diff --git a/exp_code/1_benchmark/CausVid/tests/wan/test_causal_diffusion_timestep_breakdown.py b/exp_code/1_benchmark/CausVid/tests/wan/test_causal_diffusion_timestep_breakdown.py
new file mode 100644
index 0000000000000000000000000000000000000000..61f389baaf3abd73593cb06e9db810199c0f2749
--- /dev/null
+++ b/exp_code/1_benchmark/CausVid/tests/wan/test_causal_diffusion_timestep_breakdown.py
@@ -0,0 +1,54 @@
+from causvid.models.wan.wan_wrapper import WanDiffusionWrapper, WanTextEncoder, WanVAEWrapper
+from causvid.models.wan.flow_match import FlowMatchScheduler
+from diffusers.utils import export_to_video
+import torch
+
+torch.set_grad_enabled(False)
+
+model = WanDiffusionWrapper().to("cuda").to(torch.bfloat16)
+encoder = WanTextEncoder().to("cuda").to(torch.bfloat16)
+vae = WanVAEWrapper().to("cuda").to(torch.bfloat16)
+
+model.set_module_grad(
+    {
+        "model": False
+    }
+)
+
+text_prompts = [r"""A stylish woman walks down a Tokyo street filled with warm glowing neon and animated city signage. She wears a black leather jacket, a long red dress, and black boots, and carries a black purse. She wears sunglasses and red lipstick. She walks confidently and casually. The street is damp and reflective, creating a mirror effect of the colorful lights. Many pedestrians walk about."""] * 2
+
+conditional_dict = encoder(
+    text_prompts=text_prompts
+)
+
+device = "cuda"
+
+noise = torch.randn(
+    1, 21, 16, 60, 104, generator=torch.Generator().manual_seed(42), dtype=torch.float32
+).to("cuda")
+
+scheduler = FlowMatchScheduler(shift=8.0, sigma_min=0.0, extra_one_step=True)
+scheduler.set_timesteps(1000, denoising_strength=1.0)
+scheduler.sigmas = scheduler.sigmas.to(device)
+
+data = torch.load("wan_moviegen_ode/00000.pt")
+prompt = list(data.keys())[0]
+video_latent = data[prompt][:, -1].cuda().to(torch.bfloat16)
+
+for timestep in [100, 200, 300, 400, 500, 600, 700, 800, 900]:
+    timestep = timestep * \
+        torch.ones([1, 21], device="cuda", dtype=torch.float32)
+
+    noisy_latent = scheduler.add_noise(
+        video_latent.flatten(0, 1),
+        noise.flatten(0, 1),
+        timestep.flatten(0, 1)
+    ).unflatten(0, noise.shape[:2]).type_as(video_latent)
+
+    output = model(noisy_latent, conditional_dict, timestep)
+
+    video = vae.decode_to_pixel(output)
+    video = (video * 0.5 + 0.5).cpu().detach().to(torch.float32)[0].permute(0, 2, 3, 1).numpy()
+
+    export_to_video(
+        video, f"one_stpe_output_t={timestep[0, 0].item()}.mp4", fps=16)
diff --git a/exp_code/1_benchmark/CausVid/tests/wan/test_diffusion.py b/exp_code/1_benchmark/CausVid/tests/wan/test_diffusion.py
new file mode 100644
index 0000000000000000000000000000000000000000..474a69b9468fd9edfd218a55ed1203f5a84ca780
--- /dev/null
+++ b/exp_code/1_benchmark/CausVid/tests/wan/test_diffusion.py
@@ -0,0 +1,35 @@
+from causvid.models.wan.wan_wrapper import WanDiffusionWrapper, WanTextEncoder, WanVAEWrapper
+from diffusers.utils import export_to_video
+import torch
+
+torch.set_grad_enabled(False)
+
+model = WanDiffusionWrapper().to("cuda").to(torch.float32)
+encoder = WanTextEncoder().to("cuda").to(torch.float32)
+vae = WanVAEWrapper().to("cuda").to(torch.float32)
+
+model.set_module_grad(
+    {
+        "model": False
+    }
+)
+
+text_prompts = [r"""A stylish woman walks down a Tokyo street filled with warm glowing neon and animated city signage. She wears a black leather jacket, a long red dress, and black boots, and carries a black purse. She wears sunglasses and red lipstick. She walks confidently and casually. The street is damp and reflective, creating a mirror effect of the colorful lights. Many pedestrians walk about."""]
+
+conditional_dict = encoder(
+    text_prompts=text_prompts
+)
+
+noise = torch.randn(
+    1, 21, 16, 60, 104, generator=torch.Generator().manual_seed(42), dtype=torch.float32
+).to("cuda")
+
+timetep = 999 * torch.ones([1, 21], device="cuda", dtype=torch.long)
+
+with torch.no_grad():
+    output = model(noise, conditional_dict, timetep)
+    video = vae.decode_to_pixel(output)
+
+video = (video * 0.5 + 0.5).cpu().detach().to(torch.float32)[0].permute(0, 2, 3, 1).numpy()
+
+export_to_video(video, "output.mp4", fps=8)
diff --git a/exp_code/1_benchmark/CausVid/tests/wan/test_text_encoder.py b/exp_code/1_benchmark/CausVid/tests/wan/test_text_encoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..c372443ccd5d713d4419094e6823e17ec1f3e4d7
--- /dev/null
+++ b/exp_code/1_benchmark/CausVid/tests/wan/test_text_encoder.py
@@ -0,0 +1,16 @@
+from causvid.models.wan.wan_wrapper import WanTextEncoder
+import torch
+
+torch.set_grad_enabled(False)
+
+model = WanTextEncoder().to(device="cuda:0", dtype=torch.bfloat16)
+
+prompt_list = ["a " * 50] * 10 + ["b " * 25] * 10
+
+
+print("Test Text Encoder")
+
+encoded_dict = model(prompt_list)
+
+assert encoded_dict['prompt_embeds'].shape[0] == 20 and encoded_dict[
+    'prompt_embeds'].shape[1] == 512 and encoded_dict['prompt_embeds'].shape[2] == 4096
diff --git a/exp_code/1_benchmark/CausVid/tests/wan/test_wan_ode_pairs.py b/exp_code/1_benchmark/CausVid/tests/wan/test_wan_ode_pairs.py
new file mode 100644
index 0000000000000000000000000000000000000000..6a124d3313fde0778c0fc58cdfad79e8107a8c74
--- /dev/null
+++ b/exp_code/1_benchmark/CausVid/tests/wan/test_wan_ode_pairs.py
@@ -0,0 +1,28 @@
+from causvid.models.wan.wan_wrapper import WanVAEWrapper
+from diffusers.utils import export_to_video
+import argparse
+import torch
+import glob
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--data_path", type=str, required=True)
+
+args = parser.parse_args()
+
+torch.set_grad_enabled(False)
+
+model = WanVAEWrapper().to(device="cuda:0", dtype=torch.bfloat16)
+
+
+for index, video_path in enumerate(sorted(glob.glob(args.data_path + "/*.pt"))):
+    data = torch.load(video_path)
+
+    prompt = list(data.keys())[0]
+
+    video_latent = data[prompt][:, -1].cuda().to(torch.bfloat16)
+
+    video = model.decode_to_pixel(video_latent)
+
+    video = (video * 0.5 + 0.5).clamp(0, 1)[0].permute(0, 2, 3, 1).cpu().numpy()
+    print(index, prompt)
+    export_to_video(video, f"ode_output_{index:03d}.mp4", fps=16)
diff --git a/exp_code/1_benchmark/CausVid/tests/wan/test_wan_teacher_wrapper_many_step_sampling.py b/exp_code/1_benchmark/CausVid/tests/wan/test_wan_teacher_wrapper_many_step_sampling.py
new file mode 100644
index 0000000000000000000000000000000000000000..7b8c947010c660b497907be05be58673bc1a423f
--- /dev/null
+++ b/exp_code/1_benchmark/CausVid/tests/wan/test_wan_teacher_wrapper_many_step_sampling.py
@@ -0,0 +1,90 @@
+from causvid.models.wan.wan_wrapper import WanDiffusionWrapper, WanTextEncoder, WanVAEWrapper
+from causvid.models.wan.flow_match import FlowMatchScheduler
+from diffusers.utils import export_to_video
+from causvid.data import TextDataset
+from tqdm import tqdm
+import torch
+import os
+
+torch.set_grad_enabled(False)
+torch.backends.cuda.matmul.allow_tf32 = True
+torch.backends.cudnn.allow_tf32 = True
+
+model = WanDiffusionWrapper().to("cuda").to(torch.float32)
+encoder = WanTextEncoder().to("cuda").to(torch.float32)
+vae = WanVAEWrapper().to("cuda").to(torch.float32)
+
+model.set_module_grad(
+    {
+        "model": False
+    }
+)
+
+dataset = TextDataset('sample_dataset/MovieGenVideoBench.txt')
+
+os.makedirs('wan_manystep_sampling', exist_ok=True)
+
+device = "cuda"
+num_train_timesteps = 1000
+sampling_steps = 50
+shift = 8
+
+sample_neg_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'
+
+scheduler = FlowMatchScheduler(shift=8.0, sigma_min=0.0, extra_one_step=True)
+
+scheduler.set_timesteps(num_inference_steps=50, denoising_strength=1.0)
+
+scheduler.sigmas = scheduler.sigmas.to(device)
+
+unconditional_dict = encoder(
+    text_prompts=[sample_neg_prompt]
+)
+
+for index in tqdm(range(len(dataset))):
+    prompt = dataset[index]
+
+    conditional_dict = encoder(
+        text_prompts=prompt
+    )
+
+    latents = torch.randn(
+        1, 16, 21, 60, 104, generator=torch.Generator().manual_seed(42), dtype=torch.float32
+    ).to("cuda").permute(0, 2, 1, 3, 4)
+
+    with torch.amp.autocast(dtype=torch.float32, device_type=torch.device("cuda:0").type):
+
+        for progress_id, t in enumerate(tqdm(scheduler.timesteps)):
+            timestep = t * \
+                torch.ones([1, 21], device=device, dtype=torch.float32)
+
+            x0_pred_cond = model(
+                latents, conditional_dict, timestep
+            )
+
+            x0_pred_uncond = model(
+                latents, unconditional_dict, timestep
+            )
+
+            x0_pred = x0_pred_uncond + 6 * (
+                x0_pred_cond - x0_pred_uncond
+            )
+
+            flow_pred = model._convert_x0_to_flow_pred(
+                x0_pred=x0_pred.flatten(0, 1),
+                xt=latents.flatten(0, 1),
+                timestep=timestep.flatten(0, 1),
+                scheduler=scheduler
+            ).unflatten(0, x0_pred.shape[:2])
+
+            latents = scheduler.step(
+                flow_pred.flatten(0, 1),
+                scheduler.timesteps[progress_id] * torch.ones(
+                    [1, 21], device=device, dtype=torch.long).flatten(0, 1),
+                latents.flatten(0, 1)
+            ).unflatten(dim=0, sizes=flow_pred.shape[:2])
+
+        decoded_video = vae.decode_to_pixel(latents)[0].permute(0, 2, 3, 1).cpu().numpy() * 0.5 + 0.5
+
+    export_to_video(
+        decoded_video, f"test_wanx_wrapper_{index:04d}.mp4", fps=16)
diff --git a/exp_code/1_benchmark/DiffSynth-Studio/.github/workflows/publish.yaml b/exp_code/1_benchmark/DiffSynth-Studio/.github/workflows/publish.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..f31e6bb7bf1a10f9394f9a395c093aa37eba4855
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/.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/exp_code/1_benchmark/DiffSynth-Studio/LICENSE b/exp_code/1_benchmark/DiffSynth-Studio/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..0e5a49f5f70af9e9d37278f72315d1b1afd34895
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/LICENSE
@@ -0,0 +1,201 @@
+                                 Apache License
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+                        http://www.apache.org/licenses/
+
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diff --git a/exp_code/1_benchmark/DiffSynth-Studio/README.md b/exp_code/1_benchmark/DiffSynth-Studio/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..ce3fea89dc0719c2b5c43784327778d6488bff90
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/README.md
@@ -0,0 +1,497 @@
+# DiffSynth-Studio
+
+<a href="https://github.com/modelscope/DiffSynth-Studio"><img src=".github/workflows/logo.gif" title="Logo" style="max-width:100%;" width="55" /></a> <a href="https://trendshift.io/repositories/10946" target="_blank"><img src="https://trendshift.io/api/badge/repositories/10946" alt="modelscope%2FDiffSynth-Studio | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a></p>
+
+[![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 .
+```
+
+<details>
+<summary>Other installation methods</summary>
+
+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)
+
+</details>
+
+## 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)
+
+<details>
+
+<summary>Quick Start</summary>
+
+```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/"),
+)
+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)
+image.save("image.jpg")
+```
+
+</details>
+
+<details>
+
+<summary>Model Overview</summary>
+
+|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)|
+|[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-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)|-|-|-|-|
+|[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)|-|-|-|-|
+
+</details>
+
+### FLUX Series
+
+Detail page: [./examples/flux/](./examples/flux/)
+
+![Image](https://github.com/user-attachments/assets/c01258e2-f251-441a-aa1e-ebb22f02594d)
+
+<details>
+
+<summary>Quick Start</summary>
+
+```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")
+```
+
+</details>
+
+<details>
+
+<summary>Model Overview</summary>
+
+| 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)|
+
+</details>
+
+
+
+### Wan Series
+
+Detail page: [./examples/wanvideo/](./examples/wanvideo/)
+
+https://github.com/user-attachments/assets/1d66ae74-3b02-40a9-acc3-ea95fc039314
+
+<details>
+
+<summary>Quick Start</summary>
+
+```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)
+```
+
+</details>
+
+<details>
+
+<summary>Model Overview</summary>
+
+| Model ID | Extra Parameters | Inference | Full Training | Validate After Full Training | LoRA Training | Validate After LoRA Training |
+|-|-|-|-|-|-|-|
+|[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.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/Wan2.1-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)|
+
+</details>
+
+### More Models
+
+
+
+<details>
+<summary>Image Generation Models</summary>
+
+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)|
+
+</details>
+
+
+
+<details>
+<summary>Video Generation Models</summary>
+
+- 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  
+
+</details>
+
+
+
+<details>
+<summary>Image Quality Assessment Models</summary>
+
+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)
+
+</details>
+
+
+
+## Innovative Achievements
+
+DiffSynth-Studio is not just an engineering model framework, but also a platform for incubating innovative results.
+
+<details>
+<summary>Nexus-Gen: Unified Architecture for Image Understanding, Generation, and Editing</summary>
+
+- 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)
+
+</details>
+
+<details>
+<summary>ArtAug: Aesthetic Enhancement for Image Generation Models</summary>
+
+- 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)|
+
+</details>
+
+<details>
+<summary>EliGen: Precise Image Region Control</summary>
+
+- 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)|
+
+</details>
+
+<details>
+<summary>ExVideo: Extended Training for Video Generation Models</summary>
+
+- 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
+
+</details>
+
+<details>
+<summary>Diffutoon: High-Resolution Anime-Style Video Rendering</summary>
+
+- 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
+
+</details>
+
+<details>
+<summary>DiffSynth: The Initial Version of This Project</summary>
+
+- 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
+
+</details>
+
+
+
+## Update History
+- **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)
+
+<details>
+<summary>More</summary>
+
+- **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).
+
+</details>
\ No newline at end of file
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@@ -0,0 +1,513 @@
+# DiffSynth-Studio
+
+<a href="https://github.com/modelscope/DiffSynth-Studio"><img src=".github/workflows/logo.gif" title="Logo" style="max-width:100%;" width="55" /></a> <a href="https://trendshift.io/repositories/10946" target="_blank"><img src="https://trendshift.io/api/badge/repositories/10946" alt="modelscope%2FDiffSynth-Studio | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a></p>
+
+[![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 .
+```
+
+<details>
+<summary>其他安装方式</summary>
+
+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)
+
+</details>
+
+
+
+## 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)
+
+<details>
+
+<summary>快速开始</summary>
+
+```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/"),
+)
+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")
+```
+
+</details>
+
+<details>
+
+<summary>模型总览</summary>
+
+|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)|
+|[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-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)|-|-|-|-|
+|[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)|-|-|-|-|
+
+</details>
+
+### FLUX 系列
+
+详细页面：[./examples/flux/](./examples/flux/)
+
+![Image](https://github.com/user-attachments/assets/c01258e2-f251-441a-aa1e-ebb22f02594d)
+
+<details>
+
+<summary>快速开始</summary>
+
+```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")
+```
+
+</details>
+
+<details>
+
+<summary>模型总览</summary>
+
+|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)|
+
+</details>
+
+### Wan 系列
+
+详细页面：[./examples/wanvideo/](./examples/wanvideo/)
+
+https://github.com/user-attachments/assets/1d66ae74-3b02-40a9-acc3-ea95fc039314
+
+<details>
+
+<summary>快速开始</summary>
+
+```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)
+```
+
+</details>
+
+<details>
+
+<summary>模型总览</summary>
+
+|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-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.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/Wan2.1-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)|
+
+</details>
+
+
+
+### 更多模型
+
+
+
+<details>
+<summary>图像生成模型</summary>
+
+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)|
+
+</details>
+
+
+
+<details>
+<summary>视频生成模型</summary>
+
+- 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
+
+</details>
+
+
+
+<details>
+<summary>图像质量评估模型</summary>
+
+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)
+
+</details>
+
+
+
+## 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。
+
+<details>
+<summary>Nexus-Gen: 统一架构的图像理解、生成、编辑</summary>
+
+- 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)
+
+</details>
+
+
+
+<details>
+<summary>ArtAug: 图像生成模型的美学提升</summary>
+
+- 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)|
+
+</details>
+
+
+
+<details>
+
+<summary>EliGen: 精准的图像分区控制</summary>
+
+- 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)|
+
+</details>
+
+
+
+<details>
+
+<summary>ExVideo: 视频生成模型的扩展训练</summary>
+
+- 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
+
+</details>
+
+
+
+<details>
+
+<summary>Diffutoon: 高分辨率动漫风格视频渲染</summary>
+
+- 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
+
+</details>
+
+
+
+<details>
+
+<summary>DiffSynth: 本项目的初代版本</summary>
+
+- 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
+
+</details>
+
+
+
+## 更新历史
+- **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)
+
+<details>
+<summary>更多</summary>
+
+- **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)。
+
+</details>
diff --git a/exp_code/1_benchmark/DiffSynth-Studio/apps/gradio/DiffSynth_Studio.py b/exp_code/1_benchmark/DiffSynth-Studio/apps/gradio/DiffSynth_Studio.py
new file mode 100644
index 0000000000000000000000000000000000000000..d26549202db2540a3b411cc7ba21d4d643c9d21b
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/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/exp_code/1_benchmark/DiffSynth-Studio/apps/gradio/entity_level_control.py b/exp_code/1_benchmark/DiffSynth-Studio/apps/gradio/entity_level_control.py
new file mode 100644
index 0000000000000000000000000000000000000000..58f4722d5a1b36b1746610bfb231727fc8b0a986
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/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/exp_code/1_benchmark/DiffSynth-Studio/apps/gradio/qwen_image_eligen.py b/exp_code/1_benchmark/DiffSynth-Studio/apps/gradio/qwen_image_eligen.py
new file mode 100644
index 0000000000000000000000000000000000000000..c224f01cad5e7fd41bb8102728a899a8ecb3b90f
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/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/exp_code/1_benchmark/DiffSynth-Studio/apps/streamlit/DiffSynth_Studio.py b/exp_code/1_benchmark/DiffSynth-Studio/apps/streamlit/DiffSynth_Studio.py
new file mode 100644
index 0000000000000000000000000000000000000000..cfd38565da745df53111a94b77d4354a2d475042
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/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/exp_code/1_benchmark/DiffSynth-Studio/apps/streamlit/pages/1_Image_Creator.py b/exp_code/1_benchmark/DiffSynth-Studio/apps/streamlit/pages/1_Image_Creator.py
new file mode 100644
index 0000000000000000000000000000000000000000..732d2195ce45061739c8cbe46623a181f6a30c08
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/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/exp_code/1_benchmark/DiffSynth-Studio/apps/streamlit/pages/2_Video_Creator.py b/exp_code/1_benchmark/DiffSynth-Studio/apps/streamlit/pages/2_Video_Creator.py
new file mode 100644
index 0000000000000000000000000000000000000000..87480726d3d32f48a2648266ce84d14e81e42315
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/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/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/__init__.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..ae0a45c2e2dc61f8f16354feb1b0c481776b523f
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/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 *
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diff --git a/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/configs/model_config.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/configs/model_config.py
new file mode 100644
index 0000000000000000000000000000000000000000..43fe84b0beabef16916b4d7083ef8833448b9e03
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/configs/model_config.py
@@ -0,0 +1,852 @@
+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.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
+
+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, "6bfcfb3b342cb286ce886889d519a77e", ["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, "47dbeab5e560db3180adf51dc0232fb1", ["wan_video_dit"], [WanModel], "civitai"),
+    (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, "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"),
+]
+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/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/controlnets/__init__.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/controlnets/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..a3e15add6ab116bf261804b8c83c86ff4d61c41b
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/controlnets/__init__.py
@@ -0,0 +1,2 @@
+from .controlnet_unit import ControlNetConfigUnit, ControlNetUnit, MultiControlNetManager, FluxMultiControlNetManager
+from .processors import Annotator
diff --git a/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/controlnets/__pycache__/__init__.cpython-311.pyc b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/controlnets/__pycache__/__init__.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..a258d50ea3907aafd7c25324e97ae6fbff8f0112
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diff --git a/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/controlnets/__pycache__/controlnet_unit.cpython-311.pyc b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/controlnets/__pycache__/controlnet_unit.cpython-311.pyc
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diff --git a/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/controlnets/__pycache__/processors.cpython-311.pyc b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/controlnets/__pycache__/processors.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..cc9ea04709fe216ea24367816cad5aab1448e30c
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diff --git a/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/controlnets/controlnet_unit.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/controlnets/controlnet_unit.py
new file mode 100644
index 0000000000000000000000000000000000000000..fdb4829483d208ec0295d1b5a8f82681b4251ea4
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/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/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/controlnets/processors.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/controlnets/processors.py
new file mode 100644
index 0000000000000000000000000000000000000000..06553e06d1c6d09f5a3deecfd4ea5604c5dd4352
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/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/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/data/__init__.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/data/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..749c03fbc421e30b5190703587e57af8412aab73
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/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/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/data/__pycache__/__init__.cpython-311.pyc b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/data/__pycache__/__init__.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..dc49e08e75731711974d37c7761c9fc27a6f420f
Binary files /dev/null and b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/data/__pycache__/__init__.cpython-311.pyc differ
diff --git a/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/data/__pycache__/video.cpython-311.pyc b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/data/__pycache__/video.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..cf9bdcf47554fb2638090c12a64f60d3408aeb93
Binary files /dev/null and b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/data/__pycache__/video.cpython-311.pyc differ
diff --git a/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/data/simple_text_image.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/data/simple_text_image.py
new file mode 100644
index 0000000000000000000000000000000000000000..7a9525e3c8a4d21418c1464fe11fc621450fd0d8
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/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/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/data/video.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/data/video.py
new file mode 100644
index 0000000000000000000000000000000000000000..c6b9daa41bea9d36e012d52a1d280d1cf8d92850
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/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/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/distributed/__init__.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/distributed/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/distributed/xdit_context_parallel.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/distributed/xdit_context_parallel.py
new file mode 100644
index 0000000000000000000000000000000000000000..4887e2f16a87fa3a28e8057de41c6537be83f8ed
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/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/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/ESRGAN/__init__.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/ESRGAN/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..94aff4c6fe8d75ff65e30d672dbe3e38a0d919c3
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/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/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/ESRGAN/__pycache__/__init__.cpython-311.pyc b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/ESRGAN/__pycache__/__init__.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..1b03fa06a6edbfc6cab65d4db29df4b5ece3b441
Binary files /dev/null and b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/ESRGAN/__pycache__/__init__.cpython-311.pyc differ
diff --git a/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/FastBlend/__init__.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/FastBlend/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..2bf812c2085082bfa82658dd249ebca89e9fb465
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/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/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/FastBlend/api.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/FastBlend/api.py
new file mode 100644
index 0000000000000000000000000000000000000000..2db24330e375ed62065af54613b6ab956c9c64cf
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/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/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/FastBlend/cupy_kernels.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/FastBlend/cupy_kernels.py
new file mode 100644
index 0000000000000000000000000000000000000000..70e2790a2c67a2dd537f4188b38ebfc785f1fb34
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/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/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/FastBlend/data.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/FastBlend/data.py
new file mode 100644
index 0000000000000000000000000000000000000000..dcaddd77de9eaf208cd083dd522e5eaa6b58f783
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/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/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/FastBlend/patch_match.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/FastBlend/patch_match.py
new file mode 100644
index 0000000000000000000000000000000000000000..aeb1f7f9e31b4b2ad77ec58ba8d32361315e0390
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/FastBlend/patch_match.py
@@ -0,0 +1,298 @@
+from .cupy_kernels import remapping_kernel, patch_error_kernel, pairwise_patch_error_kernel
+import numpy as np
+import cupy as cp
+import cv2
+
+
+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 = images.get()
+        images_resample = []
+        for image in images:
+            image_resample = cv2.resize(image, (width, height), interpolation=cv2.INTER_AREA)
+            images_resample.append(image_resample)
+        images_resample = cp.array(np.stack(images_resample), dtype=cp.float32)
+        return 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[:,[i for i in range(nnf.shape[0]) if i&1],:,0] += 1
+        nnf[:,:,[i for i in range(nnf.shape[0]) if i&1],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 = nnf.get().astype(np.float32)
+            nnf = [cv2.resize(n, (width, height), interpolation=cv2.INTER_LINEAR) for n in nnf]
+            nnf = cp.array(np.stack(nnf), dtype=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/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/FastBlend/runners/__init__.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/FastBlend/runners/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..078382729690d282436411661693ce22f3dcc033
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/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/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/FastBlend/runners/accurate.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/FastBlend/runners/accurate.py
new file mode 100644
index 0000000000000000000000000000000000000000..2e4a47f1981ebc1ec9a034a814dfc1130955c2e1
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/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/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/FastBlend/runners/balanced.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/FastBlend/runners/balanced.py
new file mode 100644
index 0000000000000000000000000000000000000000..1c9a2bb7e438b49c89d0786e858ccf03302fab35
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/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/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/FastBlend/runners/fast.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/FastBlend/runners/fast.py
new file mode 100644
index 0000000000000000000000000000000000000000..2ba5731475ab875929b14181e0c22f4fd466c591
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/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<<max_level)<=n:
+            max_level += 1
+        for i in range(n):
+            j = i
+            for level in range(max_level):
+                if i&(1<<level):
+                    continue
+                j |= 1<<level
+                if j>=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<<node_level)&node_index and node_index-(1<<node_level+1)+1>=leftbound:
+                node_level += 1
+            node_list.append((node_index, node_level))
+            node_index -= 1<<node_level
+        return node_list
+
+    def process_window_sum(self, frames_guide, blending_table, patch_match_engine, window_size, batch_size, desc=""):
+        n = len(blending_table)
+        tasks = []
+        frames_result = []
+        for target in range(n):
+            node_list = self.tree_query(max(target-window_size, 0), target)
+            for source, level in node_list:
+                if source!=target:
+                    meta_data = {
+                        "source": source,
+                        "target": target,
+                        "level": level
+                    }
+                    tasks.append(meta_data)
+                else:
+                    frames_result.append(blending_table[target][level])
+        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([blending_table[task["source"]][task["level"]][0] for task in tasks_batch])
+            _, target_style = patch_match_engine.estimate_nnf(source_guide, target_guide, source_style)
+            for task, frame_2 in zip(tasks_batch, target_style):
+                source, target, level = task["source"], task["target"], task["level"]
+                frame_1, weight_1 = frames_result[target]
+                weight_2 = blending_table[source][level][1]
+                weight = weight_1 + weight_2
+                frame = frame_1 * (weight_1 / weight) + frame_2 * (weight_2 / weight)
+                frames_result[target] = (frame, weight)
+        return frames_result
+
+
+class FastModeRunner:
+    def __init__(self):
+        pass
+
+    def run(self, frames_guide, frames_style, batch_size, window_size, ebsynth_config, save_path=None):
+        frames_guide = frames_guide.raw_data()
+        frames_style = frames_style.raw_data()
+        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="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, window_size, 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, 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, window_size, 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]
+        if save_path is not None:
+            for target, frame in enumerate(frames):
+                Image.fromarray(frame).save(os.path.join(save_path, "%05d.png" % target))
diff --git a/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/FastBlend/runners/interpolation.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/FastBlend/runners/interpolation.py
new file mode 100644
index 0000000000000000000000000000000000000000..62d109cf9888422914924d0fa8fdd91677c8efb0
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/FastBlend/runners/interpolation.py
@@ -0,0 +1,121 @@
+from ..patch_match import PyramidPatchMatcher
+import os
+import numpy as np
+from PIL import Image
+from tqdm import tqdm
+
+
+class InterpolationModeRunner:
+    def __init__(self):
+        pass
+
+    def get_index_dict(self, index_style):
+        index_dict = {}
+        for i, index in enumerate(index_style):
+            index_dict[index] = i
+        return index_dict
+
+    def get_weight(self, l, m, r):
+        weight_l, weight_r = abs(m - r), abs(m - l)
+        if weight_l + weight_r == 0:
+            weight_l, weight_r = 0.5, 0.5
+        else:
+            weight_l, weight_r = weight_l / (weight_l + weight_r), weight_r / (weight_l + weight_r)
+        return weight_l, weight_r
+
+    def get_task_group(self, index_style, n):
+        task_group = []
+        index_style = sorted(index_style)
+        # first frame
+        if index_style[0]>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/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/ImageQualityMetric/BLIP/__init__.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/ImageQualityMetric/BLIP/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..885dcf8f76ad77865054f0c033f8541ae08b1e04
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/ImageQualityMetric/BLIP/__init__.py
@@ -0,0 +1 @@
+from .blip_pretrain import *
diff --git a/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/ImageQualityMetric/BLIP/blip.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/ImageQualityMetric/BLIP/blip.py
new file mode 100644
index 0000000000000000000000000000000000000000..6b24c3c17fdeff6949c3692164362abb8d8d0989
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/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/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/ImageQualityMetric/BLIP/blip_pretrain.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/ImageQualityMetric/BLIP/blip_pretrain.py
new file mode 100644
index 0000000000000000000000000000000000000000..ba711e2776fd086190ca940248e022b4e083819a
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/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/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/ImageQualityMetric/BLIP/med.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/ImageQualityMetric/BLIP/med.py
new file mode 100644
index 0000000000000000000000000000000000000000..5905a346c8f3a511fc56d1e052467dcef3246e4b
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/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 <https://arxiv.org/abs/1706.03762>`__ 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/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/ImageQualityMetric/BLIP/vit.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/ImageQualityMetric/BLIP/vit.py
new file mode 100644
index 0000000000000000000000000000000000000000..cef7b650a95f56266775cf0f18b28bc0f74987ab
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/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/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/ImageQualityMetric/__init__.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/ImageQualityMetric/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..fcfb7c02b0ce2b6a2fbe345d87c31e0d1bb3a128
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/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/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/ImageQualityMetric/aesthetic.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/ImageQualityMetric/aesthetic.py
new file mode 100644
index 0000000000000000000000000000000000000000..13da98a1f45ca7eea0411e18c307cc5d0154488f
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/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/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/ImageQualityMetric/clip.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/ImageQualityMetric/clip.py
new file mode 100644
index 0000000000000000000000000000000000000000..f70941e0a45db61be87e21c347e97ad8bb390fff
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/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/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/ImageQualityMetric/config.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/ImageQualityMetric/config.py
new file mode 100644
index 0000000000000000000000000000000000000000..60faadcb1e5554c8f8f29a64fc55c3150d8a8bbe
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/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/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/ImageQualityMetric/hps.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/ImageQualityMetric/hps.py
new file mode 100644
index 0000000000000000000000000000000000000000..a4b266bd261a95676ba700d38c3a63b143bbbb40
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/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/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/ImageQualityMetric/imagereward.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/ImageQualityMetric/imagereward.py
new file mode 100644
index 0000000000000000000000000000000000000000..27607904b23fa1691c5a6966eb4030cd813567b0
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/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/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/ImageQualityMetric/mps.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/ImageQualityMetric/mps.py
new file mode 100644
index 0000000000000000000000000000000000000000..d15aad4b81026a743911512bcc569520182b31c5
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/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/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/ImageQualityMetric/open_clip/__init__.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/ImageQualityMetric/open_clip/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..1560db0b543b7b8857f39d7de435c834380666ab
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/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/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/ImageQualityMetric/open_clip/coca_model.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/ImageQualityMetric/open_clip/coca_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..039453af70d1c865dd7cc6016f732aff2f7dc3d2
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/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/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/ImageQualityMetric/open_clip/constants.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/ImageQualityMetric/open_clip/constants.py
new file mode 100644
index 0000000000000000000000000000000000000000..a670bb3fab442baeb9af53b91c312e6982af57ee
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/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/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/ImageQualityMetric/open_clip/factory.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/ImageQualityMetric/open_clip/factory.py
new file mode 100644
index 0000000000000000000000000000000000000000..5bd51a1bb6b69e0e69147c8b7cb8d7bd4899b349
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/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/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/ImageQualityMetric/open_clip/generation_utils.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/ImageQualityMetric/open_clip/generation_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/ImageQualityMetric/open_clip/hf_configs.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/ImageQualityMetric/open_clip/hf_configs.py
new file mode 100644
index 0000000000000000000000000000000000000000..e236222bafce0358445ea16953ca0b2d5a84758a
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/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/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/ImageQualityMetric/open_clip/hf_model.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/ImageQualityMetric/open_clip/hf_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..fbccc812757bf10b122ff14096980e0e38d1d221
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/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'(?<!^)(?=[A-Z])', '_', s).lower()
+
+
+# TODO: ?last - for gpt-like models
+_POOLERS = {}
+
+
+def register_pooler(cls):
+    """Decorator registering pooler class"""
+    _POOLERS[_camel2snake(cls.__name__)] = cls
+    return cls
+
+
+@register_pooler
+class MeanPooler(nn.Module):
+    """Mean pooling"""
+
+    def forward(self, x: BaseModelOutput, attention_mask: TensorType):
+        masked_output = x.last_hidden_state * attention_mask.unsqueeze(-1)
+        return masked_output.sum(dim=1) / attention_mask.sum(-1, keepdim=True)
+
+
+@register_pooler
+class MaxPooler(nn.Module):
+    """Max pooling"""
+
+    def forward(self, x: BaseModelOutput, attention_mask: TensorType):
+        masked_output = x.last_hidden_state.masked_fill(attention_mask.unsqueeze(-1), -torch.inf)
+        return masked_output.max(1).values
+
+
+@register_pooler
+class ClsPooler(nn.Module):
+    """CLS token pooling"""
+
+    def __init__(self, use_pooler_output=True):
+        super().__init__()
+        self.cls_token_position = 0
+        self.use_pooler_output = use_pooler_output
+
+    def forward(self, x: BaseModelOutput, attention_mask: TensorType):
+        if (self.use_pooler_output and
+            isinstance(x, (BaseModelOutputWithPooling, BaseModelOutputWithPoolingAndCrossAttentions)) and
+            (x.pooler_output is not None)
+        ):
+            return x.pooler_output
+
+        return x.last_hidden_state[:, self.cls_token_position, :]
+
+
+class HFTextEncoder(nn.Module):
+    """HuggingFace model adapter"""
+    output_tokens: torch.jit.Final[bool]
+
+    def __init__(
+            self,
+            model_name_or_path: str,
+            output_dim: int,
+            config: PretrainedConfig = None,
+            pooler_type: str = None,
+            proj: str = None,
+            pretrained: bool = True,
+            output_tokens: bool = False,
+    ):
+        super().__init__()
+        self.output_tokens = output_tokens
+        self.output_dim = output_dim
+
+        # TODO: find better way to get this information
+        uses_transformer_pooler = (pooler_type == "cls_pooler")
+
+        if transformers is None:
+            raise RuntimeError("Please `pip install transformers` to use pre-trained HuggingFace models")
+        if config is None:
+            self.config = AutoConfig.from_pretrained(model_name_or_path)
+            create_func, model_args = (AutoModel.from_pretrained, model_name_or_path) if pretrained else (
+                AutoModel.from_config, self.config)
+            # TODO: do all model configs have this attribute? PretrainedConfig does so yes??
+            if hasattr(self.config, "is_encoder_decoder") and self.config.is_encoder_decoder:
+                self.transformer = create_func(model_args)
+                self.transformer = self.transformer.encoder
+            else:
+                self.transformer = create_func(model_args, add_pooling_layer=uses_transformer_pooler)
+        else:
+            self.config = config
+            self.transformer = AutoModel.from_config(config)
+        if pooler_type is None:  # get default arch pooler
+            pooler_type = (arch_dict[self.config.model_type]["pooler"])
+        
+        self.pooler = _POOLERS[pooler_type]()
+
+        d_model = getattr(self.config, arch_dict[self.config.model_type]["config_names"]["width"])
+        if (d_model == output_dim) and (proj is None):  # do we always need a proj?
+            self.proj = nn.Identity()
+        elif proj == 'linear':
+            self.proj = nn.Linear(d_model, output_dim, bias=False)
+        elif proj == 'mlp':
+            hidden_size = (d_model + output_dim) // 2
+            self.proj = nn.Sequential(
+                nn.Linear(d_model, hidden_size, bias=False),
+                nn.GELU(),
+                nn.Linear(hidden_size, output_dim, bias=False),
+            )
+
+    def forward(self, x: TensorType):
+        attn_mask = (x != self.config.pad_token_id).long()
+        out = self.transformer(input_ids=x, attention_mask=attn_mask)
+        pooled_out = self.pooler(out, attn_mask)
+        projected = self.proj(pooled_out)
+
+        seq_len = out.last_hidden_state.shape[1]
+        tokens = (
+            out.last_hidden_state[:, torch.arange(seq_len) != self.pooler.cls_token_position, :] 
+            if type(self.pooler) == ClsPooler 
+            else out.last_hidden_state
+        )
+        
+        if self.output_tokens:
+            return projected, tokens
+        return projected
+
+    def lock(self, unlocked_layers: int = 0, freeze_layer_norm: bool = True):
+        if not unlocked_layers:  # full freezing
+            for n, p in self.transformer.named_parameters():
+                p.requires_grad = (not freeze_layer_norm) if "LayerNorm" in n.split(".") else False
+            return
+
+        encoder = self.transformer.encoder if hasattr(self.transformer, 'encoder') else self.transformer
+        layer_list = getattr(encoder, arch_dict[self.config.model_type]["config_names"]["layer_attr"])
+        print(f"Unlocking {unlocked_layers}/{len(layer_list) + 1} layers of hf model")
+        embeddings = getattr(
+            self.transformer, arch_dict[self.config.model_type]["config_names"]["token_embeddings_attr"])
+        modules = [embeddings, *layer_list][:-unlocked_layers]
+        # freeze layers
+        for module in modules:
+            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.transformer.gradient_checkpointing_enable()
+
+    def init_parameters(self):
+        pass
diff --git a/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/ImageQualityMetric/open_clip/loss.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/ImageQualityMetric/open_clip/loss.py
new file mode 100644
index 0000000000000000000000000000000000000000..8675e0db8085da1a3aca5b0065df3456bf1c5d30
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/ImageQualityMetric/open_clip/loss.py
@@ -0,0 +1,270 @@
+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+from torch.nn.utils.rnn import pad_sequence
+
+try:
+    import torch.distributed.nn
+    from torch import distributed as dist
+
+    has_distributed = True
+except ImportError:
+    has_distributed = False
+
+try:
+    import horovod.torch as hvd
+except ImportError:
+    hvd = None
+
+
+def gather_features(
+        image_features,
+        text_features,
+        local_loss=False,
+        gather_with_grad=False,
+        rank=0,
+        world_size=1,
+        use_horovod=False
+):
+    assert has_distributed, 'torch.distributed did not import correctly, please use a PyTorch version with support.'
+    if use_horovod:
+        assert hvd is not None, 'Please install horovod'
+        if gather_with_grad:
+            all_image_features = hvd.allgather(image_features)
+            all_text_features = hvd.allgather(text_features)
+        else:
+            with torch.no_grad():
+                all_image_features = hvd.allgather(image_features)
+                all_text_features = hvd.allgather(text_features)
+            if not local_loss:
+                # ensure grads for local rank when all_* features don't have a gradient
+                gathered_image_features = list(all_image_features.chunk(world_size, dim=0))
+                gathered_text_features = list(all_text_features.chunk(world_size, dim=0))
+                gathered_image_features[rank] = image_features
+                gathered_text_features[rank] = text_features
+                all_image_features = torch.cat(gathered_image_features, dim=0)
+                all_text_features = torch.cat(gathered_text_features, dim=0)
+    else:
+        # We gather tensors from all gpus
+        if gather_with_grad:
+            all_image_features = torch.cat(torch.distributed.nn.all_gather(image_features), dim=0)
+            all_text_features = torch.cat(torch.distributed.nn.all_gather(text_features), dim=0)
+        else:
+            gathered_image_features = [torch.zeros_like(image_features) for _ in range(world_size)]
+            gathered_text_features = [torch.zeros_like(text_features) for _ in range(world_size)]
+            dist.all_gather(gathered_image_features, image_features)
+            dist.all_gather(gathered_text_features, text_features)
+            if not local_loss:
+                # ensure grads for local rank when all_* features don't have a gradient
+                gathered_image_features[rank] = image_features
+                gathered_text_features[rank] = text_features
+            all_image_features = torch.cat(gathered_image_features, dim=0)
+            all_text_features = torch.cat(gathered_text_features, dim=0)
+
+    return all_image_features, all_text_features
+
+
+class ClipLoss(nn.Module):
+
+    def __init__(
+            self,
+            local_loss=False,
+            gather_with_grad=False,
+            cache_labels=False,
+            rank=0,
+            world_size=1,
+            use_horovod=False,
+    ):
+        super().__init__()
+        self.local_loss = local_loss
+        self.gather_with_grad = gather_with_grad
+        self.cache_labels = cache_labels
+        self.rank = rank
+        self.world_size = world_size
+        self.use_horovod = use_horovod
+
+        # cache state
+        self.prev_num_logits = 0
+        self.labels = {}
+
+    def get_ground_truth(self, device, num_logits) -> 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/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/ImageQualityMetric/open_clip/model.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/ImageQualityMetric/open_clip/model.py
new file mode 100644
index 0000000000000000000000000000000000000000..e347c42fc8df6464ca28e59adadba61e53a38add
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/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/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/ImageQualityMetric/open_clip/model_configs/ViT-H-14.json b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/ImageQualityMetric/open_clip/model_configs/ViT-H-14.json
new file mode 100644
index 0000000000000000000000000000000000000000..3e3a7e934e7f02e41f4829996c4950e05f015a74
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/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/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/ImageQualityMetric/open_clip/modified_resnet.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/ImageQualityMetric/open_clip/modified_resnet.py
new file mode 100644
index 0000000000000000000000000000000000000000..6a8d3aeda91ecb394303becbbfccc8acd8cddcd9
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/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/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/ImageQualityMetric/open_clip/openai.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/ImageQualityMetric/open_clip/openai.py
new file mode 100644
index 0000000000000000000000000000000000000000..cc4e13e876d6a7a3463b457e62c517cb063b1356
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/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/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/ImageQualityMetric/open_clip/pretrained.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/ImageQualityMetric/open_clip/pretrained.py
new file mode 100644
index 0000000000000000000000000000000000000000..87e7e527497d643fdf6ac931ac73b6e887a90d0d
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/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/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/ImageQualityMetric/open_clip/push_to_hf_hub.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/ImageQualityMetric/open_clip/push_to_hf_hub.py
new file mode 100644
index 0000000000000000000000000000000000000000..23c0631c81dcb43829b7374fac09406ecefcb436
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/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/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/ImageQualityMetric/open_clip/timm_model.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/ImageQualityMetric/open_clip/timm_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..dc71a693f9a42ec01fd88d307661bc382b4d05bc
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/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/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/ImageQualityMetric/open_clip/tokenizer.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/ImageQualityMetric/open_clip/tokenizer.py
new file mode 100644
index 0000000000000000000000000000000000000000..22ec4880b13ec73594d5c19b3d3be83aadb55aba
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/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+'</w>' for v in vocab]
+        for merge in merges:
+            vocab.append(''.join(merge))
+        if not special_tokens:
+            special_tokens = ['<start_of_text>', '<end_of_text>']
+        else:
+            special_tokens = ['<start_of_text>', '<end_of_text>'] + 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] + '</w>',)
+        pairs = get_pairs(word)
+
+        if not pairs:
+            return token+'</w>'
+
+        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('</w>', ' ')
+        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["<start_of_text>"]
+        eot_token = self.encoder["<end_of_text>"]
+        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/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/ImageQualityMetric/open_clip/transform.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/ImageQualityMetric/open_clip/transform.py
new file mode 100644
index 0000000000000000000000000000000000000000..fe4e21fa5b515f2412049f9274bd06fbe77fb9b9
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/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/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/ImageQualityMetric/open_clip/transformer.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/ImageQualityMetric/open_clip/transformer.py
new file mode 100644
index 0000000000000000000000000000000000000000..7465c1b20bf388a17e0f4f80f7b8eee3b564af92
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/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/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/ImageQualityMetric/open_clip/utils.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/ImageQualityMetric/open_clip/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..51e80c5e296b24cae130ab0459baf268e0db7673
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/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/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/ImageQualityMetric/open_clip/version.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/ImageQualityMetric/open_clip/version.py
new file mode 100644
index 0000000000000000000000000000000000000000..48aa744fb053599044caf0253b889b5cfe5b78e7
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/ImageQualityMetric/open_clip/version.py
@@ -0,0 +1 @@
+__version__ = '2.16.0'
diff --git a/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/ImageQualityMetric/pickscore.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/ImageQualityMetric/pickscore.py
new file mode 100644
index 0000000000000000000000000000000000000000..7370e099724997d98f1c4ad3fc5f14c861202665
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/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/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/ImageQualityMetric/trainer/__init__.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/ImageQualityMetric/trainer/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..cf4f59d6c0977e578ab67ec92c916c7e38842715
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/ImageQualityMetric/trainer/__init__.py
@@ -0,0 +1 @@
+from .models import *
\ No newline at end of file
diff --git a/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/ImageQualityMetric/trainer/models/__init__.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/ImageQualityMetric/trainer/models/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..c4e2b69d17f6f4603d115e79a6122318f059b385
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/ImageQualityMetric/trainer/models/__init__.py
@@ -0,0 +1,3 @@
+from .base_model import *
+from .clip_model import *
+from .cross_modeling import *
\ No newline at end of file
diff --git a/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/ImageQualityMetric/trainer/models/base_model.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/ImageQualityMetric/trainer/models/base_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..8f28caf67460a517bd9cb7cbdbd806d7b072541f
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/ImageQualityMetric/trainer/models/base_model.py
@@ -0,0 +1,7 @@
+from dataclasses import dataclass
+
+
+
+@dataclass
+class BaseModelConfig:
+    pass
diff --git a/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/ImageQualityMetric/trainer/models/clip_model.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/ImageQualityMetric/trainer/models/clip_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..0a1b37095e6b70e4722856a65fbfb30277eab03a
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/ImageQualityMetric/trainer/models/clip_model.py
@@ -0,0 +1,146 @@
+from dataclasses import dataclass
+from transformers import CLIPModel as HFCLIPModel
+from transformers import AutoTokenizer
+
+from torch import nn, einsum
+
+from .base_model import BaseModelConfig
+
+from transformers import CLIPConfig
+from typing import Any, Optional, Tuple, Union
+import torch
+
+from .cross_modeling import Cross_model
+
+import json, os
+
+class XCLIPModel(HFCLIPModel):
+    def __init__(self, config: CLIPConfig):
+        super().__init__(config)
+    
+    def get_text_features(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> torch.FloatTensor:
+
+        # Use CLIP model's config for some fields (if specified) instead of those of vision & text components.
+        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
+
+        text_outputs = self.text_model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        # pooled_output = text_outputs[1]
+        # text_features = self.text_projection(pooled_output)
+        last_hidden_state = text_outputs[0]
+        text_features = self.text_projection(last_hidden_state)
+
+        pooled_output = text_outputs[1]
+        text_features_EOS = self.text_projection(pooled_output)
+
+
+        # del last_hidden_state, text_outputs
+        # gc.collect()
+
+        return text_features, text_features_EOS
+
+    def get_image_features(
+        self,
+        pixel_values: Optional[torch.FloatTensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> torch.FloatTensor:
+        
+        # Use CLIP model's config for some fields (if specified) instead of those of vision & text components.
+        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
+
+        vision_outputs = self.vision_model(
+            pixel_values=pixel_values,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        # pooled_output = vision_outputs[1]  # pooled_output
+        # image_features = self.visual_projection(pooled_output)
+        last_hidden_state = vision_outputs[0]
+        image_features = self.visual_projection(last_hidden_state)  
+
+        return image_features
+
+
+
+@dataclass
+class ClipModelConfig(BaseModelConfig):
+    _target_: str = "diffsynth.extensions.QualityMetric.trainer.models.clip_model.CLIPModel"
+    pretrained_model_name_or_path: str ="checkpoints/clip-vit-base-patch32"
+
+
+class CLIPModel(nn.Module):
+    def __init__(self, ckpt, config_file=False):
+        super().__init__()
+        if config_file is None:
+            self.model = XCLIPModel.from_pretrained(ckpt)
+        else:
+            with open(os.path.join(ckpt, "config.json"), "r", encoding="utf-8") as f:
+                config = json.load(f)
+            config = CLIPConfig(**config)
+            self.model = XCLIPModel._from_config(config)
+        self.cross_model = Cross_model(dim=1024, layer_num=4, heads=16)
+    
+    def get_text_features(self, *args, **kwargs):
+        return self.model.get_text_features(*args, **kwargs)
+
+    def get_image_features(self, *args, **kwargs):
+        return self.model.get_image_features(*args, **kwargs)
+
+    def forward(self, text_inputs=None, image_inputs=None, condition_inputs=None):
+        outputs = ()
+
+        text_f, text_EOS = self.model.get_text_features(text_inputs) # B*77*1024
+        outputs += text_EOS,
+
+        image_f = self.model.get_image_features(image_inputs.half()) # 2B*257*1024
+        condition_f, _ = self.model.get_text_features(condition_inputs) # B*5*1024
+
+        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 > 0.01, 0, float('-inf')) # B*1*77
+
+        mask = mask.repeat(1,image_f.shape[1],1) # B*257*77
+        bc = int(image_f.shape[0]/2)
+
+        sim0 = self.cross_model(image_f[:bc,:,:], text_f,mask.half())
+        sim1 = self.cross_model(image_f[bc:,:,:], text_f,mask.half())
+        outputs += sim0[:,0,:],
+        outputs += sim1[:,0,:],
+
+        return outputs
+
+    @property
+    def logit_scale(self):
+        return self.model.logit_scale
+
+    def save(self, path):
+        self.model.save_pretrained(path)
+
diff --git a/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/ImageQualityMetric/trainer/models/cross_modeling.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/ImageQualityMetric/trainer/models/cross_modeling.py
new file mode 100644
index 0000000000000000000000000000000000000000..938f1b706e16aa0666210e91fb215304653df4cb
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/ImageQualityMetric/trainer/models/cross_modeling.py
@@ -0,0 +1,292 @@
+import torch
+from torch import einsum, nn
+import torch.nn.functional as F
+from einops import rearrange, repeat
+
+# helper functions
+
+def exists(val):
+    return val is not None
+
+def default(val, d):
+    return val if exists(val) else d
+
+# normalization
+# they use layernorm without bias, something that pytorch does not offer
+
+
+class LayerNorm(nn.Module):
+    def __init__(self, dim):
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(dim))
+        self.register_buffer("bias", torch.zeros(dim))
+
+    def forward(self, x):
+        return F.layer_norm(x, x.shape[-1:], self.weight, self.bias)
+
+# residual
+
+
+class Residual(nn.Module):
+    def __init__(self, fn):
+        super().__init__()
+        self.fn = fn
+
+    def forward(self, x, *args, **kwargs):
+        return self.fn(x, *args, **kwargs) + x
+
+
+# rotary positional embedding
+# https://arxiv.org/abs/2104.09864
+
+
+class RotaryEmbedding(nn.Module):
+    def __init__(self, dim):
+        super().__init__()
+        inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2).float() / dim))
+        self.register_buffer("inv_freq", inv_freq)
+
+    def forward(self, max_seq_len, *, device):
+        seq = torch.arange(max_seq_len, device=device, dtype=self.inv_freq.dtype)
+        freqs = einsum("i , j -> i j", seq, self.inv_freq)
+        return torch.cat((freqs, freqs), dim=-1)
+
+
+def rotate_half(x):
+    x = rearrange(x, "... (j d) -> ... j d", j=2)
+    x1, x2 = x.unbind(dim=-2)
+    return torch.cat((-x2, x1), dim=-1)
+
+
+def apply_rotary_pos_emb(pos, t):
+    return (t * pos.cos()) + (rotate_half(t) * pos.sin())
+
+
+# classic Noam Shazeer paper, except here they use SwiGLU instead of the more popular GEGLU for gating the feedforward
+# https://arxiv.org/abs/2002.05202
+
+
+class SwiGLU(nn.Module):
+    def forward(self, x):
+        x, gate = x.chunk(2, dim=-1)
+        return F.silu(gate) * x
+
+
+# parallel attention and feedforward with residual
+# discovered by Wang et al + EleutherAI from GPT-J fame
+
+class ParallelTransformerBlock(nn.Module):
+    def __init__(self, dim, dim_head=64, heads=8, ff_mult=4):
+        super().__init__()
+        self.norm = LayerNorm(dim)
+
+        attn_inner_dim = dim_head * heads
+        ff_inner_dim = dim * ff_mult
+        self.fused_dims = (attn_inner_dim, dim_head, dim_head, (ff_inner_dim * 2))
+
+        self.heads = heads
+        self.scale = dim_head**-0.5
+        self.rotary_emb = RotaryEmbedding(dim_head)
+
+        self.fused_attn_ff_proj = nn.Linear(dim, sum(self.fused_dims), bias=False)
+        self.attn_out = nn.Linear(attn_inner_dim, dim, bias=False)
+
+        self.ff_out = nn.Sequential(
+            SwiGLU(),
+            nn.Linear(ff_inner_dim, dim, bias=False)
+        )
+
+        self.register_buffer("pos_emb", None, persistent=False)
+
+
+    def get_rotary_embedding(self, n, device):
+        if self.pos_emb is not None and self.pos_emb.shape[-2] >= n:
+            return self.pos_emb[:n]
+
+        pos_emb = self.rotary_emb(n, device=device)
+        self.register_buffer("pos_emb", pos_emb, persistent=False)
+        return pos_emb
+
+    def forward(self, x, attn_mask=None):
+        """
+        einstein notation
+        b - batch
+        h - heads
+        n, i, j - sequence length (base sequence length, source, target)
+        d - feature dimension
+        """
+
+        n, device, h = x.shape[1], x.device, self.heads
+
+        # pre layernorm
+
+        x = self.norm(x)
+
+        # attention queries, keys, values, and feedforward inner
+
+        q, k, v, ff = self.fused_attn_ff_proj(x).split(self.fused_dims, dim=-1)
+
+        # split heads
+        # they use multi-query single-key-value attention, yet another Noam Shazeer paper
+        # they found no performance loss past a certain scale, and more efficient decoding obviously
+        # https://arxiv.org/abs/1911.02150
+
+        q = rearrange(q, "b n (h d) -> b h n d", h=h)
+
+        # rotary embeddings
+
+        positions = self.get_rotary_embedding(n, device)
+        q, k = map(lambda t: apply_rotary_pos_emb(positions, t), (q, k))
+
+        # scale
+
+        q = q * self.scale
+
+        # similarity
+
+        sim = einsum("b h i d, b j d -> b h i j", q, k)
+
+
+        # extra attention mask - for masking out attention from text CLS token to padding
+
+        if exists(attn_mask):
+            attn_mask = rearrange(attn_mask, 'b i j -> b 1 i j')
+            sim = sim.masked_fill(~attn_mask, -torch.finfo(sim.dtype).max)
+
+        # attention
+
+        sim = sim - sim.amax(dim=-1, keepdim=True).detach()
+        attn = sim.softmax(dim=-1)
+
+        # aggregate values
+
+        out = einsum("b h i j, b j d -> b h i d", attn, v)
+
+        # merge heads
+
+        out = rearrange(out, "b h n d -> b n (h d)")
+        return self.attn_out(out) + self.ff_out(ff)
+
+# cross attention - using multi-query + one-headed key / values as in PaLM w/ optional parallel feedforward
+
+class CrossAttention(nn.Module):
+    def __init__(
+        self,
+        dim,
+        *,
+        context_dim=None,
+        dim_head=64,
+        heads=12,
+        parallel_ff=False,
+        ff_mult=4,
+        norm_context=False
+    ):
+        super().__init__()
+        self.heads = heads
+        self.scale = dim_head ** -0.5
+        inner_dim = heads * dim_head
+        context_dim = default(context_dim, dim)
+
+        self.norm = LayerNorm(dim)
+        self.context_norm = LayerNorm(context_dim) if norm_context else nn.Identity()
+
+        self.to_q = nn.Linear(dim, inner_dim, bias=False)
+        self.to_kv = nn.Linear(context_dim, dim_head * 2, bias=False)
+        self.to_out = nn.Linear(inner_dim, dim, bias=False)
+
+        # whether to have parallel feedforward
+
+        ff_inner_dim = ff_mult * dim
+
+        self.ff = nn.Sequential(
+            nn.Linear(dim, ff_inner_dim * 2, bias=False),
+            SwiGLU(),
+            nn.Linear(ff_inner_dim, dim, bias=False)
+        ) if parallel_ff else None
+
+    def forward(self, x, context, mask):
+        """
+        einstein notation
+        b - batch
+        h - heads
+        n, i, j - sequence length (base sequence length, source, target)
+        d - feature dimension
+        """
+
+        # pre-layernorm, for queries and context
+
+        x = self.norm(x)
+        context = self.context_norm(context)
+
+        # get queries
+
+        q = self.to_q(x)
+        q = rearrange(q, 'b n (h d) -> b h n d', h = self.heads)
+
+        # scale
+
+        q = q * self.scale
+
+        # get key / values
+
+        k, v = self.to_kv(context).chunk(2, dim=-1)
+
+        # query / key similarity
+
+        sim = einsum('b h i d, b j d -> b h i j', q, k)
+
+        # attention
+        mask = mask.unsqueeze(1).repeat(1,self.heads,1,1)
+        sim = sim + mask  # context mask
+        sim = sim - sim.amax(dim=-1, keepdim=True)
+        attn = sim.softmax(dim=-1)
+
+        # aggregate
+
+        out = einsum('b h i j, b j d -> b h i d', attn, v)
+
+        # merge and combine heads
+
+        out = rearrange(out, 'b h n d -> b n (h d)')
+        out = self.to_out(out)
+
+        # add parallel feedforward (for multimodal layers)
+
+        if exists(self.ff):
+            out = out + self.ff(x)
+
+        return out
+
+
+class Cross_model(nn.Module):
+    def __init__(
+        self,
+        dim=512,
+        layer_num=4,
+        dim_head=64,
+        heads=8,
+        ff_mult=4
+    ):
+        super().__init__()
+
+        self.layers = nn.ModuleList([])
+
+
+        for ind in range(layer_num):
+            self.layers.append(nn.ModuleList([
+                Residual(CrossAttention(dim=dim, dim_head=dim_head, heads=heads, parallel_ff=True, ff_mult=ff_mult)),
+                Residual(ParallelTransformerBlock(dim=dim, dim_head=dim_head, heads=heads, ff_mult=ff_mult))
+            ]))
+
+    def forward(
+        self,
+        query_tokens,
+        context_tokens,
+        mask
+    ):
+
+        for cross_attn, self_attn_ff in self.layers:
+            query_tokens = cross_attn(query_tokens, context_tokens,mask)
+            query_tokens = self_attn_ff(query_tokens)
+
+        return query_tokens
diff --git a/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/RIFE/__init__.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/RIFE/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e76c391f0b085b3628592990a868ac09f37cced7
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/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/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/RIFE/__pycache__/__init__.cpython-311.pyc b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/extensions/RIFE/__pycache__/__init__.cpython-311.pyc
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diff --git a/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/lora/__init__.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/lora/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..33bd89c21b74ec0bba8b7b69468a3901e45360fd
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/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/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/lora/__pycache__/__init__.cpython-311.pyc b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/lora/__pycache__/__init__.cpython-311.pyc
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diff --git a/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/lora/__pycache__/flux_lora.cpython-311.pyc b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/lora/__pycache__/flux_lora.cpython-311.pyc
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diff --git a/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/lora/flux_lora.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/lora/flux_lora.py
new file mode 100644
index 0000000000000000000000000000000000000000..cb53b7342221dd749c9a704a09f6669f08f684b7
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/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/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/__init__.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/__init__.py
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--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/__init__.py
@@ -0,0 +1 @@
+from .model_manager import *
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diff --git a/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/attention.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/attention.py
new file mode 100644
index 0000000000000000000000000000000000000000..eb90e1ed1a28a0541a8d9df8313997a7d3f14da7
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/attention.py
@@ -0,0 +1,89 @@
+import torch
+from einops import rearrange
+
+
+def low_version_attention(query, key, value, attn_bias=None):
+    scale = 1 / query.shape[-1] ** 0.5
+    query = query * scale
+    attn = torch.matmul(query, key.transpose(-2, -1))
+    if attn_bias is not None:
+        attn = attn + attn_bias
+    attn = attn.softmax(-1)
+    return attn @ value
+
+
+class Attention(torch.nn.Module):
+
+    def __init__(self, q_dim, num_heads, head_dim, kv_dim=None, bias_q=False, bias_kv=False, bias_out=False):
+        super().__init__()
+        dim_inner = head_dim * num_heads
+        kv_dim = kv_dim if kv_dim is not None else q_dim
+        self.num_heads = num_heads
+        self.head_dim = head_dim
+
+        self.to_q = torch.nn.Linear(q_dim, dim_inner, bias=bias_q)
+        self.to_k = torch.nn.Linear(kv_dim, dim_inner, bias=bias_kv)
+        self.to_v = torch.nn.Linear(kv_dim, dim_inner, bias=bias_kv)
+        self.to_out = torch.nn.Linear(dim_inner, q_dim, bias=bias_out)
+
+    def interact_with_ipadapter(self, hidden_states, q, ip_k, ip_v, scale=1.0):
+        batch_size = q.shape[0]
+        ip_k = ip_k.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2)
+        ip_v = ip_v.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2)
+        ip_hidden_states = torch.nn.functional.scaled_dot_product_attention(q, ip_k, ip_v)
+        hidden_states = hidden_states + scale * ip_hidden_states
+        return hidden_states
+
+    def torch_forward(self, hidden_states, encoder_hidden_states=None, attn_mask=None, ipadapter_kwargs=None, qkv_preprocessor=None):
+        if encoder_hidden_states is None:
+            encoder_hidden_states = hidden_states
+
+        batch_size = encoder_hidden_states.shape[0]
+
+        q = self.to_q(hidden_states)
+        k = self.to_k(encoder_hidden_states)
+        v = self.to_v(encoder_hidden_states)
+
+        q = q.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2)
+        k = k.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2)
+        v = v.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2)
+
+        if qkv_preprocessor is not None:
+            q, k, v = qkv_preprocessor(q, k, v)
+
+        hidden_states = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=attn_mask)
+        if ipadapter_kwargs is not None:
+            hidden_states = self.interact_with_ipadapter(hidden_states, q, **ipadapter_kwargs)
+        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, self.num_heads * self.head_dim)
+        hidden_states = hidden_states.to(q.dtype)
+
+        hidden_states = self.to_out(hidden_states)
+
+        return hidden_states
+    
+    def xformers_forward(self, hidden_states, encoder_hidden_states=None, attn_mask=None):
+        if encoder_hidden_states is None:
+            encoder_hidden_states = hidden_states
+
+        q = self.to_q(hidden_states)
+        k = self.to_k(encoder_hidden_states)
+        v = self.to_v(encoder_hidden_states)
+
+        q = rearrange(q, "b f (n d) -> (b n) f d", n=self.num_heads)
+        k = rearrange(k, "b f (n d) -> (b n) f d", n=self.num_heads)
+        v = rearrange(v, "b f (n d) -> (b n) f d", n=self.num_heads)
+
+        if attn_mask is not None:
+            hidden_states = low_version_attention(q, k, v, attn_bias=attn_mask)
+        else:
+            import xformers.ops as xops
+            hidden_states = xops.memory_efficient_attention(q, k, v)
+        hidden_states = rearrange(hidden_states, "(b n) f d -> b f (n d)", n=self.num_heads)
+
+        hidden_states = hidden_states.to(q.dtype)
+        hidden_states = self.to_out(hidden_states)
+
+        return hidden_states
+
+    def forward(self, hidden_states, encoder_hidden_states=None, attn_mask=None, ipadapter_kwargs=None, qkv_preprocessor=None):
+        return self.torch_forward(hidden_states, encoder_hidden_states=encoder_hidden_states, attn_mask=attn_mask, ipadapter_kwargs=ipadapter_kwargs, qkv_preprocessor=qkv_preprocessor)
\ No newline at end of file
diff --git a/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/cog_dit.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/cog_dit.py
new file mode 100644
index 0000000000000000000000000000000000000000..e93c4c38684c6815c099774dd4e3c8291462cd78
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/cog_dit.py
@@ -0,0 +1,408 @@
+import torch
+from einops import rearrange, repeat
+from .sd3_dit import TimestepEmbeddings
+from .attention import Attention
+from .utils import load_state_dict_from_folder
+from .tiler import TileWorker2Dto3D
+import numpy as np
+
+
+
+class CogPatchify(torch.nn.Module):
+    def __init__(self, dim_in, dim_out, patch_size) -> None:
+        super().__init__()
+        self.proj = torch.nn.Conv3d(dim_in, dim_out, kernel_size=(1, patch_size, patch_size), stride=(1, patch_size, patch_size))
+
+    def forward(self, hidden_states):
+        hidden_states = self.proj(hidden_states)
+        hidden_states = rearrange(hidden_states, "B C T H W -> B (T H W) C")
+        return hidden_states
+    
+
+
+class CogAdaLayerNorm(torch.nn.Module):
+    def __init__(self, dim, dim_cond, single=False):
+        super().__init__()
+        self.single = single
+        self.linear = torch.nn.Linear(dim_cond, dim * (2 if single else 6))
+        self.norm = torch.nn.LayerNorm(dim, elementwise_affine=True, eps=1e-5)
+
+
+    def forward(self, hidden_states, prompt_emb, emb):
+        emb = self.linear(torch.nn.functional.silu(emb))
+        if self.single:
+            shift, scale = emb.unsqueeze(1).chunk(2, dim=2)
+            hidden_states = self.norm(hidden_states) * (1 + scale) + shift
+            return hidden_states
+        else:
+            shift_a, scale_a, gate_a, shift_b, scale_b, gate_b = emb.unsqueeze(1).chunk(6, dim=2)
+            hidden_states = self.norm(hidden_states) * (1 + scale_a) + shift_a
+            prompt_emb = self.norm(prompt_emb) * (1 + scale_b) + shift_b
+            return hidden_states, prompt_emb, gate_a, gate_b
+
+
+
+class CogDiTBlock(torch.nn.Module):
+    def __init__(self, dim, dim_cond, num_heads):
+        super().__init__()
+        self.norm1 = CogAdaLayerNorm(dim, dim_cond)
+        self.attn1 = Attention(q_dim=dim, num_heads=48, head_dim=dim//num_heads, bias_q=True, bias_kv=True, bias_out=True)
+        self.norm_q = torch.nn.LayerNorm((dim//num_heads,), eps=1e-06, elementwise_affine=True)
+        self.norm_k = torch.nn.LayerNorm((dim//num_heads,), eps=1e-06, elementwise_affine=True)
+
+        self.norm2 = CogAdaLayerNorm(dim, dim_cond)
+        self.ff = torch.nn.Sequential(
+            torch.nn.Linear(dim, dim*4),
+            torch.nn.GELU(approximate="tanh"),
+            torch.nn.Linear(dim*4, dim)
+        )
+    
+
+    def apply_rotary_emb(self, x, freqs_cis):
+        cos, sin = freqs_cis  # [S, D]
+        cos = cos[None, None]
+        sin = sin[None, None]
+        cos, sin = cos.to(x.device), sin.to(x.device)
+        x_real, x_imag = x.reshape(*x.shape[:-1], -1, 2).unbind(-1)  # [B, S, H, D//2]
+        x_rotated = torch.stack([-x_imag, x_real], dim=-1).flatten(3)
+        out = (x.float() * cos + x_rotated.float() * sin).to(x.dtype)
+        return out
+    
+
+    def process_qkv(self, q, k, v, image_rotary_emb, text_seq_length):
+        q = self.norm_q(q)
+        k = self.norm_k(k)
+        q[:, :, text_seq_length:] = self.apply_rotary_emb(q[:, :, text_seq_length:], image_rotary_emb)
+        k[:, :, text_seq_length:] = self.apply_rotary_emb(k[:, :, text_seq_length:], image_rotary_emb)
+        return q, k, v
+        
+
+    def forward(self, hidden_states, prompt_emb, time_emb, image_rotary_emb):
+        # Attention
+        norm_hidden_states, norm_encoder_hidden_states, gate_a, gate_b = self.norm1(
+            hidden_states, prompt_emb, time_emb
+        )
+        attention_io = torch.cat([norm_encoder_hidden_states, norm_hidden_states], dim=1)
+        attention_io = self.attn1(
+            attention_io,
+            qkv_preprocessor=lambda q, k, v: self.process_qkv(q, k, v, image_rotary_emb, prompt_emb.shape[1])
+        )
+
+        hidden_states = hidden_states + gate_a * attention_io[:, prompt_emb.shape[1]:]
+        prompt_emb = prompt_emb + gate_b * attention_io[:, :prompt_emb.shape[1]]
+
+        # Feed forward
+        norm_hidden_states, norm_encoder_hidden_states, gate_a, gate_b = self.norm2(
+            hidden_states, prompt_emb, time_emb
+        )
+        ff_io = torch.cat([norm_encoder_hidden_states, norm_hidden_states], dim=1)
+        ff_io = self.ff(ff_io)
+
+        hidden_states = hidden_states + gate_a * ff_io[:, prompt_emb.shape[1]:]
+        prompt_emb = prompt_emb + gate_b * ff_io[:, :prompt_emb.shape[1]]
+
+        return hidden_states, prompt_emb
+
+
+
+class CogDiT(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.patchify = CogPatchify(16, 3072, 2)
+        self.time_embedder = TimestepEmbeddings(3072, 512)
+        self.context_embedder = torch.nn.Linear(4096, 3072)
+        self.blocks = torch.nn.ModuleList([CogDiTBlock(3072, 512, 48) for _ in range(42)])
+        self.norm_final = torch.nn.LayerNorm((3072,), eps=1e-05, elementwise_affine=True)
+        self.norm_out = CogAdaLayerNorm(3072, 512, single=True)
+        self.proj_out = torch.nn.Linear(3072, 64, bias=True)
+
+
+    def get_resize_crop_region_for_grid(self, src, tgt_width, tgt_height):
+        tw = tgt_width
+        th = tgt_height
+        h, w = src
+        r = h / w
+        if r > (th / tw):
+            resize_height = th
+            resize_width = int(round(th / h * w))
+        else:
+            resize_width = tw
+            resize_height = int(round(tw / w * h))
+
+        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_3d_rotary_pos_embed(
+        self, embed_dim, crops_coords, grid_size, temporal_size, theta: int = 10000, use_real: bool = True
+    ):
+        start, stop = crops_coords
+        grid_h = np.linspace(start[0], stop[0], grid_size[0], endpoint=False, dtype=np.float32)
+        grid_w = np.linspace(start[1], stop[1], grid_size[1], endpoint=False, dtype=np.float32)
+        grid_t = np.linspace(0, temporal_size, temporal_size, endpoint=False, dtype=np.float32)
+
+        # Compute dimensions for each axis
+        dim_t = embed_dim // 4
+        dim_h = embed_dim // 8 * 3
+        dim_w = embed_dim // 8 * 3
+
+        # Temporal frequencies
+        freqs_t = 1.0 / (theta ** (torch.arange(0, dim_t, 2).float() / dim_t))
+        grid_t = torch.from_numpy(grid_t).float()
+        freqs_t = torch.einsum("n , f -> n f", grid_t, freqs_t)
+        freqs_t = freqs_t.repeat_interleave(2, dim=-1)
+
+        # Spatial frequencies for height and width
+        freqs_h = 1.0 / (theta ** (torch.arange(0, dim_h, 2).float() / dim_h))
+        freqs_w = 1.0 / (theta ** (torch.arange(0, dim_w, 2).float() / dim_w))
+        grid_h = torch.from_numpy(grid_h).float()
+        grid_w = torch.from_numpy(grid_w).float()
+        freqs_h = torch.einsum("n , f -> n f", grid_h, freqs_h)
+        freqs_w = torch.einsum("n , f -> n f", grid_w, freqs_w)
+        freqs_h = freqs_h.repeat_interleave(2, dim=-1)
+        freqs_w = freqs_w.repeat_interleave(2, dim=-1)
+
+        # Broadcast and concatenate tensors along specified dimension
+        def broadcast(tensors, dim=-1):
+            num_tensors = len(tensors)
+            shape_lens = {len(t.shape) for t in tensors}
+            assert len(shape_lens) == 1, "tensors must all have the same number of dimensions"
+            shape_len = list(shape_lens)[0]
+            dim = (dim + shape_len) if dim < 0 else dim
+            dims = list(zip(*(list(t.shape) for t in tensors)))
+            expandable_dims = [(i, val) for i, val in enumerate(dims) if i != dim]
+            assert all(
+                [*(len(set(t[1])) <= 2 for t in expandable_dims)]
+            ), "invalid dimensions for broadcastable concatenation"
+            max_dims = [(t[0], max(t[1])) for t in expandable_dims]
+            expanded_dims = [(t[0], (t[1],) * num_tensors) for t in max_dims]
+            expanded_dims.insert(dim, (dim, dims[dim]))
+            expandable_shapes = list(zip(*(t[1] for t in expanded_dims)))
+            tensors = [t[0].expand(*t[1]) for t in zip(tensors, expandable_shapes)]
+            return torch.cat(tensors, dim=dim)
+
+        freqs = broadcast((freqs_t[:, None, None, :], freqs_h[None, :, None, :], freqs_w[None, None, :, :]), dim=-1)
+
+        t, h, w, d = freqs.shape
+        freqs = freqs.view(t * h * w, d)
+
+        # Generate sine and cosine components
+        sin = freqs.sin()
+        cos = freqs.cos()
+
+        if use_real:
+            return cos, sin
+        else:
+            freqs_cis = torch.polar(torch.ones_like(freqs), freqs)
+            return freqs_cis
+    
+
+    def prepare_rotary_positional_embeddings(
+        self,
+        height: int,
+        width: int,
+        num_frames: int,
+        device: torch.device,
+    ):
+        grid_height = height // 2
+        grid_width = width // 2
+        base_size_width = 720 // (8 * 2)
+        base_size_height = 480 // (8 * 2)
+
+        grid_crops_coords = self.get_resize_crop_region_for_grid(
+            (grid_height, grid_width), base_size_width, base_size_height
+        )
+        freqs_cos, freqs_sin = self.get_3d_rotary_pos_embed(
+            embed_dim=64,
+            crops_coords=grid_crops_coords,
+            grid_size=(grid_height, grid_width),
+            temporal_size=num_frames,
+            use_real=True,
+        )
+
+        freqs_cos = freqs_cos.to(device=device)
+        freqs_sin = freqs_sin.to(device=device)
+        return freqs_cos, freqs_sin
+
+
+    def unpatchify(self, hidden_states, height, width):
+        hidden_states = rearrange(hidden_states, "B (T H W) (C P Q) -> B C T (H P) (W Q)", P=2, Q=2, H=height//2, W=width//2)
+        return hidden_states
+    
+
+    def build_mask(self, T, H, W, dtype, device, is_bound):
+        t = repeat(torch.arange(T), "T -> T H W", T=T, H=H, W=W)
+        h = repeat(torch.arange(H), "H -> T H W", T=T, H=H, W=W)
+        w = repeat(torch.arange(W), "W -> T H W", T=T, H=H, W=W)
+        border_width = (H + W) // 4
+        pad = torch.ones_like(h) * border_width
+        mask = torch.stack([
+            pad if is_bound[0] else t + 1,
+            pad if is_bound[1] else T - t,
+            pad if is_bound[2] else h + 1,
+            pad if is_bound[3] else H - h,
+            pad if is_bound[4] else w + 1,
+            pad if is_bound[5] else W - w
+        ]).min(dim=0).values
+        mask = mask.clip(1, border_width)
+        mask = (mask / border_width).to(dtype=dtype, device=device)
+        mask = rearrange(mask, "T H W -> 1 1 T H W")
+        return mask
+    
+
+    def tiled_forward(self, hidden_states, timestep, prompt_emb, tile_size=(60, 90), tile_stride=(30, 45)):
+        B, C, T, H, W = hidden_states.shape
+        value = torch.zeros((B, C, T, H, W), dtype=hidden_states.dtype, device=hidden_states.device)
+        weight = torch.zeros((B, C, T, H, W), dtype=hidden_states.dtype, device=hidden_states.device)
+
+        # Split tasks
+        tasks = []
+        for h in range(0, H, tile_stride):
+            for w in range(0, W, tile_stride):
+                if (h-tile_stride >= 0 and h-tile_stride+tile_size >= H) or (w-tile_stride >= 0 and w-tile_stride+tile_size >= W):
+                    continue
+                h_, w_ = h + tile_size, w + tile_size
+                if h_ > H: h, h_ = max(H - tile_size, 0), H
+                if w_ > W: w, w_ = max(W - tile_size, 0), W
+                tasks.append((h, h_, w, w_))
+
+        # Run
+        for hl, hr, wl, wr in tasks:
+            mask = self.build_mask(
+                value.shape[2], (hr-hl), (wr-wl),
+                hidden_states.dtype, hidden_states.device,
+                is_bound=(True, True, hl==0, hr>=H, wl==0, wr>=W)
+            )
+            model_output = self.forward(hidden_states[:, :, :, hl:hr, wl:wr], timestep, prompt_emb)
+            value[:, :, :, hl:hr, wl:wr] += model_output * mask
+            weight[:, :, :, hl:hr, wl:wr] += mask
+        value = value / weight
+
+        return value
+
+
+    def forward(self, hidden_states, timestep, prompt_emb, image_rotary_emb=None, tiled=False, tile_size=90, tile_stride=30, use_gradient_checkpointing=False):
+        if tiled:
+            return TileWorker2Dto3D().tiled_forward(
+                forward_fn=lambda x: self.forward(x, timestep, prompt_emb),
+                model_input=hidden_states,
+                tile_size=tile_size, tile_stride=tile_stride,
+                tile_device=hidden_states.device, tile_dtype=hidden_states.dtype,
+                computation_device=self.context_embedder.weight.device, computation_dtype=self.context_embedder.weight.dtype
+            )
+        num_frames, height, width = hidden_states.shape[-3:]
+        if image_rotary_emb is None:
+            image_rotary_emb = self.prepare_rotary_positional_embeddings(height, width, num_frames, device=self.context_embedder.weight.device)
+        hidden_states = self.patchify(hidden_states)
+        time_emb = self.time_embedder(timestep, dtype=hidden_states.dtype)
+        prompt_emb = self.context_embedder(prompt_emb)
+
+        def create_custom_forward(module):
+            def custom_forward(*inputs):
+                return module(*inputs)
+            return custom_forward
+        
+        for block in self.blocks:
+            if self.training and use_gradient_checkpointing:
+                hidden_states, prompt_emb = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(block),
+                    hidden_states, prompt_emb, time_emb, image_rotary_emb,
+                    use_reentrant=False,
+                )
+            else:
+                hidden_states, prompt_emb = block(hidden_states, prompt_emb, time_emb, image_rotary_emb)
+
+        hidden_states = torch.cat([prompt_emb, hidden_states], dim=1)
+        hidden_states = self.norm_final(hidden_states)
+        hidden_states = hidden_states[:, prompt_emb.shape[1]:]
+        hidden_states = self.norm_out(hidden_states, prompt_emb, time_emb)
+        hidden_states = self.proj_out(hidden_states)
+        hidden_states = self.unpatchify(hidden_states, height, width)
+
+        return hidden_states
+    
+
+    @staticmethod
+    def state_dict_converter():
+        return CogDiTStateDictConverter()
+    
+
+    @staticmethod
+    def from_pretrained(file_path, torch_dtype=torch.bfloat16):
+        model = CogDiT().to(torch_dtype)
+        state_dict = load_state_dict_from_folder(file_path, torch_dtype=torch_dtype)
+        state_dict = CogDiT.state_dict_converter().from_diffusers(state_dict)
+        model.load_state_dict(state_dict)
+        return model
+
+
+
+class CogDiTStateDictConverter:
+    def __init__(self):
+        pass
+
+
+    def from_diffusers(self, state_dict):
+        rename_dict = {
+            "patch_embed.proj.weight": "patchify.proj.weight",
+            "patch_embed.proj.bias": "patchify.proj.bias",
+            "patch_embed.text_proj.weight": "context_embedder.weight",
+            "patch_embed.text_proj.bias": "context_embedder.bias",
+            "time_embedding.linear_1.weight": "time_embedder.timestep_embedder.0.weight",
+            "time_embedding.linear_1.bias": "time_embedder.timestep_embedder.0.bias",
+            "time_embedding.linear_2.weight": "time_embedder.timestep_embedder.2.weight",
+            "time_embedding.linear_2.bias": "time_embedder.timestep_embedder.2.bias",
+
+            "norm_final.weight": "norm_final.weight",
+            "norm_final.bias": "norm_final.bias",
+            "norm_out.linear.weight": "norm_out.linear.weight",
+            "norm_out.linear.bias": "norm_out.linear.bias",
+            "norm_out.norm.weight": "norm_out.norm.weight",
+            "norm_out.norm.bias": "norm_out.norm.bias",
+            "proj_out.weight": "proj_out.weight",
+            "proj_out.bias": "proj_out.bias",
+        }
+        suffix_dict = {
+            "norm1.linear.weight": "norm1.linear.weight",
+            "norm1.linear.bias": "norm1.linear.bias",
+            "norm1.norm.weight": "norm1.norm.weight",
+            "norm1.norm.bias": "norm1.norm.bias",
+            "attn1.norm_q.weight": "norm_q.weight",
+            "attn1.norm_q.bias": "norm_q.bias",
+            "attn1.norm_k.weight": "norm_k.weight",
+            "attn1.norm_k.bias": "norm_k.bias",
+            "attn1.to_q.weight": "attn1.to_q.weight",
+            "attn1.to_q.bias": "attn1.to_q.bias",
+            "attn1.to_k.weight": "attn1.to_k.weight",
+            "attn1.to_k.bias": "attn1.to_k.bias",
+            "attn1.to_v.weight": "attn1.to_v.weight",
+            "attn1.to_v.bias": "attn1.to_v.bias",
+            "attn1.to_out.0.weight": "attn1.to_out.weight",
+            "attn1.to_out.0.bias": "attn1.to_out.bias",
+            "norm2.linear.weight": "norm2.linear.weight",
+            "norm2.linear.bias": "norm2.linear.bias",
+            "norm2.norm.weight": "norm2.norm.weight",
+            "norm2.norm.bias": "norm2.norm.bias",
+            "ff.net.0.proj.weight": "ff.0.weight",
+            "ff.net.0.proj.bias": "ff.0.bias",
+            "ff.net.2.weight": "ff.2.weight",
+            "ff.net.2.bias": "ff.2.bias",
+        }
+        state_dict_ = {}
+        for name, param in state_dict.items():
+            if name in rename_dict:
+                if name == "patch_embed.proj.weight":
+                    param = param.unsqueeze(2)
+                state_dict_[rename_dict[name]] = param
+            else:
+                names = name.split(".")
