="DiffSynth-Studio/examples_in_diffsynth", + local_dir="./", + allow_file_pattern=["data/examples/wan/depth_video.mp4", "data/examples/wan/cat_fightning.jpg"] +) + +# Depth video -> Video +control_video = VideoData("data/examples/wan/depth_video.mp4", height=480, width=832) +video = pipe( + prompt="两只可爱的橘猫戴上拳击手套，站在一个拳击台上搏斗。", + negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + vace_video=control_video, + seed=1, tiled=True +) +save_video(video, "video1_14b.mp4", fps=15, quality=5) + +# Reference image -> Video +video = pipe( + prompt="两只可爱的橘猫戴上拳击手套，站在一个拳击台上搏斗。", + negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + vace_reference_image=Image.open("data/examples/wan/cat_fightning.jpg").resize((832, 480)), + seed=1, tiled=True +) +save_video(video, "video2_14b.mp4", fps=15, quality=5) + +# Depth video + Reference image -> Video +video = pipe( + prompt="两只可爱的橘猫戴上拳击手套，站在一个拳击台上搏斗。", + negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + vace_video=control_video, + vace_reference_image=Image.open("data/examples/wan/cat_fightning.jpg").resize((832, 480)), + seed=1, tiled=True +) +save_video(video, "video3_14b.mp4", fps=15, quality=5) diff --git a/examples/wanvideo/model_inference/krea-realtime-video.py b/examples/wanvideo/model_inference/krea-realtime-video.py new file mode 100644 index 0000000000000000000000000000000000000000..f8b111bee46070ad477ea4711cd72a999f39bf8a --- /dev/null +++ b/examples/wanvideo/model_inference/krea-realtime-video.py @@ -0,0 +1,25 @@ +import torch +from diffsynth import save_video +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="krea/krea-realtime-video", origin_file_pattern="krea-realtime-video-14b.safetensors", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-14B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-14B", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"), + ], +) +pipe.enable_vram_management() + +# Text-to-video +video = pipe( + prompt="a cat sitting on a boat", + num_inference_steps=6, num_frames=81, + seed=0, tiled=True, + cfg_scale=1, + sigma_shift=20, +) +save_video(video, "video1.mp4", fps=15, quality=5) diff --git a/examples/wanvideo/model_training/full/LongCat-Video.sh b/examples/wanvideo/model_training/full/LongCat-Video.sh new file mode 100644 index 0000000000000000000000000000000000000000..2d8902e6712cb9bde92bad188dd4336591e36ca5 --- /dev/null +++ b/examples/wanvideo/model_training/full/LongCat-Video.sh @@ -0,0 +1,12 @@ +accelerate launch --config_file examples/wanvideo/model_training/full/accelerate_config_14B.yaml examples/wanvideo/model_training/train.py \ + --dataset_base_path data/example_video_dataset \ + --dataset_metadata_path data/example_video_dataset/metadata.csv \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "meituan-longcat/LongCat-Video:dit/diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.1-T2V-14B:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.1-T2V-14B:Wan2.1_VAE.pth" \ + --learning_rate 1e-5 \ + --num_epochs 2 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/LongCat-Video_full" \ + --trainable_models "dit" \ No newline at end of file diff --git a/examples/wanvideo/model_training/full/Video-As-Prompt-Wan2.1-14B.sh b/examples/wanvideo/model_training/full/Video-As-Prompt-Wan2.1-14B.sh new file mode 100644 index 0000000000000000000000000000000000000000..6be4d1239c183d77cf3312c3bfff1918013371bb --- /dev/null +++ b/examples/wanvideo/model_training/full/Video-As-Prompt-Wan2.1-14B.sh @@ -0,0 +1,16 @@ +accelerate launch --config_file examples/wanvideo/model_training/full/accelerate_config_14B.yaml examples/wanvideo/model_training/train.py \ + --dataset_base_path data/example_video_dataset \ + --dataset_metadata_path data/example_video_dataset/metadata_vap.csv \ + --data_file_keys "video,vap_video" \ + --height 480 \ + --width 832 \ + --num_frames 49 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "ByteDance/Video-As-Prompt-Wan2.1-14B:transformer/diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.1-I2V-14B-720P:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.1-I2V-14B-720P:Wan2.1_VAE.pth,Wan-AI/Wan2.1-I2V-14B-720P:models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth" \ + --learning_rate 1e-5 \ + --num_epochs 2 \ + --remove_prefix_in_ckpt "pipe.vap." \ + --output_path "./models/train/Video-As-Prompt-Wan2.1-14B_full" \ + --trainable_models "vap" \ + --extra_inputs "vap_video,input_image" \ + --use_gradient_checkpointing_offload diff --git a/examples/wanvideo/model_training/full/Wan2.1-1.3b-speedcontrol-v1.sh b/examples/wanvideo/model_training/full/Wan2.1-1.3b-speedcontrol-v1.sh new file mode 100644 index 0000000000000000000000000000000000000000..3d580ab95b96c0b727dc2874d0178110a8eec85c --- /dev/null +++ b/examples/wanvideo/model_training/full/Wan2.1-1.3b-speedcontrol-v1.sh @@ -0,0 +1,13 @@ +accelerate launch examples/wanvideo/model_training/train.py \ + --dataset_base_path data/example_video_dataset \ + --dataset_metadata_path data/example_video_dataset/metadata_motion_bucket_id.csv \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "Wan-AI/Wan2.1-T2V-1.3B:diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.1-T2V-1.3B:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.1-T2V-1.3B:Wan2.1_VAE.pth,DiffSynth-Studio/Wan2.1-1.3b-speedcontrol-v1:model.safetensors" \ + --learning_rate 1e-5 \ + --num_epochs 2 \ + --remove_prefix_in_ckpt "pipe.motion_controller." \ + --output_path "./models/train/Wan2.1-1.3b-speedcontrol-v1_full" \ + --trainable_models "motion_controller" \ + --extra_inputs "motion_bucket_id" \ No newline at end of file diff --git a/examples/wanvideo/model_training/full/Wan2.1-FLF2V-14B-720P.sh b/examples/wanvideo/model_training/full/Wan2.1-FLF2V-14B-720P.sh new file mode 100644 index 0000000000000000000000000000000000000000..c9f6f641040a5b65bd983cf337b1fb93df702718 --- /dev/null +++ b/examples/wanvideo/model_training/full/Wan2.1-FLF2V-14B-720P.sh @@ -0,0 +1,13 @@ +accelerate launch --config_file examples/wanvideo/model_training/full/accelerate_config_14B.yaml examples/wanvideo/model_training/train.py \ + --dataset_base_path data/example_video_dataset \ + --dataset_metadata_path data/example_video_dataset/metadata.csv \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "Wan-AI/Wan2.1-FLF2V-14B-720P:diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.1-FLF2V-14B-720P:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.1-FLF2V-14B-720P:Wan2.1_VAE.pth,Wan-AI/Wan2.1-FLF2V-14B-720P:models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth" \ + --learning_rate 1e-5 \ + --num_epochs 2 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.1-FLF2V-14B-720P_full" \ + --trainable_models "dit" \ + --extra_inputs "input_image,end_image" \ No newline at end of file diff --git a/examples/wanvideo/model_training/full/Wan2.1-Fun-1.3B-Control.sh b/examples/wanvideo/model_training/full/Wan2.1-Fun-1.3B-Control.sh new file mode 100644 index 0000000000000000000000000000000000000000..45a99ded5ad105c775b4ee641164fb21e2ba780f --- /dev/null +++ b/examples/wanvideo/model_training/full/Wan2.1-Fun-1.3B-Control.sh @@ -0,0 +1,14 @@ +accelerate launch examples/wanvideo/model_training/train.py \ + --dataset_base_path data/example_video_dataset \ + --dataset_metadata_path data/example_video_dataset/metadata_control.csv \ + --data_file_keys "video,control_video" \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "PAI/Wan2.1-Fun-1.3B-Control:diffusion_pytorch_model*.safetensors,PAI/Wan2.1-Fun-1.3B-Control:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.1-Fun-1.3B-Control:Wan2.1_VAE.pth,PAI/Wan2.1-Fun-1.3B-Control:models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth" \ + --learning_rate 1e-5 \ + --num_epochs 2 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.1-Fun-1.3B-Control_full" \ + --trainable_models "dit" \ + --extra_inputs "control_video" \ No newline at end of file diff --git a/examples/wanvideo/model_training/full/Wan2.1-Fun-1.3B-InP.sh b/examples/wanvideo/model_training/full/Wan2.1-Fun-1.3B-InP.sh new file mode 100644 index 0000000000000000000000000000000000000000..a202bf9890797c719ec2e67dda93ea35b0273394 --- /dev/null +++ b/examples/wanvideo/model_training/full/Wan2.1-Fun-1.3B-InP.sh @@ -0,0 +1,13 @@ +accelerate launch examples/wanvideo/model_training/train.py \ + --dataset_base_path data/example_video_dataset \ + --dataset_metadata_path data/example_video_dataset/metadata.csv \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "PAI/Wan2.1-Fun-1.3B-InP:diffusion_pytorch_model*.safetensors,PAI/Wan2.1-Fun-1.3B-InP:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.1-Fun-1.3B-InP:Wan2.1_VAE.pth,PAI/Wan2.1-Fun-1.3B-InP:models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth" \ + --learning_rate 1e-5 \ + --num_epochs 2 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.1-Fun-1.3B-InP_full" \ + --trainable_models "dit" \ + --extra_inputs "input_image,end_image" \ No newline at end of file diff --git a/examples/wanvideo/model_training/full/Wan2.1-Fun-14B-Control.sh b/examples/wanvideo/model_training/full/Wan2.1-Fun-14B-Control.sh new file mode 100644 index 0000000000000000000000000000000000000000..8a17c3f1c6127ee21b01aa9a169bcb55bdd758f1 --- /dev/null +++ b/examples/wanvideo/model_training/full/Wan2.1-Fun-14B-Control.sh @@ -0,0 +1,14 @@ +accelerate launch --config_file examples/wanvideo/model_training/full/accelerate_config_14B.yaml examples/wanvideo/model_training/train.py \ + --dataset_base_path data/example_video_dataset \ + --dataset_metadata_path data/example_video_dataset/metadata_control.csv \ + --data_file_keys "video,control_video" \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "PAI/Wan2.1-Fun-14B-Control:diffusion_pytorch_model*.safetensors,PAI/Wan2.1-Fun-14B-Control:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.1-Fun-14B-Control:Wan2.1_VAE.pth,PAI/Wan2.1-Fun-14B-Control:models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth" \ + --learning_rate 1e-5 \ + --num_epochs 2 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.1-Fun-14B-Control_full" \ + --trainable_models "dit" \ + --extra_inputs "control_video" \ No newline at end of file diff --git a/examples/wanvideo/model_training/full/Wan2.1-Fun-14B-InP.sh b/examples/wanvideo/model_training/full/Wan2.1-Fun-14B-InP.sh new file mode 100644 index 0000000000000000000000000000000000000000..86feae73cab10e414de5f3a2c6e400285495dfaa --- /dev/null +++ b/examples/wanvideo/model_training/full/Wan2.1-Fun-14B-InP.sh @@ -0,0 +1,13 @@ +accelerate launch --config_file examples/wanvideo/model_training/full/accelerate_config_14B.yaml examples/wanvideo/model_training/train.py \ + --dataset_base_path data/example_video_dataset \ + --dataset_metadata_path data/example_video_dataset/metadata.csv \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "PAI/Wan2.1-Fun-14B-InP:diffusion_pytorch_model*.safetensors,PAI/Wan2.1-Fun-14B-InP:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.1-Fun-14B-InP:Wan2.1_VAE.pth,PAI/Wan2.1-Fun-14B-InP:models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth" \ + --learning_rate 1e-5 \ + --num_epochs 2 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.1-Fun-14B-InP_full" \ + --trainable_models "dit" \ + --extra_inputs "input_image,end_image" \ No newline at end of file diff --git a/examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-1.3B-Control-Camera.sh b/examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-1.3B-Control-Camera.sh new file mode 100644 index 0000000000000000000000000000000000000000..b59ed32c5c844e40eaa053f5ccb2f9e3f5af9f0b --- /dev/null +++ b/examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-1.3B-Control-Camera.sh @@ -0,0 +1,13 @@ +accelerate launch examples/wanvideo/model_training/train.py \ + --dataset_base_path data/example_video_dataset \ + --dataset_metadata_path data/example_video_dataset/metadata_camera_control.csv \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "PAI/Wan2.1-Fun-V1.1-1.3B-Control-Camera:diffusion_pytorch_model*.safetensors,PAI/Wan2.1-Fun-V1.1-1.3B-Control-Camera:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.1-Fun-V1.1-1.3B-Control-Camera:Wan2.1_VAE.pth,PAI/Wan2.1-Fun-V1.1-1.3B-Control-Camera:models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth" \ + --learning_rate 1e-5 \ + --num_epochs 2 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.1-Fun-V1.1-1.3B-Control-Camera_full" \ + --trainable_models "dit" \ + --extra_inputs "input_image,camera_control_direction,camera_control_speed" \ No newline at end of file diff --git a/examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-1.3B-Control.sh b/examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-1.3B-Control.sh new file mode 100644 index 0000000000000000000000000000000000000000..34273c1ec50a8b4850858bfc712088812c6005e6 --- /dev/null +++ b/examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-1.3B-Control.sh @@ -0,0 +1,14 @@ +accelerate launch examples/wanvideo/model_training/train.py \ + --dataset_base_path data/example_video_dataset \ + --dataset_metadata_path data/example_video_dataset/metadata_reference_control.csv \ + --data_file_keys "video,control_video,reference_image" \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "PAI/Wan2.1-Fun-V1.1-1.3B-Control:diffusion_pytorch_model*.safetensors,PAI/Wan2.1-Fun-V1.1-1.3B-Control:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.1-Fun-V1.1-1.3B-Control:Wan2.1_VAE.pth,PAI/Wan2.1-Fun-V1.1-1.3B-Control:models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth" \ + --learning_rate 1e-5 \ + --num_epochs 2 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.1-Fun-V1.1-1.3B-Control_full" \ + --trainable_models "dit" \ + --extra_inputs "control_video,reference_image" \ No newline at end of file diff --git a/examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-1.3B-InP.sh b/examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-1.3B-InP.sh new file mode 100644 index 0000000000000000000000000000000000000000..f6eed97db8ab61860549704d3238db7426073e77 --- /dev/null +++ b/examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-1.3B-InP.sh @@ -0,0 +1,13 @@ +accelerate launch examples/wanvideo/model_training/train.py \ + --dataset_base_path data/example_video_dataset \ + --dataset_metadata_path data/example_video_dataset/metadata.csv \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "PAI/Wan2.1-Fun-V1.1-1.3B-InP:diffusion_pytorch_model*.safetensors,PAI/Wan2.1-Fun-V1.1-1.3B-InP:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.1-Fun-V1.1-1.3B-InP:Wan2.1_VAE.pth,PAI/Wan2.1-Fun-V1.1-1.3B-InP:models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth" \ + --learning_rate 1e-5 \ + --num_epochs 2 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.1-Fun-V1.1-1.3B-InP_full" \ + --trainable_models "dit" \ + --extra_inputs "input_image,end_image" \ No newline at end of file diff --git a/examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-14B-Control-Camera.sh b/examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-14B-Control-Camera.sh new file mode 100644 index 0000000000000000000000000000000000000000..41b87e990df97bff7521e81d418868d6f1d1e054 --- /dev/null +++ b/examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-14B-Control-Camera.sh @@ -0,0 +1,13 @@ +accelerate launch --config_file examples/wanvideo/model_training/full/accelerate_config_14B.yaml examples/wanvideo/model_training/train.py \ + --dataset_base_path data/example_video_dataset \ + --dataset_metadata_path data/example_video_dataset/metadata_camera_control.csv \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "PAI/Wan2.1-Fun-V1.1-14B-Control-Camera:diffusion_pytorch_model*.safetensors,PAI/Wan2.1-Fun-V1.1-14B-Control-Camera:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.1-Fun-V1.1-14B-Control-Camera:Wan2.1_VAE.pth,PAI/Wan2.1-Fun-V1.1-14B-Control-Camera:models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth" \ + --learning_rate 1e-5 \ + --num_epochs 2 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.1-Fun-V1.1-14B-Control-Camera_full" \ + --trainable_models "dit" \ + --extra_inputs "input_image,camera_control_direction,camera_control_speed" \ No newline at end of file diff --git a/examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-14B-Control.sh b/examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-14B-Control.sh new file mode 100644 index 0000000000000000000000000000000000000000..ce6640e6334f5f81372ff435c7856864594949ad --- /dev/null +++ b/examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-14B-Control.sh @@ -0,0 +1,14 @@ +accelerate launch --config_file examples/wanvideo/model_training/full/accelerate_config_14B.yaml examples/wanvideo/model_training/train.py \ + --dataset_base_path data/example_video_dataset \ + --dataset_metadata_path data/example_video_dataset/metadata_reference_control.csv \ + --data_file_keys "video,control_video,reference_image" \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "PAI/Wan2.1-Fun-V1.1-14B-Control:diffusion_pytorch_model*.safetensors,PAI/Wan2.1-Fun-V1.1-14B-Control:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.1-Fun-V1.1-14B-Control:Wan2.1_VAE.pth,PAI/Wan2.1-Fun-V1.1-14B-Control:models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth" \ + --learning_rate 1e-5 \ + --num_epochs 2 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.1-Fun-V1.1-14B-Control_full" \ + --trainable_models "dit" \ + --extra_inputs "control_video,reference_image" \ No newline at end of file diff --git a/examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-14B-InP.sh b/examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-14B-InP.sh new file mode 100644 index 0000000000000000000000000000000000000000..afb5d3dab59cebfc4af7628f63d90b107b87e522 --- /dev/null +++ b/examples/wanvideo/model_training/full/Wan2.1-Fun-V1.1-14B-InP.sh @@ -0,0 +1,13 @@ +accelerate launch --config_file examples/wanvideo/model_training/full/accelerate_config_14B.yaml examples/wanvideo/model_training/train.py \ + --dataset_base_path data/example_video_dataset \ + --dataset_metadata_path data/example_video_dataset/metadata.csv \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "PAI/Wan2.1-Fun-V1.1-14B-InP:diffusion_pytorch_model*.safetensors,PAI/Wan2.1-Fun-V1.1-14B-InP:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.1-Fun-V1.1-14B-InP:Wan2.1_VAE.pth,PAI/Wan2.1-Fun-V1.1-14B-InP:models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth" \ + --learning_rate 1e-5 \ + --num_epochs 2 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.1-Fun-V1.1-14B-InP_full" \ + --trainable_models "dit" \ + --extra_inputs "input_image,end_image" \ No newline at end of file diff --git a/examples/wanvideo/model_training/full/Wan2.1-I2V-14B-480P.sh b/examples/wanvideo/model_training/full/Wan2.1-I2V-14B-480P.sh new file mode 100644 index 0000000000000000000000000000000000000000..6d257ff55c58ca117d6b22bf84e61bcab664a2e0 --- /dev/null +++ b/examples/wanvideo/model_training/full/Wan2.1-I2V-14B-480P.sh @@ -0,0 +1,13 @@ +accelerate launch --config_file examples/wanvideo/model_training/full/accelerate_config_14B.yaml examples/wanvideo/model_training/train.py \ + --dataset_base_path data/example_video_dataset \ + --dataset_metadata_path data/example_video_dataset/metadata.csv \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "Wan-AI/Wan2.1-I2V-14B-480P:diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.1-I2V-14B-480P:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.1-I2V-14B-480P:Wan2.1_VAE.pth,Wan-AI/Wan2.1-I2V-14B-480P:models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth" \ + --learning_rate 1e-5 \ + --num_epochs 2 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.1-I2V-14B-480P_full" \ + --trainable_models "dit" \ + --extra_inputs "input_image" \ No newline at end of file diff --git a/examples/wanvideo/model_training/full/Wan2.1-I2V-14B-720P.sh b/examples/wanvideo/model_training/full/Wan2.1-I2V-14B-720P.sh new file mode 100644 index 0000000000000000000000000000000000000000..bbb2870d21c85f3f6d37c6661d56dab216211e54 --- /dev/null +++ b/examples/wanvideo/model_training/full/Wan2.1-I2V-14B-720P.sh @@ -0,0 +1,15 @@ +accelerate launch --config_file examples/wanvideo/model_training/full/accelerate_config_14B.yaml examples/wanvideo/model_training/train.py \ + --dataset_base_path data/example_video_dataset \ + --dataset_metadata_path data/example_video_dataset/metadata.csv \ + --height 720 \ + --width 1280 \ + --num_frames 49 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "Wan-AI/Wan2.1-I2V-14B-720P:diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.1-I2V-14B-720P:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.1-I2V-14B-720P:Wan2.1_VAE.pth,Wan-AI/Wan2.1-I2V-14B-720P:models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth" \ + --learning_rate 1e-5 \ + --num_epochs 2 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.1-I2V-14B-720P_full" \ + --trainable_models "dit" \ + --extra_inputs "input_image" \ + --use_gradient_checkpointing_offload \ No newline at end of file diff --git a/examples/wanvideo/model_training/full/Wan2.1-T2V-1.3B.sh b/examples/wanvideo/model_training/full/Wan2.1-T2V-1.3B.sh new file mode 100644 index 0000000000000000000000000000000000000000..e0d6e842ad12407fd5cbfc4bfa3c65618b2fa94c --- /dev/null +++ b/examples/wanvideo/model_training/full/Wan2.1-T2V-1.3B.sh @@ -0,0 +1,12 @@ +accelerate launch examples/wanvideo/model_training/train.py \ + --dataset_base_path data/example_video_dataset \ + --dataset_metadata_path data/example_video_dataset/metadata.csv \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "Wan-AI/Wan2.1-T2V-1.3B:diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.1-T2V-1.3B:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.1-T2V-1.3B:Wan2.1_VAE.pth" \ + --learning_rate 1e-5 \ + --num_epochs 2 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.1-T2V-1.3B_full" \ + --trainable_models "dit" \ No newline at end of file diff --git a/examples/wanvideo/model_training/full/Wan2.1-T2V-14B.sh b/examples/wanvideo/model_training/full/Wan2.1-T2V-14B.sh new file mode 100644 index 0000000000000000000000000000000000000000..ae804b0503cd0903e18b32bd11a18675d01a43dc --- /dev/null +++ b/examples/wanvideo/model_training/full/Wan2.1-T2V-14B.sh @@ -0,0 +1,12 @@ +accelerate launch --config_file examples/wanvideo/model_training/full/accelerate_config_14B.yaml examples/wanvideo/model_training/train.py \ + --dataset_base_path data/example_video_dataset \ + --dataset_metadata_path data/example_video_dataset/metadata.csv \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "Wan-AI/Wan2.1-T2V-14B:diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.1-T2V-14B:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.1-T2V-14B:Wan2.1_VAE.pth" \ + --learning_rate 1e-5 \ + --num_epochs 2 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.1-T2V-14B_full" \ + --trainable_models "dit" \ No newline at end of file diff --git a/examples/wanvideo/model_training/full/Wan2.1-VACE-1.3B-Preview.sh b/examples/wanvideo/model_training/full/Wan2.1-VACE-1.3B-Preview.sh new file mode 100644 index 0000000000000000000000000000000000000000..b348874f08e74f83d476444de143f4cfe4304dec --- /dev/null +++ b/examples/wanvideo/model_training/full/Wan2.1-VACE-1.3B-Preview.sh @@ -0,0 +1,16 @@ +accelerate launch examples/wanvideo/model_training/train.py \ + --dataset_base_path data/example_video_dataset \ + --dataset_metadata_path data/example_video_dataset/metadata_vace.csv \ + --data_file_keys "video,vace_video,vace_reference_image" \ + --height 480 \ + --width 832 \ + --num_frames 49 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "iic/VACE-Wan2.1-1.3B-Preview:diffusion_pytorch_model*.safetensors,iic/VACE-Wan2.1-1.3B-Preview:models_t5_umt5-xxl-enc-bf16.pth,iic/VACE-Wan2.1-1.3B-Preview:Wan2.1_VAE.pth" \ + --learning_rate 1e-4 \ + --num_epochs 2 \ + --remove_prefix_in_ckpt "pipe.vace." \ + --output_path "./models/train/Wan2.1-VACE-1.3B-Preview_full" \ + --trainable_models "vace" \ + --extra_inputs "vace_video,vace_reference_image" \ + --use_gradient_checkpointing_offload \ No newline at end of file diff --git a/examples/wanvideo/model_training/full/Wan2.1-VACE-1.3B.sh b/examples/wanvideo/model_training/full/Wan2.1-VACE-1.3B.sh new file mode 100644 index 0000000000000000000000000000000000000000..763252e14ee78e74fe001157bfb136bac62577d9 --- /dev/null +++ b/examples/wanvideo/model_training/full/Wan2.1-VACE-1.3B.sh @@ -0,0 +1,16 @@ +accelerate launch examples/wanvideo/model_training/train.py \ + --dataset_base_path data/example_video_dataset \ + --dataset_metadata_path data/example_video_dataset/metadata_vace.csv \ + --data_file_keys "video,vace_video,vace_reference_image" \ + --height 480 \ + --width 832 \ + --num_frames 49 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "Wan-AI/Wan2.1-VACE-1.3B:diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.1-VACE-1.3B:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.1-VACE-1.3B:Wan2.1_VAE.pth" \ + --learning_rate 1e-4 \ + --num_epochs 2 \ + --remove_prefix_in_ckpt "pipe.vace." \ + --output_path "./models/train/Wan2.1-VACE-1.3B_full" \ + --trainable_models "vace" \ + --extra_inputs "vace_video,vace_reference_image" \ + --use_gradient_checkpointing_offload \ No newline at end of file diff --git a/examples/wanvideo/model_training/full/Wan2.1-VACE-14B.sh b/examples/wanvideo/model_training/full/Wan2.1-VACE-14B.sh new file mode 100644 index 0000000000000000000000000000000000000000..c54926347b73cc5e96fce2fd8378161ea7cc1179 --- /dev/null +++ b/examples/wanvideo/model_training/full/Wan2.1-VACE-14B.sh @@ -0,0 +1,16 @@ +accelerate launch --config_file examples/wanvideo/model_training/full/accelerate_config_14B.yaml examples/wanvideo/model_training/train.py \ + --dataset_base_path data/example_video_dataset \ + --dataset_metadata_path data/example_video_dataset/metadata_vace.csv \ + --data_file_keys "video,vace_video,vace_reference_image" \ + --height 480 \ + --width 832 \ + --num_frames 17 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "Wan-AI/Wan2.1-VACE-14B:diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.1-VACE-14B:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.1-VACE-14B:Wan2.1_VAE.pth" \ + --learning_rate 1e-4 \ + --num_epochs 2 \ + --remove_prefix_in_ckpt "pipe.vace." \ + --output_path "./models/train/Wan2.1-VACE-14B_full" \ + --trainable_models "vace" \ + --extra_inputs "vace_video,vace_reference_image" \ + --use_gradient_checkpointing_offload \ No newline at end of file diff --git a/examples/wanvideo/model_training/full/Wan2.2-Animate-14B.sh b/examples/wanvideo/model_training/full/Wan2.2-Animate-14B.sh new file mode 100644 index 0000000000000000000000000000000000000000..ab09a78de92b242e98a61fb6ba46c4c875e1062f --- /dev/null +++ b/examples/wanvideo/model_training/full/Wan2.2-Animate-14B.sh @@ -0,0 +1,16 @@ +accelerate launch --config_file examples/wanvideo/model_training/full/accelerate_config_14B.yaml examples/wanvideo/model_training/train.py \ + --dataset_base_path data/example_video_dataset \ + --dataset_metadata_path data/example_video_dataset/metadata_animate.csv \ + --data_file_keys "video,animate_pose_video,animate_face_video" \ + --height 480 \ + --width 832 \ + --num_frames 81 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "Wan-AI/Wan2.2-Animate-14B:diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.2-Animate-14B:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.2-Animate-14B:Wan2.1_VAE.pth,Wan-AI/Wan2.2-Animate-14B:models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth" \ + --learning_rate 1e-5 \ + --num_epochs 2 \ + --remove_prefix_in_ckpt "pipe.animate_adapter." \ + --output_path "./models/train/Wan2.2-Animate-14B_full" \ + --trainable_models "animate_adapter" \ + --extra_inputs "input_image,animate_pose_video,animate_face_video" \ + --use_gradient_checkpointing_offload \ No newline at end of file diff --git a/examples/wanvideo/model_training/full/Wan2.2-Fun-A14B-Control-Camera.sh b/examples/wanvideo/model_training/full/Wan2.2-Fun-A14B-Control-Camera.sh new file mode 100644 index 0000000000000000000000000000000000000000..4352a05778bb5854f4aa871769d106d12056f674 --- /dev/null +++ b/examples/wanvideo/model_training/full/Wan2.2-Fun-A14B-Control-Camera.sh @@ -0,0 +1,35 @@ +accelerate launch --config_file examples/wanvideo/model_training/full/accelerate_config_14B.yaml examples/wanvideo/model_training/train.py \ + --dataset_base_path data/example_video_dataset \ + --dataset_metadata_path data/example_video_dataset/metadata_camera_control.csv \ + --data_file_keys "video,control_video,reference_image" \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "PAI/Wan2.2-Fun-A14B-Control-Camera:high_noise_model/diffusion_pytorch_model*.safetensors,PAI/Wan2.2-Fun-A14B-Control-Camera:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.2-Fun-A14B-Control-Camera:Wan2.1_VAE.pth" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.2-Fun-A14B-Control-Camera_high_niose_full" \ + --trainable_models "dit" \ + --extra_inputs "input_image,camera_control_direction,camera_control_speed" \ + --max_timestep_boundary 0.358 \ + --min_timestep_boundary 0 +# boundary corresponds to timesteps [900, 1000] + +accelerate launch --config_file examples/wanvideo/model_training/full/accelerate_config_14B.yaml examples/wanvideo/model_training/train.py \ + --dataset_base_path data/example_video_dataset \ + --dataset_metadata_path data/example_video_dataset/metadata_camera_control.csv \ + --data_file_keys "video,control_video,reference_image" \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "PAI/Wan2.2-Fun-A14B-Control-Camera:low_noise_model/diffusion_pytorch_model*.safetensors,PAI/Wan2.2-Fun-A14B-Control-Camera:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.2-Fun-A14B-Control-Camera:Wan2.1_VAE.pth" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.2-Fun-A14B-Control-Camera_low_noise_full" \ + --trainable_models "dit" \ + --extra_inputs "input_image,camera_control_direction,camera_control_speed" \ + --max_timestep_boundary 1 \ + --min_timestep_boundary 0.358 +# boundary corresponds to timesteps [0, 900] \ No newline at end of file diff --git a/examples/wanvideo/model_training/full/Wan2.2-Fun-A14B-Control.sh b/examples/wanvideo/model_training/full/Wan2.2-Fun-A14B-Control.sh new file mode 100644 index 0000000000000000000000000000000000000000..2cc7da0e5c6f697940e2ef5026b0c06775005562 --- /dev/null +++ b/examples/wanvideo/model_training/full/Wan2.2-Fun-A14B-Control.sh @@ -0,0 +1,35 @@ +accelerate launch --config_file examples/wanvideo/model_training/full/accelerate_config_14B.yaml examples/wanvideo/model_training/train.py \ + --dataset_base_path data/example_video_dataset \ + --dataset_metadata_path data/example_video_dataset/metadata_reference_control.csv \ + --data_file_keys "video,control_video,reference_image" \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "PAI/Wan2.2-Fun-A14B-Control:high_noise_model/diffusion_pytorch_model*.safetensors,PAI/Wan2.2-Fun-A14B-Control:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.2-Fun-A14B-Control:Wan2.1_VAE.pth" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.2-Fun-A14B-Control_high_niose_full" \ + --trainable_models "dit" \ + --extra_inputs "control_video,reference_image" \ + --max_timestep_boundary 0.358 \ + --min_timestep_boundary 0 +# boundary corresponds to timesteps [900, 1000] + +accelerate launch --config_file examples/wanvideo/model_training/full/accelerate_config_14B.yaml examples/wanvideo/model_training/train.py \ + --dataset_base_path data/example_video_dataset \ + --dataset_metadata_path data/example_video_dataset/metadata_reference_control.csv \ + --data_file_keys "video,control_video,reference_image" \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "PAI/Wan2.2-Fun-A14B-Control:low_noise_model/diffusion_pytorch_model*.safetensors,PAI/Wan2.2-Fun-A14B-Control:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.2-Fun-A14B-Control:Wan2.1_VAE.pth" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.2-Fun-A14B-Control_low_noise_full" \ + --trainable_models "dit" \ + --extra_inputs "control_video,reference_image" \ + --max_timestep_boundary 1 \ + --min_timestep_boundary 0.358 +# boundary corresponds to timesteps [0, 900] \ No newline at end of file diff --git a/examples/wanvideo/model_training/full/Wan2.2-Fun-A14B-InP.sh b/examples/wanvideo/model_training/full/Wan2.2-Fun-A14B-InP.sh new file mode 100644 index 0000000000000000000000000000000000000000..225b888df4849b0d4533903fa1ba9d0a41646d55 --- /dev/null +++ b/examples/wanvideo/model_training/full/Wan2.2-Fun-A14B-InP.sh @@ -0,0 +1,33 @@ +accelerate launch --config_file examples/wanvideo/model_training/full/accelerate_config_14B.yaml examples/wanvideo/model_training/train.py \ + --dataset_base_path data/example_video_dataset \ + --dataset_metadata_path data/example_video_dataset/metadata.csv \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "PAI/Wan2.2-Fun-A14B-InP:high_noise_model/diffusion_pytorch_model*.safetensors,PAI/Wan2.2-Fun-A14B-InP:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.2-Fun-A14B-InP:Wan2.1_VAE.pth" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.2-Fun-A14B-InP_high_niose_full" \ + --trainable_models "dit" \ + --extra_inputs "input_image,end_image" \ + --max_timestep_boundary 0.358 \ + --min_timestep_boundary 0 +# boundary corresponds to timesteps [900, 1000] + +accelerate launch --config_file examples/wanvideo/model_training/full/accelerate_config_14B.yaml examples/wanvideo/model_training/train.py \ + --dataset_base_path data/example_video_dataset \ + --dataset_metadata_path data/example_video_dataset/metadata.csv \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "PAI/Wan2.2-Fun-A14B-InP:low_noise_model/diffusion_pytorch_model*.safetensors,PAI/Wan2.2-Fun-A14B-InP:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.2-Fun-A14B-InP:Wan2.1_VAE.pth" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.2-Fun-A14B-InP_low_noise_full" \ + --trainable_models "dit" \ + --extra_inputs "input_image,end_image" \ + --max_timestep_boundary 1 \ + --min_timestep_boundary 0.358 +# boundary corresponds to timesteps [0, 900] \ No newline at end of file diff --git a/examples/wanvideo/model_training/full/Wan2.2-I2V-A14B.sh b/examples/wanvideo/model_training/full/Wan2.2-I2V-A14B.sh new file mode 100644 index 0000000000000000000000000000000000000000..10fb02f2923d6c324aab9c3985a980661b45832a --- /dev/null +++ b/examples/wanvideo/model_training/full/Wan2.2-I2V-A14B.sh @@ -0,0 +1,37 @@ +accelerate launch --config_file examples/wanvideo/model_training/full/accelerate_config_14B.yaml examples/wanvideo/model_training/train.py \ + --dataset_base_path data/example_video_dataset \ + --dataset_metadata_path data/example_video_dataset/metadata.csv \ + --height 480 \ + --width 832 \ + --num_frames 49 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "Wan-AI/Wan2.2-I2V-A14B:high_noise_model/diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.2-I2V-A14B:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.2-I2V-A14B:Wan2.1_VAE.pth" \ + --learning_rate 1e-5 \ + --num_epochs 2 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.2-I2V-A14B_high_noise_full" \ + --trainable_models "dit" \ + --extra_inputs "input_image" \ + --use_gradient_checkpointing_offload \ + --max_timestep_boundary 0.358 \ + --min_timestep_boundary 0 +# boundary corresponds to timesteps [900, 1000] + +accelerate launch --config_file examples/wanvideo/model_training/full/accelerate_config_14B.yaml examples/wanvideo/model_training/train.py \ + --dataset_base_path data/example_video_dataset \ + --dataset_metadata_path data/example_video_dataset/metadata.csv \ + --height 480 \ + --width 832 \ + --num_frames 49 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "Wan-AI/Wan2.2-I2V-A14B:low_noise_model/diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.2-I2V-A14B:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.2-I2V-A14B:Wan2.1_VAE.pth" \ + --learning_rate 1e-5 \ + --num_epochs 2 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.2-I2V-A14B_low_noise_full" \ + --trainable_models "dit" \ + --extra_inputs "input_image" \ + --use_gradient_checkpointing_offload \ + --max_timestep_boundary 1 \ + --min_timestep_boundary 0.358 +# boundary corresponds to timesteps [0, 900) \ No newline at end of file diff --git a/examples/wanvideo/model_training/full/Wan2.2-S2V-14B.sh b/examples/wanvideo/model_training/full/Wan2.2-S2V-14B.sh new file mode 100644 index 0000000000000000000000000000000000000000..3a9a87109dd437a6e452948036d073458daf2f8b --- /dev/null +++ b/examples/wanvideo/model_training/full/Wan2.2-S2V-14B.sh @@ -0,0 +1,17 @@ +accelerate launch --config_file examples/wanvideo/model_training/full/accelerate_config_14B.yaml examples/wanvideo/model_training/train.py \ + --dataset_base_path data/example_video_dataset/wans2v \ + --dataset_metadata_path data/example_video_dataset/wans2v/metadata.csv \ + --data_file_keys "video,input_audio,s2v_pose_video" \ + --height 448 \ + --width 832 \ + --num_frames 81 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "Wan-AI/Wan2.2-S2V-14B:diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.2-S2V-14B:wav2vec2-large-xlsr-53-english/model.safetensors,Wan-AI/Wan2.2-S2V-14B:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.2-S2V-14B:Wan2.1_VAE.pth" \ + --audio_processor_config "Wan-AI/Wan2.2-S2V-14B:wav2vec2-large-xlsr-53-english/" \ + --learning_rate 1e-5 \ + --num_epochs 1 \ + --trainable_models "dit" \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.2-S2V-14B_full" \ + --extra_inputs "input_image,input_audio,s2v_pose_video" \ + --use_gradient_checkpointing_offload \ No newline at end of file diff --git a/examples/wanvideo/model_training/full/Wan2.2-T2V-A14B.sh b/examples/wanvideo/model_training/full/Wan2.2-T2V-A14B.sh new file mode 100644 index 0000000000000000000000000000000000000000..89c070429723a70e15d83acdc447a097e131abca --- /dev/null +++ b/examples/wanvideo/model_training/full/Wan2.2-T2V-A14B.sh @@ -0,0 +1,33 @@ +accelerate launch --config_file examples/wanvideo/model_training/full/accelerate_config_14B.yaml examples/wanvideo/model_training/train.py \ + --dataset_base_path data/example_video_dataset \ + --dataset_metadata_path data/example_video_dataset/metadata.csv \ + --height 480 \ + --width 832 \ + --num_frames 49 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "Wan-AI/Wan2.2-T2V-A14B:high_noise_model/diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.2-T2V-A14B:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.2-T2V-A14B:Wan2.1_VAE.pth" \ + --learning_rate 1e-5 \ + --num_epochs 2 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.2-T2V-A14B_high_noise_full" \ + --trainable_models "dit" \ + --max_timestep_boundary 0.417 \ + --min_timestep_boundary 0 +# boundary corresponds to timesteps [875, 1000] + +accelerate launch --config_file examples/wanvideo/model_training/full/accelerate_config_14B.yaml examples/wanvideo/model_training/train.py \ + --dataset_base_path data/example_video_dataset \ + --dataset_metadata_path data/example_video_dataset/metadata.csv \ + --height 480 \ + --width 832 \ + --num_frames 49 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "Wan-AI/Wan2.2-T2V-A14B:low_noise_model/diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.2-T2V-A14B:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.2-T2V-A14B:Wan2.1_VAE.pth" \ + --learning_rate 1e-5 \ + --num_epochs 2 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.2-T2V-A14B_low_noise_full" \ + --trainable_models "dit" \ + --max_timestep_boundary 1 \ + --min_timestep_boundary 0.417 +# boundary corresponds to timesteps [0, 875) \ No newline at end of file diff --git a/examples/wanvideo/model_training/full/Wan2.2-TI2V-5B.sh b/examples/wanvideo/model_training/full/Wan2.2-TI2V-5B.sh new file mode 100644 index 0000000000000000000000000000000000000000..def9f897a558dc9a4f97b1d98adff11201482986 --- /dev/null +++ b/examples/wanvideo/model_training/full/Wan2.2-TI2V-5B.sh @@ -0,0 +1,14 @@ +accelerate launch examples/wanvideo/model_training/train.py \ + --dataset_base_path data/example_video_dataset \ + --dataset_metadata_path data/example_video_dataset/metadata.csv \ + --height 480 \ + --width 832 \ + --num_frames 49 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "Wan-AI/Wan2.2-TI2V-5B:diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.2-TI2V-5B:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.2-TI2V-5B:Wan2.2_VAE.pth" \ + --learning_rate 1e-5 \ + --num_epochs 2 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.2-TI2V-5B_full" \ + --trainable_models "dit" \ + --extra_inputs "input_image" \ No newline at end of file diff --git a/examples/wanvideo/model_training/full/Wan2.2-VACE-Fun-A14B.sh b/examples/wanvideo/model_training/full/Wan2.2-VACE-Fun-A14B.sh new file mode 100644 index 0000000000000000000000000000000000000000..0ee97dac7180c3193c10f48a2d20dd1fa19f51f2 --- /dev/null +++ b/examples/wanvideo/model_training/full/Wan2.2-VACE-Fun-A14B.sh @@ -0,0 +1,40 @@ +accelerate launch --config_file examples/wanvideo/model_training/full/accelerate_config_14B.yaml examples/wanvideo/model_training/train.py \ + --dataset_base_path data/example_video_dataset \ + --dataset_metadata_path data/example_video_dataset/metadata_vace.csv \ + --data_file_keys "video,vace_video,vace_reference_image" \ + --height 480 \ + --width 832 \ + --num_frames 17 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "PAI/Wan2.2-VACE-Fun-A14B:high_noise_model/diffusion_pytorch_model*.safetensors,PAI/Wan2.2-VACE-Fun-A14B:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.2-VACE-Fun-A14B:Wan2.1_VAE.pth" \ + --learning_rate 1e-4 \ + --num_epochs 2 \ + --remove_prefix_in_ckpt "pipe.vace." \ + --output_path "./models/train/Wan2.2-VACE-Fun-A14B_high_noise_full" \ + --trainable_models "vace" \ + --extra_inputs "vace_video,vace_reference_image" \ + --use_gradient_checkpointing_offload \ + --max_timestep_boundary 0.358 \ + --min_timestep_boundary 0 +# boundary corresponds to timesteps [900, 1000] + + +accelerate launch --config_file examples/wanvideo/model_training/full/accelerate_config_14B.yaml examples/wanvideo/model_training/train.py \ + --dataset_base_path data/example_video_dataset \ + --dataset_metadata_path data/example_video_dataset/metadata_vace.csv \ + --data_file_keys "video,vace_video,vace_reference_image" \ + --height 480 \ + --width 832 \ + --num_frames 17 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "PAI/Wan2.2-VACE-Fun-A14B:low_noise_model/diffusion_pytorch_model*.safetensors,PAI/Wan2.2-VACE-Fun-A14B:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.2-VACE-Fun-A14B:Wan2.1_VAE.pth" \ + --learning_rate 1e-4 \ + --num_epochs 2 \ + --remove_prefix_in_ckpt "pipe.vace." \ + --output_path "./models/train/Wan2.2-VACE-Fun-A14B_low_noise_full" \ + --trainable_models "vace" \ + --extra_inputs "vace_video,vace_reference_image" \ + --use_gradient_checkpointing_offload \ + --max_timestep_boundary 1 \ + --min_timestep_boundary 0.358 +# boundary corresponds to timesteps [0, 900] \ No newline at end of file diff --git a/examples/wanvideo/model_training/full/accelerate_config_14B.yaml b/examples/wanvideo/model_training/full/accelerate_config_14B.yaml new file mode 100644 index 0000000000000000000000000000000000000000..3875a9da2354631046baf19e61a9d5ab1d8d6aca --- /dev/null +++ b/examples/wanvideo/model_training/full/accelerate_config_14B.yaml @@ -0,0 +1,22 @@ +compute_environment: LOCAL_MACHINE +debug: false +deepspeed_config: + gradient_accumulation_steps: 1 + offload_optimizer_device: cpu + offload_param_device: cpu + zero3_init_flag: false + zero_stage: 2 +distributed_type: DEEPSPEED +downcast_bf16: 'no' +enable_cpu_affinity: false +machine_rank: 0 +main_training_function: main +mixed_precision: bf16 +num_machines: 1 +num_processes: 8 +rdzv_backend: static +same_network: true +tpu_env: [] +tpu_use_cluster: false +tpu_use_sudo: false +use_cpu: false diff --git a/examples/wanvideo/model_training/full/krea-realtime-video.sh b/examples/wanvideo/model_training/full/krea-realtime-video.sh new file mode 100644 index 0000000000000000000000000000000000000000..f0c4c85c191a72877c696a44df81f08bdf0eac25 --- /dev/null +++ b/examples/wanvideo/model_training/full/krea-realtime-video.sh @@ -0,0 +1,12 @@ +accelerate launch --config_file examples/wanvideo/model_training/full/accelerate_config_14B.yaml examples/wanvideo/model_training/train.py \ + --dataset_base_path data/example_video_dataset \ + --dataset_metadata_path data/example_video_dataset/metadata.csv \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "krea/krea-realtime-video:krea-realtime-video-14b.safetensors,Wan-AI/Wan2.1-T2V-14B:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.1-T2V-14B:Wan2.1_VAE.pth" \ + --learning_rate 1e-5 \ + --num_epochs 2 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/krea-realtime-video_full" \ + --trainable_models "dit" \ No newline at end of file diff --git a/examples/wanvideo/model_training/full/run_test.py b/examples/wanvideo/model_training/full/run_test.py new file mode 100644 index 0000000000000000000000000000000000000000..093becdec3c721f1e3ddb401bf52507a5c7646d9 --- /dev/null +++ b/examples/wanvideo/model_training/full/run_test.py @@ -0,0 +1,38 @@ +import multiprocessing, os + + +def run_task(scripts, thread_id, thread_num): + for script_id, script in enumerate(scripts): + if script_id % thread_num == thread_id: + log_file_name = script.replace("/", "_") + ".txt" + cmd = f"CUDA_VISIBLE_DEVICES={thread_id} bash {script} > data/log/{log_file_name} 2>&1" + os.makedirs("data/log", exist_ok=True) + print(cmd, flush=True) + os.system(cmd) + + +if __name__ == "__main__": + # 1.3B + scripts = [] + for file_name in os.listdir("examples/wanvideo/model_training/full"): + if file_name != "run_test.py" and "14B" not in file_name: + scripts.append(os.path.join("examples/wanvideo/model_training/full", file_name)) + + processes = [multiprocessing.Process(target=run_task, args=(scripts, i, 8)) for i in range(8)] + for p in processes: + p.start() + for p in processes: + p.join() + + # 14B + scripts = [] + for file_name in os.listdir("examples/wanvideo/model_training/full"): + if file_name != "run_test.py" and "14B" in file_name: + scripts.append(os.path.join("examples/wanvideo/model_training/full", file_name)) + for script in scripts: + log_file_name = script.replace("/", "_") + ".txt" + cmd = f"bash {script} > data/log/{log_file_name} 2>&1" + print(cmd, flush=True) + os.system(cmd) + + print("Done!") \ No newline at end of file diff --git a/examples/wanvideo/model_training/lora/LongCat-Video.sh b/examples/wanvideo/model_training/lora/LongCat-Video.sh new file mode 100644 index 0000000000000000000000000000000000000000..022048c9736bfc6f2fce94a1136ecea9ecfa757a --- /dev/null +++ b/examples/wanvideo/model_training/lora/LongCat-Video.sh @@ -0,0 +1,14 @@ +accelerate launch examples/wanvideo/model_training/train.py \ + --dataset_base_path data/example_video_dataset \ + --dataset_metadata_path data/example_video_dataset/metadata.csv \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "meituan-longcat/LongCat-Video:dit/diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.1-T2V-14B:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.1-T2V-14B:Wan2.1_VAE.pth" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/LongCat-Video_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "adaLN_modulation.1,attn.qkv,attn.proj,cross_attn.q_linear,cross_attn.kv_linear,cross_attn.proj,ffn.w1,ffn.w2,ffn.w3" \ + --lora_rank 32 \ No newline at end of file diff --git a/examples/wanvideo/model_training/lora/Video-As-Prompt-Wan2.1-14B.sh b/examples/wanvideo/model_training/lora/Video-As-Prompt-Wan2.1-14B.sh new file mode 100644 index 0000000000000000000000000000000000000000..c2c609aadc626e987c6de48aab0639fee14b15d1 --- /dev/null +++ b/examples/wanvideo/model_training/lora/Video-As-Prompt-Wan2.1-14B.sh @@ -0,0 +1,18 @@ +accelerate launch examples/wanvideo/model_training/train.py \ + --dataset_base_path data/example_video_dataset \ + --dataset_metadata_path data/example_video_dataset/metadata_vap.csv \ + --data_file_keys "video,vap_video" \ + --height 480 \ + --width 832 \ + --num_frames 49 \ + --dataset_repeat 10 \ + --model_id_with_origin_paths "ByteDance/Video-As-Prompt-Wan2.1-14B:transformer/diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.1-I2V-14B-720P:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.1-I2V-14B-720P:Wan2.1_VAE.pth,Wan-AI/Wan2.1-I2V-14B-720P:models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Video-As-Prompt-Wan2.1-14B_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \ + --lora_rank 32 \ + --extra_inputs "vap_video,input_image" \ + --use_gradient_checkpointing_offload diff --git a/examples/wanvideo/model_training/lora/Wan2.1-1.3b-speedcontrol-v1.sh b/examples/wanvideo/model_training/lora/Wan2.1-1.3b-speedcontrol-v1.sh new file mode 100644 index 0000000000000000000000000000000000000000..51ebfe45730f74c5bcfaaaf46bc3adb476d1b563 --- /dev/null +++ b/examples/wanvideo/model_training/lora/Wan2.1-1.3b-speedcontrol-v1.sh @@ -0,0 +1,15 @@ +accelerate launch examples/wanvideo/model_training/train.py \ + --dataset_base_path data/example_video_dataset \ + --dataset_metadata_path data/example_video_dataset/metadata_motion_bucket_id.csv \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "Wan-AI/Wan2.1-T2V-1.3B:diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.1-T2V-1.3B:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.1-T2V-1.3B:Wan2.1_VAE.pth,DiffSynth-Studio/Wan2.1-1.3b-speedcontrol-v1:model.safetensors" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.1-1.3b-speedcontrol-v1_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \ + --lora_rank 32 \ + --extra_inputs "motion_bucket_id" \ No newline at end of file diff --git a/examples/wanvideo/model_training/lora/Wan2.1-FLF2V-14B-720P.sh b/examples/wanvideo/model_training/lora/Wan2.1-FLF2V-14B-720P.sh new file mode 100644 index 0000000000000000000000000000000000000000..9a9622d55091685172ee2928e0842cc76018be4e --- /dev/null +++ b/examples/wanvideo/model_training/lora/Wan2.1-FLF2V-14B-720P.sh @@ -0,0 +1,15 @@ +accelerate launch examples/wanvideo/model_training/train.py \ + --dataset_base_path data/example_video_dataset \ + --dataset_metadata_path data/example_video_dataset/metadata.csv \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "Wan-AI/Wan2.1-FLF2V-14B-720P:diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.1-FLF2V-14B-720P:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.1-FLF2V-14B-720P:Wan2.1_VAE.pth,Wan-AI/Wan2.1-FLF2V-14B-720P:models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.1-FLF2V-14B-720P_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \ + --lora_rank 32 \ + --extra_inputs "input_image,end_image" \ No newline at end of file diff --git a/examples/wanvideo/model_training/lora/Wan2.1-Fun-1.3B-Control.sh b/examples/wanvideo/model_training/lora/Wan2.1-Fun-1.3B-Control.sh new file mode 100644 index 0000000000000000000000000000000000000000..03c1f4517b7af9dbc65abe515af20f59c71067a4 --- /dev/null +++ b/examples/wanvideo/model_training/lora/Wan2.1-Fun-1.3B-Control.sh @@ -0,0 +1,16 @@ +accelerate launch examples/wanvideo/model_training/train.py \ + --dataset_base_path data/example_video_dataset \ + --dataset_metadata_path data/example_video_dataset/metadata_control.csv \ + --data_file_keys "video,control_video" \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "PAI/Wan2.1-Fun-1.3B-Control:diffusion_pytorch_model*.safetensors,PAI/Wan2.1-Fun-1.3B-Control:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.1-Fun-1.3B-Control:Wan2.1_VAE.pth,PAI/Wan2.1-Fun-1.3B-Control:models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.1-Fun-1.3B-Control_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \ + --lora_rank 32 \ + --extra_inputs "control_video" \ No newline at end of file diff --git a/examples/wanvideo/model_training/lora/Wan2.1-Fun-1.3B-InP.sh b/examples/wanvideo/model_training/lora/Wan2.1-Fun-1.3B-InP.sh new file mode 100644 index 0000000000000000000000000000000000000000..d5f509be6956fe99291529b8eb3d737102302982 --- /dev/null +++ b/examples/wanvideo/model_training/lora/Wan2.1-Fun-1.3B-InP.sh @@ -0,0 +1,15 @@ +accelerate launch examples/wanvideo/model_training/train.py \ + --dataset_base_path data/example_video_dataset \ + --dataset_metadata_path data/example_video_dataset/metadata.csv \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "PAI/Wan2.1-Fun-1.3B-InP:diffusion_pytorch_model*.safetensors,PAI/Wan2.1-Fun-1.3B-InP:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.1-Fun-1.3B-InP:Wan2.1_VAE.pth,PAI/Wan2.1-Fun-1.3B-InP:models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.1-Fun-1.3B-InP_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \ + --lora_rank 32 \ + --extra_inputs "input_image,end_image" \ No newline at end of file diff --git a/examples/wanvideo/model_training/lora/Wan2.1-Fun-14B-Control.sh b/examples/wanvideo/model_training/lora/Wan2.1-Fun-14B-Control.sh new file mode 100644 index 0000000000000000000000000000000000000000..608df5ff2010ca89ca9ae6bec075ab658aed799f --- /dev/null +++ b/examples/wanvideo/model_training/lora/Wan2.1-Fun-14B-Control.sh @@ -0,0 +1,16 @@ +accelerate launch examples/wanvideo/model_training/train.py \ + --dataset_base_path data/example_video_dataset \ + --dataset_metadata_path data/example_video_dataset/metadata_control.csv \ + --data_file_keys "video,control_video" \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "PAI/Wan2.1-Fun-14B-Control:diffusion_pytorch_model*.safetensors,PAI/Wan2.1-Fun-14B-Control:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.1-Fun-14B-Control:Wan2.1_VAE.pth,PAI/Wan2.1-Fun-14B-Control:models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.1-Fun-14B-Control_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \ + --lora_rank 32 \ + --extra_inputs "control_video" \ No newline at end of file diff --git a/examples/wanvideo/model_training/lora/Wan2.1-Fun-14B-InP.sh b/examples/wanvideo/model_training/lora/Wan2.1-Fun-14B-InP.sh new file mode 100644 index 0000000000000000000000000000000000000000..37b251812c20c4012a63dde8d98b44b53b9bb1b9 --- /dev/null +++ b/examples/wanvideo/model_training/lora/Wan2.1-Fun-14B-InP.sh @@ -0,0 +1,15 @@ +accelerate launch examples/wanvideo/model_training/train.py \ + --dataset_base_path data/example_video_dataset \ + --dataset_metadata_path data/example_video_dataset/metadata.csv \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "PAI/Wan2.1-Fun-14B-InP:diffusion_pytorch_model*.safetensors,PAI/Wan2.1-Fun-14B-InP:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.1-Fun-14B-InP:Wan2.1_VAE.pth,PAI/Wan2.1-Fun-14B-InP:models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.1-Fun-14B-InP_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \ + --lora_rank 32 \ + --extra_inputs "input_image,end_image" \ No newline at end of file diff --git a/examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-1.3B-Control-Camera.sh b/examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-1.3B-Control-Camera.sh new file mode 100644 index 0000000000000000000000000000000000000000..2f809a477b7ba85f517c777060fa53efc977f711 --- /dev/null +++ b/examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-1.3B-Control-Camera.sh @@ -0,0 +1,15 @@ +accelerate launch examples/wanvideo/model_training/train.py \ + --dataset_base_path data/example_video_dataset \ + --dataset_metadata_path data/example_video_dataset/metadata_camera_control.csv \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "PAI/Wan2.1-Fun-V1.1-1.3B-Control-Camera:diffusion_pytorch_model*.safetensors,PAI/Wan2.1-Fun-V1.1-1.3B-Control-Camera:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.1-Fun-V1.1-1.3B-Control-Camera:Wan2.1_VAE.pth,PAI/Wan2.1-Fun-V1.1-1.3B-Control-Camera:models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth" \ + --learning_rate 1e-5 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.1-Fun-V1.1-1.3B-Control-Camera_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \ + --lora_rank 32 \ + --extra_inputs "input_image,camera_control_direction,camera_control_speed" \ No newline at end of file diff --git a/examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-1.3B-Control.sh b/examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-1.3B-Control.sh new file mode 100644 index 0000000000000000000000000000000000000000..1e7156df80d6f4a4a0dd752081c5feb6d465713c --- /dev/null +++ b/examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-1.3B-Control.sh @@ -0,0 +1,16 @@ +accelerate launch examples/wanvideo/model_training/train.py \ + --dataset_base_path data/example_video_dataset \ + --dataset_metadata_path data/example_video_dataset/metadata_reference_control.csv \ + --data_file_keys "video,control_video,reference_image" \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "PAI/Wan2.1-Fun-V1.1-1.3B-Control:diffusion_pytorch_model*.safetensors,PAI/Wan2.1-Fun-V1.1-1.3B-Control:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.1-Fun-V1.1-1.3B-Control:Wan2.1_VAE.pth,PAI/Wan2.1-Fun-V1.1-1.3B-Control:models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.1-Fun-V1.1-1.3B-Control_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \ + --lora_rank 32 \ + --extra_inputs "control_video,reference_image" \ No newline at end of file diff --git a/examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-1.3B-InP.sh b/examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-1.3B-InP.sh new file mode 100644 index 0000000000000000000000000000000000000000..5879f59b57104067bb3efc77ebcbf8bf28f54df5 --- /dev/null +++ b/examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-1.3B-InP.sh @@ -0,0 +1,15 @@ +accelerate launch examples/wanvideo/model_training/train.py \ + --dataset_base_path data/example_video_dataset \ + --dataset_metadata_path data/example_video_dataset/metadata.csv \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "PAI/Wan2.1-Fun-V1.1-1.3B-InP:diffusion_pytorch_model*.safetensors,PAI/Wan2.1-Fun-V1.1-1.3B-InP:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.1-Fun-V1.1-1.3B-InP:Wan2.1_VAE.pth,PAI/Wan2.1-Fun-V1.1-1.3B-InP:models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.1-Fun-V1.1-1.3B-InP_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \ + --lora_rank 32 \ + --extra_inputs "input_image,end_image" \ No newline at end of file diff --git a/examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-14B-Control-Camera.sh b/examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-14B-Control-Camera.sh new file mode 100644 index 0000000000000000000000000000000000000000..176a05fb419cc4dee753d5d282feeb6c0fa6f280 --- /dev/null +++ b/examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-14B-Control-Camera.sh @@ -0,0 +1,15 @@ +accelerate launch examples/wanvideo/model_training/train.py \ + --dataset_base_path data/example_video_dataset \ + --dataset_metadata_path data/example_video_dataset/metadata_camera_control.csv \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "PAI/Wan2.1-Fun-V1.1-14B-Control-Camera:diffusion_pytorch_model*.safetensors,PAI/Wan2.1-Fun-V1.1-14B-Control-Camera:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.1-Fun-V1.1-14B-Control-Camera:Wan2.1_VAE.pth,PAI/Wan2.1-Fun-V1.1-14B-Control-Camera:models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth" \ + --learning_rate 1e-5 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.1-Fun-V1.1-14B-Control-Camera_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \ + --lora_rank 32 \ + --extra_inputs "input_image,camera_control_direction,camera_control_speed" \ No newline at end of file diff --git a/examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-14B-Control.sh b/examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-14B-Control.sh new file mode 100644 index 0000000000000000000000000000000000000000..3ead12ce7f9a6da33c08a4c15ccf2727ae4ce3ff --- /dev/null +++ b/examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-14B-Control.sh @@ -0,0 +1,16 @@ +accelerate launch examples/wanvideo/model_training/train.py \ + --dataset_base_path data/example_video_dataset \ + --dataset_metadata_path data/example_video_dataset/metadata_reference_control.csv \ + --data_file_keys "video,control_video,reference_image" \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "PAI/Wan2.1-Fun-V1.1-14B-Control:diffusion_pytorch_model*.safetensors,PAI/Wan2.1-Fun-V1.1-14B-Control:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.1-Fun-V1.1-14B-Control:Wan2.1_VAE.pth,PAI/Wan2.1-Fun-V1.1-14B-Control:models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.1-Fun-V1.1-14B-Control_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \ + --lora_rank 32 \ + --extra_inputs "control_video,reference_image" \ No newline at end of file diff --git a/examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-14B-InP.sh b/examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-14B-InP.sh new file mode 100644 index 0000000000000000000000000000000000000000..40a8ad07950173d1b43b3595770fa4e294d44853 --- /dev/null +++ b/examples/wanvideo/model_training/lora/Wan2.1-Fun-V1.1-14B-InP.sh @@ -0,0 +1,15 @@ +accelerate launch examples/wanvideo/model_training/train.py \ + --dataset_base_path data/example_video_dataset \ + --dataset_metadata_path data/example_video_dataset/metadata.csv \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "PAI/Wan2.1-Fun-V1.1-14B-InP:diffusion_pytorch_model*.safetensors,PAI/Wan2.1-Fun-V1.1-14B-InP:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.1-Fun-V1.1-14B-InP:Wan2.1_VAE.pth,PAI/Wan2.1-Fun-V1.1-14B-InP:models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.1-Fun-V1.1-14B-InP_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \ + --lora_rank 32 \ + --extra_inputs "input_image,end_image" \ No newline at end of file diff --git a/examples/wanvideo/model_training/lora/Wan2.1-I2V-14B-480P.sh b/examples/wanvideo/model_training/lora/Wan2.1-I2V-14B-480P.sh new file mode 100644 index 0000000000000000000000000000000000000000..473d51981702868874720056795b96bf1fe337c7 --- /dev/null +++ b/examples/wanvideo/model_training/lora/Wan2.1-I2V-14B-480P.sh @@ -0,0 +1,15 @@ +accelerate launch examples/wanvideo/model_training/train.py \ + --dataset_base_path data/example_video_dataset \ + --dataset_metadata_path data/example_video_dataset/metadata.csv \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "Wan-AI/Wan2.1-I2V-14B-480P:diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.1-I2V-14B-480P:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.1-I2V-14B-480P:Wan2.1_VAE.pth,Wan-AI/Wan2.1-I2V-14B-480P:models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.1-I2V-14B-480P_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \ + --lora_rank 32 \ + --extra_inputs "input_image" \ No newline at end of file diff --git a/examples/wanvideo/model_training/lora/Wan2.1-I2V-14B-720P.sh b/examples/wanvideo/model_training/lora/Wan2.1-I2V-14B-720P.sh new file mode 100644 index 0000000000000000000000000000000000000000..ec987a8b71bf119f582839d8b603450f45b03296 --- /dev/null +++ b/examples/wanvideo/model_training/lora/Wan2.1-I2V-14B-720P.sh @@ -0,0 +1,17 @@ +accelerate launch examples/wanvideo/model_training/train.py \ + --dataset_base_path data/example_video_dataset \ + --dataset_metadata_path data/example_video_dataset/metadata.csv \ + --height 720 \ + --width 1280 \ + --num_frames 49 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "Wan-AI/Wan2.1-I2V-14B-720P:diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.1-I2V-14B-720P:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.1-I2V-14B-720P:Wan2.1_VAE.pth,Wan-AI/Wan2.1-I2V-14B-720P:models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.1-I2V-14B-720P_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \ + --lora_rank 32 \ + --extra_inputs "input_image" \ + --use_gradient_checkpointing_offload \ No newline at end of file diff --git a/examples/wanvideo/model_training/lora/Wan2.1-T2V-1.3B.sh b/examples/wanvideo/model_training/lora/Wan2.1-T2V-1.3B.sh new file mode 100644 index 0000000000000000000000000000000000000000..d16a287193286ac4893878325b1a9b6076eab627 --- /dev/null +++ b/examples/wanvideo/model_training/lora/Wan2.1-T2V-1.3B.sh @@ -0,0 +1,14 @@ +accelerate launch examples/wanvideo/model_training/train.py \ + --dataset_base_path data/example_video_dataset \ + --dataset_metadata_path data/example_video_dataset/metadata.csv \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "Wan-AI/Wan2.1-T2V-1.3B:diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.1-T2V-1.3B:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.1-T2V-1.3B:Wan2.1_VAE.pth" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.1-T2V-1.3B_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \ + --lora_rank 32 \ No newline at end of file diff --git a/examples/wanvideo/model_training/lora/Wan2.1-T2V-14B.sh b/examples/wanvideo/model_training/lora/Wan2.1-T2V-14B.sh new file mode 100644 index 0000000000000000000000000000000000000000..1fb55ac3467dca84420013e999b2bcc4e8d689d8 --- /dev/null +++ b/examples/wanvideo/model_training/lora/Wan2.1-T2V-14B.sh @@ -0,0 +1,14 @@ +accelerate launch examples/wanvideo/model_training/train.py \ + --dataset_base_path data/example_video_dataset \ + --dataset_metadata_path data/example_video_dataset/metadata.csv \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "Wan-AI/Wan2.1-T2V-14B:diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.1-T2V-14B:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.1-T2V-14B:Wan2.1_VAE.pth" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.1-T2V-14B_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \ + --lora_rank 32 \ No newline at end of file diff --git a/examples/wanvideo/model_training/lora/Wan2.1-VACE-1.3B-Preview.sh b/examples/wanvideo/model_training/lora/Wan2.1-VACE-1.3B-Preview.sh new file mode 100644 index 0000000000000000000000000000000000000000..2bcb55b9b6c1e4b02941a85f9fecc2f9ca470698 --- /dev/null +++ b/examples/wanvideo/model_training/lora/Wan2.1-VACE-1.3B-Preview.sh @@ -0,0 +1,17 @@ +accelerate launch examples/wanvideo/model_training/train.py \ + --dataset_base_path data/example_video_dataset \ + --dataset_metadata_path data/example_video_dataset/metadata_vace.csv \ + --data_file_keys "video,vace_video,vace_reference_image" \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "iic/VACE-Wan2.1-1.3B-Preview:diffusion_pytorch_model*.safetensors,iic/VACE-Wan2.1-1.3B-Preview:models_t5_umt5-xxl-enc-bf16.pth,iic/VACE-Wan2.1-1.3B-Preview:Wan2.1_VAE.pth" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.vace." \ + --output_path "./models/train/Wan2.1-VACE-1.3B-Preview_lora" \ + --lora_base_model "vace" \ + --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \ + --lora_rank 32 \ + --extra_inputs "vace_video,vace_reference_image" \ + --use_gradient_checkpointing_offload \ No newline at end of file diff --git a/examples/wanvideo/model_training/lora/Wan2.1-VACE-1.3B.sh b/examples/wanvideo/model_training/lora/Wan2.1-VACE-1.3B.sh new file mode 100644 index 0000000000000000000000000000000000000000..b56507889bd957563e874c64f2c983c09db4eeb8 --- /dev/null +++ b/examples/wanvideo/model_training/lora/Wan2.1-VACE-1.3B.sh @@ -0,0 +1,17 @@ +accelerate launch examples/wanvideo/model_training/train.py \ + --dataset_base_path data/example_video_dataset \ + --dataset_metadata_path data/example_video_dataset/metadata_vace.csv \ + --data_file_keys "video,vace_video,vace_reference_image" \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "Wan-AI/Wan2.1-VACE-1.3B:diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.1-VACE-1.3B:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.1-VACE-1.3B:Wan2.1_VAE.pth" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.vace." \ + --output_path "./models/train/Wan2.1-VACE-1.3B_lora" \ + --lora_base_model "vace" \ + --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \ + --lora_rank 32 \ + --extra_inputs "vace_video,vace_reference_image" \ + --use_gradient_checkpointing_offload \ No newline at end of file diff --git a/examples/wanvideo/model_training/lora/Wan2.1-VACE-14B.sh b/examples/wanvideo/model_training/lora/Wan2.1-VACE-14B.sh new file mode 100644 index 0000000000000000000000000000000000000000..633ea0e305b102a5b388670507429bdea869e3c5 --- /dev/null +++ b/examples/wanvideo/model_training/lora/Wan2.1-VACE-14B.sh @@ -0,0 +1,18 @@ +accelerate launch examples/wanvideo/model_training/train.py \ + --dataset_base_path data/example_video_dataset \ + --dataset_metadata_path data/example_video_dataset/metadata_vace.csv \ + --data_file_keys "video,vace_video,vace_reference_image" \ + --height 480 \ + --width 832 \ + --num_frames 17 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "Wan-AI/Wan2.1-VACE-14B:diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.1-VACE-14B:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.1-VACE-14B:Wan2.1_VAE.pth" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.vace." \ + --output_path "./models/train/Wan2.1-VACE-14B_lora" \ + --lora_base_model "vace" \ + --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \ + --lora_rank 32 \ + --extra_inputs "vace_video,vace_reference_image" \ + --use_gradient_checkpointing_offload \ No newline at end of file diff --git a/examples/wanvideo/model_training/lora/Wan2.2-Animate-14B.sh b/examples/wanvideo/model_training/lora/Wan2.2-Animate-14B.sh new file mode 100644 index 0000000000000000000000000000000000000000..0b6e5711be2cb9e1118dbb49075a1e7821864189 --- /dev/null +++ b/examples/wanvideo/model_training/lora/Wan2.2-Animate-14B.sh @@ -0,0 +1,20 @@ +# 1*80G GPU cannot train Wan2.2-Animate-14B LoRA +# We tested on 8*80G GPUs +accelerate launch --config_file examples/wanvideo/model_training/full/accelerate_config_14B.yaml examples/wanvideo/model_training/train.py \ + --dataset_base_path data/example_video_dataset \ + --dataset_metadata_path data/example_video_dataset/metadata_animate.csv \ + --data_file_keys "video,animate_pose_video,animate_face_video" \ + --height 480 \ + --width 832 \ + --num_frames 81 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "Wan-AI/Wan2.2-Animate-14B:diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.2-Animate-14B:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.2-Animate-14B:Wan2.1_VAE.pth,Wan-AI/Wan2.2-Animate-14B:models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.2-Animate-14B_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \ + --lora_rank 32 \ + --extra_inputs "input_image,animate_pose_video,animate_face_video" \ + --use_gradient_checkpointing_offload \ No newline at end of file diff --git a/examples/wanvideo/model_training/lora/Wan2.2-Fun-A14B-Control-Camera.sh b/examples/wanvideo/model_training/lora/Wan2.2-Fun-A14B-Control-Camera.sh new file mode 100644 index 0000000000000000000000000000000000000000..b97e800e16eceec817d5b170091f130eb3700f7b --- /dev/null +++ b/examples/wanvideo/model_training/lora/Wan2.2-Fun-A14B-Control-Camera.sh @@ -0,0 +1,39 @@ +accelerate launch examples/wanvideo/model_training/train.py \ + --dataset_base_path data/example_video_dataset \ + --dataset_metadata_path data/example_video_dataset/metadata_camera_control.csv \ + --data_file_keys "video,control_video,reference_image" \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "PAI/Wan2.2-Fun-A14B-Control-Camera:high_noise_model/diffusion_pytorch_model*.safetensors,PAI/Wan2.2-Fun-A14B-Control-Camera:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.2-Fun-A14B-Control-Camera:Wan2.1_VAE.pth" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.2-Fun-A14B-Control-Camera_high_niose_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \ + --lora_rank 32 \ + --extra_inputs "input_image,camera_control_direction,camera_control_speed" \ + --max_timestep_boundary 0.358 \ + --min_timestep_boundary 0 +# boundary corresponds to timesteps [900, 1000] + +accelerate launch examples/wanvideo/model_training/train.py \ + --dataset_base_path data/example_video_dataset \ + --dataset_metadata_path data/example_video_dataset/metadata_camera_control.csv \ + --data_file_keys "video,control_video,reference_image" \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "PAI/Wan2.2-Fun-A14B-Control-Camera:low_noise_model/diffusion_pytorch_model*.safetensors,PAI/Wan2.2-Fun-A14B-Control-Camera:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.2-Fun-A14B-Control-Camera:Wan2.1_VAE.pth" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.2-Fun-A14B-Control-Camera_low_noise_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \ + --lora_rank 32 \ + --extra_inputs "input_image,camera_control_direction,camera_control_speed" \ + --max_timestep_boundary 1 \ + --min_timestep_boundary 0.358 +# boundary corresponds to timesteps [0, 900] \ No newline at end of file diff --git a/examples/wanvideo/model_training/lora/Wan2.2-Fun-A14B-Control.sh b/examples/wanvideo/model_training/lora/Wan2.2-Fun-A14B-Control.sh new file mode 100644 index 0000000000000000000000000000000000000000..f0af5303a2b496d9d740e6308a0881b2cf664880 --- /dev/null +++ b/examples/wanvideo/model_training/lora/Wan2.2-Fun-A14B-Control.sh @@ -0,0 +1,39 @@ +accelerate launch examples/wanvideo/model_training/train.py \ + --dataset_base_path data/example_video_dataset \ + --dataset_metadata_path data/example_video_dataset/metadata_reference_control.csv \ + --data_file_keys "video,control_video,reference_image" \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "PAI/Wan2.2-Fun-A14B-Control:high_noise_model/diffusion_pytorch_model*.safetensors,PAI/Wan2.2-Fun-A14B-Control:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.2-Fun-A14B-Control:Wan2.1_VAE.pth" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.2-Fun-A14B-Control_high_niose_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \ + --lora_rank 32 \ + --extra_inputs "control_video,reference_image" \ + --max_timestep_boundary 0.358 \ + --min_timestep_boundary 0 +# boundary corresponds to timesteps [900, 1000] + +accelerate launch examples/wanvideo/model_training/train.py \ + --dataset_base_path data/example_video_dataset \ + --dataset_metadata_path data/example_video_dataset/metadata_reference_control.csv \ + --data_file_keys "video,control_video,reference_image" \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "PAI/Wan2.2-Fun-A14B-Control:low_noise_model/diffusion_pytorch_model*.safetensors,PAI/Wan2.2-Fun-A14B-Control:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.2-Fun-A14B-Control:Wan2.1_VAE.pth" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.2-Fun-A14B-Control_low_noise_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \ + --lora_rank 32 \ + --extra_inputs "control_video,reference_image" \ + --max_timestep_boundary 1 \ + --min_timestep_boundary 0.358 +# boundary corresponds to timesteps [0, 900] \ No newline at end of file diff --git a/examples/wanvideo/model_training/lora/Wan2.2-Fun-A14B-InP.sh b/examples/wanvideo/model_training/lora/Wan2.2-Fun-A14B-InP.sh new file mode 100644 index 0000000000000000000000000000000000000000..94cf196839643ae32debae05572f49b28cd32de1 --- /dev/null +++ b/examples/wanvideo/model_training/lora/Wan2.2-Fun-A14B-InP.sh @@ -0,0 +1,37 @@ +accelerate launch examples/wanvideo/model_training/train.py \ + --dataset_base_path data/example_video_dataset \ + --dataset_metadata_path data/example_video_dataset/metadata.csv \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "PAI/Wan2.2-Fun-A14B-InP:high_noise_model/diffusion_pytorch_model*.safetensors,PAI/Wan2.2-Fun-A14B-InP:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.2-Fun-A14B-InP:Wan2.1_VAE.pth" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.2-Fun-A14B-InP_high_niose_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \ + --lora_rank 32 \ + --extra_inputs "input_image,end_image" \ + --max_timestep_boundary 0.358 \ + --min_timestep_boundary 0 +# boundary corresponds to timesteps [900, 1000] + +accelerate launch examples/wanvideo/model_training/train.py \ + --dataset_base_path data/example_video_dataset \ + --dataset_metadata_path data/example_video_dataset/metadata.csv \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "PAI/Wan2.2-Fun-A14B-InP:low_noise_model/diffusion_pytorch_model*.safetensors,PAI/Wan2.2-Fun-A14B-InP:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.2-Fun-A14B-InP:Wan2.1_VAE.pth" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.2-Fun-A14B-InP_low_noise_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \ + --lora_rank 32 \ + --extra_inputs "input_image,end_image" \ + --max_timestep_boundary 1 \ + --min_timestep_boundary 0.358 +# boundary corresponds to timesteps [0, 900] \ No newline at end of file diff --git a/examples/wanvideo/model_training/lora/Wan2.2-I2V-A14B.sh b/examples/wanvideo/model_training/lora/Wan2.2-I2V-A14B.sh new file mode 100644 index 0000000000000000000000000000000000000000..1d9eba0aadd1f2c11d7ff6db199f1ad1e2536b59 --- /dev/null +++ b/examples/wanvideo/model_training/lora/Wan2.2-I2V-A14B.sh @@ -0,0 +1,39 @@ +accelerate launch examples/wanvideo/model_training/train.py \ + --dataset_base_path data/example_video_dataset \ + --dataset_metadata_path data/example_video_dataset/metadata.csv \ + --height 480 \ + --width 832 \ + --num_frames 49 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "Wan-AI/Wan2.2-I2V-A14B:high_noise_model/diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.2-I2V-A14B:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.2-I2V-A14B:Wan2.1_VAE.pth" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.2-I2V-A14B_high_noise_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \ + --lora_rank 32 \ + --extra_inputs "input_image" \ + --max_timestep_boundary 0.358 \ + --min_timestep_boundary 0 +# boundary corresponds to timesteps [900, 1000] + +accelerate launch examples/wanvideo/model_training/train.py \ + --dataset_base_path data/example_video_dataset \ + --dataset_metadata_path data/example_video_dataset/metadata.csv \ + --height 480 \ + --width 832 \ + --num_frames 49 