+                if names[0] == "transformer_blocks":
+                    suffix = ".".join(names[2:])
+                    state_dict_[f"blocks.{names[1]}." + suffix_dict[suffix]] = param
+        return state_dict_
+    
+
+    def from_civitai(self, state_dict):
+        return self.from_diffusers(state_dict)
diff --git a/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/cog_vae.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/cog_vae.py
new file mode 100644
index 0000000000000000000000000000000000000000..24ab3b3f37e111ffabf7d56c582637c6dc1c80b8
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/cog_vae.py
@@ -0,0 +1,518 @@
+import torch
+from einops import rearrange, repeat
+from .tiler import TileWorker2Dto3D
+
+
+
+class Downsample3D(torch.nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        kernel_size: int = 3,
+        stride: int = 2,
+        padding: int = 0,
+        compress_time: bool = False,
+    ):
+        super().__init__()
+
+        self.conv = torch.nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, padding=padding)
+        self.compress_time = compress_time
+
+    def forward(self, x: torch.Tensor, xq: torch.Tensor) -> torch.Tensor:
+        if self.compress_time:
+            batch_size, channels, frames, height, width = x.shape
+
+            # (batch_size, channels, frames, height, width) -> (batch_size, height, width, channels, frames) -> (batch_size * height * width, channels, frames)
+            x = x.permute(0, 3, 4, 1, 2).reshape(batch_size * height * width, channels, frames)
+
+            if x.shape[-1] % 2 == 1:
+                x_first, x_rest = x[..., 0], x[..., 1:]
+                if x_rest.shape[-1] > 0:
+                    # (batch_size * height * width, channels, frames - 1) -> (batch_size * height * width, channels, (frames - 1) // 2)
+                    x_rest = torch.nn.functional.avg_pool1d(x_rest, kernel_size=2, stride=2)
+
+                x = torch.cat([x_first[..., None], x_rest], dim=-1)
+                # (batch_size * height * width, channels, (frames // 2) + 1) -> (batch_size, height, width, channels, (frames // 2) + 1) -> (batch_size, channels, (frames // 2) + 1, height, width)
+                x = x.reshape(batch_size, height, width, channels, x.shape[-1]).permute(0, 3, 4, 1, 2)
+            else:
+                # (batch_size * height * width, channels, frames) -> (batch_size * height * width, channels, frames // 2)
+                x = torch.nn.functional.avg_pool1d(x, kernel_size=2, stride=2)
+                # (batch_size * height * width, channels, frames // 2) -> (batch_size, height, width, channels, frames // 2) -> (batch_size, channels, frames // 2, height, width)
+                x = x.reshape(batch_size, height, width, channels, x.shape[-1]).permute(0, 3, 4, 1, 2)
+
+        # Pad the tensor
+        pad = (0, 1, 0, 1)
+        x = torch.nn.functional.pad(x, pad, mode="constant", value=0)
+        batch_size, channels, frames, height, width = x.shape
+        # (batch_size, channels, frames, height, width) -> (batch_size, frames, channels, height, width) -> (batch_size * frames, channels, height, width)
+        x = x.permute(0, 2, 1, 3, 4).reshape(batch_size * frames, channels, height, width)
+        x = self.conv(x)
+        # (batch_size * frames, channels, height, width) -> (batch_size, frames, channels, height, width) -> (batch_size, channels, frames, height, width)
+        x = x.reshape(batch_size, frames, x.shape[1], x.shape[2], x.shape[3]).permute(0, 2, 1, 3, 4)
+        return x
+
+
+
+class Upsample3D(torch.nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        kernel_size: int = 3,
+        stride: int = 1,
+        padding: int = 1,
+        compress_time: bool = False,
+    ) -> None:
+        super().__init__()
+        self.conv = torch.nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, padding=padding)
+        self.compress_time = compress_time
+
+    def forward(self, inputs: torch.Tensor, xq: torch.Tensor) -> torch.Tensor:
+        if self.compress_time:
+            if inputs.shape[2] > 1 and inputs.shape[2] % 2 == 1:
+                # split first frame
+                x_first, x_rest = inputs[:, :, 0], inputs[:, :, 1:]
+
+                x_first = torch.nn.functional.interpolate(x_first, scale_factor=2.0)
+                x_rest = torch.nn.functional.interpolate(x_rest, scale_factor=2.0)
+                x_first = x_first[:, :, None, :, :]
+                inputs = torch.cat([x_first, x_rest], dim=2)
+            elif inputs.shape[2] > 1:
+                inputs = torch.nn.functional.interpolate(inputs, scale_factor=2.0)
+            else:
+                inputs = inputs.squeeze(2)
+                inputs = torch.nn.functional.interpolate(inputs, scale_factor=2.0)
+                inputs = inputs[:, :, None, :, :]
+        else:
+            # only interpolate 2D
+            b, c, t, h, w = inputs.shape
+            inputs = inputs.permute(0, 2, 1, 3, 4).reshape(b * t, c, h, w)
+            inputs = torch.nn.functional.interpolate(inputs, scale_factor=2.0)
+            inputs = inputs.reshape(b, t, c, *inputs.shape[2:]).permute(0, 2, 1, 3, 4)
+
+        b, c, t, h, w = inputs.shape
+        inputs = inputs.permute(0, 2, 1, 3, 4).reshape(b * t, c, h, w)
+        inputs = self.conv(inputs)
+        inputs = inputs.reshape(b, t, *inputs.shape[1:]).permute(0, 2, 1, 3, 4)
+
+        return inputs
+
+
+
+class CogVideoXSpatialNorm3D(torch.nn.Module):
+    def __init__(self, f_channels, zq_channels, groups):
+        super().__init__()
+        self.norm_layer = torch.nn.GroupNorm(num_channels=f_channels, num_groups=groups, eps=1e-6, affine=True)
+        self.conv_y = torch.nn.Conv3d(zq_channels, f_channels, kernel_size=1, stride=1)
+        self.conv_b = torch.nn.Conv3d(zq_channels, f_channels, kernel_size=1, stride=1)
+
+
+    def forward(self, f: torch.Tensor, zq: torch.Tensor) -> torch.Tensor:
+        if f.shape[2] > 1 and f.shape[2] % 2 == 1:
+            f_first, f_rest = f[:, :, :1], f[:, :, 1:]
+            f_first_size, f_rest_size = f_first.shape[-3:], f_rest.shape[-3:]
+            z_first, z_rest = zq[:, :, :1], zq[:, :, 1:]
+            z_first = torch.nn.functional.interpolate(z_first, size=f_first_size)
+            z_rest = torch.nn.functional.interpolate(z_rest, size=f_rest_size)
+            zq = torch.cat([z_first, z_rest], dim=2)
+        else:
+            zq = torch.nn.functional.interpolate(zq, size=f.shape[-3:])
+
+        norm_f = self.norm_layer(f)
+        new_f = norm_f * self.conv_y(zq) + self.conv_b(zq)
+        return new_f
+
+
+
+class Resnet3DBlock(torch.nn.Module):
+    def __init__(self, in_channels, out_channels, spatial_norm_dim, groups, eps=1e-6, use_conv_shortcut=False):
+        super().__init__()
+        self.nonlinearity = torch.nn.SiLU()
+        if spatial_norm_dim is None:
+            self.norm1 = torch.nn.GroupNorm(num_channels=in_channels, num_groups=groups, eps=eps)
+            self.norm2 = torch.nn.GroupNorm(num_channels=out_channels, num_groups=groups, eps=eps)
+        else:
+            self.norm1 = CogVideoXSpatialNorm3D(in_channels, spatial_norm_dim, groups)
+            self.norm2 = CogVideoXSpatialNorm3D(out_channels, spatial_norm_dim, groups)
+
+        self.conv1 = CachedConv3d(in_channels, out_channels, kernel_size=3, padding=(0, 1, 1))
+
+        self.conv2 = CachedConv3d(out_channels, out_channels, kernel_size=3, padding=(0, 1, 1))
+
+        if in_channels != out_channels:
+            if use_conv_shortcut:
+                self.conv_shortcut = CachedConv3d(in_channels, out_channels, kernel_size=3, padding=(0, 1, 1))
+            else:
+                self.conv_shortcut = torch.nn.Conv3d(in_channels, out_channels, kernel_size=1)
+        else:
+            self.conv_shortcut = lambda x: x
+
+
+    def forward(self, hidden_states, zq):
+        residual = hidden_states
+
+        hidden_states = self.norm1(hidden_states, zq) if isinstance(self.norm1, CogVideoXSpatialNorm3D) else self.norm1(hidden_states)
+        hidden_states = self.nonlinearity(hidden_states)
+        hidden_states = self.conv1(hidden_states)
+
+        hidden_states = self.norm2(hidden_states, zq) if isinstance(self.norm2, CogVideoXSpatialNorm3D) else self.norm2(hidden_states)
+        hidden_states = self.nonlinearity(hidden_states)
+        hidden_states = self.conv2(hidden_states)
+
+        hidden_states = hidden_states + self.conv_shortcut(residual)
+
+        return hidden_states
+    
+
+
+class CachedConv3d(torch.nn.Conv3d):
+    def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0):
+        super().__init__(in_channels, out_channels, kernel_size=kernel_size, stride=stride, padding=padding)
+        self.cached_tensor = None
+
+
+    def clear_cache(self):
+        self.cached_tensor = None
+    
+
+    def forward(self, input: torch.Tensor, use_cache = True) -> torch.Tensor:
+        if use_cache:
+            if self.cached_tensor is None:
+                self.cached_tensor = torch.concat([input[:, :, :1]] * 2, dim=2)
+            input = torch.concat([self.cached_tensor, input], dim=2)
+            self.cached_tensor = input[:, :, -2:]
+        return super().forward(input)
+
+
+
+class CogVAEDecoder(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.scaling_factor = 0.7
+        self.conv_in = CachedConv3d(16, 512, kernel_size=3, stride=1, padding=(0, 1, 1))
+
+        self.blocks = torch.nn.ModuleList([
+            Resnet3DBlock(512, 512, 16, 32),
+            Resnet3DBlock(512, 512, 16, 32),
+            Resnet3DBlock(512, 512, 16, 32),
+            Resnet3DBlock(512, 512, 16, 32),
+            Resnet3DBlock(512, 512, 16, 32),
+            Resnet3DBlock(512, 512, 16, 32),
+            Upsample3D(512, 512, compress_time=True),
+            Resnet3DBlock(512, 256, 16, 32),
+            Resnet3DBlock(256, 256, 16, 32),
+            Resnet3DBlock(256, 256, 16, 32),
+            Resnet3DBlock(256, 256, 16, 32),
+            Upsample3D(256, 256, compress_time=True),
+            Resnet3DBlock(256, 256, 16, 32),
+            Resnet3DBlock(256, 256, 16, 32),
+            Resnet3DBlock(256, 256, 16, 32),
+            Resnet3DBlock(256, 256, 16, 32),
+            Upsample3D(256, 256, compress_time=False),
+            Resnet3DBlock(256, 128, 16, 32),
+            Resnet3DBlock(128, 128, 16, 32),
+            Resnet3DBlock(128, 128, 16, 32),
+            Resnet3DBlock(128, 128, 16, 32),
+        ])
+
+        self.norm_out = CogVideoXSpatialNorm3D(128, 16, 32)
+        self.conv_act = torch.nn.SiLU()
+        self.conv_out = CachedConv3d(128, 3, kernel_size=3, stride=1, padding=(0, 1, 1))
+
+
+    def forward(self, sample):
+        sample = sample / self.scaling_factor
+        hidden_states = self.conv_in(sample)
+
+        for block in self.blocks:
+            hidden_states = block(hidden_states, sample)
+        
+        hidden_states = self.norm_out(hidden_states, sample)
+        hidden_states = self.conv_act(hidden_states)
+        hidden_states = self.conv_out(hidden_states)
+
+        return hidden_states
+    
+
+    def decode_video(self, sample, tiled=True, tile_size=(60, 90), tile_stride=(30, 45), progress_bar=lambda x:x):
+        if tiled:
+            B, C, T, H, W = sample.shape
+            return TileWorker2Dto3D().tiled_forward(
+                forward_fn=lambda x: self.decode_small_video(x),
+                model_input=sample,
+                tile_size=tile_size, tile_stride=tile_stride,
+                tile_device=sample.device, tile_dtype=sample.dtype,
+                computation_device=sample.device, computation_dtype=sample.dtype,
+                scales=(3/16, (T//2*8+T%2)/T, 8, 8),
+                progress_bar=progress_bar
+            )
+        else:
+            return self.decode_small_video(sample)
+    
+
+    def decode_small_video(self, sample):
+        B, C, T, H, W = sample.shape
+        computation_device = self.conv_in.weight.device
+        computation_dtype = self.conv_in.weight.dtype
+        value = []
+        for i in range(T//2):
+            tl = i*2 + T%2 - (T%2 and i==0)
+            tr = i*2 + 2 + T%2
+            model_input = sample[:, :, tl: tr, :, :].to(dtype=computation_dtype, device=computation_device)
+            model_output = self.forward(model_input).to(dtype=sample.dtype, device=sample.device)
+            value.append(model_output)
+        value = torch.concat(value, dim=2)
+        for name, module in self.named_modules():
+            if isinstance(module, CachedConv3d):
+                module.clear_cache()
+        return value
+    
+
+    @staticmethod
+    def state_dict_converter():
+        return CogVAEDecoderStateDictConverter()
+    
+
+
+class CogVAEEncoder(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.scaling_factor = 0.7
+        self.conv_in = CachedConv3d(3, 128, kernel_size=3, stride=1, padding=(0, 1, 1))
+
+        self.blocks = torch.nn.ModuleList([
+            Resnet3DBlock(128, 128, None, 32),
+            Resnet3DBlock(128, 128, None, 32),
+            Resnet3DBlock(128, 128, None, 32),
+            Downsample3D(128, 128, compress_time=True),
+            Resnet3DBlock(128, 256, None, 32),
+            Resnet3DBlock(256, 256, None, 32),
+            Resnet3DBlock(256, 256, None, 32),
+            Downsample3D(256, 256, compress_time=True),
+            Resnet3DBlock(256, 256, None, 32),
+            Resnet3DBlock(256, 256, None, 32),
+            Resnet3DBlock(256, 256, None, 32),
+            Downsample3D(256, 256, compress_time=False),
+            Resnet3DBlock(256, 512, None, 32),
+            Resnet3DBlock(512, 512, None, 32),
+            Resnet3DBlock(512, 512, None, 32),
+            Resnet3DBlock(512, 512, None, 32),
+            Resnet3DBlock(512, 512, None, 32),
+        ])
+
+        self.norm_out = torch.nn.GroupNorm(32, 512, eps=1e-06, affine=True)
+        self.conv_act = torch.nn.SiLU()
+        self.conv_out = CachedConv3d(512, 32, kernel_size=3, stride=1, padding=(0, 1, 1))
+
+
+    def forward(self, sample):
+        hidden_states = self.conv_in(sample)
+
+        for block in self.blocks:
+            hidden_states = block(hidden_states, sample)
+        
+        hidden_states = self.norm_out(hidden_states)
+        hidden_states = self.conv_act(hidden_states)
+        hidden_states = self.conv_out(hidden_states)[:, :16]
+        hidden_states = hidden_states * self.scaling_factor
+
+        return hidden_states
+    
+
+    def encode_video(self, sample, tiled=True, tile_size=(60, 90), tile_stride=(30, 45), progress_bar=lambda x:x):
+        if tiled:
+            B, C, T, H, W = sample.shape
+            return TileWorker2Dto3D().tiled_forward(
+                forward_fn=lambda x: self.encode_small_video(x),
+                model_input=sample,
+                tile_size=(i * 8 for i in tile_size), tile_stride=(i * 8 for i in tile_stride),
+                tile_device=sample.device, tile_dtype=sample.dtype,
+                computation_device=sample.device, computation_dtype=sample.dtype,
+                scales=(16/3, (T//4+T%2)/T, 1/8, 1/8),
+                progress_bar=progress_bar
+            )
+        else:
+            return self.encode_small_video(sample)
+    
+
+    def encode_small_video(self, sample):
+        B, C, T, H, W = sample.shape
+        computation_device = self.conv_in.weight.device
+        computation_dtype = self.conv_in.weight.dtype
+        value = []
+        for i in range(T//8):
+            t = i*8 + T%2 - (T%2 and i==0)
+            t_ = i*8 + 8 + T%2
+            model_input = sample[:, :, t: t_, :, :].to(dtype=computation_dtype, device=computation_device)
+            model_output = self.forward(model_input).to(dtype=sample.dtype, device=sample.device)
+            value.append(model_output)
+        value = torch.concat(value, dim=2)
+        for name, module in self.named_modules():
+            if isinstance(module, CachedConv3d):
+                module.clear_cache()
+        return value
+    
+
+    @staticmethod
+    def state_dict_converter():
+        return CogVAEEncoderStateDictConverter()
+
+
+
+class CogVAEEncoderStateDictConverter:
+    def __init__(self):
+        pass
+
+
+    def from_diffusers(self, state_dict):
+        rename_dict = {
+            "encoder.conv_in.conv.weight": "conv_in.weight",
+            "encoder.conv_in.conv.bias": "conv_in.bias",
+            "encoder.down_blocks.0.downsamplers.0.conv.weight": "blocks.3.conv.weight",
+            "encoder.down_blocks.0.downsamplers.0.conv.bias": "blocks.3.conv.bias",
+            "encoder.down_blocks.1.downsamplers.0.conv.weight": "blocks.7.conv.weight",
+            "encoder.down_blocks.1.downsamplers.0.conv.bias": "blocks.7.conv.bias",
+            "encoder.down_blocks.2.downsamplers.0.conv.weight": "blocks.11.conv.weight",
+            "encoder.down_blocks.2.downsamplers.0.conv.bias": "blocks.11.conv.bias",
+            "encoder.norm_out.weight": "norm_out.weight",
+            "encoder.norm_out.bias": "norm_out.bias",
+            "encoder.conv_out.conv.weight": "conv_out.weight",
+            "encoder.conv_out.conv.bias": "conv_out.bias",
+        }
+        prefix_dict = {
+            "encoder.down_blocks.0.resnets.0.": "blocks.0.",
+            "encoder.down_blocks.0.resnets.1.": "blocks.1.",
+            "encoder.down_blocks.0.resnets.2.": "blocks.2.",
+            "encoder.down_blocks.1.resnets.0.": "blocks.4.",
+            "encoder.down_blocks.1.resnets.1.": "blocks.5.",
+            "encoder.down_blocks.1.resnets.2.": "blocks.6.",
+            "encoder.down_blocks.2.resnets.0.": "blocks.8.",
+            "encoder.down_blocks.2.resnets.1.": "blocks.9.",
+            "encoder.down_blocks.2.resnets.2.": "blocks.10.",
+            "encoder.down_blocks.3.resnets.0.": "blocks.12.",
+            "encoder.down_blocks.3.resnets.1.": "blocks.13.",
+            "encoder.down_blocks.3.resnets.2.": "blocks.14.",
+            "encoder.mid_block.resnets.0.": "blocks.15.",
+            "encoder.mid_block.resnets.1.": "blocks.16.",
+        }
+        suffix_dict = {
+            "norm1.norm_layer.weight": "norm1.norm_layer.weight",
+            "norm1.norm_layer.bias": "norm1.norm_layer.bias",
+            "norm1.conv_y.conv.weight": "norm1.conv_y.weight",
+            "norm1.conv_y.conv.bias": "norm1.conv_y.bias",
+            "norm1.conv_b.conv.weight": "norm1.conv_b.weight",
+            "norm1.conv_b.conv.bias": "norm1.conv_b.bias",
+            "norm2.norm_layer.weight": "norm2.norm_layer.weight",
+            "norm2.norm_layer.bias": "norm2.norm_layer.bias",
+            "norm2.conv_y.conv.weight": "norm2.conv_y.weight",
+            "norm2.conv_y.conv.bias": "norm2.conv_y.bias",
+            "norm2.conv_b.conv.weight": "norm2.conv_b.weight",
+            "norm2.conv_b.conv.bias": "norm2.conv_b.bias",
+            "conv1.conv.weight": "conv1.weight",
+            "conv1.conv.bias": "conv1.bias",
+            "conv2.conv.weight": "conv2.weight",
+            "conv2.conv.bias": "conv2.bias",
+            "conv_shortcut.weight": "conv_shortcut.weight",
+            "conv_shortcut.bias": "conv_shortcut.bias",
+            "norm1.weight": "norm1.weight",
+            "norm1.bias": "norm1.bias",
+            "norm2.weight": "norm2.weight",
+            "norm2.bias": "norm2.bias",
+        }
+        state_dict_ = {}
+        for name, param in state_dict.items():
+            if name in rename_dict:
+                state_dict_[rename_dict[name]] = param
+            else:
+                for prefix in prefix_dict:
+                    if name.startswith(prefix):
+                        suffix = name[len(prefix):]
+                        state_dict_[prefix_dict[prefix] + suffix_dict[suffix]] = param
+        return state_dict_
+    
+
+    def from_civitai(self, state_dict):
+        return self.from_diffusers(state_dict)
+
+
+
+class CogVAEDecoderStateDictConverter:
+    def __init__(self):
+        pass
+
+
+    def from_diffusers(self, state_dict):
+        rename_dict = {
+            "decoder.conv_in.conv.weight": "conv_in.weight",
+            "decoder.conv_in.conv.bias": "conv_in.bias",
+            "decoder.up_blocks.0.upsamplers.0.conv.weight": "blocks.6.conv.weight",
+            "decoder.up_blocks.0.upsamplers.0.conv.bias": "blocks.6.conv.bias",
+            "decoder.up_blocks.1.upsamplers.0.conv.weight": "blocks.11.conv.weight",
+            "decoder.up_blocks.1.upsamplers.0.conv.bias": "blocks.11.conv.bias",
+            "decoder.up_blocks.2.upsamplers.0.conv.weight": "blocks.16.conv.weight",
+            "decoder.up_blocks.2.upsamplers.0.conv.bias": "blocks.16.conv.bias",
+            "decoder.norm_out.norm_layer.weight": "norm_out.norm_layer.weight",
+            "decoder.norm_out.norm_layer.bias": "norm_out.norm_layer.bias",
+            "decoder.norm_out.conv_y.conv.weight": "norm_out.conv_y.weight",
+            "decoder.norm_out.conv_y.conv.bias": "norm_out.conv_y.bias",
+            "decoder.norm_out.conv_b.conv.weight": "norm_out.conv_b.weight",
+            "decoder.norm_out.conv_b.conv.bias": "norm_out.conv_b.bias",
+            "decoder.conv_out.conv.weight": "conv_out.weight",
+            "decoder.conv_out.conv.bias": "conv_out.bias"
+        }
+        prefix_dict = {
+            "decoder.mid_block.resnets.0.": "blocks.0.",
+            "decoder.mid_block.resnets.1.": "blocks.1.",
+            "decoder.up_blocks.0.resnets.0.": "blocks.2.",
+            "decoder.up_blocks.0.resnets.1.": "blocks.3.",
+            "decoder.up_blocks.0.resnets.2.": "blocks.4.",
+            "decoder.up_blocks.0.resnets.3.": "blocks.5.",
+            "decoder.up_blocks.1.resnets.0.": "blocks.7.",
+            "decoder.up_blocks.1.resnets.1.": "blocks.8.",
+            "decoder.up_blocks.1.resnets.2.": "blocks.9.",
+            "decoder.up_blocks.1.resnets.3.": "blocks.10.",
+            "decoder.up_blocks.2.resnets.0.": "blocks.12.",
+            "decoder.up_blocks.2.resnets.1.": "blocks.13.",
+            "decoder.up_blocks.2.resnets.2.": "blocks.14.",
+            "decoder.up_blocks.2.resnets.3.": "blocks.15.",
+            "decoder.up_blocks.3.resnets.0.": "blocks.17.",
+            "decoder.up_blocks.3.resnets.1.": "blocks.18.",
+            "decoder.up_blocks.3.resnets.2.": "blocks.19.",
+            "decoder.up_blocks.3.resnets.3.": "blocks.20.",
+        }
+        suffix_dict = {
+            "norm1.norm_layer.weight": "norm1.norm_layer.weight",
+            "norm1.norm_layer.bias": "norm1.norm_layer.bias",
+            "norm1.conv_y.conv.weight": "norm1.conv_y.weight",
+            "norm1.conv_y.conv.bias": "norm1.conv_y.bias",
+            "norm1.conv_b.conv.weight": "norm1.conv_b.weight",
+            "norm1.conv_b.conv.bias": "norm1.conv_b.bias",
+            "norm2.norm_layer.weight": "norm2.norm_layer.weight",
+            "norm2.norm_layer.bias": "norm2.norm_layer.bias",
+            "norm2.conv_y.conv.weight": "norm2.conv_y.weight",
+            "norm2.conv_y.conv.bias": "norm2.conv_y.bias",
+            "norm2.conv_b.conv.weight": "norm2.conv_b.weight",
+            "norm2.conv_b.conv.bias": "norm2.conv_b.bias",
+            "conv1.conv.weight": "conv1.weight",
+            "conv1.conv.bias": "conv1.bias",
+            "conv2.conv.weight": "conv2.weight",
+            "conv2.conv.bias": "conv2.bias",
+            "conv_shortcut.weight": "conv_shortcut.weight",
+            "conv_shortcut.bias": "conv_shortcut.bias",
+        }
+        state_dict_ = {}
+        for name, param in state_dict.items():
+            if name in rename_dict:
+                state_dict_[rename_dict[name]] = param
+            else:
+                for prefix in prefix_dict:
+                    if name.startswith(prefix):
+                        suffix = name[len(prefix):]
+                        state_dict_[prefix_dict[prefix] + suffix_dict[suffix]] = param
+        return state_dict_
+    
+
+    def from_civitai(self, state_dict):
+        return self.from_diffusers(state_dict)
+
diff --git a/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/downloader.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/downloader.py
new file mode 100644
index 0000000000000000000000000000000000000000..6c726f628fdbdac4cba79cb4c62475506df76b20
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/downloader.py
@@ -0,0 +1,111 @@
+from huggingface_hub import hf_hub_download
+from modelscope import snapshot_download
+import os, shutil
+from typing_extensions import Literal, TypeAlias
+from typing import List
+from ..configs.model_config import preset_models_on_huggingface, preset_models_on_modelscope, Preset_model_id
+
+
+def download_from_modelscope(model_id, origin_file_path, local_dir):
+    os.makedirs(local_dir, exist_ok=True)
+    file_name = os.path.basename(origin_file_path)
+    if file_name in os.listdir(local_dir):
+        print(f"    {file_name} has been already in {local_dir}.")
+    else:
+        print(f"    Start downloading {os.path.join(local_dir, file_name)}")
+        snapshot_download(model_id, allow_file_pattern=origin_file_path, local_dir=local_dir)
+        downloaded_file_path = os.path.join(local_dir, origin_file_path)
+        target_file_path = os.path.join(local_dir, os.path.split(origin_file_path)[-1])
+        if downloaded_file_path != target_file_path:
+            shutil.move(downloaded_file_path, target_file_path)
+            shutil.rmtree(os.path.join(local_dir, origin_file_path.split("/")[0]))
+
+
+def download_from_huggingface(model_id, origin_file_path, local_dir):
+    os.makedirs(local_dir, exist_ok=True)
+    file_name = os.path.basename(origin_file_path)
+    if file_name in os.listdir(local_dir):
+        print(f"    {file_name} has been already in {local_dir}.")
+    else:
+        print(f"    Start downloading {os.path.join(local_dir, file_name)}")
+        hf_hub_download(model_id, origin_file_path, local_dir=local_dir)
+        downloaded_file_path = os.path.join(local_dir, origin_file_path)
+        target_file_path = os.path.join(local_dir, file_name)
+        if downloaded_file_path != target_file_path:
+            shutil.move(downloaded_file_path, target_file_path)
+            shutil.rmtree(os.path.join(local_dir, origin_file_path.split("/")[0]))
+
+
+Preset_model_website: TypeAlias = Literal[
+    "HuggingFace",
+    "ModelScope",
+]
+website_to_preset_models = {
+    "HuggingFace": preset_models_on_huggingface,
+    "ModelScope": preset_models_on_modelscope,
+}
+website_to_download_fn = {
+    "HuggingFace": download_from_huggingface,
+    "ModelScope": download_from_modelscope,
+}
+
+
+def download_customized_models(
+    model_id,
+    origin_file_path,
+    local_dir,
+    downloading_priority: List[Preset_model_website] = ["ModelScope", "HuggingFace"],
+):
+    downloaded_files = []
+    for website in downloading_priority:
+        # Check if the file is downloaded.
+        file_to_download = os.path.join(local_dir, os.path.basename(origin_file_path))
+        if file_to_download in downloaded_files:
+            continue
+        # Download
+        website_to_download_fn[website](model_id, origin_file_path, local_dir)
+        if os.path.basename(origin_file_path) in os.listdir(local_dir):
+            downloaded_files.append(file_to_download)
+    return downloaded_files
+
+
+def download_models(
+    model_id_list: List[Preset_model_id] = [],
+    downloading_priority: List[Preset_model_website] = ["ModelScope", "HuggingFace"],
+):
+    print(f"Downloading models: {model_id_list}")
+    downloaded_files = []
+    load_files = []
+
+    for model_id in model_id_list:
+        for website in downloading_priority:
+            if model_id in website_to_preset_models[website]:
+                
+                # Parse model metadata
+                model_metadata = website_to_preset_models[website][model_id]
+                if isinstance(model_metadata, list):
+                    file_data = model_metadata
+                else:
+                    file_data = model_metadata.get("file_list", [])
+
+                # Try downloading the model from this website.
+                model_files = []
+                for model_id, origin_file_path, local_dir in file_data:
+                    # Check if the file is downloaded.
+                    file_to_download = os.path.join(local_dir, os.path.basename(origin_file_path))
+                    if file_to_download in downloaded_files:
+                        continue
+                    # Download
+                    website_to_download_fn[website](model_id, origin_file_path, local_dir)
+                    if os.path.basename(origin_file_path) in os.listdir(local_dir):
+                        downloaded_files.append(file_to_download)
+                        model_files.append(file_to_download)
+                
+                # If the model is successfully downloaded, break.
+                if len(model_files) > 0:
+                    if isinstance(model_metadata, dict) and "load_path" in model_metadata:
+                        model_files = model_metadata["load_path"]
+                    load_files.extend(model_files)
+                    break
+                
+    return load_files
diff --git a/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/flux_controlnet.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/flux_controlnet.py
new file mode 100644
index 0000000000000000000000000000000000000000..85fccd7da016020b02f0b6480d0b5c97044126df
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/flux_controlnet.py
@@ -0,0 +1,331 @@
+import torch
+from einops import rearrange, repeat
+from .flux_dit import RoPEEmbedding, TimestepEmbeddings, FluxJointTransformerBlock, FluxSingleTransformerBlock, RMSNorm
+from .utils import hash_state_dict_keys, init_weights_on_device
+
+
+
+class FluxControlNet(torch.nn.Module):
+    def __init__(self, disable_guidance_embedder=False, num_joint_blocks=5, num_single_blocks=10, num_mode=0, mode_dict={}, additional_input_dim=0):
+        super().__init__()
+        self.pos_embedder = RoPEEmbedding(3072, 10000, [16, 56, 56])
+        self.time_embedder = TimestepEmbeddings(256, 3072)
+        self.guidance_embedder = None if disable_guidance_embedder else TimestepEmbeddings(256, 3072)
+        self.pooled_text_embedder = torch.nn.Sequential(torch.nn.Linear(768, 3072), torch.nn.SiLU(), torch.nn.Linear(3072, 3072))
+        self.context_embedder = torch.nn.Linear(4096, 3072)
+        self.x_embedder = torch.nn.Linear(64, 3072)
+
+        self.blocks = torch.nn.ModuleList([FluxJointTransformerBlock(3072, 24) for _ in range(num_joint_blocks)])
+        self.single_blocks = torch.nn.ModuleList([FluxSingleTransformerBlock(3072, 24) for _ in range(num_single_blocks)])
+
+        self.controlnet_blocks = torch.nn.ModuleList([torch.nn.Linear(3072, 3072) for _ in range(num_joint_blocks)])
+        self.controlnet_single_blocks = torch.nn.ModuleList([torch.nn.Linear(3072, 3072) for _ in range(num_single_blocks)])
+        
+        self.mode_dict = mode_dict
+        self.controlnet_mode_embedder = torch.nn.Embedding(num_mode, 3072) if len(mode_dict) > 0 else None
+        self.controlnet_x_embedder = torch.nn.Linear(64 + additional_input_dim, 3072)
+
+
+    def prepare_image_ids(self, latents):
+        batch_size, _, height, width = latents.shape
+        latent_image_ids = torch.zeros(height // 2, width // 2, 3)
+        latent_image_ids[..., 1] = latent_image_ids[..., 1] + torch.arange(height // 2)[:, None]
+        latent_image_ids[..., 2] = latent_image_ids[..., 2] + torch.arange(width // 2)[None, :]
+
+        latent_image_id_height, latent_image_id_width, latent_image_id_channels = latent_image_ids.shape
+
+        latent_image_ids = latent_image_ids[None, :].repeat(batch_size, 1, 1, 1)
+        latent_image_ids = latent_image_ids.reshape(
+            batch_size, latent_image_id_height * latent_image_id_width, latent_image_id_channels
+        )
+        latent_image_ids = latent_image_ids.to(device=latents.device, dtype=latents.dtype)
+
+        return latent_image_ids
+    
+
+    def patchify(self, hidden_states):
+        hidden_states = rearrange(hidden_states, "B C (H P) (W Q) -> B (H W) (C P Q)", P=2, Q=2)
+        return hidden_states
+    
+
+    def align_res_stack_to_original_blocks(self, res_stack, num_blocks, hidden_states):
+        if len(res_stack) == 0:
+            return [torch.zeros_like(hidden_states)] * num_blocks
+        interval = (num_blocks + len(res_stack) - 1) // len(res_stack)
+        aligned_res_stack = [res_stack[block_id // interval] for block_id in range(num_blocks)]
+        return aligned_res_stack
+
+
+    def forward(
+        self,
+        hidden_states,
+        controlnet_conditioning,
+        timestep, prompt_emb, pooled_prompt_emb, guidance, text_ids, image_ids=None,
+        processor_id=None,
+        tiled=False, tile_size=128, tile_stride=64,
+        **kwargs
+    ):
+        if image_ids is None:
+            image_ids = self.prepare_image_ids(hidden_states)
+
+        conditioning = self.time_embedder(timestep, hidden_states.dtype) + self.pooled_text_embedder(pooled_prompt_emb)
+        if self.guidance_embedder is not None:
+            guidance = guidance * 1000
+            conditioning = conditioning + self.guidance_embedder(guidance, hidden_states.dtype)
+        prompt_emb = self.context_embedder(prompt_emb)
+        if self.controlnet_mode_embedder is not None: # Different from FluxDiT
+            processor_id = torch.tensor([self.mode_dict[processor_id]], dtype=torch.int)
+            processor_id = repeat(processor_id, "D -> B D", B=1).to(text_ids.device)
+            prompt_emb = torch.concat([self.controlnet_mode_embedder(processor_id), prompt_emb], dim=1)
+            text_ids = torch.cat([text_ids[:, :1], text_ids], dim=1)
+        image_rotary_emb = self.pos_embedder(torch.cat((text_ids, image_ids), dim=1))
+
+        hidden_states = self.patchify(hidden_states)
+        hidden_states = self.x_embedder(hidden_states)
+        controlnet_conditioning = self.patchify(controlnet_conditioning) # Different from FluxDiT
+        hidden_states = hidden_states + self.controlnet_x_embedder(controlnet_conditioning) # Different from FluxDiT
+
+        controlnet_res_stack = []
+        for block, controlnet_block in zip(self.blocks, self.controlnet_blocks):
+            hidden_states, prompt_emb = block(hidden_states, prompt_emb, conditioning, image_rotary_emb)
+            controlnet_res_stack.append(controlnet_block(hidden_states))
+
+        controlnet_single_res_stack = []
+        hidden_states = torch.cat([prompt_emb, hidden_states], dim=1)
+        for block, controlnet_block in zip(self.single_blocks, self.controlnet_single_blocks):
+            hidden_states, prompt_emb = block(hidden_states, prompt_emb, conditioning, image_rotary_emb)
+            controlnet_single_res_stack.append(controlnet_block(hidden_states[:, prompt_emb.shape[1]:]))
+
+        controlnet_res_stack = self.align_res_stack_to_original_blocks(controlnet_res_stack, 19, hidden_states[:, prompt_emb.shape[1]:])
+        controlnet_single_res_stack = self.align_res_stack_to_original_blocks(controlnet_single_res_stack, 38, hidden_states[:, prompt_emb.shape[1]:])
+
+        return controlnet_res_stack, controlnet_single_res_stack
+
+
+    @staticmethod
+    def state_dict_converter():
+        return FluxControlNetStateDictConverter()
+    
+    def quantize(self):
+        def cast_to(weight, dtype=None, device=None, copy=False):
+            if device is None or weight.device == device:
+                if not copy:
+                    if dtype is None or weight.dtype == dtype:
+                        return weight
+                return weight.to(dtype=dtype, copy=copy)
+
+            r = torch.empty_like(weight, dtype=dtype, device=device)
+            r.copy_(weight)
+            return r
+
+        def cast_weight(s, input=None, dtype=None, device=None):
+            if input is not None:
+                if dtype is None:
+                    dtype = input.dtype
+                if device is None:
+                    device = input.device
+            weight = cast_to(s.weight, dtype, device)
+            return weight
+
+        def cast_bias_weight(s, input=None, dtype=None, device=None, bias_dtype=None):
+            if input is not None:
+                if dtype is None:
+                    dtype = input.dtype
+                if bias_dtype is None:
+                    bias_dtype = dtype
+                if device is None:
+                    device = input.device
+            bias = None
+            weight = cast_to(s.weight, dtype, device)
+            bias = cast_to(s.bias, bias_dtype, device)
+            return weight, bias
+
+        class quantized_layer:
+            class QLinear(torch.nn.Linear):
+                def __init__(self, *args, **kwargs):
+                    super().__init__(*args, **kwargs)
+                    
+                def forward(self,input,**kwargs):
+                    weight,bias= cast_bias_weight(self,input)
+                    return torch.nn.functional.linear(input,weight,bias)
+            
+            class QRMSNorm(torch.nn.Module):
+                def __init__(self, module):
+                    super().__init__()
+                    self.module = module
+                    
+                def forward(self,hidden_states,**kwargs):
+                    weight= cast_weight(self.module,hidden_states)
+                    input_dtype = hidden_states.dtype
+                    variance = hidden_states.to(torch.float32).square().mean(-1, keepdim=True)
+                    hidden_states = hidden_states * torch.rsqrt(variance + self.module.eps)
+                    hidden_states = hidden_states.to(input_dtype) * weight
+                    return hidden_states
+            
+            class QEmbedding(torch.nn.Embedding):
+                def __init__(self, *args, **kwargs):
+                    super().__init__(*args, **kwargs)
+                    
+                def forward(self,input,**kwargs):
+                    weight= cast_weight(self,input)
+                    return torch.nn.functional.embedding(
+                        input, weight, self.padding_idx, self.max_norm,
+                        self.norm_type, self.scale_grad_by_freq, self.sparse)
+            
+        def replace_layer(model):
+            for name, module in model.named_children():
+                if isinstance(module,quantized_layer.QRMSNorm):
+                    continue
+                if isinstance(module, torch.nn.Linear):
+                    with init_weights_on_device():
+                        new_layer = quantized_layer.QLinear(module.in_features,module.out_features)
+                    new_layer.weight = module.weight
+                    if module.bias is not None:
+                        new_layer.bias = module.bias
+                    setattr(model, name, new_layer)
+                elif isinstance(module, RMSNorm):
+                    if hasattr(module,"quantized"):
+                        continue
+                    module.quantized= True
+                    new_layer = quantized_layer.QRMSNorm(module)
+                    setattr(model, name, new_layer)
+                elif isinstance(module,torch.nn.Embedding):
+                    rows, cols = module.weight.shape
+                    new_layer = quantized_layer.QEmbedding(
+                        num_embeddings=rows,
+                        embedding_dim=cols,
+                        _weight=module.weight,
+                        # _freeze=module.freeze,
+                        padding_idx=module.padding_idx,
+                        max_norm=module.max_norm,
+                        norm_type=module.norm_type,
+                        scale_grad_by_freq=module.scale_grad_by_freq,
+                        sparse=module.sparse)
+                    setattr(model, name, new_layer)
+                else:
+                    replace_layer(module)
+
+        replace_layer(self)
+    
+
+
+class FluxControlNetStateDictConverter:
+    def __init__(self):
+        pass
+
+    def from_diffusers(self, state_dict):
+        hash_value = hash_state_dict_keys(state_dict)
+        global_rename_dict = {
+            "context_embedder": "context_embedder",
+            "x_embedder": "x_embedder",
+            "time_text_embed.timestep_embedder.linear_1": "time_embedder.timestep_embedder.0",
+            "time_text_embed.timestep_embedder.linear_2": "time_embedder.timestep_embedder.2",
+            "time_text_embed.guidance_embedder.linear_1": "guidance_embedder.timestep_embedder.0",
+            "time_text_embed.guidance_embedder.linear_2": "guidance_embedder.timestep_embedder.2",
+            "time_text_embed.text_embedder.linear_1": "pooled_text_embedder.0",
+            "time_text_embed.text_embedder.linear_2": "pooled_text_embedder.2",
+            "norm_out.linear": "final_norm_out.linear",
+            "proj_out": "final_proj_out",
+        }
+        rename_dict = {
+            "proj_out": "proj_out",
+            "norm1.linear": "norm1_a.linear",
+            "norm1_context.linear": "norm1_b.linear",
+            "attn.to_q": "attn.a_to_q",
+            "attn.to_k": "attn.a_to_k",
+            "attn.to_v": "attn.a_to_v",
+            "attn.to_out.0": "attn.a_to_out",
+            "attn.add_q_proj": "attn.b_to_q",
+            "attn.add_k_proj": "attn.b_to_k",
+            "attn.add_v_proj": "attn.b_to_v",
+            "attn.to_add_out": "attn.b_to_out",
+            "ff.net.0.proj": "ff_a.0",
+            "ff.net.2": "ff_a.2",
+            "ff_context.net.0.proj": "ff_b.0",
+            "ff_context.net.2": "ff_b.2",
+            "attn.norm_q": "attn.norm_q_a",
+            "attn.norm_k": "attn.norm_k_a",
+            "attn.norm_added_q": "attn.norm_q_b",
+            "attn.norm_added_k": "attn.norm_k_b",
+        }
+        rename_dict_single = {
+            "attn.to_q": "a_to_q",
+            "attn.to_k": "a_to_k",
+            "attn.to_v": "a_to_v",
+            "attn.norm_q": "norm_q_a",
+            "attn.norm_k": "norm_k_a",
+            "norm.linear": "norm.linear",
+            "proj_mlp": "proj_in_besides_attn",
+            "proj_out": "proj_out",
+        }
+        state_dict_ = {}
+        for name, param in state_dict.items():
+            if name.endswith(".weight") or name.endswith(".bias"):
+                suffix = ".weight" if name.endswith(".weight") else ".bias"
+                prefix = name[:-len(suffix)]
+                if prefix in global_rename_dict:
+                    state_dict_[global_rename_dict[prefix] + suffix] = param
+                elif prefix.startswith("transformer_blocks."):
+                    names = prefix.split(".")
+                    names[0] = "blocks"
+                    middle = ".".join(names[2:])
+                    if middle in rename_dict:
+                        name_ = ".".join(names[:2] + [rename_dict[middle]] + [suffix[1:]])
+                        state_dict_[name_] = param
+                elif prefix.startswith("single_transformer_blocks."):
+                    names = prefix.split(".")
+                    names[0] = "single_blocks"
+                    middle = ".".join(names[2:])
+                    if middle in rename_dict_single:
+                        name_ = ".".join(names[:2] + [rename_dict_single[middle]] + [suffix[1:]])
+                        state_dict_[name_] = param
+                    else:
+                        state_dict_[name] = param
+                else:
+                    state_dict_[name] = param
+        for name in list(state_dict_.keys()):
+            if ".proj_in_besides_attn." in name:
+                name_ = name.replace(".proj_in_besides_attn.", ".to_qkv_mlp.")
+                param = torch.concat([
+                    state_dict_[name.replace(".proj_in_besides_attn.", f".a_to_q.")],
+                    state_dict_[name.replace(".proj_in_besides_attn.", f".a_to_k.")],
+                    state_dict_[name.replace(".proj_in_besides_attn.", f".a_to_v.")],
+                    state_dict_[name],
+                ], dim=0)
+                state_dict_[name_] = param
+                state_dict_.pop(name.replace(".proj_in_besides_attn.", f".a_to_q."))
+                state_dict_.pop(name.replace(".proj_in_besides_attn.", f".a_to_k."))
+                state_dict_.pop(name.replace(".proj_in_besides_attn.", f".a_to_v."))
+                state_dict_.pop(name)
+        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.")
+                    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."))
+        if hash_value == "78d18b9101345ff695f312e7e62538c0":
+            extra_kwargs = {"num_mode": 10, "mode_dict": {"canny": 0, "tile": 1, "depth": 2, "blur": 3, "pose": 4, "gray": 5, "lq": 6}}
+        elif hash_value == "b001c89139b5f053c715fe772362dd2a":
+            extra_kwargs = {"num_single_blocks": 0}
+        elif hash_value == "52357cb26250681367488a8954c271e8":
+            extra_kwargs = {"num_joint_blocks": 6, "num_single_blocks": 0, "additional_input_dim": 4}
+        elif hash_value == "0cfd1740758423a2a854d67c136d1e8c":
+            extra_kwargs = {"num_joint_blocks": 4, "num_single_blocks": 1}
+        elif hash_value == "7f9583eb8ba86642abb9a21a4b2c9e16":
+            extra_kwargs = {"num_joint_blocks": 4, "num_single_blocks": 10}
+        elif hash_value == "43ad5aaa27dd4ee01b832ed16773fa52":
+            extra_kwargs = {"num_joint_blocks": 6, "num_single_blocks": 0}
+        else:
+            extra_kwargs = {}
+        return state_dict_, extra_kwargs
+    
+
+    def from_civitai(self, state_dict):
+        return self.from_diffusers(state_dict)
diff --git a/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/flux_dit.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/flux_dit.py
new file mode 100644
index 0000000000000000000000000000000000000000..411ac9c195a59051f1935f32a88dea5aad6842d5
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/flux_dit.py
@@ -0,0 +1,748 @@
+import torch
+from .sd3_dit import TimestepEmbeddings, AdaLayerNorm, RMSNorm
+from einops import rearrange
+from .tiler import TileWorker
+from .utils import init_weights_on_device, hash_state_dict_keys
+
+def interact_with_ipadapter(hidden_states, q, ip_k, ip_v, scale=1.0):
+    batch_size, num_tokens = hidden_states.shape[0:2]
+    ip_hidden_states = torch.nn.functional.scaled_dot_product_attention(q, ip_k, ip_v)
+    ip_hidden_states = ip_hidden_states.transpose(1, 2).reshape(batch_size, num_tokens, -1)
+    hidden_states = hidden_states + scale * ip_hidden_states
+    return hidden_states
+
+
+class RoPEEmbedding(torch.nn.Module):
+    def __init__(self, dim, theta, axes_dim):
+        super().__init__()
+        self.dim = dim
+        self.theta = theta
+        self.axes_dim = axes_dim
+
+
+    def rope(self, pos: torch.Tensor, dim: int, theta: int) -> torch.Tensor:
+        assert dim % 2 == 0, "The dimension must be even."
+
+        scale = torch.arange(0, dim, 2, dtype=torch.float64, device=pos.device) / dim
+        omega = 1.0 / (theta**scale)
+
+        batch_size, seq_length = pos.shape
+        out = torch.einsum("...n,d->...nd", pos, omega)
+        cos_out = torch.cos(out)
+        sin_out = torch.sin(out)
+
+        stacked_out = torch.stack([cos_out, -sin_out, sin_out, cos_out], dim=-1)
+        out = stacked_out.view(batch_size, -1, dim // 2, 2, 2)
+        return out.float()
+
+
+    def forward(self, ids):
+        n_axes = ids.shape[-1]
+        emb = torch.cat([self.rope(ids[..., i], self.axes_dim[i], self.theta) for i in range(n_axes)], dim=-3)
+        return emb.unsqueeze(1)
+
+
+
+class FluxJointAttention(torch.nn.Module):
+    def __init__(self, dim_a, dim_b, num_heads, head_dim, only_out_a=False):
+        super().__init__()
+        self.num_heads = num_heads
+        self.head_dim = head_dim
+        self.only_out_a = only_out_a
+
+        self.a_to_qkv = torch.nn.Linear(dim_a, dim_a * 3)
+        self.b_to_qkv = torch.nn.Linear(dim_b, dim_b * 3)
+
+        self.norm_q_a = RMSNorm(head_dim, eps=1e-6)
+        self.norm_k_a = RMSNorm(head_dim, eps=1e-6)
+        self.norm_q_b = RMSNorm(head_dim, eps=1e-6)
+        self.norm_k_b = RMSNorm(head_dim, eps=1e-6)
+
+        self.a_to_out = torch.nn.Linear(dim_a, dim_a)
+        if not only_out_a:
+            self.b_to_out = torch.nn.Linear(dim_b, dim_b)
+
+
+    def apply_rope(self, xq, xk, freqs_cis):
+        xq_ = xq.float().reshape(*xq.shape[:-1], -1, 1, 2)
+        xk_ = xk.float().reshape(*xk.shape[:-1], -1, 1, 2)
+        xq_out = freqs_cis[..., 0] * xq_[..., 0] + freqs_cis[..., 1] * xq_[..., 1]
+        xk_out = freqs_cis[..., 0] * xk_[..., 0] + freqs_cis[..., 1] * xk_[..., 1]
+        return xq_out.reshape(*xq.shape).type_as(xq), xk_out.reshape(*xk.shape).type_as(xk)
+
+    def forward(self, hidden_states_a, hidden_states_b, image_rotary_emb, attn_mask=None, ipadapter_kwargs_list=None):
+        batch_size = hidden_states_a.shape[0]
+
+        # Part A
+        qkv_a = self.a_to_qkv(hidden_states_a)
+        qkv_a = qkv_a.view(batch_size, -1, 3 * self.num_heads, self.head_dim).transpose(1, 2)
+        q_a, k_a, v_a = qkv_a.chunk(3, dim=1)
+        q_a, k_a = self.norm_q_a(q_a), self.norm_k_a(k_a)
+
+        # Part B
+        qkv_b = self.b_to_qkv(hidden_states_b)
+        qkv_b = qkv_b.view(batch_size, -1, 3 * self.num_heads, self.head_dim).transpose(1, 2)
+        q_b, k_b, v_b = qkv_b.chunk(3, dim=1)
+        q_b, k_b = self.norm_q_b(q_b), self.norm_k_b(k_b)
+
+        q = torch.concat([q_b, q_a], dim=2)
+        k = torch.concat([k_b, k_a], dim=2)
+        v = torch.concat([v_b, v_a], dim=2)
+
+        q, k = self.apply_rope(q, k, image_rotary_emb)
+
+        hidden_states = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=attn_mask)
+        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, self.num_heads * self.head_dim)
+        hidden_states = hidden_states.to(q.dtype)
+        hidden_states_b, hidden_states_a = hidden_states[:, :hidden_states_b.shape[1]], hidden_states[:, hidden_states_b.shape[1]:]
+        if ipadapter_kwargs_list is not None:
+            hidden_states_a = interact_with_ipadapter(hidden_states_a, q_a, **ipadapter_kwargs_list)
+        hidden_states_a = self.a_to_out(hidden_states_a)
+        if self.only_out_a:
+            return hidden_states_a
+        else:
+            hidden_states_b = self.b_to_out(hidden_states_b)
+            return hidden_states_a, hidden_states_b
+
+
+
+class FluxJointTransformerBlock(torch.nn.Module):
+    def __init__(self, dim, num_attention_heads):
+        super().__init__()
+        self.norm1_a = AdaLayerNorm(dim)
+        self.norm1_b = AdaLayerNorm(dim)
+
+        self.attn = FluxJointAttention(dim, dim, num_attention_heads, dim // num_attention_heads)
+
+        self.norm2_a = torch.nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
+        self.ff_a = torch.nn.Sequential(
+            torch.nn.Linear(dim, dim*4),
+            torch.nn.GELU(approximate="tanh"),
+            torch.nn.Linear(dim*4, dim)
+        )
+
+        self.norm2_b = torch.nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
+        self.ff_b = torch.nn.Sequential(
+            torch.nn.Linear(dim, dim*4),
+            torch.nn.GELU(approximate="tanh"),
+            torch.nn.Linear(dim*4, dim)
+        )
+
+
+    def forward(self, hidden_states_a, hidden_states_b, temb, image_rotary_emb, attn_mask=None, ipadapter_kwargs_list=None):
+        norm_hidden_states_a, gate_msa_a, shift_mlp_a, scale_mlp_a, gate_mlp_a = self.norm1_a(hidden_states_a, emb=temb)
+        norm_hidden_states_b, gate_msa_b, shift_mlp_b, scale_mlp_b, gate_mlp_b = self.norm1_b(hidden_states_b, emb=temb)
+
+        # Attention
+        attn_output_a, attn_output_b = self.attn(norm_hidden_states_a, norm_hidden_states_b, image_rotary_emb, attn_mask, ipadapter_kwargs_list)
+
+        # Part A
+        hidden_states_a = hidden_states_a + gate_msa_a * attn_output_a
+        norm_hidden_states_a = self.norm2_a(hidden_states_a) * (1 + scale_mlp_a) + shift_mlp_a
+        hidden_states_a = hidden_states_a + gate_mlp_a * self.ff_a(norm_hidden_states_a)
+
+        # Part B
+        hidden_states_b = hidden_states_b + gate_msa_b * attn_output_b
+        norm_hidden_states_b = self.norm2_b(hidden_states_b) * (1 + scale_mlp_b) + shift_mlp_b
+        hidden_states_b = hidden_states_b + gate_mlp_b * self.ff_b(norm_hidden_states_b)
+
+        return hidden_states_a, hidden_states_b
+
+
+
+class FluxSingleAttention(torch.nn.Module):
+    def __init__(self, dim_a, dim_b, num_heads, head_dim):
+        super().__init__()
+        self.num_heads = num_heads
+        self.head_dim = head_dim
+
+        self.a_to_qkv = torch.nn.Linear(dim_a, dim_a * 3)
+
+        self.norm_q_a = RMSNorm(head_dim, eps=1e-6)
+        self.norm_k_a = RMSNorm(head_dim, eps=1e-6)
+
+
+    def apply_rope(self, xq, xk, freqs_cis):
+        xq_ = xq.float().reshape(*xq.shape[:-1], -1, 1, 2)
+        xk_ = xk.float().reshape(*xk.shape[:-1], -1, 1, 2)
+        xq_out = freqs_cis[..., 0] * xq_[..., 0] + freqs_cis[..., 1] * xq_[..., 1]
+        xk_out = freqs_cis[..., 0] * xk_[..., 0] + freqs_cis[..., 1] * xk_[..., 1]
+        return xq_out.reshape(*xq.shape).type_as(xq), xk_out.reshape(*xk.shape).type_as(xk)
+
+
+    def forward(self, hidden_states, image_rotary_emb):
+        batch_size = hidden_states.shape[0]
+
+        qkv_a = self.a_to_qkv(hidden_states)
+        qkv_a = qkv_a.view(batch_size, -1, 3 * self.num_heads, self.head_dim).transpose(1, 2)
+        q_a, k_a, v = qkv_a.chunk(3, dim=1)
+        q_a, k_a = self.norm_q_a(q_a), self.norm_k_a(k_a)
+
+        q, k = self.apply_rope(q_a, k_a, image_rotary_emb)
+
+        hidden_states = torch.nn.functional.scaled_dot_product_attention(q, k, v)
+        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, self.num_heads * self.head_dim)
+        hidden_states = hidden_states.to(q.dtype)
+        return hidden_states
+
+
+
+class AdaLayerNormSingle(torch.nn.Module):
+    def __init__(self, dim):
+        super().__init__()
+        self.silu = torch.nn.SiLU()
+        self.linear = torch.nn.Linear(dim, 3 * dim, bias=True)
+        self.norm = torch.nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
+
+
+    def forward(self, x, emb):
+        emb = self.linear(self.silu(emb))
+        shift_msa, scale_msa, gate_msa = emb.chunk(3, dim=1)
+        x = self.norm(x) * (1 + scale_msa[:, None]) + shift_msa[:, None]
+        return x, gate_msa
+
+
+
+class FluxSingleTransformerBlock(torch.nn.Module):
+    def __init__(self, dim, num_attention_heads):
+        super().__init__()
+        self.num_heads = num_attention_heads
+        self.head_dim = dim // num_attention_heads
+        self.dim = dim
+
+        self.norm = AdaLayerNormSingle(dim)
+        self.to_qkv_mlp = torch.nn.Linear(dim, dim * (3 + 4))
+        self.norm_q_a = RMSNorm(self.head_dim, eps=1e-6)
+        self.norm_k_a = RMSNorm(self.head_dim, eps=1e-6)
+
+        self.proj_out = torch.nn.Linear(dim * 5, dim)
+
+
+    def apply_rope(self, xq, xk, freqs_cis):
+        xq_ = xq.float().reshape(*xq.shape[:-1], -1, 1, 2)
+        xk_ = xk.float().reshape(*xk.shape[:-1], -1, 1, 2)
+        xq_out = freqs_cis[..., 0] * xq_[..., 0] + freqs_cis[..., 1] * xq_[..., 1]
+        xk_out = freqs_cis[..., 0] * xk_[..., 0] + freqs_cis[..., 1] * xk_[..., 1]
+        return xq_out.reshape(*xq.shape).type_as(xq), xk_out.reshape(*xk.shape).type_as(xk)
+
+
+    def process_attention(self, hidden_states, image_rotary_emb, attn_mask=None, ipadapter_kwargs_list=None):
+        batch_size = hidden_states.shape[0]
+
+        qkv = hidden_states.view(batch_size, -1, 3 * self.num_heads, self.head_dim).transpose(1, 2)
+        q, k, v = qkv.chunk(3, dim=1)
+        q, k = self.norm_q_a(q), self.norm_k_a(k)
+
+        q, k = self.apply_rope(q, k, image_rotary_emb)
+
+        hidden_states = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=attn_mask)
+        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, self.num_heads * self.head_dim)
+        hidden_states = hidden_states.to(q.dtype)
+        if ipadapter_kwargs_list is not None:
+            hidden_states = interact_with_ipadapter(hidden_states, q, **ipadapter_kwargs_list)
+        return hidden_states
+
+
+    def forward(self, hidden_states_a, hidden_states_b, temb, image_rotary_emb, attn_mask=None, ipadapter_kwargs_list=None):
+        residual = hidden_states_a
+        norm_hidden_states, gate = self.norm(hidden_states_a, emb=temb)
+        hidden_states_a = self.to_qkv_mlp(norm_hidden_states)
+        attn_output, mlp_hidden_states = hidden_states_a[:, :, :self.dim * 3], hidden_states_a[:, :, self.dim * 3:]
+
+        attn_output = self.process_attention(attn_output, image_rotary_emb, attn_mask, ipadapter_kwargs_list)
+        mlp_hidden_states = torch.nn.functional.gelu(mlp_hidden_states, approximate="tanh")
+
+        hidden_states_a = torch.cat([attn_output, mlp_hidden_states], dim=2)
+        hidden_states_a = gate.unsqueeze(1) * self.proj_out(hidden_states_a)
+        hidden_states_a = residual + hidden_states_a
+
+        return hidden_states_a, hidden_states_b
+
+
+
+class AdaLayerNormContinuous(torch.nn.Module):
+    def __init__(self, dim):
+        super().__init__()
+        self.silu = torch.nn.SiLU()
+        self.linear = torch.nn.Linear(dim, dim * 2, bias=True)
+        self.norm = torch.nn.LayerNorm(dim, eps=1e-6, elementwise_affine=False)
+
+    def forward(self, x, conditioning):
+        emb = self.linear(self.silu(conditioning))
+        scale, shift = torch.chunk(emb, 2, dim=1)
+        x = self.norm(x) * (1 + scale)[:, None] + shift[:, None]
+        return x
+
+
+
+class FluxDiT(torch.nn.Module):
+    def __init__(self, disable_guidance_embedder=False, input_dim=64, num_blocks=19):
+        super().__init__()
+        self.pos_embedder = RoPEEmbedding(3072, 10000, [16, 56, 56])
+        self.time_embedder = TimestepEmbeddings(256, 3072)
+        self.guidance_embedder = None if disable_guidance_embedder else TimestepEmbeddings(256, 3072)
+        self.pooled_text_embedder = torch.nn.Sequential(torch.nn.Linear(768, 3072), torch.nn.SiLU(), torch.nn.Linear(3072, 3072))
+        self.context_embedder = torch.nn.Linear(4096, 3072)
+        self.x_embedder = torch.nn.Linear(input_dim, 3072)
+
+        self.blocks = torch.nn.ModuleList([FluxJointTransformerBlock(3072, 24) for _ in range(num_blocks)])
+        self.single_blocks = torch.nn.ModuleList([FluxSingleTransformerBlock(3072, 24) for _ in range(38)])
+
+        self.final_norm_out = AdaLayerNormContinuous(3072)
+        self.final_proj_out = torch.nn.Linear(3072, 64)
+        
+        self.input_dim = input_dim
+
+
+    def patchify(self, hidden_states):
+        hidden_states = rearrange(hidden_states, "B C (H P) (W Q) -> B (H W) (C P Q)", P=2, Q=2)
+        return hidden_states
+
+
+    def unpatchify(self, hidden_states, height, width):
+        hidden_states = rearrange(hidden_states, "B (H W) (C P Q) -> B C (H P) (W Q)", P=2, Q=2, H=height//2, W=width//2)
+        return hidden_states
+
+
+    def prepare_image_ids(self, latents):
+        batch_size, _, height, width = latents.shape
+        latent_image_ids = torch.zeros(height // 2, width // 2, 3)
+        latent_image_ids[..., 1] = latent_image_ids[..., 1] + torch.arange(height // 2)[:, None]
+        latent_image_ids[..., 2] = latent_image_ids[..., 2] + torch.arange(width // 2)[None, :]
+
+        latent_image_id_height, latent_image_id_width, latent_image_id_channels = latent_image_ids.shape
+
+        latent_image_ids = latent_image_ids[None, :].repeat(batch_size, 1, 1, 1)
+        latent_image_ids = latent_image_ids.reshape(
+            batch_size, latent_image_id_height * latent_image_id_width, latent_image_id_channels
+        )
+        latent_image_ids = latent_image_ids.to(device=latents.device, dtype=latents.dtype)
+
+        return latent_image_ids
+
+
+    def tiled_forward(
+        self,
+        hidden_states,
+        timestep, prompt_emb, pooled_prompt_emb, guidance, text_ids,
+        tile_size=128, tile_stride=64,
+        **kwargs
+    ):
+        # Due to the global positional embedding, we cannot implement layer-wise tiled forward.
+        hidden_states = TileWorker().tiled_forward(
+            lambda x: self.forward(x, timestep, prompt_emb, pooled_prompt_emb, guidance, text_ids, image_ids=None),
+            hidden_states,
+            tile_size,
+            tile_stride,
+            tile_device=hidden_states.device,
+            tile_dtype=hidden_states.dtype
+        )
+        return hidden_states
+
+
+    def construct_mask(self, entity_masks, prompt_seq_len, image_seq_len):
+        N = len(entity_masks)
+        batch_size = entity_masks[0].shape[0]
+        total_seq_len = N * prompt_seq_len + image_seq_len
+        patched_masks = [self.patchify(entity_masks[i]) for i in range(N)]
+        attention_mask = torch.ones((batch_size, total_seq_len, total_seq_len), dtype=torch.bool).to(device=entity_masks[0].device)
+
+        image_start = N * prompt_seq_len
+        image_end = N * prompt_seq_len + image_seq_len
+        # prompt-image mask
+        for i in range(N):
+            prompt_start = i * prompt_seq_len
+            prompt_end = (i + 1) * prompt_seq_len
+            image_mask = torch.sum(patched_masks[i], dim=-1) > 0
+            image_mask = image_mask.unsqueeze(1).repeat(1, prompt_seq_len, 1)
+            # prompt update with image
+            attention_mask[:, prompt_start:prompt_end, image_start:image_end] = image_mask
+            # image update with prompt
+            attention_mask[:, image_start:image_end, prompt_start:prompt_end] = image_mask.transpose(1, 2)
+        # prompt-prompt mask
+        for i in range(N):
+            for j in range(N):
+                if i != j:
+                    prompt_start_i = i * prompt_seq_len
+                    prompt_end_i = (i + 1) * prompt_seq_len
+                    prompt_start_j = j * prompt_seq_len
+                    prompt_end_j = (j + 1) * prompt_seq_len
+                    attention_mask[:, prompt_start_i:prompt_end_i, prompt_start_j:prompt_end_j] = False
+
+        attention_mask = attention_mask.float()
+        attention_mask[attention_mask == 0] = float('-inf')
+        attention_mask[attention_mask == 1] = 0
+        return attention_mask
+
+
+    def process_entity_masks(self, hidden_states, prompt_emb, entity_prompt_emb, entity_masks, text_ids, image_ids, repeat_dim):
+        max_masks = 0
+        attention_mask = None
+        prompt_embs = [prompt_emb]
+        if entity_masks is not None:
+            # entity_masks
+            batch_size, max_masks = entity_masks.shape[0], entity_masks.shape[1]
+            entity_masks = entity_masks.repeat(1, 1, repeat_dim, 1, 1)
+            entity_masks = [entity_masks[:, i, None].squeeze(1) for i in range(max_masks)]
+            # global mask
+            global_mask = torch.ones_like(entity_masks[0]).to(device=hidden_states.device, dtype=hidden_states.dtype)
+            entity_masks = entity_masks + [global_mask] # append global to last
+            # attention mask
+            attention_mask = self.construct_mask(entity_masks, prompt_emb.shape[1], hidden_states.shape[1])
+            attention_mask = attention_mask.to(device=hidden_states.device, dtype=hidden_states.dtype)
+            attention_mask = attention_mask.unsqueeze(1)
+            # embds: n_masks * b * seq * d
+            local_embs = [entity_prompt_emb[:, i, None].squeeze(1) for i in range(max_masks)]
+            prompt_embs = local_embs + prompt_embs # append global to last
+        prompt_embs = [self.context_embedder(prompt_emb) for prompt_emb in prompt_embs]
+        prompt_emb = torch.cat(prompt_embs, dim=1)
+
+        # positional embedding
+        text_ids = torch.cat([text_ids] * (max_masks + 1), dim=1)
+        image_rotary_emb = self.pos_embedder(torch.cat((text_ids, image_ids), dim=1))
+        return prompt_emb, image_rotary_emb, attention_mask
+
+
+    def forward(
+        self,
+        hidden_states,
+        timestep, prompt_emb, pooled_prompt_emb, guidance, text_ids, image_ids=None,
+        tiled=False, tile_size=128, tile_stride=64, entity_prompt_emb=None, entity_masks=None,
+        use_gradient_checkpointing=False,
+        **kwargs
+    ):
+        if tiled:
+            return self.tiled_forward(
+                hidden_states,
+                timestep, prompt_emb, pooled_prompt_emb, guidance, text_ids,
+                tile_size=tile_size, tile_stride=tile_stride,
+                **kwargs
+            )
+
+        if image_ids is None:
+            image_ids = self.prepare_image_ids(hidden_states)
+
+        conditioning = self.time_embedder(timestep, hidden_states.dtype) + self.pooled_text_embedder(pooled_prompt_emb)
+        if self.guidance_embedder is not None:
+            guidance = guidance * 1000
+            conditioning = conditioning + self.guidance_embedder(guidance, hidden_states.dtype)
+
+        height, width = hidden_states.shape[-2:]
+        hidden_states = self.patchify(hidden_states)
+        hidden_states = self.x_embedder(hidden_states)
+
+        if entity_prompt_emb is not None and entity_masks is not None:
+            prompt_emb, image_rotary_emb, attention_mask = self.process_entity_masks(hidden_states, prompt_emb, entity_prompt_emb, entity_masks, text_ids, image_ids)
+        else:
+            prompt_emb = self.context_embedder(prompt_emb)
+            image_rotary_emb = self.pos_embedder(torch.cat((text_ids, image_ids), dim=1))
+            attention_mask = None
+
+        def create_custom_forward(module):
+            def custom_forward(*inputs):
+                return module(*inputs)
+            return custom_forward
+
+        for block in self.blocks:
+            if self.training and use_gradient_checkpointing:
+                hidden_states, prompt_emb = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(block),
+                    hidden_states, prompt_emb, conditioning, image_rotary_emb, attention_mask,
+                    use_reentrant=False,
+                )
+            else:
+                hidden_states, prompt_emb = block(hidden_states, prompt_emb, conditioning, image_rotary_emb, attention_mask)
+
+        hidden_states = torch.cat([prompt_emb, hidden_states], dim=1)
+        for block in self.single_blocks:
+            if self.training and use_gradient_checkpointing:
+                hidden_states, prompt_emb = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(block),
+                    hidden_states, prompt_emb, conditioning, image_rotary_emb, attention_mask,
+                    use_reentrant=False,
+                )
+            else:
+                hidden_states, prompt_emb = block(hidden_states, prompt_emb, conditioning, image_rotary_emb, attention_mask)
+        hidden_states = hidden_states[:, prompt_emb.shape[1]:]
+
+        hidden_states = self.final_norm_out(hidden_states, conditioning)
+        hidden_states = self.final_proj_out(hidden_states)
+        hidden_states = self.unpatchify(hidden_states, height, width)
+
+        return hidden_states
+
+
+    def quantize(self):
+        def cast_to(weight, dtype=None, device=None, copy=False):
+            if device is None or weight.device == device:
+                if not copy:
+                    if dtype is None or weight.dtype == dtype:
+                        return weight
+                return weight.to(dtype=dtype, copy=copy)
+
+            r = torch.empty_like(weight, dtype=dtype, device=device)
+            r.copy_(weight)
+            return r
+
+        def cast_weight(s, input=None, dtype=None, device=None):
+            if input is not None:
+                if dtype is None:
+                    dtype = input.dtype
+                if device is None:
+                    device = input.device
+            weight = cast_to(s.weight, dtype, device)
+            return weight
+
+        def cast_bias_weight(s, input=None, dtype=None, device=None, bias_dtype=None):
+            if input is not None:
+                if dtype is None:
+                    dtype = input.dtype
+                if bias_dtype is None:
+                    bias_dtype = dtype
+                if device is None:
+                    device = input.device
+            bias = None
+            weight = cast_to(s.weight, dtype, device)
+            bias = cast_to(s.bias, bias_dtype, device)
+            return weight, bias
+
+        class quantized_layer:
+            class Linear(torch.nn.Linear):
+                def __init__(self, *args, **kwargs):
+                    super().__init__(*args, **kwargs)
+
+                def forward(self,input,**kwargs):
+                    weight,bias= cast_bias_weight(self,input)
+                    return torch.nn.functional.linear(input,weight,bias)
+
+            class RMSNorm(torch.nn.Module):
+                def __init__(self, module):
+                    super().__init__()
+                    self.module = module
+
+                def forward(self,hidden_states,**kwargs):
+                    weight= cast_weight(self.module,hidden_states)
+                    input_dtype = hidden_states.dtype
+                    variance = hidden_states.to(torch.float32).square().mean(-1, keepdim=True)
+                    hidden_states = hidden_states * torch.rsqrt(variance + self.module.eps)
+                    hidden_states = hidden_states.to(input_dtype) * weight
+                    return hidden_states
+
+        def replace_layer(model):
+            for name, module in model.named_children():
+                if isinstance(module, torch.nn.Linear):
+                    with init_weights_on_device():
+                        new_layer = quantized_layer.Linear(module.in_features,module.out_features)
+                    new_layer.weight = module.weight
+                    if module.bias is not None:
+                        new_layer.bias = module.bias
+                    # del module
+                    setattr(model, name, new_layer)
+                elif isinstance(module, RMSNorm):
+                    if hasattr(module,"quantized"):
+                        continue
+                    module.quantized= True
+                    new_layer = quantized_layer.RMSNorm(module)
+                    setattr(model, name, new_layer)
+                else:
+                    replace_layer(module)
+
+        replace_layer(self)
+
+
+    @staticmethod
+    def state_dict_converter():
+        return FluxDiTStateDictConverter()
+
+
+class FluxDiTStateDictConverter:
+    def __init__(self):
+        pass
+
+    def from_diffusers(self, state_dict):
+        global_rename_dict = {
+            "context_embedder": "context_embedder",
+            "x_embedder": "x_embedder",
+            "time_text_embed.timestep_embedder.linear_1": "time_embedder.timestep_embedder.0",
+            "time_text_embed.timestep_embedder.linear_2": "time_embedder.timestep_embedder.2",
+            "time_text_embed.guidance_embedder.linear_1": "guidance_embedder.timestep_embedder.0",
+            "time_text_embed.guidance_embedder.linear_2": "guidance_embedder.timestep_embedder.2",
+            "time_text_embed.text_embedder.linear_1": "pooled_text_embedder.0",
+            "time_text_embed.text_embedder.linear_2": "pooled_text_embedder.2",
+            "norm_out.linear": "final_norm_out.linear",
+            "proj_out": "final_proj_out",
+        }
+        rename_dict = {
+            "proj_out": "proj_out",
+            "norm1.linear": "norm1_a.linear",
+            "norm1_context.linear": "norm1_b.linear",
+            "attn.to_q": "attn.a_to_q",
+            "attn.to_k": "attn.a_to_k",
+            "attn.to_v": "attn.a_to_v",
+            "attn.to_out.0": "attn.a_to_out",
+            "attn.add_q_proj": "attn.b_to_q",
+            "attn.add_k_proj": "attn.b_to_k",
+            "attn.add_v_proj": "attn.b_to_v",
+            "attn.to_add_out": "attn.b_to_out",
+            "ff.net.0.proj": "ff_a.0",
+            "ff.net.2": "ff_a.2",
+            "ff_context.net.0.proj": "ff_b.0",
+            "ff_context.net.2": "ff_b.2",
+            "attn.norm_q": "attn.norm_q_a",
+            "attn.norm_k": "attn.norm_k_a",
+            "attn.norm_added_q": "attn.norm_q_b",
+            "attn.norm_added_k": "attn.norm_k_b",
+        }
+        rename_dict_single = {
+            "attn.to_q": "a_to_q",
+            "attn.to_k": "a_to_k",
+            "attn.to_v": "a_to_v",
+            "attn.norm_q": "norm_q_a",
+            "attn.norm_k": "norm_k_a",
+            "norm.linear": "norm.linear",
+            "proj_mlp": "proj_in_besides_attn",
+            "proj_out": "proj_out",
+        }
+        state_dict_ = {}
+        for name, param in state_dict.items():
+            if name.endswith(".weight") or name.endswith(".bias"):
+                suffix = ".weight" if name.endswith(".weight") else ".bias"
+                prefix = name[:-len(suffix)]
+                if prefix in global_rename_dict:
+                    state_dict_[global_rename_dict[prefix] + suffix] = param
+                elif prefix.startswith("transformer_blocks."):
+                    names = prefix.split(".")
+                    names[0] = "blocks"
+                    middle = ".".join(names[2:])
+                    if middle in rename_dict:
+                        name_ = ".".join(names[:2] + [rename_dict[middle]] + [suffix[1:]])
+                        state_dict_[name_] = param
+                elif prefix.startswith("single_transformer_blocks."):
+                    names = prefix.split(".")
+                    names[0] = "single_blocks"
+                    middle = ".".join(names[2:])
+                    if middle in rename_dict_single:
+                        name_ = ".".join(names[:2] + [rename_dict_single[middle]] + [suffix[1:]])
+                        state_dict_[name_] = param
+                    else:
+                        pass
+                else:
+                    pass
+        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:
+                    mlp = torch.zeros(4 * 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."))
+                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.")
+                    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_
+
+    def from_civitai(self, state_dict):
+        if hash_state_dict_keys(state_dict, with_shape=True) in ["3e6c61b0f9471135fc9c6d6a98e98b6d", "63c969fd37cce769a90aa781fbff5f81"]:
+            dit_state_dict = {key.replace("pipe.dit.", ""): value for key, value in state_dict.items() if key.startswith('pipe.dit.')}
+            return dit_state_dict
+        rename_dict = {
+            "time_in.in_layer.bias": "time_embedder.timestep_embedder.0.bias",
+            "time_in.in_layer.weight": "time_embedder.timestep_embedder.0.weight",
+            "time_in.out_layer.bias": "time_embedder.timestep_embedder.2.bias",
+            "time_in.out_layer.weight": "time_embedder.timestep_embedder.2.weight",
+            "txt_in.bias": "context_embedder.bias",
+            "txt_in.weight": "context_embedder.weight",
+            "vector_in.in_layer.bias": "pooled_text_embedder.0.bias",
+            "vector_in.in_layer.weight": "pooled_text_embedder.0.weight",
+            "vector_in.out_layer.bias": "pooled_text_embedder.2.bias",
+            "vector_in.out_layer.weight": "pooled_text_embedder.2.weight",
+            "final_layer.linear.bias": "final_proj_out.bias",
+            "final_layer.linear.weight": "final_proj_out.weight",
+            "guidance_in.in_layer.bias": "guidance_embedder.timestep_embedder.0.bias",
+            "guidance_in.in_layer.weight": "guidance_embedder.timestep_embedder.0.weight",
+            "guidance_in.out_layer.bias": "guidance_embedder.timestep_embedder.2.bias",
+            "guidance_in.out_layer.weight": "guidance_embedder.timestep_embedder.2.weight",
+            "img_in.bias": "x_embedder.bias",
+            "img_in.weight": "x_embedder.weight",
+            "final_layer.adaLN_modulation.1.weight": "final_norm_out.linear.weight",
+            "final_layer.adaLN_modulation.1.bias": "final_norm_out.linear.bias",
+        }
+        suffix_rename_dict = {
+            "img_attn.norm.key_norm.scale": "attn.norm_k_a.weight",
+            "img_attn.norm.query_norm.scale": "attn.norm_q_a.weight",
+            "img_attn.proj.bias": "attn.a_to_out.bias",
+            "img_attn.proj.weight": "attn.a_to_out.weight",
+            "img_attn.qkv.bias": "attn.a_to_qkv.bias",
+            "img_attn.qkv.weight": "attn.a_to_qkv.weight",
+            "img_mlp.0.bias": "ff_a.0.bias",
+            "img_mlp.0.weight": "ff_a.0.weight",
+            "img_mlp.2.bias": "ff_a.2.bias",
+            "img_mlp.2.weight": "ff_a.2.weight",
+            "img_mod.lin.bias": "norm1_a.linear.bias",
+            "img_mod.lin.weight": "norm1_a.linear.weight",
+            "txt_attn.norm.key_norm.scale": "attn.norm_k_b.weight",
+            "txt_attn.norm.query_norm.scale": "attn.norm_q_b.weight",
+            "txt_attn.proj.bias": "attn.b_to_out.bias",
+            "txt_attn.proj.weight": "attn.b_to_out.weight",
+            "txt_attn.qkv.bias": "attn.b_to_qkv.bias",
+            "txt_attn.qkv.weight": "attn.b_to_qkv.weight",
+            "txt_mlp.0.bias": "ff_b.0.bias",
+            "txt_mlp.0.weight": "ff_b.0.weight",
+            "txt_mlp.2.bias": "ff_b.2.bias",
+            "txt_mlp.2.weight": "ff_b.2.weight",
+            "txt_mod.lin.bias": "norm1_b.linear.bias",
+            "txt_mod.lin.weight": "norm1_b.linear.weight",
+
+            "linear1.bias": "to_qkv_mlp.bias",
+            "linear1.weight": "to_qkv_mlp.weight",
+            "linear2.bias": "proj_out.bias",
+            "linear2.weight": "proj_out.weight",
+            "modulation.lin.bias": "norm.linear.bias",
+            "modulation.lin.weight": "norm.linear.weight",
+            "norm.key_norm.scale": "norm_k_a.weight",
+            "norm.query_norm.scale": "norm_q_a.weight",
+        }
+        state_dict_ = {}
+        for name, param in state_dict.items():
+            if name.startswith("model.diffusion_model."):
+                name = name[len("model.diffusion_model."):]
+            names = name.split(".")
+            if name in rename_dict:
+                rename = rename_dict[name]
+                if name.startswith("final_layer.adaLN_modulation.1."):
+                    param = torch.concat([param[3072:], param[:3072]], dim=0)
+                state_dict_[rename] = param
+            elif names[0] == "double_blocks":
+                rename = f"blocks.{names[1]}." + suffix_rename_dict[".".join(names[2:])]
+                state_dict_[rename] = param
+            elif names[0] == "single_blocks":
+                if ".".join(names[2:]) in suffix_rename_dict:
+                    rename = f"single_blocks.{names[1]}." + suffix_rename_dict[".".join(names[2:])]
+                    state_dict_[rename] = param
+            else:
+                pass
+        if "guidance_embedder.timestep_embedder.0.weight" not in state_dict_:
+            return state_dict_, {"disable_guidance_embedder": True}
+        elif "blocks.8.attn.norm_k_a.weight" not in state_dict_:
+            return state_dict_, {"input_dim": 196, "num_blocks": 8}
+        else:
+            return state_dict_
diff --git a/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/flux_infiniteyou.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/flux_infiniteyou.py
new file mode 100644
index 0000000000000000000000000000000000000000..861538a4b02fb6a52edee662b6efcd60f78f6916
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/flux_infiniteyou.py
@@ -0,0 +1,129 @@
+import math
+import torch
+import torch.nn as nn
+
+
+# FFN
+def FeedForward(dim, mult=4):
+    inner_dim = int(dim * mult)
+    return nn.Sequential(
+        nn.LayerNorm(dim),
+        nn.Linear(dim, inner_dim, bias=False),
+        nn.GELU(),
+        nn.Linear(inner_dim, dim, bias=False),
+    )
+
+
+def reshape_tensor(x, heads):
+    bs, length, width = x.shape
+    #(bs, length, width) --> (bs, length, n_heads, dim_per_head)
+    x = x.view(bs, length, heads, -1)
+    # (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head)
+    x = x.transpose(1, 2)
+    # (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)
+    x = x.reshape(bs, heads, length, -1)
+    return x
+
+
+class PerceiverAttention(nn.Module):
+
+    def __init__(self, *, dim, dim_head=64, heads=8):
+        super().__init__()
+        self.scale = dim_head**-0.5
+        self.dim_head = dim_head
+        self.heads = heads
+        inner_dim = dim_head * heads
+
+        self.norm1 = nn.LayerNorm(dim)
+        self.norm2 = nn.LayerNorm(dim)
+
+        self.to_q = nn.Linear(dim, inner_dim, bias=False)
+        self.to_kv = nn.Linear(dim, inner_dim * 2, bias=False)
+        self.to_out = nn.Linear(inner_dim, dim, bias=False)
+
+    def forward(self, x, latents):
+        """
+        Args:
+            x (torch.Tensor): image features
+                shape (b, n1, D)
+            latent (torch.Tensor): latent features
+                shape (b, n2, D)
+        """
+        x = self.norm1(x)
+        latents = self.norm2(latents)
+
+        b, l, _ = latents.shape
+
+        q = self.to_q(latents)
+        kv_input = torch.cat((x, latents), dim=-2)
+        k, v = self.to_kv(kv_input).chunk(2, dim=-1)
+
+        q = reshape_tensor(q, self.heads)
+        k = reshape_tensor(k, self.heads)
+        v = reshape_tensor(v, self.heads)
+
+        # attention
+        scale = 1 / math.sqrt(math.sqrt(self.dim_head))
+        weight = (q * scale) @ (k * scale).transpose(-2, -1)  # More stable with f16 than dividing afterwards
+        weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype)
+        out = weight @ v
+
+        out = out.permute(0, 2, 1, 3).reshape(b, l, -1)
+
+        return self.to_out(out)
+
+
+class InfiniteYouImageProjector(nn.Module):
+
+    def __init__(
+        self,
+        dim=1280,
+        depth=4,
+        dim_head=64,
+        heads=20,
+        num_queries=8,
+        embedding_dim=512,
+        output_dim=4096,
+        ff_mult=4,
+    ):
+        super().__init__()
+        self.latents = nn.Parameter(torch.randn(1, num_queries, dim) / dim**0.5)
+        self.proj_in = nn.Linear(embedding_dim, dim)
+
+        self.proj_out = nn.Linear(dim, output_dim)
+        self.norm_out = nn.LayerNorm(output_dim)
+
+        self.layers = nn.ModuleList([])
+        for _ in range(depth):
+            self.layers.append(
+                nn.ModuleList([
+                    PerceiverAttention(dim=dim, dim_head=dim_head, heads=heads),
+                    FeedForward(dim=dim, mult=ff_mult),
+                ]))
+
+    def forward(self, x):
+
+        latents = self.latents.repeat(x.size(0), 1, 1)
+        latents = latents.to(dtype=x.dtype, device=x.device)
+
+        x = self.proj_in(x)
+
+        for attn, ff in self.layers:
+            latents = attn(x, latents) + latents
+            latents = ff(latents) + latents
+
+        latents = self.proj_out(latents)
+        return self.norm_out(latents)
+
+    @staticmethod
+    def state_dict_converter():
+        return FluxInfiniteYouImageProjectorStateDictConverter()
+
+
+class FluxInfiniteYouImageProjectorStateDictConverter:
+
+    def __init__(self):
+        pass
+
+    def from_diffusers(self, state_dict):
+        return state_dict['image_proj']
diff --git a/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/flux_ipadapter.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/flux_ipadapter.py
new file mode 100644
index 0000000000000000000000000000000000000000..575c75268c30f9c1d6e6b35d11b93bc57d80cb3f
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/flux_ipadapter.py
@@ -0,0 +1,94 @@
+from .svd_image_encoder import SVDImageEncoder
+from .sd3_dit import RMSNorm
+from transformers import CLIPImageProcessor
+import torch
+
+
+class MLPProjModel(torch.nn.Module):
+    def __init__(self, cross_attention_dim=768, id_embeddings_dim=512, num_tokens=4):
+        super().__init__()
+        
+        self.cross_attention_dim = cross_attention_dim
+        self.num_tokens = num_tokens
+        
+        self.proj = torch.nn.Sequential(
+            torch.nn.Linear(id_embeddings_dim, id_embeddings_dim*2),
+            torch.nn.GELU(),
+            torch.nn.Linear(id_embeddings_dim*2, cross_attention_dim*num_tokens),
+        )
+        self.norm = torch.nn.LayerNorm(cross_attention_dim)
+        
+    def forward(self, id_embeds):
+        x = self.proj(id_embeds)
+        x = x.reshape(-1, self.num_tokens, self.cross_attention_dim)
+        x = self.norm(x)
+        return x
+
+class IpAdapterModule(torch.nn.Module):
+    def __init__(self, num_attention_heads, attention_head_dim, input_dim):
+        super().__init__()
+        self.num_heads = num_attention_heads
+        self.head_dim = attention_head_dim
+        output_dim = num_attention_heads * attention_head_dim
+        self.to_k_ip = torch.nn.Linear(input_dim, output_dim, bias=False)
+        self.to_v_ip = torch.nn.Linear(input_dim, output_dim, bias=False)
+        self.norm_added_k = RMSNorm(attention_head_dim, eps=1e-5, elementwise_affine=False)
+        
+
+    def forward(self, hidden_states):
+        batch_size = hidden_states.shape[0]
+        # ip_k
+        ip_k = self.to_k_ip(hidden_states)
+        ip_k = ip_k.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2)
+        ip_k = self.norm_added_k(ip_k)
+        # ip_v
+        ip_v = self.to_v_ip(hidden_states)
+        ip_v = ip_v.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2)
+        return ip_k, ip_v
+
+
+class FluxIpAdapter(torch.nn.Module):
+    def __init__(self, num_attention_heads=24, attention_head_dim=128, cross_attention_dim=4096, num_tokens=128, num_blocks=57):
+        super().__init__()
+        self.ipadapter_modules = torch.nn.ModuleList([IpAdapterModule(num_attention_heads, attention_head_dim, cross_attention_dim) for _ in range(num_blocks)])
+        self.image_proj = MLPProjModel(cross_attention_dim=cross_attention_dim, id_embeddings_dim=1152, num_tokens=num_tokens)
+        self.set_adapter()
+
+    def set_adapter(self):
+        self.call_block_id = {i:i for i in range(len(self.ipadapter_modules))}
+
+    def forward(self, hidden_states, scale=1.0):
+        hidden_states = self.image_proj(hidden_states)
+        hidden_states = hidden_states.view(1, -1, hidden_states.shape[-1])
+        ip_kv_dict = {}
+        for block_id in self.call_block_id:
+            ipadapter_id = self.call_block_id[block_id]
+            ip_k, ip_v = self.ipadapter_modules[ipadapter_id](hidden_states)
+            ip_kv_dict[block_id] = {
+                "ip_k": ip_k,
+                "ip_v": ip_v,
+                "scale": scale
+            }
+        return ip_kv_dict
+
+    @staticmethod
+    def state_dict_converter():
+        return FluxIpAdapterStateDictConverter()
+
+
+class FluxIpAdapterStateDictConverter:
+    def __init__(self):
+        pass
+
+    def from_diffusers(self, state_dict):
+        state_dict_ = {}
+        for name in state_dict["ip_adapter"]:
+            name_ = 'ipadapter_modules.' + name
+            state_dict_[name_] = state_dict["ip_adapter"][name]
+        for name in state_dict["image_proj"]:
+            name_ = "image_proj." + name
+            state_dict_[name_] = state_dict["image_proj"][name]
+        return state_dict_
+    
+    def from_civitai(self, state_dict):
+        return self.from_diffusers(state_dict)
diff --git a/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/flux_lora_encoder.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/flux_lora_encoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..695640a8c8690f7f20f6942615c400849e9298a8
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/flux_lora_encoder.py
@@ -0,0 +1,111 @@
+import torch
+from .sd_text_encoder import CLIPEncoderLayer
+
+
+class LoRALayerBlock(torch.nn.Module):
+    def __init__(self, L, dim_in, dim_out):
+        super().__init__()
+        self.x = torch.nn.Parameter(torch.randn(1, L, dim_in))
+        self.layer_norm = torch.nn.LayerNorm(dim_out)
+
+    def forward(self, lora_A, lora_B):
+        x = self.x @ lora_A.T @ lora_B.T
+        x = self.layer_norm(x)
+        return x
+    
+
+class LoRAEmbedder(torch.nn.Module):
+    def __init__(self, lora_patterns=None, L=1, out_dim=2048):
+        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(".", "___")] = LoRALayerBlock(L, dim[0], dim[1])
+        self.model_dict = torch.nn.ModuleDict(model_dict)
+        
+        proj_dict = {}
+        for lora_pattern in lora_patterns:
+            layer_type, dim = lora_pattern["type"], lora_pattern["dim"]
+            if layer_type not in proj_dict:
+                proj_dict[layer_type.replace(".", "___")] = torch.nn.Linear(dim[1], out_dim)
+        self.proj_dict = torch.nn.ModuleDict(proj_dict)
+        
+        self.lora_patterns = lora_patterns
+        
+        
+    def default_lora_patterns(self):
+        lora_patterns = []
+        lora_dict = {
+            "attn.a_to_qkv": (3072, 9216), "attn.a_to_out": (3072, 3072), "ff_a.0": (3072, 12288), "ff_a.2": (12288, 3072), "norm1_a.linear": (3072, 18432),
+            "attn.b_to_qkv": (3072, 9216), "attn.b_to_out": (3072, 3072), "ff_b.0": (3072, 12288), "ff_b.2": (12288, 3072), "norm1_b.linear": (3072, 18432),
+        }
+        for i in range(19):
+            for suffix in lora_dict:
+                lora_patterns.append({
+                    "name": f"blocks.{i}.{suffix}",
+                    "dim": lora_dict[suffix],
+                    "type": suffix,
+                })
+        lora_dict = {"to_qkv_mlp": (3072, 21504), "proj_out": (15360, 3072), "norm.linear": (3072, 9216)}
+        for i in range(38):
+            for suffix in lora_dict:
+                lora_patterns.append({
+                    "name": f"single_blocks.{i}.{suffix}",
+                    "dim": lora_dict[suffix],
+                    "type": suffix,
+                })
+        return lora_patterns
+        
+    def forward(self, lora):
+        lora_emb = []
+        for lora_pattern in self.lora_patterns:
+            name, layer_type = lora_pattern["name"], lora_pattern["type"]
+            lora_A = lora[name + ".lora_A.default.weight"]
+            lora_B = lora[name + ".lora_B.default.weight"]
+            lora_out = self.model_dict[name.replace(".", "___")](lora_A, lora_B)
+            lora_out = self.proj_dict[layer_type.replace(".", "___")](lora_out)
+            lora_emb.append(lora_out)
+        lora_emb = torch.concat(lora_emb, dim=1)
+        return lora_emb
+    
+    
+class FluxLoRAEncoder(torch.nn.Module):
+    def __init__(self, embed_dim=4096, encoder_intermediate_size=8192, num_encoder_layers=1, num_embeds_per_lora=16, num_special_embeds=1):
+        super().__init__()
+        self.num_embeds_per_lora = num_embeds_per_lora
+        # embedder
+        self.embedder = LoRAEmbedder(L=num_embeds_per_lora, out_dim=embed_dim)
+        
+        # encoders
+        self.encoders = torch.nn.ModuleList([CLIPEncoderLayer(embed_dim, encoder_intermediate_size, num_heads=32, head_dim=128) for _ in range(num_encoder_layers)])
+
+        # special embedding
+        self.special_embeds = torch.nn.Parameter(torch.randn(1, num_special_embeds, embed_dim))
+        self.num_special_embeds = num_special_embeds
+        
+        # final layer
+        self.final_layer_norm = torch.nn.LayerNorm(embed_dim)
+        self.final_linear = torch.nn.Linear(embed_dim, embed_dim)
+
+    def forward(self, lora):
+        lora_embeds = self.embedder(lora)
+        special_embeds = self.special_embeds.to(dtype=lora_embeds.dtype, device=lora_embeds.device)
+        embeds = torch.concat([special_embeds, lora_embeds], dim=1)
+        for encoder_id, encoder in enumerate(self.encoders):
+            embeds = encoder(embeds)
+        embeds = embeds[:, :self.num_special_embeds]
+        embeds = self.final_layer_norm(embeds)
+        embeds = self.final_linear(embeds)
+        return embeds
+    
+    @staticmethod
+    def state_dict_converter():
+        return FluxLoRAEncoderStateDictConverter()
+
+
+class FluxLoRAEncoderStateDictConverter:
+    def from_civitai(self, state_dict):
+        return state_dict
diff --git a/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/flux_text_encoder.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/flux_text_encoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..bff9d2944e50e42ef9136269b3e0b1c1ea508a79
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/flux_text_encoder.py
@@ -0,0 +1,32 @@
+import torch
+from transformers import T5EncoderModel, T5Config
+from .sd_text_encoder import SDTextEncoder
+
+
+
+class FluxTextEncoder2(T5EncoderModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.eval()
+
+    def forward(self, input_ids):
+        outputs = super().forward(input_ids=input_ids)
+        prompt_emb = outputs.last_hidden_state
+        return prompt_emb
+
+    @staticmethod
+    def state_dict_converter():
+        return FluxTextEncoder2StateDictConverter()
+
+
+
+class FluxTextEncoder2StateDictConverter():
+    def __init__(self):
+        pass
+
+    def from_diffusers(self, state_dict):
+        state_dict_ = state_dict
+        return state_dict_
+
+    def from_civitai(self, state_dict):
+        return self.from_diffusers(state_dict)
diff --git a/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/flux_vae.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/flux_vae.py
new file mode 100644
index 0000000000000000000000000000000000000000..c99c65522c4d0339adcc9aa734445377b9fdd5b5
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/flux_vae.py
@@ -0,0 +1,303 @@
+from .sd3_vae_encoder import SD3VAEEncoder, SDVAEEncoderStateDictConverter
+from .sd3_vae_decoder import SD3VAEDecoder, SDVAEDecoderStateDictConverter
+
+
+class FluxVAEEncoder(SD3VAEEncoder):
+    def __init__(self):
+        super().__init__()
+        self.scaling_factor = 0.3611
+        self.shift_factor = 0.1159
+
+    @staticmethod
+    def state_dict_converter():
+        return FluxVAEEncoderStateDictConverter()
+
+
+class FluxVAEDecoder(SD3VAEDecoder):
+    def __init__(self):
+        super().__init__()
+        self.scaling_factor = 0.3611
+        self.shift_factor = 0.1159
+    
+    @staticmethod
+    def state_dict_converter():
+        return FluxVAEDecoderStateDictConverter()
+
+
+class FluxVAEEncoderStateDictConverter(SDVAEEncoderStateDictConverter):
+    def __init__(self):
+        pass
+
+    def from_civitai(self, state_dict):
+        rename_dict = {
+            "encoder.conv_in.bias": "conv_in.bias",
+            "encoder.conv_in.weight": "conv_in.weight",
+            "encoder.conv_out.bias": "conv_out.bias",
+            "encoder.conv_out.weight": "conv_out.weight",
+            "encoder.down.0.block.0.conv1.bias": "blocks.0.conv1.bias",
+            "encoder.down.0.block.0.conv1.weight": "blocks.0.conv1.weight",
+            "encoder.down.0.block.0.conv2.bias": "blocks.0.conv2.bias",
+            "encoder.down.0.block.0.conv2.weight": "blocks.0.conv2.weight",
+            "encoder.down.0.block.0.norm1.bias": "blocks.0.norm1.bias",
+            "encoder.down.0.block.0.norm1.weight": "blocks.0.norm1.weight",
+            "encoder.down.0.block.0.norm2.bias": "blocks.0.norm2.bias",
+            "encoder.down.0.block.0.norm2.weight": "blocks.0.norm2.weight",
+            "encoder.down.0.block.1.conv1.bias": "blocks.1.conv1.bias",
+            "encoder.down.0.block.1.conv1.weight": "blocks.1.conv1.weight",
+            "encoder.down.0.block.1.conv2.bias": "blocks.1.conv2.bias",
+            "encoder.down.0.block.1.conv2.weight": "blocks.1.conv2.weight",
+            "encoder.down.0.block.1.norm1.bias": "blocks.1.norm1.bias",
+            "encoder.down.0.block.1.norm1.weight": "blocks.1.norm1.weight",
+            "encoder.down.0.block.1.norm2.bias": "blocks.1.norm2.bias",
+            "encoder.down.0.block.1.norm2.weight": "blocks.1.norm2.weight",
+            "encoder.down.0.downsample.conv.bias": "blocks.2.conv.bias",
+            "encoder.down.0.downsample.conv.weight": "blocks.2.conv.weight",
+            "encoder.down.1.block.0.conv1.bias": "blocks.3.conv1.bias",
+            "encoder.down.1.block.0.conv1.weight": "blocks.3.conv1.weight",
+            "encoder.down.1.block.0.conv2.bias": "blocks.3.conv2.bias",
+            "encoder.down.1.block.0.conv2.weight": "blocks.3.conv2.weight",
+            "encoder.down.1.block.0.nin_shortcut.bias": "blocks.3.conv_shortcut.bias",
+            "encoder.down.1.block.0.nin_shortcut.weight": "blocks.3.conv_shortcut.weight",
+            "encoder.down.1.block.0.norm1.bias": "blocks.3.norm1.bias",
+            "encoder.down.1.block.0.norm1.weight": "blocks.3.norm1.weight",
+            "encoder.down.1.block.0.norm2.bias": "blocks.3.norm2.bias",
+            "encoder.down.1.block.0.norm2.weight": "blocks.3.norm2.weight",
+            "encoder.down.1.block.1.conv1.bias": "blocks.4.conv1.bias",
+            "encoder.down.1.block.1.conv1.weight": "blocks.4.conv1.weight",
+            "encoder.down.1.block.1.conv2.bias": "blocks.4.conv2.bias",
+            "encoder.down.1.block.1.conv2.weight": "blocks.4.conv2.weight",
+            "encoder.down.1.block.1.norm1.bias": "blocks.4.norm1.bias",
+            "encoder.down.1.block.1.norm1.weight": "blocks.4.norm1.weight",
+            "encoder.down.1.block.1.norm2.bias": "blocks.4.norm2.bias",
+            "encoder.down.1.block.1.norm2.weight": "blocks.4.norm2.weight",
+            "encoder.down.1.downsample.conv.bias": "blocks.5.conv.bias",
+            "encoder.down.1.downsample.conv.weight": "blocks.5.conv.weight",
+            "encoder.down.2.block.0.conv1.bias": "blocks.6.conv1.bias",
+            "encoder.down.2.block.0.conv1.weight": "blocks.6.conv1.weight",
+            "encoder.down.2.block.0.conv2.bias": "blocks.6.conv2.bias",
+            "encoder.down.2.block.0.conv2.weight": "blocks.6.conv2.weight",
+            "encoder.down.2.block.0.nin_shortcut.bias": "blocks.6.conv_shortcut.bias",
+            "encoder.down.2.block.0.nin_shortcut.weight": "blocks.6.conv_shortcut.weight",
+            "encoder.down.2.block.0.norm1.bias": "blocks.6.norm1.bias",
+            "encoder.down.2.block.0.norm1.weight": "blocks.6.norm1.weight",
+            "encoder.down.2.block.0.norm2.bias": "blocks.6.norm2.bias",
+            "encoder.down.2.block.0.norm2.weight": "blocks.6.norm2.weight",
+            "encoder.down.2.block.1.conv1.bias": "blocks.7.conv1.bias",
+            "encoder.down.2.block.1.conv1.weight": "blocks.7.conv1.weight",
+            "encoder.down.2.block.1.conv2.bias": "blocks.7.conv2.bias",
+            "encoder.down.2.block.1.conv2.weight": "blocks.7.conv2.weight",
+            "encoder.down.2.block.1.norm1.bias": "blocks.7.norm1.bias",
+            "encoder.down.2.block.1.norm1.weight": "blocks.7.norm1.weight",
+            "encoder.down.2.block.1.norm2.bias": "blocks.7.norm2.bias",
+            "encoder.down.2.block.1.norm2.weight": "blocks.7.norm2.weight",
+            "encoder.down.2.downsample.conv.bias": "blocks.8.conv.bias",
+            "encoder.down.2.downsample.conv.weight": "blocks.8.conv.weight",
+            "encoder.down.3.block.0.conv1.bias": "blocks.9.conv1.bias",
+            "encoder.down.3.block.0.conv1.weight": "blocks.9.conv1.weight",
+            "encoder.down.3.block.0.conv2.bias": "blocks.9.conv2.bias",
+            "encoder.down.3.block.0.conv2.weight": "blocks.9.conv2.weight",
+            "encoder.down.3.block.0.norm1.bias": "blocks.9.norm1.bias",
+            "encoder.down.3.block.0.norm1.weight": "blocks.9.norm1.weight",
+            "encoder.down.3.block.0.norm2.bias": "blocks.9.norm2.bias",
+            "encoder.down.3.block.0.norm2.weight": "blocks.9.norm2.weight",
+            "encoder.down.3.block.1.conv1.bias": "blocks.10.conv1.bias",
+            "encoder.down.3.block.1.conv1.weight": "blocks.10.conv1.weight",
+            "encoder.down.3.block.1.conv2.bias": "blocks.10.conv2.bias",
+            "encoder.down.3.block.1.conv2.weight": "blocks.10.conv2.weight",
+            "encoder.down.3.block.1.norm1.bias": "blocks.10.norm1.bias",
+            "encoder.down.3.block.1.norm1.weight": "blocks.10.norm1.weight",
+            "encoder.down.3.block.1.norm2.bias": "blocks.10.norm2.bias",
+            "encoder.down.3.block.1.norm2.weight": "blocks.10.norm2.weight",
+            "encoder.mid.attn_1.k.bias": "blocks.12.transformer_blocks.0.to_k.bias",
+            "encoder.mid.attn_1.k.weight": "blocks.12.transformer_blocks.0.to_k.weight",
+            "encoder.mid.attn_1.norm.bias": "blocks.12.norm.bias",
+            "encoder.mid.attn_1.norm.weight": "blocks.12.norm.weight",
+            "encoder.mid.attn_1.proj_out.bias": "blocks.12.transformer_blocks.0.to_out.bias",
+            "encoder.mid.attn_1.proj_out.weight": "blocks.12.transformer_blocks.0.to_out.weight",
+            "encoder.mid.attn_1.q.bias": "blocks.12.transformer_blocks.0.to_q.bias",
+            "encoder.mid.attn_1.q.weight": "blocks.12.transformer_blocks.0.to_q.weight",
+            "encoder.mid.attn_1.v.bias": "blocks.12.transformer_blocks.0.to_v.bias",
+            "encoder.mid.attn_1.v.weight": "blocks.12.transformer_blocks.0.to_v.weight",
+            "encoder.mid.block_1.conv1.bias": "blocks.11.conv1.bias",
+            "encoder.mid.block_1.conv1.weight": "blocks.11.conv1.weight",
+            "encoder.mid.block_1.conv2.bias": "blocks.11.conv2.bias",
+            "encoder.mid.block_1.conv2.weight": "blocks.11.conv2.weight",
+            "encoder.mid.block_1.norm1.bias": "blocks.11.norm1.bias",
+            "encoder.mid.block_1.norm1.weight": "blocks.11.norm1.weight",
+            "encoder.mid.block_1.norm2.bias": "blocks.11.norm2.bias",
+            "encoder.mid.block_1.norm2.weight": "blocks.11.norm2.weight",
+            "encoder.mid.block_2.conv1.bias": "blocks.13.conv1.bias",
+            "encoder.mid.block_2.conv1.weight": "blocks.13.conv1.weight",
+            "encoder.mid.block_2.conv2.bias": "blocks.13.conv2.bias",
+            "encoder.mid.block_2.conv2.weight": "blocks.13.conv2.weight",
+            "encoder.mid.block_2.norm1.bias": "blocks.13.norm1.bias",
+            "encoder.mid.block_2.norm1.weight": "blocks.13.norm1.weight",
+            "encoder.mid.block_2.norm2.bias": "blocks.13.norm2.bias",
+            "encoder.mid.block_2.norm2.weight": "blocks.13.norm2.weight",
+            "encoder.norm_out.bias": "conv_norm_out.bias",
+            "encoder.norm_out.weight": "conv_norm_out.weight",
+        }
+        state_dict_ = {}
+        for name in state_dict:
+            if name in rename_dict:
+                param = state_dict[name]
+                if "transformer_blocks" in rename_dict[name]:
+                    param = param.squeeze()
+                state_dict_[rename_dict[name]] = param
+        return state_dict_
+    
+
+
+class FluxVAEDecoderStateDictConverter(SDVAEDecoderStateDictConverter):
+    def __init__(self):
+        pass
+
+    def from_civitai(self, state_dict):
+        rename_dict = {
+            "decoder.conv_in.bias": "conv_in.bias",
+            "decoder.conv_in.weight": "conv_in.weight",
+            "decoder.conv_out.bias": "conv_out.bias",
+            "decoder.conv_out.weight": "conv_out.weight",
+            "decoder.mid.attn_1.k.bias": "blocks.1.transformer_blocks.0.to_k.bias",
+            "decoder.mid.attn_1.k.weight": "blocks.1.transformer_blocks.0.to_k.weight",
+            "decoder.mid.attn_1.norm.bias": "blocks.1.norm.bias",
+            "decoder.mid.attn_1.norm.weight": "blocks.1.norm.weight",
+            "decoder.mid.attn_1.proj_out.bias": "blocks.1.transformer_blocks.0.to_out.bias",
+            "decoder.mid.attn_1.proj_out.weight": "blocks.1.transformer_blocks.0.to_out.weight",
+            "decoder.mid.attn_1.q.bias": "blocks.1.transformer_blocks.0.to_q.bias",
+            "decoder.mid.attn_1.q.weight": "blocks.1.transformer_blocks.0.to_q.weight",
+            "decoder.mid.attn_1.v.bias": "blocks.1.transformer_blocks.0.to_v.bias",
+            "decoder.mid.attn_1.v.weight": "blocks.1.transformer_blocks.0.to_v.weight",
+            "decoder.mid.block_1.conv1.bias": "blocks.0.conv1.bias",
+            "decoder.mid.block_1.conv1.weight": "blocks.0.conv1.weight",
+            "decoder.mid.block_1.conv2.bias": "blocks.0.conv2.bias",
+            "decoder.mid.block_1.conv2.weight": "blocks.0.conv2.weight",
+            "decoder.mid.block_1.norm1.bias": "blocks.0.norm1.bias",
+            "decoder.mid.block_1.norm1.weight": "blocks.0.norm1.weight",
+            "decoder.mid.block_1.norm2.bias": "blocks.0.norm2.bias",
+            "decoder.mid.block_1.norm2.weight": "blocks.0.norm2.weight",
+            "decoder.mid.block_2.conv1.bias": "blocks.2.conv1.bias",
+            "decoder.mid.block_2.conv1.weight": "blocks.2.conv1.weight",
+            "decoder.mid.block_2.conv2.bias": "blocks.2.conv2.bias",
+            "decoder.mid.block_2.conv2.weight": "blocks.2.conv2.weight",
+            "decoder.mid.block_2.norm1.bias": "blocks.2.norm1.bias",
+            "decoder.mid.block_2.norm1.weight": "blocks.2.norm1.weight",
+            "decoder.mid.block_2.norm2.bias": "blocks.2.norm2.bias",
+            "decoder.mid.block_2.norm2.weight": "blocks.2.norm2.weight",
+            "decoder.norm_out.bias": "conv_norm_out.bias",
+            "decoder.norm_out.weight": "conv_norm_out.weight",
+            "decoder.up.0.block.0.conv1.bias": "blocks.15.conv1.bias",
+            "decoder.up.0.block.0.conv1.weight": "blocks.15.conv1.weight",
+            "decoder.up.0.block.0.conv2.bias": "blocks.15.conv2.bias",
+            "decoder.up.0.block.0.conv2.weight": "blocks.15.conv2.weight",
+            "decoder.up.0.block.0.nin_shortcut.bias": "blocks.15.conv_shortcut.bias",
+            "decoder.up.0.block.0.nin_shortcut.weight": "blocks.15.conv_shortcut.weight",
+            "decoder.up.0.block.0.norm1.bias": "blocks.15.norm1.bias",
+            "decoder.up.0.block.0.norm1.weight": "blocks.15.norm1.weight",
+            "decoder.up.0.block.0.norm2.bias": "blocks.15.norm2.bias",
+            "decoder.up.0.block.0.norm2.weight": "blocks.15.norm2.weight",
+            "decoder.up.0.block.1.conv1.bias": "blocks.16.conv1.bias",
+            "decoder.up.0.block.1.conv1.weight": "blocks.16.conv1.weight",
+            "decoder.up.0.block.1.conv2.bias": "blocks.16.conv2.bias",
+            "decoder.up.0.block.1.conv2.weight": "blocks.16.conv2.weight",
+            "decoder.up.0.block.1.norm1.bias": "blocks.16.norm1.bias",
+            "decoder.up.0.block.1.norm1.weight": "blocks.16.norm1.weight",
+            "decoder.up.0.block.1.norm2.bias": "blocks.16.norm2.bias",
+            "decoder.up.0.block.1.norm2.weight": "blocks.16.norm2.weight",
+            "decoder.up.0.block.2.conv1.bias": "blocks.17.conv1.bias",
+            "decoder.up.0.block.2.conv1.weight": "blocks.17.conv1.weight",
+            "decoder.up.0.block.2.conv2.bias": "blocks.17.conv2.bias",
+            "decoder.up.0.block.2.conv2.weight": "blocks.17.conv2.weight",
+            "decoder.up.0.block.2.norm1.bias": "blocks.17.norm1.bias",
+            "decoder.up.0.block.2.norm1.weight": "blocks.17.norm1.weight",
+            "decoder.up.0.block.2.norm2.bias": "blocks.17.norm2.bias",
+            "decoder.up.0.block.2.norm2.weight": "blocks.17.norm2.weight",
+            "decoder.up.1.block.0.conv1.bias": "blocks.11.conv1.bias",
+            "decoder.up.1.block.0.conv1.weight": "blocks.11.conv1.weight",
+            "decoder.up.1.block.0.conv2.bias": "blocks.11.conv2.bias",
+            "decoder.up.1.block.0.conv2.weight": "blocks.11.conv2.weight",
+            "decoder.up.1.block.0.nin_shortcut.bias": "blocks.11.conv_shortcut.bias",
+            "decoder.up.1.block.0.nin_shortcut.weight": "blocks.11.conv_shortcut.weight",
+            "decoder.up.1.block.0.norm1.bias": "blocks.11.norm1.bias",
+            "decoder.up.1.block.0.norm1.weight": "blocks.11.norm1.weight",
+            "decoder.up.1.block.0.norm2.bias": "blocks.11.norm2.bias",
+            "decoder.up.1.block.0.norm2.weight": "blocks.11.norm2.weight",
+            "decoder.up.1.block.1.conv1.bias": "blocks.12.conv1.bias",
+            "decoder.up.1.block.1.conv1.weight": "blocks.12.conv1.weight",
+            "decoder.up.1.block.1.conv2.bias": "blocks.12.conv2.bias",
+            "decoder.up.1.block.1.conv2.weight": "blocks.12.conv2.weight",
+            "decoder.up.1.block.1.norm1.bias": "blocks.12.norm1.bias",
+            "decoder.up.1.block.1.norm1.weight": "blocks.12.norm1.weight",
+            "decoder.up.1.block.1.norm2.bias": "blocks.12.norm2.bias",
+            "decoder.up.1.block.1.norm2.weight": "blocks.12.norm2.weight",
+            "decoder.up.1.block.2.conv1.bias": "blocks.13.conv1.bias",
+            "decoder.up.1.block.2.conv1.weight": "blocks.13.conv1.weight",
+            "decoder.up.1.block.2.conv2.bias": "blocks.13.conv2.bias",
+            "decoder.up.1.block.2.conv2.weight": "blocks.13.conv2.weight",
+            "decoder.up.1.block.2.norm1.bias": "blocks.13.norm1.bias",
+            "decoder.up.1.block.2.norm1.weight": "blocks.13.norm1.weight",
+            "decoder.up.1.block.2.norm2.bias": "blocks.13.norm2.bias",
+            "decoder.up.1.block.2.norm2.weight": "blocks.13.norm2.weight",
+            "decoder.up.1.upsample.conv.bias": "blocks.14.conv.bias",
+            "decoder.up.1.upsample.conv.weight": "blocks.14.conv.weight",
+            "decoder.up.2.block.0.conv1.bias": "blocks.7.conv1.bias",
+            "decoder.up.2.block.0.conv1.weight": "blocks.7.conv1.weight",
+            "decoder.up.2.block.0.conv2.bias": "blocks.7.conv2.bias",
+            "decoder.up.2.block.0.conv2.weight": "blocks.7.conv2.weight",
+            "decoder.up.2.block.0.norm1.bias": "blocks.7.norm1.bias",
+            "decoder.up.2.block.0.norm1.weight": "blocks.7.norm1.weight",
+            "decoder.up.2.block.0.norm2.bias": "blocks.7.norm2.bias",
+            "decoder.up.2.block.0.norm2.weight": "blocks.7.norm2.weight",
+            "decoder.up.2.block.1.conv1.bias": "blocks.8.conv1.bias",
+            "decoder.up.2.block.1.conv1.weight": "blocks.8.conv1.weight",
+            "decoder.up.2.block.1.conv2.bias": "blocks.8.conv2.bias",
+            "decoder.up.2.block.1.conv2.weight": "blocks.8.conv2.weight",
+            "decoder.up.2.block.1.norm1.bias": "blocks.8.norm1.bias",
+            "decoder.up.2.block.1.norm1.weight": "blocks.8.norm1.weight",
+            "decoder.up.2.block.1.norm2.bias": "blocks.8.norm2.bias",
+            "decoder.up.2.block.1.norm2.weight": "blocks.8.norm2.weight",
+            "decoder.up.2.block.2.conv1.bias": "blocks.9.conv1.bias",
+            "decoder.up.2.block.2.conv1.weight": "blocks.9.conv1.weight",
+            "decoder.up.2.block.2.conv2.bias": "blocks.9.conv2.bias",
+            "decoder.up.2.block.2.conv2.weight": "blocks.9.conv2.weight",
+            "decoder.up.2.block.2.norm1.bias": "blocks.9.norm1.bias",
+            "decoder.up.2.block.2.norm1.weight": "blocks.9.norm1.weight",
+            "decoder.up.2.block.2.norm2.bias": "blocks.9.norm2.bias",
+            "decoder.up.2.block.2.norm2.weight": "blocks.9.norm2.weight",
+            "decoder.up.2.upsample.conv.bias": "blocks.10.conv.bias",
+            "decoder.up.2.upsample.conv.weight": "blocks.10.conv.weight",
+            "decoder.up.3.block.0.conv1.bias": "blocks.3.conv1.bias",
+            "decoder.up.3.block.0.conv1.weight": "blocks.3.conv1.weight",
+            "decoder.up.3.block.0.conv2.bias": "blocks.3.conv2.bias",
+            "decoder.up.3.block.0.conv2.weight": "blocks.3.conv2.weight",
+            "decoder.up.3.block.0.norm1.bias": "blocks.3.norm1.bias",
+            "decoder.up.3.block.0.norm1.weight": "blocks.3.norm1.weight",
+            "decoder.up.3.block.0.norm2.bias": "blocks.3.norm2.bias",
+            "decoder.up.3.block.0.norm2.weight": "blocks.3.norm2.weight",
+            "decoder.up.3.block.1.conv1.bias": "blocks.4.conv1.bias",
+            "decoder.up.3.block.1.conv1.weight": "blocks.4.conv1.weight",
+            "decoder.up.3.block.1.conv2.bias": "blocks.4.conv2.bias",
+            "decoder.up.3.block.1.conv2.weight": "blocks.4.conv2.weight",
+            "decoder.up.3.block.1.norm1.bias": "blocks.4.norm1.bias",
+            "decoder.up.3.block.1.norm1.weight": "blocks.4.norm1.weight",
+            "decoder.up.3.block.1.norm2.bias": "blocks.4.norm2.bias",
+            "decoder.up.3.block.1.norm2.weight": "blocks.4.norm2.weight",
+            "decoder.up.3.block.2.conv1.bias": "blocks.5.conv1.bias",
+            "decoder.up.3.block.2.conv1.weight": "blocks.5.conv1.weight",
+            "decoder.up.3.block.2.conv2.bias": "blocks.5.conv2.bias",
+            "decoder.up.3.block.2.conv2.weight": "blocks.5.conv2.weight",
+            "decoder.up.3.block.2.norm1.bias": "blocks.5.norm1.bias",
+            "decoder.up.3.block.2.norm1.weight": "blocks.5.norm1.weight",
+            "decoder.up.3.block.2.norm2.bias": "blocks.5.norm2.bias",
+            "decoder.up.3.block.2.norm2.weight": "blocks.5.norm2.weight",
+            "decoder.up.3.upsample.conv.bias": "blocks.6.conv.bias",
+            "decoder.up.3.upsample.conv.weight": "blocks.6.conv.weight",
+        }
+        state_dict_ = {}
+        for name in state_dict:
+            if name in rename_dict:
+                param = state_dict[name]
+                if "transformer_blocks" in rename_dict[name]:
+                    param = param.squeeze()
+                state_dict_[rename_dict[name]] = param
+        return state_dict_
\ No newline at end of file
diff --git a/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/flux_value_control.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/flux_value_control.py
new file mode 100644
index 0000000000000000000000000000000000000000..69813442269350b8b48861cd938ba95cce5a0f74
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/flux_value_control.py
@@ -0,0 +1,60 @@
+import torch
+from diffsynth.models.svd_unet import TemporalTimesteps
+
+
+class MultiValueEncoder(torch.nn.Module):
+    def __init__(self, encoders=()):
+        super().__init__()
+        self.encoders = torch.nn.ModuleList(encoders)
+
+    def __call__(self, values, dtype):
+        emb = []
+        for encoder, value in zip(self.encoders, values):
+            if value is not None:
+                value = value.unsqueeze(0)
+                emb.append(encoder(value, dtype))
+        emb = torch.concat(emb, dim=0)
+        return emb
+
+
+class SingleValueEncoder(torch.nn.Module):
+    def __init__(self, dim_in=256, dim_out=4096, prefer_len=32, computation_device=None):
+        super().__init__()
+        self.prefer_len = prefer_len
+        self.prefer_proj = TemporalTimesteps(num_channels=dim_in, flip_sin_to_cos=True, downscale_freq_shift=0, computation_device=computation_device)
+        self.prefer_value_embedder = torch.nn.Sequential(
+            torch.nn.Linear(dim_in, dim_out), torch.nn.SiLU(), torch.nn.Linear(dim_out, dim_out)
+        )
+        self.positional_embedding = torch.nn.Parameter(
+            torch.randn(self.prefer_len, dim_out) 
+        )
+        self._initialize_weights()
+
+    def _initialize_weights(self):
+        last_linear = self.prefer_value_embedder[-1]
+        torch.nn.init.zeros_(last_linear.weight)
+        torch.nn.init.zeros_(last_linear.bias)
+
+    def forward(self, value, dtype):
+        value = value * 1000
+        emb = self.prefer_proj(value).to(dtype)
+        emb = self.prefer_value_embedder(emb).squeeze(0)
+        base_embeddings = emb.expand(self.prefer_len, -1)
+        positional_embedding = self.positional_embedding.to(dtype=base_embeddings.dtype, device=base_embeddings.device)
+        learned_embeddings = base_embeddings + positional_embedding
+        return learned_embeddings
+
+    @staticmethod
+    def state_dict_converter():
+        return SingleValueEncoderStateDictConverter()
+
+
+class SingleValueEncoderStateDictConverter:
+    def __init__(self):
+        pass
+
+    def from_diffusers(self, state_dict):
+        return state_dict
+
+    def from_civitai(self, state_dict):
+        return state_dict
diff --git a/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/hunyuan_dit.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/hunyuan_dit.py
new file mode 100644
index 0000000000000000000000000000000000000000..8e27183d6993e7f64eee6b9b231dcb4d8d1a6bc0
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/hunyuan_dit.py
@@ -0,0 +1,451 @@
+from .attention import Attention
+from einops import repeat, rearrange
+import math
+import torch
+
+
+class HunyuanDiTRotaryEmbedding(torch.nn.Module):
+
+    def __init__(self, q_norm_shape=88, k_norm_shape=88, rotary_emb_on_k=True):
+        super().__init__()
+        self.q_norm = torch.nn.LayerNorm((q_norm_shape,), elementwise_affine=True, eps=1e-06)
+        self.k_norm = torch.nn.LayerNorm((k_norm_shape,), elementwise_affine=True, eps=1e-06)
+        self.rotary_emb_on_k = rotary_emb_on_k
+        self.k_cache, self.v_cache = [], []
+
+    def reshape_for_broadcast(self, freqs_cis, x):
+        ndim = x.ndim
+        shape = [d if i == ndim - 2 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)]
+        return freqs_cis[0].view(*shape), freqs_cis[1].view(*shape)
+
+    def rotate_half(self, x):
+        x_real, x_imag = x.float().reshape(*x.shape[:-1], -1, 2).unbind(-1)
+        return torch.stack([-x_imag, x_real], dim=-1).flatten(3)
+
+    def apply_rotary_emb(self, xq, xk, freqs_cis):
+        xk_out = None
+        cos, sin = self.reshape_for_broadcast(freqs_cis, xq)
+        cos, sin = cos.to(xq.device), sin.to(xq.device)
+        xq_out = (xq.float() * cos + self.rotate_half(xq.float()) * sin).type_as(xq)
+        if xk is not None:
+            xk_out = (xk.float() * cos + self.rotate_half(xk.float()) * sin).type_as(xk)
+        return xq_out, xk_out
+
+    def forward(self, q, k, v, freqs_cis_img, to_cache=False):
+        # norm
+        q = self.q_norm(q)
+        k = self.k_norm(k)
+
+        # RoPE
+        if self.rotary_emb_on_k:
+            q, k = self.apply_rotary_emb(q, k, freqs_cis_img)
+        else:
+            q, _ = self.apply_rotary_emb(q, None, freqs_cis_img)
+        
+        if to_cache:
+            self.k_cache.append(k)
+            self.v_cache.append(v)
+        elif len(self.k_cache) > 0 and len(self.v_cache) > 0:
+            k = torch.concat([k] + self.k_cache, dim=2)
+            v = torch.concat([v] + self.v_cache, dim=2)
+            self.k_cache, self.v_cache = [], []
+        return q, k, v
+
+
+class FP32_Layernorm(torch.nn.LayerNorm):
+    def forward(self, inputs):
+        origin_dtype = inputs.dtype
+        return torch.nn.functional.layer_norm(inputs.float(), self.normalized_shape, self.weight.float(), self.bias.float(), self.eps).to(origin_dtype)
+
+
+class FP32_SiLU(torch.nn.SiLU):
+    def forward(self, inputs):
+        origin_dtype = inputs.dtype
+        return torch.nn.functional.silu(inputs.float(), inplace=False).to(origin_dtype)
+    
+
+class HunyuanDiTFinalLayer(torch.nn.Module):
+    def __init__(self, final_hidden_size=1408, condition_dim=1408, patch_size=2, out_channels=8):
+        super().__init__()
+        self.norm_final = torch.nn.LayerNorm(final_hidden_size, elementwise_affine=False, eps=1e-6)
+        self.linear = torch.nn.Linear(final_hidden_size, patch_size * patch_size * out_channels, bias=True)
+        self.adaLN_modulation = torch.nn.Sequential(
+            FP32_SiLU(),
+            torch.nn.Linear(condition_dim, 2 * final_hidden_size, bias=True)
+        )
+
+    def modulate(self, x, shift, scale):
+        return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
+
+    def forward(self, hidden_states, condition_emb):
+        shift, scale = self.adaLN_modulation(condition_emb).chunk(2, dim=1)
+        hidden_states = self.modulate(self.norm_final(hidden_states), shift, scale)
+        hidden_states = self.linear(hidden_states)
+        return hidden_states
+
+
+class HunyuanDiTBlock(torch.nn.Module):
+
+    def __init__(
+        self,
+        hidden_dim=1408,
+        condition_dim=1408,
+        num_heads=16,
+        mlp_ratio=4.3637,
+        text_dim=1024,
+        skip_connection=False
+    ):
+        super().__init__()
+        self.norm1 = FP32_Layernorm((hidden_dim,), eps=1e-6, elementwise_affine=True)
+        self.rota1 = HunyuanDiTRotaryEmbedding(hidden_dim//num_heads, hidden_dim//num_heads)
+        self.attn1 = Attention(hidden_dim, num_heads, hidden_dim//num_heads, bias_q=True, bias_kv=True, bias_out=True)
+        self.norm2 = FP32_Layernorm((hidden_dim,), eps=1e-6, elementwise_affine=True)
+        self.rota2 = HunyuanDiTRotaryEmbedding(hidden_dim//num_heads, hidden_dim//num_heads, rotary_emb_on_k=False)
+        self.attn2 = Attention(hidden_dim, num_heads, hidden_dim//num_heads, kv_dim=text_dim, bias_q=True, bias_kv=True, bias_out=True)
+        self.norm3 = FP32_Layernorm((hidden_dim,), eps=1e-6, elementwise_affine=True)
+        self.modulation = torch.nn.Sequential(FP32_SiLU(), torch.nn.Linear(condition_dim, hidden_dim, bias=True))
+        self.mlp = torch.nn.Sequential(
+            torch.nn.Linear(hidden_dim, int(hidden_dim*mlp_ratio), bias=True),
+            torch.nn.GELU(approximate="tanh"),
+            torch.nn.Linear(int(hidden_dim*mlp_ratio), hidden_dim, bias=True)
+        )
+        if skip_connection:
+            self.skip_norm = FP32_Layernorm((hidden_dim * 2,), eps=1e-6, elementwise_affine=True)
+            self.skip_linear = torch.nn.Linear(hidden_dim * 2, hidden_dim, bias=True)
+        else:
+            self.skip_norm, self.skip_linear = None, None
+
+    def forward(self, hidden_states, condition_emb, text_emb, freq_cis_img, residual=None, to_cache=False):
+        # Long Skip Connection
+        if self.skip_norm is not None and self.skip_linear is not None:
+            hidden_states = torch.cat([hidden_states, residual], dim=-1)
+            hidden_states = self.skip_norm(hidden_states)
+            hidden_states = self.skip_linear(hidden_states)
+
+        # Self-Attention
+        shift_msa = self.modulation(condition_emb).unsqueeze(dim=1)
+        attn_input = self.norm1(hidden_states) + shift_msa
+        hidden_states = hidden_states + self.attn1(attn_input, qkv_preprocessor=lambda q, k, v: self.rota1(q, k, v, freq_cis_img, to_cache=to_cache))
+
+        # Cross-Attention
+        attn_input = self.norm3(hidden_states)
+        hidden_states = hidden_states + self.attn2(attn_input, text_emb, qkv_preprocessor=lambda q, k, v: self.rota2(q, k, v, freq_cis_img))
+
+        # FFN Layer
+        mlp_input = self.norm2(hidden_states)
+        hidden_states = hidden_states + self.mlp(mlp_input)
+        return hidden_states
+    
+
+class AttentionPool(torch.nn.Module):
+    def __init__(self, spacial_dim, embed_dim, num_heads, output_dim = None):
+        super().__init__()
+        self.positional_embedding = torch.nn.Parameter(torch.randn(spacial_dim + 1, embed_dim) / embed_dim ** 0.5)
+        self.k_proj = torch.nn.Linear(embed_dim, embed_dim)
+        self.q_proj = torch.nn.Linear(embed_dim, embed_dim)
+        self.v_proj = torch.nn.Linear(embed_dim, embed_dim)
+        self.c_proj = torch.nn.Linear(embed_dim, output_dim or embed_dim)
+        self.num_heads = num_heads
+
+    def forward(self, x):
+        x = x.permute(1, 0, 2)  # NLC -> LNC
+        x = torch.cat([x.mean(dim=0, keepdim=True), x], dim=0)  # (L+1)NC
+        x = x + self.positional_embedding[:, None, :].to(x.dtype)  # (L+1)NC
+        x, _ = torch.nn.functional.multi_head_attention_forward(
+            query=x[:1], 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.squeeze(0)
+    
+
+class PatchEmbed(torch.nn.Module):
+    def __init__(
+        self,
+        patch_size=(2, 2),
+        in_chans=4,
+        embed_dim=1408,
+        bias=True,
+    ):
+        super().__init__()
+        self.proj = torch.nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size, bias=bias)
+
+    def forward(self, x):
+        x = self.proj(x)
+        x = x.flatten(2).transpose(1, 2)  # BCHW -> BNC
+        return x
+    
+
+def timestep_embedding(t, dim, max_period=10000, repeat_only=False):
+    # https://github.com/openai/glide-text2im/blob/main/glide_text2im/nn.py
+    if not repeat_only:
+        half = dim // 2
+        freqs = torch.exp(
+            -math.log(max_period)
+            * torch.arange(start=0, end=half, dtype=torch.float32)
+            / half
+        ).to(device=t.device)   # size: [dim/2], 一个指数衰减的曲线
+        args = t[:, None].float() * freqs[None]
+        embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
+        if dim % 2:
+            embedding = torch.cat(
+                [embedding, torch.zeros_like(embedding[:, :1])], dim=-1
+            )
+    else:
+        embedding = repeat(t, "b -> b d", d=dim)
+    return embedding
+    
+
+class TimestepEmbedder(torch.nn.Module):
+    def __init__(self, hidden_size=1408, frequency_embedding_size=256):
+        super().__init__()
+        self.mlp = torch.nn.Sequential(
+            torch.nn.Linear(frequency_embedding_size, hidden_size, bias=True),
+            torch.nn.SiLU(),
+            torch.nn.Linear(hidden_size, hidden_size, bias=True),
+        )
+        self.frequency_embedding_size = frequency_embedding_size
+
+    def forward(self, t):
+        t_freq = timestep_embedding(t, self.frequency_embedding_size).type(self.mlp[0].weight.dtype)
+        t_emb = self.mlp(t_freq)
+        return t_emb
+
+
+class HunyuanDiT(torch.nn.Module):
+    def __init__(self, num_layers_down=21, num_layers_up=19, in_channels=4, out_channels=8, hidden_dim=1408, text_dim=1024, t5_dim=2048, text_length=77, t5_length=256):
+        super().__init__()
+
+        # Embedders
+        self.text_emb_padding = torch.nn.Parameter(torch.randn(text_length + t5_length, text_dim, dtype=torch.float32))
+        self.t5_embedder = torch.nn.Sequential(
+            torch.nn.Linear(t5_dim, t5_dim * 4, bias=True),
+            FP32_SiLU(),
+            torch.nn.Linear(t5_dim * 4, text_dim, bias=True),
+        )
+        self.t5_pooler = AttentionPool(t5_length, t5_dim, num_heads=8, output_dim=1024)
+        self.style_embedder = torch.nn.Parameter(torch.randn(hidden_dim))
+        self.patch_embedder = PatchEmbed(in_chans=in_channels)
+        self.timestep_embedder = TimestepEmbedder()
+        self.extra_embedder = torch.nn.Sequential(
+            torch.nn.Linear(256 * 6 + 1024 + hidden_dim, hidden_dim * 4),
+            FP32_SiLU(),
+            torch.nn.Linear(hidden_dim * 4, hidden_dim),
+        )
+
+        # Transformer blocks
+        self.num_layers_down = num_layers_down
+        self.num_layers_up = num_layers_up
+        self.blocks = torch.nn.ModuleList(
+            [HunyuanDiTBlock(skip_connection=False) for _ in range(num_layers_down)] + \
+            [HunyuanDiTBlock(skip_connection=True) for _ in range(num_layers_up)]
+        )
+
+        # Output layers
+        self.final_layer = HunyuanDiTFinalLayer()
+        self.out_channels = out_channels
+
+    def prepare_text_emb(self, text_emb, text_emb_t5, text_emb_mask, text_emb_mask_t5):
+        text_emb_mask = text_emb_mask.bool()
+        text_emb_mask_t5 = text_emb_mask_t5.bool()
+        text_emb_t5 = self.t5_embedder(text_emb_t5)
+        text_emb = torch.cat([text_emb, text_emb_t5], dim=1)
+        text_emb_mask = torch.cat([text_emb_mask, text_emb_mask_t5], dim=-1)
+        text_emb = torch.where(text_emb_mask.unsqueeze(2), text_emb, self.text_emb_padding.to(text_emb))
+        return text_emb
+    
+    def prepare_extra_emb(self, text_emb_t5, timestep, size_emb, dtype, batch_size):
+        # Text embedding
+        pooled_text_emb_t5 = self.t5_pooler(text_emb_t5)
+
+        # Timestep embedding
+        timestep_emb = self.timestep_embedder(timestep)
+
+        # Size embedding
+        size_emb = timestep_embedding(size_emb.view(-1), 256).to(dtype)
+        size_emb = size_emb.view(-1, 6 * 256)
+
+        # Style embedding
+        style_emb = repeat(self.style_embedder, "D -> B D", B=batch_size)
+
+        # Concatenate all extra vectors
+        extra_emb = torch.cat([pooled_text_emb_t5, size_emb, style_emb], dim=1)
+        condition_emb = timestep_emb + self.extra_embedder(extra_emb)
+
+        return condition_emb
+
+    def unpatchify(self, x, h, w):
+        return rearrange(x, "B (H W) (P Q C) -> B C (H P) (W Q)", H=h, W=w, P=2, Q=2)
+    
+    def build_mask(self, data, is_bound):
+        _, _, H, W = data.shape
+        h = repeat(torch.arange(H), "H -> H W", H=H, W=W)
+        w = repeat(torch.arange(W), "W -> H W", H=H, W=W)
+        border_width = (H + W) // 4
+        pad = torch.ones_like(h) * border_width
+        mask = torch.stack([
+            pad if is_bound[0] else h + 1,
+            pad if is_bound[1] else H - h,
+            pad if is_bound[2] else w + 1,
+            pad if is_bound[3] else W - w
+        ]).min(dim=0).values
+        mask = mask.clip(1, border_width)
+        mask = (mask / border_width).to(dtype=data.dtype, device=data.device)
+        mask = rearrange(mask, "H W -> 1 H W")
+        return mask
+    
+    def tiled_block_forward(self, block, hidden_states, condition_emb, text_emb, freq_cis_img, residual, torch_dtype, data_device, computation_device, tile_size, tile_stride):
+        B, C, H, W = hidden_states.shape
+
+        weight = torch.zeros((1, 1, H, W), dtype=torch_dtype, device=data_device)
+        values = torch.zeros((B, C, H, W), dtype=torch_dtype, device=data_device)
+
+        # Split tasks
+        tasks = []
+        for h in range(0, H, tile_stride):
+            for w in range(0, W, tile_stride):
+                if (h-tile_stride >= 0 and h-tile_stride+tile_size >= H) or (w-tile_stride >= 0 and w-tile_stride+tile_size >= W):
+                    continue
+                h_, w_ = h + tile_size, w + tile_size
+                if h_ > H: h, h_ = H - tile_size, H
+                if w_ > W: w, w_ = W - tile_size, W
+                tasks.append((h, h_, w, w_))
+
+        # Run
+        for hl, hr, wl, wr in tasks:
+            hidden_states_batch = hidden_states[:, :, hl:hr, wl:wr].to(computation_device)
+            hidden_states_batch = rearrange(hidden_states_batch, "B C H W -> B (H W) C")
+            if residual is not None:
+                residual_batch = residual[:, :, hl:hr, wl:wr].to(computation_device)
+                residual_batch = rearrange(residual_batch, "B C H W -> B (H W) C")
+            else:
+                residual_batch = None
+
+            # Forward
+            hidden_states_batch = block(hidden_states_batch, condition_emb, text_emb, freq_cis_img, residual_batch).to(data_device)
+            hidden_states_batch = rearrange(hidden_states_batch, "B (H W) C -> B C H W", H=hr-hl)
+
+            mask = self.build_mask(hidden_states_batch, is_bound=(hl==0, hr>=H, wl==0, wr>=W))
+            values[:, :, hl:hr, wl:wr] += hidden_states_batch * mask
+            weight[:, :, hl:hr, wl:wr] += mask
+        values /= weight
+        return values
+
+    def forward(
+        self, hidden_states, text_emb, text_emb_t5, text_emb_mask, text_emb_mask_t5, timestep, size_emb, freq_cis_img,
+        tiled=False, tile_size=64, tile_stride=32,
+        to_cache=False,
+        use_gradient_checkpointing=False,
+    ):
+        # Embeddings
+        text_emb = self.prepare_text_emb(text_emb, text_emb_t5, text_emb_mask, text_emb_mask_t5)
+        condition_emb = self.prepare_extra_emb(text_emb_t5, timestep, size_emb, hidden_states.dtype, hidden_states.shape[0])
+        
+        # Input
+        height, width = hidden_states.shape[-2], hidden_states.shape[-1]
+        hidden_states = self.patch_embedder(hidden_states)
+
+        # Blocks
+        def create_custom_forward(module):
+            def custom_forward(*inputs):
+                return module(*inputs)
+            return custom_forward
+        if tiled:
+            hidden_states = rearrange(hidden_states, "B (H W) C -> B C H W", H=height//2)
+            residuals = []
+            for block_id, block in enumerate(self.blocks):
+                residual = residuals.pop() if block_id >= self.num_layers_down else None
+                hidden_states = self.tiled_block_forward(
+                    block, hidden_states, condition_emb, text_emb, freq_cis_img, residual,
+                    torch_dtype=hidden_states.dtype, data_device=hidden_states.device, computation_device=hidden_states.device,
+                    tile_size=tile_size, tile_stride=tile_stride
+                )
+                if block_id < self.num_layers_down - 2:
+                    residuals.append(hidden_states)
+            hidden_states = rearrange(hidden_states, "B C H W -> B (H W) C")
+        else:
+            residuals = []
+            for block_id, block in enumerate(self.blocks):
+                residual = residuals.pop() if block_id >= self.num_layers_down else None
+                if self.training and use_gradient_checkpointing:
+                    hidden_states = torch.utils.checkpoint.checkpoint(
+                        create_custom_forward(block),
+                        hidden_states, condition_emb, text_emb, freq_cis_img, residual,
+                        use_reentrant=False,
+                    )
+                else:
+                    hidden_states = block(hidden_states, condition_emb, text_emb, freq_cis_img, residual, to_cache=to_cache)
+                if block_id < self.num_layers_down - 2:
+                    residuals.append(hidden_states)
+
+        # Output
+        hidden_states = self.final_layer(hidden_states, condition_emb)
+        hidden_states = self.unpatchify(hidden_states, height//2, width//2)
+        hidden_states, _ = hidden_states.chunk(2, dim=1)
+        return hidden_states
+    
+    @staticmethod
+    def state_dict_converter():
+        return HunyuanDiTStateDictConverter()
+
+
+
+class HunyuanDiTStateDictConverter():
+    def __init__(self):
+        pass
+
+    def from_diffusers(self, state_dict):
+        state_dict_ = {}
+        for name, param in state_dict.items():
+            name_ = name
+            name_ = name_.replace(".default_modulation.", ".modulation.")
+            name_ = name_.replace(".mlp.fc1.", ".mlp.0.")
+            name_ = name_.replace(".mlp.fc2.", ".mlp.2.")
+            name_ = name_.replace(".attn1.q_norm.", ".rota1.q_norm.")
+            name_ = name_.replace(".attn2.q_norm.", ".rota2.q_norm.")
+            name_ = name_.replace(".attn1.k_norm.", ".rota1.k_norm.")
+            name_ = name_.replace(".attn2.k_norm.", ".rota2.k_norm.")
+            name_ = name_.replace(".q_proj.", ".to_q.")
+            name_ = name_.replace(".out_proj.", ".to_out.")
+            name_ = name_.replace("text_embedding_padding", "text_emb_padding")
+            name_ = name_.replace("mlp_t5.0.", "t5_embedder.0.")
+            name_ = name_.replace("mlp_t5.2.", "t5_embedder.2.")
+            name_ = name_.replace("pooler.", "t5_pooler.")
+            name_ = name_.replace("x_embedder.", "patch_embedder.")
+            name_ = name_.replace("t_embedder.", "timestep_embedder.")
+            name_ = name_.replace("t5_pooler.to_q.", "t5_pooler.q_proj.")
+            name_ = name_.replace("style_embedder.weight", "style_embedder")
+            if ".kv_proj." in name_:
+                param_k = param[:param.shape[0]//2]
+                param_v = param[param.shape[0]//2:]
+                state_dict_[name_.replace(".kv_proj.", ".to_k.")] = param_k
+                state_dict_[name_.replace(".kv_proj.", ".to_v.")] = param_v
+            elif ".Wqkv." in name_:
+                param_q = param[:param.shape[0]//3]
+                param_k = param[param.shape[0]//3:param.shape[0]//3*2]
+                param_v = param[param.shape[0]//3*2:]
+                state_dict_[name_.replace(".Wqkv.", ".to_q.")] = param_q
+                state_dict_[name_.replace(".Wqkv.", ".to_k.")] = param_k
+                state_dict_[name_.replace(".Wqkv.", ".to_v.")] = param_v
+            elif "style_embedder" in name_:
+                state_dict_[name_] = param.squeeze()
+            else:
+                state_dict_[name_] = param
+        return state_dict_
+    
+    def from_civitai(self, state_dict):
+        return self.from_diffusers(state_dict)
diff --git a/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/hunyuan_dit_text_encoder.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/hunyuan_dit_text_encoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..33999a8b10a319b736981dd8f3a911bbe9568e8d
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/hunyuan_dit_text_encoder.py
@@ -0,0 +1,163 @@
+from transformers import BertModel, BertConfig, T5EncoderModel, T5Config
+import torch
+
+
+
+class HunyuanDiTCLIPTextEncoder(BertModel):
+    def __init__(self):
+        config = BertConfig(
+            _name_or_path = "",
+            architectures = ["BertModel"],
+            attention_probs_dropout_prob = 0.1,
+            bos_token_id = 0,
+            classifier_dropout = None,
+            directionality = "bidi",
+            eos_token_id = 2,
+            hidden_act = "gelu",
+            hidden_dropout_prob = 0.1,
+            hidden_size = 1024,
+            initializer_range = 0.02,
+            intermediate_size = 4096,
+            layer_norm_eps = 1e-12,
+            max_position_embeddings = 512,
+            model_type = "bert",
+            num_attention_heads = 16,
+            num_hidden_layers = 24,
+            output_past = True,
+            pad_token_id = 0,
+            pooler_fc_size = 768,
+            pooler_num_attention_heads = 12,
+            pooler_num_fc_layers = 3,
+            pooler_size_per_head = 128,
+            pooler_type = "first_token_transform",
+            position_embedding_type = "absolute",
+            torch_dtype = "float32",
+            transformers_version = "4.37.2",
+            type_vocab_size = 2,
+            use_cache = True,
+            vocab_size = 47020
+        )
+        super().__init__(config, add_pooling_layer=False)
+        self.eval()
+
+    def forward(self, input_ids, attention_mask, clip_skip=1):
+        input_shape = input_ids.size()
+
+        batch_size, seq_length = input_shape
+        device = input_ids.device
+
+        past_key_values_length = 0
+
+        if attention_mask is None:
+            attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), device=device)
+
+        extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape)
+
+        embedding_output = self.embeddings(
+            input_ids=input_ids,
+            position_ids=None,
+            token_type_ids=None,
+            inputs_embeds=None,
+            past_key_values_length=0,
+        )
+        encoder_outputs = self.encoder(
+            embedding_output,
+            attention_mask=extended_attention_mask,
+            head_mask=None,
+            encoder_hidden_states=None,
+            encoder_attention_mask=None,
+            past_key_values=None,
+            use_cache=False,
+            output_attentions=False,
+            output_hidden_states=True,
+            return_dict=True,
+        )
+        all_hidden_states = encoder_outputs.hidden_states
+        prompt_emb = all_hidden_states[-clip_skip]
+        if clip_skip > 1:
+            mean, std = all_hidden_states[-1].mean(), all_hidden_states[-1].std()
+            prompt_emb = (prompt_emb - prompt_emb.mean()) / prompt_emb.std() * std + mean
+        return prompt_emb
+
+    @staticmethod
+    def state_dict_converter():
+        return HunyuanDiTCLIPTextEncoderStateDictConverter()
+
+
+
+class HunyuanDiTT5TextEncoder(T5EncoderModel):
+    def __init__(self):
+        config = T5Config(
+            _name_or_path = "../HunyuanDiT/t2i/mt5",
+            architectures = ["MT5ForConditionalGeneration"],
+            classifier_dropout = 0.0,
+            d_ff = 5120,
+            d_kv = 64,
+            d_model = 2048,
+            decoder_start_token_id = 0,
+            dense_act_fn = "gelu_new",
+            dropout_rate = 0.1,
+            eos_token_id = 1,
+            feed_forward_proj = "gated-gelu",
+            initializer_factor = 1.0,
+            is_encoder_decoder = True,
+            is_gated_act = True,
+            layer_norm_epsilon = 1e-06,
+            model_type = "t5",
+            num_decoder_layers = 24,
+            num_heads = 32,
+            num_layers = 24,
+            output_past = True,
+            pad_token_id = 0,
+            relative_attention_max_distance = 128,
+            relative_attention_num_buckets = 32,
+            tie_word_embeddings = False,
+            tokenizer_class = "T5Tokenizer",
+            transformers_version = "4.37.2",
+            use_cache = True,
+            vocab_size = 250112
+        )
+        super().__init__(config)
+        self.eval()
+
+    def forward(self, input_ids, attention_mask, clip_skip=1):
+        outputs = super().forward(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            output_hidden_states=True,
+        )
+        prompt_emb = outputs.hidden_states[-clip_skip]
+        if clip_skip > 1:
+            mean, std = outputs.hidden_states[-1].mean(), outputs.hidden_states[-1].std()
+            prompt_emb = (prompt_emb - prompt_emb.mean()) / prompt_emb.std() * std + mean
+        return prompt_emb
+    
+    @staticmethod
+    def state_dict_converter():
+        return HunyuanDiTT5TextEncoderStateDictConverter()
+
+
+
+class HunyuanDiTCLIPTextEncoderStateDictConverter():
+    def __init__(self):
+        pass
+
+    def from_diffusers(self, state_dict):
+        state_dict_ = {name[5:]: param for name, param in state_dict.items() if name.startswith("bert.")}
+        return state_dict_
+    
+    def from_civitai(self, state_dict):
+        return self.from_diffusers(state_dict)
+
+
+class HunyuanDiTT5TextEncoderStateDictConverter():
+    def __init__(self):
+        pass
+
+    def from_diffusers(self, state_dict):
+        state_dict_ = {name: param for name, param in state_dict.items() if name.startswith("encoder.")}
+        state_dict_["shared.weight"] = state_dict["shared.weight"]
+        return state_dict_
+    
+    def from_civitai(self, state_dict):
+        return self.from_diffusers(state_dict)
diff --git a/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/hunyuan_video_dit.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/hunyuan_video_dit.py
new file mode 100644
index 0000000000000000000000000000000000000000..13155361734f8ec4cb7947941a1824f5064f1642
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/hunyuan_video_dit.py
@@ -0,0 +1,920 @@
+import torch
+from .sd3_dit import TimestepEmbeddings, RMSNorm
+from .utils import init_weights_on_device
+from einops import rearrange, repeat
+from tqdm import tqdm
+from typing import Union, Tuple, List
+from .utils import hash_state_dict_keys
+
+
+def HunyuanVideoRope(latents):
+    def _to_tuple(x, dim=2):
+        if isinstance(x, int):
+            return (x,) * dim
+        elif len(x) == dim:
+            return x
+        else:
+            raise ValueError(f"Expected length {dim} or int, but got {x}")
+
+
+    def get_meshgrid_nd(start, *args, dim=2):
+        """
+        Get n-D meshgrid with start, stop and num.
+
+        Args:
+            start (int or tuple): If len(args) == 0, start is num; If len(args) == 1, start is start, args[0] is stop,
+                step is 1; If len(args) == 2, start is start, args[0] is stop, args[1] is num. For n-dim, start/stop/num
+                should be int or n-tuple. If n-tuple is provided, the meshgrid will be stacked following the dim order in
+                n-tuples.
+            *args: See above.
+            dim (int): Dimension of the meshgrid. Defaults to 2.
+
+        Returns:
+            grid (np.ndarray): [dim, ...]
+        """
+        if len(args) == 0:
+            # start is grid_size
+            num = _to_tuple(start, dim=dim)
+            start = (0,) * dim
+            stop = num
+        elif len(args) == 1:
+            # start is start, args[0] is stop, step is 1
+            start = _to_tuple(start, dim=dim)
+            stop = _to_tuple(args[0], dim=dim)
+            num = [stop[i] - start[i] for i in range(dim)]
+        elif len(args) == 2:
+            # start is start, args[0] is stop, args[1] is num
+            start = _to_tuple(start, dim=dim)  # Left-Top       eg: 12,0
+            stop = _to_tuple(args[0], dim=dim)  # Right-Bottom   eg: 20,32
+            num = _to_tuple(args[1], dim=dim)  # Target Size    eg: 32,124
+        else:
+            raise ValueError(f"len(args) should be 0, 1 or 2, but got {len(args)}")
+
+        # PyTorch implement of np.linspace(start[i], stop[i], num[i], endpoint=False)
+        axis_grid = []
+        for i in range(dim):
+            a, b, n = start[i], stop[i], num[i]
+            g = torch.linspace(a, b, n + 1, dtype=torch.float32)[:n]
+            axis_grid.append(g)
+        grid = torch.meshgrid(*axis_grid, indexing="ij")  # dim x [W, H, D]
+        grid = torch.stack(grid, dim=0)  # [dim, W, H, D]
+
+        return grid
+
+
+    def get_1d_rotary_pos_embed(
+        dim: int,
+        pos: Union[torch.FloatTensor, int],
+        theta: float = 10000.0,
+        use_real: bool = False,
+        theta_rescale_factor: float = 1.0,
+        interpolation_factor: float = 1.0,
+    ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
+        """
+        Precompute the frequency tensor for complex exponential (cis) with given dimensions.
+        (Note: `cis` means `cos + i * sin`, where i is the imaginary unit.)
+
+        This function calculates a frequency tensor with complex exponential using the given dimension 'dim'
+        and the end index 'end'. The 'theta' parameter scales the frequencies.
+        The returned tensor contains complex values in complex64 data type.
+
+        Args:
+            dim (int): Dimension of the frequency tensor.
+            pos (int or torch.FloatTensor): Position indices for the frequency tensor. [S] or scalar
+            theta (float, optional): Scaling factor for frequency computation. Defaults to 10000.0.
+            use_real (bool, optional): If True, return real part and imaginary part separately.
+                                    Otherwise, return complex numbers.
+            theta_rescale_factor (float, optional): Rescale factor for theta. Defaults to 1.0.
+
+        Returns:
+            freqs_cis: Precomputed frequency tensor with complex exponential. [S, D/2]
+            freqs_cos, freqs_sin: Precomputed frequency tensor with real and imaginary parts separately. [S, D]
+        """
+        if isinstance(pos, int):
+            pos = torch.arange(pos).float()
+
+        # proposed by reddit user bloc97, to rescale rotary embeddings to longer sequence length without fine-tuning
+        # has some connection to NTK literature
+        if theta_rescale_factor != 1.0:
+            theta *= theta_rescale_factor ** (dim / (dim - 2))
+
+        freqs = 1.0 / (
+            theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim)
+        )  # [D/2]
+        # assert interpolation_factor == 1.0, f"interpolation_factor: {interpolation_factor}"
+        freqs = torch.outer(pos * interpolation_factor, freqs)  # [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 get_nd_rotary_pos_embed(
+        rope_dim_list,
+        start,
+        *args,
+        theta=10000.0,
+        use_real=False,
+        theta_rescale_factor: Union[float, List[float]] = 1.0,
+        interpolation_factor: Union[float, List[float]] = 1.0,
+    ):
+        """
+        This is a n-d version of precompute_freqs_cis, which is a RoPE for tokens with n-d structure.
+
+        Args:
+            rope_dim_list (list of int): Dimension of each rope. len(rope_dim_list) should equal to n.
+                sum(rope_dim_list) should equal to head_dim of attention layer.
+            start (int | tuple of int | list of int): If len(args) == 0, start is num; If len(args) == 1, start is start,
+                args[0] is stop, step is 1; If len(args) == 2, start is start, args[0] is stop, args[1] is num.
+            *args: See above.
+            theta (float): Scaling factor for frequency computation. Defaults to 10000.0.
+            use_real (bool): If True, return real part and imaginary part separately. Otherwise, return complex numbers.
+                Some libraries such as TensorRT does not support complex64 data type. So it is useful to provide a real
+                part and an imaginary part separately.
+            theta_rescale_factor (float): Rescale factor for theta. Defaults to 1.0.