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "Wan-AI/Wan2.2-I2V-A14B:low_noise_model/diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.2-I2V-A14B:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.2-I2V-A14B:Wan2.1_VAE.pth" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.2-I2V-A14B_low_noise_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \ + --lora_rank 32 \ + --extra_inputs "input_image" \ + --max_timestep_boundary 1 \ + --min_timestep_boundary 0.358 +# boundary corresponds to timesteps [0, 900) \ No newline at end of file diff --git a/examples/wanvideo/model_training/lora/Wan2.2-S2V-14B.sh b/examples/wanvideo/model_training/lora/Wan2.2-S2V-14B.sh new file mode 100644 index 0000000000000000000000000000000000000000..84723ec61afd13838b594addbfa8718948e1b374 --- /dev/null +++ b/examples/wanvideo/model_training/lora/Wan2.2-S2V-14B.sh @@ -0,0 +1,19 @@ +accelerate launch --config_file examples/wanvideo/model_training/full/accelerate_config_14B.yaml examples/wanvideo/model_training/train.py \ + --dataset_base_path data/example_video_dataset/wans2v \ + --dataset_metadata_path data/example_video_dataset/wans2v/metadata.csv \ + --data_file_keys "video,input_audio,s2v_pose_video" \ + --height 448 \ + --width 832 \ + --num_frames 81 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "Wan-AI/Wan2.2-S2V-14B:diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.2-S2V-14B:wav2vec2-large-xlsr-53-english/model.safetensors,Wan-AI/Wan2.2-S2V-14B:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.2-S2V-14B:Wan2.1_VAE.pth" \ + --audio_processor_config "Wan-AI/Wan2.2-S2V-14B:wav2vec2-large-xlsr-53-english/" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.2-S2V-14B_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \ + --lora_rank 32 \ + --extra_inputs "input_image,input_audio,s2v_pose_video" \ + --use_gradient_checkpointing_offload \ No newline at end of file diff --git a/examples/wanvideo/model_training/lora/Wan2.2-T2V-A14B.sh b/examples/wanvideo/model_training/lora/Wan2.2-T2V-A14B.sh new file mode 100644 index 0000000000000000000000000000000000000000..f47c96b7197a99c3e1332c5a858f0a9755130ead --- /dev/null +++ b/examples/wanvideo/model_training/lora/Wan2.2-T2V-A14B.sh @@ -0,0 +1,38 @@ +accelerate launch examples/wanvideo/model_training/train.py \ + --dataset_base_path data/example_video_dataset \ + --dataset_metadata_path data/example_video_dataset/metadata.csv \ + --height 480 \ + --width 832 \ + --num_frames 49 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "Wan-AI/Wan2.2-T2V-A14B:high_noise_model/diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.2-T2V-A14B:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.2-T2V-A14B:Wan2.1_VAE.pth" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.2-T2V-A14B_high_noise_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \ + --lora_rank 32 \ + --max_timestep_boundary 0.417 \ + --min_timestep_boundary 0 +# boundary corresponds to timesteps [875, 1000] + + +accelerate launch examples/wanvideo/model_training/train.py \ + --dataset_base_path data/example_video_dataset \ + --dataset_metadata_path data/example_video_dataset/metadata.csv \ + --height 480 \ + --width 832 \ + --num_frames 49 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "Wan-AI/Wan2.2-T2V-A14B:low_noise_model/diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.2-T2V-A14B:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.2-T2V-A14B:Wan2.1_VAE.pth" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.2-T2V-A14B_low_noise_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \ + --lora_rank 32 \ + --max_timestep_boundary 1 \ + --min_timestep_boundary 0.417 +# boundary corresponds to timesteps [0, 875) \ No newline at end of file diff --git a/examples/wanvideo/model_training/lora/Wan2.2-TI2V-5B.sh b/examples/wanvideo/model_training/lora/Wan2.2-TI2V-5B.sh new file mode 100644 index 0000000000000000000000000000000000000000..6a33b5799e71f6fc68cc00386ff28b1368e2673b --- /dev/null +++ b/examples/wanvideo/model_training/lora/Wan2.2-TI2V-5B.sh @@ -0,0 +1,16 @@ +accelerate launch examples/wanvideo/model_training/train.py \ + --dataset_base_path data/example_video_dataset \ + --dataset_metadata_path data/example_video_dataset/metadata.csv \ + --height 480 \ + --width 832 \ + --num_frames 49 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "Wan-AI/Wan2.2-TI2V-5B:diffusion_pytorch_model*.safetensors,Wan-AI/Wan2.2-TI2V-5B:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.2-TI2V-5B:Wan2.2_VAE.pth" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/Wan2.2-TI2V-5B_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \ + --lora_rank 32 \ + --extra_inputs "input_image" \ No newline at end of file diff --git a/examples/wanvideo/model_training/lora/Wan2.2-VACE-Fun-A14B.sh b/examples/wanvideo/model_training/lora/Wan2.2-VACE-Fun-A14B.sh new file mode 100644 index 0000000000000000000000000000000000000000..93b38cfd11bed9a315dd87d2b5a9fe857ced27fe --- /dev/null +++ b/examples/wanvideo/model_training/lora/Wan2.2-VACE-Fun-A14B.sh @@ -0,0 +1,43 @@ +accelerate launch examples/wanvideo/model_training/train.py \ + --dataset_base_path data/example_video_dataset \ + --dataset_metadata_path data/example_video_dataset/metadata_vace.csv \ + --data_file_keys "video,vace_video,vace_reference_image" \ + --height 480 \ + --width 832 \ + --num_frames 17 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "PAI/Wan2.2-VACE-Fun-A14B:high_noise_model/diffusion_pytorch_model*.safetensors,PAI/Wan2.2-VACE-Fun-A14B:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.2-VACE-Fun-A14B:Wan2.1_VAE.pth" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.vace." \ + --output_path "./models/train/Wan2.2-VACE-Fun-A14B_high_noise_lora" \ + --lora_base_model "vace" \ + --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \ + --lora_rank 32 \ + --extra_inputs "vace_video,vace_reference_image" \ + --use_gradient_checkpointing_offload \ + --max_timestep_boundary 0.358 \ + --min_timestep_boundary 0 +# boundary corresponds to timesteps [900, 1000] + +accelerate launch examples/wanvideo/model_training/train.py \ + --dataset_base_path data/example_video_dataset \ + --dataset_metadata_path data/example_video_dataset/metadata_vace.csv \ + --data_file_keys "video,vace_video,vace_reference_image" \ + --height 480 \ + --width 832 \ + --num_frames 17 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "PAI/Wan2.2-VACE-Fun-A14B:low_noise_model/diffusion_pytorch_model*.safetensors,PAI/Wan2.2-VACE-Fun-A14B:models_t5_umt5-xxl-enc-bf16.pth,PAI/Wan2.2-VACE-Fun-A14B:Wan2.1_VAE.pth" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.vace." \ + --output_path "./models/train/Wan2.2-VACE-Fun-A14B_low_noise_lora" \ + --lora_base_model "vace" \ + --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \ + --lora_rank 32 \ + --extra_inputs "vace_video,vace_reference_image" \ + --use_gradient_checkpointing_offload \ + --max_timestep_boundary 1 \ + --min_timestep_boundary 0.358 +# boundary corresponds to timesteps [0, 900] \ No newline at end of file diff --git a/examples/wanvideo/model_training/lora/krea-realtime-video.sh b/examples/wanvideo/model_training/lora/krea-realtime-video.sh new file mode 100644 index 0000000000000000000000000000000000000000..94c64d17d4e2aacb4842b54f9064ac3440a60a7b --- /dev/null +++ b/examples/wanvideo/model_training/lora/krea-realtime-video.sh @@ -0,0 +1,14 @@ +accelerate launch examples/wanvideo/model_training/train.py \ + --dataset_base_path data/example_video_dataset \ + --dataset_metadata_path data/example_video_dataset/metadata.csv \ + --height 480 \ + --width 832 \ + --dataset_repeat 100 \ + --model_id_with_origin_paths "krea/krea-realtime-video:krea-realtime-video-14b.safetensors,Wan-AI/Wan2.1-T2V-14B:models_t5_umt5-xxl-enc-bf16.pth,Wan-AI/Wan2.1-T2V-14B:Wan2.1_VAE.pth" \ + --learning_rate 1e-4 \ + --num_epochs 5 \ + --remove_prefix_in_ckpt "pipe.dit." \ + --output_path "./models/train/krea-realtime-video_lora" \ + --lora_base_model "dit" \ + --lora_target_modules "q,k,v,o,ffn.0,ffn.2" \ + --lora_rank 32 \ No newline at end of file diff --git a/examples/wanvideo/model_training/lora/run_test.py b/examples/wanvideo/model_training/lora/run_test.py new file mode 100644 index 0000000000000000000000000000000000000000..ec0f9e2ddcf86e38c735d36122a7ca417eddd6b4 --- /dev/null +++ b/examples/wanvideo/model_training/lora/run_test.py @@ -0,0 +1,25 @@ +import multiprocessing, os + + +def run_task(scripts, thread_id, thread_num): + for script_id, script in enumerate(scripts): + if script_id % thread_num == thread_id: + log_file_name = script.replace("/", "_") + ".txt" + cmd = f"CUDA_VISIBLE_DEVICES={thread_id} bash {script} > data/log/{log_file_name} 2>&1" + os.makedirs("data/log", exist_ok=True) + print(cmd, flush=True) + os.system(cmd) + + +if __name__ == "__main__": + scripts = [] + for file_name in os.listdir("examples/wanvideo/model_training/lora"): + if file_name != "run_test.py": + scripts.append(os.path.join("examples/wanvideo/model_training/lora", file_name)) + + processes = [multiprocessing.Process(target=run_task, args=(scripts, i, 8)) for i in range(8)] + for p in processes: + p.start() + for p in processes: + p.join() + print("Done!") \ No newline at end of file diff --git a/examples/wanvideo/model_training/train.py b/examples/wanvideo/model_training/train.py new file mode 100644 index 0000000000000000000000000000000000000000..643c8e28132a4ac73eee28b0116cd8b1216253bd --- /dev/null +++ b/examples/wanvideo/model_training/train.py @@ -0,0 +1,136 @@ +import torch, os, json +from diffsynth import load_state_dict +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig +from diffsynth.trainers.utils import DiffusionTrainingModule, ModelLogger, launch_training_task, wan_parser +from diffsynth.trainers.unified_dataset import UnifiedDataset, LoadVideo, LoadAudio, ImageCropAndResize, ToAbsolutePath +os.environ["TOKENIZERS_PARALLELISM"] = "false" + + + +class WanTrainingModule(DiffusionTrainingModule): + def __init__( + self, + model_paths=None, model_id_with_origin_paths=None, audio_processor_config=None, + trainable_models=None, + lora_base_model=None, lora_target_modules="q,k,v,o,ffn.0,ffn.2", lora_rank=32, lora_checkpoint=None, + use_gradient_checkpointing=True, + use_gradient_checkpointing_offload=False, + extra_inputs=None, + max_timestep_boundary=1.0, + min_timestep_boundary=0.0, + ): + super().__init__() + # Load models + model_configs = self.parse_model_configs(model_paths, model_id_with_origin_paths, enable_fp8_training=False) + if audio_processor_config is not None: + audio_processor_config = ModelConfig(model_id=audio_processor_config.split(":")[0], origin_file_pattern=audio_processor_config.split(":")[1]) + self.pipe = WanVideoPipeline.from_pretrained(torch_dtype=torch.bfloat16, device="cpu", model_configs=model_configs, audio_processor_config=audio_processor_config) + + # Training mode + self.switch_pipe_to_training_mode( + self.pipe, trainable_models, + lora_base_model, lora_target_modules, lora_rank, lora_checkpoint=lora_checkpoint, + enable_fp8_training=False, + ) + + # Store other configs + self.use_gradient_checkpointing = use_gradient_checkpointing + self.use_gradient_checkpointing_offload = use_gradient_checkpointing_offload + self.extra_inputs = extra_inputs.split(",") if extra_inputs is not None else [] + self.max_timestep_boundary = max_timestep_boundary + self.min_timestep_boundary = min_timestep_boundary + + + def forward_preprocess(self, data): + # CFG-sensitive parameters + inputs_posi = {"prompt": data["prompt"]} + inputs_nega = {} + + # CFG-unsensitive parameters + inputs_shared = { + # Assume you are using this pipeline for inference, + # please fill in the input parameters. + "input_video": data["video"], + "height": data["video"][0].size[1], + "width": data["video"][0].size[0], + "num_frames": len(data["video"]), + # Please do not modify the following parameters + # unless you clearly know what this will cause. + "cfg_scale": 1, + "tiled": False, + "rand_device": self.pipe.device, + "use_gradient_checkpointing": self.use_gradient_checkpointing, + "use_gradient_checkpointing_offload": self.use_gradient_checkpointing_offload, + "cfg_merge": False, + "vace_scale": 1, + "max_timestep_boundary": self.max_timestep_boundary, + "min_timestep_boundary": self.min_timestep_boundary, + } + + # Extra inputs + for extra_input in self.extra_inputs: + if extra_input == "input_image": + inputs_shared["input_image"] = data["video"][0] + elif extra_input == "end_image": + inputs_shared["end_image"] = data["video"][-1] + elif extra_input == "reference_image" or extra_input == "vace_reference_image": + inputs_shared[extra_input] = data[extra_input][0] + else: + inputs_shared[extra_input] = data[extra_input] + + # Pipeline units will automatically process the input parameters. + for unit in self.pipe.units: + inputs_shared, inputs_posi, inputs_nega = self.pipe.unit_runner(unit, self.pipe, inputs_shared, inputs_posi, inputs_nega) + return {**inputs_shared, **inputs_posi} + + + def forward(self, data, inputs=None): + if inputs is None: inputs = self.forward_preprocess(data) + models = {name: getattr(self.pipe, name) for name in self.pipe.in_iteration_models} + loss = self.pipe.training_loss(**models, **inputs) + return loss + + +if __name__ == "__main__": + parser = wan_parser() + args = parser.parse_args() + dataset = UnifiedDataset( + base_path=args.dataset_base_path, + metadata_path=args.dataset_metadata_path, + repeat=args.dataset_repeat, + data_file_keys=args.data_file_keys.split(","), + main_data_operator=UnifiedDataset.default_video_operator( + base_path=args.dataset_base_path, + max_pixels=args.max_pixels, + height=args.height, + width=args.width, + height_division_factor=16, + width_division_factor=16, + num_frames=args.num_frames, + time_division_factor=4, + time_division_remainder=1, + ), + special_operator_map={ + "animate_face_video": ToAbsolutePath(args.dataset_base_path) >> LoadVideo(args.num_frames, 4, 1, frame_processor=ImageCropAndResize(512, 512, None, 16, 16)), + "input_audio": ToAbsolutePath(args.dataset_base_path) >> LoadAudio(sr=16000), + } + ) + model = WanTrainingModule( + model_paths=args.model_paths, + model_id_with_origin_paths=args.model_id_with_origin_paths, + audio_processor_config=args.audio_processor_config, + trainable_models=args.trainable_models, + lora_base_model=args.lora_base_model, + lora_target_modules=args.lora_target_modules, + lora_rank=args.lora_rank, + lora_checkpoint=args.lora_checkpoint, + use_gradient_checkpointing_offload=args.use_gradient_checkpointing_offload, + extra_inputs=args.extra_inputs, + max_timestep_boundary=args.max_timestep_boundary, + min_timestep_boundary=args.min_timestep_boundary, + ) + model_logger = ModelLogger( + args.output_path, + remove_prefix_in_ckpt=args.remove_prefix_in_ckpt + ) + launch_training_task(dataset, model, model_logger, args=args) diff --git a/examples/wanvideo/model_training/validate_full/LongCat-Video.py b/examples/wanvideo/model_training/validate_full/LongCat-Video.py new file mode 100644 index 0000000000000000000000000000000000000000..31ee9a74da24d30798992a14fe6e711a5a1ef1fa --- /dev/null +++ b/examples/wanvideo/model_training/validate_full/LongCat-Video.py @@ -0,0 +1,25 @@ +import torch +from PIL import Image +from diffsynth import save_video, VideoData, load_state_dict +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="meituan-longcat/LongCat-Video", origin_file_pattern="dit/diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-14B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-14B", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"), + ], +) +state_dict = load_state_dict("models/train/LongCat-Video_full/epoch-1.safetensors") +pipe.dit.load_state_dict(state_dict) +pipe.enable_vram_management() + +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + seed=1, tiled=True +) +save_video(video, "video_LongCat-Video.mp4", fps=15, quality=5) diff --git a/examples/wanvideo/model_training/validate_full/Video-As-Prompt-Wan2.1-14B.py b/examples/wanvideo/model_training/validate_full/Video-As-Prompt-Wan2.1-14B.py new file mode 100644 index 0000000000000000000000000000000000000000..97af5bb0d1532b81cb7a8c0cbae723c3d176e399 --- /dev/null +++ b/examples/wanvideo/model_training/validate_full/Video-As-Prompt-Wan2.1-14B.py @@ -0,0 +1,43 @@ +import torch +from PIL import Image +from diffsynth import save_video, VideoData, load_state_dict +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="ByteDance/Video-As-Prompt-Wan2.1-14B", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.1-I2V-14B-720P", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.1-I2V-14B-720P", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.1-I2V-14B-720P", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth", offload_device="cpu"), + ], +) +state_dict = load_state_dict("models/train/Video-As-Prompt-Wan2.1-14B_full/epoch-1.safetensors") +pipe.vap.load_state_dict(state_dict) +pipe.enable_vram_management() + +ref_video_path = 'data/example_video_dataset/wanvap/vap_ref.mp4' +target_image_path = 'data/example_video_dataset/wanvap/input_image.jpg' + +image = Image.open(target_image_path).convert("RGB") +ref_video = VideoData(ref_video_path, height=480, width=832) +ref_frames = [ref_video[i] for i in range(49)] + +vap_prompt = "A man stands with his back to the camera on a dirt path overlooking sun-drenched, rolling green tea plantations. He wears a blue and green plaid shirt, dark pants, and white shoes. As he turns to face the camera and spreads his arms, a brief, magical burst of sparkling golden light particles envelops him. Through this shimmer, he seamlessly transforms into a Labubu toy character. His head morphs into the iconic large, furry-eared head of the toy, featuring a wide grin with pointed teeth and red cheek markings. The character retains the man's original plaid shirt and clothing, which now fit its stylized, cartoonish body. The camera remains static throughout the transformation, positioned low among the tea bushes, maintaining a consistent view of the subject and the expansive scenery." +prompt = "A young woman with curly hair, wearing a green hijab and a floral dress, plays a violin in front of a vintage green car on a tree-lined street. She executes a swift counter-clockwise turn to face the camera. During the turn, a brilliant shower of golden, sparkling particles erupts and momentarily obscures her figure. As the particles fade, she is revealed to have seamlessly transformed into a Labubu toy character. This new figure, now with the toy's signature large ears, big eyes, and toothy grin, maintains the original pose and continues playing the violin. The character's clothing—the green hijab, floral dress, and black overcoat—remains identical to the woman's. Throughout this transition, the camera stays static, and the street-side environment remains completely consistent." +negative_prompt = "Bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, worst quality, low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, misshapen limbs, fused fingers, still picture, messy background, three legs, many people in the background, walking backwards" + +video = pipe( + prompt=prompt, + negative_prompt=negative_prompt, + input_image=image, + seed=42, tiled=True, + height=480, width=832, + num_frames=49, + vap_video=ref_frames, + vap_prompt=vap_prompt, + negative_vap_prompt=negative_prompt, +) +save_video(video, "video_Video-As-Prompt-Wan2.1-14B.mp4", fps=15, quality=5) diff --git a/examples/wanvideo/model_training/validate_full/Wan2.1-1.3b-speedcontrol-v1.py b/examples/wanvideo/model_training/validate_full/Wan2.1-1.3b-speedcontrol-v1.py new file mode 100644 index 0000000000000000000000000000000000000000..124749a8ebe2e5d87fff6aadcf1872988b487c29 --- /dev/null +++ b/examples/wanvideo/model_training/validate_full/Wan2.1-1.3b-speedcontrol-v1.py @@ -0,0 +1,28 @@ +import torch +from PIL import Image +from diffsynth import save_video, VideoData, load_state_dict +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"), + ModelConfig(model_id="DiffSynth-Studio/Wan2.1-1.3b-speedcontrol-v1", origin_file_pattern="model.safetensors", offload_device="cpu"), + ], +) +state_dict = load_state_dict("models/train/Wan2.1-1.3b-speedcontrol-v1_full/epoch-1.safetensors") +pipe.motion_controller.load_state_dict(state_dict) +pipe.enable_vram_management() + +# Text-to-video +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + seed=1, tiled=True, + motion_bucket_id=50 +) +save_video(video, "video_Wan2.1-1.3b-speedcontrol-v1.mp4", fps=15, quality=5) diff --git a/examples/wanvideo/model_training/validate_full/Wan2.1-FLF2V-14B-720P.py b/examples/wanvideo/model_training/validate_full/Wan2.1-FLF2V-14B-720P.py new file mode 100644 index 0000000000000000000000000000000000000000..41a67edfea24e40caf7fefbc013ae6517c2f7100 --- /dev/null +++ b/examples/wanvideo/model_training/validate_full/Wan2.1-FLF2V-14B-720P.py @@ -0,0 +1,33 @@ +import torch +from PIL import Image +from diffsynth import save_video, VideoData, load_state_dict +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.1-FLF2V-14B-720P", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.1-FLF2V-14B-720P", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.1-FLF2V-14B-720P", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.1-FLF2V-14B-720P", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth", offload_device="cpu"), + ], +) +state_dict = load_state_dict("models/train/Wan2.1-FLF2V-14B-720P_full/epoch-1.safetensors") +pipe.dit.load_state_dict(state_dict) +pipe.enable_vram_management() + +video = VideoData("data/example_video_dataset/video1.mp4", height=480, width=832) + +# First and last frame to video +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + input_image=video[0], + end_image=video[80], + seed=0, tiled=True, + sigma_shift=16, +) +save_video(video, "video_Wan2.1-FLF2V-14B-720P.mp4", fps=15, quality=5) diff --git a/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-1.3B-Control.py b/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-1.3B-Control.py new file mode 100644 index 0000000000000000000000000000000000000000..6726e9c3a71fabedd15a58844c103919af06a555 --- /dev/null +++ b/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-1.3B-Control.py @@ -0,0 +1,32 @@ +import torch +from PIL import Image +from diffsynth import save_video, VideoData, load_state_dict +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="PAI/Wan2.1-Fun-1.3B-Control", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.1-Fun-1.3B-Control", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.1-Fun-1.3B-Control", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.1-Fun-1.3B-Control", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth", offload_device="cpu"), + ], +) +state_dict = load_state_dict("models/train/Wan2.1-Fun-1.3B-Control_full/epoch-1.safetensors") +pipe.dit.load_state_dict(state_dict) +pipe.enable_vram_management() + +video = VideoData("data/example_video_dataset/video1_softedge.mp4", height=480, width=832) +video = [video[i] for i in range(81)] + +# Control video +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + control_video=video, + seed=1, tiled=True +) +save_video(video, "video_Wan2.1-Fun-1.3B-Control.mp4", fps=15, quality=5) diff --git a/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-1.3B-InP.py b/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-1.3B-InP.py new file mode 100644 index 0000000000000000000000000000000000000000..3e1e6f3cac2ebe7b22008e3c797dd7b75fe5d5fa --- /dev/null +++ b/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-1.3B-InP.py @@ -0,0 +1,31 @@ +import torch +from PIL import Image +from diffsynth import save_video, VideoData, load_state_dict +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="PAI/Wan2.1-Fun-1.3B-InP", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.1-Fun-1.3B-InP", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.1-Fun-1.3B-InP", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.1-Fun-1.3B-InP", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth", offload_device="cpu"), + ], +) +state_dict = load_state_dict("models/train/Wan2.1-Fun-1.3B-InP_full/epoch-1.safetensors") +pipe.dit.load_state_dict(state_dict) +pipe.enable_vram_management() + +video = VideoData("data/example_video_dataset/video1.mp4", height=480, width=832) + +# First and last frame to video +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + input_image=video[0], end_image=video[80], + seed=0, tiled=True +) +save_video(video, "video_Wan2.1-Fun-1.3B-InP.mp4", fps=15, quality=5) diff --git a/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-14B-Control.py b/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-14B-Control.py new file mode 100644 index 0000000000000000000000000000000000000000..08b0acb5a7e209dae1e0b096a113398a1b6faf26 --- /dev/null +++ b/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-14B-Control.py @@ -0,0 +1,32 @@ +import torch +from PIL import Image +from diffsynth import save_video, VideoData, load_state_dict +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="PAI/Wan2.1-Fun-14B-Control", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.1-Fun-14B-Control", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.1-Fun-14B-Control", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.1-Fun-14B-Control", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth", offload_device="cpu"), + ], +) +state_dict = load_state_dict("models/train/Wan2.1-Fun-14B-Control_full/epoch-1.safetensors") +pipe.dit.load_state_dict(state_dict) +pipe.enable_vram_management() + +video = VideoData("data/example_video_dataset/video1_softedge.mp4", height=480, width=832) +video = [video[i] for i in range(81)] + +# Control video +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + control_video=video, + seed=1, tiled=True +) +save_video(video, "video_Wan2.1-Fun-14B-Control.mp4", fps=15, quality=5) diff --git a/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-14B-InP.py b/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-14B-InP.py new file mode 100644 index 0000000000000000000000000000000000000000..d7e39d7e8dbcbbb82eca12e6441bc22c2ed36f85 --- /dev/null +++ b/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-14B-InP.py @@ -0,0 +1,31 @@ +import torch +from PIL import Image +from diffsynth import save_video, VideoData, load_state_dict +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="PAI/Wan2.1-Fun-14B-InP", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.1-Fun-14B-InP", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.1-Fun-14B-InP", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.1-Fun-14B-InP", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth", offload_device="cpu"), + ], +) +state_dict = load_state_dict("models/train/Wan2.1-Fun-14B-InP_full/epoch-1.safetensors") +pipe.dit.load_state_dict(state_dict) +pipe.enable_vram_management() + +video = VideoData("data/example_video_dataset/video1.mp4", height=480, width=832) + +# First and last frame to video +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + input_image=video[0], end_image=video[80], + seed=0, tiled=True +) +save_video(video, "video_Wan2.1-Fun-14B-InP.mp4", fps=15, quality=5) diff --git a/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-1.3B-Control-Camera.py b/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-1.3B-Control-Camera.py new file mode 100644 index 0000000000000000000000000000000000000000..c5a210a59039c085fdc602bae6c2f600af10ac22 --- /dev/null +++ b/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-1.3B-Control-Camera.py @@ -0,0 +1,32 @@ +import torch +from PIL import Image +from diffsynth import save_video, VideoData, load_state_dict +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-Control-Camera", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-Control-Camera", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-Control-Camera", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-Control-Camera", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth", offload_device="cpu"), + ], +) +state_dict = load_state_dict("models/train/Wan2.1-Fun-V1.1-1.3B-Control-Camera_full/epoch-1.safetensors") +pipe.dit.load_state_dict(state_dict) +pipe.enable_vram_management() + +video = VideoData("data/example_video_dataset/video1.mp4", height=480, width=832) + +# First and last frame to video +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + input_image=video[0], + camera_control_direction="Left", camera_control_speed=0.0, + seed=0, tiled=True +) +save_video(video, "video_Wan2.1-Fun-V1.1-1.3B-Control-Camera.mp4", fps=15, quality=5) diff --git a/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-1.3B-Control.py b/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-1.3B-Control.py new file mode 100644 index 0000000000000000000000000000000000000000..6497e1b875d2a6186777594b895dae46bbfe6924 --- /dev/null +++ b/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-1.3B-Control.py @@ -0,0 +1,33 @@ +import torch +from PIL import Image +from diffsynth import save_video, VideoData, load_state_dict +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-Control", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-Control", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-Control", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-Control", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth", offload_device="cpu"), + ], +) +state_dict = load_state_dict("models/train/Wan2.1-Fun-V1.1-1.3B-Control_full/epoch-1.safetensors") +pipe.dit.load_state_dict(state_dict) +pipe.enable_vram_management() + +video = VideoData("data/example_video_dataset/video1_softedge.mp4", height=480, width=832) +video = [video[i] for i in range(81)] +reference_image = VideoData("data/example_video_dataset/video1.mp4", height=480, width=832)[0] + +# Control video +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + control_video=video, reference_image=reference_image, + seed=1, tiled=True +) +save_video(video, "video_Wan2.1-Fun-V1.1-1.3B-Control.mp4", fps=15, quality=5) diff --git a/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-1.3B-InP.py b/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-1.3B-InP.py new file mode 100644 index 0000000000000000000000000000000000000000..cd8ee204e6d79b522523262b75cf3290b55176ad --- /dev/null +++ b/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-1.3B-InP.py @@ -0,0 +1,31 @@ +import torch +from PIL import Image +from diffsynth import save_video, VideoData, load_state_dict +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-InP", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-InP", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-InP", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-InP", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth", offload_device="cpu"), + ], +) +state_dict = load_state_dict("models/train/Wan2.1-Fun-V1.1-1.3B-InP_full/epoch-1.safetensors") +pipe.dit.load_state_dict(state_dict) +pipe.enable_vram_management() + +video = VideoData("data/example_video_dataset/video1.mp4", height=480, width=832) + +# First and last frame to video +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + input_image=video[0], end_image=video[80], + seed=0, tiled=True +) +save_video(video, "video_Wan2.1-Fun-V1.1-1.3B-InP.mp4", fps=15, quality=5) diff --git a/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-14B-Control-Camera.py b/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-14B-Control-Camera.py new file mode 100644 index 0000000000000000000000000000000000000000..2de51028922c429625d20d2d0c9b09cb3473a673 --- /dev/null +++ b/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-14B-Control-Camera.py @@ -0,0 +1,32 @@ +import torch +from PIL import Image +from diffsynth import save_video, VideoData, load_state_dict +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-14B-Control-Camera", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-14B-Control-Camera", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-14B-Control-Camera", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-14B-Control-Camera", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth", offload_device="cpu"), + ], +) +state_dict = load_state_dict("models/train/Wan2.1-Fun-V1.1-14B-Control-Camera_full/epoch-1.safetensors") +pipe.dit.load_state_dict(state_dict) +pipe.enable_vram_management() + +video = VideoData("data/example_video_dataset/video1.mp4", height=480, width=832) + +# First and last frame to video +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + input_image=video[0], + camera_control_direction="Left", camera_control_speed=0.0, + seed=0, tiled=True +) +save_video(video, "video_Wan2.1-Fun-V1.1-14B-Control-Camera.mp4", fps=15, quality=5) diff --git a/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-14B-Control.py b/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-14B-Control.py new file mode 100644 index 0000000000000000000000000000000000000000..0dd2516e9c6588e0161dc6e36b44e623f3745b75 --- /dev/null +++ b/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-14B-Control.py @@ -0,0 +1,33 @@ +import torch +from PIL import Image +from diffsynth import save_video, VideoData, load_state_dict +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-14B-Control", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-14B-Control", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-14B-Control", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-14B-Control", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth", offload_device="cpu"), + ], +) +state_dict = load_state_dict("models/train/Wan2.1-Fun-V1.1-14B-Control_full/epoch-1.safetensors") +pipe.dit.load_state_dict(state_dict) +pipe.enable_vram_management() + +video = VideoData("data/example_video_dataset/video1_softedge.mp4", height=480, width=832) +video = [video[i] for i in range(81)] +reference_image = VideoData("data/example_video_dataset/video1.mp4", height=480, width=832)[0] + +# Control video +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + control_video=video, reference_image=reference_image, + seed=1, tiled=True +) +save_video(video, "video_Wan2.1-Fun-V1.1-14B-Control.mp4", fps=15, quality=5) diff --git a/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-14B-InP.py b/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-14B-InP.py new file mode 100644 index 0000000000000000000000000000000000000000..7e944b064ab1d732bcdcd24894a3e4138f4a8282 --- /dev/null +++ b/examples/wanvideo/model_training/validate_full/Wan2.1-Fun-V1.1-14B-InP.py @@ -0,0 +1,31 @@ +import torch +from PIL import Image +from diffsynth import save_video, VideoData, load_state_dict +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-14B-InP", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-14B-InP", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-14B-InP", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-14B-InP", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth", offload_device="cpu"), + ], +) +state_dict = load_state_dict("models/train/Wan2.1-Fun-V1.1-14B-InP_full/epoch-1.safetensors") +pipe.dit.load_state_dict(state_dict) +pipe.enable_vram_management() + +video = VideoData("data/example_video_dataset/video1.mp4", height=480, width=832) + +# First and last frame to video +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + input_image=video[0], end_image=video[80], + seed=0, tiled=True +) +save_video(video, "video_Wan2.1-Fun-V1.1-14B-InP.mp4", fps=15, quality=5) diff --git a/examples/wanvideo/model_training/validate_full/Wan2.1-I2V-14B-480P.py b/examples/wanvideo/model_training/validate_full/Wan2.1-I2V-14B-480P.py new file mode 100644 index 0000000000000000000000000000000000000000..c1c8615aac171b81a702d7e1826ef48948d74d14 --- /dev/null +++ b/examples/wanvideo/model_training/validate_full/Wan2.1-I2V-14B-480P.py @@ -0,0 +1,30 @@ +import torch +from PIL import Image +from diffsynth import save_video, VideoData, load_state_dict +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.1-I2V-14B-480P", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.1-I2V-14B-480P", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.1-I2V-14B-480P", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.1-I2V-14B-480P", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth", offload_device="cpu"), + ], +) +state_dict = load_state_dict("models/train/Wan2.1-I2V-14B-480P_full/epoch-1.safetensors") +pipe.dit.load_state_dict(state_dict) +pipe.enable_vram_management() + +input_image = VideoData("data/example_video_dataset/video1.mp4", height=480, width=832)[0] + +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + input_image=input_image, + seed=1, tiled=True +) +save_video(video, "video_Wan2.1-I2V-14B-480P.mp4", fps=15, quality=5) diff --git a/examples/wanvideo/model_training/validate_full/Wan2.1-I2V-14B-720P.py b/examples/wanvideo/model_training/validate_full/Wan2.1-I2V-14B-720P.py new file mode 100644 index 0000000000000000000000000000000000000000..d9d39c88047b68ebe10f39fc548df3810bc13ae7 --- /dev/null +++ b/examples/wanvideo/model_training/validate_full/Wan2.1-I2V-14B-720P.py @@ -0,0 +1,31 @@ +import torch +from PIL import Image +from diffsynth import save_video, VideoData, load_state_dict +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.1-I2V-14B-720P", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.1-I2V-14B-720P", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.1-I2V-14B-720P", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.1-I2V-14B-720P", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth", offload_device="cpu"), + ], +) +state_dict = load_state_dict("models/train/Wan2.1-I2V-14B-720P_full/epoch-1.safetensors") +pipe.dit.load_state_dict(state_dict) +pipe.enable_vram_management() + +input_image = VideoData("data/example_video_dataset/video1.mp4", height=720, width=1280)[0] + +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + input_image=input_image, + height=720, width=1280, num_frames=49, + seed=1, tiled=True +) +save_video(video, "video_Wan2.1-I2V-14B-720P.mp4", fps=15, quality=5) diff --git a/examples/wanvideo/model_training/validate_full/Wan2.1-T2V-1.3B.py b/examples/wanvideo/model_training/validate_full/Wan2.1-T2V-1.3B.py new file mode 100644 index 0000000000000000000000000000000000000000..1420514e0ed5ee01fc7a144708c70ee1910cd459 --- /dev/null +++ b/examples/wanvideo/model_training/validate_full/Wan2.1-T2V-1.3B.py @@ -0,0 +1,25 @@ +import torch +from PIL import Image +from diffsynth import save_video, VideoData, load_state_dict +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"), + ], +) +state_dict = load_state_dict("models/train/Wan2.1-T2V-1.3B_full/epoch-1.safetensors") +pipe.dit.load_state_dict(state_dict) +pipe.enable_vram_management() + +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + seed=1, tiled=True +) +save_video(video, "video_Wan2.1-T2V-1.3B.mp4", fps=15, quality=5) diff --git a/examples/wanvideo/model_training/validate_full/Wan2.1-T2V-14B.py b/examples/wanvideo/model_training/validate_full/Wan2.1-T2V-14B.py new file mode 100644 index 0000000000000000000000000000000000000000..a0107aef39e38565c2457562343124e0e45440d0 --- /dev/null +++ b/examples/wanvideo/model_training/validate_full/Wan2.1-T2V-14B.py @@ -0,0 +1,25 @@ +import torch +from PIL import Image +from diffsynth import save_video, VideoData, load_state_dict +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-14B", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-14B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-14B", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"), + ], +) +state_dict = load_state_dict("models/train/Wan2.1-T2V-14B_full/epoch-1.safetensors") +pipe.dit.load_state_dict(state_dict) +pipe.enable_vram_management() + +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + seed=1, tiled=True +) +save_video(video, "video_Wan2.1-T2V-14B.mp4", fps=15, quality=5) diff --git a/examples/wanvideo/model_training/validate_full/Wan2.1-VACE-1.3B-Preview.py b/examples/wanvideo/model_training/validate_full/Wan2.1-VACE-1.3B-Preview.py new file mode 100644 index 0000000000000000000000000000000000000000..a916745155be47e0ce0ff7ec41f1dc1220c8e23b --- /dev/null +++ b/examples/wanvideo/model_training/validate_full/Wan2.1-VACE-1.3B-Preview.py @@ -0,0 +1,30 @@ +import torch +from PIL import Image +from diffsynth import save_video, VideoData, load_state_dict +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="iic/VACE-Wan2.1-1.3B-Preview", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="iic/VACE-Wan2.1-1.3B-Preview", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"), + ModelConfig(model_id="iic/VACE-Wan2.1-1.3B-Preview", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"), + ], +) +state_dict = load_state_dict("models/train/Wan2.1-VACE-1.3B-Preview_full/epoch-1.safetensors") +pipe.vace.load_state_dict(state_dict) +pipe.enable_vram_management() + +video = VideoData("data/example_video_dataset/video1_softedge.mp4", height=480, width=832) +video = [video[i] for i in range(49)] +reference_image = VideoData("data/example_video_dataset/video1.mp4", height=480, width=832)[0] + +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + vace_video=video, vace_reference_image=reference_image, num_frames=49, + seed=1, tiled=True +) +save_video(video, "video_Wan2.1-VACE-1.3B-Preview.mp4", fps=15, quality=5) diff --git a/examples/wanvideo/model_training/validate_full/Wan2.1-VACE-1.3B.py b/examples/wanvideo/model_training/validate_full/Wan2.1-VACE-1.3B.py new file mode 100644 index 0000000000000000000000000000000000000000..5a371e7456af056f6afdf7c1dd8aefb2082f77cf --- /dev/null +++ b/examples/wanvideo/model_training/validate_full/Wan2.1-VACE-1.3B.py @@ -0,0 +1,30 @@ +import torch +from PIL import Image +from diffsynth import save_video, VideoData, load_state_dict +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.1-VACE-1.3B", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.1-VACE-1.3B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.1-VACE-1.3B", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"), + ], +) +state_dict = load_state_dict("models/train/Wan2.1-VACE-1.3B_full/epoch-1.safetensors") +pipe.vace.load_state_dict(state_dict) +pipe.enable_vram_management() + +video = VideoData("data/example_video_dataset/video1_softedge.mp4", height=480, width=832) +video = [video[i] for i in range(49)] +reference_image = VideoData("data/example_video_dataset/video1.mp4", height=480, width=832)[0] + +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + vace_video=video, vace_reference_image=reference_image, num_frames=49, + seed=1, tiled=True +) +save_video(video, "video_Wan2.1-VACE-1.3B.mp4", fps=15, quality=5) diff --git a/examples/wanvideo/model_training/validate_full/Wan2.1-VACE-14B.py b/examples/wanvideo/model_training/validate_full/Wan2.1-VACE-14B.py new file mode 100644 index 0000000000000000000000000000000000000000..5553471bec03e201f8d896b06ab7019ab488e408 --- /dev/null +++ b/examples/wanvideo/model_training/validate_full/Wan2.1-VACE-14B.py @@ -0,0 +1,30 @@ +import torch +from PIL import Image +from diffsynth import save_video, VideoData, load_state_dict +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.1-VACE-14B", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.1-VACE-14B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.1-VACE-14B", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"), + ], +) +state_dict = load_state_dict("models/train/Wan2.1-VACE-14B_full/epoch-1.safetensors") +pipe.vace.load_state_dict(state_dict) +pipe.enable_vram_management() + +video = VideoData("data/example_video_dataset/video1_softedge.mp4", height=480, width=832) +video = [video[i] for i in range(17)] +reference_image = VideoData("data/example_video_dataset/video1.mp4", height=480, width=832)[0] + +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + vace_video=video, vace_reference_image=reference_image, num_frames=17, + seed=1, tiled=True +) +save_video(video, "video_Wan2.1-VACE-14B.mp4", fps=15, quality=5) diff --git a/examples/wanvideo/model_training/validate_full/Wan2.2-Animate-14B.py b/examples/wanvideo/model_training/validate_full/Wan2.2-Animate-14B.py new file mode 100644 index 0000000000000000000000000000000000000000..d6fbfc1a9392dc16ab3da39a5a336662eacfcd49 --- /dev/null +++ b/examples/wanvideo/model_training/validate_full/Wan2.2-Animate-14B.py @@ -0,0 +1,33 @@ +import torch +from PIL import Image +from diffsynth import save_video, VideoData, load_state_dict +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.2-Animate-14B", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.2-Animate-14B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.2-Animate-14B", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.2-Animate-14B", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth", offload_device="cpu"), + ], +) +state_dict = load_state_dict("models/train/Wan2.2-Animate-14B_full/epoch-1.safetensors") +pipe.animate_adapter.load_state_dict(state_dict, strict=False) +pipe.enable_vram_management() + +input_image = VideoData("data/example_video_dataset/animate/animate_output.mp4", height=480, width=832)[0] +animate_pose_video = VideoData("data/examples/wan/animate/animate_pose_video.mp4", height=480, width=832).raw_data()[:81-4] +animate_face_video = VideoData("data/examples/wan/animate/animate_face_video.mp4", height=512, width=512).raw_data()[:81-4] +video = pipe( + prompt="视频中的人在做动作", + seed=0, tiled=True, + input_image=input_image, + animate_pose_video=animate_pose_video, + animate_face_video=animate_face_video, + num_frames=81, height=480, width=832, + num_inference_steps=20, cfg_scale=1, +) +save_video(video, "video_Wan2.2-Animate-14B.mp4", fps=15, quality=5) \ No newline at end of file diff --git a/examples/wanvideo/model_training/validate_full/Wan2.2-Fun-A14B-Control-Camera.py b/examples/wanvideo/model_training/validate_full/Wan2.2-Fun-A14B-Control-Camera.py new file mode 100644 index 0000000000000000000000000000000000000000..700d503cbbc7eb1af68f218777c1fd938c065920 --- /dev/null +++ b/examples/wanvideo/model_training/validate_full/Wan2.2-Fun-A14B-Control-Camera.py @@ -0,0 +1,34 @@ +import torch +from PIL import Image +from diffsynth import save_video, VideoData, load_state_dict +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-Control-Camera", origin_file_pattern="high_noise_model/diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-Control-Camera", origin_file_pattern="low_noise_model/diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-Control-Camera", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-Control-Camera", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"), + ], +) +state_dict = load_state_dict("models/train/Wan2.2-Fun-A14B-Control-Camera_high_noise_full/epoch-1.safetensors") +pipe.dit.load_state_dict(state_dict) +state_dict = load_state_dict("models/train/Wan2.2-Fun-A14B-Control-Camera_low_noise_full/epoch-1.safetensors") +pipe.dit2.load_state_dict(state_dict) +pipe.enable_vram_management() + +video = VideoData("data/example_video_dataset/video1.mp4", height=480, width=832) + +# First and last frame to video +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + input_image=video[0], + camera_control_direction="Left", camera_control_speed=0.0, + seed=0, tiled=True +) +save_video(video, "video_Wan2.2-Fun-A14B-Control-Camera.mp4", fps=15, quality=5) diff --git a/examples/wanvideo/model_training/validate_full/Wan2.2-Fun-A14B-Control.py b/examples/wanvideo/model_training/validate_full/Wan2.2-Fun-A14B-Control.py new file mode 100644 index 0000000000000000000000000000000000000000..685b48c32b767381a023cb47573e2550cec352c5 --- /dev/null +++ b/examples/wanvideo/model_training/validate_full/Wan2.2-Fun-A14B-Control.py @@ -0,0 +1,35 @@ +import torch +from PIL import Image +from diffsynth import save_video, VideoData, load_state_dict +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-Control", origin_file_pattern="high_noise_model/diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-Control", origin_file_pattern="low_noise_model/diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-Control", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-Control", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"), + ], +) +state_dict = load_state_dict("models/train/Wan2.2-Fun-A14B-Control_high_noise_full/epoch-1.safetensors") +pipe.dit.load_state_dict(state_dict) +state_dict = load_state_dict("models/train/Wan2.2-Fun-A14B-Control_low_noise_full/epoch-1.safetensors") +pipe.dit2.load_state_dict(state_dict) +pipe.enable_vram_management() + +video = VideoData("data/example_video_dataset/video1_softedge.mp4", height=480, width=832) +video = [video[i] for i in range(81)] +reference_image = VideoData("data/example_video_dataset/video1.mp4", height=480, width=832)[0] + +# Control video +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + control_video=video, reference_image=reference_image, + seed=1, tiled=True +) +save_video(video, "video_Wan2.2-Fun-A14B-Control.mp4", fps=15, quality=5) diff --git a/examples/wanvideo/model_training/validate_full/Wan2.2-Fun-A14B-InP.py b/examples/wanvideo/model_training/validate_full/Wan2.2-Fun-A14B-InP.py new file mode 100644 index 0000000000000000000000000000000000000000..154c4e4f57e21da02d3ce2753a70c676884c50d0 --- /dev/null +++ b/examples/wanvideo/model_training/validate_full/Wan2.2-Fun-A14B-InP.py @@ -0,0 +1,32 @@ +import torch +from PIL import Image +from diffsynth import save_video, VideoData, load_state_dict +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-InP", origin_file_pattern="high_noise_model/diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-InP", origin_file_pattern="low_noise_model/diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-InP", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-InP", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"), + ], +) +state_dict = load_state_dict("models/train/Wan2.2-Fun-A14B-InP_high_noise_full/epoch-1.safetensors") +pipe.dit.load_state_dict(state_dict) +state_dict = load_state_dict("models/train/Wan2.2-Fun-A14B-InP_low_noise_full/epoch-1.safetensors") +pipe.dit2.load_state_dict(state_dict) +pipe.enable_vram_management() +video = VideoData("data/example_video_dataset/video1.mp4", height=480, width=832) + +# First and last frame to video +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + input_image=video[0], end_image=video[80], + seed=0, tiled=True +) +save_video(video, "video_Wan2.2-Fun-A14B-InP.mp4", fps=15, quality=5) diff --git a/examples/wanvideo/model_training/validate_full/Wan2.2-I2V-A14B.py b/examples/wanvideo/model_training/validate_full/Wan2.2-I2V-A14B.py new file mode 100644 index 0000000000000000000000000000000000000000..3f6d253cfb6612061a4954a1eeeb9b3af70b1726 --- /dev/null +++ b/examples/wanvideo/model_training/validate_full/Wan2.2-I2V-A14B.py @@ -0,0 +1,33 @@ +import torch +from PIL import Image +from diffsynth import save_video, VideoData, load_state_dict +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.2-I2V-A14B", origin_file_pattern="high_noise_model/diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.2-I2V-A14B", origin_file_pattern="low_noise_model/diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.2-I2V-A14B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.2-I2V-A14B", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"), + ], +) +state_dict = load_state_dict("models/train/Wan2.2-I2V-A14B_high_noise_full/epoch-1.safetensors") +pipe.dit.load_state_dict(state_dict) +state_dict = load_state_dict("models/train/Wan2.2-I2V-A14B_low_noise_full/epoch-1.safetensors") +pipe.dit2.load_state_dict(state_dict) +pipe.enable_vram_management() + +input_image = VideoData("data/example_video_dataset/video1.mp4", height=480, width=832)[0] + +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + input_image=input_image, + num_frames=49, + seed=1, tiled=True, +) +save_video(video, "video_Wan2.2-I2V-A14B.mp4", fps=15, quality=5) diff --git a/examples/wanvideo/model_training/validate_full/Wan2.2-S2V-14B.py b/examples/wanvideo/model_training/validate_full/Wan2.2-S2V-14B.py new file mode 100644 index 0000000000000000000000000000000000000000..2df08d2632d7a0682082844363570dfd85984fba --- /dev/null +++ b/examples/wanvideo/model_training/validate_full/Wan2.2-S2V-14B.py @@ -0,0 +1,53 @@ +import torch +from PIL import Image +import librosa +from diffsynth import VideoData, save_video_with_audio, load_state_dict +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.2-S2V-14B", origin_file_pattern="diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Wan-AI/Wan2.2-S2V-14B", origin_file_pattern="wav2vec2-large-xlsr-53-english/model.safetensors"), + ModelConfig(model_id="Wan-AI/Wan2.2-S2V-14B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="Wan-AI/Wan2.2-S2V-14B", origin_file_pattern="Wan2.1_VAE.pth"), + ], + audio_processor_config=ModelConfig(model_id="Wan-AI/Wan2.2-S2V-14B", origin_file_pattern="wav2vec2-large-xlsr-53-english/"), +) + +state_dict = load_state_dict("models/train/Wan2.2-S2V-14B_full/epoch-0.safetensors") +pipe.dit.load_state_dict(state_dict, strict=False) +pipe.enable_vram_management() + + +num_frames = 81 # 4n+1 +height = 448 +width = 832 + +prompt = "a person is singing" +negative_prompt = "画面模糊，最差质量，画面模糊，细节模糊不清，情绪激动剧烈，手快速抖动，字幕，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" +input_image = Image.open("data/example_video_dataset/wans2v/pose.png").convert("RGB").resize((width, height)) +# s2v audio input, recommend 16kHz sampling rate +audio_path = 'data/example_video_dataset/wans2v/sing.MP3' +input_audio, sample_rate = librosa.load(audio_path, sr=16000) +# S2V pose video input +pose_video_path = 'data/example_video_dataset/wans2v/pose.mp4' +pose_video = VideoData(pose_video_path, height=height, width=width) + +# Speech-to-video with pose +video = pipe( + prompt=prompt, + input_image=input_image, + negative_prompt=negative_prompt, + seed=0, + num_frames=num_frames, + height=height, + width=width, + audio_sample_rate=sample_rate, + input_audio=input_audio, + s2v_pose_video=pose_video, + num_inference_steps=40, +) +save_video_with_audio(video[1:], "video_pose_with_audio.mp4", audio_path, fps=16, quality=5) diff --git a/examples/wanvideo/model_training/validate_full/Wan2.2-T2V-A14B.py b/examples/wanvideo/model_training/validate_full/Wan2.2-T2V-A14B.py new file mode 100644 index 0000000000000000000000000000000000000000..be0e000e7da227afd641e8e395495d4d4eff849d --- /dev/null +++ b/examples/wanvideo/model_training/validate_full/Wan2.2-T2V-A14B.py @@ -0,0 +1,28 @@ +import torch +from PIL import Image +from diffsynth import save_video, VideoData, load_state_dict +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.2-T2V-A14B", origin_file_pattern="high_noise_model/diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.2-T2V-A14B", origin_file_pattern="low_noise_model/diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.2-T2V-A14B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.2-T2V-A14B", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"), + ], +) +state_dict = load_state_dict("models/train/Wan2.2-T2V-A14B_high_noise_full/epoch-1.safetensors") +pipe.dit.load_state_dict(state_dict) +state_dict = load_state_dict("models/train/Wan2.2-T2V-A14B_low_noise_full/epoch-1.safetensors") +pipe.dit2.load_state_dict(state_dict) +pipe.enable_vram_management() + +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + seed=1, tiled=True +) +save_video(video, "video_Wan2.2-T2V-A14B.mp4", fps=15, quality=5) diff --git a/examples/wanvideo/model_training/validate_full/Wan2.2-TI2V-5B.py b/examples/wanvideo/model_training/validate_full/Wan2.2-TI2V-5B.py new file mode 100644 index 0000000000000000000000000000000000000000..8a3d36cdb68c8a25cca04dce8c6975e85eb7974b --- /dev/null +++ b/examples/wanvideo/model_training/validate_full/Wan2.2-TI2V-5B.py @@ -0,0 +1,30 @@ +import torch +from PIL import Image +from diffsynth import save_video, VideoData, load_state_dict +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.2-TI2V-5B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.2-TI2V-5B", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.2-TI2V-5B", origin_file_pattern="Wan2.2_VAE.pth", offload_device="cpu"), + ], +) +state_dict = load_state_dict("models/train/Wan2.2-TI2V-5B_full/epoch-1.safetensors") +pipe.dit.load_state_dict(state_dict) +pipe.enable_vram_management() + +input_image = VideoData("data/example_video_dataset/video1.mp4", height=480, width=832)[0] + +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + input_image=input_image, + num_frames=49, + seed=1, tiled=True, +) +save_video(video, "video_Wan2.2-TI2V-5B.mp4", fps=15, quality=5) diff --git a/examples/wanvideo/model_training/validate_full/Wan2.2-VACE-Fun-A14B.py b/examples/wanvideo/model_training/validate_full/Wan2.2-VACE-Fun-A14B.py new file mode 100644 index 0000000000000000000000000000000000000000..e566dbadbf69b615708b84c0ff1d9cac67628743 --- /dev/null +++ b/examples/wanvideo/model_training/validate_full/Wan2.2-VACE-Fun-A14B.py @@ -0,0 +1,33 @@ +import torch +from PIL import Image +from diffsynth import save_video, VideoData, load_state_dict +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="PAI/Wan2.2-VACE-Fun-A14B", origin_file_pattern="high_noise_model/diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.2-VACE-Fun-A14B", origin_file_pattern="low_noise_model/diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.2-VACE-Fun-A14B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.2-VACE-Fun-A14B", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"), + ], +) +state_dict = load_state_dict("models/train/Wan2.2-VACE-Fun-A14B_high_noise_full/epoch-1.safetensors") +pipe.vace.load_state_dict(state_dict) +state_dict = load_state_dict("models/train/Wan2.2-VACE-Fun-A14B_low_noise_full/epoch-1.safetensors") +pipe.vace2.load_state_dict(state_dict) +pipe.enable_vram_management() + +video = VideoData("data/example_video_dataset/video1_softedge.mp4", height=480, width=832) +video = [video[i] for i in range(17)] +reference_image = VideoData("data/example_video_dataset/video1.mp4", height=480, width=832)[0] + +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + vace_video=video, vace_reference_image=reference_image, num_frames=17, + seed=1, tiled=True +) +save_video(video, "video_Wan2.2-VACE-A14B.mp4", fps=15, quality=5) diff --git a/examples/wanvideo/model_training/validate_full/krea-realtime-video.py b/examples/wanvideo/model_training/validate_full/krea-realtime-video.py new file mode 100644 index 0000000000000000000000000000000000000000..d67577322d5527545fa4cab0e5f6c540118b372f --- /dev/null +++ b/examples/wanvideo/model_training/validate_full/krea-realtime-video.py @@ -0,0 +1,28 @@ +import torch +from PIL import Image +from diffsynth import save_video, VideoData, load_state_dict +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="krea/krea-realtime-video", origin_file_pattern="krea-realtime-video-14b.safetensors", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-14B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-14B", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"), + ], +) +state_dict = load_state_dict("models/train/krea-realtime-video_full/epoch-1.safetensors") +pipe.dit.load_state_dict(state_dict) +pipe.enable_vram_management() + +# Text-to-video +video = pipe( + prompt="a cat sitting on a boat", + num_inference_steps=6, num_frames=81, + seed=0, tiled=True, + cfg_scale=1, + sigma_shift=20, +) +save_video(video, "output.mp4", fps=15, quality=5) diff --git a/examples/wanvideo/model_training/validate_full/run_test.py b/examples/wanvideo/model_training/validate_full/run_test.py new file mode 100644 index 0000000000000000000000000000000000000000..a4e32039a6e80c7f18c1fe8a6b7e4fdb84ae85d4 --- /dev/null +++ b/examples/wanvideo/model_training/validate_full/run_test.py @@ -0,0 +1,25 @@ +import multiprocessing, os + + +def run_task(scripts, thread_id, thread_num): + for script_id, script in enumerate(scripts): + if script_id % thread_num == thread_id: + log_file_name = script.replace("/", "_") + ".txt" + cmd = f"CUDA_VISIBLE_DEVICES={thread_id} python -u {script} > data/log/{log_file_name} 2>&1" + os.makedirs("data/log", exist_ok=True) + print(cmd, flush=True) + os.system(cmd) + + +if __name__ == "__main__": + scripts = [] + for file_name in os.listdir("examples/wanvideo/model_training/validate_full"): + if file_name != "run_test.py": + scripts.append(os.path.join("examples/wanvideo/model_training/validate_full", file_name)) + + processes = [multiprocessing.Process(target=run_task, args=(scripts, i, 8)) for i in range(8)] + for p in processes: + p.start() + for p in processes: + p.join() + print("Done!") \ No newline at end of file diff --git a/examples/wanvideo/model_training/validate_lora/LongCat-Video.py b/examples/wanvideo/model_training/validate_lora/LongCat-Video.py new file mode 100644 index 0000000000000000000000000000000000000000..45c1ddb3dfe3d71e58c40f6552da3b62a41d7481 --- /dev/null +++ b/examples/wanvideo/model_training/validate_lora/LongCat-Video.py @@ -0,0 +1,24 @@ +import torch +from PIL import Image +from diffsynth import save_video, VideoData +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="meituan-longcat/LongCat-Video", origin_file_pattern="dit/diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-14B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-14B", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"), + ], +) +pipe.load_lora(pipe.dit, "models/train/LongCat-Video_lora/epoch-4.safetensors", alpha=1) +pipe.enable_vram_management() + +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + seed=1, tiled=True +) +save_video(video, "video_LongCat-Video.mp4", fps=15, quality=5) diff --git a/examples/wanvideo/model_training/validate_lora/Video-As-Prompt-Wan2.1-14B.py b/examples/wanvideo/model_training/validate_lora/Video-As-Prompt-Wan2.1-14B.py new file mode 100644 index 0000000000000000000000000000000000000000..13da5c089eabacb19baacfc33613094b105018ff --- /dev/null +++ b/examples/wanvideo/model_training/validate_lora/Video-As-Prompt-Wan2.1-14B.py @@ -0,0 +1,42 @@ +import torch +from PIL import Image +from diffsynth import save_video, VideoData, load_state_dict +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="ByteDance/Video-As-Prompt-Wan2.1-14B", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.1-I2V-14B-720P", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.1-I2V-14B-720P", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.1-I2V-14B-720P", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth", offload_device="cpu"), + ], +) +pipe.load_lora(pipe.dit, "models/train/Video-As-Prompt-Wan2.1-14B_lora/epoch-4.safetensors", alpha=1) +pipe.enable_vram_management() + +ref_video_path = 'data/example_video_dataset/wanvap/vap_ref.mp4' +target_image_path = 'data/example_video_dataset/wanvap/input_image.jpg' + +image = Image.open(target_image_path).convert("RGB") +ref_video = VideoData(ref_video_path, height=480, width=832) +ref_frames = [ref_video[i] for i in range(49)] + +vap_prompt = "A man stands with his back to the camera on a dirt path overlooking sun-drenched, rolling green tea plantations. He wears a blue and green plaid shirt, dark pants, and white shoes. As he turns to face the camera and spreads his arms, a brief, magical burst of sparkling golden light particles envelops him. Through this shimmer, he seamlessly transforms into a Labubu toy character. His head morphs into the iconic large, furry-eared head of the toy, featuring a wide grin with pointed teeth and red cheek markings. The character retains the man's original plaid shirt and clothing, which now fit its stylized, cartoonish body. The camera remains static throughout the transformation, positioned low among the tea bushes, maintaining a consistent view of the subject and the expansive scenery." +prompt = "A young woman with curly hair, wearing a green hijab and a floral dress, plays a violin in front of a vintage green car on a tree-lined street. She executes a swift counter-clockwise turn to face the camera. During the turn, a brilliant shower of golden, sparkling particles erupts and momentarily obscures her figure. As the particles fade, she is revealed to have seamlessly transformed into a Labubu toy character. This new figure, now with the toy's signature large ears, big eyes, and toothy grin, maintains the original pose and continues playing the violin. The character's clothing—the green hijab, floral dress, and black overcoat—remains identical to the woman's. Throughout this transition, the camera stays static, and the street-side environment remains completely consistent." +negative_prompt = "Bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, worst quality, low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, misshapen limbs, fused fingers, still picture, messy background, three legs, many people in the background, walking backwards" + +video = pipe( + prompt=prompt, + negative_prompt=negative_prompt, + input_image=image, + seed=42, tiled=True, + height=480, width=832, + num_frames=49, + vap_video=ref_frames, + vap_prompt=vap_prompt, + negative_vap_prompt=negative_prompt, +) +save_video(video, "video_Video-As-Prompt-Wan2.1-14B.mp4", fps=15, quality=5) diff --git a/examples/wanvideo/model_training/validate_lora/Wan2.1-1.3b-speedcontrol-v1.py b/examples/wanvideo/model_training/validate_lora/Wan2.1-1.3b-speedcontrol-v1.py new file mode 100644 index 0000000000000000000000000000000000000000..167b8714bf01c83bc337d87fb4aaff356685b637 --- /dev/null +++ b/examples/wanvideo/model_training/validate_lora/Wan2.1-1.3b-speedcontrol-v1.py @@ -0,0 +1,27 @@ +import torch +from PIL import Image +from diffsynth import save_video, VideoData, load_state_dict +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"), + ModelConfig(model_id="DiffSynth-Studio/Wan2.1-1.3b-speedcontrol-v1", origin_file_pattern="model.safetensors", offload_device="cpu"), + ], +) +pipe.load_lora(pipe.dit, "models/train/Wan2.1-1.3b-speedcontrol-v1_lora/epoch-4.safetensors", alpha=1) +pipe.enable_vram_management() + +# Text-to-video +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + seed=1, tiled=True, + motion_bucket_id=50 +) +save_video(video, "video_Wan2.1-1.3b-speedcontrol-v1.mp4", fps=15, quality=5) diff --git a/examples/wanvideo/model_training/validate_lora/Wan2.1-FLF2V-14B-720P.py b/examples/wanvideo/model_training/validate_lora/Wan2.1-FLF2V-14B-720P.py new file mode 100644 index 0000000000000000000000000000000000000000..cd68f0eca7bcf58402ec0ebebcb2c4f69cf053c3 --- /dev/null +++ b/examples/wanvideo/model_training/validate_lora/Wan2.1-FLF2V-14B-720P.py @@ -0,0 +1,32 @@ +import torch +from PIL import Image +from diffsynth import save_video, VideoData +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.1-FLF2V-14B-720P", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.1-FLF2V-14B-720P", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.1-FLF2V-14B-720P", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.1-FLF2V-14B-720P", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth", offload_device="cpu"), + ], +) +pipe.load_lora(pipe.dit, "models/train/Wan2.1-FLF2V-14B-720P_lora/epoch-4.safetensors", alpha=1) +pipe.enable_vram_management() + +video = VideoData("data/example_video_dataset/video1.mp4", height=480, width=832) + +# First and last frame to video +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + input_image=video[0], + end_image=video[80], + seed=0, tiled=True, + sigma_shift=16, +) +save_video(video, "video_Wan2.1-FLF2V-14B-720P.mp4", fps=15, quality=5) diff --git a/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-1.3B-Control.py b/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-1.3B-Control.py new file mode 100644 index 0000000000000000000000000000000000000000..7270c38e75640d2a6af0f97c67e03cbdc5cec83e --- /dev/null +++ b/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-1.3B-Control.py @@ -0,0 +1,31 @@ +import torch +from PIL import Image +from diffsynth import save_video, VideoData +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="PAI/Wan2.1-Fun-1.3B-Control", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.1-Fun-1.3B-Control", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.1-Fun-1.3B-Control", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.1-Fun-1.3B-Control", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth", offload_device="cpu"), + ], +) +pipe.load_lora(pipe.dit, "models/train/Wan2.1-Fun-1.3B-Control_lora/epoch-4.safetensors", alpha=1) +pipe.enable_vram_management() + +video = VideoData("data/example_video_dataset/video1_softedge.mp4", height=480, width=832) +video = [video[i] for i in range(81)] + +# Control video +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + control_video=video, + seed=1, tiled=True +) +save_video(video, "video_Wan2.1-Fun-1.3B-Control.mp4", fps=15, quality=5) diff --git a/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-1.3B-InP.py b/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-1.3B-InP.py new file mode 100644 index 0000000000000000000000000000000000000000..c904dfa4d01b537702092409fc73fddcb3fd6342 --- /dev/null +++ b/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-1.3B-InP.py @@ -0,0 +1,30 @@ +import torch +from PIL import Image +from diffsynth import save_video, VideoData +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="PAI/Wan2.1-Fun-1.3B-InP", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.1-Fun-1.3B-InP", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.1-Fun-1.3B-InP", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.1-Fun-1.3B-InP", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth", offload_device="cpu"), + ], +) +pipe.load_lora(pipe.dit, "models/train/Wan2.1-Fun-1.3B-InP_lora/epoch-4.safetensors", alpha=1) +pipe.enable_vram_management() + +video = VideoData("data/example_video_dataset/video1.mp4", height=480, width=832) + +# First and last frame to video +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + input_image=video[0], end_image=video[80], + seed=0, tiled=True +) +save_video(video, "video_Wan2.1-Fun-1.3B-InP.mp4", fps=15, quality=5) diff --git a/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-14B-Control.py b/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-14B-Control.py new file mode 100644 index 0000000000000000000000000000000000000000..8631d054434554469a62d98dfa1eaaa208339773 --- /dev/null +++ b/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-14B-Control.py @@ -0,0 +1,31 @@ +import torch +from PIL import Image +from diffsynth import save_video, VideoData +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="PAI/Wan2.1-Fun-14B-Control", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.1-Fun-14B-Control", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.1-Fun-14B-Control", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.1-Fun-14B-Control", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth", offload_device="cpu"), + ], +) +pipe.load_lora(pipe.dit, "models/train/Wan2.1-Fun-14B-Control_lora/epoch-4.safetensors", alpha=1) +pipe.enable_vram_management() + +video = VideoData("data/example_video_dataset/video1_softedge.mp4", height=480, width=832) +video = [video[i] for i in range(81)] + +# Control video +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + control_video=video, + seed=1, tiled=True +) +save_video(video, "video_Wan2.1-Fun-14B-Control.mp4", fps=15, quality=5) diff --git a/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-14B-InP.py b/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-14B-InP.py new file mode 100644 index 0000000000000000000000000000000000000000..e020aacbe8c15e46f7a5dd21d3a79df0fb5f2e65 --- /dev/null +++ b/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-14B-InP.py @@ -0,0 +1,30 @@ +import torch +from PIL import Image +from diffsynth import save_video, VideoData +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="PAI/Wan2.1-Fun-14B-InP", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.1-Fun-14B-InP", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.1-Fun-14B-InP", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.1-Fun-14B-InP", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth", offload_device="cpu"), + ], +) +pipe.load_lora(pipe.dit, "models/train/Wan2.1-Fun-14B-InP_lora/epoch-4.safetensors", alpha=1) +pipe.enable_vram_management() + +video = VideoData("data/example_video_dataset/video1.mp4", height=480, width=832) + +# First and last frame to video +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + input_image=video[0], end_image=video[80], + seed=0, tiled=True +) +save_video(video, "video_Wan2.1-Fun-14B-InP.mp4", fps=15, quality=5) diff --git a/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-1.3B-Control-Camera.py b/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-1.3B-Control-Camera.py new file mode 100644 index 0000000000000000000000000000000000000000..3023692448034848795a6b847a51ba9d6fc04d2d --- /dev/null +++ b/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-1.3B-Control-Camera.py @@ -0,0 +1,31 @@ +import torch +from PIL import Image +from diffsynth import save_video, VideoData, load_state_dict +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-Control-Camera", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-Control-Camera", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-Control-Camera", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-Control-Camera", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth", offload_device="cpu"), + ], +) +pipe.load_lora(pipe.dit, "models/train/Wan2.1-Fun-V1.1-1.3B-Control-Camera_lora/epoch-4.safetensors", alpha=1) +pipe.enable_vram_management() + +video = VideoData("data/example_video_dataset/video1.mp4", height=480, width=832) + +# First and last frame to video +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + input_image=video[0], + camera_control_direction="Left", camera_control_speed=0.0, + seed=0, tiled=True +) +save_video(video, "video_Wan2.1-Fun-V1.1-1.3B-Control-Camera.mp4", fps=15, quality=5) diff --git a/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-1.3B-Control.py b/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-1.3B-Control.py new file mode 100644 index 0000000000000000000000000000000000000000..ebcfd2f85d6625d66aaa7a49e439f3788eaba516 --- /dev/null +++ b/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-1.3B-Control.py @@ -0,0 +1,32 @@ +import torch +from PIL import Image +from diffsynth import save_video, VideoData +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-Control", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-Control", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-Control", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-Control", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth", offload_device="cpu"), + ], +) +pipe.load_lora(pipe.dit, "models/train/Wan2.1-Fun-V1.1-1.3B-Control_lora/epoch-4.safetensors", alpha=1) +pipe.enable_vram_management() + +video = VideoData("data/example_video_dataset/video1_softedge.mp4", height=480, width=832) +video = [video[i] for i in range(81)] +reference_image = VideoData("data/example_video_dataset/video1.mp4", height=480, width=832)[0] + +# Control video +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + control_video=video, reference_image=reference_image, + seed=1, tiled=True +) +save_video(video, "video_Wan2.1-Fun-V1.1-1.3B-Control.mp4", fps=15, quality=5) diff --git a/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-1.3B-InP.py b/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-1.3B-InP.py new file mode 100644 index 0000000000000000000000000000000000000000..99eb2b48a4aacd52cb1361ce7f3d5d689d5902e0 --- /dev/null +++ b/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-1.3B-InP.py @@ -0,0 +1,30 @@ +import torch +from PIL import Image +from diffsynth import save_video, VideoData +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-InP", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-InP", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-InP", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-InP", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth", offload_device="cpu"), + ], +) +pipe.load_lora(pipe.dit, "models/train/Wan2.1-Fun-V1.1-1.3B-InP_lora/epoch-4.safetensors", alpha=1) +pipe.enable_vram_management() + +video = VideoData("data/example_video_dataset/video1.mp4", height=480, width=832) + +# First and last frame to video +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + input_image=video[0], end_image=video[80], + seed=0, tiled=True +) +save_video(video, "video_Wan2.1-Fun-V1.1-1.3B-InP.mp4", fps=15, quality=5) diff --git a/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-14B-Control-Camera.py b/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-14B-Control-Camera.py new file mode 100644 index 0000000000000000000000000000000000000000..0edb2d0224f97bf385f16bb81a62d77e6bf7807e --- /dev/null +++ b/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-14B-Control-Camera.py @@ -0,0 +1,31 @@ +import torch +from PIL import Image +from diffsynth import save_video, VideoData, load_state_dict +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-14B-Control-Camera", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-14B-Control-Camera", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-14B-Control-Camera", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-14B-Control-Camera", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth", offload_device="cpu"), + ], +) +pipe.load_lora(pipe.dit, "models/train/Wan2.1-Fun-V1.1-1.3B-Control-Camera_lora/epoch-4.safetensors", alpha=1) +pipe.enable_vram_management() + +video = VideoData("data/example_video_dataset/video1.mp4", height=480, width=832) + +# First and last frame to video +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + input_image=video[0], + camera_control_direction="Left", camera_control_speed=0.0, + seed=0, tiled=True +) +save_video(video, "video_Wan2.1-Fun-V1.1-14B-Control-Camera.mp4", fps=15, quality=5) diff --git a/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-14B-Control.py