+
+        Returns:
+            pos_embed (torch.Tensor): [HW, D/2]
+        """
+
+        grid = get_meshgrid_nd(
+            start, *args, dim=len(rope_dim_list)
+        )  # [3, W, H, D] / [2, W, H]
+
+        if isinstance(theta_rescale_factor, int) or isinstance(theta_rescale_factor, float):
+            theta_rescale_factor = [theta_rescale_factor] * len(rope_dim_list)
+        elif isinstance(theta_rescale_factor, list) and len(theta_rescale_factor) == 1:
+            theta_rescale_factor = [theta_rescale_factor[0]] * len(rope_dim_list)
+        assert len(theta_rescale_factor) == len(
+            rope_dim_list
+        ), "len(theta_rescale_factor) should equal to len(rope_dim_list)"
+
+        if isinstance(interpolation_factor, int) or isinstance(interpolation_factor, float):
+            interpolation_factor = [interpolation_factor] * len(rope_dim_list)
+        elif isinstance(interpolation_factor, list) and len(interpolation_factor) == 1:
+            interpolation_factor = [interpolation_factor[0]] * len(rope_dim_list)
+        assert len(interpolation_factor) == len(
+            rope_dim_list
+        ), "len(interpolation_factor) should equal to len(rope_dim_list)"
+
+        # use 1/ndim of dimensions to encode grid_axis
+        embs = []
+        for i in range(len(rope_dim_list)):
+            emb = get_1d_rotary_pos_embed(
+                rope_dim_list[i],
+                grid[i].reshape(-1),
+                theta,
+                use_real=use_real,
+                theta_rescale_factor=theta_rescale_factor[i],
+                interpolation_factor=interpolation_factor[i],
+            )  # 2 x [WHD, rope_dim_list[i]]
+            embs.append(emb)
+
+        if use_real:
+            cos = torch.cat([emb[0] for emb in embs], dim=1)  # (WHD, D/2)
+            sin = torch.cat([emb[1] for emb in embs], dim=1)  # (WHD, D/2)
+            return cos, sin
+        else:
+            emb = torch.cat(embs, dim=1)  # (WHD, D/2)
+            return emb
+
+    freqs_cos, freqs_sin = get_nd_rotary_pos_embed(
+        [16, 56, 56],
+        [latents.shape[2], latents.shape[3] // 2, latents.shape[4] // 2],
+        theta=256,
+        use_real=True,
+        theta_rescale_factor=1,
+    )
+    return freqs_cos, freqs_sin
+
+
+class PatchEmbed(torch.nn.Module):
+    def __init__(self, patch_size=(1, 2, 2), in_channels=16, embed_dim=3072):
+        super().__init__()
+        self.proj = torch.nn.Conv3d(in_channels, embed_dim, kernel_size=patch_size, stride=patch_size)
+
+    def forward(self, x):
+        x = self.proj(x)
+        x = x.flatten(2).transpose(1, 2)
+        return x
+
+
+class IndividualTokenRefinerBlock(torch.nn.Module):
+    def __init__(self, hidden_size=3072, num_heads=24):
+        super().__init__()
+        self.num_heads = num_heads
+        self.norm1 = torch.nn.LayerNorm(hidden_size, elementwise_affine=True, eps=1e-6)
+        self.self_attn_qkv = torch.nn.Linear(hidden_size, hidden_size * 3)
+        self.self_attn_proj = torch.nn.Linear(hidden_size, hidden_size)
+
+        self.norm2 = torch.nn.LayerNorm(hidden_size, elementwise_affine=True, eps=1e-6)
+        self.mlp = torch.nn.Sequential(
+            torch.nn.Linear(hidden_size, hidden_size * 4),
+            torch.nn.SiLU(),
+            torch.nn.Linear(hidden_size * 4, hidden_size)
+        )
+        self.adaLN_modulation = torch.nn.Sequential(
+            torch.nn.SiLU(),
+            torch.nn.Linear(hidden_size, hidden_size * 2, device="cuda", dtype=torch.bfloat16),
+        )
+
+    def forward(self, x, c, attn_mask=None):
+        gate_msa, gate_mlp = self.adaLN_modulation(c).chunk(2, dim=1)
+
+        norm_x = self.norm1(x)
+        qkv = self.self_attn_qkv(norm_x)
+        q, k, v = rearrange(qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads)
+
+        attn = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=attn_mask)
+        attn = rearrange(attn, "B H L D -> B L (H D)")
+
+        x = x + self.self_attn_proj(attn) * gate_msa.unsqueeze(1)
+        x = x + self.mlp(self.norm2(x)) * gate_mlp.unsqueeze(1)
+
+        return x
+
+
+class SingleTokenRefiner(torch.nn.Module):
+    def __init__(self, in_channels=4096, hidden_size=3072, depth=2):
+        super().__init__()
+        self.input_embedder = torch.nn.Linear(in_channels, hidden_size, bias=True)
+        self.t_embedder = TimestepEmbeddings(256, hidden_size, computation_device="cpu")
+        self.c_embedder = torch.nn.Sequential(
+            torch.nn.Linear(in_channels, hidden_size),
+            torch.nn.SiLU(),
+            torch.nn.Linear(hidden_size, hidden_size)
+        )
+        self.blocks = torch.nn.ModuleList([IndividualTokenRefinerBlock(hidden_size=hidden_size) for _ in range(depth)])
+
+    def forward(self, x, t, mask=None):
+        timestep_aware_representations = self.t_embedder(t, dtype=torch.float32)
+
+        mask_float = mask.float().unsqueeze(-1)
+        context_aware_representations = (x * mask_float).sum(dim=1) / mask_float.sum(dim=1)
+        context_aware_representations = self.c_embedder(context_aware_representations)
+        c = timestep_aware_representations + context_aware_representations
+
+        x = self.input_embedder(x)
+
+        mask = mask.to(device=x.device, dtype=torch.bool)
+        mask = repeat(mask, "B L -> B 1 D L", D=mask.shape[-1])
+        mask = mask & mask.transpose(2, 3)
+        mask[:, :, :, 0] = True
+
+        for block in self.blocks:
+            x = block(x, c, mask)
+
+        return x
+
+
+class ModulateDiT(torch.nn.Module):
+    def __init__(self, hidden_size, factor=6):
+        super().__init__()
+        self.act = torch.nn.SiLU()
+        self.linear = torch.nn.Linear(hidden_size, factor * hidden_size)
+
+    def forward(self, x):
+        return self.linear(self.act(x))
+
+
+def modulate(x, shift=None, scale=None, tr_shift=None, tr_scale=None, tr_token=None):
+    if tr_shift is not None:
+        x_zero = x[:, :tr_token] * (1 + tr_scale.unsqueeze(1)) + tr_shift.unsqueeze(1)
+        x_orig = x[:, tr_token:] * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
+        x = torch.concat((x_zero, x_orig), dim=1)
+        return x
+    if scale is None and shift is None:
+        return x
+    elif shift is None:
+        return x * (1 + scale.unsqueeze(1))
+    elif scale is None:
+        return x + shift.unsqueeze(1)
+    else:
+        return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
+
+
+def reshape_for_broadcast(
+    freqs_cis,
+    x: torch.Tensor,
+    head_first=False,
+):
+    ndim = x.ndim
+    assert 0 <= 1 < ndim
+
+    if isinstance(freqs_cis, tuple):
+        # freqs_cis: (cos, sin) in real space
+        if head_first:
+            assert freqs_cis[0].shape == (
+                x.shape[-2],
+                x.shape[-1],
+            ), f"freqs_cis shape {freqs_cis[0].shape} does not match x shape {x.shape}"
+            shape = [
+                d if i == ndim - 2 or i == ndim - 1 else 1
+                for i, d in enumerate(x.shape)
+            ]
+        else:
+            assert freqs_cis[0].shape == (
+                x.shape[1],
+                x.shape[-1],
+            ), f"freqs_cis shape {freqs_cis[0].shape} does not match x shape {x.shape}"
+            shape = [d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)]
+        return freqs_cis[0].view(*shape), freqs_cis[1].view(*shape)
+    else:
+        # freqs_cis: values in complex space
+        if head_first:
+            assert freqs_cis.shape == (
+                x.shape[-2],
+                x.shape[-1],
+            ), f"freqs_cis shape {freqs_cis.shape} does not match x shape {x.shape}"
+            shape = [
+                d if i == ndim - 2 or i == ndim - 1 else 1
+                for i, d in enumerate(x.shape)
+            ]
+        else:
+            assert freqs_cis.shape == (
+                x.shape[1],
+                x.shape[-1],
+            ), f"freqs_cis shape {freqs_cis.shape} does not match x shape {x.shape}"
+            shape = [d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)]
+        return freqs_cis.view(*shape)
+
+
+def rotate_half(x):
+    x_real, x_imag = (
+        x.float().reshape(*x.shape[:-1], -1, 2).unbind(-1)
+    )  # [B, S, H, D//2]
+    return torch.stack([-x_imag, x_real], dim=-1).flatten(3)
+
+
+def apply_rotary_emb(
+    xq: torch.Tensor,
+    xk: torch.Tensor,
+    freqs_cis,
+    head_first: bool = False,
+):
+    xk_out = None
+    if isinstance(freqs_cis, tuple):
+        cos, sin = reshape_for_broadcast(freqs_cis, xq, head_first)  # [S, D]
+        cos, sin = cos.to(xq.device), sin.to(xq.device)
+        # real * cos - imag * sin
+        # imag * cos + real * sin
+        xq_out = (xq.float() * cos + rotate_half(xq.float()) * sin).type_as(xq)
+        xk_out = (xk.float() * cos + rotate_half(xk.float()) * sin).type_as(xk)
+    else:
+        # view_as_complex will pack [..., D/2, 2](real) to [..., D/2](complex)
+        xq_ = torch.view_as_complex(
+            xq.float().reshape(*xq.shape[:-1], -1, 2)
+        )  # [B, S, H, D//2]
+        freqs_cis = reshape_for_broadcast(freqs_cis, xq_, head_first).to(
+            xq.device
+        )  # [S, D//2] --> [1, S, 1, D//2]
+        # (real, imag) * (cos, sin) = (real * cos - imag * sin, imag * cos + real * sin)
+        # view_as_real will expand [..., D/2](complex) to [..., D/2, 2](real)
+        xq_out = torch.view_as_real(xq_ * freqs_cis).flatten(3).type_as(xq)
+        xk_ = torch.view_as_complex(
+            xk.float().reshape(*xk.shape[:-1], -1, 2)
+        )  # [B, S, H, D//2]
+        xk_out = torch.view_as_real(xk_ * freqs_cis).flatten(3).type_as(xk)
+
+    return xq_out, xk_out
+
+
+def attention(q, k, v):
+    q, k, v = q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2)
+    x = torch.nn.functional.scaled_dot_product_attention(q, k, v)
+    x = x.transpose(1, 2).flatten(2, 3)
+    return x
+
+
+def apply_gate(x, gate, tr_gate=None, tr_token=None):
+    if tr_gate is not None:
+        x_zero = x[:, :tr_token] * tr_gate.unsqueeze(1)
+        x_orig = x[:, tr_token:] * gate.unsqueeze(1)
+        return torch.concat((x_zero, x_orig), dim=1)
+    else:
+        return x * gate.unsqueeze(1)
+
+
+class MMDoubleStreamBlockComponent(torch.nn.Module):
+    def __init__(self, hidden_size=3072, heads_num=24, mlp_width_ratio=4):
+        super().__init__()
+        self.heads_num = heads_num
+
+        self.mod = ModulateDiT(hidden_size)
+        self.norm1 = torch.nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
+
+        self.to_qkv = torch.nn.Linear(hidden_size, hidden_size * 3)
+        self.norm_q = RMSNorm(dim=hidden_size // heads_num, eps=1e-6)
+        self.norm_k = RMSNorm(dim=hidden_size // heads_num, eps=1e-6)
+        self.to_out = torch.nn.Linear(hidden_size, hidden_size)
+
+        self.norm2 = torch.nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
+        self.ff = torch.nn.Sequential(
+            torch.nn.Linear(hidden_size, hidden_size * mlp_width_ratio),
+            torch.nn.GELU(approximate="tanh"),
+            torch.nn.Linear(hidden_size * mlp_width_ratio, hidden_size)
+        )
+
+    def forward(self, hidden_states, conditioning, freqs_cis=None, token_replace_vec=None, tr_token=None):
+        mod1_shift, mod1_scale, mod1_gate, mod2_shift, mod2_scale, mod2_gate = self.mod(conditioning).chunk(6, dim=-1)
+        if token_replace_vec is not None:
+            assert tr_token is not None
+            tr_mod1_shift, tr_mod1_scale, tr_mod1_gate, tr_mod2_shift, tr_mod2_scale, tr_mod2_gate = self.mod(token_replace_vec).chunk(6, dim=-1)
+        else:
+            tr_mod1_shift, tr_mod1_scale, tr_mod1_gate, tr_mod2_shift, tr_mod2_scale, tr_mod2_gate = None, None, None, None, None, None
+
+        norm_hidden_states = self.norm1(hidden_states)
+        norm_hidden_states = modulate(norm_hidden_states, shift=mod1_shift, scale=mod1_scale,
+                                      tr_shift=tr_mod1_shift, tr_scale=tr_mod1_scale, tr_token=tr_token)
+        qkv = self.to_qkv(norm_hidden_states)
+        q, k, v = rearrange(qkv, "B L (K H D) -> K B L H D", K=3, H=self.heads_num)
+
+        q = self.norm_q(q)
+        k = self.norm_k(k)
+
+        if freqs_cis is not None:
+            q, k = apply_rotary_emb(q, k, freqs_cis, head_first=False)
+        return (q, k, v), (mod1_gate, mod2_shift, mod2_scale, mod2_gate), (tr_mod1_gate, tr_mod2_shift, tr_mod2_scale, tr_mod2_gate)
+
+    def process_ff(self, hidden_states, attn_output, mod, mod_tr=None, tr_token=None):
+        mod1_gate, mod2_shift, mod2_scale, mod2_gate = mod
+        if mod_tr is not None:
+            tr_mod1_gate, tr_mod2_shift, tr_mod2_scale, tr_mod2_gate = mod_tr
+        else:
+            tr_mod1_gate, tr_mod2_shift, tr_mod2_scale, tr_mod2_gate = None, None, None, None
+        hidden_states = hidden_states + apply_gate(self.to_out(attn_output), mod1_gate, tr_mod1_gate, tr_token)
+        x = self.ff(modulate(self.norm2(hidden_states), shift=mod2_shift, scale=mod2_scale, tr_shift=tr_mod2_shift, tr_scale=tr_mod2_scale, tr_token=tr_token))
+        hidden_states = hidden_states + apply_gate(x, mod2_gate, tr_mod2_gate, tr_token)
+        return hidden_states
+
+
+class MMDoubleStreamBlock(torch.nn.Module):
+    def __init__(self, hidden_size=3072, heads_num=24, mlp_width_ratio=4):
+        super().__init__()
+        self.component_a = MMDoubleStreamBlockComponent(hidden_size, heads_num, mlp_width_ratio)
+        self.component_b = MMDoubleStreamBlockComponent(hidden_size, heads_num, mlp_width_ratio)
+
+    def forward(self, hidden_states_a, hidden_states_b, conditioning, freqs_cis, token_replace_vec=None, tr_token=None, split_token=71):
+        (q_a, k_a, v_a), mod_a, mod_tr = self.component_a(hidden_states_a, conditioning, freqs_cis, token_replace_vec, tr_token)
+        (q_b, k_b, v_b), mod_b, _ = self.component_b(hidden_states_b, conditioning, freqs_cis=None)
+
+        q_a, q_b = torch.concat([q_a, q_b[:, :split_token]], dim=1), q_b[:, split_token:].contiguous()
+        k_a, k_b = torch.concat([k_a, k_b[:, :split_token]], dim=1), k_b[:, split_token:].contiguous()
+        v_a, v_b = torch.concat([v_a, v_b[:, :split_token]], dim=1), v_b[:, split_token:].contiguous()
+        attn_output_a = attention(q_a, k_a, v_a)
+        attn_output_b = attention(q_b, k_b, v_b)
+        attn_output_a, attn_output_b = attn_output_a[:, :-split_token].contiguous(), torch.concat([attn_output_a[:, -split_token:], attn_output_b], dim=1)
+
+        hidden_states_a = self.component_a.process_ff(hidden_states_a, attn_output_a, mod_a, mod_tr, tr_token)
+        hidden_states_b = self.component_b.process_ff(hidden_states_b, attn_output_b, mod_b)
+        return hidden_states_a, hidden_states_b
+
+
+class MMSingleStreamBlockOriginal(torch.nn.Module):
+    def __init__(self, hidden_size=3072, heads_num=24, mlp_width_ratio=4):
+        super().__init__()
+        self.hidden_size = hidden_size
+        self.heads_num = heads_num
+        self.mlp_hidden_dim = hidden_size * mlp_width_ratio
+
+        self.linear1 = torch.nn.Linear(hidden_size, hidden_size * 3 + self.mlp_hidden_dim)
+        self.linear2 = torch.nn.Linear(hidden_size + self.mlp_hidden_dim, hidden_size)
+
+        self.q_norm = RMSNorm(dim=hidden_size // heads_num, eps=1e-6)
+        self.k_norm = RMSNorm(dim=hidden_size // heads_num, eps=1e-6)
+
+        self.pre_norm = torch.nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
+
+        self.mlp_act = torch.nn.GELU(approximate="tanh")
+        self.modulation = ModulateDiT(hidden_size, factor=3)
+
+    def forward(self, x, vec, freqs_cis=None, txt_len=256):
+        mod_shift, mod_scale, mod_gate = self.modulation(vec).chunk(3, dim=-1)
+        x_mod = modulate(self.pre_norm(x), shift=mod_shift, scale=mod_scale)
+        qkv, mlp = torch.split(self.linear1(x_mod), [3 * self.hidden_size, self.mlp_hidden_dim], dim=-1)
+        q, k, v = rearrange(qkv, "B L (K H D) -> K B L H D", K=3, H=self.heads_num)
+        q = self.q_norm(q)
+        k = self.k_norm(k)
+
+        q_a, q_b = q[:, :-txt_len, :, :], q[:, -txt_len:, :, :]
+        k_a, k_b = k[:, :-txt_len, :, :], k[:, -txt_len:, :, :]
+        q_a, k_a = apply_rotary_emb(q_a, k_a, freqs_cis, head_first=False)
+        q = torch.cat((q_a, q_b), dim=1)
+        k = torch.cat((k_a, k_b), dim=1)
+
+        attn_output_a = attention(q[:, :-185].contiguous(), k[:, :-185].contiguous(), v[:, :-185].contiguous())
+        attn_output_b = attention(q[:, -185:].contiguous(), k[:, -185:].contiguous(), v[:, -185:].contiguous())
+        attn_output = torch.concat([attn_output_a, attn_output_b], dim=1)
+
+        output = self.linear2(torch.cat((attn_output, self.mlp_act(mlp)), 2))
+        return x + output * mod_gate.unsqueeze(1)
+
+
+class MMSingleStreamBlock(torch.nn.Module):
+    def __init__(self, hidden_size=3072, heads_num=24, mlp_width_ratio=4):
+        super().__init__()
+        self.heads_num = heads_num
+
+        self.mod = ModulateDiT(hidden_size, factor=3)
+        self.norm = torch.nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
+
+        self.to_qkv = torch.nn.Linear(hidden_size, hidden_size * 3)
+        self.norm_q = RMSNorm(dim=hidden_size // heads_num, eps=1e-6)
+        self.norm_k = RMSNorm(dim=hidden_size // heads_num, eps=1e-6)
+        self.to_out = torch.nn.Linear(hidden_size, hidden_size)
+
+        self.ff = torch.nn.Sequential(
+            torch.nn.Linear(hidden_size, hidden_size * mlp_width_ratio),
+            torch.nn.GELU(approximate="tanh"),
+            torch.nn.Linear(hidden_size * mlp_width_ratio, hidden_size, bias=False)
+        )
+
+    def forward(self, hidden_states, conditioning, freqs_cis=None, txt_len=256, token_replace_vec=None, tr_token=None, split_token=71):
+        mod_shift, mod_scale, mod_gate = self.mod(conditioning).chunk(3, dim=-1)
+        if token_replace_vec is not None:
+            assert tr_token is not None
+            tr_mod_shift, tr_mod_scale, tr_mod_gate = self.mod(token_replace_vec).chunk(3, dim=-1)
+        else:
+            tr_mod_shift, tr_mod_scale, tr_mod_gate = None, None, None
+
+        norm_hidden_states = self.norm(hidden_states)
+        norm_hidden_states = modulate(norm_hidden_states, shift=mod_shift, scale=mod_scale,
+                                      tr_shift=tr_mod_shift, tr_scale=tr_mod_scale, tr_token=tr_token)
+        qkv = self.to_qkv(norm_hidden_states)
+
+        q, k, v = rearrange(qkv, "B L (K H D) -> K B L H D", K=3, H=self.heads_num)
+
+        q = self.norm_q(q)
+        k = self.norm_k(k)
+
+        q_a, q_b = q[:, :-txt_len, :, :], q[:, -txt_len:, :, :]
+        k_a, k_b = k[:, :-txt_len, :, :], k[:, -txt_len:, :, :]
+        q_a, k_a = apply_rotary_emb(q_a, k_a, freqs_cis, head_first=False)
+
+        v_len = txt_len - split_token
+        q_a, q_b = torch.concat([q_a, q_b[:, :split_token]], dim=1), q_b[:, split_token:].contiguous()
+        k_a, k_b = torch.concat([k_a, k_b[:, :split_token]], dim=1), k_b[:, split_token:].contiguous()
+        v_a, v_b = v[:, :-v_len].contiguous(), v[:, -v_len:].contiguous()
+
+        attn_output_a = attention(q_a, k_a, v_a)
+        attn_output_b = attention(q_b, k_b, v_b)
+        attn_output = torch.concat([attn_output_a, attn_output_b], dim=1)
+
+        hidden_states = hidden_states + apply_gate(self.to_out(attn_output), mod_gate, tr_mod_gate, tr_token)
+        hidden_states = hidden_states + apply_gate(self.ff(norm_hidden_states), mod_gate, tr_mod_gate, tr_token)
+        return hidden_states
+
+
+class FinalLayer(torch.nn.Module):
+    def __init__(self, hidden_size=3072, patch_size=(1, 2, 2), out_channels=16):
+        super().__init__()
+
+        self.norm_final = torch.nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
+        self.linear = torch.nn.Linear(hidden_size, patch_size[0] * patch_size[1] * patch_size[2] * out_channels)
+
+        self.adaLN_modulation = torch.nn.Sequential(torch.nn.SiLU(), torch.nn.Linear(hidden_size, 2 * hidden_size))
+
+    def forward(self, x, c):
+        shift, scale = self.adaLN_modulation(c).chunk(2, dim=1)
+        x = modulate(self.norm_final(x), shift=shift, scale=scale)
+        x = self.linear(x)
+        return x
+
+
+class HunyuanVideoDiT(torch.nn.Module):
+    def __init__(self, in_channels=16, hidden_size=3072, text_dim=4096, num_double_blocks=20, num_single_blocks=40, guidance_embed=True):
+        super().__init__()
+        self.img_in = PatchEmbed(in_channels=in_channels, embed_dim=hidden_size)
+        self.txt_in = SingleTokenRefiner(in_channels=text_dim, hidden_size=hidden_size)
+        self.time_in = TimestepEmbeddings(256, hidden_size, computation_device="cpu")
+        self.vector_in = torch.nn.Sequential(
+            torch.nn.Linear(768, hidden_size),
+            torch.nn.SiLU(),
+            torch.nn.Linear(hidden_size, hidden_size)
+        )
+        self.guidance_in = TimestepEmbeddings(256, hidden_size, computation_device="cpu") if guidance_embed else None
+        self.double_blocks = torch.nn.ModuleList([MMDoubleStreamBlock(hidden_size) for _ in range(num_double_blocks)])
+        self.single_blocks = torch.nn.ModuleList([MMSingleStreamBlock(hidden_size) for _ in range(num_single_blocks)])
+        self.final_layer = FinalLayer(hidden_size)
+
+        # TODO: remove these parameters
+        self.dtype = torch.bfloat16
+        self.patch_size = [1, 2, 2]
+        self.hidden_size = 3072
+        self.heads_num = 24
+        self.rope_dim_list = [16, 56, 56]
+
+    def unpatchify(self, x, T, H, W):
+        x = rearrange(x, "B (T H W) (C pT pH pW) -> B C (T pT) (H pH) (W pW)", H=H, W=W, pT=1, pH=2, pW=2)
+        return x
+
+    def enable_block_wise_offload(self, warm_device="cuda", cold_device="cpu"):
+        self.warm_device = warm_device
+        self.cold_device = cold_device
+        self.to(self.cold_device)
+
+    def load_models_to_device(self, loadmodel_names=[], device="cpu"):
+        for model_name in loadmodel_names:
+            model = getattr(self, model_name)
+            if model is not None:
+                model.to(device)
+        torch.cuda.empty_cache()
+
+    def prepare_freqs(self, latents):
+        return HunyuanVideoRope(latents)
+
+    def forward(
+        self,
+        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,
+        **kwargs
+    ):
+        B, C, T, H, W = x.shape
+
+        vec = self.time_in(t, dtype=torch.float32) + self.vector_in(pooled_prompt_emb)
+        if self.guidance_in is not None:
+            vec += self.guidance_in(guidance * 1000, dtype=torch.float32)
+        img = self.img_in(x)
+        txt = self.txt_in(prompt_emb, t, text_mask)
+
+        for block in tqdm(self.double_blocks, desc="Double stream blocks"):
+            img, txt = block(img, txt, vec, (freqs_cos, freqs_sin))
+
+        x = torch.concat([img, txt], dim=1)
+        for block in tqdm(self.single_blocks, desc="Single stream blocks"):
+            x = block(x, vec, (freqs_cos, freqs_sin))
+
+        img = x[:, :-256]
+        img = self.final_layer(img, vec)
+        img = self.unpatchify(img, T=T//1, H=H//2, W=W//2)
+        return img
+
+
+    def enable_auto_offload(self, dtype=torch.bfloat16, device="cuda"):
+        def cast_to(weight, dtype=None, device=None, copy=False):
+            if device is None or weight.device == device:
+                if not copy:
+                    if dtype is None or weight.dtype == dtype:
+                        return weight
+                return weight.to(dtype=dtype, copy=copy)
+
+            r = torch.empty_like(weight, dtype=dtype, device=device)
+            r.copy_(weight)
+            return r
+
+        def cast_weight(s, input=None, dtype=None, device=None):
+            if input is not None:
+                if dtype is None:
+                    dtype = input.dtype
+                if device is None:
+                    device = input.device
+            weight = cast_to(s.weight, dtype, device)
+            return weight
+
+        def cast_bias_weight(s, input=None, dtype=None, device=None, bias_dtype=None):
+            if input is not None:
+                if dtype is None:
+                    dtype = input.dtype
+                if bias_dtype is None:
+                    bias_dtype = dtype
+                if device is None:
+                    device = input.device
+            weight = cast_to(s.weight, dtype, device)
+            bias = cast_to(s.bias, bias_dtype, device) if s.bias is not None else None
+            return weight, bias
+
+        class quantized_layer:
+            class Linear(torch.nn.Linear):
+                def __init__(self, *args, dtype=torch.bfloat16, device="cuda", **kwargs):
+                    super().__init__(*args, **kwargs)
+                    self.dtype = dtype
+                    self.device = device
+
+                def block_forward_(self, x, i, j, dtype, device):
+                    weight_ = cast_to(
+                        self.weight[j * self.block_size: (j + 1) * self.block_size, i * self.block_size: (i + 1) * self.block_size],
+                        dtype=dtype, device=device
+                    )
+                    if self.bias is None or i > 0:
+                        bias_ = None
+                    else:
+                        bias_ = cast_to(self.bias[j * self.block_size: (j + 1) * self.block_size], dtype=dtype, device=device)
+                    x_ = x[..., i * self.block_size: (i + 1) * self.block_size]
+                    y_ = torch.nn.functional.linear(x_, weight_, bias_)
+                    del x_, weight_, bias_
+                    torch.cuda.empty_cache()
+                    return y_
+
+                def block_forward(self, x, **kwargs):
+                    # This feature can only reduce 2GB VRAM, so we disable it.
+                    y = torch.zeros(x.shape[:-1] + (self.out_features,), dtype=x.dtype, device=x.device)
+                    for i in range((self.in_features + self.block_size - 1) // self.block_size):
+                        for j in range((self.out_features + self.block_size - 1) // self.block_size):
+                            y[..., j * self.block_size: (j + 1) * self.block_size] += self.block_forward_(x, i, j, dtype=x.dtype, device=x.device)
+                    return y
+
+                def forward(self, x, **kwargs):
+                    weight, bias = cast_bias_weight(self, x, dtype=self.dtype, device=self.device)
+                    return torch.nn.functional.linear(x, weight, bias)
+
+
+            class RMSNorm(torch.nn.Module):
+                def __init__(self, module, dtype=torch.bfloat16, device="cuda"):
+                    super().__init__()
+                    self.module = module
+                    self.dtype = dtype
+                    self.device = device
+
+                def forward(self, hidden_states, **kwargs):
+                    input_dtype = hidden_states.dtype
+                    variance = hidden_states.to(torch.float32).square().mean(-1, keepdim=True)
+                    hidden_states = hidden_states * torch.rsqrt(variance + self.module.eps)
+                    hidden_states = hidden_states.to(input_dtype)
+                    if self.module.weight is not None:
+                        weight = cast_weight(self.module, hidden_states, dtype=torch.bfloat16, device="cuda")
+                        hidden_states = hidden_states * weight
+                    return hidden_states
+
+            class Conv3d(torch.nn.Conv3d):
+                def __init__(self, *args, dtype=torch.bfloat16, device="cuda", **kwargs):
+                    super().__init__(*args, **kwargs)
+                    self.dtype = dtype
+                    self.device = device
+
+                def forward(self, x):
+                    weight, bias = cast_bias_weight(self, x, dtype=self.dtype, device=self.device)
+                    return torch.nn.functional.conv3d(x, weight, bias, self.stride, self.padding, self.dilation, self.groups)
+
+            class LayerNorm(torch.nn.LayerNorm):
+                def __init__(self, *args, dtype=torch.bfloat16, device="cuda", **kwargs):
+                    super().__init__(*args, **kwargs)
+                    self.dtype = dtype
+                    self.device = device
+
+                def forward(self, x):
+                    if self.weight is not None and self.bias is not None:
+                        weight, bias = cast_bias_weight(self, x, dtype=self.dtype, device=self.device)
+                        return torch.nn.functional.layer_norm(x, self.normalized_shape, weight, bias, self.eps)
+                    else:
+                        return torch.nn.functional.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)
+
+        def replace_layer(model, dtype=torch.bfloat16, device="cuda"):
+            for name, module in model.named_children():
+                if isinstance(module, torch.nn.Linear):
+                    with init_weights_on_device():
+                        new_layer = quantized_layer.Linear(
+                            module.in_features, module.out_features, bias=module.bias is not None,
+                            dtype=dtype, device=device
+                        )
+                    new_layer.load_state_dict(module.state_dict(), assign=True)
+                    setattr(model, name, new_layer)
+                elif isinstance(module, torch.nn.Conv3d):
+                    with init_weights_on_device():
+                        new_layer = quantized_layer.Conv3d(
+                            module.in_channels, module.out_channels, kernel_size=module.kernel_size, stride=module.stride,
+                            dtype=dtype, device=device
+                        )
+                    new_layer.load_state_dict(module.state_dict(), assign=True)
+                    setattr(model, name, new_layer)
+                elif isinstance(module, RMSNorm):
+                    new_layer = quantized_layer.RMSNorm(
+                        module,
+                        dtype=dtype, device=device
+                    )
+                    setattr(model, name, new_layer)
+                elif isinstance(module, torch.nn.LayerNorm):
+                    with init_weights_on_device():
+                        new_layer = quantized_layer.LayerNorm(
+                            module.normalized_shape, elementwise_affine=module.elementwise_affine, eps=module.eps,
+                            dtype=dtype, device=device
+                        )
+                    new_layer.load_state_dict(module.state_dict(), assign=True)
+                    setattr(model, name, new_layer)
+                else:
+                    replace_layer(module, dtype=dtype, device=device)
+
+        replace_layer(self, dtype=dtype, device=device)
+
+    @staticmethod
+    def state_dict_converter():
+        return HunyuanVideoDiTStateDictConverter()
+
+
+class HunyuanVideoDiTStateDictConverter:
+    def __init__(self):
+        pass
+
+    def from_civitai(self, state_dict):
+        origin_hash_key = hash_state_dict_keys(state_dict, with_shape=True)
+        if "module" in state_dict:
+            state_dict = state_dict["module"]
+        direct_dict = {
+            "img_in.proj": "img_in.proj",
+            "time_in.mlp.0": "time_in.timestep_embedder.0",
+            "time_in.mlp.2": "time_in.timestep_embedder.2",
+            "vector_in.in_layer": "vector_in.0",
+            "vector_in.out_layer": "vector_in.2",
+            "guidance_in.mlp.0": "guidance_in.timestep_embedder.0",
+            "guidance_in.mlp.2": "guidance_in.timestep_embedder.2",
+            "txt_in.input_embedder": "txt_in.input_embedder",
+            "txt_in.t_embedder.mlp.0": "txt_in.t_embedder.timestep_embedder.0",
+            "txt_in.t_embedder.mlp.2": "txt_in.t_embedder.timestep_embedder.2",
+            "txt_in.c_embedder.linear_1": "txt_in.c_embedder.0",
+            "txt_in.c_embedder.linear_2": "txt_in.c_embedder.2",
+            "final_layer.linear": "final_layer.linear",
+            "final_layer.adaLN_modulation.1": "final_layer.adaLN_modulation.1",
+        }
+        txt_suffix_dict = {
+            "norm1": "norm1",
+            "self_attn_qkv": "self_attn_qkv",
+            "self_attn_proj": "self_attn_proj",
+            "norm2": "norm2",
+            "mlp.fc1": "mlp.0",
+            "mlp.fc2": "mlp.2",
+            "adaLN_modulation.1": "adaLN_modulation.1",
+        }
+        double_suffix_dict = {
+            "img_mod.linear": "component_a.mod.linear",
+            "img_attn_qkv": "component_a.to_qkv",
+            "img_attn_q_norm": "component_a.norm_q",
+            "img_attn_k_norm": "component_a.norm_k",
+            "img_attn_proj": "component_a.to_out",
+            "img_mlp.fc1": "component_a.ff.0",
+            "img_mlp.fc2": "component_a.ff.2",
+            "txt_mod.linear": "component_b.mod.linear",
+            "txt_attn_qkv": "component_b.to_qkv",
+            "txt_attn_q_norm": "component_b.norm_q",
+            "txt_attn_k_norm": "component_b.norm_k",
+            "txt_attn_proj": "component_b.to_out",
+            "txt_mlp.fc1": "component_b.ff.0",
+            "txt_mlp.fc2": "component_b.ff.2",
+        }
+        single_suffix_dict = {
+            "linear1": ["to_qkv", "ff.0"],
+            "linear2": ["to_out", "ff.2"],
+            "q_norm": "norm_q",
+            "k_norm": "norm_k",
+            "modulation.linear": "mod.linear",
+        }
+        # single_suffix_dict = {
+        #     "linear1": "linear1",
+        #     "linear2": "linear2",
+        #     "q_norm": "q_norm",
+        #     "k_norm": "k_norm",
+        #     "modulation.linear": "modulation.linear",
+        # }
+        state_dict_ = {}
+        for name, param in state_dict.items():
+            names = name.split(".")
+            direct_name = ".".join(names[:-1])
+            if direct_name in direct_dict:
+                name_ = direct_dict[direct_name] + "." + names[-1]
+                state_dict_[name_] = param
+            elif names[0] == "double_blocks":
+                prefix = ".".join(names[:2])
+                suffix = ".".join(names[2:-1])
+                name_ = prefix + "." + double_suffix_dict[suffix] + "." + names[-1]
+                state_dict_[name_] = param
+            elif names[0] == "single_blocks":
+                prefix = ".".join(names[:2])
+                suffix = ".".join(names[2:-1])
+                if isinstance(single_suffix_dict[suffix], list):
+                    if suffix == "linear1":
+                        name_a, name_b = single_suffix_dict[suffix]
+                        param_a, param_b = torch.split(param, (3072*3, 3072*4), dim=0)
+                        state_dict_[prefix + "." + name_a + "." + names[-1]] = param_a
+                        state_dict_[prefix + "." + name_b + "." + names[-1]] = param_b
+                    elif suffix == "linear2":
+                        if names[-1] == "weight":
+                            name_a, name_b = single_suffix_dict[suffix]
+                            param_a, param_b = torch.split(param, (3072*1, 3072*4), dim=-1)
+                            state_dict_[prefix + "." + name_a + "." + names[-1]] = param_a
+                            state_dict_[prefix + "." + name_b + "." + names[-1]] = param_b
+                        else:
+                            name_a, name_b = single_suffix_dict[suffix]
+                            state_dict_[prefix + "." + name_a + "." + names[-1]] = param
+                    else:
+                        pass
+                else:
+                    name_ = prefix + "." + single_suffix_dict[suffix] + "." + names[-1]
+                    state_dict_[name_] = param
+            elif names[0] == "txt_in":
+                prefix = ".".join(names[:4]).replace(".individual_token_refiner.", ".")
+                suffix = ".".join(names[4:-1])
+                name_ = prefix + "." + txt_suffix_dict[suffix] + "." + names[-1]
+                state_dict_[name_] = param
+            else:
+                pass
+
+        return state_dict_
diff --git a/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/hunyuan_video_text_encoder.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/hunyuan_video_text_encoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..ce7a6805a163c709a4f3de82784538b300541119
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/hunyuan_video_text_encoder.py
@@ -0,0 +1,68 @@
+from transformers import LlamaModel, LlamaConfig, DynamicCache, LlavaForConditionalGeneration
+from copy import deepcopy
+import torch
+
+
+class HunyuanVideoLLMEncoder(LlamaModel):
+
+    def __init__(self, config: LlamaConfig):
+        super().__init__(config)
+        self.auto_offload = False
+
+    def enable_auto_offload(self, **kwargs):
+        self.auto_offload = True
+
+    def forward(self, input_ids, attention_mask, hidden_state_skip_layer=2):
+        embed_tokens = deepcopy(self.embed_tokens).to(input_ids.device) if self.auto_offload else self.embed_tokens
+        inputs_embeds = embed_tokens(input_ids)
+
+        past_key_values = DynamicCache()
+
+        cache_position = torch.arange(0, inputs_embeds.shape[1], device=inputs_embeds.device)
+        position_ids = cache_position.unsqueeze(0)
+
+        causal_mask = self._update_causal_mask(attention_mask, inputs_embeds, cache_position, None, False)
+        hidden_states = inputs_embeds
+
+        # create position embeddings to be shared across the decoder layers
+        rotary_emb = deepcopy(self.rotary_emb).to(input_ids.device) if self.auto_offload else self.rotary_emb
+        position_embeddings = rotary_emb(hidden_states, position_ids)
+
+        # decoder layers
+        for layer_id, decoder_layer in enumerate(self.layers):
+            if self.auto_offload:
+                decoder_layer = deepcopy(decoder_layer).to(hidden_states.device)
+            layer_outputs = decoder_layer(
+                hidden_states,
+                attention_mask=causal_mask,
+                position_ids=position_ids,
+                past_key_value=past_key_values,
+                output_attentions=False,
+                use_cache=True,
+                cache_position=cache_position,
+                position_embeddings=position_embeddings,
+            )
+            hidden_states = layer_outputs[0]
+            if layer_id + hidden_state_skip_layer + 1 >= len(self.layers):
+                break
+
+        return hidden_states
+
+
+class HunyuanVideoMLLMEncoder(LlavaForConditionalGeneration):
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.auto_offload = False
+
+    def enable_auto_offload(self, **kwargs):
+        self.auto_offload = True
+
+    # TODO: implement the low VRAM inference for MLLM.
+    def forward(self, input_ids, pixel_values, attention_mask, hidden_state_skip_layer=2):
+        outputs = super().forward(input_ids=input_ids,
+                                  attention_mask=attention_mask,
+                                  output_hidden_states=True,
+                                  pixel_values=pixel_values)
+        hidden_state = outputs.hidden_states[-(hidden_state_skip_layer + 1)]
+        return hidden_state
diff --git a/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/hunyuan_video_vae_decoder.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/hunyuan_video_vae_decoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..ae09ff85a9149edd46a36e7bfeb29a372a81e12c
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/hunyuan_video_vae_decoder.py
@@ -0,0 +1,507 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from einops import rearrange
+import numpy as np
+from tqdm import tqdm
+from einops import repeat
+
+
+class CausalConv3d(nn.Module):
+
+    def __init__(self, in_channel, out_channel, kernel_size, stride=1, dilation=1, pad_mode='replicate', **kwargs):
+        super().__init__()
+        self.pad_mode = pad_mode
+        self.time_causal_padding = (kernel_size // 2, kernel_size // 2, kernel_size // 2, kernel_size // 2, kernel_size - 1, 0
+                                   )  # W, H, T
+        self.conv = nn.Conv3d(in_channel, out_channel, kernel_size, stride=stride, dilation=dilation, **kwargs)
+
+    def forward(self, x):
+        x = F.pad(x, self.time_causal_padding, mode=self.pad_mode)
+        return self.conv(x)
+
+
+class UpsampleCausal3D(nn.Module):
+
+    def __init__(self, channels, use_conv=False, out_channels=None, kernel_size=None, bias=True, upsample_factor=(2, 2, 2)):
+        super().__init__()
+        self.channels = channels
+        self.out_channels = out_channels or channels
+        self.upsample_factor = upsample_factor
+        self.conv = None
+        if use_conv:
+            kernel_size = 3 if kernel_size is None else kernel_size
+            self.conv = CausalConv3d(self.channels, self.out_channels, kernel_size=kernel_size, bias=bias)
+
+    def forward(self, hidden_states):
+        # Cast to float32 to as 'upsample_nearest2d_out_frame' op does not support bfloat16
+        dtype = hidden_states.dtype
+        if dtype == torch.bfloat16:
+            hidden_states = hidden_states.to(torch.float32)
+
+        # upsample_nearest_nhwc fails with large batch sizes. see https://github.com/huggingface/diffusers/issues/984
+        if hidden_states.shape[0] >= 64:
+            hidden_states = hidden_states.contiguous()
+
+        # interpolate
+        B, C, T, H, W = hidden_states.shape
+        first_h, other_h = hidden_states.split((1, T - 1), dim=2)
+        if T > 1:
+            other_h = F.interpolate(other_h, scale_factor=self.upsample_factor, mode="nearest")
+        first_h = F.interpolate(first_h.squeeze(2), scale_factor=self.upsample_factor[1:], mode="nearest").unsqueeze(2)
+        hidden_states = torch.cat((first_h, other_h), dim=2) if T > 1 else first_h
+
+        # If the input is bfloat16, we cast back to bfloat16
+        if dtype == torch.bfloat16:
+            hidden_states = hidden_states.to(dtype)
+
+        if self.conv:
+            hidden_states = self.conv(hidden_states)
+
+        return hidden_states
+
+
+class ResnetBlockCausal3D(nn.Module):
+
+    def __init__(self, in_channels, out_channels=None, dropout=0.0, groups=32, eps=1e-6, conv_shortcut_bias=True):
+        super().__init__()
+        self.pre_norm = True
+        self.in_channels = in_channels
+        out_channels = in_channels if out_channels is None else out_channels
+        self.out_channels = out_channels
+
+        self.norm1 = nn.GroupNorm(num_groups=groups, num_channels=in_channels, eps=eps, affine=True)
+        self.conv1 = CausalConv3d(in_channels, out_channels, kernel_size=3, stride=1)
+
+        self.norm2 = nn.GroupNorm(num_groups=groups, num_channels=out_channels, eps=eps, affine=True)
+        self.conv2 = CausalConv3d(out_channels, out_channels, kernel_size=3, stride=1)
+
+        self.dropout = nn.Dropout(dropout)
+        self.nonlinearity = nn.SiLU()
+
+        self.conv_shortcut = None
+        if in_channels != out_channels:
+            self.conv_shortcut = CausalConv3d(in_channels, out_channels, kernel_size=1, stride=1, bias=conv_shortcut_bias)
+
+    def forward(self, input_tensor):
+        hidden_states = input_tensor
+        # conv1
+        hidden_states = self.norm1(hidden_states)
+        hidden_states = self.nonlinearity(hidden_states)
+        hidden_states = self.conv1(hidden_states)
+
+        # conv2
+        hidden_states = self.norm2(hidden_states)
+        hidden_states = self.nonlinearity(hidden_states)
+        hidden_states = self.dropout(hidden_states)
+        hidden_states = self.conv2(hidden_states)
+        # shortcut
+        if self.conv_shortcut is not None:
+            input_tensor = (self.conv_shortcut(input_tensor))
+        # shortcut and scale
+        output_tensor = input_tensor + hidden_states
+
+        return output_tensor
+
+
+def prepare_causal_attention_mask(n_frame, n_hw, dtype, device, batch_size=None):
+    seq_len = n_frame * n_hw
+    mask = torch.full((seq_len, seq_len), float("-inf"), dtype=dtype, device=device)
+    for i in range(seq_len):
+        i_frame = i // n_hw
+        mask[i, :(i_frame + 1) * n_hw] = 0
+    if batch_size is not None:
+        mask = mask.unsqueeze(0).expand(batch_size, -1, -1)
+    return mask
+
+
+class Attention(nn.Module):
+
+    def __init__(self,
+                 in_channels,
+                 num_heads,
+                 head_dim,
+                 num_groups=32,
+                 dropout=0.0,
+                 eps=1e-6,
+                 bias=True,
+                 residual_connection=True):
+        super().__init__()
+        self.num_heads = num_heads
+        self.head_dim = head_dim
+        self.residual_connection = residual_connection
+        dim_inner = head_dim * num_heads
+        self.group_norm = nn.GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=eps, affine=True)
+        self.to_q = nn.Linear(in_channels, dim_inner, bias=bias)
+        self.to_k = nn.Linear(in_channels, dim_inner, bias=bias)
+        self.to_v = nn.Linear(in_channels, dim_inner, bias=bias)
+        self.to_out = nn.Sequential(nn.Linear(dim_inner, in_channels, bias=bias), nn.Dropout(dropout))
+
+    def forward(self, input_tensor, attn_mask=None):
+        hidden_states = self.group_norm(input_tensor.transpose(1, 2)).transpose(1, 2)
+        batch_size = hidden_states.shape[0]
+
+        q = self.to_q(hidden_states)
+        k = self.to_k(hidden_states)
+        v = self.to_v(hidden_states)
+
+        q = q.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2)
+        k = k.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2)
+        v = v.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2)
+
+        if attn_mask is not None:
+            attn_mask = attn_mask.view(batch_size, self.num_heads, -1, attn_mask.shape[-1])
+        hidden_states = F.scaled_dot_product_attention(q, k, v, attn_mask=attn_mask)
+        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, self.num_heads * self.head_dim)
+        hidden_states = self.to_out(hidden_states)
+        if self.residual_connection:
+            output_tensor = input_tensor + hidden_states
+        return output_tensor
+
+
+class UNetMidBlockCausal3D(nn.Module):
+
+    def __init__(self, in_channels, dropout=0.0, num_layers=1, eps=1e-6, num_groups=32, attention_head_dim=None):
+        super().__init__()
+        resnets = [
+            ResnetBlockCausal3D(
+                in_channels=in_channels,
+                out_channels=in_channels,
+                dropout=dropout,
+                groups=num_groups,
+                eps=eps,
+            )
+        ]
+        attentions = []
+        attention_head_dim = attention_head_dim or in_channels
+
+        for _ in range(num_layers):
+            attentions.append(
+                Attention(
+                    in_channels,
+                    num_heads=in_channels // attention_head_dim,
+                    head_dim=attention_head_dim,
+                    num_groups=num_groups,
+                    dropout=dropout,
+                    eps=eps,
+                    bias=True,
+                    residual_connection=True,
+                ))
+
+            resnets.append(
+                ResnetBlockCausal3D(
+                    in_channels=in_channels,
+                    out_channels=in_channels,
+                    dropout=dropout,
+                    groups=num_groups,
+                    eps=eps,
+                ))
+
+        self.attentions = nn.ModuleList(attentions)
+        self.resnets = nn.ModuleList(resnets)
+
+    def forward(self, hidden_states):
+        hidden_states = self.resnets[0](hidden_states)
+        for attn, resnet in zip(self.attentions, self.resnets[1:]):
+            B, C, T, H, W = hidden_states.shape
+            hidden_states = rearrange(hidden_states, "b c f h w -> b (f h w) c")
+            attn_mask = prepare_causal_attention_mask(T, H * W, hidden_states.dtype, hidden_states.device, batch_size=B)
+            hidden_states = attn(hidden_states, attn_mask=attn_mask)
+            hidden_states = rearrange(hidden_states, "b (f h w) c -> b c f h w", f=T, h=H, w=W)
+            hidden_states = resnet(hidden_states)
+
+        return hidden_states
+
+
+class UpDecoderBlockCausal3D(nn.Module):
+
+    def __init__(
+            self,
+            in_channels,
+            out_channels,
+            dropout=0.0,
+            num_layers=1,
+            eps=1e-6,
+            num_groups=32,
+            add_upsample=True,
+            upsample_scale_factor=(2, 2, 2),
+    ):
+        super().__init__()
+        resnets = []
+        for i in range(num_layers):
+            cur_in_channel = in_channels if i == 0 else out_channels
+            resnets.append(
+                ResnetBlockCausal3D(
+                    in_channels=cur_in_channel,
+                    out_channels=out_channels,
+                    groups=num_groups,
+                    dropout=dropout,
+                    eps=eps,
+                ))
+        self.resnets = nn.ModuleList(resnets)
+
+        self.upsamplers = None
+        if add_upsample:
+            self.upsamplers = nn.ModuleList([
+                UpsampleCausal3D(
+                    out_channels,
+                    use_conv=True,
+                    out_channels=out_channels,
+                    upsample_factor=upsample_scale_factor,
+                )
+            ])
+
+    def forward(self, hidden_states):
+        for resnet in self.resnets:
+            hidden_states = resnet(hidden_states)
+        if self.upsamplers is not None:
+            for upsampler in self.upsamplers:
+                hidden_states = upsampler(hidden_states)
+        return hidden_states
+
+
+class DecoderCausal3D(nn.Module):
+
+    def __init__(
+        self,
+        in_channels=16,
+        out_channels=3,
+        eps=1e-6,
+        dropout=0.0,
+        block_out_channels=[128, 256, 512, 512],
+        layers_per_block=2,
+        num_groups=32,
+        time_compression_ratio=4,
+        spatial_compression_ratio=8,
+        gradient_checkpointing=False,
+    ):
+        super().__init__()
+        self.layers_per_block = layers_per_block
+
+        self.conv_in = CausalConv3d(in_channels, block_out_channels[-1], kernel_size=3, stride=1)
+        self.up_blocks = nn.ModuleList([])
+
+        # mid
+        self.mid_block = UNetMidBlockCausal3D(
+            in_channels=block_out_channels[-1],
+            dropout=dropout,
+            eps=eps,
+            num_groups=num_groups,
+            attention_head_dim=block_out_channels[-1],
+        )
+
+        # up
+        reversed_block_out_channels = list(reversed(block_out_channels))
+        output_channel = reversed_block_out_channels[0]
+        for i in range(len(block_out_channels)):
+            prev_output_channel = output_channel
+            output_channel = reversed_block_out_channels[i]
+            is_final_block = i == len(block_out_channels) - 1
+            num_spatial_upsample_layers = int(np.log2(spatial_compression_ratio))
+            num_time_upsample_layers = int(np.log2(time_compression_ratio))
+
+            add_spatial_upsample = bool(i < num_spatial_upsample_layers)
+            add_time_upsample = bool(i >= len(block_out_channels) - 1 - num_time_upsample_layers and not is_final_block)
+
+            upsample_scale_factor_HW = (2, 2) if add_spatial_upsample else (1, 1)
+            upsample_scale_factor_T = (2,) if add_time_upsample else (1,)
+            upsample_scale_factor = tuple(upsample_scale_factor_T + upsample_scale_factor_HW)
+
+            up_block = UpDecoderBlockCausal3D(
+                in_channels=prev_output_channel,
+                out_channels=output_channel,
+                dropout=dropout,
+                num_layers=layers_per_block + 1,
+                eps=eps,
+                num_groups=num_groups,
+                add_upsample=bool(add_spatial_upsample or add_time_upsample),
+                upsample_scale_factor=upsample_scale_factor,
+            )
+
+            self.up_blocks.append(up_block)
+            prev_output_channel = output_channel
+
+        # out
+        self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[0], num_groups=num_groups, eps=eps)
+        self.conv_act = nn.SiLU()
+        self.conv_out = CausalConv3d(block_out_channels[0], out_channels, kernel_size=3)
+
+        self.gradient_checkpointing = gradient_checkpointing
+
+    def forward(self, hidden_states):
+        hidden_states = self.conv_in(hidden_states)
+        if self.training and self.gradient_checkpointing:
+
+            def create_custom_forward(module):
+
+                def custom_forward(*inputs):
+                    return module(*inputs)
+
+                return custom_forward
+
+            # middle
+            hidden_states = torch.utils.checkpoint.checkpoint(
+                create_custom_forward(self.mid_block),
+                hidden_states,
+                use_reentrant=False,
+            )
+            # up
+            for up_block in self.up_blocks:
+                hidden_states = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(up_block),
+                    hidden_states,
+                    use_reentrant=False,
+                )
+        else:
+            # middle
+            hidden_states = self.mid_block(hidden_states)
+            # up
+            for up_block in self.up_blocks:
+                hidden_states = up_block(hidden_states)
+        # post-process
+        hidden_states = self.conv_norm_out(hidden_states)
+        hidden_states = self.conv_act(hidden_states)
+        hidden_states = self.conv_out(hidden_states)
+
+        return hidden_states
+
+
+class HunyuanVideoVAEDecoder(nn.Module):
+
+    def __init__(
+        self,
+        in_channels=16,
+        out_channels=3,
+        eps=1e-6,
+        dropout=0.0,
+        block_out_channels=[128, 256, 512, 512],
+        layers_per_block=2,
+        num_groups=32,
+        time_compression_ratio=4,
+        spatial_compression_ratio=8,
+        gradient_checkpointing=False,
+    ):
+        super().__init__()
+        self.decoder = DecoderCausal3D(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            eps=eps,
+            dropout=dropout,
+            block_out_channels=block_out_channels,
+            layers_per_block=layers_per_block,
+            num_groups=num_groups,
+            time_compression_ratio=time_compression_ratio,
+            spatial_compression_ratio=spatial_compression_ratio,
+            gradient_checkpointing=gradient_checkpointing,
+        )
+        self.post_quant_conv = nn.Conv3d(in_channels, in_channels, kernel_size=1)
+        self.scaling_factor = 0.476986
+
+
+    def forward(self, latents):
+        latents = latents / self.scaling_factor
+        latents = self.post_quant_conv(latents)
+        dec = self.decoder(latents)
+        return dec
+    
+
+    def build_1d_mask(self, length, left_bound, right_bound, border_width):
+        x = torch.ones((length,))
+        if not left_bound:
+            x[:border_width] = (torch.arange(border_width) + 1) / border_width
+        if not right_bound:
+            x[-border_width:] = torch.flip((torch.arange(border_width) + 1) / border_width, dims=(0,))
+        return x
+    
+
+    def build_mask(self, data, is_bound, border_width):
+        _, _, T, H, W = data.shape
+        t = self.build_1d_mask(T, is_bound[0], is_bound[1], border_width[0])
+        h = self.build_1d_mask(H, is_bound[2], is_bound[3], border_width[1])
+        w = self.build_1d_mask(W, is_bound[4], is_bound[5], border_width[2])
+
+        t = repeat(t, "T -> T H W", T=T, H=H, W=W)
+        h = repeat(h, "H -> T H W", T=T, H=H, W=W)
+        w = repeat(w, "W -> T H W", T=T, H=H, W=W)
+
+        mask = torch.stack([t, h, w]).min(dim=0).values
+        mask = rearrange(mask, "T H W -> 1 1 T H W")
+        return mask
+    
+
+    def tile_forward(self, hidden_states, tile_size, tile_stride):
+        B, C, T, H, W = hidden_states.shape
+        size_t, size_h, size_w = tile_size
+        stride_t, stride_h, stride_w = tile_stride
+
+        # Split tasks
+        tasks = []
+        for t in range(0, T, stride_t):
+            if (t-stride_t >= 0 and t-stride_t+size_t >= T): continue
+            for h in range(0, H, stride_h):
+                if (h-stride_h >= 0 and h-stride_h+size_h >= H): continue
+                for w in range(0, W, stride_w):
+                    if (w-stride_w >= 0 and w-stride_w+size_w >= W): continue
+                    t_, h_, w_ = t + size_t, h + size_h, w + size_w
+                    tasks.append((t, t_, h, h_, w, w_))
+
+        # Run
+        torch_dtype = self.post_quant_conv.weight.dtype
+        data_device = hidden_states.device
+        computation_device = self.post_quant_conv.weight.device
+
+        weight = torch.zeros((1, 1, (T - 1) * 4 + 1, H * 8, W * 8), dtype=torch_dtype, device=data_device)
+        values = torch.zeros((B, 3, (T - 1) * 4 + 1, H * 8, W * 8), dtype=torch_dtype, device=data_device)
+
+        for t, t_, h, h_, w, w_ in tqdm(tasks, desc="VAE decoding"):
+            hidden_states_batch = hidden_states[:, :, t:t_, h:h_, w:w_].to(computation_device)
+            hidden_states_batch = self.forward(hidden_states_batch).to(data_device)
+            if t > 0:
+                hidden_states_batch = hidden_states_batch[:, :, 1:]
+
+            mask = self.build_mask(
+                hidden_states_batch,
+                is_bound=(t==0, t_>=T, h==0, h_>=H, w==0, w_>=W),
+                border_width=((size_t - stride_t) * 4, (size_h - stride_h) * 8, (size_w - stride_w) * 8)
+            ).to(dtype=torch_dtype, device=data_device)
+
+            target_t = 0 if t==0 else t * 4 + 1
+            target_h = h * 8
+            target_w = w * 8
+            values[
+                :,
+                :,
+                target_t: target_t + hidden_states_batch.shape[2],
+                target_h: target_h + hidden_states_batch.shape[3],
+                target_w: target_w + hidden_states_batch.shape[4],
+            ] += hidden_states_batch * mask
+            weight[
+                :,
+                :,
+                target_t: target_t + hidden_states_batch.shape[2],
+                target_h: target_h + hidden_states_batch.shape[3],
+                target_w: target_w + hidden_states_batch.shape[4],
+            ] += mask
+        return values / weight
+
+
+    def decode_video(self, latents, tile_size=(17, 32, 32), tile_stride=(12, 24, 24)):
+        latents = latents.to(self.post_quant_conv.weight.dtype)
+        return self.tile_forward(latents, tile_size=tile_size, tile_stride=tile_stride)
+
+    @staticmethod
+    def state_dict_converter():
+        return HunyuanVideoVAEDecoderStateDictConverter()
+
+
+class HunyuanVideoVAEDecoderStateDictConverter:
+
+    def __init__(self):
+        pass
+
+    def from_diffusers(self, state_dict):
+        state_dict_ = {}
+        for name in state_dict:
+            if name.startswith('decoder.') or name.startswith('post_quant_conv.'):
+                state_dict_[name] = state_dict[name]
+        return state_dict_
diff --git a/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/hunyuan_video_vae_encoder.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/hunyuan_video_vae_encoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..faaaeb95f7d688d57fb61a707c7658a89bb2c92a
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/hunyuan_video_vae_encoder.py
@@ -0,0 +1,307 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from einops import rearrange, repeat
+import numpy as np
+from tqdm import tqdm
+from .hunyuan_video_vae_decoder import CausalConv3d, ResnetBlockCausal3D, UNetMidBlockCausal3D
+
+
+class DownsampleCausal3D(nn.Module):
+
+    def __init__(self, channels, out_channels, kernel_size=3, bias=True, stride=2):
+        super().__init__()
+        self.conv = CausalConv3d(channels, out_channels, kernel_size, stride=stride, bias=bias)
+
+    def forward(self, hidden_states):
+        hidden_states = self.conv(hidden_states)
+        return hidden_states
+
+
+class DownEncoderBlockCausal3D(nn.Module):
+
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        dropout=0.0,
+        num_layers=1,
+        eps=1e-6,
+        num_groups=32,
+        add_downsample=True,
+        downsample_stride=2,
+    ):
+
+        super().__init__()
+        resnets = []
+        for i in range(num_layers):
+            cur_in_channel = in_channels if i == 0 else out_channels
+            resnets.append(
+                ResnetBlockCausal3D(
+                    in_channels=cur_in_channel,
+                    out_channels=out_channels,
+                    groups=num_groups,
+                    dropout=dropout,
+                    eps=eps,
+                ))
+        self.resnets = nn.ModuleList(resnets)
+
+        self.downsamplers = None
+        if add_downsample:
+            self.downsamplers = nn.ModuleList([DownsampleCausal3D(
+                out_channels,
+                out_channels,
+                stride=downsample_stride,
+            )])
+
+    def forward(self, hidden_states):
+        for resnet in self.resnets:
+            hidden_states = resnet(hidden_states)
+
+        if self.downsamplers is not None:
+            for downsampler in self.downsamplers:
+                hidden_states = downsampler(hidden_states)
+
+        return hidden_states
+
+
+class EncoderCausal3D(nn.Module):
+
+    def __init__(
+        self,
+        in_channels: int = 3,
+        out_channels: int = 16,
+        eps=1e-6,
+        dropout=0.0,
+        block_out_channels=[128, 256, 512, 512],
+        layers_per_block=2,
+        num_groups=32,
+        time_compression_ratio: int = 4,
+        spatial_compression_ratio: int = 8,
+        gradient_checkpointing=False,
+    ):
+        super().__init__()
+        self.conv_in = CausalConv3d(in_channels, block_out_channels[0], kernel_size=3, stride=1)
+        self.down_blocks = nn.ModuleList([])
+
+        # down
+        output_channel = block_out_channels[0]
+        for i in range(len(block_out_channels)):
+            input_channel = output_channel
+            output_channel = block_out_channels[i]
+            is_final_block = i == len(block_out_channels) - 1
+            num_spatial_downsample_layers = int(np.log2(spatial_compression_ratio))
+            num_time_downsample_layers = int(np.log2(time_compression_ratio))
+
+            add_spatial_downsample = bool(i < num_spatial_downsample_layers)
+            add_time_downsample = bool(i >= (len(block_out_channels) - 1 - num_time_downsample_layers) and not is_final_block)
+
+            downsample_stride_HW = (2, 2) if add_spatial_downsample else (1, 1)
+            downsample_stride_T = (2,) if add_time_downsample else (1,)
+            downsample_stride = tuple(downsample_stride_T + downsample_stride_HW)
+            down_block = DownEncoderBlockCausal3D(
+                in_channels=input_channel,
+                out_channels=output_channel,
+                dropout=dropout,
+                num_layers=layers_per_block,
+                eps=eps,
+                num_groups=num_groups,
+                add_downsample=bool(add_spatial_downsample or add_time_downsample),
+                downsample_stride=downsample_stride,
+            )
+            self.down_blocks.append(down_block)
+
+        # mid
+        self.mid_block = UNetMidBlockCausal3D(
+            in_channels=block_out_channels[-1],
+            dropout=dropout,
+            eps=eps,
+            num_groups=num_groups,
+            attention_head_dim=block_out_channels[-1],
+        )
+        # out
+        self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[-1], num_groups=num_groups, eps=eps)
+        self.conv_act = nn.SiLU()
+        self.conv_out = CausalConv3d(block_out_channels[-1], 2 * out_channels, kernel_size=3)
+
+        self.gradient_checkpointing = gradient_checkpointing
+
+    def forward(self, hidden_states):
+        hidden_states = self.conv_in(hidden_states)
+        if self.training and self.gradient_checkpointing:
+
+            def create_custom_forward(module):
+
+                def custom_forward(*inputs):
+                    return module(*inputs)
+
+                return custom_forward
+
+            # down
+            for down_block in self.down_blocks:
+                torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(down_block),
+                    hidden_states,
+                    use_reentrant=False,
+                )
+            # middle
+            hidden_states = torch.utils.checkpoint.checkpoint(
+                create_custom_forward(self.mid_block),
+                hidden_states,
+                use_reentrant=False,
+            )
+        else:
+            # down
+            for down_block in self.down_blocks:
+                hidden_states = down_block(hidden_states)
+            # middle
+            hidden_states = self.mid_block(hidden_states)
+        # post-process
+        hidden_states = self.conv_norm_out(hidden_states)
+        hidden_states = self.conv_act(hidden_states)
+        hidden_states = self.conv_out(hidden_states)
+
+        return hidden_states
+
+
+class HunyuanVideoVAEEncoder(nn.Module):
+
+    def __init__(
+        self,
+        in_channels=3,
+        out_channels=16,
+        eps=1e-6,
+        dropout=0.0,
+        block_out_channels=[128, 256, 512, 512],
+        layers_per_block=2,
+        num_groups=32,
+        time_compression_ratio=4,
+        spatial_compression_ratio=8,
+        gradient_checkpointing=False,
+    ):
+        super().__init__()
+        self.encoder = EncoderCausal3D(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            eps=eps,
+            dropout=dropout,
+            block_out_channels=block_out_channels,
+            layers_per_block=layers_per_block,
+            num_groups=num_groups,
+            time_compression_ratio=time_compression_ratio,
+            spatial_compression_ratio=spatial_compression_ratio,
+            gradient_checkpointing=gradient_checkpointing,
+        )
+        self.quant_conv = nn.Conv3d(2 * out_channels, 2 * out_channels, kernel_size=1)
+        self.scaling_factor = 0.476986
+
+
+    def forward(self, images):
+        latents = self.encoder(images)
+        latents = self.quant_conv(latents)
+        latents = latents[:, :16]
+        latents = latents * self.scaling_factor
+        return latents
+    
+
+    def build_1d_mask(self, length, left_bound, right_bound, border_width):
+        x = torch.ones((length,))
+        if not left_bound:
+            x[:border_width] = (torch.arange(border_width) + 1) / border_width
+        if not right_bound:
+            x[-border_width:] = torch.flip((torch.arange(border_width) + 1) / border_width, dims=(0,))
+        return x
+    
+
+    def build_mask(self, data, is_bound, border_width):
+        _, _, T, H, W = data.shape
+        t = self.build_1d_mask(T, is_bound[0], is_bound[1], border_width[0])
+        h = self.build_1d_mask(H, is_bound[2], is_bound[3], border_width[1])
+        w = self.build_1d_mask(W, is_bound[4], is_bound[5], border_width[2])
+
+        t = repeat(t, "T -> T H W", T=T, H=H, W=W)
+        h = repeat(h, "H -> T H W", T=T, H=H, W=W)
+        w = repeat(w, "W -> T H W", T=T, H=H, W=W)
+
+        mask = torch.stack([t, h, w]).min(dim=0).values
+        mask = rearrange(mask, "T H W -> 1 1 T H W")
+        return mask
+    
+
+    def tile_forward(self, hidden_states, tile_size, tile_stride):
+        B, C, T, H, W = hidden_states.shape
+        size_t, size_h, size_w = tile_size
+        stride_t, stride_h, stride_w = tile_stride
+
+        # Split tasks
+        tasks = []
+        for t in range(0, T, stride_t):
+            if (t-stride_t >= 0 and t-stride_t+size_t >= T): continue
+            for h in range(0, H, stride_h):
+                if (h-stride_h >= 0 and h-stride_h+size_h >= H): continue
+                for w in range(0, W, stride_w):
+                    if (w-stride_w >= 0 and w-stride_w+size_w >= W): continue
+                    t_, h_, w_ = t + size_t, h + size_h, w + size_w
+                    tasks.append((t, t_, h, h_, w, w_))
+
+        # Run
+        torch_dtype = self.quant_conv.weight.dtype
+        data_device = hidden_states.device
+        computation_device = self.quant_conv.weight.device
+
+        weight = torch.zeros((1, 1,  (T - 1) // 4 + 1, H // 8, W // 8), dtype=torch_dtype, device=data_device)
+        values = torch.zeros((B, 16, (T - 1) // 4 + 1, H // 8, W // 8), dtype=torch_dtype, device=data_device)
+
+        for t, t_, h, h_, w, w_ in tqdm(tasks, desc="VAE encoding"):
+            hidden_states_batch = hidden_states[:, :, t:t_, h:h_, w:w_].to(computation_device)
+            hidden_states_batch = self.forward(hidden_states_batch).to(data_device)
+            if t > 0:
+                hidden_states_batch = hidden_states_batch[:, :, 1:]
+
+            mask = self.build_mask(
+                hidden_states_batch,
+                is_bound=(t==0, t_>=T, h==0, h_>=H, w==0, w_>=W),
+                border_width=((size_t - stride_t) // 4, (size_h - stride_h) // 8, (size_w - stride_w) // 8)
+            ).to(dtype=torch_dtype, device=data_device)
+
+            target_t = 0 if t==0 else t // 4 + 1
+            target_h = h // 8
+            target_w = w // 8
+            values[
+                :,
+                :,
+                target_t: target_t + hidden_states_batch.shape[2],
+                target_h: target_h + hidden_states_batch.shape[3],
+                target_w: target_w + hidden_states_batch.shape[4],
+            ] += hidden_states_batch * mask
+            weight[
+                :,
+                :,
+                target_t: target_t + hidden_states_batch.shape[2],
+                target_h: target_h + hidden_states_batch.shape[3],
+                target_w: target_w + hidden_states_batch.shape[4],
+            ] += mask
+        return values / weight
+
+
+    def encode_video(self, latents, tile_size=(65, 256, 256), tile_stride=(48, 192, 192)):
+        latents = latents.to(self.quant_conv.weight.dtype)
+        return self.tile_forward(latents, tile_size=tile_size, tile_stride=tile_stride)
+
+
+    @staticmethod
+    def state_dict_converter():
+        return HunyuanVideoVAEEncoderStateDictConverter()
+
+
+class HunyuanVideoVAEEncoderStateDictConverter:
+
+    def __init__(self):
+        pass
+
+    def from_diffusers(self, state_dict):
+        state_dict_ = {}
+        for name in state_dict:
+            if name.startswith('encoder.') or name.startswith('quant_conv.'):
+                state_dict_[name] = state_dict[name]
+        return state_dict_
diff --git a/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/kolors_text_encoder.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/kolors_text_encoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..2e9212141ba1808d9fafeefc905fb4f52eb93fd5
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/kolors_text_encoder.py
@@ -0,0 +1,1551 @@
+"""
+This model is copied from https://github.com/Kwai-Kolors/Kolors/tree/master/kolors/models.
+We didn't modify this model.
+The tensor operation is performed in the prompter.
+"""
+
+
+""" PyTorch ChatGLM model. """
+
+import math
+import copy
+import warnings
+import re
+import sys
+
+import torch
+import torch.utils.checkpoint
+import torch.nn.functional as F
+from torch import nn
+from torch.nn import CrossEntropyLoss, LayerNorm
+from torch.nn import CrossEntropyLoss, LayerNorm, MSELoss, BCEWithLogitsLoss
+from torch.nn.utils import skip_init
+from typing import Optional, Tuple, Union, List, Callable, Dict, Any
+from copy import deepcopy
+
+from transformers.modeling_outputs import (
+    BaseModelOutputWithPast,
+    CausalLMOutputWithPast,
+    SequenceClassifierOutputWithPast,
+)
+from transformers.modeling_utils import PreTrainedModel
+from transformers.utils import logging
+from transformers.generation.logits_process import LogitsProcessor
+from transformers.generation.utils import LogitsProcessorList, StoppingCriteriaList, GenerationConfig, ModelOutput
+from transformers import PretrainedConfig
+from torch.nn.parameter import Parameter
+import bz2
+import torch
+import base64
+import ctypes
+from transformers.utils import logging
+from typing import List
+
+
+
+logger = logging.get_logger(__name__)
+
+try:
+    from cpm_kernels.kernels.base import LazyKernelCModule, KernelFunction, round_up
+
+
+    class Kernel:
+        def __init__(self, code: bytes, function_names: List[str]):
+            self.code = code
+            self._function_names = function_names
+            self._cmodule = LazyKernelCModule(self.code)
+
+            for name in self._function_names:
+                setattr(self, name, KernelFunction(self._cmodule, name))
+
+
+    quantization_code = "$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"
+
+    kernels = Kernel(
+        bz2.decompress(base64.b64decode(quantization_code)),
+        [
+            "int4WeightCompression",
+            "int4WeightExtractionFloat",
+            "int4WeightExtractionHalf",
+            "int8WeightExtractionFloat",
+            "int8WeightExtractionHalf",
+        ],
+    )
+except Exception as exception:
+    kernels = None
+
+
+class W8A16Linear(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, inp: torch.Tensor, quant_w: torch.Tensor, scale_w: torch.Tensor, weight_bit_width):
+        ctx.inp_shape = inp.size()
+        ctx.weight_bit_width = weight_bit_width
+        out_features = quant_w.size(0)
+        inp = inp.contiguous().view(-1, inp.size(-1))
+        weight = extract_weight_to_half(quant_w, scale_w, weight_bit_width)
+        ctx.weight_shape = weight.size()
+        output = inp.mm(weight.t())
+        ctx.save_for_backward(inp, quant_w, scale_w)
+        return output.view(*(ctx.inp_shape[:-1] + (out_features,)))
+
+    @staticmethod
+    def backward(ctx, grad_output: torch.Tensor):
+        inp, quant_w, scale_w = ctx.saved_tensors
+        weight = extract_weight_to_half(quant_w, scale_w, ctx.weight_bit_width)
+        grad_output = grad_output.contiguous().view(-1, weight.size(0))
+        grad_input = grad_output.mm(weight)
+        grad_weight = grad_output.t().mm(inp)
+        return grad_input.view(ctx.inp_shape), grad_weight.view(ctx.weight_shape), None, None
+
+
+def compress_int4_weight(weight: torch.Tensor):  # (n, m)
+    with torch.cuda.device(weight.device):
+        n, m = weight.size(0), weight.size(1)
+        assert m % 2 == 0
+        m = m // 2
+        out = torch.empty(n, m, dtype=torch.int8, device="cuda")
+        stream = torch.cuda.current_stream()
+
+        gridDim = (n, 1, 1)
+        blockDim = (min(round_up(m, 32), 1024), 1, 1)
+
+        kernels.int4WeightCompression(
+            gridDim,
+            blockDim,
+            0,
+            stream,
+            [ctypes.c_void_p(weight.data_ptr()), ctypes.c_void_p(out.data_ptr()), ctypes.c_int32(n), ctypes.c_int32(m)],
+        )
+        return out
+
+
+def extract_weight_to_half(weight: torch.Tensor, scale_list: torch.Tensor, source_bit_width: int):
+    assert scale_list.dtype in [torch.half, torch.bfloat16]
+    assert weight.dtype in [torch.int8]
+    if source_bit_width == 8:
+        return weight.to(scale_list.dtype) * scale_list[:, None]
+    elif source_bit_width == 4:
+        func = (
+            kernels.int4WeightExtractionHalf if scale_list.dtype == torch.half else kernels.int4WeightExtractionBFloat16
+        )
+    else:
+        assert False, "Unsupported bit-width"
+
+    with torch.cuda.device(weight.device):
+        n, m = weight.size(0), weight.size(1)
+        out = torch.empty(n, m * (8 // source_bit_width), dtype=scale_list.dtype, device="cuda")
+        stream = torch.cuda.current_stream()
+
+        gridDim = (n, 1, 1)
+        blockDim = (min(round_up(m, 32), 1024), 1, 1)
+
+        func(
+            gridDim,
+            blockDim,
+            0,
+            stream,
+            [
+                ctypes.c_void_p(weight.data_ptr()),
+                ctypes.c_void_p(scale_list.data_ptr()),
+                ctypes.c_void_p(out.data_ptr()),
+                ctypes.c_int32(n),
+                ctypes.c_int32(m),
+            ],
+        )
+        return out
+
+
+class QuantizedLinear(torch.nn.Module):
+    def __init__(self, weight_bit_width: int, weight, bias=None, device="cuda", dtype=None, empty_init=False):
+        super().__init__()
+        weight = weight.to(device)  # ensure the weight is on the cuda device
+        assert str(weight.device).startswith(
+            'cuda'), 'The weights that need to be quantified should be on the CUDA device'
+        self.weight_bit_width = weight_bit_width
+        shape = weight.shape
+
+        if weight is None or empty_init:
+            self.weight = torch.empty(shape[0], shape[1] * weight_bit_width // 8, dtype=torch.int8, device=device)
+            self.weight_scale = torch.empty(shape[0], dtype=dtype, device=device)
+        else:
+            self.weight_scale = weight.abs().max(dim=-1).values / ((2 ** (weight_bit_width - 1)) - 1)
+            self.weight = torch.round(weight / self.weight_scale[:, None]).to(torch.int8)
+            if weight_bit_width == 4:
+                self.weight = compress_int4_weight(self.weight)
+
+        self.weight = Parameter(self.weight.to(device), requires_grad=False)
+        self.weight_scale = Parameter(self.weight_scale.to(device), requires_grad=False)
+        self.bias = Parameter(bias.to(device), requires_grad=False) if bias is not None else None
+
+    def forward(self, input):
+        output = W8A16Linear.apply(input, self.weight, self.weight_scale, self.weight_bit_width)
+        if self.bias is not None:
+            output = output + self.bias
+        return output
+
+
+def quantize(model, weight_bit_width, empty_init=False, device=None):
+    """Replace fp16 linear with quantized linear"""
+    for layer in model.layers:
+        layer.self_attention.query_key_value = QuantizedLinear(
+            weight_bit_width=weight_bit_width,
+            weight=layer.self_attention.query_key_value.weight,
+            bias=layer.self_attention.query_key_value.bias,
+            dtype=layer.self_attention.query_key_value.weight.dtype,
+            device=layer.self_attention.query_key_value.weight.device if device is None else device,
+            empty_init=empty_init
+        )
+        layer.self_attention.dense = QuantizedLinear(
+            weight_bit_width=weight_bit_width,
+            weight=layer.self_attention.dense.weight,
+            bias=layer.self_attention.dense.bias,
+            dtype=layer.self_attention.dense.weight.dtype,
+            device=layer.self_attention.dense.weight.device if device is None else device,
+            empty_init=empty_init
+        )
+        layer.mlp.dense_h_to_4h = QuantizedLinear(
+            weight_bit_width=weight_bit_width,
+            weight=layer.mlp.dense_h_to_4h.weight,
+            bias=layer.mlp.dense_h_to_4h.bias,
+            dtype=layer.mlp.dense_h_to_4h.weight.dtype,
+            device=layer.mlp.dense_h_to_4h.weight.device if device is None else device,
+            empty_init=empty_init
+        )
+        layer.mlp.dense_4h_to_h = QuantizedLinear(
+            weight_bit_width=weight_bit_width,
+            weight=layer.mlp.dense_4h_to_h.weight,
+            bias=layer.mlp.dense_4h_to_h.bias,
+            dtype=layer.mlp.dense_4h_to_h.weight.dtype,
+            device=layer.mlp.dense_4h_to_h.weight.device if device is None else device,
+            empty_init=empty_init
+        )
+
+    return model
+
+
+
+class ChatGLMConfig(PretrainedConfig):
+    model_type = "chatglm"
+    def __init__(
+        self,
+        num_layers=28,
+        padded_vocab_size=65024,
+        hidden_size=4096,
+        ffn_hidden_size=13696,
+        kv_channels=128,
+        num_attention_heads=32,
+        seq_length=2048,
+        hidden_dropout=0.0,
+        classifier_dropout=None,
+        attention_dropout=0.0,
+        layernorm_epsilon=1e-5,
+        rmsnorm=True,
+        apply_residual_connection_post_layernorm=False,
+        post_layer_norm=True,
+        add_bias_linear=False,
+        add_qkv_bias=False,
+        bias_dropout_fusion=True,
+        multi_query_attention=False,
+        multi_query_group_num=1,
+        apply_query_key_layer_scaling=True,
+        attention_softmax_in_fp32=True,
+        fp32_residual_connection=False,
+        quantization_bit=0,
+        pre_seq_len=None,
+        prefix_projection=False,
+        **kwargs
+    ):
+        self.num_layers = num_layers
+        self.vocab_size = padded_vocab_size
+        self.padded_vocab_size = padded_vocab_size
+        self.hidden_size = hidden_size
+        self.ffn_hidden_size = ffn_hidden_size
+        self.kv_channels = kv_channels
+        self.num_attention_heads = num_attention_heads
+        self.seq_length = seq_length
+        self.hidden_dropout = hidden_dropout
+        self.classifier_dropout = classifier_dropout
+        self.attention_dropout = attention_dropout
+        self.layernorm_epsilon = layernorm_epsilon
+        self.rmsnorm = rmsnorm
+        self.apply_residual_connection_post_layernorm = apply_residual_connection_post_layernorm
+        self.post_layer_norm = post_layer_norm
+        self.add_bias_linear = add_bias_linear
+        self.add_qkv_bias = add_qkv_bias
+        self.bias_dropout_fusion = bias_dropout_fusion
+        self.multi_query_attention = multi_query_attention
+        self.multi_query_group_num = multi_query_group_num
+        self.apply_query_key_layer_scaling = apply_query_key_layer_scaling
+        self.attention_softmax_in_fp32 = attention_softmax_in_fp32
+        self.fp32_residual_connection = fp32_residual_connection
+        self.quantization_bit = quantization_bit
+        self.pre_seq_len = pre_seq_len
+        self.prefix_projection = prefix_projection
+        super().__init__(**kwargs)
+
+
+
+# flags required to enable jit fusion kernels
+
+if sys.platform != 'darwin':
+    torch._C._jit_set_profiling_mode(False)
+    torch._C._jit_set_profiling_executor(False)
+    torch._C._jit_override_can_fuse_on_cpu(True)
+    torch._C._jit_override_can_fuse_on_gpu(True)
+
+logger = logging.get_logger(__name__)
+
+_CHECKPOINT_FOR_DOC = "THUDM/ChatGLM"
+_CONFIG_FOR_DOC = "ChatGLM6BConfig"
+
+CHATGLM_6B_PRETRAINED_MODEL_ARCHIVE_LIST = [
+    "THUDM/chatglm3-6b-base",
+    # See all ChatGLM models at https://huggingface.co/models?filter=chatglm
+]
+
+
+def default_init(cls, *args, **kwargs):
+    return cls(*args, **kwargs)
+
+
+class InvalidScoreLogitsProcessor(LogitsProcessor):
+    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
+        if torch.isnan(scores).any() or torch.isinf(scores).any():
+            scores.zero_()
+            scores[..., 5] = 5e4
+        return scores
+
+
+class PrefixEncoder(torch.nn.Module):
+    """
+    The torch.nn model to encode the prefix
+    Input shape: (batch-size, prefix-length)
+    Output shape: (batch-size, prefix-length, 2*layers*hidden)
+    """
+
+    def __init__(self, config: ChatGLMConfig):
+        super().__init__()
+        self.prefix_projection = config.prefix_projection
+        if self.prefix_projection:
+            # Use a two-layer MLP to encode the prefix
+            kv_size = config.num_layers * config.kv_channels * config.multi_query_group_num * 2
+            self.embedding = torch.nn.Embedding(config.pre_seq_len, kv_size)
+            self.trans = torch.nn.Sequential(
+                torch.nn.Linear(kv_size, config.hidden_size),
+                torch.nn.Tanh(),
+                torch.nn.Linear(config.hidden_size, kv_size)
+            )
+        else:
+            self.embedding = torch.nn.Embedding(config.pre_seq_len,
+                                                config.num_layers * config.kv_channels * config.multi_query_group_num * 2)
+
+    def forward(self, prefix: torch.Tensor):
+        if self.prefix_projection:
+            prefix_tokens = self.embedding(prefix)
+            past_key_values = self.trans(prefix_tokens)
+        else:
+            past_key_values = self.embedding(prefix)
+        return past_key_values
+
+
+def split_tensor_along_last_dim(
+        tensor: torch.Tensor,
+        num_partitions: int,
+        contiguous_split_chunks: bool = False,
+) -> List[torch.Tensor]:
+    """Split a tensor along its last dimension.