b/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-14B-Control.py new file mode 100644 index 0000000000000000000000000000000000000000..6b1109826b6583f637ceaf38dac3d02034815107 --- /dev/null +++ b/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-14B-Control.py @@ -0,0 +1,32 @@ +import torch +from PIL import Image +from diffsynth import save_video, VideoData +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-14B-Control", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-14B-Control", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-14B-Control", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-14B-Control", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth", offload_device="cpu"), + ], +) +pipe.load_lora(pipe.dit, "models/train/Wan2.1-Fun-V1.1-14B-Control_lora/epoch-4.safetensors", alpha=1) +pipe.enable_vram_management() + +video = VideoData("data/example_video_dataset/video1_softedge.mp4", height=480, width=832) +video = [video[i] for i in range(81)] +reference_image = VideoData("data/example_video_dataset/video1.mp4", height=480, width=832)[0] + +# Control video +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + control_video=video, reference_image=reference_image, + seed=1, tiled=True +) +save_video(video, "video_Wan2.1-Fun-V1.1-14B-Control.mp4", fps=15, quality=5) diff --git a/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-14B-InP.py b/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-14B-InP.py new file mode 100644 index 0000000000000000000000000000000000000000..35088fb7b28885d861e30b18f052827dd97ffae8 --- /dev/null +++ b/examples/wanvideo/model_training/validate_lora/Wan2.1-Fun-V1.1-14B-InP.py @@ -0,0 +1,30 @@ +import torch +from PIL import Image +from diffsynth import save_video, VideoData +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-14B-InP", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-14B-InP", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-14B-InP", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-14B-InP", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth", offload_device="cpu"), + ], +) +pipe.load_lora(pipe.dit, "models/train/Wan2.1-Fun-V1.1-14B-InP_lora/epoch-4.safetensors", alpha=1) +pipe.enable_vram_management() + +video = VideoData("data/example_video_dataset/video1.mp4", height=480, width=832) + +# First and last frame to video +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + input_image=video[0], end_image=video[80], + seed=0, tiled=True +) +save_video(video, "video_Wan2.1-Fun-V1.1-14B-InP.mp4", fps=15, quality=5) diff --git a/examples/wanvideo/model_training/validate_lora/Wan2.1-I2V-14B-480P.py b/examples/wanvideo/model_training/validate_lora/Wan2.1-I2V-14B-480P.py new file mode 100644 index 0000000000000000000000000000000000000000..1687e36b1873c06aadfed3bb29076ffe365b9f4e --- /dev/null +++ b/examples/wanvideo/model_training/validate_lora/Wan2.1-I2V-14B-480P.py @@ -0,0 +1,29 @@ +import torch +from PIL import Image +from diffsynth import save_video, VideoData +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.1-I2V-14B-480P", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.1-I2V-14B-480P", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.1-I2V-14B-480P", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.1-I2V-14B-480P", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth", offload_device="cpu"), + ], +) +pipe.load_lora(pipe.dit, "models/train/Wan2.1-I2V-14B-480P_lora/epoch-4.safetensors", alpha=1) +pipe.enable_vram_management() + +input_image = VideoData("data/example_video_dataset/video1.mp4", height=480, width=832)[0] + +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + input_image=input_image, + seed=1, tiled=True +) +save_video(video, "video_Wan2.1-I2V-14B-480P.mp4", fps=15, quality=5) diff --git a/examples/wanvideo/model_training/validate_lora/Wan2.1-I2V-14B-720P.py b/examples/wanvideo/model_training/validate_lora/Wan2.1-I2V-14B-720P.py new file mode 100644 index 0000000000000000000000000000000000000000..cd60f378329b71c3b26b36e70b254187c5c2e9cb --- /dev/null +++ b/examples/wanvideo/model_training/validate_lora/Wan2.1-I2V-14B-720P.py @@ -0,0 +1,30 @@ +import torch +from PIL import Image +from diffsynth import save_video, VideoData +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.1-I2V-14B-720P", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.1-I2V-14B-720P", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.1-I2V-14B-720P", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.1-I2V-14B-720P", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth", offload_device="cpu"), + ], +) +pipe.load_lora(pipe.dit, "models/train/Wan2.1-I2V-14B-720P_lora/epoch-4.safetensors", alpha=1) +pipe.enable_vram_management() + +input_image = VideoData("data/example_video_dataset/video1.mp4", height=720, width=1280)[0] + +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + input_image=input_image, + height=720, width=1280, num_frames=49, + seed=1, tiled=True +) +save_video(video, "video_Wan2.1-I2V-14B-720P.mp4", fps=15, quality=5) diff --git a/examples/wanvideo/model_training/validate_lora/Wan2.1-T2V-1.3B.py b/examples/wanvideo/model_training/validate_lora/Wan2.1-T2V-1.3B.py new file mode 100644 index 0000000000000000000000000000000000000000..7cb6c020a7966853ee586e638af6522913d28628 --- /dev/null +++ b/examples/wanvideo/model_training/validate_lora/Wan2.1-T2V-1.3B.py @@ -0,0 +1,24 @@ +import torch +from PIL import Image +from diffsynth import save_video, VideoData +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"), + ], +) +pipe.load_lora(pipe.dit, "models/train/Wan2.1-T2V-1.3B_lora/epoch-4.safetensors", alpha=1) +pipe.enable_vram_management() + +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + seed=1, tiled=True +) +save_video(video, "video_Wan2.1-T2V-1.3B.mp4", fps=15, quality=5) diff --git a/examples/wanvideo/model_training/validate_lora/Wan2.1-T2V-14B.py b/examples/wanvideo/model_training/validate_lora/Wan2.1-T2V-14B.py new file mode 100644 index 0000000000000000000000000000000000000000..3b66a4996c273755b4172d7a1e39ac89a6938141 --- /dev/null +++ b/examples/wanvideo/model_training/validate_lora/Wan2.1-T2V-14B.py @@ -0,0 +1,24 @@ +import torch +from PIL import Image +from diffsynth import save_video, VideoData +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-14B", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-14B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-14B", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"), + ], +) +pipe.load_lora(pipe.dit, "models/train/Wan2.1-T2V-14B_lora/epoch-4.safetensors", alpha=1) +pipe.enable_vram_management() + +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + seed=1, tiled=True +) +save_video(video, "video_Wan2.1-T2V-14B.mp4", fps=15, quality=5) diff --git a/examples/wanvideo/model_training/validate_lora/Wan2.1-VACE-1.3B-Preview.py b/examples/wanvideo/model_training/validate_lora/Wan2.1-VACE-1.3B-Preview.py new file mode 100644 index 0000000000000000000000000000000000000000..91cbf9271c23a2e6ae1d4aac8d1e081915a7e6bc --- /dev/null +++ b/examples/wanvideo/model_training/validate_lora/Wan2.1-VACE-1.3B-Preview.py @@ -0,0 +1,29 @@ +import torch +from PIL import Image +from diffsynth import save_video, VideoData +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="iic/VACE-Wan2.1-1.3B-Preview", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="iic/VACE-Wan2.1-1.3B-Preview", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"), + ModelConfig(model_id="iic/VACE-Wan2.1-1.3B-Preview", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"), + ], +) +pipe.load_lora(pipe.vace, "models/train/Wan2.1-VACE-1.3B-Preview_lora/epoch-4.safetensors", alpha=1) +pipe.enable_vram_management() + +video = VideoData("data/example_video_dataset/video1_softedge.mp4", height=480, width=832) +video = [video[i] for i in range(49)] +reference_image = VideoData("data/example_video_dataset/video1.mp4", height=480, width=832)[0] + +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + vace_video=video, vace_reference_image=reference_image, num_frames=49, + seed=1, tiled=True +) +save_video(video, "video_Wan2.1-VACE-1.3B-Preview.mp4", fps=15, quality=5) diff --git a/examples/wanvideo/model_training/validate_lora/Wan2.1-VACE-1.3B.py b/examples/wanvideo/model_training/validate_lora/Wan2.1-VACE-1.3B.py new file mode 100644 index 0000000000000000000000000000000000000000..b5fd2037a5f9efefe60fe65b21bbf6fdb01cf632 --- /dev/null +++ b/examples/wanvideo/model_training/validate_lora/Wan2.1-VACE-1.3B.py @@ -0,0 +1,29 @@ +import torch +from PIL import Image +from diffsynth import save_video, VideoData +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.1-VACE-1.3B", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.1-VACE-1.3B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.1-VACE-1.3B", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"), + ], +) +pipe.load_lora(pipe.vace, "models/train/Wan2.1-VACE-1.3B_lora/epoch-4.safetensors", alpha=1) +pipe.enable_vram_management() + +video = VideoData("data/example_video_dataset/video1_softedge.mp4", height=480, width=832) +video = [video[i] for i in range(49)] +reference_image = VideoData("data/example_video_dataset/video1.mp4", height=480, width=832)[0] + +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + vace_video=video, vace_reference_image=reference_image, num_frames=49, + seed=1, tiled=True +) +save_video(video, "video_Wan2.1-VACE-1.3B.mp4", fps=15, quality=5) diff --git a/examples/wanvideo/model_training/validate_lora/Wan2.1-VACE-14B.py b/examples/wanvideo/model_training/validate_lora/Wan2.1-VACE-14B.py new file mode 100644 index 0000000000000000000000000000000000000000..bec5df394b2297e885ad65ac9d903af74655eafc --- /dev/null +++ b/examples/wanvideo/model_training/validate_lora/Wan2.1-VACE-14B.py @@ -0,0 +1,29 @@ +import torch +from PIL import Image +from diffsynth import save_video, VideoData +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.1-VACE-14B", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.1-VACE-14B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.1-VACE-14B", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"), + ], +) +pipe.load_lora(pipe.vace, "models/train/Wan2.1-VACE-14B_lora/epoch-4.safetensors", alpha=1) +pipe.enable_vram_management() + +video = VideoData("data/example_video_dataset/video1_softedge.mp4", height=480, width=832) +video = [video[i] for i in range(17)] +reference_image = VideoData("data/example_video_dataset/video1.mp4", height=480, width=832)[0] + +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + vace_video=video, vace_reference_image=reference_image, num_frames=17, + seed=1, tiled=True +) +save_video(video, "video_Wan2.1-VACE-14B.mp4", fps=15, quality=5) diff --git a/examples/wanvideo/model_training/validate_lora/Wan2.2-Animate-14B.py b/examples/wanvideo/model_training/validate_lora/Wan2.2-Animate-14B.py new file mode 100644 index 0000000000000000000000000000000000000000..9f6d7c48dc0ca4817e258eeb928458086332368a --- /dev/null +++ b/examples/wanvideo/model_training/validate_lora/Wan2.2-Animate-14B.py @@ -0,0 +1,32 @@ +import torch +from PIL import Image +from diffsynth import save_video, VideoData, load_state_dict +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.2-Animate-14B", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.2-Animate-14B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.2-Animate-14B", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.2-Animate-14B", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth", offload_device="cpu"), + ], +) +pipe.load_lora(pipe.dit, "models/train/Wan2.2-Animate-14B_lora/epoch-4.safetensors", alpha=1) +pipe.enable_vram_management() + +input_image = VideoData("data/example_video_dataset/animate/animate_output.mp4", height=480, width=832)[0] +animate_pose_video = VideoData("data/examples/wan/animate/animate_pose_video.mp4", height=480, width=832).raw_data()[:81-4] +animate_face_video = VideoData("data/examples/wan/animate/animate_face_video.mp4", height=512, width=512).raw_data()[:81-4] +video = pipe( + prompt="视频中的人在做动作", + seed=0, tiled=True, + input_image=input_image, + animate_pose_video=animate_pose_video, + animate_face_video=animate_face_video, + num_frames=81, height=480, width=832, + num_inference_steps=20, cfg_scale=1, +) +save_video(video, "video_Wan2.2-Animate-14B.mp4", fps=15, quality=5) \ No newline at end of file diff --git a/examples/wanvideo/model_training/validate_lora/Wan2.2-Fun-A14B-Control-Camera.py b/examples/wanvideo/model_training/validate_lora/Wan2.2-Fun-A14B-Control-Camera.py new file mode 100644 index 0000000000000000000000000000000000000000..f1e18407cab28e5d7191745476b3771d02d4660b --- /dev/null +++ b/examples/wanvideo/model_training/validate_lora/Wan2.2-Fun-A14B-Control-Camera.py @@ -0,0 +1,32 @@ +import torch +from PIL import Image +from diffsynth import save_video, VideoData, load_state_dict +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-Control-Camera", origin_file_pattern="high_noise_model/diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-Control-Camera", origin_file_pattern="low_noise_model/diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-Control-Camera", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-Control-Camera", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"), + ], +) +pipe.load_lora(pipe.dit, "models/train/Wan2.2-Fun-A14B-Control-Camera_high_noise_lora/epoch-4.safetensors", alpha=1) +pipe.load_lora(pipe.dit2, "models/train/Wan2.2-Fun-A14B-Control-Camera_low_noise_lora/epoch-4.safetensors", alpha=1) +pipe.enable_vram_management() + +video = VideoData("data/example_video_dataset/video1.mp4", height=480, width=832) + +# First and last frame to video +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + input_image=video[0], + camera_control_direction="Left", camera_control_speed=0.0, + seed=0, tiled=True +) +save_video(video, "video_Wan2.2-Fun-A14B-Control-Camera.mp4", fps=15, quality=5) diff --git a/examples/wanvideo/model_training/validate_lora/Wan2.2-Fun-A14B-Control.py b/examples/wanvideo/model_training/validate_lora/Wan2.2-Fun-A14B-Control.py new file mode 100644 index 0000000000000000000000000000000000000000..11dda5bd3fe93ab77dd599799a3105b3e7612994 --- /dev/null +++ b/examples/wanvideo/model_training/validate_lora/Wan2.2-Fun-A14B-Control.py @@ -0,0 +1,33 @@ +import torch +from PIL import Image +from diffsynth import save_video, VideoData +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-Control", origin_file_pattern="high_noise_model/diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-Control", origin_file_pattern="low_noise_model/diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-Control", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-Control", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"), + ], +) +pipe.load_lora(pipe.dit, "models/train/Wan2.2-Fun-A14B-Control_high_noise_lora/epoch-4.safetensors", alpha=1) +pipe.load_lora(pipe.dit2, "models/train/Wan2.2-Fun-A14B-Control_low_noise_lora/epoch-4.safetensors", alpha=1) +pipe.enable_vram_management() + +video = VideoData("data/example_video_dataset/video1_softedge.mp4", height=480, width=832) +video = [video[i] for i in range(81)] +reference_image = VideoData("data/example_video_dataset/video1.mp4", height=480, width=832)[0] + +# Control video +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + control_video=video, reference_image=reference_image, + seed=1, tiled=True +) +save_video(video, "video_Wan2.2-Fun-A14B-Control.mp4", fps=15, quality=5) diff --git a/examples/wanvideo/model_training/validate_lora/Wan2.2-Fun-A14B-InP.py b/examples/wanvideo/model_training/validate_lora/Wan2.2-Fun-A14B-InP.py new file mode 100644 index 0000000000000000000000000000000000000000..90afa4695cf9a0b22c0b657638839a16a88b7aeb --- /dev/null +++ b/examples/wanvideo/model_training/validate_lora/Wan2.2-Fun-A14B-InP.py @@ -0,0 +1,31 @@ +import torch +from PIL import Image +from diffsynth import save_video, VideoData +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-InP", origin_file_pattern="high_noise_model/diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-InP", origin_file_pattern="low_noise_model/diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-InP", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.2-Fun-A14B-InP", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"), + ], +) +pipe.load_lora(pipe.dit, "models/train/Wan2.2-Fun-A14B-InP_high_noise_lora/epoch-4.safetensors", alpha=1) +pipe.load_lora(pipe.dit2, "models/train/Wan2.2-Fun-A14B-InP_low_noise_lora/epoch-4.safetensors", alpha=1) +pipe.enable_vram_management() + +video = VideoData("data/example_video_dataset/video1.mp4", height=480, width=832) + +# First and last frame to video +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + input_image=video[0], end_image=video[80], + seed=0, tiled=True +) +save_video(video, "video_Wan2.2-Fun-A14B-InP.mp4", fps=15, quality=5) diff --git a/examples/wanvideo/model_training/validate_lora/Wan2.2-I2V-A14B.py b/examples/wanvideo/model_training/validate_lora/Wan2.2-I2V-A14B.py new file mode 100644 index 0000000000000000000000000000000000000000..f221ef71383cb03f53c45ac948805b924fec8664 --- /dev/null +++ b/examples/wanvideo/model_training/validate_lora/Wan2.2-I2V-A14B.py @@ -0,0 +1,31 @@ +import torch +from PIL import Image +from diffsynth import save_video, VideoData +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.2-I2V-A14B", origin_file_pattern="high_noise_model/diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.2-I2V-A14B", origin_file_pattern="low_noise_model/diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.2-I2V-A14B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.2-I2V-A14B", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"), + ], +) +pipe.load_lora(pipe.dit, "models/train/Wan2.2-I2V-A14B_high_noise_lora/epoch-4.safetensors", alpha=1) +pipe.load_lora(pipe.dit2, "models/train/Wan2.2-I2V-A14B_low_noise_lora/epoch-4.safetensors", alpha=1) +pipe.enable_vram_management() + +input_image = VideoData("data/example_video_dataset/video1.mp4", height=480, width=832)[0] + +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + input_image=input_image, + num_frames=49, + seed=1, tiled=True, +) +save_video(video, "video_Wan2.2-I2V-A14B.mp4", fps=15, quality=5) diff --git a/examples/wanvideo/model_training/validate_lora/Wan2.2-S2V-14B.py b/examples/wanvideo/model_training/validate_lora/Wan2.2-S2V-14B.py new file mode 100644 index 0000000000000000000000000000000000000000..a6166b9174f26e4f89714bfff50b32a99f4c4475 --- /dev/null +++ b/examples/wanvideo/model_training/validate_lora/Wan2.2-S2V-14B.py @@ -0,0 +1,51 @@ +import torch +from PIL import Image +import librosa +from diffsynth import VideoData, save_video_with_audio +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.2-S2V-14B", origin_file_pattern="diffusion_pytorch_model*.safetensors"), + ModelConfig(model_id="Wan-AI/Wan2.2-S2V-14B", origin_file_pattern="wav2vec2-large-xlsr-53-english/model.safetensors"), + ModelConfig(model_id="Wan-AI/Wan2.2-S2V-14B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"), + ModelConfig(model_id="Wan-AI/Wan2.2-S2V-14B", origin_file_pattern="Wan2.1_VAE.pth"), + ], + audio_processor_config=ModelConfig(model_id="Wan-AI/Wan2.2-S2V-14B", origin_file_pattern="wav2vec2-large-xlsr-53-english/"), +) + +pipe.load_lora(pipe.dit, "models/train/Wan2.2-S2V-14B_lora/epoch-4.safetensors", alpha=1) +pipe.enable_vram_management() + + +num_frames = 81 # 4n+1 +height = 448 +width = 832 + +prompt = "a person is singing" +negative_prompt = "画面模糊，最差质量，画面模糊，细节模糊不清，情绪激动剧烈，手快速抖动，字幕，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" +input_image = Image.open("data/example_video_dataset/wans2v/pose.png").convert("RGB").resize((width, height)) +# s2v audio input, recommend 16kHz sampling rate +audio_path = 'data/example_video_dataset/wans2v/sing.MP3' +input_audio, sample_rate = librosa.load(audio_path, sr=16000) +# Pose video input +pose_video_path = 'data/example_video_dataset/wans2v/pose.mp4' +pose_video = VideoData(pose_video_path, height=height, width=width) + +# Speech-to-video with pose +video = pipe( + prompt=prompt, + input_image=input_image, + negative_prompt=negative_prompt, + seed=0, + num_frames=num_frames, + height=height, + width=width, + audio_sample_rate=sample_rate, + input_audio=input_audio, + s2v_pose_video=pose_video, + num_inference_steps=40, +) +save_video_with_audio(video[1:], "video_pose_with_audio.mp4", audio_path, fps=16, quality=5) diff --git a/examples/wanvideo/model_training/validate_lora/Wan2.2-T2V-A14B.py b/examples/wanvideo/model_training/validate_lora/Wan2.2-T2V-A14B.py new file mode 100644 index 0000000000000000000000000000000000000000..ab439274bb9307da38df841edacff920a0d48bb3 --- /dev/null +++ b/examples/wanvideo/model_training/validate_lora/Wan2.2-T2V-A14B.py @@ -0,0 +1,28 @@ +import torch +from PIL import Image +from diffsynth import save_video, VideoData +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.2-T2V-A14B", origin_file_pattern="high_noise_model/diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.2-T2V-A14B", origin_file_pattern="low_noise_model/diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.2-T2V-A14B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.2-T2V-A14B", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"), + ], +) +pipe.load_lora(pipe.dit, "models/train/Wan2.2-T2V-A14B_high_noise_lora/epoch-4.safetensors", alpha=1) +pipe.load_lora(pipe.dit2, "models/train/Wan2.2-T2V-A14B_low_noise_lora/epoch-4.safetensors", alpha=1) +pipe.enable_vram_management() + +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + num_frames=49, + seed=1, tiled=True +) +save_video(video, "video_Wan2.2-T2V-A14B.mp4", fps=15, quality=5) diff --git a/examples/wanvideo/model_training/validate_lora/Wan2.2-TI2V-5B.py b/examples/wanvideo/model_training/validate_lora/Wan2.2-TI2V-5B.py new file mode 100644 index 0000000000000000000000000000000000000000..e5b16c8d19917ce487557297621a3a01a0925305 --- /dev/null +++ b/examples/wanvideo/model_training/validate_lora/Wan2.2-TI2V-5B.py @@ -0,0 +1,29 @@ +import torch +from PIL import Image +from diffsynth import save_video, VideoData, load_state_dict +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig +from modelscope import dataset_snapshot_download + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="Wan-AI/Wan2.2-TI2V-5B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.2-TI2V-5B", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.2-TI2V-5B", origin_file_pattern="Wan2.2_VAE.pth", offload_device="cpu"), + ], +) +pipe.load_lora(pipe.dit, "models/train/Wan2.2-TI2V-5B_lora/epoch-4.safetensors", alpha=1) +pipe.enable_vram_management() + +input_image = VideoData("data/example_video_dataset/video1.mp4", height=480, width=832)[0] + +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + input_image=input_image, + num_frames=49, + seed=1, tiled=True, +) +save_video(video, "video_Wan2.2-TI2V-5B.mp4", fps=15, quality=5) diff --git a/examples/wanvideo/model_training/validate_lora/Wan2.2-VACE-Fun-A14B.py b/examples/wanvideo/model_training/validate_lora/Wan2.2-VACE-Fun-A14B.py new file mode 100644 index 0000000000000000000000000000000000000000..b6e6aff24740e9f321ba5e130afe4ce0d4766198 --- /dev/null +++ b/examples/wanvideo/model_training/validate_lora/Wan2.2-VACE-Fun-A14B.py @@ -0,0 +1,31 @@ +import torch +from PIL import Image +from diffsynth import save_video, VideoData +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="PAI/Wan2.2-VACE-Fun-A14B", origin_file_pattern="high_noise_model/diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.2-VACE-Fun-A14B", origin_file_pattern="low_noise_model/diffusion_pytorch_model*.safetensors", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.2-VACE-Fun-A14B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"), + ModelConfig(model_id="PAI/Wan2.2-VACE-Fun-A14B", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"), + ], +) +pipe.load_lora(pipe.vace, "models/train/Wan2.2-VACE-Fun-A14B_high_noise_lora/epoch-4.safetensors", alpha=1) +pipe.load_lora(pipe.vace2, "models/train/Wan2.2-VACE-Fun-A14B_low_noise_lora/epoch-4.safetensors", alpha=1) +pipe.enable_vram_management() + +video = VideoData("data/example_video_dataset/video1_softedge.mp4", height=480, width=832) +video = [video[i] for i in range(17)] +reference_image = VideoData("data/example_video_dataset/video1.mp4", height=480, width=832)[0] + +video = pipe( + prompt="from sunset to night, a small town, light, house, river", + negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走", + vace_video=video, vace_reference_image=reference_image, num_frames=17, + seed=1, tiled=True +) +save_video(video, "video_Wan2.2-VACE-Fun-A14B.mp4", fps=15, quality=5) diff --git a/examples/wanvideo/model_training/validate_lora/krea-realtime-video.py b/examples/wanvideo/model_training/validate_lora/krea-realtime-video.py new file mode 100644 index 0000000000000000000000000000000000000000..0742d5b00571b224502e3863b24f410a4ed44c94 --- /dev/null +++ b/examples/wanvideo/model_training/validate_lora/krea-realtime-video.py @@ -0,0 +1,28 @@ +import torch +from PIL import Image +from diffsynth import save_video, VideoData, load_state_dict +from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig + + +pipe = WanVideoPipeline.from_pretrained( + torch_dtype=torch.bfloat16, + device="cuda", + model_configs=[ + ModelConfig(model_id="krea/krea-realtime-video", origin_file_pattern="krea-realtime-video-14b.safetensors", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-14B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"), + ModelConfig(model_id="Wan-AI/Wan2.1-T2V-14B", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"), + ], +) + +pipe.load_lora(pipe.dit, "models/train/krea-realtime-video_lora/epoch-4.safetensors", alpha=1) +pipe.enable_vram_management() + +# Text-to-video +video = pipe( + prompt="a cat sitting on a boat", + num_inference_steps=6, num_frames=81, + seed=0, tiled=True, + cfg_scale=1, + sigma_shift=20, +) +save_video(video, "output.mp4", fps=15, quality=5) diff --git a/examples/wanvideo/model_training/validate_lora/run_test.py b/examples/wanvideo/model_training/validate_lora/run_test.py new file mode 100644 index 0000000000000000000000000000000000000000..367ee9d3c6afb4f781ed742a75a3e2a2a187564b --- /dev/null +++ b/examples/wanvideo/model_training/validate_lora/run_test.py @@ -0,0 +1,25 @@ +import multiprocessing, os + + +def run_task(scripts, thread_id, thread_num): + for script_id, script in enumerate(scripts): + if script_id % thread_num == thread_id: + log_file_name = script.replace("/", "_") + ".txt" + cmd = f"CUDA_VISIBLE_DEVICES={thread_id} python -u {script} > data/log/{log_file_name} 2>&1" + os.makedirs("data/log", exist_ok=True) + print(cmd, flush=True) + os.system(cmd) + + +if __name__ == "__main__": + scripts = [] + for file_name in os.listdir("examples/wanvideo/model_training/validate_lora"): + if file_name != "run_test.py": + scripts.append(os.path.join("examples/wanvideo/model_training/validate_lora", file_name)) + + processes = [multiprocessing.Process(target=run_task, args=(scripts, i, 8)) for i in range(8)] + for p in processes: + p.start() + for p in processes: + p.join() + print("Done!") \ No newline at end of file diff --git a/nano_api.py b/nano_api.py new file mode 100644 index 0000000000000000000000000000000000000000..9e0f823f546f023568e3683a4b504b33d63956e6 --- /dev/null +++ b/nano_api.py @@ -0,0 +1,344 @@ +import io +import requests +import json +import base64 +import re +import os +import os.path as osp +import datetime +from typing import Optional, Tuple +from PIL import Image +from io import BytesIO +from tqdm import tqdm +# from api_class.utils import base64_to_image,encode_pil_to_base64 +def encode_pil_to_base64(image_pil): + # 将PIL图像编码为base64字符串 + buffered = BytesIO() + image_pil.save(buffered, format="PNG") + img_bytes = buffered.getvalue() + img_base64 = base64.b64encode(img_bytes).decode('utf-8') + mime_type = "image/png" + return img_base64, mime_type + +def base64_to_image(base64_str): + # 将base64字符串解码为PIL图像 + img_bytes = base64.b64decode(base64_str) + image_pil = Image.open(BytesIO(img_bytes)) + return image_pil +class GeminiImageGenerator: + def __init__(self, api_url: str = "https://api.apiyi.com/v1beta/models/gemini-3-pro-image-preview:generateContent"): + self.api_key = "sk-MC5B3H948s5YhiVN591f578fC74a4eC484659cC6005bB603" + self.api_url = api_url + self.api_url_compre="https://api.apiyi.com/v1/chat/completions" + self.headers = { + "Content-Type": "application/json", + "Authorization": f"Bearer {self.api_key}" + } + self.SUPPORTED_ASPECT_RATIOS = [ + "21:9", "16:9", "4:3", "3:2", "1:1", + "9:16", "3:4", "2:3", "5:4", "4:5"] + + self.SUPPORTED_RESOLUTION=["1K","2K","4K"] + + def nano_imageEditing(self,data_dict): + required_keys = {"prompt","image_list","ratio","resolution"} + assert required_keys <= data_dict.keys(), \ + f"缺少必要字段，必须包含: {required_keys}，实际提供: {list(data_dict.keys())}" + ratio=data_dict["ratio"] + resolution=data_dict["resolution"] + assert ratio in self.SUPPORTED_ASPECT_RATIOS, f"不支持的比例,支持比例为{self.SUPPORTED_ASPECT_RATIOS}" + assert resolution in self.SUPPORTED_RESOLUTION,f"不支持的分辨率,支持分辨率为{self.SUPPORTED_RESOLUTION}" + prompt=data_dict["prompt"] + img_payload=[] + for image_pil in data_dict["image_list"]: + image_pil = Image.open(image_pil) + image_base64, mime_type=encode_pil_to_base64(image_pil) + img_payload.append({ + "inline_data": { + "mime_type": mime_type, + "data": image_base64 + } + }) + try: + payload = { + "contents": [{ + "parts": [{"text": prompt}]+img_payload + }] + } + + if ratio: + payload["generationConfig"] = { + "responseModalities": ["IMAGE"], + "imageConfig": { + "aspectRatio": ratio, + "image_size": resolution + } + } + + print("📡 发送请求到 Gemini API...") + # 发送非流式请求 + response = requests.post( + self.api_url, + headers=self.headers, + json=payload, + timeout=120 + ) + + print("✅ API请求成功，正在解析响应...") + + # 解析非流式JSON响应 + try: + result = response.json() + print("✅ 成功解析JSON响应") + except json.JSONDecodeError as e: + return False, f"JSON解析失败: {str(e)}",None + + if "candidates" not in result or len(result["candidates"]) == 0: + return False, "未找到图片数据", None + + candidate = result["candidates"][0] + if "content" not in candidate or "parts" not in candidate["content"]: + return False, "响应格式错误",None + + parts = candidate["content"]["parts"] + output_image_data = None + + for part in parts: + if "inlineData" in part and "data" in part["inlineData"]: + output_image_data = part["inlineData"]["data"] + break + + if not output_image_data: + return False, "未找到图片数据",None + + try: + pil_img=base64_to_image(output_image_data) + return pil_img + except Exception as e: + raise ValueError(f"图片加载失败: {e}") + + + except requests.exceptions.Timeout: + raise RuntimeError("请求超时（300秒）") + except requests.exceptions.ConnectionError as e: + raise RuntimeError(f"连接错误: {str(e)}") + except Exception as e: + raise ValueError(f"未知错误: {str(e)}") + + + + def nano_text2image(self, data_dict) : + required_keys = {"prompt", "ratio","resolution"} + assert required_keys <= data_dict.keys(), \ + f"缺少必要字段，必须包含: {required_keys}，实际提供: {list(data_dict.keys())}" + ratio=data_dict["ratio"] + assert ratio in self.SUPPORTED_ASPECT_RATIOS, f"不支持的比例,支持比例为{self.SUPPORTED_ASPECT_RATIOS}" + + prompt="帮我生成图片,图片提示词如下: "+data_dict["prompt"] + resolution=data_dict["resolution"] + print("🚀 开始生成图片...") + print(f"提示词: {prompt}") + + try: + # 构建请求数据 + payload = { + "contents": [{ + "parts": [{"text": prompt}] + }] + } + + if ratio: + payload["generationConfig"] = { + "responseModalities": ["IMAGE"], + "imageConfig": { + "aspectRatio": ratio, + "image_size": resolution + } + } + + print("📡 发送请求到 Gemini API...") + # 发送非流式请求 + + response = requests.post( + self.api_url, + headers=self.headers, + json=payload, + timeout=120 + ) + + if response.status_code != 200: + error_msg = f"API请求失败，状态码: {response.status_code}" + try: + error_detail = response.json() + error_msg += f", 错误详情: {error_detail}" + except: + error_msg += f", 响应内容: {response.text[:500]}" + return False, error_msg,None + + print("✅ API请求成功，正在解析响应...") + + # 解析非流式JSON响应 + try: + result = response.json() + print("✅ 成功解析JSON响应") + except json.JSONDecodeError as e: + return False, f"JSON解析失败: {str(e)}",None + + #提取图片数据 + if "candidates" not in result or len(result["candidates"]) == 0: + return False, "未找到图片数据",None + + candidate = result["candidates"][0] + if "content" not in candidate or "parts" not in candidate["content"]: + return False, "响应格式错误",None + + parts = candidate["content"]["parts"] + image_data = None + + for part in parts: + if "inlineData" in part and "data" in part["inlineData"]: + image_data = part["inlineData"]["data"] + break + + if not image_data: + return False, "未找到图片数据",None + try: + pil_img=base64_to_image(image_data) + return pil_img + except Exception as e: + raise ValueError(f"图片加载失败: {e}") + + except requests.exceptions.Timeout: + raise RuntimeError("请求超时（300秒）") + except requests.exceptions.ConnectionError as e: + raise RuntimeError(f"连接错误: {str(e)}") + except Exception as e: + raise ValueError(f"未知错误: {str(e)}") + + def _extract_image_from_base64(self,content: str) -> Tuple[bool, Optional[Image.Image], str]: + """ + 高效提取base64图片并返回PIL Image对象 + + Args: + content: 包含图片数据的内容 + + Returns: + Tuple[是否成功, PIL Image对象(或None), 消息] + """ + try: + print(f"📄 内容预览（前200字符）: {content[:200]}") + + # 匹配 base64 图片数据 + base64_pattern = r'data:image/([^;]+);base64,([A-Za-z0-9+/=]+)' + match = re.search(base64_pattern, content) + + if not match: + print('⚠️ 未找到base64图片数据') + raise ValueError("No image founded!") + + image_format = match.group(1) + b64_data = match.group(2) + + print(f'🎨 图像格式: {image_format}') + print(f'📏 Base64数据长度: {len(b64_data)} 字符') + + # 解码 base64 + image_data = base64.b64decode(b64_data) + + if len(image_data) < 100: + return False, None, "解码后的图片数据太小，可能无效" + + # 使用 PIL 读取图像 + image = Image.open(io.BytesIO(image_data)) + print(f'🖼️ 图片加载成功，尺寸: {image.size}, 模式: {image.mode}') + + return True, image, f"成功提取图像 ({image_format})" + + except Exception as e: + return False, None, f"处理图片时发生错误: {str(e)}" + + def nano_image_comprehension(self, data_dict,prompt): + required_keys = {"image"} + assert required_keys <= data_dict.keys(), \ + f"缺少必要字段，必须包含: {required_keys}，实际提供: {list(data_dict.keys())}" + im=data_dict["image"] + im = Image.open(im) + im_base64,_=encode_pil_to_base64(im) + headers = { + "Authorization": f"Bearer {self.api_key}", + "Content-Type": "application/json" + } + # Prepare payload - generated image first, then ground truth + payload = { + "model": "gemini-2.5-flash", + "messages": [ + { + "role": "user", + "content": [ + {"type": "text", "text": prompt}, + { + "type": "image_url", + "image_url": {"url": im_base64} + }, + ] + } + ], + "max_tokens": 5000 + } + + # Make API call + response = requests.post(self.api_url_compre, headers=headers, json=payload, timeout=600) + response.raise_for_status() + + + # Extract response content + response_data = response.json() + content = response_data['choices'][0]['message']['content'] + + print(content) + return content + +if __name__=="__main__": + g = GeminiImageGenerator() + # 描述性提示词（根据需要修改） + prompt = """You are a concise storyboard narrator focused on core scene and composition description. Based on the 1 vertically stitched image containing multiple storyboards (identified as Storyboard 1 to Storyboard N in top-to-bottom order, N = actual number), output ONLY a simple story background and concise composition descriptions for each shot. Strictly follow the JSON format below, with each "Image Composition" limited to ~100 words: +{"Simple Story Background": "1-2 sentences summarizing the basic story context (e.g., 'A girl searches for her lost cat in a suburban neighborhood on a sunny afternoon')","Storyboard_List": [{"Shot Number": 1,"Scene": "Specific location (e.g., front yard of a cottage, forest trail, downtown café)","Image Composition": "Concise description of characters (appearance, posture), key props, framing (shot type: close-up/medium/long/wide), lighting, and core visual elements (max 100 words)","Emotional Tone": "Brief atmosphere (e.g., warm, tense, peaceful)"},{"Shot Number": N,"Scene": "Same as above","Image Composition": "Same as above (max 100 words)","Emotional Tone": "Same as above"}]} +Requirements +All fields are mandatory; no redundant content. +"Image Composition" focuses only on critical visual information (characters, framing, key props, lighting) – no excessive details. +Strictly match the number/order of storyboards in the image (top-to-bottom numbering). +JSON format must be error-free, ready for direct use. +No extra text outside the JSON structure.""" + + INPUT_DIR = "dataset/spotlight_sketch_cat/GT" + OUTPUT_DIR = "dataset/spotlight_sketch_cat" + RATIO = "16:9" + # 对输入文件夹内的图片进行排序处理 + + output_path = os.path.join(OUTPUT_DIR, "spotlight_nano_comprehension_1203.txt") + os.makedirs(OUTPUT_DIR, exist_ok=True) + + input_files = sorted( + fname for fname in os.listdir(INPUT_DIR) + if os.path.isfile(os.path.join(INPUT_DIR, fname)) + ) + + for idx, fname in tqdm(enumerate(input_files), total=len(input_files)): + src_path = os.path.join(INPUT_DIR, fname) + # 调用nano_image_comprehension + # try: + # pil_in = Image.open(src_path).convert("RGB") + + result = g.nano_image_comprehension({ + "image": src_path, + },prompt) + base_name = os.path.splitext(fname)[0] + with open(output_path, "a", encoding="utf-8") as f: + result = result.replace("\n", "") + result = result.replace("```", "") + result = result.replace("json", "") + result = result.replace('"Simple Story Background"', f'"Image_Name": "{base_name}", "Simple Story Background"') + + f.write(result.strip("\n") + "\n") + # except Exception as e: + # print(f"处理文件 {fname} 时出错: {e}") \ No newline at end of file diff --git a/nano_comprehension_1201.txt b/nano_comprehension_1201.txt new file mode 100644 index 0000000000000000000000000000000000000000..51b3f768895a023696da44a4cd7011c099956850 --- /dev/null +++ b/nano_comprehension_1201.txt @@ -0,0 +1,6 @@ +{"Image_Name": "000","Simple Story Background": "A young girl in a sun-drenched meadow observes and interacts with butterflies, experiencing moments of wonder and joy as they land on her.", "Storyboard_List": [{"Shot Number": 1, "Scene": "Sunlit meadow", "Image Composition": "A medium shot captures a young girl with braided red hair, wearing a simple dress, looking upwards and to the right in a field of wildflowers. A butterfly flies in the upper left. Golden hour lighting illuminates the scene from the back, highlighting her profile and the gentle glow of the field.", "Emotional Tone": "Wonder, contemplation"}, {"Shot Number": 2, "Scene": "Sunlit meadow", "Image Composition": "A medium close-up shows the girl with her head tilted, looking down intently. She is gently holding a small natural element between her fingers, her expression focused and careful. Soft, warm light falls on her face and the surrounding foliage.", "Emotional Tone": "Curiosity, gentleness"}, {"Shot Number": 3, "Scene": "Sunlit meadow", "Image Composition": "A close-up frames the girl's face and outstretched hands. A monarch butterfly rests delicately on her index finger, captivating her gaze. Her eyes are wide with awe, and her mouth is slightly open, revealing a sense of pure wonder. Soft, warm light accentuates the scene.", "Emotional Tone": "Awe, delight"}, {"Shot Number": 4, "Scene": "Sunlit meadow", "Image Composition": "A medium close-up reveals the girl smiling radiantly, looking towards the upper right. A butterfly is perched on her hair, while another appears to be flying away from her open left hand. The scene is bathed in warm, golden light, emphasizing her joyous expression.", "Emotional Tone": "Joy, happiness"}]} +{ "Image_Name": "001", "Simple Story Background": "A woman spends a quiet, contemplative morning or afternoon in her sunlit home, lost in moments of serene reflection.", "Storyboard_List": [ { "Shot Number": 1, "Scene": "An antique-style living room/bedroom with large windows.", "Image Composition": "A young woman in a sheer, light-colored nightgown lies on her back on a plush, light brown sofa. Her left arm is raised as she basks in bright sunlight streaming from a large window. The framing is a medium shot, highlighting her peaceful expression and the warm, ethereal lighting.", "Emotional Tone": "Serene, peaceful, dreamlike" }, { "Shot Number": 2, "Scene": "The same sunlit living room/bedroom.", "Image Composition": "The woman, still in her nightgown, stands in a medium long shot, facing slightly away and looking down, her long dark hair falling over her shoulders. Strong shafts of sunlight diagonally cut across the room, illuminating her and the sofa behind her. A small table with fruit is visible in the foreground.", "Emotional Tone": "Thoughtful, quiet, gentle" }, { "Shot Number": 3, "Scene": "The same living room/bedroom, from a slightly lower angle.", "Image Composition": "The woman is seated on the floor in a medium shot, facing away slightly, looking down with a pensive expression. She appears to be adjusting something near her leg. Sunlight continues to filter into the room, softly illuminating her and parts of the sofa and a decorative side table with fruit.", "Emotional Tone": "Pensive, introspective, calm" } ]} +{ "Image_Name": "002", "Simple Story Background": "A young woman thoughtfully collects and arranges flowers in a sun-kissed field, eventually carrying her floral creation across the landscape as evening descends.", "Storyboard_List": [ { "Shot Number": 1, "Scene": "Flower-filled outdoor setting", "Image Composition": "Medium close-up of a young woman's profile, head tilted slightly, looking right. Her dark hair is adorned with small white flowers, framing her serene face. Warm, golden sunlight bathes her from the side, with soft-focus foliage and flowers in the foreground and background.", "Emotional Tone": "Serene, reflective, warm" }, { "Shot Number": 2, "Scene": "Grassy field at sunset", "Image Composition": "Medium shot of the young woman in a green tunic, bending forward in a golden field. She delicately touches a structure made of bamboo poles adorned with white flowers. The setting sun casts a warm, orange backlight, illuminating her hair and the landscape, with distant hills visible.", "Emotional Tone": "Engaged, peaceful, warm" }, { "Shot Number": 3, "Scene": "Open field at dusk", "Image Composition": "Wide shot of a silhouetted young woman walking right across a field, carrying a large, flower-laden structure over her shoulder. A prominent, bare-branched tree stands on the left. The background features a vibrant orange and purple sunset sky over distant hills.", "Emotional Tone": "Poetic, solitary, evocative" } ]} +{ "Image_Name": "003", "Simple Story Background": "A young artist finds inspiration in a sun-drenched forest, capturing the ethereal beauty on her canvas during golden hour.", "Storyboard_List": [ { "Shot Number": 1, "Scene": "Enchanted forest", "Image Composition": "Medium shot of a young woman in a cream sweater, seen from behind/side, holding a palette and brush. Her hand is raised towards dramatic sunbeams cutting through the trees. An easel with a canvas is visible to her right. Strong backlighting creates a radiant halo.", "Emotional Tone": "Magical, inspired, focused" }, { "Shot Number": 2, "Scene": "Enchanted forest", "Image Composition": "Close-up of the young woman's right profile, focusing on her reflective eyes and softly lit face. Her gaze is directed upwards and to the right, bathed in warm, golden light. The background is a soft, blurred wash of greens and yellows.", "Emotional Tone": "Reflective, serene, thoughtful" }, { "Shot Number": 3, "Scene": "Enchanted forest", "Image Composition": "Wide shot of the young woman, seen from behind, painting at an easel on the left. She wears the same cream sweater and holds a palette. The dense forest is illuminated by prominent, misty sunbeams streaming through the trees from the right, creating a deeply atmospheric scene.", "Emotional Tone": "Peaceful, immersive, ethereal" } ]} +{ "Image_Name": "004", "Simple Story Background": "The story begins with a serene natural landscape at dawn or dusk, transitioning to a young woman deeply engrossed in reading a book outdoors.", "Storyboard_List": [ { "Shot Number": 1, "Scene": "Misty, open field with distant trees at sunrise/sunset", "Image Composition": "A wide shot captures a misty, grassy field bathed in the warm, golden light of sunrise or sunset. The sun's flare is prominent on the left, illuminating distant dark trees and creating a soft glow over the landscape. Text "A WORKING TITLE PRODUCTION" is centered.", "Emotional Tone": "Serene, expansive, hopeful" }, { "Shot Number": 2, "Scene": "Grassy outdoor area, possibly a field or garden", "Image Composition": "A medium close-up of a young woman with dark hair, wearing a dark top, looking down intently at an open book. Her expression is focused and calm. Golden sunlight from the upper left beautifully lights her face and hair, with blurred green foliage and trees in the background.", "Emotional Tone": "Contemplative, peaceful, studious" }, { "Shot Number": 3, "Scene": "Grassy outdoor area", "Image Composition": "A close-up shot, from an overhead perspective, focuses on an open book held by hands, resting on lush green grass. The person's shoulder, clad in a reddish-brown garment, is visible in the lower left. The pages are filled with text, softly illuminated by natural light.", "Emotional Tone": "Intimate, focused, calm" } ]} +{ "Image_Name": "005", "Simple Story Background": "An older gentleman shares a story or engages in a conversation with several young women, who react with amusement and anticipation within a domestic, period setting.", "Storyboard_List": [ { "Shot Number": 1, "Scene": "An opulent living room or study", "Image Composition": "An elderly man with long white hair and beard, dressed in a brown vest and light shirt, sits in an ornate dark armchair, looking off-camera with a gentle smile. A window and potted plant are to his left; an intricate, dark cabinet is behind him. Medium shot, soft, warm lighting.", "Emotional Tone": "Content, calm, reflective" }, { "Shot Number": 2, "Scene": "A crowded interior space", "Image Composition": "A young woman with dark brown hair and a light-colored period dress is shown in a close-up, smiling broadly with an open mouth, her head tilted up. She rests her chin on a striped pillow or armrest, her gaze directed upwards and off-camera. Soft, warm lighting.", "Emotional Tone": "Joyful, animated, amused" }, { "Shot Number": 3, "Scene": "A doorway within a home", "Image Composition": "A young woman with dark, curly hair and a patterned dress smiles brightly, looking off-camera from a doorway. Behind her, two other young women are partially visible; one appears curious or surprised. The shot is framed by a dark doorframe on the left and a golden pillar on the right. Medium shot, warm lighting.", "Emotional Tone": "Enthusiastic, delighted, curious" } ]} \ No newline at end of file diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..dd8aab031586615077ccf65d0036adeddfd23833 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,15 @@ +torch>=2.0.0 +torchvision +transformers +imageio +imageio[ffmpeg] +safetensors +einops +sentencepiece +protobuf +modelscope +ftfy +pynvml +pandas +accelerate +peft diff --git a/setup.py b/setup.py new file mode 100644 index 0000000000000000000000000000000000000000..e72b901c32c11d264f369c5ffa146fb0624d8680 --- /dev/null +++ b/setup.py @@ -0,0 +1,30 @@ +import os +from setuptools import setup, find_packages +import pkg_resources + +# Path to the requirements file +requirements_path = os.path.join(os.path.dirname(__file__), "requirements.txt") + +# Read the requirements from the requirements file +if os.path.exists(requirements_path): + with open(requirements_path, 'r') as f: + install_requires = [str(r) for r in pkg_resources.parse_requirements(f)] +else: + install_requires = [] + +setup( + name="diffsynth", + version="1.1.9", + description="Enjoy the magic of Diffusion models!", + author="Artiprocher", + packages=find_packages(), + install_requires=install_requires, + include_package_data=True, + classifiers=[ + "Programming Language :: Python :: 3", + "License :: OSI Approved :: Apache Software License", + "Operating System :: OS Independent", + ], + package_data={"diffsynth": ["tokenizer_configs/**/**/*.*"]}, + python_requires='>=3.6', +) diff --git a/visualize.py b/visualize.py new file mode 100644 index 0000000000000000000000000000000000000000..5f1326c5d8bce39d5940b2c93752022b064c1261 --- /dev/null +++ b/visualize.py @@ -0,0 +1,146 @@ +import os +import os.path as osp +import base64 # 新增：导入base64模块 +from jinja2 import Environment, BaseLoader +step = 4 +# 设置文件和文件夹路径 +caption_visualized_folders = [ + f"dataset/spotlight_sketch_cat/GT", + f"dataset/spotlight_sketch_cat/epoch0" + # f"test_1126_step10/epoch0", + # f"test_1126_step25/epoch0", + # f"test_1126_step{step}/epoch3", + # f"test_1126_step{step}/epoch4" +] + +# 提取模型名称 +model_names = ["GT", "sktech"] + +# 图片格式映射：后缀名 -> MIME类型（用于Base64的data URI） +IMG_EXT_TO_MIME = { + ".jpg": "image/jpeg", + ".jpeg": "image/jpeg", + ".png": "image/png", + ".bmp": "image/bmp", + ".tiff": "image/tiff", + ".webp": "image/webp" +} +IMG_EXTS = set(IMG_EXT_TO_MIME.keys()) # 复用格式集合 + +# 收集所有文件基名（stem）的并集，按字典序排序 +all_basenames = set() +for folder in caption_visualized_folders: + folder_local = osp.join(os.getcwd(), folder) + if osp.isdir(folder_local): + for fname in os.listdir(folder_local): + ext = osp.splitext(fname)[1].lower() + if ext in IMG_EXTS: + all_basenames.add(osp.splitext(fname)[0]) + else: + print(f"Warning: folder not found: {folder_local}") + +res_data = [] +for base in sorted(all_basenames): + # 初始化前几个占位字段（模板主要使用索引后面的图片列） + res_row_info = [base, base] + + # 对每个文件夹按基名查找对应的图片，编码为Base64 + for folder in caption_visualized_folders: + base64_img = None # 存储Base64编码后的图片字符串 + img_filename = None # 存储图片文件名（用于显示） + folder_local = osp.join(os.getcwd(), folder) + + if osp.isdir(folder_local): + # 优先尝试常见扩展的有序匹配 + found = False + for ext in [".jpg", ".jpeg", ".png", ".bmp", ".tiff", ".webp"]: + candidate = osp.join(folder_local, base + ext) + if osp.isfile(candidate): + img_filename = base + ext + # 读取图片并编码为Base64 + with open(candidate, "rb") as f: + img_data = f.read() + mime_type = IMG_EXT_TO_MIME[ext] + # 拼接Base64的data URI格式 + base64_img = f"data:{mime_type};base64,{base64.b64encode(img_data).decode('utf-8')}" + found = True + break + # 如果按扩展没有找到，尝试更宽松的匹配（忽略大小写后缀） + if not found: + for fname in sorted(os.listdir(folder_local)): + fname_base, fname_ext = osp.splitext(fname) + fname_ext_lower = fname_ext.lower() + if fname_base == base and fname_ext_lower in IMG_EXTS: + img_filename = fname + candidate = osp.join(folder_local, fname) + with open(candidate, "rb") as f: + img_data = f.read() + mime_type = IMG_EXT_TO_MIME[fname_ext_lower] + base64_img = f"data:{mime_type};base64,{base64.b64encode(img_data).decode('utf-8')}" + found = True + break + # 将Base64编码和文件名加入结果（无图则为None） + res_row_info.append({ + "base64": base64_img, + "filename": img_filename + }) + + res_data.append(res_row_info) + +# Jinja2模板（修改img的src为Base64编码） +template_string = """ + + + + Images + + + + +

epoch0-1

+ + + + + + + + + + {% for row in data %} + + + {% for img_info in row[2:4] %} {# 要改 #} + + {% endfor %} + + {% endfor %} + +
#GTsketch
{{ loop.index }} + {% if img_info.base64 %} + img +
{{ img_info.filename or '未知文件' }}
+ {% else %} +
(无图)
+ {% endif %} +
+ + +""" + +# 渲染模板 +template = Environment(loader=BaseLoader).from_string(template_string) + +# 输出HTML文件 +output_html_path = f"showcase1203.html" +with open(output_html_path, "w", encoding='utf-8') as f: + f.write(template.render(data=res_data, model_names=model_names)) + +print(f"Base64版HTML文件已保存为 {output_html_path}") \ No newline at end of file