+
+    Arguments:
+        tensor: input tensor.
+        num_partitions: number of partitions to split the tensor
+        contiguous_split_chunks: If True, make each chunk contiguous
+                                 in memory.
+
+    Returns:
+        A list of Tensors
+    """
+    # Get the size and dimension.
+    last_dim = tensor.dim() - 1
+    last_dim_size = tensor.size()[last_dim] // num_partitions
+    # Split.
+    tensor_list = torch.split(tensor, last_dim_size, dim=last_dim)
+    # Note: torch.split does not create contiguous tensors by default.
+    if contiguous_split_chunks:
+        return tuple(chunk.contiguous() for chunk in tensor_list)
+
+    return tensor_list
+
+
+class RotaryEmbedding(nn.Module):
+    def __init__(self, dim, original_impl=False, device=None, dtype=None):
+        super().__init__()
+        inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2, device=device).to(dtype=dtype) / dim))
+        self.register_buffer("inv_freq", inv_freq)
+        self.dim = dim
+        self.original_impl = original_impl
+
+    def forward_impl(
+            self, seq_len: int, n_elem: int, dtype: torch.dtype, device: torch.device, base: int = 10000
+    ):
+        """Enhanced Transformer with Rotary Position Embedding.
+
+        Derived from: https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/
+        transformers/rope/__init__.py. MIT License:
+        https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/license.
+        """
+        # $\Theta = {\theta_i = 10000^{\frac{2(i-1)}{d}}, i \in [1, 2, ..., \frac{d}{2}]}$
+        theta = 1.0 / (base ** (torch.arange(0, n_elem, 2, dtype=torch.float, device=device) / n_elem))
+
+        # Create position indexes `[0, 1, ..., seq_len - 1]`
+        seq_idx = torch.arange(seq_len, dtype=torch.float, device=device)
+
+        # Calculate the product of position index and $\theta_i$
+        idx_theta = torch.outer(seq_idx, theta).float()
+
+        cache = torch.stack([torch.cos(idx_theta), torch.sin(idx_theta)], dim=-1)
+
+        # this is to mimic the behaviour of complex32, else we will get different results
+        if dtype in (torch.float16, torch.bfloat16, torch.int8):
+            cache = cache.bfloat16() if dtype == torch.bfloat16 else cache.half()
+        return cache
+
+    def forward(self, max_seq_len, offset=0):
+        return self.forward_impl(
+            max_seq_len, self.dim, dtype=self.inv_freq.dtype, device=self.inv_freq.device
+        )
+
+
+@torch.jit.script
+def apply_rotary_pos_emb(x: torch.Tensor, rope_cache: torch.Tensor) -> torch.Tensor:
+    # x: [sq, b, np, hn]
+    sq, b, np, hn = x.size(0), x.size(1), x.size(2), x.size(3)
+    rot_dim = rope_cache.shape[-2] * 2
+    x, x_pass = x[..., :rot_dim], x[..., rot_dim:]
+    # truncate to support variable sizes
+    rope_cache = rope_cache[:sq]
+    xshaped = x.reshape(sq, -1, np, rot_dim // 2, 2)
+    rope_cache = rope_cache.view(sq, -1, 1, xshaped.size(3), 2)
+    x_out2 = torch.stack(
+        [
+            xshaped[..., 0] * rope_cache[..., 0] - xshaped[..., 1] * rope_cache[..., 1],
+            xshaped[..., 1] * rope_cache[..., 0] + xshaped[..., 0] * rope_cache[..., 1],
+        ],
+        -1,
+    )
+    x_out2 = x_out2.flatten(3)
+    return torch.cat((x_out2, x_pass), dim=-1)
+
+
+class RMSNorm(torch.nn.Module):
+    def __init__(self, normalized_shape, eps=1e-5, device=None, dtype=None, **kwargs):
+        super().__init__()
+        self.weight = torch.nn.Parameter(torch.empty(normalized_shape, device=device, dtype=dtype))
+        self.eps = eps
+
+    def forward(self, hidden_states: torch.Tensor):
+        input_dtype = hidden_states.dtype
+        variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
+        hidden_states = hidden_states * torch.rsqrt(variance + self.eps)
+
+        return (self.weight * hidden_states).to(input_dtype)
+
+
+class CoreAttention(torch.nn.Module):
+    def __init__(self, config: ChatGLMConfig, layer_number):
+        super(CoreAttention, self).__init__()
+
+        self.apply_query_key_layer_scaling = config.apply_query_key_layer_scaling
+        self.attention_softmax_in_fp32 = config.attention_softmax_in_fp32
+        if self.apply_query_key_layer_scaling:
+            self.attention_softmax_in_fp32 = True
+        self.layer_number = max(1, layer_number)
+
+        projection_size = config.kv_channels * config.num_attention_heads
+
+        # Per attention head and per partition values.
+        self.hidden_size_per_partition = projection_size
+        self.hidden_size_per_attention_head = projection_size // config.num_attention_heads
+        self.num_attention_heads_per_partition = config.num_attention_heads
+
+        coeff = None
+        self.norm_factor = math.sqrt(self.hidden_size_per_attention_head)
+        if self.apply_query_key_layer_scaling:
+            coeff = self.layer_number
+            self.norm_factor *= coeff
+        self.coeff = coeff
+
+        self.attention_dropout = torch.nn.Dropout(config.attention_dropout)
+
+    def forward(self, query_layer, key_layer, value_layer, attention_mask):
+        pytorch_major_version = int(torch.__version__.split('.')[0])
+        if pytorch_major_version >= 2:
+            query_layer, key_layer, value_layer = [k.permute(1, 2, 0, 3) for k in [query_layer, key_layer, value_layer]]
+            if attention_mask is None and query_layer.shape[2] == key_layer.shape[2]:
+                context_layer = torch.nn.functional.scaled_dot_product_attention(query_layer, key_layer, value_layer,
+                                                                                 is_causal=True)
+            else:
+                if attention_mask is not None:
+                    attention_mask = ~attention_mask
+                context_layer = torch.nn.functional.scaled_dot_product_attention(query_layer, key_layer, value_layer,
+                                                                                 attention_mask)
+            context_layer = context_layer.permute(2, 0, 1, 3)
+            new_context_layer_shape = context_layer.size()[:-2] + (self.hidden_size_per_partition,)
+            context_layer = context_layer.reshape(*new_context_layer_shape)
+        else:
+            # Raw attention scores
+
+            # [b, np, sq, sk]
+            output_size = (query_layer.size(1), query_layer.size(2), query_layer.size(0), key_layer.size(0))
+
+            # [sq, b, np, hn] -> [sq, b * np, hn]
+            query_layer = query_layer.view(output_size[2], output_size[0] * output_size[1], -1)
+            # [sk, b, np, hn] -> [sk, b * np, hn]
+            key_layer = key_layer.view(output_size[3], output_size[0] * output_size[1], -1)
+
+            # preallocting input tensor: [b * np, sq, sk]
+            matmul_input_buffer = torch.empty(
+                output_size[0] * output_size[1], output_size[2], output_size[3], dtype=query_layer.dtype,
+                device=query_layer.device
+            )
+
+            # Raw attention scores. [b * np, sq, sk]
+            matmul_result = torch.baddbmm(
+                matmul_input_buffer,
+                query_layer.transpose(0, 1),  # [b * np, sq, hn]
+                key_layer.transpose(0, 1).transpose(1, 2),  # [b * np, hn, sk]
+                beta=0.0,
+                alpha=(1.0 / self.norm_factor),
+            )
+
+            # change view to [b, np, sq, sk]
+            attention_scores = matmul_result.view(*output_size)
+
+            # ===========================
+            # Attention probs and dropout
+            # ===========================
+
+            # attention scores and attention mask [b, np, sq, sk]
+            if self.attention_softmax_in_fp32:
+                attention_scores = attention_scores.float()
+            if self.coeff is not None:
+                attention_scores = attention_scores * self.coeff
+            if attention_mask is None and attention_scores.shape[2] == attention_scores.shape[3]:
+                attention_mask = torch.ones(output_size[0], 1, output_size[2], output_size[3],
+                                            device=attention_scores.device, dtype=torch.bool)
+                attention_mask.tril_()
+                attention_mask = ~attention_mask
+            if attention_mask is not None:
+                attention_scores = attention_scores.masked_fill(attention_mask, float("-inf"))
+            attention_probs = F.softmax(attention_scores, dim=-1)
+            attention_probs = attention_probs.type_as(value_layer)
+
+            # 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 = self.attention_dropout(attention_probs)
+            # =========================
+            # Context layer. [sq, b, hp]
+            # =========================
+
+            # value_layer -> context layer.
+            # [sk, b, np, hn] --> [b, np, sq, hn]
+
+            # context layer shape: [b, np, sq, hn]
+            output_size = (value_layer.size(1), value_layer.size(2), query_layer.size(0), value_layer.size(3))
+            # change view [sk, b * np, hn]
+            value_layer = value_layer.view(value_layer.size(0), output_size[0] * output_size[1], -1)
+            # change view [b * np, sq, sk]
+            attention_probs = attention_probs.view(output_size[0] * output_size[1], output_size[2], -1)
+            # matmul: [b * np, sq, hn]
+            context_layer = torch.bmm(attention_probs, value_layer.transpose(0, 1))
+            # change view [b, np, sq, hn]
+            context_layer = context_layer.view(*output_size)
+            # [b, np, sq, hn] --> [sq, b, np, hn]
+            context_layer = context_layer.permute(2, 0, 1, 3).contiguous()
+            # [sq, b, np, hn] --> [sq, b, hp]
+            new_context_layer_shape = context_layer.size()[:-2] + (self.hidden_size_per_partition,)
+            context_layer = context_layer.view(*new_context_layer_shape)
+
+        return context_layer
+
+
+class SelfAttention(torch.nn.Module):
+    """Parallel self-attention layer abstract class.
+
+    Self-attention layer takes input with size [s, b, h]
+    and returns output of the same size.
+    """
+
+    def __init__(self, config: ChatGLMConfig, layer_number, device=None):
+        super(SelfAttention, self).__init__()
+        self.layer_number = max(1, layer_number)
+
+        self.projection_size = config.kv_channels * config.num_attention_heads
+
+        # Per attention head and per partition values.
+        self.hidden_size_per_attention_head = self.projection_size // config.num_attention_heads
+        self.num_attention_heads_per_partition = config.num_attention_heads
+
+        self.multi_query_attention = config.multi_query_attention
+        self.qkv_hidden_size = 3 * self.projection_size
+        if self.multi_query_attention:
+            self.num_multi_query_groups_per_partition = config.multi_query_group_num
+            self.qkv_hidden_size = (
+                    self.projection_size + 2 * self.hidden_size_per_attention_head * config.multi_query_group_num
+            )
+        self.query_key_value = nn.Linear(config.hidden_size, self.qkv_hidden_size,
+                                         bias=config.add_bias_linear or config.add_qkv_bias,
+                                         device=device, **_config_to_kwargs(config)
+                                         )
+
+        self.core_attention = CoreAttention(config, self.layer_number)
+
+        # Output.
+        self.dense = nn.Linear(self.projection_size, config.hidden_size, bias=config.add_bias_linear,
+                               device=device, **_config_to_kwargs(config)
+                               )
+
+    def _allocate_memory(self, inference_max_sequence_len, batch_size, device=None, dtype=None):
+        if self.multi_query_attention:
+            num_attention_heads = self.num_multi_query_groups_per_partition
+        else:
+            num_attention_heads = self.num_attention_heads_per_partition
+        return torch.empty(
+            inference_max_sequence_len,
+            batch_size,
+            num_attention_heads,
+            self.hidden_size_per_attention_head,
+            dtype=dtype,
+            device=device,
+        )
+
+    def forward(
+            self, hidden_states, attention_mask, rotary_pos_emb, kv_cache=None, use_cache=True
+    ):
+        # hidden_states: [sq, b, h]
+
+        # =================================================
+        # Pre-allocate memory for key-values for inference.
+        # =================================================
+        # =====================
+        # Query, Key, and Value
+        # =====================
+
+        # Attention heads [sq, b, h] --> [sq, b, (np * 3 * hn)]
+        mixed_x_layer = self.query_key_value(hidden_states)
+
+        if self.multi_query_attention:
+            (query_layer, key_layer, value_layer) = mixed_x_layer.split(
+                [
+                    self.num_attention_heads_per_partition * self.hidden_size_per_attention_head,
+                    self.num_multi_query_groups_per_partition * self.hidden_size_per_attention_head,
+                    self.num_multi_query_groups_per_partition * self.hidden_size_per_attention_head,
+                ],
+                dim=-1,
+            )
+            query_layer = query_layer.view(
+                query_layer.size()[:-1] + (self.num_attention_heads_per_partition, self.hidden_size_per_attention_head)
+            )
+            key_layer = key_layer.view(
+                key_layer.size()[:-1] + (self.num_multi_query_groups_per_partition, self.hidden_size_per_attention_head)
+            )
+            value_layer = value_layer.view(
+                value_layer.size()[:-1]
+                + (self.num_multi_query_groups_per_partition, self.hidden_size_per_attention_head)
+            )
+        else:
+            new_tensor_shape = mixed_x_layer.size()[:-1] + \
+                               (self.num_attention_heads_per_partition,
+                                3 * self.hidden_size_per_attention_head)
+            mixed_x_layer = mixed_x_layer.view(*new_tensor_shape)
+
+            # [sq, b, np, 3 * hn] --> 3 [sq, b, np, hn]
+            (query_layer, key_layer, value_layer) = split_tensor_along_last_dim(mixed_x_layer, 3)
+
+        # apply relative positional encoding (rotary embedding)
+        if rotary_pos_emb is not None:
+            query_layer = apply_rotary_pos_emb(query_layer, rotary_pos_emb)
+            key_layer = apply_rotary_pos_emb(key_layer, rotary_pos_emb)
+
+        # adjust key and value for inference
+        if kv_cache is not None:
+            cache_k, cache_v = kv_cache
+            key_layer = torch.cat((cache_k, key_layer), dim=0)
+            value_layer = torch.cat((cache_v, value_layer), dim=0)
+        if use_cache:
+            kv_cache = (key_layer, value_layer)
+        else:
+            kv_cache = None
+
+        if self.multi_query_attention:
+            key_layer = key_layer.unsqueeze(-2)
+            key_layer = key_layer.expand(
+                -1, -1, -1, self.num_attention_heads_per_partition // self.num_multi_query_groups_per_partition, -1
+            )
+            key_layer = key_layer.contiguous().view(
+                key_layer.size()[:2] + (self.num_attention_heads_per_partition, self.hidden_size_per_attention_head)
+            )
+            value_layer = value_layer.unsqueeze(-2)
+            value_layer = value_layer.expand(
+                -1, -1, -1, self.num_attention_heads_per_partition // self.num_multi_query_groups_per_partition, -1
+            )
+            value_layer = value_layer.contiguous().view(
+                value_layer.size()[:2] + (self.num_attention_heads_per_partition, self.hidden_size_per_attention_head)
+            )
+
+        # ==================================
+        # core attention computation
+        # ==================================
+
+        context_layer = self.core_attention(query_layer, key_layer, value_layer, attention_mask)
+
+        # =================
+        # Output. [sq, b, h]
+        # =================
+
+        output = self.dense(context_layer)
+
+        return output, kv_cache
+
+
+def _config_to_kwargs(args):
+    common_kwargs = {
+        "dtype": args.torch_dtype,
+    }
+    return common_kwargs
+
+
+class MLP(torch.nn.Module):
+    """MLP.
+
+    MLP will take the input with h hidden state, project it to 4*h
+    hidden dimension, perform nonlinear transformation, and project the
+    state back into h hidden dimension.
+    """
+
+    def __init__(self, config: ChatGLMConfig, device=None):
+        super(MLP, self).__init__()
+
+        self.add_bias = config.add_bias_linear
+
+        # Project to 4h. If using swiglu double the output width, see https://arxiv.org/pdf/2002.05202.pdf
+        self.dense_h_to_4h = nn.Linear(
+            config.hidden_size,
+            config.ffn_hidden_size * 2,
+            bias=self.add_bias,
+            device=device,
+            **_config_to_kwargs(config)
+        )
+
+        def swiglu(x):
+            x = torch.chunk(x, 2, dim=-1)
+            return F.silu(x[0]) * x[1]
+
+        self.activation_func = swiglu
+
+        # Project back to h.
+        self.dense_4h_to_h = nn.Linear(
+            config.ffn_hidden_size,
+            config.hidden_size,
+            bias=self.add_bias,
+            device=device,
+            **_config_to_kwargs(config)
+        )
+
+    def forward(self, hidden_states):
+        # [s, b, 4hp]
+        intermediate_parallel = self.dense_h_to_4h(hidden_states)
+        intermediate_parallel = self.activation_func(intermediate_parallel)
+        # [s, b, h]
+        output = self.dense_4h_to_h(intermediate_parallel)
+        return output
+
+
+class GLMBlock(torch.nn.Module):
+    """A single transformer layer.
+
+    Transformer layer takes input with size [s, b, h] and returns an
+    output of the same size.
+    """
+
+    def __init__(self, config: ChatGLMConfig, layer_number, device=None):
+        super(GLMBlock, self).__init__()
+        self.layer_number = layer_number
+
+        self.apply_residual_connection_post_layernorm = config.apply_residual_connection_post_layernorm
+
+        self.fp32_residual_connection = config.fp32_residual_connection
+
+        LayerNormFunc = RMSNorm if config.rmsnorm else LayerNorm
+        # Layernorm on the input data.
+        self.input_layernorm = LayerNormFunc(config.hidden_size, eps=config.layernorm_epsilon, device=device,
+                                             dtype=config.torch_dtype)
+
+        # Self attention.
+        self.self_attention = SelfAttention(config, layer_number, device=device)
+        self.hidden_dropout = config.hidden_dropout
+
+        # Layernorm on the attention output
+        self.post_attention_layernorm = LayerNormFunc(config.hidden_size, eps=config.layernorm_epsilon, device=device,
+                                                      dtype=config.torch_dtype)
+
+        # MLP
+        self.mlp = MLP(config, device=device)
+
+    def forward(
+            self, hidden_states, attention_mask, rotary_pos_emb, kv_cache=None, use_cache=True,
+    ):
+        # hidden_states: [s, b, h]
+
+        # Layer norm at the beginning of the transformer layer.
+        layernorm_output = self.input_layernorm(hidden_states)
+        # Self attention.
+        attention_output, kv_cache = self.self_attention(
+            layernorm_output,
+            attention_mask,
+            rotary_pos_emb,
+            kv_cache=kv_cache,
+            use_cache=use_cache
+        )
+
+        # Residual connection.
+        if self.apply_residual_connection_post_layernorm:
+            residual = layernorm_output
+        else:
+            residual = hidden_states
+
+        layernorm_input = torch.nn.functional.dropout(attention_output, p=self.hidden_dropout, training=self.training)
+        layernorm_input = residual + layernorm_input
+
+        # Layer norm post the self attention.
+        layernorm_output = self.post_attention_layernorm(layernorm_input)
+
+        # MLP.
+        mlp_output = self.mlp(layernorm_output)
+
+        # Second residual connection.
+        if self.apply_residual_connection_post_layernorm:
+            residual = layernorm_output
+        else:
+            residual = layernorm_input
+
+        output = torch.nn.functional.dropout(mlp_output, p=self.hidden_dropout, training=self.training)
+        output = residual + output
+
+        return output, kv_cache
+
+
+class GLMTransformer(torch.nn.Module):
+    """Transformer class."""
+
+    def __init__(self, config: ChatGLMConfig, device=None):
+        super(GLMTransformer, self).__init__()
+
+        self.fp32_residual_connection = config.fp32_residual_connection
+        self.post_layer_norm = config.post_layer_norm
+
+        # Number of layers.
+        self.num_layers = config.num_layers
+
+        # Transformer layers.
+        def build_layer(layer_number):
+            return GLMBlock(config, layer_number, device=device)
+
+        self.layers = torch.nn.ModuleList([build_layer(i + 1) for i in range(self.num_layers)])
+
+        if self.post_layer_norm:
+            LayerNormFunc = RMSNorm if config.rmsnorm else LayerNorm
+            # Final layer norm before output.
+            self.final_layernorm = LayerNormFunc(config.hidden_size, eps=config.layernorm_epsilon, device=device,
+                                                 dtype=config.torch_dtype)
+
+        self.gradient_checkpointing = False
+
+    def _get_layer(self, layer_number):
+        return self.layers[layer_number]
+
+    def forward(
+            self, hidden_states, attention_mask, rotary_pos_emb, kv_caches=None,
+            use_cache: Optional[bool] = True,
+            output_hidden_states: Optional[bool] = False,
+    ):
+        if not kv_caches:
+            kv_caches = [None for _ in range(self.num_layers)]
+        presents = () if use_cache else None
+        if self.gradient_checkpointing and self.training:
+            if use_cache:
+                logger.warning_once(
+                    "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
+                )
+                use_cache = False
+
+        all_self_attentions = None
+        all_hidden_states = () if output_hidden_states else None
+        for index in range(self.num_layers):
+            if output_hidden_states:
+                all_hidden_states = all_hidden_states + (hidden_states,)
+
+            layer = self._get_layer(index)
+            if self.gradient_checkpointing and self.training:
+                layer_ret = torch.utils.checkpoint.checkpoint(
+                    layer,
+                    hidden_states,
+                    attention_mask,
+                    rotary_pos_emb,
+                    kv_caches[index],
+                    use_cache
+                )
+            else:
+                layer_ret = layer(
+                    hidden_states,
+                    attention_mask,
+                    rotary_pos_emb,
+                    kv_cache=kv_caches[index],
+                    use_cache=use_cache
+                )
+            hidden_states, kv_cache = layer_ret
+            if use_cache:
+                presents = presents + (kv_cache,)
+
+        if output_hidden_states:
+            all_hidden_states = all_hidden_states + (hidden_states,)
+
+        # Final layer norm.
+        if self.post_layer_norm:
+            hidden_states = self.final_layernorm(hidden_states)
+
+        return hidden_states, presents, all_hidden_states, all_self_attentions
+
+
+class ChatGLMPreTrainedModel(PreTrainedModel):
+    """
+    An abstract class to handle weights initialization and
+    a simple interface for downloading and loading pretrained models.
+    """
+
+    is_parallelizable = False
+    supports_gradient_checkpointing = True
+    config_class = ChatGLMConfig
+    base_model_prefix = "transformer"
+    _no_split_modules = ["GLMBlock"]
+
+    def _init_weights(self, module: nn.Module):
+        """Initialize the weights."""
+        return
+
+    def get_masks(self, input_ids, past_key_values, padding_mask=None):
+        batch_size, seq_length = input_ids.shape
+        full_attention_mask = torch.ones(batch_size, seq_length, seq_length, device=input_ids.device)
+        full_attention_mask.tril_()
+        past_length = 0
+        if past_key_values:
+            past_length = past_key_values[0][0].shape[0]
+        if past_length:
+            full_attention_mask = torch.cat((torch.ones(batch_size, seq_length, past_length,
+                                                        device=input_ids.device), full_attention_mask), dim=-1)
+        if padding_mask is not None:
+            full_attention_mask = full_attention_mask * padding_mask.unsqueeze(1)
+        if not past_length and padding_mask is not None:
+            full_attention_mask -= padding_mask.unsqueeze(-1) - 1
+        full_attention_mask = (full_attention_mask < 0.5).bool()
+        full_attention_mask.unsqueeze_(1)
+        return full_attention_mask
+
+    def get_position_ids(self, input_ids, device):
+        batch_size, seq_length = input_ids.shape
+        position_ids = torch.arange(seq_length, dtype=torch.long, device=device).unsqueeze(0).repeat(batch_size, 1)
+        return position_ids
+
+    def _set_gradient_checkpointing(self, module, value=False):
+        if isinstance(module, GLMTransformer):
+            module.gradient_checkpointing = value
+
+
+class Embedding(torch.nn.Module):
+    """Language model embeddings."""
+
+    def __init__(self, config: ChatGLMConfig, device=None):
+        super(Embedding, self).__init__()
+
+        self.hidden_size = config.hidden_size
+        # Word embeddings (parallel).
+        self.word_embeddings = nn.Embedding(
+            config.padded_vocab_size,
+            self.hidden_size,
+            dtype=config.torch_dtype,
+            device=device
+        )
+        self.fp32_residual_connection = config.fp32_residual_connection
+
+    def forward(self, input_ids):
+        # Embeddings.
+        words_embeddings = self.word_embeddings(input_ids)
+        embeddings = words_embeddings
+        # Data format change to avoid explicit transposes : [b s h] --> [s b h].
+        embeddings = embeddings.transpose(0, 1).contiguous()
+        # If the input flag for fp32 residual connection is set, convert for float.
+        if self.fp32_residual_connection:
+            embeddings = embeddings.float()
+        return embeddings
+
+
+class ChatGLMModel(ChatGLMPreTrainedModel):
+    def __init__(self, config: ChatGLMConfig, device=None, empty_init=True):
+        super().__init__(config)
+        if empty_init:
+            init_method = skip_init
+        else:
+            init_method = default_init
+        init_kwargs = {}
+        if device is not None:
+            init_kwargs["device"] = device
+        self.embedding = init_method(Embedding, config, **init_kwargs)
+        self.num_layers = config.num_layers
+        self.multi_query_group_num = config.multi_query_group_num
+        self.kv_channels = config.kv_channels
+
+        # Rotary positional embeddings
+        self.seq_length = config.seq_length
+        rotary_dim = (
+            config.hidden_size // config.num_attention_heads if config.kv_channels is None else config.kv_channels
+        )
+
+        self.rotary_pos_emb = RotaryEmbedding(rotary_dim // 2, original_impl=config.original_rope, device=device,
+                                              dtype=config.torch_dtype)
+        self.encoder = init_method(GLMTransformer, config, **init_kwargs)
+        self.output_layer = init_method(nn.Linear, config.hidden_size, config.padded_vocab_size, bias=False,
+                                        dtype=config.torch_dtype, **init_kwargs)
+        self.pre_seq_len = config.pre_seq_len
+        self.prefix_projection = config.prefix_projection
+        if self.pre_seq_len is not None:
+            for param in self.parameters():
+                param.requires_grad = False
+            self.prefix_tokens = torch.arange(self.pre_seq_len).long()
+            self.prefix_encoder = PrefixEncoder(config)
+            self.dropout = torch.nn.Dropout(0.1)
+
+    def get_input_embeddings(self):
+        return self.embedding.word_embeddings
+
+    def get_prompt(self, batch_size, device, dtype=torch.half):
+        prefix_tokens = self.prefix_tokens.unsqueeze(0).expand(batch_size, -1).to(device)
+        past_key_values = self.prefix_encoder(prefix_tokens).type(dtype)
+        past_key_values = past_key_values.view(
+            batch_size,
+            self.pre_seq_len,
+            self.num_layers * 2,
+            self.multi_query_group_num,
+            self.kv_channels
+        )
+        # seq_len, b, nh, hidden_size
+        past_key_values = self.dropout(past_key_values)
+        past_key_values = past_key_values.permute([2, 1, 0, 3, 4]).split(2)
+        return past_key_values
+
+    def forward(
+            self,
+            input_ids,
+            position_ids: Optional[torch.Tensor] = None,
+            attention_mask: Optional[torch.BoolTensor] = None,
+            full_attention_mask: Optional[torch.BoolTensor] = None,
+            past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
+            inputs_embeds: Optional[torch.Tensor] = None,
+            use_cache: Optional[bool] = None,
+            output_hidden_states: Optional[bool] = None,
+            return_dict: Optional[bool] = None,
+    ):
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        batch_size, seq_length = input_ids.shape
+
+        if inputs_embeds is None:
+            inputs_embeds = self.embedding(input_ids)
+
+        if self.pre_seq_len is not None:
+            if past_key_values is None:
+                past_key_values = self.get_prompt(batch_size=batch_size, device=input_ids.device,
+                                                  dtype=inputs_embeds.dtype)
+            if attention_mask is not None:
+                attention_mask = torch.cat([attention_mask.new_ones((batch_size, self.pre_seq_len)),
+                                            attention_mask], dim=-1)
+
+        if full_attention_mask is None:
+            if (attention_mask is not None and not attention_mask.all()) or (past_key_values and seq_length != 1):
+                full_attention_mask = self.get_masks(input_ids, past_key_values, padding_mask=attention_mask)
+
+        # Rotary positional embeddings
+        rotary_pos_emb = self.rotary_pos_emb(self.seq_length)
+        if position_ids is not None:
+            rotary_pos_emb = rotary_pos_emb[position_ids]
+        else:
+            rotary_pos_emb = rotary_pos_emb[None, :seq_length]
+        rotary_pos_emb = rotary_pos_emb.transpose(0, 1).contiguous()
+
+        # Run encoder.
+        hidden_states, presents, all_hidden_states, all_self_attentions = self.encoder(
+            inputs_embeds, full_attention_mask, rotary_pos_emb=rotary_pos_emb,
+            kv_caches=past_key_values, use_cache=use_cache, output_hidden_states=output_hidden_states
+        )
+
+        if not return_dict:
+            return tuple(v for v in [hidden_states, presents, all_hidden_states, all_self_attentions] if v is not None)
+
+        return BaseModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=presents,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attentions,
+        )
+
+    def quantize(self, weight_bit_width: int):
+        # from .quantization import quantize
+        quantize(self.encoder, weight_bit_width)
+        return self
+
+
+class ChatGLMForConditionalGeneration(ChatGLMPreTrainedModel):
+    def __init__(self, config: ChatGLMConfig, empty_init=True, device=None):
+        super().__init__(config)
+
+        self.max_sequence_length = config.max_length
+        self.transformer = ChatGLMModel(config, empty_init=empty_init, device=device)
+        self.config = config
+        self.quantized = False
+
+        if self.config.quantization_bit:
+            self.quantize(self.config.quantization_bit, empty_init=True)
+
+    def _update_model_kwargs_for_generation(
+            self,
+            outputs: ModelOutput,
+            model_kwargs: Dict[str, Any],
+            is_encoder_decoder: bool = False,
+            standardize_cache_format: bool = False,
+    ) -> Dict[str, Any]:
+        # update past_key_values
+        model_kwargs["past_key_values"] = self._extract_past_from_model_output(
+            outputs, standardize_cache_format=standardize_cache_format
+        )
+
+        # update attention mask
+        if "attention_mask" in model_kwargs:
+            attention_mask = model_kwargs["attention_mask"]
+            model_kwargs["attention_mask"] = torch.cat(
+                [attention_mask, attention_mask.new_ones((attention_mask.shape[0], 1))], dim=-1
+            )
+
+        # update position ids
+        if "position_ids" in model_kwargs:
+            position_ids = model_kwargs["position_ids"]
+            new_position_id = position_ids[..., -1:].clone()
+            new_position_id += 1
+            model_kwargs["position_ids"] = torch.cat(
+                [position_ids, new_position_id], dim=-1
+            )
+
+        model_kwargs["is_first_forward"] = False
+        return model_kwargs
+
+    def prepare_inputs_for_generation(
+            self,
+            input_ids: torch.LongTensor,
+            past_key_values: Optional[torch.Tensor] = None,
+            attention_mask: Optional[torch.Tensor] = None,
+            position_ids: Optional[torch.Tensor] = None,
+            use_cache: Optional[bool] = None,
+            is_first_forward: bool = True,
+            **kwargs
+    ) -> dict:
+        # only last token for input_ids if past is not None
+        if position_ids is None:
+            position_ids = self.get_position_ids(input_ids, device=input_ids.device)
+        if not is_first_forward:
+            if past_key_values is not None:
+                position_ids = position_ids[..., -1:]
+                input_ids = input_ids[:, -1:]
+        return {
+            "input_ids": input_ids,
+            "past_key_values": past_key_values,
+            "position_ids": position_ids,
+            "attention_mask": attention_mask,
+            "return_last_logit": True,
+            "use_cache": use_cache
+        }
+
+    def forward(
+            self,
+            input_ids: Optional[torch.Tensor] = None,
+            position_ids: Optional[torch.Tensor] = None,
+            attention_mask: Optional[torch.Tensor] = None,
+            past_key_values: Optional[Tuple[torch.FloatTensor]] = None,
+            inputs_embeds: Optional[torch.Tensor] = None,
+            labels: Optional[torch.Tensor] = None,
+            use_cache: Optional[bool] = None,
+            output_attentions: Optional[bool] = None,
+            output_hidden_states: Optional[bool] = None,
+            return_dict: Optional[bool] = None,
+            return_last_logit: Optional[bool] = False,
+    ):
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        transformer_outputs = self.transformer(
+            input_ids=input_ids,
+            position_ids=position_ids,
+            attention_mask=attention_mask,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        hidden_states = transformer_outputs[0]
+        if return_last_logit:
+            hidden_states = hidden_states[-1:]
+        lm_logits = self.transformer.output_layer(hidden_states)
+        lm_logits = lm_logits.transpose(0, 1).contiguous()
+
+        loss = None
+        if labels is not None:
+            lm_logits = lm_logits.to(torch.float32)
+
+            # Shift so that tokens < n predict n
+            shift_logits = lm_logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            # Flatten the tokens
+            loss_fct = CrossEntropyLoss(ignore_index=-100)
+            loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
+
+            lm_logits = lm_logits.to(hidden_states.dtype)
+            loss = loss.to(hidden_states.dtype)
+
+        if not return_dict:
+            output = (lm_logits,) + transformer_outputs[1:]
+            return ((loss,) + output) if loss is not None else output
+
+        return CausalLMOutputWithPast(
+            loss=loss,
+            logits=lm_logits,
+            past_key_values=transformer_outputs.past_key_values,
+            hidden_states=transformer_outputs.hidden_states,
+            attentions=transformer_outputs.attentions,
+        )
+
+    @staticmethod
+    def _reorder_cache(
+            past: Tuple[Tuple[torch.Tensor, torch.Tensor], ...], beam_idx: torch.LongTensor
+    ) -> Tuple[Tuple[torch.Tensor, torch.Tensor], ...]:
+        """
+        This function is used to re-order the `past_key_values` cache if [`~PreTrainedModel.beam_search`] or
+        [`~PreTrainedModel.beam_sample`] is called. This is required to match `past_key_values` with the correct
+        beam_idx at every generation step.
+
+        Output shares the same memory storage as `past`.
+        """
+        return tuple(
+            (
+                layer_past[0].index_select(1, beam_idx.to(layer_past[0].device)),
+                layer_past[1].index_select(1, beam_idx.to(layer_past[1].device)),
+            )
+            for layer_past in past
+        )
+
+    def process_response(self, output, history):
+        content = ""
+        history = deepcopy(history)
+        for response in output.split("<|assistant|>"):
+            metadata, content = response.split("\n", maxsplit=1)
+            if not metadata.strip():
+                content = content.strip()
+                history.append({"role": "assistant", "metadata": metadata, "content": content})
+                content = content.replace("[[训练时间]]", "2023年")
+            else:
+                history.append({"role": "assistant", "metadata": metadata, "content": content})
+                if history[0]["role"] == "system" and "tools" in history[0]:
+                    content = "\n".join(content.split("\n")[1:-1])
+                    def tool_call(**kwargs):
+                        return kwargs
+                    parameters = eval(content)
+                    content = {"name": metadata.strip(), "parameters": parameters}
+                else:
+                    content = {"name": metadata.strip(), "content": content}
+        return content, history
+
+    @torch.inference_mode()
+    def chat(self, tokenizer, query: str, history: List[Tuple[str, str]] = None, role: str = "user",
+             max_length: int = 8192, num_beams=1, do_sample=True, top_p=0.8, temperature=0.8, logits_processor=None,
+             **kwargs):
+        if history is None:
+            history = []
+        if logits_processor is None:
+            logits_processor = LogitsProcessorList()
+        logits_processor.append(InvalidScoreLogitsProcessor())
+        gen_kwargs = {"max_length": max_length, "num_beams": num_beams, "do_sample": do_sample, "top_p": top_p,
+                      "temperature": temperature, "logits_processor": logits_processor, **kwargs}
+        inputs = tokenizer.build_chat_input(query, history=history, role=role)
+        inputs = inputs.to(self.device)
+        eos_token_id = [tokenizer.eos_token_id, tokenizer.get_command("<|user|>"),
+                        tokenizer.get_command("<|observation|>")]
+        outputs = self.generate(**inputs, **gen_kwargs, eos_token_id=eos_token_id)
+        outputs = outputs.tolist()[0][len(inputs["input_ids"][0]):-1]
+        response = tokenizer.decode(outputs)
+        history.append({"role": role, "content": query})
+        response, history = self.process_response(response, history)
+        return response, history
+
+    @torch.inference_mode()
+    def stream_chat(self, tokenizer, query: str, history: List[Tuple[str, str]] = None, role: str = "user",
+                    past_key_values=None,max_length: int = 8192, do_sample=True, top_p=0.8, temperature=0.8,
+                    logits_processor=None, return_past_key_values=False, **kwargs):
+        if history is None:
+            history = []
+        if logits_processor is None:
+            logits_processor = LogitsProcessorList()
+        logits_processor.append(InvalidScoreLogitsProcessor())
+        eos_token_id = [tokenizer.eos_token_id, tokenizer.get_command("<|user|>"),
+                        tokenizer.get_command("<|observation|>")]
+        gen_kwargs = {"max_length": max_length, "do_sample": do_sample, "top_p": top_p,
+                      "temperature": temperature, "logits_processor": logits_processor, **kwargs}
+        if past_key_values is None:
+            inputs = tokenizer.build_chat_input(query, history=history, role=role)
+        else:
+            inputs = tokenizer.build_chat_input(query, role=role)
+        inputs = inputs.to(self.device)
+        if past_key_values is not None:
+            past_length = past_key_values[0][0].shape[0]
+            if self.transformer.pre_seq_len is not None:
+                past_length -= self.transformer.pre_seq_len
+            inputs.position_ids += past_length
+            attention_mask = inputs.attention_mask
+            attention_mask = torch.cat((attention_mask.new_ones(1, past_length), attention_mask), dim=1)
+            inputs['attention_mask'] = attention_mask
+        history.append({"role": role, "content": query})
+        for outputs in self.stream_generate(**inputs, past_key_values=past_key_values,
+                                            eos_token_id=eos_token_id, return_past_key_values=return_past_key_values,
+                                            **gen_kwargs):
+            if return_past_key_values:
+                outputs, past_key_values = outputs
+            outputs = outputs.tolist()[0][len(inputs["input_ids"][0]):-1]
+            response = tokenizer.decode(outputs)
+            if response and response[-1] != "�":
+                response, new_history = self.process_response(response, history)
+                if return_past_key_values:
+                    yield response, new_history, past_key_values
+                else:
+                    yield response, new_history
+
+    @torch.inference_mode()
+    def stream_generate(
+            self,
+            input_ids,
+            generation_config: Optional[GenerationConfig] = None,
+            logits_processor: Optional[LogitsProcessorList] = None,
+            stopping_criteria: Optional[StoppingCriteriaList] = None,
+            prefix_allowed_tokens_fn: Optional[Callable[[int, torch.Tensor], List[int]]] = None,
+            return_past_key_values=False,
+            **kwargs,
+    ):
+        batch_size, input_ids_seq_length = input_ids.shape[0], input_ids.shape[-1]
+
+        if generation_config is None:
+            generation_config = self.generation_config
+        generation_config = copy.deepcopy(generation_config)
+        model_kwargs = generation_config.update(**kwargs)
+        model_kwargs["use_cache"] = generation_config.use_cache
+        bos_token_id, eos_token_id = generation_config.bos_token_id, generation_config.eos_token_id
+
+        if isinstance(eos_token_id, int):
+            eos_token_id = [eos_token_id]
+        eos_token_id_tensor = torch.tensor(eos_token_id).to(input_ids.device) if eos_token_id is not None else None
+
+        has_default_max_length = kwargs.get("max_length") is None and generation_config.max_length is not None
+        if has_default_max_length and generation_config.max_new_tokens is None:
+            warnings.warn(
+                f"Using `max_length`'s default ({generation_config.max_length}) to control the generation length. "
+                "This behaviour is deprecated and will be removed from the config in v5 of Transformers -- we"
+                " recommend using `max_new_tokens` to control the maximum length of the generation.",
+                UserWarning,
+            )
+        elif generation_config.max_new_tokens is not None:
+            generation_config.max_length = generation_config.max_new_tokens + input_ids_seq_length
+            if not has_default_max_length:
+                logger.warning(
+                    f"Both `max_new_tokens` (={generation_config.max_new_tokens}) and `max_length`(="
+                    f"{generation_config.max_length}) seem to have been set. `max_new_tokens` will take precedence. "
+                    "Please refer to the documentation for more information. "
+                    "(https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)",
+                    UserWarning,
+                )
+
+        if input_ids_seq_length >= generation_config.max_length:
+            input_ids_string = "decoder_input_ids" if self.config.is_encoder_decoder else "input_ids"
+            logger.warning(
+                f"Input length of {input_ids_string} is {input_ids_seq_length}, but `max_length` is set to"
+                f" {generation_config.max_length}. This can lead to unexpected behavior. You should consider"
+                " increasing `max_new_tokens`."
+            )
+
+        # 2. Set generation parameters if not already defined
+        logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList()
+        stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList()
+
+        logits_processor = self._get_logits_processor(
+            generation_config=generation_config,
+            input_ids_seq_length=input_ids_seq_length,
+            encoder_input_ids=input_ids,
+            prefix_allowed_tokens_fn=prefix_allowed_tokens_fn,
+            logits_processor=logits_processor,
+        )
+
+        stopping_criteria = self._get_stopping_criteria(
+            generation_config=generation_config, stopping_criteria=stopping_criteria
+        )
+        logits_warper = self._get_logits_warper(generation_config)
+
+        unfinished_sequences = input_ids.new(input_ids.shape[0]).fill_(1)
+        scores = None
+        while True:
+            model_inputs = self.prepare_inputs_for_generation(input_ids, **model_kwargs)
+            # forward pass to get next token
+            outputs = self(
+                **model_inputs,
+                return_dict=True,
+                output_attentions=False,
+                output_hidden_states=False,
+            )
+
+            next_token_logits = outputs.logits[:, -1, :]
+
+            # pre-process distribution
+            next_token_scores = logits_processor(input_ids, next_token_logits)
+            next_token_scores = logits_warper(input_ids, next_token_scores)
+
+            # sample
+            probs = nn.functional.softmax(next_token_scores, dim=-1)
+            if generation_config.do_sample:
+                next_tokens = torch.multinomial(probs, num_samples=1).squeeze(1)
+            else:
+                next_tokens = torch.argmax(probs, dim=-1)
+            # update generated ids, model inputs, and length for next step
+            input_ids = torch.cat([input_ids, next_tokens[:, None]], dim=-1)
+            model_kwargs = self._update_model_kwargs_for_generation(
+                outputs, model_kwargs, is_encoder_decoder=self.config.is_encoder_decoder
+            )
+            unfinished_sequences = unfinished_sequences.mul(
+                next_tokens.tile(eos_token_id_tensor.shape[0], 1).ne(eos_token_id_tensor.unsqueeze(1)).prod(dim=0)
+            )
+            if return_past_key_values:
+                yield input_ids, outputs.past_key_values
+            else:
+                yield input_ids
+            # stop when each sentence is finished, or if we exceed the maximum length
+            if unfinished_sequences.max() == 0 or stopping_criteria(input_ids, scores):
+                break
+
+    def quantize(self, bits: int, empty_init=False, device=None, **kwargs):
+        if bits == 0:
+            return
+
+        # from .quantization import quantize
+
+        if self.quantized:
+            logger.info("Already quantized.")
+            return self
+
+        self.quantized = True
+
+        self.config.quantization_bit = bits
+
+        self.transformer.encoder = quantize(self.transformer.encoder, bits, empty_init=empty_init, device=device,
+                                            **kwargs)
+        return self
+
+
+class ChatGLMForSequenceClassification(ChatGLMPreTrainedModel):
+    def __init__(self, config: ChatGLMConfig, empty_init=True, device=None):
+        super().__init__(config)
+
+        self.num_labels = config.num_labels
+        self.transformer = ChatGLMModel(config, empty_init=empty_init, device=device)
+
+        self.classifier_head = nn.Linear(config.hidden_size, config.num_labels, bias=True, dtype=torch.half)
+        if config.classifier_dropout is not None:
+            self.dropout = nn.Dropout(config.classifier_dropout)
+        else:
+            self.dropout = None
+        self.config = config
+
+        if self.config.quantization_bit:
+            self.quantize(self.config.quantization_bit, empty_init=True)
+
+    def forward(
+            self,
+            input_ids: Optional[torch.LongTensor] = None,
+            position_ids: Optional[torch.LongTensor] = None,
+            attention_mask: Optional[torch.Tensor] = None,
+            full_attention_mask: Optional[torch.Tensor] = None,
+            past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
+            inputs_embeds: Optional[torch.LongTensor] = None,
+            labels: Optional[torch.LongTensor] = None,
+            use_cache: Optional[bool] = None,
+            output_hidden_states: Optional[bool] = None,
+            return_dict: Optional[bool] = None,
+    ) -> Union[Tuple[torch.Tensor, ...], SequenceClassifierOutputWithPast]:
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        transformer_outputs = self.transformer(
+            input_ids=input_ids,
+            position_ids=position_ids,
+            attention_mask=attention_mask,
+            full_attention_mask=full_attention_mask,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        hidden_states = transformer_outputs[0]
+        pooled_hidden_states = hidden_states[-1]
+        if self.dropout is not None:
+            pooled_hidden_states = self.dropout(pooled_hidden_states)
+        logits = self.classifier_head(pooled_hidden_states)
+
+        loss = None
+        if labels is not None:
+            if self.config.problem_type is None:
+                if self.num_labels == 1:
+                    self.config.problem_type = "regression"
+                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+                    self.config.problem_type = "single_label_classification"
+                else:
+                    self.config.problem_type = "multi_label_classification"
+
+            if self.config.problem_type == "regression":
+                loss_fct = MSELoss()
+                if self.num_labels == 1:
+                    loss = loss_fct(logits.squeeze().float(), labels.squeeze())
+                else:
+                    loss = loss_fct(logits.float(), labels)
+            elif self.config.problem_type == "single_label_classification":
+                loss_fct = CrossEntropyLoss()
+                loss = loss_fct(logits.view(-1, self.num_labels).float(), labels.view(-1))
+            elif self.config.problem_type == "multi_label_classification":
+                loss_fct = BCEWithLogitsLoss()
+                loss = loss_fct(logits.float(), labels.view(-1, self.num_labels))
+
+        if not return_dict:
+            output = (logits,) + transformer_outputs[1:]
+            return ((loss,) + output) if loss is not None else output
+
+        return SequenceClassifierOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=transformer_outputs.past_key_values,
+            hidden_states=transformer_outputs.hidden_states,
+            attentions=transformer_outputs.attentions,
+        )
diff --git a/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/lora.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/lora.py
new file mode 100644
index 0000000000000000000000000000000000000000..0278bb1b27af467d6214bbf040a74a5ddcd86b93
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/lora.py
@@ -0,0 +1,402 @@
+import torch
+from .sd_unet import SDUNet
+from .sdxl_unet import SDXLUNet
+from .sd_text_encoder import SDTextEncoder
+from .sdxl_text_encoder import SDXLTextEncoder, SDXLTextEncoder2
+from .sd3_dit import SD3DiT
+from .flux_dit import FluxDiT
+from .hunyuan_dit import HunyuanDiT
+from .cog_dit import CogDiT
+from .hunyuan_video_dit import HunyuanVideoDiT
+from .wan_video_dit import WanModel
+
+
+
+class LoRAFromCivitai:
+    def __init__(self):
+        self.supported_model_classes = []
+        self.lora_prefix = []
+        self.renamed_lora_prefix = {}
+        self.special_keys = {}
+
+
+    def convert_state_dict(self, state_dict, lora_prefix="lora_unet_", alpha=1.0):
+        for key in state_dict:
+            if ".lora_up" in key:
+                return self.convert_state_dict_up_down(state_dict, lora_prefix, alpha)
+        return self.convert_state_dict_AB(state_dict, lora_prefix, alpha)
+
+
+    def convert_state_dict_up_down(self, state_dict, lora_prefix="lora_unet_", alpha=1.0):
+        renamed_lora_prefix = self.renamed_lora_prefix.get(lora_prefix, "")
+        state_dict_ = {}
+        for key in state_dict:
+            if ".lora_up" not in key:
+                continue
+            if not key.startswith(lora_prefix):
+                continue
+            weight_up = state_dict[key].to(device="cuda", dtype=torch.float16)
+            weight_down = state_dict[key.replace(".lora_up", ".lora_down")].to(device="cuda", dtype=torch.float16)
+            if len(weight_up.shape) == 4:
+                weight_up = weight_up.squeeze(3).squeeze(2).to(torch.float32)
+                weight_down = weight_down.squeeze(3).squeeze(2).to(torch.float32)
+                lora_weight = alpha * torch.mm(weight_up, weight_down).unsqueeze(2).unsqueeze(3)
+            else:
+                lora_weight = alpha * torch.mm(weight_up, weight_down)
+            target_name = key.split(".")[0].replace(lora_prefix, renamed_lora_prefix).replace("_", ".") + ".weight"
+            for special_key in self.special_keys:
+                target_name = target_name.replace(special_key, self.special_keys[special_key])
+            state_dict_[target_name] = lora_weight.cpu()
+        return state_dict_
+    
+
+    def convert_state_dict_AB(self, state_dict, lora_prefix="", alpha=1.0, device="cuda", torch_dtype=torch.float16):
+        state_dict_ = {}
+        for key in state_dict:
+            if ".lora_B." not in key:
+                continue
+            if not key.startswith(lora_prefix):
+                continue
+            weight_up = state_dict[key].to(device=device, dtype=torch_dtype)
+            weight_down = state_dict[key.replace(".lora_B.", ".lora_A.")].to(device=device, dtype=torch_dtype)
+            if len(weight_up.shape) == 4:
+                weight_up = weight_up.squeeze(3).squeeze(2)
+                weight_down = weight_down.squeeze(3).squeeze(2)
+                lora_weight = alpha * torch.mm(weight_up, weight_down).unsqueeze(2).unsqueeze(3)
+            else:
+                lora_weight = alpha * torch.mm(weight_up, weight_down)
+            keys = key.split(".")
+            keys.pop(keys.index("lora_B"))
+            target_name = ".".join(keys)
+            target_name = target_name[len(lora_prefix):]
+            state_dict_[target_name] = lora_weight.cpu()
+        return state_dict_
+    
+
+    def load(self, model, state_dict_lora, lora_prefix, alpha=1.0, model_resource=None):
+        state_dict_model = model.state_dict()
+        state_dict_lora = self.convert_state_dict(state_dict_lora, lora_prefix=lora_prefix, alpha=alpha)
+        if model_resource == "diffusers":
+            state_dict_lora = model.__class__.state_dict_converter().from_diffusers(state_dict_lora)
+        elif model_resource == "civitai":
+            state_dict_lora = model.__class__.state_dict_converter().from_civitai(state_dict_lora)
+        if isinstance(state_dict_lora, tuple):
+            state_dict_lora = state_dict_lora[0]
+        if len(state_dict_lora) > 0:
+            print(f"    {len(state_dict_lora)} tensors are updated.")
+            for name in state_dict_lora:
+                fp8=False
+                if state_dict_model[name].dtype == torch.float8_e4m3fn:
+                    state_dict_model[name]= state_dict_model[name].to(state_dict_lora[name].dtype)
+                    fp8=True
+                state_dict_model[name] += state_dict_lora[name].to(
+                    dtype=state_dict_model[name].dtype, device=state_dict_model[name].device)
+                if fp8:
+                    state_dict_model[name] = state_dict_model[name].to(torch.float8_e4m3fn)
+            model.load_state_dict(state_dict_model)
+    
+
+    def match(self, model, state_dict_lora):
+        for lora_prefix, model_class in zip(self.lora_prefix, self.supported_model_classes):
+            if not isinstance(model, model_class):
+                continue
+            state_dict_model = model.state_dict()
+            for model_resource in ["diffusers", "civitai"]:
+                try:
+                    state_dict_lora_ = self.convert_state_dict(state_dict_lora, lora_prefix=lora_prefix, alpha=1.0)
+                    converter_fn = model.__class__.state_dict_converter().from_diffusers if model_resource == "diffusers" \
+                        else model.__class__.state_dict_converter().from_civitai
+                    state_dict_lora_ = converter_fn(state_dict_lora_)
+                    if isinstance(state_dict_lora_, tuple):
+                        state_dict_lora_ = state_dict_lora_[0]
+                    if len(state_dict_lora_) == 0:
+                        continue
+                    for name in state_dict_lora_:
+                        if name not in state_dict_model:
+                            break
+                    else:
+                        return lora_prefix, model_resource
+                except:
+                    pass
+        return None
+
+
+
+class SDLoRAFromCivitai(LoRAFromCivitai):
+    def __init__(self):
+        super().__init__()
+        self.supported_model_classes = [SDUNet, SDTextEncoder]
+        self.lora_prefix = ["lora_unet_", "lora_te_"]
+        self.special_keys = {
+            "down.blocks": "down_blocks",
+            "up.blocks": "up_blocks",
+            "mid.block": "mid_block",
+            "proj.in": "proj_in",
+            "proj.out": "proj_out",
+            "transformer.blocks": "transformer_blocks",
+            "to.q": "to_q",
+            "to.k": "to_k",
+            "to.v": "to_v",
+            "to.out": "to_out",
+            "text.model": "text_model",
+            "self.attn.q.proj": "self_attn.q_proj",
+            "self.attn.k.proj": "self_attn.k_proj",
+            "self.attn.v.proj": "self_attn.v_proj",
+            "self.attn.out.proj": "self_attn.out_proj",
+            "input.blocks": "model.diffusion_model.input_blocks",
+            "middle.block": "model.diffusion_model.middle_block",
+            "output.blocks": "model.diffusion_model.output_blocks",
+        }
+
+
+class SDXLLoRAFromCivitai(LoRAFromCivitai):
+    def __init__(self):
+        super().__init__()
+        self.supported_model_classes = [SDXLUNet, SDXLTextEncoder, SDXLTextEncoder2]
+        self.lora_prefix = ["lora_unet_", "lora_te1_", "lora_te2_"]
+        self.renamed_lora_prefix = {"lora_te2_": "2"}
+        self.special_keys = {
+            "down.blocks": "down_blocks",
+            "up.blocks": "up_blocks",
+            "mid.block": "mid_block",
+            "proj.in": "proj_in",
+            "proj.out": "proj_out",
+            "transformer.blocks": "transformer_blocks",
+            "to.q": "to_q",
+            "to.k": "to_k",
+            "to.v": "to_v",
+            "to.out": "to_out",
+            "text.model": "conditioner.embedders.0.transformer.text_model",
+            "self.attn.q.proj": "self_attn.q_proj",
+            "self.attn.k.proj": "self_attn.k_proj",
+            "self.attn.v.proj": "self_attn.v_proj",
+            "self.attn.out.proj": "self_attn.out_proj",
+            "input.blocks": "model.diffusion_model.input_blocks",
+            "middle.block": "model.diffusion_model.middle_block",
+            "output.blocks": "model.diffusion_model.output_blocks",
+            "2conditioner.embedders.0.transformer.text_model.encoder.layers": "text_model.encoder.layers"
+        }
+        
+
+class FluxLoRAFromCivitai(LoRAFromCivitai):
+    def __init__(self):
+        super().__init__()
+        self.supported_model_classes = [FluxDiT, FluxDiT]
+        self.lora_prefix = ["lora_unet_", "transformer."]
+        self.renamed_lora_prefix = {}
+        self.special_keys = {
+            "single.blocks": "single_blocks",
+            "double.blocks": "double_blocks",
+            "img.attn": "img_attn",
+            "img.mlp": "img_mlp",
+            "img.mod": "img_mod",
+            "txt.attn": "txt_attn",
+            "txt.mlp": "txt_mlp",
+            "txt.mod": "txt_mod",
+        }
+
+    
+    
+class GeneralLoRAFromPeft:
+    def __init__(self):
+        self.supported_model_classes = [SDUNet, SDXLUNet, SD3DiT, HunyuanDiT, FluxDiT, CogDiT, WanModel]
+    
+    
+    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)
+            target_name = ".".join(keys)
+            lora_name_dict[target_name] = (key, key.replace(".lora_B.", ".lora_A."))
+        return lora_name_dict
+    
+    
+    def match(self, model: torch.nn.Module, state_dict_lora):
+        lora_name_dict = self.get_name_dict(state_dict_lora)
+        model_name_dict = {name: None for name, _ in model.named_parameters()}
+        matched_num = sum([i in model_name_dict for i in lora_name_dict])
+        if matched_num == len(lora_name_dict):
+            return "", ""
+        else:
+            return None
+    
+    
+    def fetch_device_and_dtype(self, state_dict):
+        device, dtype = None, None
+        for name, param in state_dict.items():
+            device, dtype = param.device, param.dtype
+            break
+        computation_device = device
+        computation_dtype = dtype
+        if computation_device == torch.device("cpu"):
+            if torch.cuda.is_available():
+                computation_device = torch.device("cuda")
+        if computation_dtype == torch.float8_e4m3fn:
+            computation_dtype = torch.float32
+        return device, dtype, computation_device, computation_dtype
+
+
+    def load(self, model, state_dict_lora, lora_prefix="", alpha=1.0, model_resource=""):
+        state_dict_model = model.state_dict()
+        device, dtype, computation_device, computation_dtype = self.fetch_device_and_dtype(state_dict_model)
+        lora_name_dict = self.get_name_dict(state_dict_lora)
+        for name in lora_name_dict:
+            weight_up = state_dict_lora[lora_name_dict[name][0]].to(device=computation_device, dtype=computation_dtype)
+            weight_down = state_dict_lora[lora_name_dict[name][1]].to(device=computation_device, dtype=computation_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)
+            weight_model = state_dict_model[name].to(device=computation_device, dtype=computation_dtype)
+            weight_patched = weight_model + weight_lora
+            state_dict_model[name] = weight_patched.to(device=device, dtype=dtype)
+        print(f"    {len(lora_name_dict)} tensors are updated.")
+        model.load_state_dict(state_dict_model)
+    
+    
+
+class HunyuanVideoLoRAFromCivitai(LoRAFromCivitai):
+    def __init__(self):
+        super().__init__()
+        self.supported_model_classes = [HunyuanVideoDiT, HunyuanVideoDiT]
+        self.lora_prefix = ["diffusion_model.", "transformer."]
+        self.special_keys = {}
+    
+
+class FluxLoRAConverter:
+    def __init__(self):
+        pass
+
+    @staticmethod
+    def align_to_opensource_format(state_dict, alpha=None):
+        prefix_rename_dict = {
+            "single_blocks": "lora_unet_single_blocks",
+            "blocks": "lora_unet_double_blocks",
+        }
+        middle_rename_dict = {
+            "norm.linear": "modulation_lin",
+            "to_qkv_mlp": "linear1",
+            "proj_out": "linear2",
+
+            "norm1_a.linear": "img_mod_lin",
+            "norm1_b.linear": "txt_mod_lin",
+            "attn.a_to_qkv": "img_attn_qkv",
+            "attn.b_to_qkv": "txt_attn_qkv",
+            "attn.a_to_out": "img_attn_proj",
+            "attn.b_to_out": "txt_attn_proj",
+            "ff_a.0": "img_mlp_0",
+            "ff_a.2": "img_mlp_2",
+            "ff_b.0": "txt_mlp_0",
+            "ff_b.2": "txt_mlp_2",
+        }
+        suffix_rename_dict = {
+            "lora_B.weight": "lora_up.weight",
+            "lora_A.weight": "lora_down.weight",
+        }
+        state_dict_ = {}
+        for name, param in state_dict.items():
+            names = name.split(".")
+            if names[-2] != "lora_A" and names[-2] != "lora_B":
+                names.pop(-2)
+            prefix = names[0]
+            middle = ".".join(names[2:-2])
+            suffix = ".".join(names[-2:])
+            block_id = names[1]
+            if middle not in middle_rename_dict:
+                continue
+            rename = prefix_rename_dict[prefix] + "_" + block_id + "_" + middle_rename_dict[middle] + "." + suffix_rename_dict[suffix]
+            state_dict_[rename] = param
+            if rename.endswith("lora_up.weight"):
+                lora_alpha = alpha if alpha is not None else param.shape[-1]
+                state_dict_[rename.replace("lora_up.weight", "alpha")] = torch.tensor((lora_alpha,))[0]
+        return state_dict_
+    
+    @staticmethod
+    def align_to_diffsynth_format(state_dict):
+        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 guess_block_id(name):
+            names = name.split("_")
+            for i in names:
+                if i.isdigit():
+                    return i, name.replace(f"_{i}_", "_blockid_")
+            return None, None
+        state_dict_ = {}
+        for name, param in state_dict.items():
+            block_id, source_name = guess_block_id(name)
+            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
+        return state_dict_
+
+
+class WanLoRAConverter:
+    def __init__(self):
+        pass
+
+    @staticmethod
+    def align_to_opensource_format(state_dict, **kwargs):
+        state_dict = {"diffusion_model." + name.replace(".default.", "."): param for name, param in state_dict.items()}
+        return state_dict
+    
+    @staticmethod
+    def align_to_diffsynth_format(state_dict, **kwargs):
+        state_dict = {name.replace("diffusion_model.", "").replace(".lora_A.weight", ".lora_A.default.weight").replace(".lora_B.weight", ".lora_B.default.weight"): param for name, param in state_dict.items()}
+        return state_dict
+
+
+class QwenImageLoRAConverter:
+    def __init__(self):
+        pass
+
+    @staticmethod
+    def align_to_opensource_format(state_dict, **kwargs):
+        state_dict = {name.replace(".default.", "."): param for name, param in state_dict.items()}
+        return state_dict
+    
+    @staticmethod
+    def align_to_diffsynth_format(state_dict, **kwargs):
+        state_dict = {name.replace(".lora_A.weight", ".lora_A.default.weight").replace(".lora_B.weight", ".lora_B.default.weight"): param for name, param in state_dict.items()}
+        return state_dict
+
+
+def get_lora_loaders():
+    return [SDLoRAFromCivitai(), SDXLLoRAFromCivitai(), FluxLoRAFromCivitai(), HunyuanVideoLoRAFromCivitai(), GeneralLoRAFromPeft()]
diff --git a/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/model_manager.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/model_manager.py
new file mode 100644
index 0000000000000000000000000000000000000000..d46eedfa639ab6afeaee71748971d85724692fb6
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/model_manager.py
@@ -0,0 +1,467 @@
+import os, torch, json, importlib
+from typing import List
+
+from .downloader import download_models, download_customized_models, Preset_model_id, Preset_model_website
+
+from .sd_text_encoder import SDTextEncoder
+from .sd_unet import SDUNet
+from .sd_vae_encoder import SDVAEEncoder
+from .sd_vae_decoder import SDVAEDecoder
+from .lora import get_lora_loaders
+
+from .sdxl_text_encoder import SDXLTextEncoder, SDXLTextEncoder2
+from .sdxl_unet import SDXLUNet
+from .sdxl_vae_decoder import SDXLVAEDecoder
+from .sdxl_vae_encoder import SDXLVAEEncoder
+
+from .sd3_text_encoder import SD3TextEncoder1, SD3TextEncoder2, SD3TextEncoder3
+from .sd3_dit import SD3DiT
+from .sd3_vae_decoder import SD3VAEDecoder
+from .sd3_vae_encoder import SD3VAEEncoder
+
+from .sd_controlnet import SDControlNet
+from .sdxl_controlnet import SDXLControlNetUnion
+
+from .sd_motion import SDMotionModel
+from .sdxl_motion import SDXLMotionModel
+
+from .svd_image_encoder import SVDImageEncoder
+from .svd_unet import SVDUNet
+from .svd_vae_decoder import SVDVAEDecoder
+from .svd_vae_encoder import SVDVAEEncoder
+
+from .sd_ipadapter import SDIpAdapter, IpAdapterCLIPImageEmbedder
+from .sdxl_ipadapter import SDXLIpAdapter, IpAdapterXLCLIPImageEmbedder
+
+from .hunyuan_dit_text_encoder import HunyuanDiTCLIPTextEncoder, HunyuanDiTT5TextEncoder
+from .hunyuan_dit import HunyuanDiT
+from .hunyuan_video_vae_decoder import HunyuanVideoVAEDecoder
+from .hunyuan_video_vae_encoder import HunyuanVideoVAEEncoder
+
+from .flux_dit import FluxDiT
+from .flux_text_encoder import FluxTextEncoder2
+from .flux_vae import FluxVAEEncoder, FluxVAEDecoder
+from .flux_ipadapter import FluxIpAdapter
+
+from .cog_vae import CogVAEEncoder, CogVAEDecoder
+from .cog_dit import CogDiT
+
+from ..extensions.RIFE import IFNet
+from ..extensions.ESRGAN import RRDBNet
+
+from ..configs.model_config import model_loader_configs, huggingface_model_loader_configs, patch_model_loader_configs
+from .utils import load_state_dict, init_weights_on_device, hash_state_dict_keys, split_state_dict_with_prefix
+
+
+def load_model_from_single_file(state_dict, model_names, model_classes, model_resource, torch_dtype, device):
+    loaded_model_names, loaded_models = [], []
+    for model_name, model_class in zip(model_names, model_classes):
+        print(f"    model_name: {model_name} model_class: {model_class.__name__}")
+        state_dict_converter = model_class.state_dict_converter()
+        if model_resource == "civitai":
+            state_dict_results = state_dict_converter.from_civitai(state_dict)
+        elif model_resource == "diffusers":
+            state_dict_results = state_dict_converter.from_diffusers(state_dict)
+        if isinstance(state_dict_results, tuple):
+            model_state_dict, extra_kwargs = state_dict_results
+            print(f"        This model is initialized with extra kwargs: {extra_kwargs}")
+        else:
+            model_state_dict, extra_kwargs = state_dict_results, {}
+        torch_dtype = torch.float32 if extra_kwargs.get("upcast_to_float32", False) else torch_dtype
+        with init_weights_on_device():
+            model = model_class(**extra_kwargs)
+        if hasattr(model, "eval"):
+            model = model.eval()
+        model.load_state_dict(model_state_dict, assign=True)
+        model = model.to(dtype=torch_dtype, device=device)
+        loaded_model_names.append(model_name)
+        loaded_models.append(model)
+    return loaded_model_names, loaded_models
+
+
+def load_model_from_huggingface_folder(file_path, model_names, model_classes, torch_dtype, device):
+    loaded_model_names, loaded_models = [], []
+    for model_name, model_class in zip(model_names, model_classes):
+        if torch_dtype in [torch.float32, torch.float16, torch.bfloat16]:
+            model = model_class.from_pretrained(file_path, torch_dtype=torch_dtype).eval()
+        else:
+            model = model_class.from_pretrained(file_path).eval().to(dtype=torch_dtype)
+        if torch_dtype == torch.float16 and hasattr(model, "half"):
+            model = model.half()
+        try:
+            model = model.to(device=device)
+        except:
+            pass
+        loaded_model_names.append(model_name)
+        loaded_models.append(model)
+    return loaded_model_names, loaded_models
+
+
+def load_single_patch_model_from_single_file(state_dict, model_name, model_class, base_model, extra_kwargs, torch_dtype, device):
+    print(f"    model_name: {model_name} model_class: {model_class.__name__} extra_kwargs: {extra_kwargs}")
+    base_state_dict = base_model.state_dict()
+    base_model.to("cpu")
+    del base_model
+    model = model_class(**extra_kwargs)
+    model.load_state_dict(base_state_dict, strict=False)
+    model.load_state_dict(state_dict, strict=False)
+    model.to(dtype=torch_dtype, device=device)
+    return model
+
+
+def load_patch_model_from_single_file(state_dict, model_names, model_classes, extra_kwargs, model_manager, torch_dtype, device):
+    loaded_model_names, loaded_models = [], []
+    for model_name, model_class in zip(model_names, model_classes):
+        while True:
+            for model_id in range(len(model_manager.model)):
+                base_model_name = model_manager.model_name[model_id]
+                if base_model_name == model_name:
+                    base_model_path = model_manager.model_path[model_id]
+                    base_model = model_manager.model[model_id]
+                    print(f"    Adding patch model to {base_model_name} ({base_model_path})")
+                    patched_model = load_single_patch_model_from_single_file(
+                        state_dict, model_name, model_class, base_model, extra_kwargs, torch_dtype, device)
+                    loaded_model_names.append(base_model_name)
+                    loaded_models.append(patched_model)
+                    model_manager.model.pop(model_id)
+                    model_manager.model_path.pop(model_id)
+                    model_manager.model_name.pop(model_id)
+                    break
+            else:
+                break
+    return loaded_model_names, loaded_models
+
+
+
+class ModelDetectorTemplate:
+    def __init__(self):
+        pass
+
+    def match(self, file_path="", state_dict={}):
+        return False
+    
+    def load(self, file_path="", state_dict={}, device="cuda", torch_dtype=torch.float16, **kwargs):
+        return [], []
+    
+
+
+class ModelDetectorFromSingleFile:
+    def __init__(self, model_loader_configs=[]):
+        self.keys_hash_with_shape_dict = {}
+        self.keys_hash_dict = {}
+        for metadata in model_loader_configs:
+            self.add_model_metadata(*metadata)
+
+
+    def add_model_metadata(self, keys_hash, keys_hash_with_shape, model_names, model_classes, model_resource):
+        self.keys_hash_with_shape_dict[keys_hash_with_shape] = (model_names, model_classes, model_resource)
+        if keys_hash is not None:
+            self.keys_hash_dict[keys_hash] = (model_names, model_classes, model_resource)
+
+
+    def match(self, file_path="", state_dict={}):
+        if isinstance(file_path, str) and os.path.isdir(file_path):
+            return False
+        if len(state_dict) == 0:
+            state_dict = load_state_dict(file_path)
+        keys_hash_with_shape = hash_state_dict_keys(state_dict, with_shape=True)
+        if keys_hash_with_shape in self.keys_hash_with_shape_dict:
+            return True
+        keys_hash = hash_state_dict_keys(state_dict, with_shape=False)
+        if keys_hash in self.keys_hash_dict:
+            return True
+        return False
+
+
+    def load(self, file_path="", state_dict={}, device="cuda", torch_dtype=torch.float16, **kwargs):
+        if len(state_dict) == 0:
+            state_dict = load_state_dict(file_path)
+
+        # Load models with strict matching
+        keys_hash_with_shape = hash_state_dict_keys(state_dict, with_shape=True)
+        if keys_hash_with_shape in self.keys_hash_with_shape_dict:
+            model_names, model_classes, model_resource = self.keys_hash_with_shape_dict[keys_hash_with_shape]
+            loaded_model_names, loaded_models = load_model_from_single_file(state_dict, model_names, model_classes, model_resource, torch_dtype, device)
+            return loaded_model_names, loaded_models
+
+        # Load models without strict matching
+        # (the shape of parameters may be inconsistent, and the state_dict_converter will modify the model architecture)
+        keys_hash = hash_state_dict_keys(state_dict, with_shape=False)
+        if keys_hash in self.keys_hash_dict:
+            model_names, model_classes, model_resource = self.keys_hash_dict[keys_hash]
+            loaded_model_names, loaded_models = load_model_from_single_file(state_dict, model_names, model_classes, model_resource, torch_dtype, device)
+            return loaded_model_names, loaded_models
+
+        return loaded_model_names, loaded_models
+
+
+
+class ModelDetectorFromSplitedSingleFile(ModelDetectorFromSingleFile):
+    def __init__(self, model_loader_configs=[]):
+        super().__init__(model_loader_configs)
+
+
+    def match(self, file_path="", state_dict={}):
+        if isinstance(file_path, str) and os.path.isdir(file_path):
+            return False
+        if len(state_dict) == 0:
+            state_dict = load_state_dict(file_path)
+        splited_state_dict = split_state_dict_with_prefix(state_dict)
+        for sub_state_dict in splited_state_dict:
+            if super().match(file_path, sub_state_dict):
+                return True
+        return False
+
+
+    def load(self, file_path="", state_dict={}, device="cuda", torch_dtype=torch.float16, **kwargs):
+        # Split the state_dict and load from each component
+        splited_state_dict = split_state_dict_with_prefix(state_dict)
+        valid_state_dict = {}
+        for sub_state_dict in splited_state_dict:
+            if super().match(file_path, sub_state_dict):
+                valid_state_dict.update(sub_state_dict)
+        if super().match(file_path, valid_state_dict):
+            loaded_model_names, loaded_models = super().load(file_path, valid_state_dict, device, torch_dtype)
+        else:
+            loaded_model_names, loaded_models = [], []
+            for sub_state_dict in splited_state_dict:
+                if super().match(file_path, sub_state_dict):
+                    loaded_model_names_, loaded_models_ = super().load(file_path, valid_state_dict, device, torch_dtype)
+                    loaded_model_names += loaded_model_names_
+                    loaded_models += loaded_models_
+        return loaded_model_names, loaded_models
+    
+
+
+class ModelDetectorFromHuggingfaceFolder:
+    def __init__(self, model_loader_configs=[]):
+        self.architecture_dict = {}
+        for metadata in model_loader_configs:
+            self.add_model_metadata(*metadata)
+
+
+    def add_model_metadata(self, architecture, huggingface_lib, model_name, redirected_architecture):
+        self.architecture_dict[architecture] = (huggingface_lib, model_name, redirected_architecture)
+
+
+    def match(self, file_path="", state_dict={}):
+        if not isinstance(file_path, str) or os.path.isfile(file_path):
+            return False
+        file_list = os.listdir(file_path)
+        if "config.json" not in file_list:
+            return False
+        with open(os.path.join(file_path, "config.json"), "r") as f:
+            config = json.load(f)
+        if "architectures" not in config and "_class_name" not in config:
+            return False
+        return True
+
+
+    def load(self, file_path="", state_dict={}, device="cuda", torch_dtype=torch.float16, **kwargs):
+        with open(os.path.join(file_path, "config.json"), "r") as f:
+            config = json.load(f)
+        loaded_model_names, loaded_models = [], []
+        architectures = config["architectures"] if "architectures" in config else [config["_class_name"]]
+        for architecture in architectures:
+            huggingface_lib, model_name, redirected_architecture = self.architecture_dict[architecture]
+            if redirected_architecture is not None:
+                architecture = redirected_architecture
+            model_class = importlib.import_module(huggingface_lib).__getattribute__(architecture)
+            loaded_model_names_, loaded_models_ = load_model_from_huggingface_folder(file_path, [model_name], [model_class], torch_dtype, device)
+            loaded_model_names += loaded_model_names_
+            loaded_models += loaded_models_
+        return loaded_model_names, loaded_models
+    
+
+
+class ModelDetectorFromPatchedSingleFile:
+    def __init__(self, model_loader_configs=[]):
+        self.keys_hash_with_shape_dict = {}
+        for metadata in model_loader_configs:
+            self.add_model_metadata(*metadata)
+
+
+    def add_model_metadata(self, keys_hash_with_shape, model_name, model_class, extra_kwargs):
+        self.keys_hash_with_shape_dict[keys_hash_with_shape] = (model_name, model_class, extra_kwargs)
+
+
+    def match(self, file_path="", state_dict={}):
+        if not isinstance(file_path, str) or os.path.isdir(file_path):
+            return False
+        if len(state_dict) == 0:
+            state_dict = load_state_dict(file_path)
+        keys_hash_with_shape = hash_state_dict_keys(state_dict, with_shape=True)
+        if keys_hash_with_shape in self.keys_hash_with_shape_dict:
+            return True
+        return False
+
+
+    def load(self, file_path="", state_dict={}, device="cuda", torch_dtype=torch.float16, model_manager=None, **kwargs):
+        if len(state_dict) == 0:
+            state_dict = load_state_dict(file_path)
+
+        # Load models with strict matching
+        loaded_model_names, loaded_models = [], []
+        keys_hash_with_shape = hash_state_dict_keys(state_dict, with_shape=True)
+        if keys_hash_with_shape in self.keys_hash_with_shape_dict:
+            model_names, model_classes, extra_kwargs = self.keys_hash_with_shape_dict[keys_hash_with_shape]
+            loaded_model_names_, loaded_models_ = load_patch_model_from_single_file(
+                state_dict, model_names, model_classes, extra_kwargs, model_manager, torch_dtype, device)
+            loaded_model_names += loaded_model_names_
+            loaded_models += loaded_models_
+        return loaded_model_names, loaded_models
+
+
+
+class ModelManager:
+    def __init__(
+        self,
+        torch_dtype=torch.float16,
+        device="cuda",
+        model_id_list: List[Preset_model_id] = [],
+        downloading_priority: List[Preset_model_website] = ["ModelScope", "HuggingFace"],
+        file_path_list: List[str] = [],
+    ):
+        self.torch_dtype = torch_dtype
+        self.device = device
+        self.model = []
+        self.model_path = []
+        self.model_name = []
+        downloaded_files = download_models(model_id_list, downloading_priority) if len(model_id_list) > 0 else []
+        self.model_detector = [
+            ModelDetectorFromSingleFile(model_loader_configs),
+            ModelDetectorFromSplitedSingleFile(model_loader_configs),
+            ModelDetectorFromHuggingfaceFolder(huggingface_model_loader_configs),
+            ModelDetectorFromPatchedSingleFile(patch_model_loader_configs),
+        ]
+        self.load_models(downloaded_files + file_path_list)
+
+
+    def load_model_from_single_file(self, file_path="", state_dict={}, model_names=[], model_classes=[], model_resource=None):
+        print(f"Loading models from file: {file_path}")
+        if len(state_dict) == 0:
+            state_dict = load_state_dict(file_path)
+        model_names, models = load_model_from_single_file(state_dict, model_names, model_classes, model_resource, self.torch_dtype, self.device)
+        for model_name, model in zip(model_names, models):
+            self.model.append(model)
+            self.model_path.append(file_path)
+            self.model_name.append(model_name)
+        print(f"    The following models are loaded: {model_names}.")
+
+
+    def load_model_from_huggingface_folder(self, file_path="", model_names=[], model_classes=[]):
+        print(f"Loading models from folder: {file_path}")
+        model_names, models = load_model_from_huggingface_folder(file_path, model_names, model_classes, self.torch_dtype, self.device)
+        for model_name, model in zip(model_names, models):
+            self.model.append(model)
+            self.model_path.append(file_path)
+            self.model_name.append(model_name)
+        print(f"    The following models are loaded: {model_names}.")
+
+
+    def load_patch_model_from_single_file(self, file_path="", state_dict={}, model_names=[], model_classes=[], extra_kwargs={}):
+        print(f"Loading patch models from file: {file_path}")
+        model_names, models = load_patch_model_from_single_file(
+            state_dict, model_names, model_classes, extra_kwargs, self, self.torch_dtype, self.device)
+        for model_name, model in zip(model_names, models):
+            self.model.append(model)
+            self.model_path.append(file_path)
+            self.model_name.append(model_name)
+        print(f"    The following patched models are loaded: {model_names}.")
+
+
+    def load_lora(self, file_path="", state_dict={}, lora_alpha=1.0):
+        if isinstance(file_path, list):
+            for file_path_ in file_path:
+                self.load_lora(file_path_, state_dict=state_dict, lora_alpha=lora_alpha)
+        else:
+            print(f"Loading LoRA models from file: {file_path}")
+            is_loaded = False
+            if len(state_dict) == 0:
+                state_dict = load_state_dict(file_path)
+            for model_name, model, model_path in zip(self.model_name, self.model, self.model_path):
+                for lora in get_lora_loaders():
+                    match_results = lora.match(model, state_dict)
+                    if match_results is not None:
+                        print(f"    Adding LoRA to {model_name} ({model_path}).")
+                        lora_prefix, model_resource = match_results
+                        lora.load(model, state_dict, lora_prefix, alpha=lora_alpha, model_resource=model_resource)
+                        is_loaded = True
+                        break
+            if not is_loaded:
+                print(f"    Cannot load LoRA: {file_path}")
+
+
+    def load_model(self, file_path, model_names=None, device=None, torch_dtype=None):
+        print(f"Loading models from: {file_path}")
+        if device is None: device = self.device
+        if torch_dtype is None: torch_dtype = self.torch_dtype
+        if isinstance(file_path, list):
+            state_dict = {}
+            for path in file_path:
+                state_dict.update(load_state_dict(path))
+        elif os.path.isfile(file_path):
+            state_dict = load_state_dict(file_path)
+        else:
+            state_dict = None
+        for model_detector in self.model_detector:
+            if model_detector.match(file_path, state_dict):
+                model_names, models = model_detector.load(
+                    file_path, state_dict,
+                    device=device, torch_dtype=torch_dtype,
+                    allowed_model_names=model_names, model_manager=self
+                )
+                for model_name, model in zip(model_names, models):
+                    self.model.append(model)
+                    self.model_path.append(file_path)
+                    self.model_name.append(model_name)
+                print(f"    The following models are loaded: {model_names}.")
+                break
+        else:
+            print(f"    We cannot detect the model type. No models are loaded.")
+        
+
+    def load_models(self, file_path_list, model_names=None, device=None, torch_dtype=None):
+        for file_path in file_path_list:
+            self.load_model(file_path, model_names, device=device, torch_dtype=torch_dtype)
+
+    
+    def fetch_model(self, model_name, file_path=None, require_model_path=False, index=None):
+        fetched_models = []
+        fetched_model_paths = []
+        for model, model_path, model_name_ in zip(self.model, self.model_path, self.model_name):
+            if file_path is not None and file_path != model_path:
+                continue
+            if model_name == model_name_:
+                fetched_models.append(model)
+                fetched_model_paths.append(model_path)
+        if len(fetched_models) == 0:
+            print(f"No {model_name} models available.")
+            return None
+        if len(fetched_models) == 1:
+            print(f"Using {model_name} from {fetched_model_paths[0]}.")
+            model = fetched_models[0]
+            path = fetched_model_paths[0]
+        else:
+            if index is None:
+                model = fetched_models[0]
+                path = fetched_model_paths[0]
+                print(f"More than one {model_name} models are loaded in model manager: {fetched_model_paths}. Using {model_name} from {fetched_model_paths[0]}.")
+            elif isinstance(index, int):
+                model = fetched_models[:index]
+                path = fetched_model_paths[:index]
+                print(f"More than one {model_name} models are loaded in model manager: {fetched_model_paths}. Using {model_name} from {fetched_model_paths[:index]}.")
+            else:
+                model = fetched_models
+                path = fetched_model_paths
+                print(f"More than one {model_name} models are loaded in model manager: {fetched_model_paths}. Using {model_name} from {fetched_model_paths}.")
+        if require_model_path:
+            return model, path
+        else:
+            return model
+        
+
+    def to(self, device):
+        for model in self.model:
+            model.to(device)
+
diff --git a/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/nexus_gen.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/nexus_gen.py
new file mode 100644
index 0000000000000000000000000000000000000000..011039842312b01a0bbb69b999bc868902736e9a
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/nexus_gen.py
@@ -0,0 +1,161 @@
+import torch
+from PIL import Image
+
+
+class NexusGenAutoregressiveModel(torch.nn.Module):
+    def __init__(self, max_length=1024, max_pixels=262640):
+        super(NexusGenAutoregressiveModel, self).__init__()
+        from .nexus_gen_ar_model import Qwen2_5_VLForConditionalGeneration
+        from transformers import Qwen2_5_VLConfig
+        self.max_length = max_length
+        self.max_pixels = max_pixels
+        model_config = Qwen2_5_VLConfig(**{
+            "_name_or_path": "DiffSynth-Studio/Nexus-GenV2",
+            "architectures": [
+                "Qwen2_5_VLForConditionalGeneration"
+            ],
+            "attention_dropout": 0.0,
+            "auto_map": {
+                "AutoConfig": "configuration_qwen2_5_vl.Qwen2_5_VLConfig",
+                "AutoModel": "modeling_qwen2_5_vl.Qwen2_5_VLModel",
+                "AutoModelForCausalLM": "modeling_qwen2_5_vl.Qwen2_5_VLForConditionalGeneration"
+            },
+            "bos_token_id": 151643,
+            "eos_token_id": 151645,
+            "hidden_act": "silu",
+            "hidden_size": 3584,
+            "image_token_id": 151655,
+            "initializer_range": 0.02,
+            "intermediate_size": 18944,
+            "max_position_embeddings": 128000,
+            "max_window_layers": 28,
+            "model_type": "qwen2_5_vl",
+            "num_attention_heads": 28,
+            "num_hidden_layers": 28,
+            "num_key_value_heads": 4,
+            "pad_token_id": 151643,
+            "rms_norm_eps": 1e-06,
+            "rope_scaling": {
+                "mrope_section": [
+                16,
+                24,
+                24
+                ],
+                "rope_type": "default",
+                "type": "default"
+            },
+            "rope_theta": 1000000.0,
+            "sliding_window": 32768,
+            "tie_word_embeddings": False,
+            "torch_dtype": "bfloat16",
+            "transformers_version": "4.49.0",
+            "use_cache": False,
+            "use_sliding_window": False,
+            "video_token_id": 151656,
+            "vision_config": {
+                "hidden_size": 1280,
+                "in_chans": 3,
+                "model_type": "qwen2_5_vl",
+                "spatial_patch_size": 14,
+                "tokens_per_second": 2,
+                "torch_dtype": "bfloat16"
+            },
+            "vision_end_token_id": 151653,
+            "vision_start_token_id": 151652,
+            "vision_token_id": 151654,
+            "vocab_size": 152064
+        })
+        self.model = Qwen2_5_VLForConditionalGeneration(model_config)
+        self.processor = None
+        
+        
+    def load_processor(self, path):
+        from .nexus_gen_ar_model import Qwen2_5_VLProcessor
+        self.processor = Qwen2_5_VLProcessor.from_pretrained(path)
+
+
+    @staticmethod
+    def state_dict_converter():
+        return NexusGenAutoregressiveModelStateDictConverter()
+
+    def bound_image(self, image, max_pixels=262640):
+        from qwen_vl_utils import smart_resize
+        resized_height, resized_width = smart_resize(
+            image.height,
+            image.width,
+            max_pixels=max_pixels,
+        )
+        return image.resize((resized_width, resized_height))
+
+    def get_editing_msg(self, instruction):
+        if '<image>' not in instruction:
+            instruction = '<image> ' + instruction
+        messages = [{"role":"user", "content":instruction}, {"role":"assistant", "content":"Here is the image: <image>"}]
+        return messages
+
+    def get_generation_msg(self, instruction):
+        instruction = "Generate an image according to the following description: {}".format(instruction)
+        messages = [{"role":"user", "content":instruction}, {"role":"assistant", "content":"Here is an image based on the description: <image>"}]
+        return messages
+
+    def forward(self, instruction, ref_image=None, num_img_tokens=81):
+        """
+        Generate target embeddings for the given instruction and reference image.
+        """
+        if ref_image is not None:
+            messages = self.get_editing_msg(instruction)
+            images = [self.bound_image(ref_image)] + [Image.new(mode='RGB', size=(252, 252), color=(255, 255, 255))]
+            output_image_embeddings = self.get_target_embeddings(images, messages, self.processor, self.model, num_img_tokens)
+        else:
+            messages = self.get_generation_msg(instruction)
+            images = [Image.new(mode='RGB', size=(252, 252), color=(255, 255, 255))]
+            output_image_embeddings = self.get_target_embeddings(images, messages, self.processor, self.model, num_img_tokens)
+
+        return output_image_embeddings
+
+    def get_target_embeddings(self, images, messages, processor, model, num_img_tokens=81):
+        text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=False)
+        text = text.replace('<image>', '<|vision_start|><|image_pad|><|vision_end|>')
+        inputs = processor(
+            text=[text],
+            images=images,
+            padding=True,
+            return_tensors="pt",
+        )
+        inputs = inputs.to(model.device)
+
+        input_embeds = model.model.embed_tokens(inputs['input_ids'])
+        image_embeds = model.visual(inputs['pixel_values'], grid_thw=inputs['image_grid_thw'])
+        ground_truth_image_embeds = image_embeds[-num_img_tokens:]
+        input_image_embeds = image_embeds[:-num_img_tokens]
+
+        image_mask = inputs['input_ids'] == model.config.image_token_id
+        indices = image_mask.cumsum(dim=1)
+        input_image_mask = torch.logical_and(indices <= (image_embeds.shape[0] - ground_truth_image_embeds.shape[0]), image_mask)
+        gt_image_mask = torch.logical_and(image_mask, ~input_image_mask)
+        input_image_mask = input_image_mask.unsqueeze(-1).expand_as(input_embeds)
+        input_embeds = input_embeds.masked_scatter(input_image_mask, input_image_embeds)
+
+        image_prefill_embeds = model.image_prefill_embeds(
+            torch.arange(81, device=model.device).long()
+        )
+        input_embeds = input_embeds.masked_scatter(gt_image_mask.unsqueeze(-1).expand_as(input_embeds), image_prefill_embeds)
+
+        position_ids, _ = model.get_rope_index(
+            inputs['input_ids'],
+            inputs['image_grid_thw'],
+            attention_mask=inputs['attention_mask'])
+        position_ids = position_ids.contiguous()
+        outputs = model(inputs_embeds=input_embeds, position_ids=position_ids, attention_mask=inputs['attention_mask'], return_dict=True)
+        output_image_embeddings = outputs.image_embeddings[:, :-1, :]
+        output_image_embeddings = output_image_embeddings[gt_image_mask[:, 1:]]
+        return output_image_embeddings, input_image_embeds, inputs['image_grid_thw']
+
+
+class NexusGenAutoregressiveModelStateDictConverter:
+    def __init__(self):
+        pass
+
+    def from_civitai(self, state_dict):
+        state_dict = {"model." + key: value for key, value in state_dict.items()}
+        return state_dict
diff --git a/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/nexus_gen_ar_model.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/nexus_gen_ar_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..d5a29735e1ca71fcbf7847928a309e7799718ec7
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/nexus_gen_ar_model.py
@@ -0,0 +1,1143 @@
+import os
+import re
+from dataclasses import dataclass
+from typing import Any, Dict, List, Optional, Tuple, Union
+
+import torch
+import torch.nn as nn
+from torch.nn import CrossEntropyLoss
+
+from transformers.cache_utils import Cache
+from transformers.generation import GenerationMixin, LogitsProcessorList, StoppingCriteriaList, GenerationConfig, GenerateDecoderOnlyOutput, GenerateEncoderDecoderOutput
+from transformers.utils import add_start_docstrings_to_model_forward, logging, replace_return_docstrings
+from transformers.modeling_outputs import ModelOutput
+from transformers.models.qwen2_5_vl.configuration_qwen2_5_vl import Qwen2_5_VLConfig
+from transformers.models.qwen2_5_vl.modeling_qwen2_5_vl import (
+    Qwen2_5_VisionTransformerPretrainedModel,
+    Qwen2_5_VLModel,
+    Qwen2_5_VLPreTrainedModel,
+    QWEN2_5_VL_INPUTS_DOCSTRING,
+    )
+
+from transformers.feature_extraction_utils import BatchFeature
+from transformers.image_utils import ImageInput, VideoInput
+from transformers.processing_utils import ProcessingKwargs, ProcessorMixin, Unpack, VideosKwargs
+from transformers.tokenization_utils_base import PreTokenizedInput, TextInput
+
+GenerateNonBeamOutput = Union[GenerateDecoderOnlyOutput, GenerateEncoderDecoderOutput]
+
+logger = logging.get_logger(__name__)
+
+_CONFIG_FOR_DOC = "Qwen2_5_VLConfig"
+
+
+@dataclass
+class Qwen2_5_VLCausalLMOutputWithPast(ModelOutput):
+    """
+    Base class for Qwen2_5_VL causal language model (or autoregressive) outputs.
+
+    Args:
+        loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
+            Language modeling loss (for next-token prediction).
+        logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
+            Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
+        past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+            Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
+            `(batch_size, num_heads, sequence_length, embed_size_per_head)`)
+
+            Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
+            `past_key_values` input) to speed up sequential decoding.
+        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+            Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
+            one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
+
+            Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
+        attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
+            sequence_length)`.
+
+            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
+            heads.
+        rope_deltas (`torch.LongTensor` of shape `(batch_size, )`, *optional*):
+            The rope index difference between sequence length and multimodal rope.
+    """
+
+    loss: Optional[torch.FloatTensor] = None
+    logits: torch.FloatTensor = None
+    image_embeddings: torch.FloatTensor = None
+    past_key_values: Optional[List[torch.FloatTensor]] = None
+    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
+    attentions: Optional[Tuple[torch.FloatTensor]] = None
+    rope_deltas: Optional[torch.LongTensor] = None
+
+
+class Qwen2_5_VLForConditionalGeneration(Qwen2_5_VLPreTrainedModel, GenerationMixin):
+    _tied_weights_keys = ["lm_head.weight"]
+    config_class = Qwen2_5_VLConfig
+    _no_split_modules = ["Qwen2_5_VLDecoderLayer", "Qwen2_5_VLVisionBlock"]
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.visual = Qwen2_5_VisionTransformerPretrainedModel._from_config(config.vision_config)
+        self.model = Qwen2_5_VLModel(config)
+        self.vocab_size = config.vocab_size
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+        self.vision_head = nn.Linear(config.hidden_size, config.hidden_size, bias=False)
+        self.rope_deltas = None  # cache rope_deltas here
+        self.image_prefill_embeds = nn.Embedding(81, config.hidden_size)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.model.embed_tokens = value
+
+    def get_output_embeddings(self):
+        return self.lm_head
+
+    def set_output_embeddings(self, new_embeddings):
+        self.lm_head = new_embeddings
+
+    def set_decoder(self, decoder):
+        self.model = decoder
+
+    def get_decoder(self):
+        return self.model
+
+    def get_rope_index(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        image_grid_thw: Optional[torch.LongTensor] = None,
+        video_grid_thw: Optional[torch.LongTensor] = None,
+        second_per_grid_ts: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """
+        Calculate the 3D rope index based on image and video's temporal, height and width in LLM.
+
+        Explanation:
+            Each embedding sequence contains vision embedding and text embedding or just contains text embedding.
+
+            For pure text embedding sequence, the rotary position embedding has no difference with modern LLMs.
+            Examples:
+                input_ids: [T T T T T], here T is for text.
+                temporal position_ids: [0, 1, 2, 3, 4]
+                height position_ids: [0, 1, 2, 3, 4]
+                width position_ids: [0, 1, 2, 3, 4]
+
+            For vision and text embedding sequence, we calculate 3D rotary position embedding for vision part
+            and 1D rotary position embedding for text part.
+            Examples:
+                Temporal (Time): 3 patches, representing different segments of the video in time.
+                Height: 2 patches, dividing each frame vertically.
+                Width: 2 patches, dividing each frame horizontally.
+                We also have some important parameters:
+                fps (Frames Per Second): The video's frame rate, set to 1. This means one frame is processed each second.
+                tokens_per_second: This is a crucial parameter. It dictates how many "time-steps" or "temporal tokens" are conceptually packed into a one-second interval of the video. In this case, we have 25 tokens per second. So each second of the video will be represented with 25 separate time points. It essentially defines the temporal granularity.
+                temporal_patch_size: The number of frames that compose one temporal patch. Here, it's 2 frames.
+                interval: The step size for the temporal position IDs, calculated as tokens_per_second * temporal_patch_size / fps. In this case, 25 * 2 / 1 = 50. This means that each temporal patch will be have a difference of 50 in the temporal position IDs.
+                input_ids: [V V V V V V V V V V V V T T T T T], here V is for vision.
+                vision temporal position_ids: [0, 0, 0, 0, 50, 50, 50, 50, 100, 100, 100, 100]
+                vision height position_ids: [0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1]
+                vision width position_ids: [0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1]
+                text temporal position_ids: [101, 102, 103, 104, 105]
+                text height position_ids: [101, 102, 103, 104, 105]
+                text width position_ids: [101, 102, 103, 104, 105]
+                Here we calculate the text start position_ids as the max vision position_ids plus 1.
+
+        Args:
+            input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+                Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
+                it.
+            image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*):
+                The temporal, height and width of feature shape of each image in LLM.
+            video_grid_thw (`torch.LongTensor` of shape `(num_videos, 3)`, *optional*):
+                The temporal, height and width of feature shape of each video in LLM.
+            second_per_grid_ts (`torch.Tensor` of shape `(num_videos)`, *optional*):
+                The time interval (in seconds) for each grid along the temporal dimension in the 3D position IDs.
+            attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+                Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+                - 1 for tokens that are **not masked**,
+                - 0 for tokens that are **masked**.
+
+        Returns:
+            position_ids (`torch.LongTensor` of shape `(3, batch_size, sequence_length)`)
+            mrope_position_deltas (`torch.Tensor` of shape `(batch_size)`)
+        """
+        spatial_merge_size = self.config.vision_config.spatial_merge_size
+        image_token_id = self.config.image_token_id
+        video_token_id = self.config.video_token_id
+        vision_start_token_id = self.config.vision_start_token_id
+        mrope_position_deltas = []
+        if input_ids is not None and (image_grid_thw is not None or video_grid_thw is not None):
+            total_input_ids = input_ids
+            if attention_mask is None:
+                attention_mask = torch.ones_like(total_input_ids)
+            position_ids = torch.ones(
+                3,
+                input_ids.shape[0],
+                input_ids.shape[1],
+                dtype=input_ids.dtype,
+                device=input_ids.device,
+            )
+            image_index, video_index = 0, 0
+            attention_mask = attention_mask.to(total_input_ids.device)
+            for i, input_ids in enumerate(total_input_ids):
+                input_ids = input_ids[attention_mask[i] == 1]
+                image_nums, video_nums = 0, 0
+                vision_start_indices = torch.argwhere(input_ids == vision_start_token_id).squeeze(1)
+                vision_tokens = input_ids[vision_start_indices + 1]
+                image_nums = (vision_tokens == image_token_id).sum()
+                video_nums = (vision_tokens == video_token_id).sum()
+                input_tokens = input_ids.tolist()
+                llm_pos_ids_list: list = []
+                st = 0
+                remain_images, remain_videos = image_nums, video_nums
+                for _ in range(image_nums + video_nums):
+                    if image_token_id in input_tokens and remain_images > 0:
+                        ed_image = input_tokens.index(image_token_id, st)
+                    else:
+                        ed_image = len(input_tokens) + 1
+                    if video_token_id in input_tokens and remain_videos > 0:
+                        ed_video = input_tokens.index(video_token_id, st)
+                    else:
+                        ed_video = len(input_tokens) + 1
+                    if ed_image < ed_video:
+                        t, h, w = (
+                            image_grid_thw[image_index][0],
+                            image_grid_thw[image_index][1],
+                            image_grid_thw[image_index][2],
+                        )
+                        second_per_grid_t = 0
+                        image_index += 1
+                        remain_images -= 1
+                        ed = ed_image
+
+                    else:
+                        t, h, w = (
+                            video_grid_thw[video_index][0],
+                            video_grid_thw[video_index][1],
+                            video_grid_thw[video_index][2],
+                        )
+                        if second_per_grid_ts is not None:
+                            second_per_grid_t = second_per_grid_ts[video_index]
+                        else:
+                            second_per_grid_t = 1.0
+                        video_index += 1
+                        remain_videos -= 1
+                        ed = ed_video
+                    llm_grid_t, llm_grid_h, llm_grid_w = (
+                        t.item(),
+                        h.item() // spatial_merge_size,
+                        w.item() // spatial_merge_size,
+                    )
+                    text_len = ed - st
+
+                    st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0
+                    llm_pos_ids_list.append(torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx)
+
+                    range_tensor = torch.arange(llm_grid_t).view(-1, 1)
+                    expanded_range = range_tensor.expand(-1, llm_grid_h * llm_grid_w)
+
+                    time_tensor = expanded_range * second_per_grid_t * self.config.vision_config.tokens_per_second
+
+                    time_tensor_long = time_tensor.long()
+                    t_index = time_tensor_long.flatten()
+
+                    h_index = torch.arange(llm_grid_h).view(1, -1, 1).expand(llm_grid_t, -1, llm_grid_w).flatten()
+                    w_index = torch.arange(llm_grid_w).view(1, 1, -1).expand(llm_grid_t, llm_grid_h, -1).flatten()
+                    llm_pos_ids_list.append(torch.stack([t_index, h_index, w_index]) + text_len + st_idx)
+                    st = ed + llm_grid_t * llm_grid_h * llm_grid_w
+
+                if st < len(input_tokens):
+                    st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0
+                    text_len = len(input_tokens) - st
+                    llm_pos_ids_list.append(torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx)
+
+                llm_positions = torch.cat(llm_pos_ids_list, dim=1).reshape(3, -1)
+                position_ids[..., i, attention_mask[i] == 1] = llm_positions.to(position_ids.device)
+                mrope_position_deltas.append(llm_positions.max() + 1 - len(total_input_ids[i]))
+            mrope_position_deltas = torch.tensor(mrope_position_deltas, device=input_ids.device).unsqueeze(1)
+            return position_ids, mrope_position_deltas
+        else:
+            if attention_mask is not None:
+                position_ids = attention_mask.long().cumsum(-1) - 1
+                position_ids.masked_fill_(attention_mask == 0, 1)
+                position_ids = position_ids.unsqueeze(0).expand(3, -1, -1).to(attention_mask.device)
+                max_position_ids = position_ids.max(0, keepdim=False)[0].max(-1, keepdim=True)[0]
+                mrope_position_deltas = max_position_ids + 1 - attention_mask.shape[-1]
+            else:
+                position_ids = (
+                    torch.arange(input_ids.shape[1], device=input_ids.device)
+                    .view(1, 1, -1)
+                    .expand(3, input_ids.shape[0], -1)
+                )
+                mrope_position_deltas = torch.zeros(
+                    [input_ids.shape[0], 1],
+                    device=input_ids.device,
+                    dtype=input_ids.dtype,
+                )
+
+            return position_ids, mrope_position_deltas
+
+    @add_start_docstrings_to_model_forward(QWEN2_5_VL_INPUTS_DOCSTRING)
+    @replace_return_docstrings(output_type=Qwen2_5_VLCausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        pixel_values: Optional[torch.Tensor] = None,
+        pixel_values_videos: Optional[torch.FloatTensor] = None,
+        image_grid_thw: Optional[torch.LongTensor] = None,
+        video_grid_thw: Optional[torch.LongTensor] = None,
+        rope_deltas: Optional[torch.LongTensor] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        second_per_grid_ts: Optional[torch.Tensor] = None,
+        image_embeddings: Optional[torch.Tensor] = None,
+        token_loss_weight: Optional[float] = 0.1,
+        img_loss_weight: Optional[float] = 1.0,
+    ) -> Union[Tuple, Qwen2_5_VLCausalLMOutputWithPast]:
+        r"""
+            labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+                Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+                config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+                (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+        Returns:
+
+        Example:
+
+        ```python
+        >>> from PIL import Image
+        >>> import requests
+        >>> from transformers import AutoProcessor, Qwen2_5_VLForConditionalGeneration
+
+        >>> model = Qwen2_5_VLForConditionalGeneration.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct")
+        >>> processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct")
+
+        >>> messages = [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "image"},
+                    {"type": "text", "text": "What is shown in this image?"},
+                ],
+            },
+        ]
+        >>> url = "https://www.ilankelman.org/stopsigns/australia.jpg"
+        >>> image = Image.open(requests.get(url, stream=True).raw)
+
+        >>> text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+        >>> inputs = processor(text=[text], images=[image], vision_infos=[vision_infos])
+
+        >>> # Generate
+        >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+        >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+        "The image shows a street scene with a red stop sign in the foreground. In the background, there is a large red gate with Chinese characters ..."
+        ```"""
+
+        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 inputs_embeds is None:
+            # test feature
+            inputs_embeds = self.model.embed_tokens(input_ids)
+            # for image encoding and training
+            if pixel_values is not None:
+                pixel_values = pixel_values.type(self.visual.dtype)
+                image_embeds = self.visual(pixel_values, grid_thw=image_grid_thw)
+                n_image_tokens = (input_ids == self.config.image_token_id).sum().item()
+                n_image_features = image_embeds.shape[0]
+                if n_image_tokens != n_image_features:
+                    raise ValueError(
+                        f"Image features and image tokens do not match: tokens: {n_image_tokens}, features {n_image_features}"
+                    )
+
+                mask = input_ids == self.config.image_token_id
+                mask_unsqueezed = mask.unsqueeze(-1)
+                mask_expanded = mask_unsqueezed.expand_as(inputs_embeds)
+                image_mask = mask_expanded.to(inputs_embeds.device)
+
+                image_embeds = image_embeds.to(inputs_embeds.device, inputs_embeds.dtype)
+                inputs_embeds = inputs_embeds.masked_scatter(image_mask, image_embeds)
+
+            if pixel_values_videos is not None:
+                pixel_values_videos = pixel_values_videos.type(self.visual.dtype)
+                video_embeds = self.visual(pixel_values_videos, grid_thw=video_grid_thw)
+                n_video_tokens = (input_ids == self.config.video_token_id).sum().item()
+                n_video_features = video_embeds.shape[0]
+                if n_video_tokens != n_video_features:
+                    raise ValueError(
+                        f"Video features and video tokens do not match: tokens: {n_video_tokens}, features {n_video_features}"
+                    )
+
+                mask = input_ids == self.config.video_token_id
+                mask_unsqueezed = mask.unsqueeze(-1)
+                mask_expanded = mask_unsqueezed.expand_as(inputs_embeds)
+                video_mask = mask_expanded.to(inputs_embeds.device)
+
+                video_embeds = video_embeds.to(inputs_embeds.device, inputs_embeds.dtype)
+                inputs_embeds = inputs_embeds.masked_scatter(video_mask, video_embeds)
+
+            if attention_mask is not None:
+                attention_mask = attention_mask.to(inputs_embeds.device)
+
+        # if we get 4D attention mask we cannot calculate rope deltas anymore. TODO @raushan fixme
+        if position_ids is None and (attention_mask is None or attention_mask.ndim == 2):
+            # calculate RoPE index once per generation in the pre-fill stage only
+            if (
+                (cache_position is not None and cache_position[0] == 0)
+                or self.rope_deltas is None
+                or (past_key_values is None or past_key_values.get_seq_length() == 0)
+            ):
+                position_ids, rope_deltas = self.get_rope_index(
+                    input_ids,
+                    image_grid_thw,
+                    video_grid_thw,
+                    second_per_grid_ts,
+                    attention_mask,
+                )
+                self.rope_deltas = rope_deltas
+            # then use the prev pre-calculated rope-deltas to get the correct position ids
+            else:
+                batch_size, seq_length, _ = inputs_embeds.shape
+                delta = (
+                    (cache_position[0] + self.rope_deltas).to(inputs_embeds.device)
+                    if cache_position is not None
+                    else 0
+                )
+                position_ids = torch.arange(seq_length, device=inputs_embeds.device)
+                position_ids = position_ids.view(1, -1).expand(batch_size, -1)
+                if cache_position is not None:  # otherwise `deltas` is an int `0`
+                    delta = delta.repeat_interleave(batch_size // delta.shape[0], dim=0)
+                position_ids = position_ids.add(delta)
+                position_ids = position_ids.unsqueeze(0).expand(3, -1, -1)
+        # position_ids [3, B, L]
+
+        outputs = self.model(
+            input_ids=None,
+            position_ids=position_ids,
+            attention_mask=attention_mask,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            cache_position=cache_position,
+        )
+
+        hidden_states = outputs[0]
+        logits = self.lm_head(hidden_states)
+        image_embeds = self.vision_head(hidden_states)
+
+        loss = None
+        if labels is not None:
+            # Upcast to float if we need to compute the loss to avoid potential precision issues
+            # prepare labels for logits
+            logits_labels = labels.clone().detach()
+            image_tokens = (labels == self.config.image_token_id)
+            logits_labels[image_tokens] = -100
+
+            logits = logits.float()
+            # Shift so that tokens < n predict n
+            shift_logits = logits[..., :-1, :].contiguous()
+            shift_labels = logits_labels[..., 1:].contiguous()
+            # Flatten the tokens
+            loss_fct = CrossEntropyLoss()
+            shift_logits = shift_logits.view(-1, self.config.vocab_size)
+            shift_labels = shift_labels.view(-1)
+            # Enable model parallelism
+            shift_labels = shift_labels.to(shift_logits.device)
+            loss = loss_fct(shift_logits, shift_labels) * token_loss_weight
+
+            shift_image_tokens_2d = (labels[..., 1:].contiguous() == self.config.image_token_id) # (B, L-1)
+            shifted_image_embeds = image_embeds[:, :-1, :].contiguous()  # (B, L-1, D)
+            masked_image_embeds = shifted_image_embeds[shift_image_tokens_2d]  # (num_image_tokens, D)
+
+            mse_loss_fct = nn.MSELoss()
+            mse_loss_fct = mse_loss_fct.to(shift_logits.device)
+            if image_embeddings is None:
+                image_embeddings = torch.zeros_like(masked_image_embeds)
+            img_loss = mse_loss_fct(masked_image_embeds, image_embeddings)
+
+            cos_sim = torch.cosine_similarity(
+                masked_image_embeds,
+                image_embeddings,
+                dim=-1
+            )
+            cos_loss = (1 - cos_sim).mean()
+            img_loss = 0.5 * img_loss + 0.5 * cos_loss
+            # fix nan for empty image tokens
+            if image_embeddings.size(0) == 0:
+                img_loss = img_loss.nan_to_num(0.0)
+            # combine the loss
+            loss = loss + img_loss_weight * img_loss
+
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            return (loss,) + output if loss is not None else output
+
+        return Qwen2_5_VLCausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            image_embeddings=image_embeds,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+            rope_deltas=self.rope_deltas,
+        )
+
+
+
+    def _sample(
+        self,
+        input_ids: torch.LongTensor,
+        logits_processor: LogitsProcessorList,
+        stopping_criteria: StoppingCriteriaList,
+        generation_config: GenerationConfig,
+        synced_gpus: bool,
+        streamer: Optional["BaseStreamer"],
+        **model_kwargs,
+    ) -> Union[GenerateNonBeamOutput, torch.LongTensor]:
+        r"""
+        Generates sequences of token ids for models with a language modeling head using **multinomial sampling** and
+        can be used for text-decoder, text-to-text, speech-to-text, and vision-to-text models.
+
+        Parameters:
+            input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+                The sequence used as a prompt for the generation.
+            logits_processor (`LogitsProcessorList`):
+                An instance of [`LogitsProcessorList`]. List of instances of class derived from [`LogitsProcessor`]
+                used to modify the prediction scores of the language modeling head applied at each generation step.
+            stopping_criteria (`StoppingCriteriaList`):
+                An instance of [`StoppingCriteriaList`]. List of instances of class derived from [`StoppingCriteria`]
+                used to tell if the generation loop should stop.
+            generation_config ([`~generation.GenerationConfig`]):
+                The generation configuration to be used as parametrization of the decoding method.
+            synced_gpus (`bool`):
+                Whether to continue running the while loop until max_length (needed to avoid deadlocking with
+                `FullyShardedDataParallel` and DeepSpeed ZeRO Stage 3).
+            streamer (`BaseStreamer`, *optional*):
+                Streamer object that will be used to stream the generated sequences. Generated tokens are passed
+                through `streamer.put(token_ids)` and the streamer is responsible for any further processing.
+            model_kwargs:
+                Additional model specific kwargs will be forwarded to the `forward` function of the model. If model is
+                an encoder-decoder model the kwargs should include `encoder_outputs`.
+
+        Return:
+            [`~generation.GenerateDecoderOnlyOutput`], [`~generation.GenerateEncoderDecoderOutput`] or `torch.LongTensor`:
+            A `torch.LongTensor` containing the generated tokens (default behaviour) or a
+            [`~generation.GenerateDecoderOnlyOutput`] if `model.config.is_encoder_decoder=False` and
+            `return_dict_in_generate=True` or a [`~generation.GenerateEncoderDecoderOutput`] if
+            `model.config.is_encoder_decoder=True`.
+        """
+        # init values
+        pad_token_id = generation_config._pad_token_tensor
+        output_attentions = generation_config.output_attentions
+        output_hidden_states = generation_config.output_hidden_states
+        output_scores = generation_config.output_scores
+        output_logits = generation_config.output_logits
+        return_dict_in_generate = generation_config.return_dict_in_generate
+        max_length = generation_config.max_length
+        has_eos_stopping_criteria = any(hasattr(criteria, "eos_token_id") for criteria in stopping_criteria)
+        do_sample = generation_config.do_sample
+
+        # init attention / hidden states / scores tuples
+        scores = () if (return_dict_in_generate and output_scores) else None
+        raw_logits = () if (return_dict_in_generate and output_logits) else None
+        decoder_attentions = () if (return_dict_in_generate and output_attentions) else None
+        cross_attentions = () if (return_dict_in_generate and output_attentions) else None
+        decoder_hidden_states = () if (return_dict_in_generate and output_hidden_states) else None
+
+        # if model is an encoder-decoder, retrieve encoder attention weights and hidden states
+        if return_dict_in_generate and self.config.is_encoder_decoder:
+            encoder_attentions = model_kwargs["encoder_outputs"].get("attentions") if output_attentions else None
+            encoder_hidden_states = (
+                model_kwargs["encoder_outputs"].get("hidden_states") if output_hidden_states else None
+            )
+
+        # keep track of which sequences are already finished
+        batch_size, cur_len = input_ids.shape
+        this_peer_finished = False
+        unfinished_sequences = torch.ones(batch_size, dtype=torch.long, device=input_ids.device)
+        model_kwargs = self._get_initial_cache_position(input_ids, model_kwargs)
+
+        model_forward = self.__call__
+        if isinstance(model_kwargs.get("past_key_values"), Cache):
+            is_compileable = model_kwargs["past_key_values"].is_compileable and self._supports_static_cache
+            is_compileable = is_compileable and not self.generation_config.disable_compile
+            if is_compileable and (
+                self.device.type == "cuda" or generation_config.compile_config._compile_all_devices
+            ):
+                os.environ["TOKENIZERS_PARALLELISM"] = "0"
+                model_forward = self.get_compiled_call(generation_config.compile_config)
+
+        is_prefill = True
+        is_sampling_img = input_ids[:, -1] == self.config.vision_start_token_id
+        generation_image_grid_thw = model_kwargs.pop("generation_image_grid_thw", self.get_default_image_grid_thw())
+        num_img_tokens = self.get_num_image_tokens(generation_image_grid_thw)
+        output_image_embeddings = []
+        while self._has_unfinished_sequences(
+            this_peer_finished, synced_gpus, device=input_ids.device, cur_len=cur_len, max_length=max_length
+        ):
+            # prepare model inputs
+            model_inputs = self.prepare_inputs_for_generation(input_ids, **model_kwargs)
+
+            # prepare prefilled embeds
+            model_inputs.update(self.prepare_prefilled_image_embeds(len(output_image_embeddings), num_img_tokens, is_sampling_img, **model_kwargs))
+
+            # parse position_ids from model_kwargs
+            model_inputs.update(self.prepare_image_position_ids(input_ids, generation_image_grid_thw, is_sampling_img, **model_kwargs))
+
+            # prepare variable output controls (note: some models won't accept all output controls)
+            model_inputs.update({"output_attentions": output_attentions} if output_attentions else {})
+            model_inputs.update({"output_hidden_states": output_hidden_states} if output_hidden_states else {})
+
+            if is_prefill:
+                outputs = self(**model_inputs, return_dict=True)
+                is_prefill = False
+            else:
+                outputs = model_forward(**model_inputs, return_dict=True)
+
+            # synced_gpus: don't waste resources running the code we don't need; kwargs must be updated before skipping
+            model_kwargs = self._update_model_kwargs_for_generation(
+                outputs,
+                model_kwargs,
+                is_encoder_decoder=self.config.is_encoder_decoder,
+            )
+            # TODO: support batch image sampling
+            if bool(is_sampling_img) and len(output_image_embeddings) < num_img_tokens:
+                output_image_embeddings.append(outputs.image_embeddings[:, -1, :].unsqueeze(1))
+
+            if synced_gpus and this_peer_finished:
+                continue
+            # Clone is needed to avoid keeping a hanging ref to outputs.logits which may be very large for first iteration
+            # (the clone itself is always small)
+            next_token_logits = outputs.logits[:, -1, :].clone().float()
+            next_token_logits = next_token_logits.to(input_ids.device)
+
+            # do not sample <vision_end> token
+            next_token_logits[:, self.config.vision_end_token_id] = -float('inf')
+            # pre-process distribution
+            next_token_scores = logits_processor(input_ids, next_token_logits)
+            # Store scores, attentions and hidden_states when required
+            if return_dict_in_generate:
+                if output_scores:
+                    scores += (next_token_scores,)
+                if output_logits:
+                    raw_logits += (next_token_logits,)
+                if output_attentions:
+                    decoder_attentions += (
+                        (outputs.decoder_attentions,) if self.config.is_encoder_decoder else (outputs.attentions,)
+                    )
+                    if self.config.is_encoder_decoder:
+                        cross_attentions += (outputs.cross_attentions,)
+
+                if output_hidden_states:
+                    decoder_hidden_states += (
+                        (outputs.decoder_hidden_states,)
+                        if self.config.is_encoder_decoder
+                        else (outputs.hidden_states,)
+                    )
+
+            # token selection
+            if do_sample:
+                probs = nn.functional.softmax(next_token_scores, dim=-1)
+                # TODO (joao): this OP throws "skipping cudagraphs due to ['incompatible ops']", find solution
+                next_tokens = torch.multinomial(probs, num_samples=1).squeeze(1)
+                # while not bool(is_sampling_img) and torch.any(next_tokens == self.config.vision_end_token_id):
+                #     probs[:, self.config.vision_end_token_id] = 0
+                #     next_tokens = torch.multinomial(probs, num_samples=1).squeeze(1)
+            else:
+                next_tokens = torch.argmax(next_token_scores, dim=-1)
+
+            # finished sentences should have their next token be a padding token
+            if has_eos_stopping_criteria:
+                next_tokens = next_tokens * unfinished_sequences + pad_token_id * (1 - unfinished_sequences)
+
+            #TODO: support batch image sample
+            if num_img_tokens is not None:
+                cur_img_tokens = (input_ids == self.config.vision_start_token_id).flip(dims=[1]).float().argmax(dim=1)
+                # check whether is sampling images
+                is_end_img = torch.logical_and(cur_img_tokens == num_img_tokens, is_sampling_img)
+                is_sampling_img = torch.logical_and(is_sampling_img, cur_img_tokens < num_img_tokens)
+                next_tokens[is_sampling_img] = self.config.image_token_id
+                # check whether to end sampling images
+                next_tokens[is_end_img] = self.config.vision_end_token_id
+            else:
+                # check whether to end sampling images
+                is_sampling_img = torch.logical_and(is_sampling_img, (next_tokens != self.config.vision_end_token_id))
+                # replace the next token with the image token if is sampling image
+                next_tokens[is_sampling_img] = self.config.image_token_id
+            # check whether to start sampling images
+            is_sampling_img = torch.logical_or(is_sampling_img, (next_tokens == self.config.vision_start_token_id))
+
+            # update generated ids, model inputs, and length for next step
+            input_ids = torch.cat([input_ids, next_tokens[:, None]], dim=-1)
+
+            if streamer is not None:
+                streamer.put(next_tokens.cpu())
+
+            unfinished_sequences = unfinished_sequences & ~stopping_criteria(input_ids, scores)
+            this_peer_finished = unfinished_sequences.max() == 0
+            cur_len += 1
+
+            # This is needed to properly delete outputs.logits which may be very large for first iteration
+            # Otherwise a reference to outputs is kept which keeps the logits alive in the next iteration
+            del outputs
+
+        if streamer is not None:
+            streamer.end()
+
+        # output the image embeddings
+        output_image_embeddings = torch.cat(output_image_embeddings, dim=1) if len(output_image_embeddings) > 0 else None
+
+        if return_dict_in_generate:
+            return GenerateDecoderOnlyAll2AllOutput(
+                sequences=input_ids,
+                scores=scores,
+                logits=raw_logits,
+                attentions=decoder_attentions,
+                hidden_states=decoder_hidden_states,
+                past_key_values=model_kwargs.get("past_key_values"),
+                output_image_embeddings=output_image_embeddings,
+            )
+        else:
+            return input_ids
+
+
+    def prepare_prefilled_image_embeds(self, cur_image_tokens, num_img_tokens, is_sampling_img, **model_kwargs):
+        if cur_image_tokens == 0 or cur_image_tokens > num_img_tokens or not bool(is_sampling_img):
+            return {}
+        # TODO: support batch image sample
+        image_idx = torch.tensor([cur_image_tokens-1]).to(self.device).long().unsqueeze(0)
+        inputs_embeds = self.image_prefill_embeds(image_idx)
+        return {"inputs_embeds": inputs_embeds}
+
+
+    def get_default_image_grid_thw(self,):
+        return torch.tensor([[1, 18, 18]]).to(self.device)
+
+
+    def get_num_image_tokens(self, image_grid_thw):
+        return int(torch.prod(image_grid_thw, dim=1).sum() // 4)
+
+
+    def _validate_model_kwargs(self, model_kwargs: Dict[str, Any]):
+        num_img_tokens = model_kwargs.pop("generation_image_grid_thw", None)
+        super()._validate_model_kwargs(model_kwargs)
+        model_kwargs["generation_image_grid_thw"] = num_img_tokens
+
+    def prepare_image_position_ids(self, input_ids, generation_image_grid_thw, is_sampling_img, **model_kwargs):
+        # Overwritten -- prepare position_ids for image tokens
+        cur_img_tokens = int((input_ids == self.config.vision_start_token_id).flip(dims=[1]).float().argmax(dim=1))
+        # TODO: support batch image sample
+        if cur_img_tokens > 0 and bool(is_sampling_img):
+            image_grid_thw = generation_image_grid_thw
+            if model_kwargs.get('image_grid_thw') is not None:
+                image_grid_thw = torch.cat([model_kwargs.get('image_grid_thw'), image_grid_thw])
+            remaining_img_tokens = self.get_num_image_tokens(generation_image_grid_thw) - cur_img_tokens
+            padding_ids = input_ids.new_full((1, remaining_img_tokens), fill_value=self.config.image_token_id)
+            padded_ids = torch.cat([input_ids, padding_ids], dim=1)
+            position_ids, _ = self.get_rope_index(padded_ids, image_grid_thw, None, None)
+            if model_kwargs.get("use_cache", True):
+                position_ids = position_ids[:, :, input_ids.shape[1] - 1].unsqueeze(-1)
+            else:
+                position_ids = position_ids[:, :, :input_ids.shape[1]]
+            return {"position_ids": position_ids}
+        return {}
+
+    def prepare_inputs_for_generation(
+        self,
+        input_ids,
+        past_key_values=None,
+        attention_mask=None,
+        inputs_embeds=None,
+        cache_position=None,
+        position_ids=None,
+        use_cache=True,
+        pixel_values=None,
+        pixel_values_videos=None,
+        image_grid_thw=None,
+        video_grid_thw=None,
+        second_per_grid_ts=None,
+        image_embeddings=None,
+        **kwargs,
+    ):
+        # Overwritten -- in specific circumstances we don't want to forward image inputs to the model
+
+        model_inputs = super().prepare_inputs_for_generation(
+            input_ids,
+            past_key_values=past_key_values,
+            attention_mask=attention_mask,
+            inputs_embeds=inputs_embeds,
+            cache_position=cache_position,
+            position_ids=position_ids,
+            pixel_values=pixel_values,
+            pixel_values_videos=pixel_values_videos,
+            image_grid_thw=image_grid_thw,
+            video_grid_thw=video_grid_thw,
+            second_per_grid_ts=second_per_grid_ts,
+            use_cache=use_cache,
+            **kwargs,
+        )
+
+        # Qwen2-5-VL position_ids are prepared with rope_deltas in forward
+        model_inputs["position_ids"] = None
+
+        if cache_position[0] != 0:
+            model_inputs["pixel_values"] = None
+            model_inputs["pixel_values_videos"] = None
+        return model_inputs
+
+    def _get_image_nums_and_video_nums(
+        self,
+        input_ids: Optional[torch.LongTensor],
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """
+        Get the number of images and videos for each sample to calculate the separation length of the sample tensor.
+        These parameters are not passed through the processor to avoid unpredictable impacts from interface modifications.
+
+        Args:
+            input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+                Indices of input sequence tokens in the vocabulary.
+
+        Returns:
+            image_nums (`torch.LongTensor` of shape `(batch_size, num_images_sample)`)
+            video_nums (`torch.LongTensor` of shape `(batch_size, num_videos_sample)`)
+        """
+        image_token_id = self.config.image_token_id
+        video_token_id = self.config.video_token_id
+        vision_start_token_id = self.config.vision_start_token_id
+
+        vision_start_mask = input_ids == vision_start_token_id
+        vision_first_mask = torch.roll(vision_start_mask, shifts=1, dims=1)
+        image_mask = input_ids == image_token_id
+        video_mask = input_ids == video_token_id
+        image_nums = torch.sum(vision_first_mask & image_mask, dim=1)
+        video_nums = torch.sum(vision_first_mask & video_mask, dim=1)
+
+        return image_nums, video_nums
+
+    def _expand_inputs_for_generation(
+        self,
+        expand_size: int = 1,
+        is_encoder_decoder: bool = False,
+        input_ids: Optional[torch.LongTensor] = None,
+        **model_kwargs,
+    ) -> Tuple[torch.LongTensor, Dict[str, Any]]:
+        # Overwritten -- Support for expanding tensors without a batch size dimension
+        # e.g., pixel_values, image_grid_thw, pixel_values_videos, video_grid_thw, second_per_grid_t
+        # pixel_values.shape[0] is sum(seqlen_images for samples)
+        # image_grid_thw.shape[0] is sum(num_images for samples)
+
+        if expand_size == 1:
+            return input_ids, model_kwargs
+
+        visual_keys = ["pixel_values", "image_grid_thw", "pixel_values_videos", "video_grid_thw", "second_per_grid_ts"]
+
+        def _expand_dict_for_generation_visual(dict_to_expand):
+            image_grid_thw = model_kwargs.get("image_grid_thw", None)
+            video_grid_thw = model_kwargs.get("video_grid_thw", None)
+            image_nums, video_nums = self._get_image_nums_and_video_nums(input_ids)
+
+            def _repeat_interleave_samples(x, lengths, repeat_times):
+                samples = torch.split(x, lengths)
+                repeat_args = [repeat_times] + [1] * (x.dim() - 1)
+                result = torch.cat([sample.repeat(*repeat_args) for sample in samples], dim=0)
+                return result
+
+            for key in dict_to_expand:
+                if key == "pixel_values":
+                    # split images into samples
+                    samples = torch.split(image_grid_thw, list(image_nums))
+                    # compute the sequence length of images for each sample
+                    lengths = [torch.prod(sample, dim=1).sum() for sample in samples]
+                    dict_to_expand[key] = _repeat_interleave_samples(
+                        dict_to_expand[key], lengths=lengths, repeat_times=expand_size
+                    )
+                elif key == "image_grid_thw":
+                    # get the num of images for each sample
+                    lengths = list(image_nums)
+                    dict_to_expand[key] = _repeat_interleave_samples(
+                        dict_to_expand[key], lengths=lengths, repeat_times=expand_size
+                    )
+                elif key == "pixel_values_videos":
+                    samples = torch.split(video_grid_thw, list(video_nums))
+                    lengths = [torch.prod(sample, dim=1).sum() for sample in samples]
+                    dict_to_expand[key] = _repeat_interleave_samples(
+                        dict_to_expand[key], lengths=lengths, repeat_times=expand_size
+                    )
+                elif key == "video_grid_thw":
+                    lengths = list(video_nums)
+                    dict_to_expand[key] = _repeat_interleave_samples(
+                        dict_to_expand[key], lengths=lengths, repeat_times=expand_size
+                    )
+                elif key == "second_per_grid_ts":
+                    if not isinstance(dict_to_expand[key], list):
+                        raise TypeError(
+                            f"Expected value for key '{key}' to be a list, but got {type(dict_to_expand[key])} instead."
+                        )
+                    tensor = torch.tensor(dict_to_expand[key])
+                    lengths = list(video_nums)
+                    tensor = _repeat_interleave_samples(tensor, lengths=lengths, repeat_times=expand_size)
+                    dict_to_expand[key] = tensor.tolist()
+            return dict_to_expand
+
+        def _expand_dict_for_generation(dict_to_expand):
+            for key in dict_to_expand:
+                if (
+                    key != "cache_position"
+                    and dict_to_expand[key] is not None
+                    and isinstance(dict_to_expand[key], torch.Tensor)
+                    and key not in visual_keys
+                ):
+                    dict_to_expand[key] = dict_to_expand[key].repeat_interleave(expand_size, dim=0)
+            return dict_to_expand
+
+        # input_ids is required for expanding visual inputs
+        # If input_ids is unavailable, visual inputs will not be used; therefore, there is no need to expand visual inputs.
+        if input_ids is not None and input_ids.numel() != 0:
+            model_kwargs = _expand_dict_for_generation_visual(model_kwargs)
+
+        if input_ids is not None:
+            input_ids = input_ids.repeat_interleave(expand_size, dim=0)
+
+        model_kwargs = _expand_dict_for_generation(model_kwargs)
+
+        if is_encoder_decoder:
+            if model_kwargs.get("encoder_outputs") is None:
+                raise ValueError("If `is_encoder_decoder` is True, make sure that `encoder_outputs` is defined.")
+            model_kwargs["encoder_outputs"] = _expand_dict_for_generation(model_kwargs["encoder_outputs"])
+
+        return input_ids, model_kwargs
+
+
+__all__ = ["Qwen2_5_VLForConditionalGeneration", "Qwen2_5_VLModel", "Qwen2_5_VLPreTrainedModel"]
+
+
+
+class Qwen2_5_VLVideosProcessorKwargs(VideosKwargs, total=False):
+    fps: Union[List[float], float]
+
+
+class Qwen2_5_VLProcessorKwargs(ProcessingKwargs, total=False):
+    videos_kwargs: Qwen2_5_VLVideosProcessorKwargs
+    _defaults = {
+        "text_kwargs": {
+            "padding": False,
+        },
+        "videos_kwargs": {"fps": 2.0},
+    }
+
+
+class Qwen2_5_VLProcessor(ProcessorMixin):
+    r"""
+    Constructs a Qwen2.5-VL processor which wraps a Qwen2.5-VL image processor and a Qwen2 tokenizer into a single processor.
+    [`Qwen2_5_VLProcessor`] offers all the functionalities of [`Qwen2VLImageProcessor`] and [`Qwen2TokenizerFast`]. See the
+    [`~Qwen2_5_VLProcessor.__call__`] and [`~Qwen2_5_VLProcessor.decode`] for more information.
+    Args:
+        image_processor ([`Qwen2VLImageProcessor`], *optional*):
+            The image processor is a required input.
+        tokenizer ([`Qwen2TokenizerFast`], *optional*):
+            The tokenizer is a required input.
+        chat_template (`str`, *optional*): A Jinja template which will be used to convert lists of messages
+            in a chat into a tokenizable string.
+    """
+
+    attributes = ["image_processor", "tokenizer"]
+    valid_kwargs = ["chat_template"]
+
+    image_processor_class = "AutoImageProcessor"
+    tokenizer_class = ("Qwen2Tokenizer", "Qwen2TokenizerFast")
+
+    def __init__(self, image_processor=None, tokenizer=None, chat_template=None, **kwargs):
+        self.image_token = "<|image_pad|>" if not hasattr(tokenizer, "image_token") else tokenizer.image_token
+        self.video_token = "<|video_pad|>" if not hasattr(tokenizer, "video_token") else tokenizer.video_token
+        super().__init__(image_processor, tokenizer, chat_template=chat_template)
+
+    def __call__(
+        self,
+        images: ImageInput = None,
+        text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,
+        videos: VideoInput = None,
+        **kwargs: Unpack[Qwen2_5_VLProcessorKwargs],
+    ) -> BatchFeature:
+        """
+        Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text`
+        and `kwargs` arguments to Qwen2TokenizerFast's [`~Qwen2TokenizerFast.__call__`] if `text` is not `None` to encode
+        the text. To prepare the vision inputs, this method forwards the `vision_infos` and `kwrags` arguments to
+        Qwen2VLImageProcessor's [`~Qwen2VLImageProcessor.__call__`] if `vision_infos` is not `None`.
+
+        Args:
+            images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`):
+                The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
+                tensor. Both channels-first and channels-last formats are supported.
+            text (`str`, `List[str]`, `List[List[str]]`):
+                The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
+                (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
+                `is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
+            videos (`np.ndarray`, `torch.Tensor`, `List[np.ndarray]`, `List[torch.Tensor]`):
+                The image or batch of videos to be prepared. Each video can be a 4D NumPy array or PyTorch
+                tensor, or a nested list of 3D frames. Both channels-first and channels-last formats are supported.
+            return_tensors (`str` or [`~utils.TensorType`], *optional*):
+                If set, will return tensors of a particular framework. Acceptable values are:
+                - `'tf'`: Return TensorFlow `tf.constant` objects.
+                - `'pt'`: Return PyTorch `torch.Tensor` objects.
+                - `'np'`: Return NumPy `np.ndarray` objects.
+                - `'jax'`: Return JAX `jnp.ndarray` objects.
+
+        Returns:
+            [`BatchFeature`]: A [`BatchFeature`] with the following fields:
+
+            - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
+            - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
+              `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
+              `None`).
+            - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
+            - **pixel_values_videos** -- Pixel values of videos to be fed to a model. Returned when `videos` is not `None`.
+            - **image_grid_thw** -- List of image 3D grid in LLM. Returned when `images` is not `None`.
+            - **video_grid_thw** -- List of video 3D grid in LLM. Returned when `videos` is not `None`.
+            - **second_per_grid_ts** -- List of video seconds per time grid. Returned when `videos` is not `None`.
+        """
+        output_kwargs = self._merge_kwargs(
+            Qwen2_5_VLProcessorKwargs,
+            tokenizer_init_kwargs=self.tokenizer.init_kwargs,
+            **kwargs,
+        )
+        if images is not None:
+            image_inputs = self.image_processor(images=images, videos=None, **output_kwargs["images_kwargs"])
+            image_grid_thw = image_inputs["image_grid_thw"]
+        else:
+            image_inputs = {}
+            image_grid_thw = None
+
+        if videos is not None:
+            videos_inputs = self.image_processor(images=None, videos=videos, **output_kwargs["images_kwargs"])
+            video_grid_thw = videos_inputs["video_grid_thw"]
+
+            fps = output_kwargs["videos_kwargs"].pop("fps", 2.0)
+            if isinstance(fps, (int, float)):
+                second_per_grid_ts = [self.image_processor.temporal_patch_size / fps] * len(video_grid_thw)
+            elif hasattr(fps, "__len__") and len(fps) == len(video_grid_thw):
+                second_per_grid_ts = [self.image_processor.temporal_patch_size / tmp for tmp in fps]
+            else:
+                raise ValueError(
+                    f"The length of fps ({len(fps) if hasattr(fps, '__len__') else fps}) must be equal to the length of video_grid_thw ({len(video_grid_thw)}) or fps should be a single number."
+                )
+            videos_inputs.update({"second_per_grid_ts": second_per_grid_ts})
+
+        else:
+            videos_inputs = {}
+            video_grid_thw = None
+
+        if not isinstance(text, list):
+            text = [text]
+
+        if image_grid_thw is not None:
+            merge_length = self.image_processor.merge_size**2
+            index = 0
+            for i in range(len(text)):
+                while self.image_token in text[i]:
+                    text[i] = text[i].replace(
+                        self.image_token,
+                        "<|placeholder|>" * (image_grid_thw[index].prod() // merge_length),
+                        1,
+                    )
+                    index += 1
+                text[i] = text[i].replace("<|placeholder|>", self.image_token)
+
+        if video_grid_thw is not None:
+            merge_length = self.image_processor.merge_size**2
+            index = 0
+            for i in range(len(text)):
+                while self.video_token in text[i]:
+                    text[i] = text[i].replace(
+                        self.video_token,
+                        "<|placeholder|>" * (video_grid_thw[index].prod() // merge_length),
+                        1,
+                    )
+                    index += 1
+                text[i] = text[i].replace("<|placeholder|>", self.video_token)
+
+        text_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"])
+
+        return BatchFeature(data={**text_inputs, **image_inputs, **videos_inputs})
+
+    def batch_decode(self, *args, **kwargs):
+        """
+        This method forwards all its arguments to Qwen2TokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
+        refer to the docstring of this method for more information.
+        """
+        return self.tokenizer.batch_decode(*args, **kwargs)
+
+    def batch_decode_all2all(self, *args, **kwargs):
+        """
+        This method forwards all its arguments to Qwen2TokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
+        refer to the docstring of this method for more information.
+        """
+        decoded = self.tokenizer.batch_decode(*args, **kwargs)
+        pattern = r'<\|vision_start\|>.*?<\|vision_end\|>'
+        decoded_with_image_tag = [re.sub(pattern, '<image>', d, flags=re.DOTALL) for d in decoded]
+        decoded_with_image_tag = [re.sub(r'<\|im_end\|>', '', d) for d in decoded_with_image_tag]
+        return decoded_with_image_tag
+
+    def decode(self, *args, **kwargs):
+        """
+        This method forwards all its arguments to Qwen2TokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to
+        the docstring of this method for more information.
+        """
+        return self.tokenizer.decode(*args, **kwargs)
+
+    def post_process_image_text_to_text(
+        self, generated_outputs, skip_special_tokens=True, clean_up_tokenization_spaces=False, **kwargs
+    ):
+        """
+        Post-process the output of the model to decode the text.
+
+        Args:
+            generated_outputs (`torch.Tensor` or `np.ndarray`):
+                The output of the model `generate` function. The output is expected to be a tensor of shape `(batch_size, sequence_length)`
+                or `(sequence_length,)`.
+            skip_special_tokens (`bool`, *optional*, defaults to `True`):
+                Whether or not to remove special tokens in the output. Argument passed to the tokenizer's `batch_decode` method.
+            Clean_up_tokenization_spaces (`bool`, *optional*, defaults to `False`):
+                Whether or not to clean up the tokenization spaces. Argument passed to the tokenizer's `batch_decode` method.
+            **kwargs:
+                Additional arguments to be passed to the tokenizer's `batch_decode method`.
+
+        Returns:
+            `List[str]`: The decoded text.
+        """
+        return self.tokenizer.batch_decode(
+            generated_outputs,
+            skip_special_tokens=skip_special_tokens,
+            clean_up_tokenization_spaces=clean_up_tokenization_spaces,
+            **kwargs,
+        )
+
+    @property
+    def model_input_names(self):
+        tokenizer_input_names = self.tokenizer.model_input_names
+        image_processor_input_names = self.image_processor.model_input_names
+        names_from_processor = list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
+        return names_from_processor + ["second_per_grid_ts"]
+
+
+__all__ = ["Qwen2_5_VLProcessor"]
diff --git a/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/nexus_gen_projector.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/nexus_gen_projector.py
new file mode 100644
index 0000000000000000000000000000000000000000..d69b3e1bfd50fc3b9c098f7775afae4020f9b320
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/nexus_gen_projector.py
@@ -0,0 +1,417 @@
+import math
+import torch
+import torch.nn as nn
+from typing import Optional, Tuple
+
+
+
+def rotate_half(x):
+    """Rotates half the hidden dims of the input."""
+    x1 = x[..., : x.shape[-1] // 2]
+    x2 = x[..., x.shape[-1] // 2 :]
+    return torch.cat((-x2, x1), dim=-1)
+
+
+def apply_multimodal_rotary_pos_emb(q, k, cos, sin, mrope_section, unsqueeze_dim=1):
+    mrope_section = mrope_section * 2
+    cos = torch.cat([m[i % 3] for i, m in enumerate(cos.split(mrope_section, dim=-1))], dim=-1).unsqueeze(
+        unsqueeze_dim
+    )
+    sin = torch.cat([m[i % 3] for i, m in enumerate(sin.split(mrope_section, dim=-1))], dim=-1).unsqueeze(
+        unsqueeze_dim
+    )
+
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+    return q_embed, k_embed
+
+
+class Qwen2_5_VLRotaryEmbedding(nn.Module):
+    def __init__(self, config, device=None):
+        super().__init__()
+        # BC: "rope_type" was originally "type"
+        if hasattr(config, "rope_scaling") and config.rope_scaling is not None:
+            self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
+        else:
+            self.rope_type = "default"
+        self.max_seq_len_cached = config.max_position_embeddings
+        self.original_max_seq_len = config.max_position_embeddings
+
+        self.config = config
+        from transformers.modeling_rope_utils import _compute_default_rope_parameters
+        self.rope_init_fn = _compute_default_rope_parameters
+
+        inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device)
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+        self.original_inv_freq = self.inv_freq
+
+
+    def _dynamic_frequency_update(self, position_ids, device):
+        """
+        dynamic RoPE layers should recompute `inv_freq` in the following situations:
+        1 - growing beyond the cached sequence length (allow scaling)
+        2 - the current sequence length is in the original scale (avoid losing precision with small sequences)
+        """
+        seq_len = torch.max(position_ids) + 1
+        if seq_len > self.max_seq_len_cached:  # growth
+            inv_freq, self.attention_scaling = self.rope_init_fn(
+                self.config, device, seq_len=seq_len, **self.rope_kwargs
+            )
+            self.register_buffer("inv_freq", inv_freq, persistent=False)  # TODO joao: may break with compilation
+            self.max_seq_len_cached = seq_len
+
+        if seq_len < self.original_max_seq_len and self.max_seq_len_cached > self.original_max_seq_len:  # reset
+            self.register_buffer("inv_freq", self.original_inv_freq, persistent=False)
+            self.max_seq_len_cached = self.original_max_seq_len
+
+
+    @torch.no_grad()
+    def forward(self, x, position_ids):
+        if "dynamic" in self.rope_type:
+            self._dynamic_frequency_update(position_ids, device=x.device)
+
+        # Core RoPE block. In contrast to other models, Qwen2_5_VL has different position ids for the grids
+        # So we expand the inv_freq to shape (3, ...)
+        inv_freq_expanded = self.inv_freq[None, None, :, None].float().expand(3, position_ids.shape[1], -1, 1)
+        position_ids_expanded = position_ids[:, :, None, :].float()  # shape (3, bs, 1, positions)
+        # Force float32 (see https://github.com/huggingface/transformers/pull/29285)
+        device_type = x.device.type
+        device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu"
+        with torch.autocast(device_type=device_type, enabled=False):
+            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(2, 3)
+            emb = torch.cat((freqs, freqs), dim=-1)
+            cos = emb.cos()
+            sin = emb.sin()
+
+        # Advanced RoPE types (e.g. yarn) apply a post-processing scaling factor, equivalent to scaling attention
+        cos = cos * self.attention_scaling
+        sin = sin * self.attention_scaling
+
+        return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
+
+
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+    """
+    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
+    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
+    """
+    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+    if n_rep == 1:
+        return hidden_states
+    hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
+    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+
+class Qwen2_5_VLAttention(nn.Module):
+    def __init__(self, config, layer_idx: Optional[int] = None):
+        super().__init__()
+        self.config = config
+        self.layer_idx = layer_idx
+
+        self.hidden_size = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.head_dim = self.hidden_size // self.num_heads
+        self.num_key_value_heads = config.num_key_value_heads
+        self.num_key_value_groups = self.num_heads // self.num_key_value_heads
+        self.is_causal = True
+        self.attention_dropout = config.attention_dropout
+        self.rope_scaling = config.rope_scaling
+
+        if (self.head_dim * self.num_heads) != self.hidden_size:
+            raise ValueError(
+                f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
+                f" and `num_heads`: {self.num_heads})."
+            )
+        self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=True)
+        self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=True)
+        self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=True)
+        self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
+
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,  # necessary, but kept here for BC
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        bsz, q_len, _ = hidden_states.size()
+
+        query_states = self.q_proj(hidden_states)
+        key_states = self.k_proj(hidden_states)
+        value_states = self.v_proj(hidden_states)
+
+        query_states = query_states.view(bsz, q_len, -1, self.head_dim).transpose(1, 2)
+        key_states = key_states.view(bsz, q_len, -1, self.head_dim).transpose(1, 2)
+        value_states = value_states.view(bsz, q_len, -1, self.head_dim).transpose(1, 2)
+
+        cos, sin = position_embeddings
+        query_states, key_states = apply_multimodal_rotary_pos_emb(
+            query_states, key_states, cos, sin, self.rope_scaling["mrope_section"]
+        )
+
+        # repeat k/v heads if n_kv_heads < n_heads
+        key_states = repeat_kv(key_states, self.num_key_value_groups)
+        value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+        attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
+
+        # Fix precision issues in Qwen2-VL float16 inference
+        # Replace inf values with zeros in attention weights to prevent NaN propagation
+        if query_states.dtype == torch.float16:
+            attn_weights = torch.where(torch.isinf(attn_weights), torch.zeros_like(attn_weights), attn_weights)
+
+        # upcast attention to fp32
+        attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
+        attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
+        attn_output = torch.matmul(attn_weights, value_states)
+
+        if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
+            raise ValueError(
+                f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
+                f" {attn_output.size()}"
+            )
+
+        attn_output = attn_output.transpose(1, 2).contiguous()
+        attn_output = attn_output.reshape(bsz, q_len, -1)
+
+        attn_output = self.o_proj(attn_output)
+
+        return attn_output
+
+
+class Qwen2MLP(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        from transformers.activations import ACT2FN
+        self.config = config
+        self.hidden_size = config.hidden_size
+        self.intermediate_size = config.intermediate_size
+        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
+        self.act_fn = ACT2FN[config.hidden_act]
+
+    def forward(self, x):
+        down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
+        return down_proj
+
+
+class Qwen2RMSNorm(nn.Module):
+    def __init__(self, hidden_size, eps=1e-6):
+        """
+        Qwen2RMSNorm is equivalent to T5LayerNorm
+        """
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.variance_epsilon = eps
+
+    def forward(self, hidden_states):
+        input_dtype = hidden_states.dtype
+        hidden_states = hidden_states.to(torch.float32)
+        variance = hidden_states.pow(2).mean(-1, keepdim=True)
+        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+        return self.weight * hidden_states.to(input_dtype)
+
+    def extra_repr(self):
+        return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
+
+
+class Qwen2_5_VLDecoderLayer(nn.Module):
+    def __init__(self, config, layer_idx):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+
+        self.self_attn = Qwen2_5_VLAttention(config, layer_idx)
+
+        self.mlp = Qwen2MLP(config)
+        self.input_layernorm = Qwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.post_attention_layernorm = Qwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,  # necessary, but kept here for BC
+    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
+
+        residual = hidden_states
+
+        hidden_states = self.input_layernorm(hidden_states)
+
+        # Self Attention
+        hidden_states = self.self_attn(
+            hidden_states=hidden_states,
+            position_embeddings=position_embeddings,
+        )
+        hidden_states = residual + hidden_states
+
+        # Fully Connected
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+
+        return hidden_states
+
+
+class NexusGenImageEmbeddingMerger(nn.Module):
+    def __init__(self, num_layers=1, out_channel=4096, expand_ratio=4, device='cpu'):
+        super().__init__()
+        from transformers import Qwen2_5_VLConfig
+        from transformers.activations import ACT2FN
+        config = Qwen2_5_VLConfig(**{
+            "_name_or_path": "DiffSynth-Studio/Nexus-GenV2",
+            "architectures": [
+                "Qwen2_5_VLForConditionalGeneration"
+            ],
+            "attention_dropout": 0.0,
+            "auto_map": {
+                "AutoConfig": "configuration_qwen2_5_vl.Qwen2_5_VLConfig",
+                "AutoModel": "modeling_qwen2_5_vl.Qwen2_5_VLModel",
+                "AutoModelForCausalLM": "modeling_qwen2_5_vl.Qwen2_5_VLForConditionalGeneration"
+            },
+            "bos_token_id": 151643,
+            "eos_token_id": 151645,
+            "hidden_act": "silu",
+            "hidden_size": 3584,
+            "image_token_id": 151655,
+            "initializer_range": 0.02,
+            "intermediate_size": 18944,
+            "max_position_embeddings": 128000,
+            "max_window_layers": 28,
+            "model_type": "qwen2_5_vl",
+            "num_attention_heads": 28,
+            "num_hidden_layers": 28,
+            "num_key_value_heads": 4,
+            "pad_token_id": 151643,
+            "rms_norm_eps": 1e-06,
+            "rope_scaling": {
+                "mrope_section": [
+                16,
+                24,
+                24
+                ],
+                "rope_type": "default",
+                "type": "default"
+            },
+            "rope_theta": 1000000.0,
+            "sliding_window": 32768,
+            "tie_word_embeddings": False,
+            "torch_dtype": "bfloat16",
+            "transformers_version": "4.49.0",
+            "use_cache": False,
+            "use_sliding_window": False,
+            "video_token_id": 151656,
+            "vision_config": {
+                "hidden_size": 1280,
+                "in_chans": 3,
+                "model_type": "qwen2_5_vl",
+                "spatial_patch_size": 14,
+                "tokens_per_second": 2,
+                "torch_dtype": "bfloat16"
+            },
+            "vision_end_token_id": 151653,
+            "vision_start_token_id": 151652,
+            "vision_token_id": 151654,
+            "vocab_size": 152064
+        })
+        self.config = config
+        self.num_layers = num_layers
+        self.layers = nn.ModuleList([Qwen2_5_VLDecoderLayer(config, layer_idx) for layer_idx in range(num_layers)])
+        self.projector = nn.Sequential(Qwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps),
+                                       nn.Linear(config.hidden_size, out_channel * expand_ratio),
+                                       Qwen2RMSNorm(out_channel * expand_ratio, eps=config.rms_norm_eps),
+                                       ACT2FN[config.hidden_act], nn.Linear(out_channel * expand_ratio, out_channel),
+                                       Qwen2RMSNorm(out_channel, eps=config.rms_norm_eps))
+        self.base_grid = torch.tensor([[1, 72, 72]], device=device)
+        self.rotary_emb = Qwen2_5_VLRotaryEmbedding(config=config, device=device)
+
+    def get_position_ids(self, image_grid_thw):
+        """
+        Generates position ids for the input embeddings grid.
+        modified from the qwen2_vl mrope.
+        """
+        batch_size = image_grid_thw.shape[0]
+        spatial_merge_size = self.config.vision_config.spatial_merge_size
+        t, h, w = (
+            image_grid_thw[0][0],
+            image_grid_thw[0][1],
+            image_grid_thw[0][2],
+        )
+        llm_grid_t, llm_grid_h, llm_grid_w = (
+            t.item(),
+            h.item() // spatial_merge_size,
+            w.item() // spatial_merge_size,
+        )
+        scale_h = self.base_grid[0][1].item() / h.item()
+        scale_w = self.base_grid[0][2].item() / w.item()
+
+        range_tensor = torch.arange(llm_grid_t).view(-1, 1)
+        expanded_range = range_tensor.expand(-1, llm_grid_h * llm_grid_w)
+        time_tensor = expanded_range * self.config.vision_config.tokens_per_second
+        t_index = time_tensor.long().flatten().to(image_grid_thw.device)
+        h_index = torch.arange(llm_grid_h).view(1, -1, 1).expand(llm_grid_t, -1, llm_grid_w).flatten().to(image_grid_thw.device) * scale_h
+        w_index = torch.arange(llm_grid_w).view(1, 1, -1).expand(llm_grid_t, llm_grid_h, -1).flatten().to(image_grid_thw.device) * scale_w
+        # 3, B, L
+        position_ids = torch.stack([t_index, h_index, w_index]).unsqueeze(0).repeat(batch_size, 1, 1).permute(1, 0, 2)
+        return position_ids
+
+    def forward(self, embeds, embeds_grid, ref_embeds=None, ref_embeds_grid=None):
+        position_ids = self.get_position_ids(embeds_grid)
+        hidden_states = embeds
+        if ref_embeds is not None:
+            position_ids_ref_embeds = self.get_position_ids(ref_embeds_grid)
+            position_ids = torch.cat((position_ids, position_ids_ref_embeds), dim=-1)
+            hidden_states = torch.cat((embeds, ref_embeds), dim=1)
+
+        position_embeddings = self.rotary_emb(hidden_states, position_ids)
+        for layer in self.layers:
+            hidden_states = layer(hidden_states, position_embeddings)
+
+        hidden_states = self.projector(hidden_states)
+        return hidden_states
+
+    @staticmethod
+    def state_dict_converter():
+        return NexusGenMergerStateDictConverter()
+
+
+class NexusGenMergerStateDictConverter:
+    def __init__(self):
+        pass
+
+    def from_diffusers(self, state_dict):
+        return state_dict
+    
+    def from_civitai(self, state_dict):
+        merger_state_dict = {key.replace("embedding_merger.", ""): value for key, value in state_dict.items() if key.startswith('embedding_merger.')}
+        return merger_state_dict
+
+
+class NexusGenAdapter(nn.Module):
+    """
+    Adapter for Nexus-Gen generation decoder.
+    """
+    def __init__(self, input_dim=3584, output_dim=4096):
+        super(NexusGenAdapter, self).__init__()
+        self.adapter = nn.Sequential(nn.Linear(input_dim, output_dim),
+                                     nn.LayerNorm(output_dim), nn.ReLU(),
+                                     nn.Linear(output_dim, output_dim),
+                                     nn.LayerNorm(output_dim))
+
+    def forward(self, x):
+        return self.adapter(x)
+
+    @staticmethod
+    def state_dict_converter():
+        return NexusGenAdapterStateDictConverter()
+
+
+class NexusGenAdapterStateDictConverter:
+    def __init__(self):
+        pass
+
+    def from_diffusers(self, state_dict):
+        return state_dict
+    
+    def from_civitai(self, state_dict):
+        adapter_state_dict = {key: value for key, value in state_dict.items() if key.startswith('adapter.')}
+        return adapter_state_dict
diff --git a/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/omnigen.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/omnigen.py
new file mode 100644
index 0000000000000000000000000000000000000000..571d6c0e71e5bb28cd0c4e56a2d0437dd82be4c0
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/omnigen.py
@@ -0,0 +1,803 @@
+# The code is revised from DiT
+import os
+import torch
+import torch.nn as nn
+import numpy as np
+import math
+from safetensors.torch import load_file
+from typing import List, Optional, Tuple, Union
+import torch.utils.checkpoint
+from huggingface_hub import snapshot_download
+from transformers.modeling_outputs import BaseModelOutputWithPast
+from transformers import Phi3Config, Phi3Model
+from transformers.cache_utils import Cache, DynamicCache
+from transformers.utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+
+class Phi3Transformer(Phi3Model):
+    """
+    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`Phi3DecoderLayer`]
+    We only modified the attention mask
+    Args:
+        config: Phi3Config
+    """
+    def prefetch_layer(self, layer_idx: int, device: torch.device):
+        "Starts prefetching the next layer cache"
+        with torch.cuda.stream(self.prefetch_stream):
+            # Prefetch next layer tensors to GPU
+            for name, param in self.layers[layer_idx].named_parameters():
+                param.data = param.data.to(device, non_blocking=True)
+
+    def evict_previous_layer(self, layer_idx: int):
+        "Moves the previous layer cache to the CPU"
+        prev_layer_idx = layer_idx - 1
+        for name, param in self.layers[prev_layer_idx].named_parameters():
+            param.data = param.data.to("cpu", non_blocking=True)
+            
+    def get_offlaod_layer(self, layer_idx: int, device: torch.device):
+        # init stream
+        if not hasattr(self, "prefetch_stream"):
+            self.prefetch_stream = torch.cuda.Stream()
+
+        # delete previous layer
+        torch.cuda.current_stream().synchronize()
+        self.evict_previous_layer(layer_idx)
+        
+        # make sure the current layer is ready
+        torch.cuda.synchronize(self.prefetch_stream)
+
+        # load next layer
+        self.prefetch_layer((layer_idx + 1) % len(self.layers), device)
+        
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        offload_model: Optional[bool] = False,
+    ) -> Union[Tuple, BaseModelOutputWithPast]:
+        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
+        )
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        if (input_ids is None) ^ (inputs_embeds is not None):
+            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
+
+        if self.gradient_checkpointing and self.training:
+            if use_cache:
+                logger.warning_once(
+                    "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
+                )
+                use_cache = False
+
+        # kept for BC (non `Cache` `past_key_values` inputs)
+        return_legacy_cache = False
+        if use_cache and not isinstance(past_key_values, Cache):
+            return_legacy_cache = True
+            if past_key_values is None:
+                past_key_values = DynamicCache()
+            else:
+                past_key_values = DynamicCache.from_legacy_cache(past_key_values)
+                logger.warning_once(
+                    "We detected that you are passing `past_key_values` as a tuple of tuples. This is deprecated and "
+                    "will be removed in v4.47. Please convert your cache or use an appropriate `Cache` class "
+                    "(https://huggingface.co/docs/transformers/kv_cache#legacy-cache-format)"
+                )
+
+        # if inputs_embeds is None:
+        #     inputs_embeds = self.embed_tokens(input_ids)
+
+        # if cache_position is None:
+        #     past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+        #     cache_position = torch.arange(
+        #         past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
+        #     )
+        # if position_ids is None:
+        #     position_ids = cache_position.unsqueeze(0)
+
+        if attention_mask is not None and attention_mask.dim() == 3:
+            dtype = inputs_embeds.dtype
+            min_dtype = torch.finfo(dtype).min
+            attention_mask = (1 - attention_mask) * min_dtype
+            attention_mask = attention_mask.unsqueeze(1).to(inputs_embeds.dtype)
+        else:
+            raise Exception("attention_mask parameter was unavailable or invalid")
+            # causal_mask = self._update_causal_mask(
+            #     attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
+            # )
+
+        hidden_states = inputs_embeds
+
+        # decoder layers
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attns = () if output_attentions else None
+        next_decoder_cache = None
+
+        layer_idx = -1
+        for decoder_layer in self.layers:
+            layer_idx += 1
+
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+
+            if self.gradient_checkpointing and self.training:
+                layer_outputs = self._gradient_checkpointing_func(
+                    decoder_layer.__call__,
+                    hidden_states,
+                    attention_mask,
+                    position_ids,
+                    past_key_values,
+                    output_attentions,
+                    use_cache,
+                    cache_position,
+                )
+            else:
+                if offload_model and not self.training:
+                    self.get_offlaod_layer(layer_idx, device=inputs_embeds.device)
+                layer_outputs = decoder_layer(
+                    hidden_states,
+                    attention_mask=attention_mask,
+                    position_ids=position_ids,
+                    past_key_value=past_key_values,
+                    output_attentions=output_attentions,
+                    use_cache=use_cache,
+                    cache_position=cache_position,
+                )
+
+            hidden_states = layer_outputs[0]
+
+            if use_cache:
+                next_decoder_cache = layer_outputs[2 if output_attentions else 1]
+
+            if output_attentions:
+                all_self_attns += (layer_outputs[1],)
+
+        hidden_states = self.norm(hidden_states)
+
+        # add hidden states from the last decoder layer
+        if output_hidden_states:
+            print('************')
+            all_hidden_states += (hidden_states,)
+
+        next_cache = next_decoder_cache if use_cache else None
+        if return_legacy_cache:
+            next_cache = next_cache.to_legacy_cache()
+
+        if not return_dict:
+            return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
+        return BaseModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=next_cache,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attns,
+        )
+
+
+def modulate(x, shift, scale):
+    return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
+ 
+
+class TimestepEmbedder(nn.Module):
+    """
+    Embeds scalar timesteps into vector representations.
+    """
+    def __init__(self, hidden_size, frequency_embedding_size=256):
+        super().__init__()
+        self.mlp = nn.Sequential(
+            nn.Linear(frequency_embedding_size, hidden_size, bias=True),
+            nn.SiLU(),
+            nn.Linear(hidden_size, hidden_size, bias=True),
+        )
+        self.frequency_embedding_size = frequency_embedding_size
+
+    @staticmethod
+    def timestep_embedding(t, dim, max_period=10000):
+        """
+        Create sinusoidal timestep embeddings.
+        :param t: a 1-D Tensor of N indices, one per batch element.
+                          These may be fractional.
+        :param dim: the dimension of the output.
+        :param max_period: controls the minimum frequency of the embeddings.
+        :return: an (N, D) Tensor of positional embeddings.
+        """
+        # https://github.com/openai/glide-text2im/blob/main/glide_text2im/nn.py
+        half = dim // 2
+        freqs = torch.exp(
+            -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half
+        ).to(device=t.device)
+        args = t[:, None].float() * freqs[None]
+        embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
+        if dim % 2:
+            embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
+        return embedding
+
+    def forward(self, t, dtype=torch.float32):
+        t_freq = self.timestep_embedding(t, self.frequency_embedding_size).to(dtype)
+        t_emb = self.mlp(t_freq)
+        return t_emb
+
+
+class FinalLayer(nn.Module):
+    """
+    The final layer of DiT.
+    """
+    def __init__(self, hidden_size, patch_size, out_channels):
+        super().__init__()
+        self.norm_final = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
+        self.linear = nn.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True)
+        self.adaLN_modulation = nn.Sequential(
+            nn.SiLU(),
+            nn.Linear(hidden_size, 2 * hidden_size, bias=True)
+        )
+
+    def forward(self, x, c):
+        shift, scale = self.adaLN_modulation(c).chunk(2, dim=1)
+        x = modulate(self.norm_final(x), shift, scale)
+        x = self.linear(x)
+        return x
+
+
+def get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False, extra_tokens=0, interpolation_scale=1.0, base_size=1):
+    """
+    grid_size: int of the grid height and width return: pos_embed: [grid_size*grid_size, embed_dim] or
+    [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token)
+    """
+    if isinstance(grid_size, int):
+        grid_size = (grid_size, grid_size)
+
+    grid_h = np.arange(grid_size[0], dtype=np.float32) / (grid_size[0] / base_size) / interpolation_scale
+    grid_w = np.arange(grid_size[1], dtype=np.float32) / (grid_size[1] / base_size) / interpolation_scale
+    grid = np.meshgrid(grid_w, grid_h)  # here w goes first
+    grid = np.stack(grid, axis=0)
+
+    grid = grid.reshape([2, 1, grid_size[1], grid_size[0]])
+    pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid)
+    if cls_token and extra_tokens > 0:
+        pos_embed = np.concatenate([np.zeros([extra_tokens, embed_dim]), pos_embed], axis=0)
+    return pos_embed
+
+
+def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
+    assert embed_dim % 2 == 0
+
+    # use half of dimensions to encode grid_h
+    emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0])  # (H*W, D/2)
+    emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1])  # (H*W, D/2)
+
+    emb = np.concatenate([emb_h, emb_w], axis=1) # (H*W, D)
+    return emb
+
+
+def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
+    """
+    embed_dim: output dimension for each position
+    pos: a list of positions to be encoded: size (M,)
+    out: (M, D)
+    """
+    assert embed_dim % 2 == 0
+    omega = np.arange(embed_dim // 2, dtype=np.float64)
+    omega /= embed_dim / 2.
+    omega = 1. / 10000**omega  # (D/2,)
+
+    pos = pos.reshape(-1)  # (M,)
+    out = np.einsum('m,d->md', pos, omega)  # (M, D/2), outer product
+
+    emb_sin = np.sin(out) # (M, D/2)
+    emb_cos = np.cos(out) # (M, D/2)
+
+    emb = np.concatenate([emb_sin, emb_cos], axis=1)  # (M, D)
+    return emb
+
+
+class PatchEmbedMR(nn.Module):
+    """ 2D Image to Patch Embedding
+    """
+    def __init__(
+            self,
+            patch_size: int = 2,
+            in_chans: int = 4,
+            embed_dim: int = 768,
+            bias: bool = True,
+    ):
+        super().__init__()
+        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size, bias=bias)
+
+    def forward(self, x):
+        x = self.proj(x)
+        x = x.flatten(2).transpose(1, 2)  # NCHW -> NLC
+        return x
+
+
+class OmniGenOriginalModel(nn.Module):
+    """
+    Diffusion model with a Transformer backbone.
+    """
+    def __init__(
+        self,
+        transformer_config: Phi3Config,
+        patch_size=2,
+        in_channels=4,
+        pe_interpolation: float = 1.0,
+        pos_embed_max_size: int = 192,
+    ):
+        super().__init__()
+        self.in_channels = in_channels
+        self.out_channels = in_channels
+        self.patch_size = patch_size
+        self.pos_embed_max_size = pos_embed_max_size
+
+        hidden_size = transformer_config.hidden_size
+
+        self.x_embedder = PatchEmbedMR(patch_size, in_channels, hidden_size, bias=True)
+        self.input_x_embedder = PatchEmbedMR(patch_size, in_channels, hidden_size, bias=True)
+
+        self.time_token = TimestepEmbedder(hidden_size)
+        self.t_embedder = TimestepEmbedder(hidden_size)
+        
+        self.pe_interpolation = pe_interpolation
+        pos_embed = get_2d_sincos_pos_embed(hidden_size, pos_embed_max_size, interpolation_scale=self.pe_interpolation, base_size=64)
+        self.register_buffer("pos_embed", torch.from_numpy(pos_embed).float().unsqueeze(0), persistent=True)
+
+        self.final_layer = FinalLayer(hidden_size, patch_size, self.out_channels)
+
+        self.initialize_weights()
+
+        self.llm = Phi3Transformer(config=transformer_config)
+        self.llm.config.use_cache = False
+    
+    @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,
+                                           ignore_patterns=['flax_model.msgpack', 'rust_model.ot', 'tf_model.h5'])
+        config = Phi3Config.from_pretrained(model_name)
+        model = cls(config)
+        if os.path.exists(os.path.join(model_name, 'model.safetensors')):
+            print("Loading safetensors")
+            ckpt = load_file(os.path.join(model_name, 'model.safetensors'))
+        else:
+            ckpt = torch.load(os.path.join(model_name, 'model.pt'), map_location='cpu')
+        model.load_state_dict(ckpt)
+        return model
+
+    def initialize_weights(self):
+        assert not hasattr(self, "llama")
+
+        # Initialize transformer layers:
+        def _basic_init(module):
+            if isinstance(module, nn.Linear):
+                torch.nn.init.xavier_uniform_(module.weight)
+                if module.bias is not None:
+                    nn.init.constant_(module.bias, 0)
+        self.apply(_basic_init)
+        
+        # Initialize patch_embed like nn.Linear (instead of nn.Conv2d):
+        w = self.x_embedder.proj.weight.data
+        nn.init.xavier_uniform_(w.view([w.shape[0], -1]))
+        nn.init.constant_(self.x_embedder.proj.bias, 0)
+
+        w = self.input_x_embedder.proj.weight.data
+        nn.init.xavier_uniform_(w.view([w.shape[0], -1]))
+        nn.init.constant_(self.x_embedder.proj.bias, 0)
+
+
+        # Initialize timestep embedding MLP:
+        nn.init.normal_(self.t_embedder.mlp[0].weight, std=0.02)
+        nn.init.normal_(self.t_embedder.mlp[2].weight, std=0.02)
+        nn.init.normal_(self.time_token.mlp[0].weight, std=0.02)
+        nn.init.normal_(self.time_token.mlp[2].weight, std=0.02)
+
+        # Zero-out output layers:
+        nn.init.constant_(self.final_layer.adaLN_modulation[-1].weight, 0)
+        nn.init.constant_(self.final_layer.adaLN_modulation[-1].bias, 0)
+        nn.init.constant_(self.final_layer.linear.weight, 0)
+        nn.init.constant_(self.final_layer.linear.bias, 0)
+
+    def unpatchify(self, x, h, w):
+        """
+        x: (N, T, patch_size**2 * C)
+        imgs: (N, H, W, C)
+        """
+        c = self.out_channels
+
+        x = x.reshape(shape=(x.shape[0], h//self.patch_size, w//self.patch_size, self.patch_size, self.patch_size, c))
+        x = torch.einsum('nhwpqc->nchpwq', x)
+        imgs = x.reshape(shape=(x.shape[0], c, h, w))
+        return imgs
+
+
+    def cropped_pos_embed(self, height, width):
+        """Crops positional embeddings for SD3 compatibility."""
+        if self.pos_embed_max_size is None:
+            raise ValueError("`pos_embed_max_size` must be set for cropping.")
+
+        height = height // self.patch_size
+        width = width // self.patch_size
+        if height > self.pos_embed_max_size:
+            raise ValueError(
+                f"Height ({height}) cannot be greater than `pos_embed_max_size`: {self.pos_embed_max_size}."
+            )
+        if width > self.pos_embed_max_size:
+            raise ValueError(
+                f"Width ({width}) cannot be greater than `pos_embed_max_size`: {self.pos_embed_max_size}."
+            )
+
+        top = (self.pos_embed_max_size - height) // 2
+        left = (self.pos_embed_max_size - width) // 2
+        spatial_pos_embed = self.pos_embed.reshape(1, self.pos_embed_max_size, self.pos_embed_max_size, -1)
+        spatial_pos_embed = spatial_pos_embed[:, top : top + height, left : left + width, :]
+        # print(top, top + height, left, left + width, spatial_pos_embed.size())
+        spatial_pos_embed = spatial_pos_embed.reshape(1, -1, spatial_pos_embed.shape[-1])
+        return spatial_pos_embed
+
+
+    def patch_multiple_resolutions(self, latents, padding_latent=None, is_input_images:bool=False):
+        if isinstance(latents, list):
+            return_list = False
+            if padding_latent is None:
+                padding_latent = [None] * len(latents)
+                return_list = True
+            patched_latents, num_tokens, shapes = [], [], []
+            for latent, padding in zip(latents, padding_latent):
+                height, width = latent.shape[-2:]
+                if is_input_images:
+                    latent = self.input_x_embedder(latent)
+                else:
+                    latent = self.x_embedder(latent)
+                pos_embed = self.cropped_pos_embed(height, width)    
+                latent = latent + pos_embed
+                if padding is not None:
+                    latent = torch.cat([latent, padding], dim=-2)
+                patched_latents.append(latent)
+
+                num_tokens.append(pos_embed.size(1))
+                shapes.append([height, width])
+            if not return_list:
+                latents = torch.cat(patched_latents, dim=0)
+            else:
+                latents = patched_latents
+        else:
+            height, width = latents.shape[-2:]
+            if is_input_images:
+                latents = self.input_x_embedder(latents)
+            else:
+                latents = self.x_embedder(latents)
+            pos_embed = self.cropped_pos_embed(height, width)  
+            latents = latents + pos_embed
+            num_tokens = latents.size(1)
+            shapes = [height, width]
+        return latents, num_tokens, shapes
+
+    
+    def forward(self, x, timestep, input_ids, input_img_latents, input_image_sizes, attention_mask, position_ids, padding_latent=None, past_key_values=None, return_past_key_values=True, offload_model:bool=False):
+        """
+        
+        """
+        input_is_list = isinstance(x, list)
+        x, num_tokens, shapes = self.patch_multiple_resolutions(x, padding_latent)
+        time_token = self.time_token(timestep, dtype=x[0].dtype).unsqueeze(1)   
+        
+        if input_img_latents is not None:
+            input_latents, _, _ = self.patch_multiple_resolutions(input_img_latents, is_input_images=True)
+        if input_ids is not None:
+            condition_embeds = self.llm.embed_tokens(input_ids).clone()
+            input_img_inx = 0
+            for b_inx in input_image_sizes.keys():
+                for start_inx, end_inx in input_image_sizes[b_inx]:
+                    condition_embeds[b_inx, start_inx: end_inx] = input_latents[input_img_inx]
+                    input_img_inx += 1
+            if input_img_latents is not None:
+                assert input_img_inx == len(input_latents) 
+
+            input_emb = torch.cat([condition_embeds, time_token, x], dim=1)
+        else:
+            input_emb = torch.cat([time_token, x], dim=1)
+        output = self.llm(inputs_embeds=input_emb, attention_mask=attention_mask, position_ids=position_ids, past_key_values=past_key_values, offload_model=offload_model)
+        output, past_key_values = output.last_hidden_state, output.past_key_values
+        if input_is_list:
+            image_embedding = output[:, -max(num_tokens):]
+            time_emb = self.t_embedder(timestep, dtype=x.dtype)
+            x = self.final_layer(image_embedding, time_emb)
+            latents = []
+            for i in range(x.size(0)):
+                latent = x[i:i+1, :num_tokens[i]]
+                latent = self.unpatchify(latent, shapes[i][0], shapes[i][1])
+                latents.append(latent)
+        else:
+            image_embedding = output[:, -num_tokens:]
+            time_emb = self.t_embedder(timestep, dtype=x.dtype)
+            x = self.final_layer(image_embedding, time_emb)
+            latents = self.unpatchify(x, shapes[0], shapes[1])
+
+        if return_past_key_values:
+            return latents, past_key_values
+        return latents
+
+    @torch.no_grad()
+    def forward_with_cfg(self, x, timestep, input_ids, input_img_latents, input_image_sizes, attention_mask, position_ids, cfg_scale, use_img_cfg, img_cfg_scale, past_key_values, use_kv_cache, offload_model):      
+        self.llm.config.use_cache = use_kv_cache
+        model_out, past_key_values = self.forward(x, timestep, input_ids, input_img_latents, input_image_sizes, attention_mask, position_ids, past_key_values=past_key_values, return_past_key_values=True, offload_model=offload_model)
+        if use_img_cfg:
+            cond, uncond, img_cond = torch.split(model_out, len(model_out) // 3, dim=0)
+            cond = uncond + img_cfg_scale * (img_cond - uncond) + cfg_scale * (cond - img_cond)
+            model_out = [cond, cond, cond]
+        else:
+            cond, uncond = torch.split(model_out, len(model_out) // 2, dim=0)
+            cond = uncond + cfg_scale * (cond - uncond)
+            model_out = [cond, cond]
+        
+        return torch.cat(model_out, dim=0), past_key_values
+
+
+    @torch.no_grad()
+    def forward_with_separate_cfg(self, x, timestep, input_ids, input_img_latents, input_image_sizes, attention_mask, position_ids, cfg_scale, use_img_cfg, img_cfg_scale, past_key_values, use_kv_cache, offload_model):
+        self.llm.config.use_cache = use_kv_cache
+        if past_key_values is None:
+            past_key_values = [None] * len(attention_mask)
+
+        x = torch.split(x, len(x) // len(attention_mask), dim=0)
+        timestep = timestep.to(x[0].dtype)
+        timestep = torch.split(timestep, len(timestep) // len(input_ids), dim=0)
+
+        model_out, pask_key_values = [], []
+        for i in range(len(input_ids)):
+            temp_out, temp_pask_key_values = self.forward(x[i], timestep[i], input_ids[i], input_img_latents[i], input_image_sizes[i], attention_mask[i], position_ids[i], past_key_values=past_key_values[i], return_past_key_values=True, offload_model=offload_model)
+            model_out.append(temp_out)
+            pask_key_values.append(temp_pask_key_values)
+
+        if len(model_out) == 3:
+            cond, uncond, img_cond = model_out
+            cond = uncond + img_cfg_scale * (img_cond - uncond) + cfg_scale * (cond - img_cond)
+            model_out = [cond, cond, cond]
+        elif len(model_out) == 2:
+            cond, uncond = model_out
+            cond = uncond + cfg_scale * (cond - uncond)
+            model_out = [cond, cond]
+        else:
+            return model_out[0]
+        
+        return torch.cat(model_out, dim=0), pask_key_values
+
+
+
+class OmniGenTransformer(OmniGenOriginalModel):
+    def __init__(self):
+        config = {
+            "_name_or_path": "Phi-3-vision-128k-instruct",
+            "architectures": [
+                "Phi3ForCausalLM"
+            ],
+            "attention_dropout": 0.0,
+            "bos_token_id": 1,
+            "eos_token_id": 2,
+            "hidden_act": "silu",
+            "hidden_size": 3072,
+            "initializer_range": 0.02,
+            "intermediate_size": 8192,
+            "max_position_embeddings": 131072,
+            "model_type": "phi3",
+            "num_attention_heads": 32,
+            "num_hidden_layers": 32,
+            "num_key_value_heads": 32,
+            "original_max_position_embeddings": 4096,
+            "rms_norm_eps": 1e-05,
+            "rope_scaling": {
+                "long_factor": [
+                1.0299999713897705,
+                1.0499999523162842,
+                1.0499999523162842,
+                1.0799999237060547,
+                1.2299998998641968,
+                1.2299998998641968,
+                1.2999999523162842,
+                1.4499999284744263,
+                1.5999999046325684,
+                1.6499998569488525,
+                1.8999998569488525,
+                2.859999895095825,
+                3.68999981880188,
+                5.419999599456787,
+                5.489999771118164,
+                5.489999771118164,
+                9.09000015258789,
+                11.579999923706055,
+                15.65999984741211,
+                15.769999504089355,
+                15.789999961853027,
+                18.360000610351562,
+                21.989999771118164,
+                23.079999923706055,
+                30.009998321533203,
+                32.35000228881836,
+                32.590003967285156,
+                35.56000518798828,
+                39.95000457763672,
+                53.840003967285156,
+                56.20000457763672,
+                57.95000457763672,
+                59.29000473022461,
+                59.77000427246094,
+                59.920005798339844,
+                61.190006256103516,
+                61.96000671386719,
+                62.50000762939453,
+                63.3700065612793,
+                63.48000717163086,
+                63.48000717163086,
+                63.66000747680664,
+                63.850006103515625,
+                64.08000946044922,
+                64.760009765625,
+                64.80001068115234,
+                64.81001281738281,
+                64.81001281738281
+                ],
+                "short_factor": [
+                1.05,
+                1.05,
+                1.05,
+                1.1,
+                1.1,
+                1.1,
+                1.2500000000000002,
+                1.2500000000000002,
+                1.4000000000000004,
+                1.4500000000000004,
+                1.5500000000000005,
+                1.8500000000000008,
+                1.9000000000000008,
+                2.000000000000001,
+                2.000000000000001,
+                2.000000000000001,
+                2.000000000000001,
+                2.000000000000001,
+                2.000000000000001,
+                2.000000000000001,
+                2.000000000000001,
+                2.000000000000001,
+                2.000000000000001,
+                2.000000000000001,
+                2.000000000000001,
+                2.000000000000001,
+                2.000000000000001,
+                2.000000000000001,
+                2.000000000000001,
+                2.000000000000001,
+                2.000000000000001,
+                2.000000000000001,
+                2.1000000000000005,
+                2.1000000000000005,
+                2.2,
+                2.3499999999999996,
+                2.3499999999999996,
+                2.3499999999999996,
+                2.3499999999999996,
+                2.3999999999999995,
+                2.3999999999999995,
+                2.6499999999999986,
+                2.6999999999999984,
+                2.8999999999999977,
+                2.9499999999999975,
+                3.049999999999997,
+                3.049999999999997,
+                3.049999999999997
+                ],
+                "type": "su"
+            },
+            "rope_theta": 10000.0,
+            "sliding_window": 131072,
+            "tie_word_embeddings": False,
+            "torch_dtype": "bfloat16",
+            "transformers_version": "4.38.1",
+            "use_cache": True,
+            "vocab_size": 32064,
+            "_attn_implementation": "sdpa"
+        }
+        config = Phi3Config(**config)
+        super().__init__(config)
+
+    
+    def forward(self, x, timestep, input_ids, input_img_latents, input_image_sizes, attention_mask, position_ids, padding_latent=None, past_key_values=None, return_past_key_values=True, offload_model:bool=False):
+        input_is_list = isinstance(x, list)
+        x, num_tokens, shapes = self.patch_multiple_resolutions(x, padding_latent)
+        time_token = self.time_token(timestep, dtype=x[0].dtype).unsqueeze(1)   
+        
+        if input_img_latents is not None:
+            input_latents, _, _ = self.patch_multiple_resolutions(input_img_latents, is_input_images=True)
+        if input_ids is not None:
+            condition_embeds = self.llm.embed_tokens(input_ids).clone()
+            input_img_inx = 0
+            for b_inx in input_image_sizes.keys():
+                for start_inx, end_inx in input_image_sizes[b_inx]:
+                    condition_embeds[b_inx, start_inx: end_inx] = input_latents[input_img_inx]
+                    input_img_inx += 1
+            if input_img_latents is not None:
+                assert input_img_inx == len(input_latents) 
+
+            input_emb = torch.cat([condition_embeds, time_token, x], dim=1)
+        else:
+            input_emb = torch.cat([time_token, x], dim=1)
+        output = self.llm(inputs_embeds=input_emb, attention_mask=attention_mask, position_ids=position_ids, past_key_values=past_key_values, offload_model=offload_model)
+        output, past_key_values = output.last_hidden_state, output.past_key_values
+        if input_is_list:
+            image_embedding = output[:, -max(num_tokens):]
+            time_emb = self.t_embedder(timestep, dtype=x.dtype)
+            x = self.final_layer(image_embedding, time_emb)
+            latents = []
+            for i in range(x.size(0)):
+                latent = x[i:i+1, :num_tokens[i]]
+                latent = self.unpatchify(latent, shapes[i][0], shapes[i][1])
+                latents.append(latent)
+        else:
+            image_embedding = output[:, -num_tokens:]
+            time_emb = self.t_embedder(timestep, dtype=x.dtype)
+            x = self.final_layer(image_embedding, time_emb)
+            latents = self.unpatchify(x, shapes[0], shapes[1])
+
+        if return_past_key_values:
+            return latents, past_key_values
+        return latents
+    
+
+    @torch.no_grad()
+    def forward_with_separate_cfg(self, x, timestep, input_ids, input_img_latents, input_image_sizes, attention_mask, position_ids, cfg_scale, use_img_cfg, img_cfg_scale, past_key_values, use_kv_cache, offload_model):
+        self.llm.config.use_cache = use_kv_cache
+        if past_key_values is None:
+            past_key_values = [None] * len(attention_mask)
+
+        x = torch.split(x, len(x) // len(attention_mask), dim=0)
+        timestep = timestep.to(x[0].dtype)
+        timestep = torch.split(timestep, len(timestep) // len(input_ids), dim=0)
+
+        model_out, pask_key_values = [], []
+        for i in range(len(input_ids)):
+            temp_out, temp_pask_key_values = self.forward(x[i], timestep[i], input_ids[i], input_img_latents[i], input_image_sizes[i], attention_mask[i], position_ids[i], past_key_values=past_key_values[i], return_past_key_values=True, offload_model=offload_model)
+            model_out.append(temp_out)
+            pask_key_values.append(temp_pask_key_values)
+
+        if len(model_out) == 3:
+            cond, uncond, img_cond = model_out
+            cond = uncond + img_cfg_scale * (img_cond - uncond) + cfg_scale * (cond - img_cond)
+            model_out = [cond, cond, cond]
+        elif len(model_out) == 2:
+            cond, uncond = model_out
+            cond = uncond + cfg_scale * (cond - uncond)
+            model_out = [cond, cond]
+        else:
+            return model_out[0]
+        
+        return torch.cat(model_out, dim=0), pask_key_values
+    
+
+    @staticmethod
+    def state_dict_converter():
+        return OmniGenTransformerStateDictConverter()
+
+
+
+class OmniGenTransformerStateDictConverter:
+    def __init__(self):
+        pass
+
+    def from_diffusers(self, state_dict):
+        return state_dict
+    
+    def from_civitai(self, state_dict):
+        return state_dict
diff --git a/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/qwen_image_controlnet.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/qwen_image_controlnet.py
new file mode 100644
index 0000000000000000000000000000000000000000..fc88eaa3fb1b314991d78dc4d8e3cb2730397704
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/qwen_image_controlnet.py
@@ -0,0 +1,74 @@
+import torch
+import torch.nn as nn
+from .sd3_dit import RMSNorm
+from .utils import hash_state_dict_keys
+
+
+class BlockWiseControlBlock(torch.nn.Module):
+    # [linear, gelu, linear]
+    def __init__(self, dim: int = 3072):
+        super().__init__()
+        self.x_rms = RMSNorm(dim, eps=1e-6)
+        self.y_rms = RMSNorm(dim, eps=1e-6)
+        self.input_proj = nn.Linear(dim, dim)
+        self.act = nn.GELU()
+        self.output_proj = nn.Linear(dim, dim)
+
+    def forward(self, x, y):
+        x, y = self.x_rms(x), self.y_rms(y)
+        x = self.input_proj(x + y)
+        x = self.act(x)
+        x = self.output_proj(x)
+        return x
+
+    def init_weights(self):
+        # zero initialize output_proj
+        nn.init.zeros_(self.output_proj.weight)
+        nn.init.zeros_(self.output_proj.bias)
+
+
+class QwenImageBlockWiseControlNet(torch.nn.Module):
+    def __init__(
+        self,
+        num_layers: int = 60,
+        in_dim: int = 64,
+        additional_in_dim: int = 0,
+        dim: int = 3072,
+    ):
+        super().__init__()
+        self.img_in = nn.Linear(in_dim + additional_in_dim, dim)
+        self.controlnet_blocks = nn.ModuleList(
+            [
+                BlockWiseControlBlock(dim)
+                for _ in range(num_layers)
+            ]
+        )
+
+    def init_weight(self):
+        nn.init.zeros_(self.img_in.weight)
+        nn.init.zeros_(self.img_in.bias)
+        for block in self.controlnet_blocks:
+            block.init_weights()
+
+    def process_controlnet_conditioning(self, controlnet_conditioning):
+        return self.img_in(controlnet_conditioning)
+
+    def blockwise_forward(self, img, controlnet_conditioning, block_id):
+        return self.controlnet_blocks[block_id](img, controlnet_conditioning)
+
+    @staticmethod
+    def state_dict_converter():
+        return QwenImageBlockWiseControlNetStateDictConverter()
+
+
+class QwenImageBlockWiseControlNetStateDictConverter():
+    def __init__(self):
+        pass
+
+    def from_civitai(self, state_dict):
+        hash_value = hash_state_dict_keys(state_dict)
+        extra_kwargs = {}
+        if hash_value == "a9e54e480a628f0b956a688a81c33bab":
+            # inpaint controlnet
+            extra_kwargs = {"additional_in_dim": 4}
+        return state_dict, extra_kwargs
diff --git a/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/qwen_image_dit.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/qwen_image_dit.py
new file mode 100644
index 0000000000000000000000000000000000000000..13cb5ca20ca6adc78ce7fb83b928ad0fd0ea6d6e
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/qwen_image_dit.py
@@ -0,0 +1,547 @@
+import torch, math
+import torch.nn as nn
+from typing import Tuple, Optional, Union, List
+from einops import rearrange
+from .sd3_dit import TimestepEmbeddings, RMSNorm
+from .flux_dit import AdaLayerNorm
+
+try:
+    import flash_attn_interface
+    FLASH_ATTN_3_AVAILABLE = True
+except ModuleNotFoundError:
+    FLASH_ATTN_3_AVAILABLE = False
+
+
+def qwen_image_flash_attention(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, num_heads: int, attention_mask = None, enable_fp8_attention: bool = False):
+    if FLASH_ATTN_3_AVAILABLE and attention_mask is None:
+        if not enable_fp8_attention:
+            q = rearrange(q, "b n s d -> b s n d", n=num_heads)
+            k = rearrange(k, "b n s d -> b s n d", n=num_heads)
+            v = rearrange(v, "b n s d -> b s n d", n=num_heads)
+            x = flash_attn_interface.flash_attn_func(q, k, v)
+            if isinstance(x, tuple):
+                x = x[0]
+            x = rearrange(x, "b s n d -> b s (n d)", n=num_heads)
+        else:
+            origin_dtype = q.dtype
+            q_std, k_std, v_std = q.std(), k.std(), v.std()
+            q, k, v = (q / q_std).to(torch.float8_e4m3fn), (k / k_std).to(torch.float8_e4m3fn), (v / v_std).to(torch.float8_e4m3fn)
+            q = rearrange(q, "b n s d -> b s n d", n=num_heads)
+            k = rearrange(k, "b n s d -> b s n d", n=num_heads)
+            v = rearrange(v, "b n s d -> b s n d", n=num_heads)
+            x = flash_attn_interface.flash_attn_func(q, k, v, softmax_scale=q_std * k_std / math.sqrt(q.size(-1)))
+            if isinstance(x, tuple):
+                x = x[0]
+            x = x.to(origin_dtype) * v_std
+            x = rearrange(x, "b s n d -> b s (n d)", n=num_heads)
+    else:
+        x = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=attention_mask)
+        x = rearrange(x, "b n s d -> b s (n d)", n=num_heads)
+    return x
+
+
+class ApproximateGELU(nn.Module):
+    def __init__(self, dim_in: int, dim_out: int, bias: bool = True):
+        super().__init__()
+        self.proj = nn.Linear(dim_in, dim_out, bias=bias)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.proj(x)
+        return x * torch.sigmoid(1.702 * x)
+
+def apply_rotary_emb_qwen(
+    x: torch.Tensor,
+    freqs_cis: Union[torch.Tensor, Tuple[torch.Tensor]]
+):
+    x_rotated = torch.view_as_complex(x.float().reshape(*x.shape[:-1], -1, 2))
+    x_out = torch.view_as_real(x_rotated * freqs_cis).flatten(3)
+    return x_out.type_as(x)
+
+
+class QwenEmbedRope(nn.Module):
+    def __init__(self, theta: int, axes_dim: list[int], scale_rope=False):
+        super().__init__()
+        self.theta = theta
+        self.axes_dim = axes_dim
+        pos_index = torch.arange(4096)
+        neg_index = torch.arange(4096).flip(0) * -1 - 1
+        self.pos_freqs = torch.cat([
+            self.rope_params(pos_index, self.axes_dim[0], self.theta),
+            self.rope_params(pos_index, self.axes_dim[1], self.theta),
+            self.rope_params(pos_index, self.axes_dim[2], self.theta),
+        ], dim=1)
+        self.neg_freqs = torch.cat([
+            self.rope_params(neg_index, self.axes_dim[0], self.theta),
+            self.rope_params(neg_index, self.axes_dim[1], self.theta),
+            self.rope_params(neg_index, self.axes_dim[2], self.theta),
+        ], dim=1)
+        self.rope_cache = {}
+        self.scale_rope = scale_rope
+        
+    def rope_params(self, index, dim, theta=10000):
+        """
+            Args:
+                index: [0, 1, 2, 3] 1D Tensor representing the position index of the token
+        """
+        assert dim % 2 == 0
+        freqs = torch.outer(
+            index,
+            1.0 / torch.pow(theta, torch.arange(0, dim, 2).to(torch.float32).div(dim))
+        )
+        freqs = torch.polar(torch.ones_like(freqs), freqs)
+        return freqs
+
+
+    def _expand_pos_freqs_if_needed(self, video_fhw, txt_seq_lens):
+        if isinstance(video_fhw, list):
+            video_fhw = tuple(max([i[j] for i in video_fhw]) for j in range(3))
+        _, height, width = video_fhw
+        if self.scale_rope:
+            max_vid_index = max(height // 2, width // 2)
+        else:
+            max_vid_index = max(height, width)
+        required_len = max_vid_index + max(txt_seq_lens)
+        cur_max_len = self.pos_freqs.shape[0]
+        if required_len <= cur_max_len:
+            return
+
+        new_max_len = math.ceil(required_len / 512) * 512
+        pos_index = torch.arange(new_max_len)
+        neg_index = torch.arange(new_max_len).flip(0) * -1 - 1
+        self.pos_freqs = torch.cat([
+            self.rope_params(pos_index, self.axes_dim[0], self.theta),
+            self.rope_params(pos_index, self.axes_dim[1], self.theta),
+            self.rope_params(pos_index, self.axes_dim[2], self.theta),
+        ], dim=1)
+        self.neg_freqs = torch.cat([
+            self.rope_params(neg_index, self.axes_dim[0], self.theta),
+            self.rope_params(neg_index, self.axes_dim[1], self.theta),
+            self.rope_params(neg_index, self.axes_dim[2], self.theta),
+        ], dim=1)
+        return
+
+
+    def forward(self, video_fhw, txt_seq_lens, device):
+        self._expand_pos_freqs_if_needed(video_fhw, txt_seq_lens)
+        if self.pos_freqs.device != device:
+            self.pos_freqs = self.pos_freqs.to(device)
+            self.neg_freqs = self.neg_freqs.to(device)
+
+        vid_freqs = []
+        max_vid_index = 0
+        for idx, fhw in enumerate(video_fhw):
+            frame, height, width = fhw
+            rope_key = f"{idx}_{height}_{width}"
+
+            if rope_key not in self.rope_cache:
+                seq_lens = frame * height * width
+                freqs_pos = self.pos_freqs.split([x // 2 for x in self.axes_dim], dim=1)
+                freqs_neg = self.neg_freqs.split([x // 2 for x in self.axes_dim], dim=1)
+                freqs_frame = freqs_pos[0][idx : idx + frame].view(frame, 1, 1, -1).expand(frame, height, width, -1)
+                if self.scale_rope:
+                    freqs_height = torch.cat(
+                        [freqs_neg[1][-(height - height // 2) :], freqs_pos[1][: height // 2]], dim=0
+                    )
+                    freqs_height = freqs_height.view(1, height, 1, -1).expand(frame, height, width, -1)
+                    freqs_width = torch.cat([freqs_neg[2][-(width - width // 2) :], freqs_pos[2][: width // 2]], dim=0)
+                    freqs_width = freqs_width.view(1, 1, width, -1).expand(frame, height, width, -1)
+
+                else:
+                    freqs_height = freqs_pos[1][:height].view(1, height, 1, -1).expand(frame, height, width, -1)
+                    freqs_width = freqs_pos[2][:width].view(1, 1, width, -1).expand(frame, height, width, -1)
+
+                freqs = torch.cat([freqs_frame, freqs_height, freqs_width], dim=-1).reshape(seq_lens, -1)
+                self.rope_cache[rope_key] = freqs.clone().contiguous()
+            vid_freqs.append(self.rope_cache[rope_key])
+
+            if self.scale_rope:
+                max_vid_index = max(height // 2, width // 2, max_vid_index)
+            else:
+                max_vid_index = max(height, width, max_vid_index)
+
+        max_len = max(txt_seq_lens)
+        txt_freqs = self.pos_freqs[max_vid_index : max_vid_index + max_len, ...]
+        vid_freqs = torch.cat(vid_freqs, dim=0)
+
+        return vid_freqs, txt_freqs
+
+
+    def forward_sampling(self, video_fhw, txt_seq_lens, device):
+        self._expand_pos_freqs_if_needed(video_fhw, txt_seq_lens)
+        if self.pos_freqs.device != device:
+            self.pos_freqs = self.pos_freqs.to(device)
+            self.neg_freqs = self.neg_freqs.to(device)
+
+        vid_freqs = []
+        max_vid_index = 0
+        for idx, fhw in enumerate(video_fhw):
+            frame, height, width = fhw
+            rope_key = f"{idx}_{height}_{width}"
+            if idx > 0 and f"{0}_{height}_{width}" not in self.rope_cache:
+                frame_0, height_0, width_0 = video_fhw[0]
+
+                rope_key_0 = f"0_{height_0}_{width_0}"
+                spatial_freqs_0 = self.rope_cache[rope_key_0].reshape(frame_0, height_0, width_0, -1)
+                h_indices = torch.linspace(0, height_0 - 1, height).long()
+                w_indices = torch.linspace(0, width_0 - 1, width).long()
+                h_grid, w_grid = torch.meshgrid(h_indices, w_indices, indexing='ij')
+                sampled_rope = spatial_freqs_0[:, h_grid, w_grid, :]
+
+                freqs_pos = self.pos_freqs.split([x // 2 for x in self.axes_dim], dim=1)
+                freqs_frame = freqs_pos[0][idx : idx + frame].view(frame, 1, 1, -1).expand(frame, height, width, -1)
+                sampled_rope[:, :, :, :freqs_frame.shape[-1]] = freqs_frame
+
+                seq_lens = frame * height * width
+                self.rope_cache[rope_key] = sampled_rope.reshape(seq_lens, -1).clone()
+            if rope_key not in self.rope_cache:
+                seq_lens = frame * height * width
+                freqs_pos = self.pos_freqs.split([x // 2 for x in self.axes_dim], dim=1)
+                freqs_neg = self.neg_freqs.split([x // 2 for x in self.axes_dim], dim=1)
+                freqs_frame = freqs_pos[0][idx : idx + frame].view(frame, 1, 1, -1).expand(frame, height, width, -1)
+                if self.scale_rope:
+                    freqs_height = torch.cat(
+                        [freqs_neg[1][-(height - height // 2) :], freqs_pos[1][: height // 2]], dim=0
+                    )
+                    freqs_height = freqs_height.view(1, height, 1, -1).expand(frame, height, width, -1)
+                    freqs_width = torch.cat([freqs_neg[2][-(width - width // 2) :], freqs_pos[2][: width // 2]], dim=0)
+                    freqs_width = freqs_width.view(1, 1, width, -1).expand(frame, height, width, -1)
+
+                else:
+                    freqs_height = freqs_pos[1][:height].view(1, height, 1, -1).expand(frame, height, width, -1)
+                    freqs_width = freqs_pos[2][:width].view(1, 1, width, -1).expand(frame, height, width, -1)
+
+                freqs = torch.cat([freqs_frame, freqs_height, freqs_width], dim=-1).reshape(seq_lens, -1)
+                self.rope_cache[rope_key] = freqs.clone()
+            vid_freqs.append(self.rope_cache[rope_key].contiguous())
+
+            if self.scale_rope:
+                max_vid_index = max(height // 2, width // 2, max_vid_index)
+            else:
+                max_vid_index = max(height, width, max_vid_index)
+
+        max_len = max(txt_seq_lens)
+        txt_freqs = self.pos_freqs[max_vid_index : max_vid_index + max_len, ...]
+        vid_freqs = torch.cat(vid_freqs, dim=0)
+
+        return vid_freqs, txt_freqs
+
+
+class QwenFeedForward(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        dim_out: Optional[int] = None,
+        dropout: float = 0.0,
+    ):
+        super().__init__()
+        inner_dim = int(dim * 4)
+        self.net = nn.ModuleList([])
+        self.net.append(ApproximateGELU(dim, inner_dim))
+        self.net.append(nn.Dropout(dropout))
+        self.net.append(nn.Linear(inner_dim, dim_out))
+
+    def forward(self, hidden_states: torch.Tensor, *args, **kwargs) -> torch.Tensor:
+        for module in self.net:
+            hidden_states = module(hidden_states)
+        return hidden_states
+
+class QwenDoubleStreamAttention(nn.Module):
+    def __init__(
+        self,
+        dim_a,
+        dim_b,
+        num_heads,
+        head_dim,
+    ):
+        super().__init__()
+        self.num_heads = num_heads
+        self.head_dim = head_dim
+
+        self.to_q = nn.Linear(dim_a, dim_a)
+        self.to_k = nn.Linear(dim_a, dim_a)
+        self.to_v = nn.Linear(dim_a, dim_a)
+        self.norm_q = RMSNorm(head_dim, eps=1e-6)
+        self.norm_k = RMSNorm(head_dim, eps=1e-6)
+
+        self.add_q_proj = nn.Linear(dim_b, dim_b)
+        self.add_k_proj = nn.Linear(dim_b, dim_b)
+        self.add_v_proj = nn.Linear(dim_b, dim_b)
+        self.norm_added_q = RMSNorm(head_dim, eps=1e-6)
+        self.norm_added_k = RMSNorm(head_dim, eps=1e-6)
+
+        self.to_out = torch.nn.Sequential(nn.Linear(dim_a, dim_a))
+        self.to_add_out = nn.Linear(dim_b, dim_b)
+
+    def forward(
+        self,
+        image: torch.FloatTensor,
+        text: torch.FloatTensor,
+        image_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        enable_fp8_attention: bool = False,
+    ) -> Tuple[torch.FloatTensor, torch.FloatTensor]:
+        img_q, img_k, img_v = self.to_q(image), self.to_k(image), self.to_v(image)
+        txt_q, txt_k, txt_v = self.add_q_proj(text), self.add_k_proj(text), self.add_v_proj(text)
+        seq_txt = txt_q.shape[1]
+
+        img_q = rearrange(img_q, 'b s (h d) -> b h s d', h=self.num_heads)
+        img_k = rearrange(img_k, 'b s (h d) -> b h s d', h=self.num_heads)
+        img_v = rearrange(img_v, 'b s (h d) -> b h s d', h=self.num_heads)
+
+        txt_q = rearrange(txt_q, 'b s (h d) -> b h s d', h=self.num_heads)
+        txt_k = rearrange(txt_k, 'b s (h d) -> b h s d', h=self.num_heads)
+        txt_v = rearrange(txt_v, 'b s (h d) -> b h s d', h=self.num_heads)
+
+        img_q, img_k = self.norm_q(img_q), self.norm_k(img_k)
+        txt_q, txt_k = self.norm_added_q(txt_q), self.norm_added_k(txt_k)
+        
+        if image_rotary_emb is not None:
+            img_freqs, txt_freqs = image_rotary_emb
+            img_q = apply_rotary_emb_qwen(img_q, img_freqs)
+            img_k = apply_rotary_emb_qwen(img_k, img_freqs)
+            txt_q = apply_rotary_emb_qwen(txt_q, txt_freqs)
+            txt_k = apply_rotary_emb_qwen(txt_k, txt_freqs)
+
+        joint_q = torch.cat([txt_q, img_q], dim=2)
+        joint_k = torch.cat([txt_k, img_k], dim=2)
+        joint_v = torch.cat([txt_v, img_v], dim=2)
+
+        joint_attn_out = qwen_image_flash_attention(joint_q, joint_k, joint_v, num_heads=joint_q.shape[1], attention_mask=attention_mask, enable_fp8_attention=enable_fp8_attention).to(joint_q.dtype)
+
+        txt_attn_output = joint_attn_out[:, :seq_txt, :]
+        img_attn_output = joint_attn_out[:, seq_txt:, :]
+
+        img_attn_output = self.to_out(img_attn_output)
+        txt_attn_output = self.to_add_out(txt_attn_output)
+
+        return img_attn_output, txt_attn_output
+
+
+class QwenImageTransformerBlock(nn.Module):
+    def __init__(
+        self, 
+        dim: int, 
+        num_attention_heads: int, 
+        attention_head_dim: int, 
+        eps: float = 1e-6,
+    ):    
+        super().__init__()
+        
+        self.dim = dim
+        self.num_attention_heads = num_attention_heads
+        self.attention_head_dim = attention_head_dim
+
+        self.img_mod = nn.Sequential(
+            nn.SiLU(),
+            nn.Linear(dim, 6 * dim), 
+        )
+        self.img_norm1 = nn.LayerNorm(dim, elementwise_affine=False, eps=eps)
+        self.attn = QwenDoubleStreamAttention(
+            dim_a=dim,
+            dim_b=dim,
+            num_heads=num_attention_heads,
+            head_dim=attention_head_dim,
+        )
+        self.img_norm2 = nn.LayerNorm(dim, elementwise_affine=False, eps=eps)
+        self.img_mlp = QwenFeedForward(dim=dim, dim_out=dim)
+
+        self.txt_mod = nn.Sequential(
+            nn.SiLU(),
+            nn.Linear(dim, 6 * dim, bias=True), 
+        )
+        self.txt_norm1 = nn.LayerNorm(dim, elementwise_affine=False, eps=eps)
+        self.txt_norm2 = nn.LayerNorm(dim, elementwise_affine=False, eps=eps)
+        self.txt_mlp = QwenFeedForward(dim=dim, dim_out=dim)
+    
+    def _modulate(self, x, mod_params):
+        shift, scale, gate = mod_params.chunk(3, dim=-1)
+        return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1), gate.unsqueeze(1)    
+
+    def forward(
+        self,
+        image: torch.Tensor,  
+        text: torch.Tensor,
+        temb: torch.Tensor, 
+        image_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        enable_fp8_attention = False,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+
+        img_mod_attn, img_mod_mlp = self.img_mod(temb).chunk(2, dim=-1)  # [B, 3*dim] each
+        txt_mod_attn, txt_mod_mlp = self.txt_mod(temb).chunk(2, dim=-1)  # [B, 3*dim] each
+
+        img_normed = self.img_norm1(image)
+        img_modulated, img_gate = self._modulate(img_normed, img_mod_attn)
+
+        txt_normed = self.txt_norm1(text)
+        txt_modulated, txt_gate = self._modulate(txt_normed, txt_mod_attn)
+
+        img_attn_out, txt_attn_out = self.attn(
+            image=img_modulated,
+            text=txt_modulated,
+            image_rotary_emb=image_rotary_emb,
+            attention_mask=attention_mask,
+            enable_fp8_attention=enable_fp8_attention,
+        )
+        
+        image = image + img_gate * img_attn_out
+        text = text + txt_gate * txt_attn_out
+
+        img_normed_2 = self.img_norm2(image)
+        img_modulated_2, img_gate_2 = self._modulate(img_normed_2, img_mod_mlp)
+
+        txt_normed_2 = self.txt_norm2(text)
+        txt_modulated_2, txt_gate_2 = self._modulate(txt_normed_2, txt_mod_mlp)
+
+        img_mlp_out = self.img_mlp(img_modulated_2)
+        txt_mlp_out = self.txt_mlp(txt_modulated_2)
+
+        image = image + img_gate_2 * img_mlp_out
+        text = text + txt_gate_2 * txt_mlp_out
+
+        return text, image
+
+
+class QwenImageDiT(torch.nn.Module):
+    def __init__(
+        self,
+        num_layers: int = 60,
+    ):
+        super().__init__()
+
+        self.pos_embed = QwenEmbedRope(theta=10000, axes_dim=[16,56,56], scale_rope=True) 
+
+        self.time_text_embed = TimestepEmbeddings(256, 3072, diffusers_compatible_format=True, scale=1000, align_dtype_to_timestep=True)
+        self.txt_norm = RMSNorm(3584, eps=1e-6)
+
+        self.img_in = nn.Linear(64, 3072)
+        self.txt_in = nn.Linear(3584, 3072)
+
+        self.transformer_blocks = nn.ModuleList(
+            [
+                QwenImageTransformerBlock(
+                    dim=3072,
+                    num_attention_heads=24,
+                    attention_head_dim=128,
+                )
+                for _ in range(num_layers)
+            ]
+        )
+        self.norm_out = AdaLayerNorm(3072, single=True)
+        self.proj_out = nn.Linear(3072, 64)
+
+
+    def process_entity_masks(self, latents, prompt_emb, prompt_emb_mask, entity_prompt_emb, entity_prompt_emb_mask, entity_masks, height, width, image, img_shapes):
+        # prompt_emb
+        all_prompt_emb = entity_prompt_emb + [prompt_emb]
+        all_prompt_emb = [self.txt_in(self.txt_norm(local_prompt_emb)) for local_prompt_emb in all_prompt_emb]
+        all_prompt_emb = torch.cat(all_prompt_emb, dim=1)
+
+        # image_rotary_emb
+        txt_seq_lens = prompt_emb_mask.sum(dim=1).tolist()
+        image_rotary_emb = self.pos_embed(img_shapes, txt_seq_lens, device=latents.device)
+        entity_seq_lens = [emb_mask.sum(dim=1).tolist() for emb_mask in entity_prompt_emb_mask]
+        entity_rotary_emb = [self.pos_embed(img_shapes, entity_seq_len, device=latents.device)[1] for entity_seq_len in entity_seq_lens]
+        txt_rotary_emb = torch.cat(entity_rotary_emb + [image_rotary_emb[1]], dim=0)
+        image_rotary_emb = (image_rotary_emb[0], txt_rotary_emb)
+
+        # attention_mask
+        repeat_dim = latents.shape[1]
+        max_masks = entity_masks.shape[1]
+        entity_masks = entity_masks.repeat(1, 1, repeat_dim, 1, 1)
+        entity_masks = [entity_masks[:, i, None].squeeze(1) for i in range(max_masks)]
+        global_mask = torch.ones_like(entity_masks[0]).to(device=latents.device, dtype=latents.dtype)
+        entity_masks = entity_masks + [global_mask]
+
+        N = len(entity_masks)
+        batch_size = entity_masks[0].shape[0]
+        seq_lens = [mask_.sum(dim=1).item() for mask_ in entity_prompt_emb_mask] + [prompt_emb_mask.sum(dim=1).item()]
+        total_seq_len = sum(seq_lens) + image.shape[1]
+        patched_masks = []
+        for i in range(N):
+            patched_mask = rearrange(entity_masks[i], "B C (H P) (W Q) -> B (H W) (C P Q)", H=height//16, W=width//16, P=2, Q=2)
+            patched_masks.append(patched_mask)
+        attention_mask = torch.ones((batch_size, total_seq_len, total_seq_len), dtype=torch.bool).to(device=entity_masks[0].device)
+
+        # prompt-image attention mask
+        image_start = sum(seq_lens)
+        image_end = total_seq_len
+        cumsum = [0]
+        single_image_seq = image_end - image_start
+        for length in seq_lens:
+            cumsum.append(cumsum[-1] + length)
+        for i in range(N):
+            prompt_start = cumsum[i]
+            prompt_end = cumsum[i+1]
+            image_mask = torch.sum(patched_masks[i], dim=-1) > 0
+            image_mask = image_mask.unsqueeze(1).repeat(1, seq_lens[i], 1)
+            # repeat image mask to match the single image sequence length
+            repeat_time = single_image_seq // image_mask.shape[-1]
+            image_mask = image_mask.repeat(1, 1, repeat_time)
+            # prompt update with image
+            attention_mask[:, prompt_start:prompt_end, image_start:image_end] = image_mask
+            # image update with prompt
+            attention_mask[:, image_start:image_end, prompt_start:prompt_end] = image_mask.transpose(1, 2)
+        # prompt-prompt attention mask, let the prompt tokens not attend to each other
+        for i in range(N):
+            for j in range(N):
+                if i == j:
+                    continue
+                start_i, end_i = cumsum[i], cumsum[i+1]
+                start_j, end_j = cumsum[j], cumsum[j+1]
+                attention_mask[:, start_i:end_i, start_j:end_j] = False
+
+        attention_mask = attention_mask.float()
+        attention_mask[attention_mask == 0] = float('-inf')
+        attention_mask[attention_mask == 1] = 0
+        attention_mask = attention_mask.to(device=latents.device, dtype=latents.dtype).unsqueeze(1)
+
+        return all_prompt_emb, image_rotary_emb, attention_mask
+
+
+    def forward(
+        self,
+        latents=None,
+        timestep=None,
+        prompt_emb=None,
+        prompt_emb_mask=None,
+        height=None,
+        width=None,
+    ):
+        img_shapes = [(latents.shape[0], latents.shape[2]//2, latents.shape[3]//2)]
+        txt_seq_lens = prompt_emb_mask.sum(dim=1).tolist()
+        
+        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 = self.img_in(image)
+        text = self.txt_in(self.txt_norm(prompt_emb))
+
+        conditioning = self.time_text_embed(timestep, image.dtype)
+
+        image_rotary_emb = self.pos_embed(img_shapes, txt_seq_lens, device=latents.device)
+
+        for block in self.transformer_blocks:
+            text, image = block(
+                image=image,
+                text=text,
+                temb=conditioning,
+                image_rotary_emb=image_rotary_emb,
+            )
+        
+        image = self.norm_out(image, conditioning)
+        image = self.proj_out(image)
+        
+        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 image
+    
+    @staticmethod
+    def state_dict_converter():
+        return QwenImageDiTStateDictConverter()
+
+
+
+class QwenImageDiTStateDictConverter():
+    def __init__(self):
+        pass
+
+    def from_civitai(self, state_dict):
+        return state_dict
diff --git a/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/qwen_image_text_encoder.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/qwen_image_text_encoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..dc10f9599dd06be89b0262a0eff087c1115de82a
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/qwen_image_text_encoder.py
@@ -0,0 +1,255 @@
+from transformers import Qwen2_5_VLModel
+import torch
+from typing import Optional, Union
+
+
+class QwenImageTextEncoder(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        from transformers import Qwen2_5_VLConfig
+        config = Qwen2_5_VLConfig(**{
+            "architectures": [
+                "Qwen2_5_VLForConditionalGeneration"
+            ],
+            "attention_dropout": 0.0,
+            "bos_token_id": 151643,
+            "eos_token_id": 151645,
+            "hidden_act": "silu",
+            "hidden_size": 3584,
+            "image_token_id": 151655,
+            "initializer_range": 0.02,
+            "intermediate_size": 18944,
+            "max_position_embeddings": 128000,
+            "max_window_layers": 28,
+            "model_type": "qwen2_5_vl",
+            "num_attention_heads": 28,
+            "num_hidden_layers": 28,
+            "num_key_value_heads": 4,
+            "rms_norm_eps": 1e-06,
+            "rope_scaling": {
+                "mrope_section": [
+                    16,
+                    24,
+                    24
+                ],
+                "rope_type": "default",
+                "type": "default"
+            },
+            "rope_theta": 1000000.0,
+            "sliding_window": 32768,
+            "text_config": {
+                "architectures": [
+                "Qwen2_5_VLForConditionalGeneration"
+                ],
+                "attention_dropout": 0.0,
+                "bos_token_id": 151643,
+                "eos_token_id": 151645,
+                "hidden_act": "silu",
+                "hidden_size": 3584,
+                "image_token_id": None,
+                "initializer_range": 0.02,
+                "intermediate_size": 18944,
+                "layer_types": [
+                "full_attention",
+                "full_attention",
+                "full_attention",
+                "full_attention",
+                "full_attention",
+                "full_attention",
+                "full_attention",
+                "full_attention",
+                "full_attention",
+                "full_attention",
+                "full_attention",
+                "full_attention",
+                "full_attention",
+                "full_attention",
+                "full_attention",
+                "full_attention",
+                "full_attention",
+                "full_attention",
+                "full_attention",
+                "full_attention",
+                "full_attention",
+                "full_attention",
+                "full_attention",
+                "full_attention",
+                "full_attention",
+                "full_attention",
+                "full_attention",
+                "full_attention"
+                ],
+                "max_position_embeddings": 128000,
+                "max_window_layers": 28,
+                "model_type": "qwen2_5_vl_text",
+                "num_attention_heads": 28,
+                "num_hidden_layers": 28,
+                "num_key_value_heads": 4,
+                "rms_norm_eps": 1e-06,
+                "rope_scaling": {
+                "mrope_section": [
+                    16,
+                    24,
+                    24
+                ],
+                "rope_type": "default",
+                "type": "default"
+                },
+                "rope_theta": 1000000.0,
+                "sliding_window": None,
+                "torch_dtype": "float32",
+                "use_cache": True,
+                "use_sliding_window": False,
+                "video_token_id": None,
+                "vision_end_token_id": 151653,
+                "vision_start_token_id": 151652,
+                "vision_token_id": 151654,
+                "vocab_size": 152064
+            },
+            "tie_word_embeddings": False,
+            "torch_dtype": "float32",
+            "transformers_version": "4.54.0",
+            "use_cache": True,
+            "use_sliding_window": False,
+            "video_token_id": 151656,
+            "vision_config": {
+                "depth": 32,
+                "fullatt_block_indexes": [
+                    7,
+                    15,
+                    23,
+                    31
+                ],
+                "hidden_act": "silu",
+                "hidden_size": 1280,
+                "in_channels": 3,
+                "in_chans": 3,
+                "initializer_range": 0.02,
+                "intermediate_size": 3420,
+                "model_type": "qwen2_5_vl",
+                "num_heads": 16,
+                "out_hidden_size": 3584,
+                "patch_size": 14,
+                "spatial_merge_size": 2,
+                "spatial_patch_size": 14,
+                "temporal_patch_size": 2,
+                "tokens_per_second": 2,
+                "torch_dtype": "float32",
+                "window_size": 112
+            },
+            "vision_end_token_id": 151653,
+            "vision_start_token_id": 151652,
+            "vision_token_id": 151654,
+            "vocab_size": 152064
+        })
+        self.model = Qwen2_5_VLModel(config)
+        self.lm_head = torch.nn.Linear(config.text_config.hidden_size, config.text_config.vocab_size, bias=False)
+        
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        pixel_values: Optional[torch.Tensor] = None,
+        pixel_values_videos: Optional[torch.FloatTensor] = None,
+        image_grid_thw: Optional[torch.LongTensor] = None,
+        video_grid_thw: Optional[torch.LongTensor] = None,
+        rope_deltas: Optional[torch.LongTensor] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        second_per_grid_ts: Optional[torch.Tensor] = None,
+        logits_to_keep: Union[int, torch.Tensor] = 0,
+        **kwargs,
+    ):
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+            config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+            (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+        image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*):
+            The temporal, height and width of feature shape of each image in LLM.
+        video_grid_thw (`torch.LongTensor` of shape `(num_videos, 3)`, *optional*):
+            The temporal, height and width of feature shape of each video in LLM.
+        rope_deltas (`torch.LongTensor` of shape `(batch_size, )`, *optional*):
+            The rope index difference between sequence length and multimodal rope.
+        second_per_grid_ts (`torch.Tensor` of shape `(num_videos)`, *optional*):
+            The time interval (in seconds) for each grid along the temporal dimension in the 3D position IDs.
+
+        Example:
+
+        ```python
+        >>> from PIL import Image
+        >>> import requests
+        >>> from transformers import AutoProcessor, Qwen2_5_VLForConditionalGeneration
+
+        >>> model = Qwen2_5_VLForConditionalGeneration.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct")
+        >>> processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct")
+
+        >>> messages = [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "image"},
+                    {"type": "text", "text": "What is shown in this image?"},
+                ],
+            },
+        ]
+        >>> url = "https://www.ilankelman.org/stopsigns/australia.jpg"
+        >>> image = Image.open(requests.get(url, stream=True).raw)
+
+        >>> text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+        >>> inputs = processor(text=[text], images=[image], vision_infos=[vision_infos])
+
+        >>> # Generate
+        >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+        >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+        "The image shows a street scene with a red stop sign in the foreground. In the background, there is a large red gate with Chinese characters ..."
+        ```"""
+
+        output_attentions = False
+        output_hidden_states = True
+
+        outputs = self.model(
+            input_ids=input_ids,
+            pixel_values=pixel_values,
+            pixel_values_videos=pixel_values_videos,
+            image_grid_thw=image_grid_thw,
+            video_grid_thw=video_grid_thw,
+            second_per_grid_ts=second_per_grid_ts,
+            position_ids=position_ids,
+            attention_mask=attention_mask,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=True,
+            cache_position=cache_position,
+            **kwargs,
+        )
+        return outputs.hidden_states
+    
+    @staticmethod
+    def state_dict_converter():
+        return QwenImageTextEncoderStateDictConverter()
+
+
+
+class QwenImageTextEncoderStateDictConverter():
+    def __init__(self):
+        pass
+
+    def from_diffusers(self, state_dict):
+        state_dict_ = {}
+        for k, v in state_dict.items():
+            if k.startswith("visual."):
+                k = "model." + k
+            elif k.startswith("model."):
+                k = k.replace("model.", "model.language_model.")
+            state_dict_[k] = v
+        return state_dict_
diff --git a/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/qwen_image_vae.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/qwen_image_vae.py
new file mode 100644
index 0000000000000000000000000000000000000000..63de5484ef7c47968b8b93a8bcd1f438730fccd6
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/qwen_image_vae.py
@@ -0,0 +1,736 @@
+import torch
+from typing import List, Optional, Tuple, Union
+from torch import nn
+
+
+CACHE_T = 2
+
+class QwenImageCausalConv3d(torch.nn.Conv3d):
+    r"""
+    A custom 3D causal convolution layer with feature caching support.
+
+    This layer extends the standard Conv3D layer by ensuring causality in the time dimension and handling feature
+    caching for efficient inference.
+
+    Args:
+        in_channels (int): Number of channels in the input image
+        out_channels (int): Number of channels produced by the convolution
+        kernel_size (int or tuple): Size of the convolving kernel
+        stride (int or tuple, optional): Stride of the convolution. Default: 1
+        padding (int or tuple, optional): Zero-padding added to all three sides of the input. Default: 0
+    """
+
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        kernel_size: Union[int, Tuple[int, int, int]],
+        stride: Union[int, Tuple[int, int, int]] = 1,
+        padding: Union[int, Tuple[int, int, int]] = 0,
+    ) -> None:
+        super().__init__(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            kernel_size=kernel_size,
+            stride=stride,
+            padding=padding,
+        )
+
+        # Set up causal padding
+        self._padding = (self.padding[2], self.padding[2], self.padding[1], self.padding[1], 2 * self.padding[0], 0)
+        self.padding = (0, 0, 0)
+
+    def forward(self, x, cache_x=None):
+        padding = list(self._padding)
+        if cache_x is not None and self._padding[4] > 0:
+            cache_x = cache_x.to(x.device)
+            x = torch.cat([cache_x, x], dim=2)
+            padding[4] -= cache_x.shape[2]
+        x = torch.nn.functional.pad(x, padding)
+        return super().forward(x)
+
+
+
+class QwenImageRMS_norm(nn.Module):
+    r"""
+    A custom RMS normalization layer.
+
+    Args:
+        dim (int): The number of dimensions to normalize over.
+        channel_first (bool, optional): Whether the input tensor has channels as the first dimension.
+            Default is True.
+        images (bool, optional): Whether the input represents image data. Default is True.
+        bias (bool, optional): Whether to include a learnable bias term. Default is False.
+    """
+
+    def __init__(self, dim: int, channel_first: bool = True, images: bool = True, bias: bool = False) -> None:
+        super().__init__()
+        broadcastable_dims = (1, 1, 1) if not images else (1, 1)
+        shape = (dim, *broadcastable_dims) if channel_first else (dim,)
+
+        self.channel_first = channel_first
+        self.scale = dim**0.5
+        self.gamma = nn.Parameter(torch.ones(shape))
+        self.bias = nn.Parameter(torch.zeros(shape)) if bias else 0.0
+
+    def forward(self, x):
+        return torch.nn.functional.normalize(x, dim=(1 if self.channel_first else -1)) * self.scale * self.gamma + self.bias
+
+
+
+class QwenImageResidualBlock(nn.Module):
+    r"""
+    A custom residual block module.
+
+    Args:
+        in_dim (int): Number of input channels.
+        out_dim (int): Number of output channels.
+        dropout (float, optional): Dropout rate for the dropout layer. Default is 0.0.
+        non_linearity (str, optional): Type of non-linearity to use. Default is "silu".
+    """
+
+    def __init__(
+        self,
+        in_dim: int,
+        out_dim: int,
+        dropout: float = 0.0,
+        non_linearity: str = "silu",
+    ) -> None:
+        super().__init__()
+        self.in_dim = in_dim
+        self.out_dim = out_dim
+        self.nonlinearity = torch.nn.SiLU()
+
+        # layers
+        self.norm1 = QwenImageRMS_norm(in_dim, images=False)
+        self.conv1 = QwenImageCausalConv3d(in_dim, out_dim, 3, padding=1)
+        self.norm2 = QwenImageRMS_norm(out_dim, images=False)
+        self.dropout = nn.Dropout(dropout)
+        self.conv2 = QwenImageCausalConv3d(out_dim, out_dim, 3, padding=1)
+        self.conv_shortcut = QwenImageCausalConv3d(in_dim, out_dim, 1) if in_dim != out_dim else nn.Identity()
+
+    def forward(self, x, feat_cache=None, feat_idx=[0]):
+        # Apply shortcut connection
+        h = self.conv_shortcut(x)
+
+        # First normalization and activation
+        x = self.norm1(x)
+        x = self.nonlinearity(x)
+
+        if feat_cache is not None:
+            idx = feat_idx[0]
+            cache_x = x[:, :, -CACHE_T:, :, :].clone()
+            if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
+                cache_x = torch.cat([feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(cache_x.device), cache_x], dim=2)
+
+            x = self.conv1(x, feat_cache[idx])
+            feat_cache[idx] = cache_x
+            feat_idx[0] += 1
+        else:
+            x = self.conv1(x)
+
+        # Second normalization and activation
+        x = self.norm2(x)
+        x = self.nonlinearity(x)
+
+        # Dropout
+        x = self.dropout(x)
+
+        if feat_cache is not None:
+            idx = feat_idx[0]
+            cache_x = x[:, :, -CACHE_T:, :, :].clone()
+            if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
+                cache_x = torch.cat([feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(cache_x.device), cache_x], dim=2)
+
+            x = self.conv2(x, feat_cache[idx])
+            feat_cache[idx] = cache_x
+            feat_idx[0] += 1
+        else:
+            x = self.conv2(x)
+
+        # Add residual connection
+        return x + h
+
+
+
+class QwenImageAttentionBlock(nn.Module):
+    r"""
+    Causal self-attention with a single head.
+
+    Args:
+        dim (int): The number of channels in the input tensor.
+    """
+
+    def __init__(self, dim):
+        super().__init__()
+        self.dim = dim
+
+        # layers
+        self.norm = QwenImageRMS_norm(dim)
+        self.to_qkv = nn.Conv2d(dim, dim * 3, 1)
+        self.proj = nn.Conv2d(dim, dim, 1)
+
+    def forward(self, x):
+        identity = x
+        batch_size, channels, time, height, width = x.size()
+
+        x = x.permute(0, 2, 1, 3, 4).reshape(batch_size * time, channels, height, width)
+        x = self.norm(x)
+
+        # compute query, key, value
+        qkv = self.to_qkv(x)
+        qkv = qkv.reshape(batch_size * time, 1, channels * 3, -1)
+        qkv = qkv.permute(0, 1, 3, 2).contiguous()
+        q, k, v = qkv.chunk(3, dim=-1)
+
+        # apply attention
+        x = torch.nn.functional.scaled_dot_product_attention(q, k, v)
+
+        x = x.squeeze(1).permute(0, 2, 1).reshape(batch_size * time, channels, height, width)
+
+        # output projection
+        x = self.proj(x)
+
+        # Reshape back: [(b*t), c, h, w] -> [b, c, t, h, w]
+        x = x.view(batch_size, time, channels, height, width)
+        x = x.permute(0, 2, 1, 3, 4)
+
+        return x + identity
+
+
+
+class QwenImageUpsample(nn.Upsample):
+    r"""
+    Perform upsampling while ensuring the output tensor has the same data type as the input.
+
+    Args:
+        x (torch.Tensor): Input tensor to be upsampled.
+
+    Returns:
+        torch.Tensor: Upsampled tensor with the same data type as the input.
+    """
+
+    def forward(self, x):
+        return super().forward(x.float()).type_as(x)
+
+
+
+class QwenImageResample(nn.Module):
+    r"""
+    A custom resampling module for 2D and 3D data.
+
+    Args:
+        dim (int): The number of input/output channels.
+        mode (str): The resampling mode. Must be one of:
+            - 'none': No resampling (identity operation).
+            - 'upsample2d': 2D upsampling with nearest-exact interpolation and convolution.
+            - 'upsample3d': 3D upsampling with nearest-exact interpolation, convolution, and causal 3D convolution.
+            - 'downsample2d': 2D downsampling with zero-padding and convolution.
+            - 'downsample3d': 3D downsampling with zero-padding, convolution, and causal 3D convolution.
+    """
+
+    def __init__(self, dim: int, mode: str) -> None:
+        super().__init__()
+        self.dim = dim
+        self.mode = mode
+
+        # layers
+        if mode == "upsample2d":
+            self.resample = nn.Sequential(
+                QwenImageUpsample(scale_factor=(2.0, 2.0), mode="nearest-exact"), nn.Conv2d(dim, dim // 2, 3, padding=1)
+            )
+        elif mode == "upsample3d":
+            self.resample = nn.Sequential(
+                QwenImageUpsample(scale_factor=(2.0, 2.0), mode="nearest-exact"), nn.Conv2d(dim, dim // 2, 3, padding=1)
+            )
+            self.time_conv = QwenImageCausalConv3d(dim, dim * 2, (3, 1, 1), padding=(1, 0, 0))
+
+        elif mode == "downsample2d":
+            self.resample = nn.Sequential(nn.ZeroPad2d((0, 1, 0, 1)), nn.Conv2d(dim, dim, 3, stride=(2, 2)))
+        elif mode == "downsample3d":
+            self.resample = nn.Sequential(nn.ZeroPad2d((0, 1, 0, 1)), nn.Conv2d(dim, dim, 3, stride=(2, 2)))
+            self.time_conv = QwenImageCausalConv3d(dim, dim, (3, 1, 1), stride=(2, 1, 1), padding=(0, 0, 0))
+
+        else:
+            self.resample = nn.Identity()
+
+    def forward(self, x, feat_cache=None, feat_idx=[0]):
+        b, c, t, h, w = x.size()
+        if self.mode == "upsample3d":
+            if feat_cache is not None:
+                idx = feat_idx[0]
+                if feat_cache[idx] is None:
+                    feat_cache[idx] = "Rep"
+                    feat_idx[0] += 1
+                else:
+                    cache_x = x[:, :, -CACHE_T:, :, :].clone()
+                    if cache_x.shape[2] < 2 and feat_cache[idx] is not None and feat_cache[idx] != "Rep":
+                        # cache last frame of last two chunk
+                        cache_x = torch.cat(
+                            [feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(cache_x.device), cache_x], dim=2
+                        )
+                    if cache_x.shape[2] < 2 and feat_cache[idx] is not None and feat_cache[idx] == "Rep":
+                        cache_x = torch.cat([torch.zeros_like(cache_x).to(cache_x.device), cache_x], dim=2)
+                    if feat_cache[idx] == "Rep":
+                        x = self.time_conv(x)
+                    else:
+                        x = self.time_conv(x, feat_cache[idx])
+                    feat_cache[idx] = cache_x
+                    feat_idx[0] += 1
+
+                    x = x.reshape(b, 2, c, t, h, w)
+                    x = torch.stack((x[:, 0, :, :, :, :], x[:, 1, :, :, :, :]), 3)
+                    x = x.reshape(b, c, t * 2, h, w)
+        t = x.shape[2]
+        x = x.permute(0, 2, 1, 3, 4).reshape(b * t, c, h, w)
+        x = self.resample(x)
+        x = x.view(b, t, x.size(1), x.size(2), x.size(3)).permute(0, 2, 1, 3, 4)
+
+        if self.mode == "downsample3d":
+            if feat_cache is not None:
+                idx = feat_idx[0]
+                if feat_cache[idx] is None:
+                    feat_cache[idx] = x.clone()
+                    feat_idx[0] += 1
+                else:
+                    cache_x = x[:, :, -1:, :, :].clone()
+                    x = self.time_conv(torch.cat([feat_cache[idx][:, :, -1:, :, :], x], 2))
+                    feat_cache[idx] = cache_x
+                    feat_idx[0] += 1
+        return x
+
+
+
+class QwenImageMidBlock(nn.Module):
+    """
+    Middle block for WanVAE encoder and decoder.
+
+    Args:
+        dim (int): Number of input/output channels.
+        dropout (float): Dropout rate.
+        non_linearity (str): Type of non-linearity to use.
+    """
+
+    def __init__(self, dim: int, dropout: float = 0.0, non_linearity: str = "silu", num_layers: int = 1):
+        super().__init__()
+        self.dim = dim
+
+        # Create the components
+        resnets = [QwenImageResidualBlock(dim, dim, dropout, non_linearity)]
+        attentions = []
+        for _ in range(num_layers):
+            attentions.append(QwenImageAttentionBlock(dim))
+            resnets.append(QwenImageResidualBlock(dim, dim, dropout, non_linearity))
+        self.attentions = nn.ModuleList(attentions)
+        self.resnets = nn.ModuleList(resnets)
+
+        self.gradient_checkpointing = False
+
+    def forward(self, x, feat_cache=None, feat_idx=[0]):
+        # First residual block
+        x = self.resnets[0](x, feat_cache, feat_idx)
+
+        # Process through attention and residual blocks
+        for attn, resnet in zip(self.attentions, self.resnets[1:]):
+            if attn is not None:
+                x = attn(x)
+
+            x = resnet(x, feat_cache, feat_idx)
+
+        return x
+
+
+
+class QwenImageEncoder3d(nn.Module):
+    r"""
+    A 3D encoder module.
+
+    Args:
+        dim (int): The base number of channels in the first layer.
+        z_dim (int): The dimensionality of the latent space.
+        dim_mult (list of int): Multipliers for the number of channels in each block.
+        num_res_blocks (int): Number of residual blocks in each block.
+        attn_scales (list of float): Scales at which to apply attention mechanisms.
+        temperal_downsample (list of bool): Whether to downsample temporally in each block.
+        dropout (float): Dropout rate for the dropout layers.
+        non_linearity (str): Type of non-linearity to use.
+    """
+
+    def __init__(
+        self,
+        dim=128,
+        z_dim=4,
+        dim_mult=[1, 2, 4, 4],
+        num_res_blocks=2,
+        attn_scales=[],
+        temperal_downsample=[True, True, False],
+        dropout=0.0,
+        non_linearity: str = "silu",
+    ):
+        super().__init__()
+        self.dim = dim
+        self.z_dim = z_dim
+        self.dim_mult = dim_mult
+        self.num_res_blocks = num_res_blocks
+        self.attn_scales = attn_scales
+        self.temperal_downsample = temperal_downsample
+        self.nonlinearity = torch.nn.SiLU()
+
+        # dimensions
+        dims = [dim * u for u in [1] + dim_mult]
+        scale = 1.0
+
+        # init block
+        self.conv_in = QwenImageCausalConv3d(3, dims[0], 3, padding=1)
+
+        # downsample blocks
+        self.down_blocks = torch.nn.ModuleList([])
+        for i, (in_dim, out_dim) in enumerate(zip(dims[:-1], dims[1:])):
+            # residual (+attention) blocks
+            for _ in range(num_res_blocks):
+                self.down_blocks.append(QwenImageResidualBlock(in_dim, out_dim, dropout))
+                if scale in attn_scales:
+                    self.down_blocks.append(QwenImageAttentionBlock(out_dim))
+                in_dim = out_dim
+
+            # downsample block
+            if i != len(dim_mult) - 1:
+                mode = "downsample3d" if temperal_downsample[i] else "downsample2d"
+                self.down_blocks.append(QwenImageResample(out_dim, mode=mode))
+                scale /= 2.0
+
+        # middle blocks
+        self.mid_block = QwenImageMidBlock(out_dim, dropout, non_linearity, num_layers=1)
+
+        # output blocks
+        self.norm_out = QwenImageRMS_norm(out_dim, images=False)
+        self.conv_out = QwenImageCausalConv3d(out_dim, z_dim, 3, padding=1)
+
+        self.gradient_checkpointing = False
+
+    def forward(self, x, feat_cache=None, feat_idx=[0]):
+        if feat_cache is not None:
+            idx = feat_idx[0]
+            cache_x = x[:, :, -CACHE_T:, :, :].clone()
+            if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
+                # cache last frame of last two chunk
+                cache_x = torch.cat([feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(cache_x.device), cache_x], dim=2)
+            x = self.conv_in(x, feat_cache[idx])
+            feat_cache[idx] = cache_x
+            feat_idx[0] += 1
+        else:
+            x = self.conv_in(x)
+
+        ## downsamples
+        for layer in self.down_blocks:
+            if feat_cache is not None:
+                x = layer(x, feat_cache, feat_idx)
+            else:
+                x = layer(x)
+
+        ## middle
+        x = self.mid_block(x, feat_cache, feat_idx)
+
+        ## head
+        x = self.norm_out(x)
+        x = self.nonlinearity(x)
+        if feat_cache is not None:
+            idx = feat_idx[0]
+            cache_x = x[:, :, -CACHE_T:, :, :].clone()
+            if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
+                # cache last frame of last two chunk
+                cache_x = torch.cat([feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(cache_x.device), cache_x], dim=2)
+            x = self.conv_out(x, feat_cache[idx])
+            feat_cache[idx] = cache_x
+            feat_idx[0] += 1
+        else:
+            x = self.conv_out(x)
+        return x
+
+
+
+class QwenImageUpBlock(nn.Module):
+    """
+    A block that handles upsampling for the WanVAE decoder.
+
+    Args:
+        in_dim (int): Input dimension
+        out_dim (int): Output dimension
+        num_res_blocks (int): Number of residual blocks
+        dropout (float): Dropout rate
+        upsample_mode (str, optional): Mode for upsampling ('upsample2d' or 'upsample3d')
+        non_linearity (str): Type of non-linearity to use
+    """
+
+    def __init__(
+        self,
+        in_dim: int,
+        out_dim: int,
+        num_res_blocks: int,
+        dropout: float = 0.0,
+        upsample_mode: Optional[str] = None,
+        non_linearity: str = "silu",
+    ):
+        super().__init__()
+        self.in_dim = in_dim
+        self.out_dim = out_dim
+
+        # Create layers list
+        resnets = []
+        # Add residual blocks and attention if needed
+        current_dim = in_dim
+        for _ in range(num_res_blocks + 1):
+            resnets.append(QwenImageResidualBlock(current_dim, out_dim, dropout, non_linearity))
+            current_dim = out_dim
+
+        self.resnets = nn.ModuleList(resnets)
+
+        # Add upsampling layer if needed
+        self.upsamplers = None
+        if upsample_mode is not None:
+            self.upsamplers = nn.ModuleList([QwenImageResample(out_dim, mode=upsample_mode)])
+
+        self.gradient_checkpointing = False
+
+    def forward(self, x, feat_cache=None, feat_idx=[0]):
+        """
+        Forward pass through the upsampling block.
+
+        Args:
+            x (torch.Tensor): Input tensor
+            feat_cache (list, optional): Feature cache for causal convolutions
+            feat_idx (list, optional): Feature index for cache management
+
+        Returns:
+            torch.Tensor: Output tensor
+        """
+        for resnet in self.resnets:
+            if feat_cache is not None:
+                x = resnet(x, feat_cache, feat_idx)
+            else:
+                x = resnet(x)
+
+        if self.upsamplers is not None:
+            if feat_cache is not None:
+                x = self.upsamplers[0](x, feat_cache, feat_idx)
+            else:
+                x = self.upsamplers[0](x)
+        return x
+
+
+
+class QwenImageDecoder3d(nn.Module):
+    r"""
+    A 3D decoder module.
+
+    Args:
+        dim (int): The base number of channels in the first layer.
+        z_dim (int): The dimensionality of the latent space.
+        dim_mult (list of int): Multipliers for the number of channels in each block.
+        num_res_blocks (int): Number of residual blocks in each block.
+        attn_scales (list of float): Scales at which to apply attention mechanisms.
+        temperal_upsample (list of bool): Whether to upsample temporally in each block.
+        dropout (float): Dropout rate for the dropout layers.
+        non_linearity (str): Type of non-linearity to use.
+    """
+
+    def __init__(
+        self,
+        dim=128,
+        z_dim=4,
+        dim_mult=[1, 2, 4, 4],
+        num_res_blocks=2,
+        attn_scales=[],
+        temperal_upsample=[False, True, True],
+        dropout=0.0,
+        non_linearity: str = "silu",
+    ):
+        super().__init__()
+        self.dim = dim
+        self.z_dim = z_dim
+        self.dim_mult = dim_mult
+        self.num_res_blocks = num_res_blocks
+        self.attn_scales = attn_scales
+        self.temperal_upsample = temperal_upsample
+
+        self.nonlinearity = torch.nn.SiLU()
+
+        # dimensions
+        dims = [dim * u for u in [dim_mult[-1]] + dim_mult[::-1]]
+        scale = 1.0 / 2 ** (len(dim_mult) - 2)
+
+        # init block
+        self.conv_in = QwenImageCausalConv3d(z_dim, dims[0], 3, padding=1)
+
+        # middle blocks
+        self.mid_block = QwenImageMidBlock(dims[0], dropout, non_linearity, num_layers=1)
+
+        # upsample blocks
+        self.up_blocks = nn.ModuleList([])
+        for i, (in_dim, out_dim) in enumerate(zip(dims[:-1], dims[1:])):
+            # residual (+attention) blocks
+            if i > 0:
+                in_dim = in_dim // 2
+
+            # Determine if we need upsampling
+            upsample_mode = None
+            if i != len(dim_mult) - 1:
+                upsample_mode = "upsample3d" if temperal_upsample[i] else "upsample2d"
+
+            # Create and add the upsampling block
+            up_block = QwenImageUpBlock(
+                in_dim=in_dim,
+                out_dim=out_dim,
+                num_res_blocks=num_res_blocks,
+                dropout=dropout,
+                upsample_mode=upsample_mode,
+                non_linearity=non_linearity,
+            )
+            self.up_blocks.append(up_block)
+
+            # Update scale for next iteration
+            if upsample_mode is not None:
+                scale *= 2.0
+
+        # output blocks
+        self.norm_out = QwenImageRMS_norm(out_dim, images=False)
+        self.conv_out = QwenImageCausalConv3d(out_dim, 3, 3, padding=1)
+
+        self.gradient_checkpointing = False
+
+    def forward(self, x, feat_cache=None, feat_idx=[0]):
+        ## conv1
+        if feat_cache is not None:
+            idx = feat_idx[0]
+            cache_x = x[:, :, -CACHE_T:, :, :].clone()
+            if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
+                # cache last frame of last two chunk
+                cache_x = torch.cat([feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(cache_x.device), cache_x], dim=2)
+            x = self.conv_in(x, feat_cache[idx])
+            feat_cache[idx] = cache_x
+            feat_idx[0] += 1
+        else:
+            x = self.conv_in(x)
+
+        ## middle
+        x = self.mid_block(x, feat_cache, feat_idx)
+
+        ## upsamples
+        for up_block in self.up_blocks:
+            x = up_block(x, feat_cache, feat_idx)
+
+        ## head
+        x = self.norm_out(x)
+        x = self.nonlinearity(x)
+        if feat_cache is not None:
+            idx = feat_idx[0]
+            cache_x = x[:, :, -CACHE_T:, :, :].clone()
+            if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
+                # cache last frame of last two chunk
+                cache_x = torch.cat([feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(cache_x.device), cache_x], dim=2)
+            x = self.conv_out(x, feat_cache[idx])
+            feat_cache[idx] = cache_x
+            feat_idx[0] += 1
+        else:
+            x = self.conv_out(x)
+        return x
+
+
+
+class QwenImageVAE(torch.nn.Module):
+    def __init__(
+        self,
+        base_dim: int = 96,
+        z_dim: int = 16,
+        dim_mult: Tuple[int] = [1, 2, 4, 4],
+        num_res_blocks: int = 2,
+        attn_scales: List[float] = [],
+        temperal_downsample: List[bool] = [False, True, True],
+        dropout: float = 0.0,
+    ) -> None:
+        super().__init__()
+
+        self.z_dim = z_dim
+        self.temperal_downsample = temperal_downsample
+        self.temperal_upsample = temperal_downsample[::-1]
+
+        self.encoder = QwenImageEncoder3d(
+            base_dim, z_dim * 2, dim_mult, num_res_blocks, attn_scales, self.temperal_downsample, dropout
+        )
+        self.quant_conv = QwenImageCausalConv3d(z_dim * 2, z_dim * 2, 1)
+        self.post_quant_conv = QwenImageCausalConv3d(z_dim, z_dim, 1)
+
+        self.decoder = QwenImageDecoder3d(
+            base_dim, z_dim, dim_mult, num_res_blocks, attn_scales, self.temperal_upsample, dropout
+        )
+
+        mean = [
+            -0.7571,
+            -0.7089,
+            -0.9113,
+            0.1075,
+            -0.1745,
+            0.9653,
+            -0.1517,
+            1.5508,
+            0.4134,
+            -0.0715,
+            0.5517,
+            -0.3632,
+            -0.1922,
+            -0.9497,
+            0.2503,
+            -0.2921,
+        ]
+        std = [
+            2.8184,
+            1.4541,
+            2.3275,
+            2.6558,
+            1.2196,
+            1.7708,
+            2.6052,
+            2.0743,
+            3.2687,
+            2.1526,
+            2.8652,
+            1.5579,
+            1.6382,
+            1.1253,
+            2.8251,
+            1.9160,
+        ]
+        self.mean = torch.tensor(mean).view(1, 16, 1, 1, 1)
+        self.std = 1 / torch.tensor(std).view(1, 16, 1, 1, 1)
+
+    def encode(self, x, **kwargs):
+        x = x.unsqueeze(2)
+        x = self.encoder(x)
+        x = self.quant_conv(x)
+        x = x[:, :16]
+        mean, std = self.mean.to(dtype=x.dtype, device=x.device), self.std.to(dtype=x.dtype, device=x.device)
+        x = (x - mean) * std
+        x = x.squeeze(2)
+        return x
+    
+    def decode(self, x, **kwargs):
+        x = x.unsqueeze(2)
+        mean, std = self.mean.to(dtype=x.dtype, device=x.device), self.std.to(dtype=x.dtype, device=x.device)
+        x = x / std + mean
+        x = self.post_quant_conv(x)
+        x = self.decoder(x)
+        x = x.squeeze(2)
+        return x
+    
+    @staticmethod
+    def state_dict_converter():
+        return QwenImageVAEStateDictConverter()
+
+
+
+class QwenImageVAEStateDictConverter():
+    def __init__(self):
+        pass
+
+    def from_diffusers(self, state_dict):
+        return state_dict
diff --git a/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/qwenvl.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/qwenvl.py
new file mode 100644
index 0000000000000000000000000000000000000000..9677488e1a8e7b5b46ed6c84b74bff57bf65bd1e
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/qwenvl.py
@@ -0,0 +1,168 @@
+import torch
+
+
+class Qwen25VL_7b_Embedder(torch.nn.Module):
+    def __init__(self, model_path, max_length=640, dtype=torch.bfloat16, device="cuda"):
+        super(Qwen25VL_7b_Embedder, self).__init__()
+        self.max_length = max_length
+        self.dtype = dtype
+        self.device = device
+        
+        from transformers import AutoProcessor, Qwen2_5_VLForConditionalGeneration
+
+        self.model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
+            model_path,
+            torch_dtype=dtype,
+        ).to(torch.cuda.current_device())
+
+        self.model.requires_grad_(False)
+        self.processor = AutoProcessor.from_pretrained(
+            model_path, min_pixels=256 * 28 * 28, max_pixels=324 * 28 * 28
+        )
+        
+        Qwen25VL_7b_PREFIX = '''Given a user prompt, generate an "Enhanced prompt" that provides detailed visual descriptions suitable for image generation. Evaluate the level of detail in the user prompt:
+- If the prompt is simple, focus on adding specifics about colors, shapes, sizes, textures, and spatial relationships to create vivid and concrete scenes.
+- If the prompt is already detailed, refine and enhance the existing details slightly without overcomplicating.\n
+Here are examples of how to transform or refine prompts:
+- User Prompt: A cat sleeping -> Enhanced: A small, fluffy white cat curled up in a round shape, sleeping peacefully on a warm sunny windowsill, surrounded by pots of blooming red flowers.
+- User Prompt: A busy city street -> Enhanced: A bustling city street scene at dusk, featuring glowing street lamps, a diverse crowd of people in colorful clothing, and a double-decker bus passing by towering glass skyscrapers.\n
+Please generate only the enhanced description for the prompt below and avoid including any additional commentary or evaluations:
+User Prompt:'''
+
+        self.prefix = Qwen25VL_7b_PREFIX
+        
+    @staticmethod
+    def from_pretrained(path, torch_dtype=torch.bfloat16, device="cuda"):
+        return Qwen25VL_7b_Embedder(path, dtype=torch_dtype, device=device)
+
+    def forward(self, caption, ref_images):
+        text_list = caption
+        embs = torch.zeros(
+            len(text_list),
+            self.max_length,
+            self.model.config.hidden_size,
+            dtype=torch.bfloat16,
+            device=torch.cuda.current_device(),
+        )
+        hidden_states = torch.zeros(
+            len(text_list),
+            self.max_length,
+            self.model.config.hidden_size,
+            dtype=torch.bfloat16,
+            device=torch.cuda.current_device(),
+        )
+        masks = torch.zeros(
+            len(text_list),
+            self.max_length,
+            dtype=torch.long,
+            device=torch.cuda.current_device(),
+        )
+        input_ids_list = []
+        attention_mask_list = []
+        emb_list = []
+
+        def split_string(s):
+            s = s.replace("“", '"').replace("”", '"').replace("'", '''"''')  # use english quotes
+            result = []
+            in_quotes = False
+            temp = ""
+
+            for idx,char in enumerate(s):
+                if char == '"' and idx>155:
+                    temp += char
+                    if not in_quotes:
+                        result.append(temp)
+                        temp = ""
+
+                    in_quotes = not in_quotes
+                    continue
+                if in_quotes:
+                    if char.isspace():
+                        pass  # have space token
+
+                    result.append("“" + char + "”")
+                else:
+                    temp += char
+
+            if temp:
+                result.append(temp)
+
+            return result
+
+        for idx, (txt, imgs) in enumerate(zip(text_list, ref_images)):
+
+            messages = [{"role": "user", "content": []}]
+
+            messages[0]["content"].append({"type": "text", "text": f"{self.prefix}"})
+
+            messages[0]["content"].append({"type": "image", "image": imgs})
+
+            # 再添加 text
+            messages[0]["content"].append({"type": "text", "text": f"{txt}"})
+
+            # Preparation for inference
+            text = self.processor.apply_chat_template(
+                messages, tokenize=False, add_generation_prompt=True, add_vision_id=True
+            )
+
+            image_inputs = [imgs]
+
+            inputs = self.processor(
+                text=[text],
+                images=image_inputs,
+                padding=True,
+                return_tensors="pt",
+            )
+
+            old_inputs_ids = inputs.input_ids
+            text_split_list = split_string(text)
+
+            token_list = []
+            for text_each in text_split_list:
+                txt_inputs = self.processor(
+                    text=text_each,
+                    images=None,
+                    videos=None,
+                    padding=True,
+                    return_tensors="pt",
+                )
+                token_each = txt_inputs.input_ids
+                if token_each[0][0] == 2073 and token_each[0][-1] == 854:
+                    token_each = token_each[:, 1:-1]
+                    token_list.append(token_each)
+                else:
+                    token_list.append(token_each)
+
+            new_txt_ids = torch.cat(token_list, dim=1).to("cuda")
+
+            new_txt_ids = new_txt_ids.to(old_inputs_ids.device)
+
+            idx1 = (old_inputs_ids == 151653).nonzero(as_tuple=True)[1][0]
+            idx2 = (new_txt_ids == 151653).nonzero(as_tuple=True)[1][0]
+            inputs.input_ids = (
+                torch.cat([old_inputs_ids[0, :idx1], new_txt_ids[0, idx2:]], dim=0)
+                .unsqueeze(0)
+                .to("cuda")
+            )
+            inputs.attention_mask = (inputs.input_ids > 0).long().to("cuda")
+            outputs = self.model(
+                input_ids=inputs.input_ids,
+                attention_mask=inputs.attention_mask,
+                pixel_values=inputs.pixel_values.to("cuda"),
+                image_grid_thw=inputs.image_grid_thw.to("cuda"),
+                output_hidden_states=True,
+            )
+
+            emb = outputs["hidden_states"][-1]
+
+            embs[idx, : min(self.max_length, emb.shape[1] - 217)] = emb[0, 217:][
+                : self.max_length
+            ]
+
+            masks[idx, : min(self.max_length, emb.shape[1] - 217)] = torch.ones(
+                (min(self.max_length, emb.shape[1] - 217)),
+                dtype=torch.long,
+                device=torch.cuda.current_device(),
+            )
+
+        return embs, masks
\ No newline at end of file
diff --git a/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/sd3_dit.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/sd3_dit.py
new file mode 100644
index 0000000000000000000000000000000000000000..eae0afa38d8f78748bf58a35846ea23995f45475
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/sd3_dit.py
@@ -0,0 +1,567 @@
+import torch
+from einops import rearrange
+from .svd_unet import TemporalTimesteps
+from .tiler import TileWorker
+
+
+
+class RMSNorm(torch.nn.Module):
+    def __init__(self, dim, eps, elementwise_affine=True):
+        super().__init__()
+        self.eps = eps
+        if elementwise_affine:
+            self.weight = torch.nn.Parameter(torch.ones((dim,)))
+        else:
+            self.weight = None
+
+    def forward(self, hidden_states):
+        input_dtype = hidden_states.dtype
+        variance = hidden_states.to(torch.float32).square().mean(-1, keepdim=True)
+        hidden_states = hidden_states * torch.rsqrt(variance + self.eps)
+        hidden_states = hidden_states.to(input_dtype)
+        if self.weight is not None:
+            hidden_states = hidden_states * self.weight
+        return hidden_states
+
+
+
+class PatchEmbed(torch.nn.Module):
+    def __init__(self, patch_size=2, in_channels=16, embed_dim=1536, pos_embed_max_size=192):
+        super().__init__()
+        self.pos_embed_max_size = pos_embed_max_size
+        self.patch_size = patch_size
+
+        self.proj = torch.nn.Conv2d(in_channels, embed_dim, kernel_size=(patch_size, patch_size), stride=patch_size)
+        self.pos_embed = torch.nn.Parameter(torch.zeros(1, self.pos_embed_max_size, self.pos_embed_max_size, embed_dim))
+
+    def cropped_pos_embed(self, height, width):
+        height = height // self.patch_size
+        width = width // self.patch_size
+        top = (self.pos_embed_max_size - height) // 2
+        left = (self.pos_embed_max_size - width) // 2
+        spatial_pos_embed = self.pos_embed[:, top : top + height, left : left + width, :].flatten(1, 2)
+        return spatial_pos_embed
+
+    def forward(self, latent):
+        height, width = latent.shape[-2:]
+        latent = self.proj(latent)
+        latent = latent.flatten(2).transpose(1, 2)
+        pos_embed = self.cropped_pos_embed(height, width)
+        return latent + pos_embed
+
+
+class DiffusersCompatibleTimestepProj(torch.nn.Module):
+    def __init__(self, dim_in, dim_out):
+        super().__init__()
+        self.linear_1 = torch.nn.Linear(dim_in, dim_out)
+        self.act = torch.nn.SiLU()
+        self.linear_2 = torch.nn.Linear(dim_out, dim_out)
+
+    def forward(self, x):
+        x = self.linear_1(x)
+        x = self.act(x)
+        x = self.linear_2(x)
+        return x
+
+
+class TimestepEmbeddings(torch.nn.Module):
+    def __init__(self, dim_in, dim_out, computation_device=None, diffusers_compatible_format=False, scale=1, align_dtype_to_timestep=False):
+        super().__init__()
+        self.time_proj = TemporalTimesteps(num_channels=dim_in, flip_sin_to_cos=True, downscale_freq_shift=0, computation_device=computation_device, scale=scale, align_dtype_to_timestep=align_dtype_to_timestep)
+        if diffusers_compatible_format:
+            self.timestep_embedder = DiffusersCompatibleTimestepProj(dim_in, dim_out)
+        else:
+            self.timestep_embedder = torch.nn.Sequential(
+                torch.nn.Linear(dim_in, dim_out), torch.nn.SiLU(), torch.nn.Linear(dim_out, dim_out)
+            )
+
+    def forward(self, timestep, dtype):
+        time_emb = self.time_proj(timestep).to(dtype)
+        time_emb = self.timestep_embedder(time_emb)
+        return time_emb
+
+
+
+class AdaLayerNorm(torch.nn.Module):
+    def __init__(self, dim, single=False, dual=False):
+        super().__init__()
+        self.single = single
+        self.dual = dual
+        self.linear = torch.nn.Linear(dim, dim * [[6, 2][single], 9][dual])
+        self.norm = torch.nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
+
+    def forward(self, x, emb):
+        emb = self.linear(torch.nn.functional.silu(emb))
+        if self.single:
+            scale, shift = emb.unsqueeze(1).chunk(2, dim=2)
+            x = self.norm(x) * (1 + scale) + shift
+            return x
+        elif self.dual:
+            shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp, shift_msa2, scale_msa2, gate_msa2 = emb.unsqueeze(1).chunk(9, dim=2)
+            norm_x = self.norm(x)
+            x = norm_x * (1 + scale_msa) + shift_msa
+            norm_x2 = norm_x * (1 + scale_msa2) + shift_msa2
+            return x, gate_msa, shift_mlp, scale_mlp, gate_mlp, norm_x2, gate_msa2
+        else:
+            shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = emb.unsqueeze(1).chunk(6, dim=2)
+            x = self.norm(x) * (1 + scale_msa) + shift_msa
+            return x, gate_msa, shift_mlp, scale_mlp, gate_mlp
+
+
+
+class JointAttention(torch.nn.Module):
+    def __init__(self, dim_a, dim_b, num_heads, head_dim, only_out_a=False, use_rms_norm=False):
+        super().__init__()
+        self.num_heads = num_heads
+        self.head_dim = head_dim
+        self.only_out_a = only_out_a
+
+        self.a_to_qkv = torch.nn.Linear(dim_a, dim_a * 3)
+        self.b_to_qkv = torch.nn.Linear(dim_b, dim_b * 3)
+
+        self.a_to_out = torch.nn.Linear(dim_a, dim_a)
+        if not only_out_a:
+            self.b_to_out = torch.nn.Linear(dim_b, dim_b)
+
+        if use_rms_norm:
+            self.norm_q_a = RMSNorm(head_dim, eps=1e-6)
+            self.norm_k_a = RMSNorm(head_dim, eps=1e-6)
+            self.norm_q_b = RMSNorm(head_dim, eps=1e-6)
+            self.norm_k_b = RMSNorm(head_dim, eps=1e-6)
+        else:
+            self.norm_q_a = None
+            self.norm_k_a = None
+            self.norm_q_b = None
+            self.norm_k_b = None
+
+
+    def process_qkv(self, hidden_states, to_qkv, norm_q, norm_k):
+        batch_size = hidden_states.shape[0]
+        qkv = to_qkv(hidden_states)
+        qkv = qkv.view(batch_size, -1, 3 * self.num_heads, self.head_dim).transpose(1, 2)
+        q, k, v = qkv.chunk(3, dim=1)
+        if norm_q is not None:
+            q = norm_q(q)
+        if norm_k is not None:
+            k = norm_k(k)
+        return q, k, v
+
+
+    def forward(self, hidden_states_a, hidden_states_b):
+        batch_size = hidden_states_a.shape[0]
+
+        qa, ka, va = self.process_qkv(hidden_states_a, self.a_to_qkv, self.norm_q_a, self.norm_k_a)
+        qb, kb, vb = self.process_qkv(hidden_states_b, self.b_to_qkv, self.norm_q_b, self.norm_k_b)
+        q = torch.concat([qa, qb], dim=2)
+        k = torch.concat([ka, kb], dim=2)
+        v = torch.concat([va, vb], dim=2)
+
+        hidden_states = torch.nn.functional.scaled_dot_product_attention(q, k, v)
+        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, self.num_heads * self.head_dim)
+        hidden_states = hidden_states.to(q.dtype)
+        hidden_states_a, hidden_states_b = hidden_states[:, :hidden_states_a.shape[1]], hidden_states[:, hidden_states_a.shape[1]:]
+        hidden_states_a = self.a_to_out(hidden_states_a)
+        if self.only_out_a:
+            return hidden_states_a
+        else:
+            hidden_states_b = self.b_to_out(hidden_states_b)
+            return hidden_states_a, hidden_states_b
+        
+
+
+class SingleAttention(torch.nn.Module):
+    def __init__(self, dim_a, num_heads, head_dim, use_rms_norm=False):
+        super().__init__()
+        self.num_heads = num_heads
+        self.head_dim = head_dim
+
+        self.a_to_qkv = torch.nn.Linear(dim_a, dim_a * 3)
+        self.a_to_out = torch.nn.Linear(dim_a, dim_a)
+
+        if use_rms_norm:
+            self.norm_q_a = RMSNorm(head_dim, eps=1e-6)
+            self.norm_k_a = RMSNorm(head_dim, eps=1e-6)
+        else:
+            self.norm_q_a = None
+            self.norm_k_a = None
+
+
+    def process_qkv(self, hidden_states, to_qkv, norm_q, norm_k):
+        batch_size = hidden_states.shape[0]
+        qkv = to_qkv(hidden_states)
+        qkv = qkv.view(batch_size, -1, 3 * self.num_heads, self.head_dim).transpose(1, 2)
+        q, k, v = qkv.chunk(3, dim=1)
+        if norm_q is not None:
+            q = norm_q(q)
+        if norm_k is not None:
+            k = norm_k(k)
+        return q, k, v
+
+
+    def forward(self, hidden_states_a):
+        batch_size = hidden_states_a.shape[0]
+        q, k, v = self.process_qkv(hidden_states_a, self.a_to_qkv, self.norm_q_a, self.norm_k_a)
+
+        hidden_states = torch.nn.functional.scaled_dot_product_attention(q, k, v)
+        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, self.num_heads * self.head_dim)
+        hidden_states = hidden_states.to(q.dtype)
+        hidden_states = self.a_to_out(hidden_states)
+        return hidden_states
+        
+
+
+class DualTransformerBlock(torch.nn.Module):
+    def __init__(self, dim, num_attention_heads, use_rms_norm=False):
+        super().__init__()
+        self.norm1_a = AdaLayerNorm(dim, dual=True)
+        self.norm1_b = AdaLayerNorm(dim)
+
+        self.attn = JointAttention(dim, dim, num_attention_heads, dim // num_attention_heads, use_rms_norm=use_rms_norm)
+        self.attn2 = JointAttention(dim, dim, num_attention_heads, dim // num_attention_heads, use_rms_norm=use_rms_norm)
+
+        self.norm2_a = torch.nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
+        self.ff_a = torch.nn.Sequential(
+            torch.nn.Linear(dim, dim*4),
+            torch.nn.GELU(approximate="tanh"),
+            torch.nn.Linear(dim*4, dim)
+        )
+
+        self.norm2_b = torch.nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
+        self.ff_b = torch.nn.Sequential(
+            torch.nn.Linear(dim, dim*4),
+            torch.nn.GELU(approximate="tanh"),
+            torch.nn.Linear(dim*4, dim)
+        )
+
+
+    def forward(self, hidden_states_a, hidden_states_b, temb):
+        norm_hidden_states_a, gate_msa_a, shift_mlp_a, scale_mlp_a, gate_mlp_a, norm_hidden_states_a_2, gate_msa_a_2 = self.norm1_a(hidden_states_a, emb=temb)
+        norm_hidden_states_b, gate_msa_b, shift_mlp_b, scale_mlp_b, gate_mlp_b = self.norm1_b(hidden_states_b, emb=temb)
+
+        # Attention
+        attn_output_a, attn_output_b = self.attn(norm_hidden_states_a, norm_hidden_states_b)
+
+        # Part A
+        hidden_states_a = hidden_states_a + gate_msa_a * attn_output_a
+        hidden_states_a = hidden_states_a + gate_msa_a_2 * self.attn2(norm_hidden_states_a_2)
+        norm_hidden_states_a = self.norm2_a(hidden_states_a) * (1 + scale_mlp_a) + shift_mlp_a
+        hidden_states_a = hidden_states_a + gate_mlp_a * self.ff_a(norm_hidden_states_a)
+
+        # Part B
+        hidden_states_b = hidden_states_b + gate_msa_b * attn_output_b
+        norm_hidden_states_b = self.norm2_b(hidden_states_b) * (1 + scale_mlp_b) + shift_mlp_b
+        hidden_states_b = hidden_states_b + gate_mlp_b * self.ff_b(norm_hidden_states_b)
+
+        return hidden_states_a, hidden_states_b
+
+
+
+class JointTransformerBlock(torch.nn.Module):
+    def __init__(self, dim, num_attention_heads, use_rms_norm=False, dual=False):
+        super().__init__()
+        self.norm1_a = AdaLayerNorm(dim, dual=dual)
+        self.norm1_b = AdaLayerNorm(dim)
+
+        self.attn = JointAttention(dim, dim, num_attention_heads, dim // num_attention_heads, use_rms_norm=use_rms_norm)
+        if dual:
+            self.attn2 = SingleAttention(dim, num_attention_heads, dim // num_attention_heads, use_rms_norm=use_rms_norm)
+
+        self.norm2_a = torch.nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
+        self.ff_a = torch.nn.Sequential(
+            torch.nn.Linear(dim, dim*4),
+            torch.nn.GELU(approximate="tanh"),
+            torch.nn.Linear(dim*4, dim)
+        )
+
+        self.norm2_b = torch.nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
+        self.ff_b = torch.nn.Sequential(
+            torch.nn.Linear(dim, dim*4),
+            torch.nn.GELU(approximate="tanh"),
+            torch.nn.Linear(dim*4, dim)
+        )
+
+
+    def forward(self, hidden_states_a, hidden_states_b, temb):
+        if self.norm1_a.dual:
+            norm_hidden_states_a, gate_msa_a, shift_mlp_a, scale_mlp_a, gate_mlp_a, norm_hidden_states_a_2, gate_msa_a_2 = self.norm1_a(hidden_states_a, emb=temb)
+        else:
+            norm_hidden_states_a, gate_msa_a, shift_mlp_a, scale_mlp_a, gate_mlp_a = self.norm1_a(hidden_states_a, emb=temb)
+        norm_hidden_states_b, gate_msa_b, shift_mlp_b, scale_mlp_b, gate_mlp_b = self.norm1_b(hidden_states_b, emb=temb)
+
+        # Attention
+        attn_output_a, attn_output_b = self.attn(norm_hidden_states_a, norm_hidden_states_b)
+
+        # Part A
+        hidden_states_a = hidden_states_a + gate_msa_a * attn_output_a
+        if self.norm1_a.dual:
+            hidden_states_a = hidden_states_a + gate_msa_a_2 * self.attn2(norm_hidden_states_a_2)
+        norm_hidden_states_a = self.norm2_a(hidden_states_a) * (1 + scale_mlp_a) + shift_mlp_a
+        hidden_states_a = hidden_states_a + gate_mlp_a * self.ff_a(norm_hidden_states_a)
+
+        # Part B
+        hidden_states_b = hidden_states_b + gate_msa_b * attn_output_b
+        norm_hidden_states_b = self.norm2_b(hidden_states_b) * (1 + scale_mlp_b) + shift_mlp_b
+        hidden_states_b = hidden_states_b + gate_mlp_b * self.ff_b(norm_hidden_states_b)
+
+        return hidden_states_a, hidden_states_b
+
+
+
+class JointTransformerFinalBlock(torch.nn.Module):
+    def __init__(self, dim, num_attention_heads, use_rms_norm=False):
+        super().__init__()
+        self.norm1_a = AdaLayerNorm(dim)
+        self.norm1_b = AdaLayerNorm(dim, single=True)
+
+        self.attn = JointAttention(dim, dim, num_attention_heads, dim // num_attention_heads, only_out_a=True, use_rms_norm=use_rms_norm)
+
+        self.norm2_a = torch.nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
+        self.ff_a = torch.nn.Sequential(
+            torch.nn.Linear(dim, dim*4),
+            torch.nn.GELU(approximate="tanh"),
+            torch.nn.Linear(dim*4, dim)
+        )
+
+
+    def forward(self, hidden_states_a, hidden_states_b, temb):
+        norm_hidden_states_a, gate_msa_a, shift_mlp_a, scale_mlp_a, gate_mlp_a = self.norm1_a(hidden_states_a, emb=temb)
+        norm_hidden_states_b = self.norm1_b(hidden_states_b, emb=temb)
+
+        # Attention
+        attn_output_a = self.attn(norm_hidden_states_a, norm_hidden_states_b)
+
+        # Part A
+        hidden_states_a = hidden_states_a + gate_msa_a * attn_output_a
+        norm_hidden_states_a = self.norm2_a(hidden_states_a) * (1 + scale_mlp_a) + shift_mlp_a
+        hidden_states_a = hidden_states_a + gate_mlp_a * self.ff_a(norm_hidden_states_a)
+
+        return hidden_states_a, hidden_states_b
+
+
+
+class SD3DiT(torch.nn.Module):
+    def __init__(self, embed_dim=1536, num_layers=24, use_rms_norm=False, num_dual_blocks=0, pos_embed_max_size=192):
+        super().__init__()
+        self.pos_embedder = PatchEmbed(patch_size=2, in_channels=16, embed_dim=embed_dim, pos_embed_max_size=pos_embed_max_size)
+        self.time_embedder = TimestepEmbeddings(256, embed_dim)
+        self.pooled_text_embedder = torch.nn.Sequential(torch.nn.Linear(2048, embed_dim), torch.nn.SiLU(), torch.nn.Linear(embed_dim, embed_dim))
+        self.context_embedder = torch.nn.Linear(4096, embed_dim)
+        self.blocks = torch.nn.ModuleList([JointTransformerBlock(embed_dim, embed_dim//64, use_rms_norm=use_rms_norm, dual=True) for _ in range(num_dual_blocks)]
+                                          + [JointTransformerBlock(embed_dim, embed_dim//64, use_rms_norm=use_rms_norm) for _ in range(num_layers-1-num_dual_blocks)]
+                                          + [JointTransformerFinalBlock(embed_dim, embed_dim//64, use_rms_norm=use_rms_norm)])
+        self.norm_out = AdaLayerNorm(embed_dim, single=True)
+        self.proj_out = torch.nn.Linear(embed_dim, 64)
+
+    def tiled_forward(self, hidden_states, timestep, prompt_emb, pooled_prompt_emb, tile_size=128, tile_stride=64):
+        # Due to the global positional embedding, we cannot implement layer-wise tiled forward.
+        hidden_states = TileWorker().tiled_forward(
+            lambda x: self.forward(x, timestep, prompt_emb, pooled_prompt_emb),
+            hidden_states,
+            tile_size,
+            tile_stride,
+            tile_device=hidden_states.device,
+            tile_dtype=hidden_states.dtype
+        )
+        return hidden_states
+
+    def forward(self, hidden_states, timestep, prompt_emb, pooled_prompt_emb, tiled=False, tile_size=128, tile_stride=64, use_gradient_checkpointing=False):
+        if tiled:
+            return self.tiled_forward(hidden_states, timestep, prompt_emb, pooled_prompt_emb, tile_size, tile_stride)
+        conditioning = self.time_embedder(timestep, hidden_states.dtype) + self.pooled_text_embedder(pooled_prompt_emb)
+        prompt_emb = self.context_embedder(prompt_emb)
+
+        height, width = hidden_states.shape[-2:]
+        hidden_states = self.pos_embedder(hidden_states)
+
+        def create_custom_forward(module):
+            def custom_forward(*inputs):
+                return module(*inputs)
+            return custom_forward
+        
+        for block in self.blocks:
+            if self.training and use_gradient_checkpointing:
+                hidden_states, prompt_emb = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(block),
+                    hidden_states, prompt_emb, conditioning,
+                    use_reentrant=False,
+                )
+            else:
+                hidden_states, prompt_emb = block(hidden_states, prompt_emb, conditioning)
+        
+        hidden_states = self.norm_out(hidden_states, conditioning)
+        hidden_states = self.proj_out(hidden_states)
+        hidden_states = rearrange(hidden_states, "B (H W) (P Q C) -> B C (H P) (W Q)", P=2, Q=2, H=height//2, W=width//2)
+        return hidden_states
+        
+    @staticmethod
+    def state_dict_converter():
+        return SD3DiTStateDictConverter()
+
+
+
+class SD3DiTStateDictConverter:
+    def __init__(self):
+        pass
+
+    def infer_architecture(self, state_dict):
+        embed_dim = state_dict["blocks.0.ff_a.0.weight"].shape[1]
+        num_layers = 100
+        while num_layers > 0 and f"blocks.{num_layers-1}.ff_a.0.bias" not in state_dict:
+            num_layers -= 1
+        use_rms_norm = "blocks.0.attn.norm_q_a.weight" in state_dict
+        num_dual_blocks = 0
+        while f"blocks.{num_dual_blocks}.attn2.a_to_out.bias" in state_dict:
+            num_dual_blocks += 1
+        pos_embed_max_size = state_dict["pos_embedder.pos_embed"].shape[1]
+        return {
+            "embed_dim": embed_dim,
+            "num_layers": num_layers,
+            "use_rms_norm": use_rms_norm,
+            "num_dual_blocks": num_dual_blocks,
+            "pos_embed_max_size": pos_embed_max_size
+        }
+
+    def from_diffusers(self, state_dict):
+        rename_dict = {
+            "context_embedder": "context_embedder",
+            "pos_embed.pos_embed": "pos_embedder.pos_embed",
+            "pos_embed.proj": "pos_embedder.proj",
+            "time_text_embed.timestep_embedder.linear_1": "time_embedder.timestep_embedder.0",
+            "time_text_embed.timestep_embedder.linear_2": "time_embedder.timestep_embedder.2",
+            "time_text_embed.text_embedder.linear_1": "pooled_text_embedder.0",
+            "time_text_embed.text_embedder.linear_2": "pooled_text_embedder.2",
+            "norm_out.linear": "norm_out.linear",
+            "proj_out": "proj_out",
+
+            "norm1.linear": "norm1_a.linear",
+            "norm1_context.linear": "norm1_b.linear",
+            "attn.to_q": "attn.a_to_q",
+            "attn.to_k": "attn.a_to_k",
+            "attn.to_v": "attn.a_to_v",
+            "attn.to_out.0": "attn.a_to_out",
+            "attn.add_q_proj": "attn.b_to_q",
+            "attn.add_k_proj": "attn.b_to_k",
+            "attn.add_v_proj": "attn.b_to_v",
+            "attn.to_add_out": "attn.b_to_out",
+            "ff.net.0.proj": "ff_a.0",
+            "ff.net.2": "ff_a.2",
+            "ff_context.net.0.proj": "ff_b.0",
+            "ff_context.net.2": "ff_b.2",
+
+            "attn.norm_q": "attn.norm_q_a",
+            "attn.norm_k": "attn.norm_k_a",
+            "attn.norm_added_q": "attn.norm_q_b",
+            "attn.norm_added_k": "attn.norm_k_b",
+        }
+        state_dict_ = {}
+        for name, param in state_dict.items():
+            if name in rename_dict:
+                if name == "pos_embed.pos_embed":
+                    param = param.reshape((1, 192, 192, param.shape[-1]))
+                state_dict_[rename_dict[name]] = param
+            elif name.endswith(".weight") or name.endswith(".bias"):
+                suffix = ".weight" if name.endswith(".weight") else ".bias"
+                prefix = name[:-len(suffix)]
+                if prefix in rename_dict:
+                    state_dict_[rename_dict[prefix] + suffix] = param
+                elif prefix.startswith("transformer_blocks."):
+                    names = prefix.split(".")
+                    names[0] = "blocks"
+                    middle = ".".join(names[2:])
+                    if middle in rename_dict:
+                        name_ = ".".join(names[:2] + [rename_dict[middle]] + [suffix[1:]])
+                        state_dict_[name_] = param
+        merged_keys = [name for name in state_dict_ if ".a_to_q." in name or ".b_to_q." in name]
+        for key in merged_keys:
+            param = torch.concat([
+                state_dict_[key.replace("to_q", "to_q")],
+                state_dict_[key.replace("to_q", "to_k")],
+                state_dict_[key.replace("to_q", "to_v")],
+            ], dim=0)
+            name = key.replace("to_q", "to_qkv")
+            state_dict_.pop(key.replace("to_q", "to_q"))
+            state_dict_.pop(key.replace("to_q", "to_k"))
+            state_dict_.pop(key.replace("to_q", "to_v"))
+            state_dict_[name] = param
+        return state_dict_, self.infer_architecture(state_dict_)
+    
+    def from_civitai(self, state_dict):
+        rename_dict = {
+            "model.diffusion_model.context_embedder.bias": "context_embedder.bias",
+            "model.diffusion_model.context_embedder.weight": "context_embedder.weight",
+            "model.diffusion_model.final_layer.linear.bias": "proj_out.bias",
+            "model.diffusion_model.final_layer.linear.weight": "proj_out.weight",
+
+            "model.diffusion_model.pos_embed": "pos_embedder.pos_embed",
+            "model.diffusion_model.t_embedder.mlp.0.bias": "time_embedder.timestep_embedder.0.bias",
+            "model.diffusion_model.t_embedder.mlp.0.weight": "time_embedder.timestep_embedder.0.weight",
+            "model.diffusion_model.t_embedder.mlp.2.bias": "time_embedder.timestep_embedder.2.bias",
+            "model.diffusion_model.t_embedder.mlp.2.weight": "time_embedder.timestep_embedder.2.weight",
+            "model.diffusion_model.x_embedder.proj.bias": "pos_embedder.proj.bias",
+            "model.diffusion_model.x_embedder.proj.weight": "pos_embedder.proj.weight",
+            "model.diffusion_model.y_embedder.mlp.0.bias": "pooled_text_embedder.0.bias",
+            "model.diffusion_model.y_embedder.mlp.0.weight": "pooled_text_embedder.0.weight",
+            "model.diffusion_model.y_embedder.mlp.2.bias": "pooled_text_embedder.2.bias",
+            "model.diffusion_model.y_embedder.mlp.2.weight": "pooled_text_embedder.2.weight",
+            
+            "model.diffusion_model.joint_blocks.23.context_block.adaLN_modulation.1.weight": "blocks.23.norm1_b.linear.weight",
+            "model.diffusion_model.joint_blocks.23.context_block.adaLN_modulation.1.bias": "blocks.23.norm1_b.linear.bias",
+            "model.diffusion_model.final_layer.adaLN_modulation.1.weight": "norm_out.linear.weight",
+            "model.diffusion_model.final_layer.adaLN_modulation.1.bias": "norm_out.linear.bias",
+        }
+        for i in range(40):
+            rename_dict.update({
+                f"model.diffusion_model.joint_blocks.{i}.context_block.adaLN_modulation.1.bias": f"blocks.{i}.norm1_b.linear.bias",
+                f"model.diffusion_model.joint_blocks.{i}.context_block.adaLN_modulation.1.weight": f"blocks.{i}.norm1_b.linear.weight",
+                f"model.diffusion_model.joint_blocks.{i}.context_block.attn.proj.bias": f"blocks.{i}.attn.b_to_out.bias",
+                f"model.diffusion_model.joint_blocks.{i}.context_block.attn.proj.weight": f"blocks.{i}.attn.b_to_out.weight",
+                f"model.diffusion_model.joint_blocks.{i}.context_block.attn.qkv.bias": [f'blocks.{i}.attn.b_to_q.bias', f'blocks.{i}.attn.b_to_k.bias', f'blocks.{i}.attn.b_to_v.bias'],
+                f"model.diffusion_model.joint_blocks.{i}.context_block.attn.qkv.weight": [f'blocks.{i}.attn.b_to_q.weight', f'blocks.{i}.attn.b_to_k.weight', f'blocks.{i}.attn.b_to_v.weight'],
+                f"model.diffusion_model.joint_blocks.{i}.context_block.mlp.fc1.bias": f"blocks.{i}.ff_b.0.bias",
+                f"model.diffusion_model.joint_blocks.{i}.context_block.mlp.fc1.weight": f"blocks.{i}.ff_b.0.weight",
+                f"model.diffusion_model.joint_blocks.{i}.context_block.mlp.fc2.bias": f"blocks.{i}.ff_b.2.bias",
+                f"model.diffusion_model.joint_blocks.{i}.context_block.mlp.fc2.weight": f"blocks.{i}.ff_b.2.weight",
+                f"model.diffusion_model.joint_blocks.{i}.x_block.adaLN_modulation.1.bias": f"blocks.{i}.norm1_a.linear.bias",
+                f"model.diffusion_model.joint_blocks.{i}.x_block.adaLN_modulation.1.weight": f"blocks.{i}.norm1_a.linear.weight",
+                f"model.diffusion_model.joint_blocks.{i}.x_block.attn.proj.bias": f"blocks.{i}.attn.a_to_out.bias",
+                f"model.diffusion_model.joint_blocks.{i}.x_block.attn.proj.weight": f"blocks.{i}.attn.a_to_out.weight",
+                f"model.diffusion_model.joint_blocks.{i}.x_block.attn.qkv.bias": [f'blocks.{i}.attn.a_to_q.bias', f'blocks.{i}.attn.a_to_k.bias', f'blocks.{i}.attn.a_to_v.bias'],
+                f"model.diffusion_model.joint_blocks.{i}.x_block.attn.qkv.weight": [f'blocks.{i}.attn.a_to_q.weight', f'blocks.{i}.attn.a_to_k.weight', f'blocks.{i}.attn.a_to_v.weight'],
+                f"model.diffusion_model.joint_blocks.{i}.x_block.mlp.fc1.bias": f"blocks.{i}.ff_a.0.bias",
+                f"model.diffusion_model.joint_blocks.{i}.x_block.mlp.fc1.weight": f"blocks.{i}.ff_a.0.weight",
+                f"model.diffusion_model.joint_blocks.{i}.x_block.mlp.fc2.bias": f"blocks.{i}.ff_a.2.bias",
+                f"model.diffusion_model.joint_blocks.{i}.x_block.mlp.fc2.weight": f"blocks.{i}.ff_a.2.weight",
+                f"model.diffusion_model.joint_blocks.{i}.x_block.attn.ln_q.weight": f"blocks.{i}.attn.norm_q_a.weight",
+                f"model.diffusion_model.joint_blocks.{i}.x_block.attn.ln_k.weight": f"blocks.{i}.attn.norm_k_a.weight",
+                f"model.diffusion_model.joint_blocks.{i}.context_block.attn.ln_q.weight": f"blocks.{i}.attn.norm_q_b.weight",
+                f"model.diffusion_model.joint_blocks.{i}.context_block.attn.ln_k.weight": f"blocks.{i}.attn.norm_k_b.weight",
+
+                f"model.diffusion_model.joint_blocks.{i}.x_block.attn2.ln_q.weight": f"blocks.{i}.attn2.norm_q_a.weight",
+                f"model.diffusion_model.joint_blocks.{i}.x_block.attn2.ln_k.weight": f"blocks.{i}.attn2.norm_k_a.weight",
+                f"model.diffusion_model.joint_blocks.{i}.x_block.attn2.qkv.weight": f"blocks.{i}.attn2.a_to_qkv.weight",
+                f"model.diffusion_model.joint_blocks.{i}.x_block.attn2.qkv.bias": f"blocks.{i}.attn2.a_to_qkv.bias",
+                f"model.diffusion_model.joint_blocks.{i}.x_block.attn2.proj.weight": f"blocks.{i}.attn2.a_to_out.weight",
+                f"model.diffusion_model.joint_blocks.{i}.x_block.attn2.proj.bias": f"blocks.{i}.attn2.a_to_out.bias",
+            })
+        state_dict_ = {}
+        for name in state_dict:
+            if name in rename_dict:
+                param = state_dict[name]
+                if name == "model.diffusion_model.pos_embed":
+                    pos_embed_max_size = int(param.shape[1] ** 0.5 + 0.4)
+                    param = param.reshape((1, pos_embed_max_size, pos_embed_max_size, param.shape[-1]))
+                if isinstance(rename_dict[name], str):
+                    state_dict_[rename_dict[name]] = param
+                else:
+                    name_ = rename_dict[name][0].replace(".a_to_q.", ".a_to_qkv.").replace(".b_to_q.", ".b_to_qkv.")
+                    state_dict_[name_] = param
+        extra_kwargs = self.infer_architecture(state_dict_)
+        num_layers = extra_kwargs["num_layers"]
+        for name in [
+            f"blocks.{num_layers-1}.norm1_b.linear.weight", f"blocks.{num_layers-1}.norm1_b.linear.bias", "norm_out.linear.weight", "norm_out.linear.bias",
+        ]:
+            param = state_dict_[name]
+            dim = param.shape[0] // 2
+            param = torch.concat([param[dim:], param[:dim]], axis=0)
+            state_dict_[name] = param
+        return state_dict_, self.infer_architecture(state_dict_)
diff --git a/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/sd3_text_encoder.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/sd3_text_encoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..efe29ca8ae99586ae197ee633a9c3d7f2c074f77
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/sd3_text_encoder.py
@@ -0,0 +1,1120 @@
+import torch
+from transformers import T5EncoderModel, T5Config
+from .sd_text_encoder import SDTextEncoder
+from .sdxl_text_encoder import SDXLTextEncoder2, SDXLTextEncoder2StateDictConverter
+
+
+class SD3TextEncoder1(SDTextEncoder):
+    def __init__(self, vocab_size=49408):
+        super().__init__(vocab_size=vocab_size)
+
+    def forward(self, input_ids, clip_skip=2, extra_mask=None):
+        embeds = self.token_embedding(input_ids)
+        embeds = embeds + self.position_embeds.to(dtype=embeds.dtype, device=input_ids.device)
+        attn_mask = self.attn_mask.to(device=embeds.device, dtype=embeds.dtype)
+        if extra_mask is not None:
+            attn_mask[:, extra_mask[0]==0] = float("-inf")
+        for encoder_id, encoder in enumerate(self.encoders):
+            embeds = encoder(embeds, attn_mask=attn_mask)
+            if encoder_id + clip_skip == len(self.encoders):
+                hidden_states = embeds
+        embeds = self.final_layer_norm(embeds)
+        pooled_embeds = embeds[torch.arange(embeds.shape[0]), input_ids.to(dtype=torch.int).argmax(dim=-1)]
+        return pooled_embeds, hidden_states
+    
+    @staticmethod
+    def state_dict_converter():
+        return SD3TextEncoder1StateDictConverter()
+    
+
+
+class SD3TextEncoder2(SDXLTextEncoder2):
+    def __init__(self):
+        super().__init__()
+
+    @staticmethod
+    def state_dict_converter():
+        return SD3TextEncoder2StateDictConverter()
+
+
+class SD3TextEncoder3(T5EncoderModel):
+    def __init__(self):
+        config = T5Config(
+            _name_or_path = ".",
+            architectures = ["T5EncoderModel"],
+            classifier_dropout = 0.0,
+            d_ff = 10240,
+            d_kv = 64,
+            d_model = 4096,
+            decoder_start_token_id = 0,
+            dense_act_fn = "gelu_new",
+            dropout_rate = 0.1,
+            eos_token_id = 1,
+            feed_forward_proj = "gated-gelu",
+            initializer_factor = 1.0,
+            is_encoder_decoder = True,
+            is_gated_act = True,
+            layer_norm_epsilon = 1e-06,
+            model_type = "t5",
+            num_decoder_layers = 24,
+            num_heads = 64,
+            num_layers = 24,
+            output_past = True,
+            pad_token_id = 0,
+            relative_attention_max_distance = 128,
+            relative_attention_num_buckets = 32,
+            tie_word_embeddings = False,
+            torch_dtype = torch.float16,
+            transformers_version = "4.41.2",
+            use_cache = True,
+            vocab_size = 32128
+        )
+        super().__init__(config)
+        self.eval()
+
+    def forward(self, input_ids):
+        outputs = super().forward(input_ids=input_ids)
+        prompt_emb = outputs.last_hidden_state
+        return prompt_emb
+    
+    @staticmethod
+    def state_dict_converter():
+        return SD3TextEncoder3StateDictConverter()
+
+
+
+class SD3TextEncoder1StateDictConverter:
+    def __init__(self):
+        pass
+
+    def from_diffusers(self, state_dict):
+        rename_dict = {
+            "text_model.embeddings.token_embedding.weight": "token_embedding.weight",
+            "text_model.embeddings.position_embedding.weight": "position_embeds",
+            "text_model.final_layer_norm.weight": "final_layer_norm.weight",
+            "text_model.final_layer_norm.bias": "final_layer_norm.bias",
+        }
+        attn_rename_dict = {
+            "self_attn.q_proj": "attn.to_q",
+            "self_attn.k_proj": "attn.to_k",
+            "self_attn.v_proj": "attn.to_v",
+            "self_attn.out_proj": "attn.to_out",
+            "layer_norm1": "layer_norm1",
+            "layer_norm2": "layer_norm2",
+            "mlp.fc1": "fc1",
+            "mlp.fc2": "fc2",
+        }
+        state_dict_ = {}
+        for name in state_dict:
+            if name in rename_dict:
+                param = state_dict[name]
+                if name == "text_model.embeddings.position_embedding.weight":
+                    param = param.reshape((1, param.shape[0], param.shape[1]))
+                state_dict_[rename_dict[name]] = param
+            elif name.startswith("text_model.encoder.layers."):
+                param = state_dict[name]
+                names = name.split(".")
+                layer_id, layer_type, tail = names[3], ".".join(names[4:-1]), names[-1]
+                name_ = ".".join(["encoders", layer_id, attn_rename_dict[layer_type], tail])
+                state_dict_[name_] = param
+        return state_dict_
+    
+    def from_civitai(self, state_dict):
+        rename_dict = {
+            "text_encoders.clip_l.transformer.text_model.embeddings.position_embedding.weight": "position_embeds",
+            "text_encoders.clip_l.transformer.text_model.embeddings.token_embedding.weight": "token_embedding.weight",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.0.layer_norm1.bias": "encoders.0.layer_norm1.bias",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.0.layer_norm1.weight": "encoders.0.layer_norm1.weight",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.0.layer_norm2.bias": "encoders.0.layer_norm2.bias",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.0.layer_norm2.weight": "encoders.0.layer_norm2.weight",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.0.mlp.fc1.bias": "encoders.0.fc1.bias",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.0.mlp.fc1.weight": "encoders.0.fc1.weight",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.0.mlp.fc2.bias": "encoders.0.fc2.bias",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.0.mlp.fc2.weight": "encoders.0.fc2.weight",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.0.self_attn.k_proj.bias": "encoders.0.attn.to_k.bias",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.0.self_attn.k_proj.weight": "encoders.0.attn.to_k.weight",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.0.self_attn.out_proj.bias": "encoders.0.attn.to_out.bias",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.0.self_attn.out_proj.weight": "encoders.0.attn.to_out.weight",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.0.self_attn.q_proj.bias": "encoders.0.attn.to_q.bias",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.0.self_attn.q_proj.weight": "encoders.0.attn.to_q.weight",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.0.self_attn.v_proj.bias": "encoders.0.attn.to_v.bias",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.0.self_attn.v_proj.weight": "encoders.0.attn.to_v.weight",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.1.layer_norm1.bias": "encoders.1.layer_norm1.bias",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.1.layer_norm1.weight": "encoders.1.layer_norm1.weight",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.1.layer_norm2.bias": "encoders.1.layer_norm2.bias",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.1.layer_norm2.weight": "encoders.1.layer_norm2.weight",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.1.mlp.fc1.bias": "encoders.1.fc1.bias",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.1.mlp.fc1.weight": "encoders.1.fc1.weight",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.1.mlp.fc2.bias": "encoders.1.fc2.bias",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.1.mlp.fc2.weight": "encoders.1.fc2.weight",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.1.self_attn.k_proj.bias": "encoders.1.attn.to_k.bias",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.1.self_attn.k_proj.weight": "encoders.1.attn.to_k.weight",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.1.self_attn.out_proj.bias": "encoders.1.attn.to_out.bias",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.1.self_attn.out_proj.weight": "encoders.1.attn.to_out.weight",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.1.self_attn.q_proj.bias": "encoders.1.attn.to_q.bias",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.1.self_attn.q_proj.weight": "encoders.1.attn.to_q.weight",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.1.self_attn.v_proj.bias": "encoders.1.attn.to_v.bias",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.1.self_attn.v_proj.weight": "encoders.1.attn.to_v.weight",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.10.layer_norm1.bias": "encoders.10.layer_norm1.bias",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.10.layer_norm1.weight": "encoders.10.layer_norm1.weight",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.10.layer_norm2.bias": "encoders.10.layer_norm2.bias",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.10.layer_norm2.weight": "encoders.10.layer_norm2.weight",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.10.mlp.fc1.bias": "encoders.10.fc1.bias",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.10.mlp.fc1.weight": "encoders.10.fc1.weight",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.10.mlp.fc2.bias": "encoders.10.fc2.bias",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.10.mlp.fc2.weight": "encoders.10.fc2.weight",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.10.self_attn.k_proj.bias": "encoders.10.attn.to_k.bias",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.10.self_attn.k_proj.weight": "encoders.10.attn.to_k.weight",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.10.self_attn.out_proj.bias": "encoders.10.attn.to_out.bias",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.10.self_attn.out_proj.weight": "encoders.10.attn.to_out.weight",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.10.self_attn.q_proj.bias": "encoders.10.attn.to_q.bias",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.10.self_attn.q_proj.weight": "encoders.10.attn.to_q.weight",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.10.self_attn.v_proj.bias": "encoders.10.attn.to_v.bias",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.10.self_attn.v_proj.weight": "encoders.10.attn.to_v.weight",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.11.layer_norm1.bias": "encoders.11.layer_norm1.bias",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.11.layer_norm1.weight": "encoders.11.layer_norm1.weight",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.11.layer_norm2.bias": "encoders.11.layer_norm2.bias",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.11.layer_norm2.weight": "encoders.11.layer_norm2.weight",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.11.mlp.fc1.bias": "encoders.11.fc1.bias",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.11.mlp.fc1.weight": "encoders.11.fc1.weight",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.11.mlp.fc2.bias": "encoders.11.fc2.bias",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.11.mlp.fc2.weight": "encoders.11.fc2.weight",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.11.self_attn.k_proj.bias": "encoders.11.attn.to_k.bias",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.11.self_attn.k_proj.weight": "encoders.11.attn.to_k.weight",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.11.self_attn.out_proj.bias": "encoders.11.attn.to_out.bias",
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+            "text_encoders.clip_l.transformer.text_model.encoder.layers.6.layer_norm2.bias": "encoders.6.layer_norm2.bias",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.6.layer_norm2.weight": "encoders.6.layer_norm2.weight",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.6.mlp.fc1.bias": "encoders.6.fc1.bias",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.6.mlp.fc1.weight": "encoders.6.fc1.weight",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.6.mlp.fc2.bias": "encoders.6.fc2.bias",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.6.mlp.fc2.weight": "encoders.6.fc2.weight",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.6.self_attn.k_proj.bias": "encoders.6.attn.to_k.bias",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.6.self_attn.k_proj.weight": "encoders.6.attn.to_k.weight",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.6.self_attn.out_proj.bias": "encoders.6.attn.to_out.bias",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.6.self_attn.out_proj.weight": "encoders.6.attn.to_out.weight",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.6.self_attn.q_proj.bias": "encoders.6.attn.to_q.bias",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.6.self_attn.q_proj.weight": "encoders.6.attn.to_q.weight",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.6.self_attn.v_proj.bias": "encoders.6.attn.to_v.bias",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.6.self_attn.v_proj.weight": "encoders.6.attn.to_v.weight",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.7.layer_norm1.bias": "encoders.7.layer_norm1.bias",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.7.layer_norm1.weight": "encoders.7.layer_norm1.weight",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.7.layer_norm2.bias": "encoders.7.layer_norm2.bias",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.7.layer_norm2.weight": "encoders.7.layer_norm2.weight",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.7.mlp.fc1.bias": "encoders.7.fc1.bias",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.7.mlp.fc1.weight": "encoders.7.fc1.weight",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.7.mlp.fc2.bias": "encoders.7.fc2.bias",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.7.mlp.fc2.weight": "encoders.7.fc2.weight",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.7.self_attn.k_proj.bias": "encoders.7.attn.to_k.bias",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.7.self_attn.k_proj.weight": "encoders.7.attn.to_k.weight",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.7.self_attn.out_proj.bias": "encoders.7.attn.to_out.bias",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.7.self_attn.out_proj.weight": "encoders.7.attn.to_out.weight",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.7.self_attn.q_proj.bias": "encoders.7.attn.to_q.bias",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.7.self_attn.q_proj.weight": "encoders.7.attn.to_q.weight",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.7.self_attn.v_proj.bias": "encoders.7.attn.to_v.bias",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.7.self_attn.v_proj.weight": "encoders.7.attn.to_v.weight",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.8.layer_norm1.bias": "encoders.8.layer_norm1.bias",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.8.layer_norm1.weight": "encoders.8.layer_norm1.weight",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.8.layer_norm2.bias": "encoders.8.layer_norm2.bias",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.8.layer_norm2.weight": "encoders.8.layer_norm2.weight",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.8.mlp.fc1.bias": "encoders.8.fc1.bias",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.8.mlp.fc1.weight": "encoders.8.fc1.weight",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.8.mlp.fc2.bias": "encoders.8.fc2.bias",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.8.mlp.fc2.weight": "encoders.8.fc2.weight",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.8.self_attn.k_proj.bias": "encoders.8.attn.to_k.bias",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.8.self_attn.k_proj.weight": "encoders.8.attn.to_k.weight",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.8.self_attn.out_proj.bias": "encoders.8.attn.to_out.bias",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.8.self_attn.out_proj.weight": "encoders.8.attn.to_out.weight",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.8.self_attn.q_proj.bias": "encoders.8.attn.to_q.bias",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.8.self_attn.q_proj.weight": "encoders.8.attn.to_q.weight",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.8.self_attn.v_proj.bias": "encoders.8.attn.to_v.bias",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.8.self_attn.v_proj.weight": "encoders.8.attn.to_v.weight",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.9.layer_norm1.bias": "encoders.9.layer_norm1.bias",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.9.layer_norm1.weight": "encoders.9.layer_norm1.weight",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.9.layer_norm2.bias": "encoders.9.layer_norm2.bias",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.9.layer_norm2.weight": "encoders.9.layer_norm2.weight",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.9.mlp.fc1.bias": "encoders.9.fc1.bias",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.9.mlp.fc1.weight": "encoders.9.fc1.weight",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.9.mlp.fc2.bias": "encoders.9.fc2.bias",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.9.mlp.fc2.weight": "encoders.9.fc2.weight",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.9.self_attn.k_proj.bias": "encoders.9.attn.to_k.bias",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.9.self_attn.k_proj.weight": "encoders.9.attn.to_k.weight",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.9.self_attn.out_proj.bias": "encoders.9.attn.to_out.bias",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.9.self_attn.out_proj.weight": "encoders.9.attn.to_out.weight",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.9.self_attn.q_proj.bias": "encoders.9.attn.to_q.bias",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.9.self_attn.q_proj.weight": "encoders.9.attn.to_q.weight",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.9.self_attn.v_proj.bias": "encoders.9.attn.to_v.bias",
+            "text_encoders.clip_l.transformer.text_model.encoder.layers.9.self_attn.v_proj.weight": "encoders.9.attn.to_v.weight",
+            "text_encoders.clip_l.transformer.text_model.final_layer_norm.bias": "final_layer_norm.bias",
+            "text_encoders.clip_l.transformer.text_model.final_layer_norm.weight": "final_layer_norm.weight",
+        }
+        state_dict_ = {}
+        for name in state_dict:
+            if name in rename_dict:
+                param = state_dict[name]
+                if name == "text_encoders.clip_l.transformer.text_model.embeddings.position_embedding.weight":
+                    param = param.reshape((1, param.shape[0], param.shape[1]))
+                state_dict_[rename_dict[name]] = param
+            elif ("text_encoders.clip_l.transformer." + name) in rename_dict:
+                param = state_dict[name]
+                if name == "text_model.embeddings.position_embedding.weight":
+                    param = param.reshape((1, param.shape[0], param.shape[1]))
+                state_dict_[rename_dict["text_encoders.clip_l.transformer." + name]] = param
+        return state_dict_
+    
+
+
+class SD3TextEncoder2StateDictConverter(SDXLTextEncoder2StateDictConverter):
+    def __init__(self):
+        pass
+
+    def from_diffusers(self, state_dict):
+        return super().from_diffusers(state_dict)
+    
+    def from_civitai(self, state_dict):
+        rename_dict = {
+            "text_encoders.clip_g.transformer.text_model.embeddings.position_embedding.weight": "position_embeds",
+            "text_encoders.clip_g.transformer.text_model.embeddings.token_embedding.weight": "token_embedding.weight",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.0.layer_norm1.bias": "encoders.0.layer_norm1.bias",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.0.layer_norm1.weight": "encoders.0.layer_norm1.weight",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.0.layer_norm2.bias": "encoders.0.layer_norm2.bias",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.0.layer_norm2.weight": "encoders.0.layer_norm2.weight",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.0.mlp.fc1.bias": "encoders.0.fc1.bias",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.0.mlp.fc1.weight": "encoders.0.fc1.weight",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.0.mlp.fc2.bias": "encoders.0.fc2.bias",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.0.mlp.fc2.weight": "encoders.0.fc2.weight",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.0.self_attn.k_proj.bias": "encoders.0.attn.to_k.bias",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.0.self_attn.k_proj.weight": "encoders.0.attn.to_k.weight",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.0.self_attn.out_proj.bias": "encoders.0.attn.to_out.bias",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.0.self_attn.out_proj.weight": "encoders.0.attn.to_out.weight",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.0.self_attn.q_proj.bias": "encoders.0.attn.to_q.bias",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.0.self_attn.q_proj.weight": "encoders.0.attn.to_q.weight",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.0.self_attn.v_proj.bias": "encoders.0.attn.to_v.bias",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.0.self_attn.v_proj.weight": "encoders.0.attn.to_v.weight",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.1.layer_norm1.bias": "encoders.1.layer_norm1.bias",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.1.layer_norm1.weight": "encoders.1.layer_norm1.weight",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.1.layer_norm2.bias": "encoders.1.layer_norm2.bias",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.1.layer_norm2.weight": "encoders.1.layer_norm2.weight",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.1.mlp.fc1.bias": "encoders.1.fc1.bias",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.1.mlp.fc1.weight": "encoders.1.fc1.weight",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.1.mlp.fc2.bias": "encoders.1.fc2.bias",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.1.mlp.fc2.weight": "encoders.1.fc2.weight",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.1.self_attn.k_proj.bias": "encoders.1.attn.to_k.bias",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.1.self_attn.k_proj.weight": "encoders.1.attn.to_k.weight",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.1.self_attn.out_proj.bias": "encoders.1.attn.to_out.bias",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.1.self_attn.out_proj.weight": "encoders.1.attn.to_out.weight",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.1.self_attn.q_proj.bias": "encoders.1.attn.to_q.bias",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.1.self_attn.q_proj.weight": "encoders.1.attn.to_q.weight",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.1.self_attn.v_proj.bias": "encoders.1.attn.to_v.bias",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.1.self_attn.v_proj.weight": "encoders.1.attn.to_v.weight",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.10.layer_norm1.bias": "encoders.10.layer_norm1.bias",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.10.layer_norm1.weight": "encoders.10.layer_norm1.weight",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.10.layer_norm2.bias": "encoders.10.layer_norm2.bias",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.10.layer_norm2.weight": "encoders.10.layer_norm2.weight",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.10.mlp.fc1.bias": "encoders.10.fc1.bias",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.10.mlp.fc1.weight": "encoders.10.fc1.weight",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.10.mlp.fc2.bias": "encoders.10.fc2.bias",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.10.mlp.fc2.weight": "encoders.10.fc2.weight",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.10.self_attn.k_proj.bias": "encoders.10.attn.to_k.bias",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.10.self_attn.k_proj.weight": "encoders.10.attn.to_k.weight",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.10.self_attn.out_proj.bias": "encoders.10.attn.to_out.bias",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.10.self_attn.out_proj.weight": "encoders.10.attn.to_out.weight",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.10.self_attn.q_proj.bias": "encoders.10.attn.to_q.bias",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.10.self_attn.q_proj.weight": "encoders.10.attn.to_q.weight",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.10.self_attn.v_proj.bias": "encoders.10.attn.to_v.bias",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.10.self_attn.v_proj.weight": "encoders.10.attn.to_v.weight",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.11.layer_norm1.bias": "encoders.11.layer_norm1.bias",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.11.layer_norm1.weight": "encoders.11.layer_norm1.weight",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.11.layer_norm2.bias": "encoders.11.layer_norm2.bias",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.11.layer_norm2.weight": "encoders.11.layer_norm2.weight",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.11.mlp.fc1.bias": "encoders.11.fc1.bias",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.11.mlp.fc1.weight": "encoders.11.fc1.weight",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.11.mlp.fc2.bias": "encoders.11.fc2.bias",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.11.mlp.fc2.weight": "encoders.11.fc2.weight",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.11.self_attn.k_proj.bias": "encoders.11.attn.to_k.bias",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.11.self_attn.k_proj.weight": "encoders.11.attn.to_k.weight",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.11.self_attn.out_proj.bias": "encoders.11.attn.to_out.bias",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.11.self_attn.out_proj.weight": "encoders.11.attn.to_out.weight",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.11.self_attn.q_proj.bias": "encoders.11.attn.to_q.bias",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.11.self_attn.q_proj.weight": "encoders.11.attn.to_q.weight",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.11.self_attn.v_proj.bias": "encoders.11.attn.to_v.bias",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.11.self_attn.v_proj.weight": "encoders.11.attn.to_v.weight",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.12.layer_norm1.bias": "encoders.12.layer_norm1.bias",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.12.layer_norm1.weight": "encoders.12.layer_norm1.weight",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.12.layer_norm2.bias": "encoders.12.layer_norm2.bias",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.12.layer_norm2.weight": "encoders.12.layer_norm2.weight",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.12.mlp.fc1.bias": "encoders.12.fc1.bias",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.12.mlp.fc1.weight": "encoders.12.fc1.weight",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.12.mlp.fc2.bias": "encoders.12.fc2.bias",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.12.mlp.fc2.weight": "encoders.12.fc2.weight",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.12.self_attn.k_proj.bias": "encoders.12.attn.to_k.bias",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.12.self_attn.k_proj.weight": "encoders.12.attn.to_k.weight",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.12.self_attn.out_proj.bias": "encoders.12.attn.to_out.bias",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.12.self_attn.out_proj.weight": "encoders.12.attn.to_out.weight",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.12.self_attn.q_proj.bias": "encoders.12.attn.to_q.bias",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.12.self_attn.q_proj.weight": "encoders.12.attn.to_q.weight",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.12.self_attn.v_proj.bias": "encoders.12.attn.to_v.bias",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.12.self_attn.v_proj.weight": "encoders.12.attn.to_v.weight",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.13.layer_norm1.bias": "encoders.13.layer_norm1.bias",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.13.layer_norm1.weight": "encoders.13.layer_norm1.weight",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.13.layer_norm2.bias": "encoders.13.layer_norm2.bias",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.13.layer_norm2.weight": "encoders.13.layer_norm2.weight",
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+            "text_encoders.clip_g.transformer.text_model.encoder.layers.30.self_attn.v_proj.bias": "encoders.30.attn.to_v.bias",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.30.self_attn.v_proj.weight": "encoders.30.attn.to_v.weight",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.31.layer_norm1.bias": "encoders.31.layer_norm1.bias",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.31.layer_norm1.weight": "encoders.31.layer_norm1.weight",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.31.layer_norm2.bias": "encoders.31.layer_norm2.bias",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.31.layer_norm2.weight": "encoders.31.layer_norm2.weight",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.31.mlp.fc1.bias": "encoders.31.fc1.bias",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.31.mlp.fc1.weight": "encoders.31.fc1.weight",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.31.mlp.fc2.bias": "encoders.31.fc2.bias",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.31.mlp.fc2.weight": "encoders.31.fc2.weight",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.31.self_attn.k_proj.bias": "encoders.31.attn.to_k.bias",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.31.self_attn.k_proj.weight": "encoders.31.attn.to_k.weight",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.31.self_attn.out_proj.bias": "encoders.31.attn.to_out.bias",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.31.self_attn.out_proj.weight": "encoders.31.attn.to_out.weight",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.31.self_attn.q_proj.bias": "encoders.31.attn.to_q.bias",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.31.self_attn.q_proj.weight": "encoders.31.attn.to_q.weight",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.31.self_attn.v_proj.bias": "encoders.31.attn.to_v.bias",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.31.self_attn.v_proj.weight": "encoders.31.attn.to_v.weight",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.4.layer_norm1.bias": "encoders.4.layer_norm1.bias",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.4.layer_norm1.weight": "encoders.4.layer_norm1.weight",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.4.layer_norm2.bias": "encoders.4.layer_norm2.bias",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.4.layer_norm2.weight": "encoders.4.layer_norm2.weight",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.4.mlp.fc1.bias": "encoders.4.fc1.bias",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.4.mlp.fc1.weight": "encoders.4.fc1.weight",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.4.mlp.fc2.bias": "encoders.4.fc2.bias",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.4.mlp.fc2.weight": "encoders.4.fc2.weight",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.4.self_attn.k_proj.bias": "encoders.4.attn.to_k.bias",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.4.self_attn.k_proj.weight": "encoders.4.attn.to_k.weight",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.4.self_attn.out_proj.bias": "encoders.4.attn.to_out.bias",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.4.self_attn.out_proj.weight": "encoders.4.attn.to_out.weight",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.4.self_attn.q_proj.bias": "encoders.4.attn.to_q.bias",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.4.self_attn.q_proj.weight": "encoders.4.attn.to_q.weight",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.4.self_attn.v_proj.bias": "encoders.4.attn.to_v.bias",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.4.self_attn.v_proj.weight": "encoders.4.attn.to_v.weight",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.5.layer_norm1.bias": "encoders.5.layer_norm1.bias",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.5.layer_norm1.weight": "encoders.5.layer_norm1.weight",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.5.layer_norm2.bias": "encoders.5.layer_norm2.bias",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.5.layer_norm2.weight": "encoders.5.layer_norm2.weight",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.5.mlp.fc1.bias": "encoders.5.fc1.bias",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.5.mlp.fc1.weight": "encoders.5.fc1.weight",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.5.mlp.fc2.bias": "encoders.5.fc2.bias",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.5.mlp.fc2.weight": "encoders.5.fc2.weight",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.5.self_attn.k_proj.bias": "encoders.5.attn.to_k.bias",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.5.self_attn.k_proj.weight": "encoders.5.attn.to_k.weight",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.5.self_attn.out_proj.bias": "encoders.5.attn.to_out.bias",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.5.self_attn.out_proj.weight": "encoders.5.attn.to_out.weight",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.5.self_attn.q_proj.bias": "encoders.5.attn.to_q.bias",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.5.self_attn.q_proj.weight": "encoders.5.attn.to_q.weight",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.5.self_attn.v_proj.bias": "encoders.5.attn.to_v.bias",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.5.self_attn.v_proj.weight": "encoders.5.attn.to_v.weight",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.6.layer_norm1.bias": "encoders.6.layer_norm1.bias",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.6.layer_norm1.weight": "encoders.6.layer_norm1.weight",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.6.layer_norm2.bias": "encoders.6.layer_norm2.bias",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.6.layer_norm2.weight": "encoders.6.layer_norm2.weight",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.6.mlp.fc1.bias": "encoders.6.fc1.bias",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.6.mlp.fc1.weight": "encoders.6.fc1.weight",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.6.mlp.fc2.bias": "encoders.6.fc2.bias",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.6.mlp.fc2.weight": "encoders.6.fc2.weight",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.6.self_attn.k_proj.bias": "encoders.6.attn.to_k.bias",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.6.self_attn.k_proj.weight": "encoders.6.attn.to_k.weight",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.6.self_attn.out_proj.bias": "encoders.6.attn.to_out.bias",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.6.self_attn.out_proj.weight": "encoders.6.attn.to_out.weight",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.6.self_attn.q_proj.bias": "encoders.6.attn.to_q.bias",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.6.self_attn.q_proj.weight": "encoders.6.attn.to_q.weight",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.6.self_attn.v_proj.bias": "encoders.6.attn.to_v.bias",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.6.self_attn.v_proj.weight": "encoders.6.attn.to_v.weight",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.7.layer_norm1.bias": "encoders.7.layer_norm1.bias",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.7.layer_norm1.weight": "encoders.7.layer_norm1.weight",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.7.layer_norm2.bias": "encoders.7.layer_norm2.bias",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.7.layer_norm2.weight": "encoders.7.layer_norm2.weight",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.7.mlp.fc1.bias": "encoders.7.fc1.bias",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.7.mlp.fc1.weight": "encoders.7.fc1.weight",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.7.mlp.fc2.bias": "encoders.7.fc2.bias",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.7.mlp.fc2.weight": "encoders.7.fc2.weight",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.7.self_attn.k_proj.bias": "encoders.7.attn.to_k.bias",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.7.self_attn.k_proj.weight": "encoders.7.attn.to_k.weight",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.7.self_attn.out_proj.bias": "encoders.7.attn.to_out.bias",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.7.self_attn.out_proj.weight": "encoders.7.attn.to_out.weight",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.7.self_attn.q_proj.bias": "encoders.7.attn.to_q.bias",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.7.self_attn.q_proj.weight": "encoders.7.attn.to_q.weight",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.7.self_attn.v_proj.bias": "encoders.7.attn.to_v.bias",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.7.self_attn.v_proj.weight": "encoders.7.attn.to_v.weight",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.8.layer_norm1.bias": "encoders.8.layer_norm1.bias",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.8.layer_norm1.weight": "encoders.8.layer_norm1.weight",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.8.layer_norm2.bias": "encoders.8.layer_norm2.bias",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.8.layer_norm2.weight": "encoders.8.layer_norm2.weight",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.8.mlp.fc1.bias": "encoders.8.fc1.bias",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.8.mlp.fc1.weight": "encoders.8.fc1.weight",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.8.mlp.fc2.bias": "encoders.8.fc2.bias",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.8.mlp.fc2.weight": "encoders.8.fc2.weight",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.8.self_attn.k_proj.bias": "encoders.8.attn.to_k.bias",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.8.self_attn.k_proj.weight": "encoders.8.attn.to_k.weight",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.8.self_attn.out_proj.bias": "encoders.8.attn.to_out.bias",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.8.self_attn.out_proj.weight": "encoders.8.attn.to_out.weight",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.8.self_attn.q_proj.bias": "encoders.8.attn.to_q.bias",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.8.self_attn.q_proj.weight": "encoders.8.attn.to_q.weight",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.8.self_attn.v_proj.bias": "encoders.8.attn.to_v.bias",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.8.self_attn.v_proj.weight": "encoders.8.attn.to_v.weight",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.9.layer_norm1.bias": "encoders.9.layer_norm1.bias",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.9.layer_norm1.weight": "encoders.9.layer_norm1.weight",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.9.layer_norm2.bias": "encoders.9.layer_norm2.bias",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.9.layer_norm2.weight": "encoders.9.layer_norm2.weight",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.9.mlp.fc1.bias": "encoders.9.fc1.bias",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.9.mlp.fc1.weight": "encoders.9.fc1.weight",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.9.mlp.fc2.bias": "encoders.9.fc2.bias",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.9.mlp.fc2.weight": "encoders.9.fc2.weight",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.9.self_attn.k_proj.bias": "encoders.9.attn.to_k.bias",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.9.self_attn.k_proj.weight": "encoders.9.attn.to_k.weight",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.9.self_attn.out_proj.bias": "encoders.9.attn.to_out.bias",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.9.self_attn.out_proj.weight": "encoders.9.attn.to_out.weight",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.9.self_attn.q_proj.bias": "encoders.9.attn.to_q.bias",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.9.self_attn.q_proj.weight": "encoders.9.attn.to_q.weight",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.9.self_attn.v_proj.bias": "encoders.9.attn.to_v.bias",
+            "text_encoders.clip_g.transformer.text_model.encoder.layers.9.self_attn.v_proj.weight": "encoders.9.attn.to_v.weight",
+            "text_encoders.clip_g.transformer.text_model.final_layer_norm.bias": "final_layer_norm.bias",
+            "text_encoders.clip_g.transformer.text_model.final_layer_norm.weight": "final_layer_norm.weight",
+            "text_encoders.clip_g.transformer.text_projection.weight": "text_projection.weight",
+        }
+        state_dict_ = {}
+        for name in state_dict:
+            if name in rename_dict:
+                param = state_dict[name]
+                if name == "text_encoders.clip_g.transformer.text_model.embeddings.position_embedding.weight":
+                    param = param.reshape((1, param.shape[0], param.shape[1]))
+                state_dict_[rename_dict[name]] = param
+            elif ("text_encoders.clip_g.transformer." + name) in rename_dict:
+                param = state_dict[name]
+                if name == "text_model.embeddings.position_embedding.weight":
+                    param = param.reshape((1, param.shape[0], param.shape[1]))
+                state_dict_[rename_dict["text_encoders.clip_g.transformer." + name]] = param
+        return state_dict_
+
+
+
+class SD3TextEncoder3StateDictConverter():
+    def __init__(self):
+        pass
+
+    def from_diffusers(self, state_dict):
+        state_dict_ = state_dict
+        return state_dict_
+    
+    def from_civitai(self, state_dict):
+        prefix = "text_encoders.t5xxl.transformer."
+        state_dict_ = {name[len(prefix):]: param for name, param in state_dict.items() if name.startswith(prefix)}
+        if len(state_dict_) > 0:
+            return self.from_diffusers(state_dict_)
+        name_list = [
+            "encoder.block.0.layer.0.SelfAttention.k.weight",
+            "encoder.block.0.layer.0.SelfAttention.o.weight",
+            "encoder.block.0.layer.0.SelfAttention.q.weight",
+            "encoder.block.0.layer.0.SelfAttention.relative_attention_bias.weight",
+            "encoder.block.0.layer.0.SelfAttention.v.weight",
+            "encoder.block.0.layer.0.layer_norm.weight",
+            "encoder.block.0.layer.1.DenseReluDense.wi_0.weight",
+            "encoder.block.0.layer.1.DenseReluDense.wi_1.weight",
+            "encoder.block.0.layer.1.DenseReluDense.wo.weight",
+            "encoder.block.0.layer.1.layer_norm.weight",
+            "encoder.block.1.layer.0.SelfAttention.k.weight",
+            "encoder.block.1.layer.0.SelfAttention.o.weight",
+            "encoder.block.1.layer.0.SelfAttention.q.weight",
+            "encoder.block.1.layer.0.SelfAttention.v.weight",
+            "encoder.block.1.layer.0.layer_norm.weight",
+            "encoder.block.1.layer.1.DenseReluDense.wi_0.weight",
+            "encoder.block.1.layer.1.DenseReluDense.wi_1.weight",
+            "encoder.block.1.layer.1.DenseReluDense.wo.weight",
+            "encoder.block.1.layer.1.layer_norm.weight",
+            "encoder.block.10.layer.0.SelfAttention.k.weight",
+            "encoder.block.10.layer.0.SelfAttention.o.weight",
+            "encoder.block.10.layer.0.SelfAttention.q.weight",
+            "encoder.block.10.layer.0.SelfAttention.v.weight",
+            "encoder.block.10.layer.0.layer_norm.weight",
+            "encoder.block.10.layer.1.DenseReluDense.wi_0.weight",
+            "encoder.block.10.layer.1.DenseReluDense.wi_1.weight",
+            "encoder.block.10.layer.1.DenseReluDense.wo.weight",
+            "encoder.block.10.layer.1.layer_norm.weight",
+            "encoder.block.11.layer.0.SelfAttention.k.weight",
+            "encoder.block.11.layer.0.SelfAttention.o.weight",
+            "encoder.block.11.layer.0.SelfAttention.q.weight",
+            "encoder.block.11.layer.0.SelfAttention.v.weight",
+            "encoder.block.11.layer.0.layer_norm.weight",
+            "encoder.block.11.layer.1.DenseReluDense.wi_0.weight",
+            "encoder.block.11.layer.1.DenseReluDense.wi_1.weight",
+            "encoder.block.11.layer.1.DenseReluDense.wo.weight",
+            "encoder.block.11.layer.1.layer_norm.weight",
+            "encoder.block.12.layer.0.SelfAttention.k.weight",
+            "encoder.block.12.layer.0.SelfAttention.o.weight",
+            "encoder.block.12.layer.0.SelfAttention.q.weight",
+            "encoder.block.12.layer.0.SelfAttention.v.weight",
+            "encoder.block.12.layer.0.layer_norm.weight",
+            "encoder.block.12.layer.1.DenseReluDense.wi_0.weight",
+            "encoder.block.12.layer.1.DenseReluDense.wi_1.weight",
+            "encoder.block.12.layer.1.DenseReluDense.wo.weight",
+            "encoder.block.12.layer.1.layer_norm.weight",
+            "encoder.block.13.layer.0.SelfAttention.k.weight",
+            "encoder.block.13.layer.0.SelfAttention.o.weight",
+            "encoder.block.13.layer.0.SelfAttention.q.weight",
+            "encoder.block.13.layer.0.SelfAttention.v.weight",
+            "encoder.block.13.layer.0.layer_norm.weight",
+            "encoder.block.13.layer.1.DenseReluDense.wi_0.weight",
+            "encoder.block.13.layer.1.DenseReluDense.wi_1.weight",
+            "encoder.block.13.layer.1.DenseReluDense.wo.weight",
+            "encoder.block.13.layer.1.layer_norm.weight",
+            "encoder.block.14.layer.0.SelfAttention.k.weight",
+            "encoder.block.14.layer.0.SelfAttention.o.weight",
+            "encoder.block.14.layer.0.SelfAttention.q.weight",
+            "encoder.block.14.layer.0.SelfAttention.v.weight",
+            "encoder.block.14.layer.0.layer_norm.weight",
+            "encoder.block.14.layer.1.DenseReluDense.wi_0.weight",
+            "encoder.block.14.layer.1.DenseReluDense.wi_1.weight",
+            "encoder.block.14.layer.1.DenseReluDense.wo.weight",
+            "encoder.block.14.layer.1.layer_norm.weight",
+            "encoder.block.15.layer.0.SelfAttention.k.weight",
+            "encoder.block.15.layer.0.SelfAttention.o.weight",
+            "encoder.block.15.layer.0.SelfAttention.q.weight",
+            "encoder.block.15.layer.0.SelfAttention.v.weight",
+            "encoder.block.15.layer.0.layer_norm.weight",
+            "encoder.block.15.layer.1.DenseReluDense.wi_0.weight",
+            "encoder.block.15.layer.1.DenseReluDense.wi_1.weight",
+            "encoder.block.15.layer.1.DenseReluDense.wo.weight",
+            "encoder.block.15.layer.1.layer_norm.weight",
+            "encoder.block.16.layer.0.SelfAttention.k.weight",
+            "encoder.block.16.layer.0.SelfAttention.o.weight",
+            "encoder.block.16.layer.0.SelfAttention.q.weight",
+            "encoder.block.16.layer.0.SelfAttention.v.weight",
+            "encoder.block.16.layer.0.layer_norm.weight",
+            "encoder.block.16.layer.1.DenseReluDense.wi_0.weight",
+            "encoder.block.16.layer.1.DenseReluDense.wi_1.weight",
+            "encoder.block.16.layer.1.DenseReluDense.wo.weight",
+            "encoder.block.16.layer.1.layer_norm.weight",
+            "encoder.block.17.layer.0.SelfAttention.k.weight",
+            "encoder.block.17.layer.0.SelfAttention.o.weight",
+            "encoder.block.17.layer.0.SelfAttention.q.weight",
+            "encoder.block.17.layer.0.SelfAttention.v.weight",
+            "encoder.block.17.layer.0.layer_norm.weight",
+            "encoder.block.17.layer.1.DenseReluDense.wi_0.weight",
+            "encoder.block.17.layer.1.DenseReluDense.wi_1.weight",
+            "encoder.block.17.layer.1.DenseReluDense.wo.weight",
+            "encoder.block.17.layer.1.layer_norm.weight",
+            "encoder.block.18.layer.0.SelfAttention.k.weight",
+            "encoder.block.18.layer.0.SelfAttention.o.weight",
+            "encoder.block.18.layer.0.SelfAttention.q.weight",
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+            "encoder.block.18.layer.0.layer_norm.weight",
+            "encoder.block.18.layer.1.DenseReluDense.wi_0.weight",
+            "encoder.block.18.layer.1.DenseReluDense.wi_1.weight",
+            "encoder.block.18.layer.1.DenseReluDense.wo.weight",
+            "encoder.block.18.layer.1.layer_norm.weight",
+            "encoder.block.19.layer.0.SelfAttention.k.weight",
+            "encoder.block.19.layer.0.SelfAttention.o.weight",
+            "encoder.block.19.layer.0.SelfAttention.q.weight",
+            "encoder.block.19.layer.0.SelfAttention.v.weight",
+            "encoder.block.19.layer.0.layer_norm.weight",
+            "encoder.block.19.layer.1.DenseReluDense.wi_0.weight",
+            "encoder.block.19.layer.1.DenseReluDense.wi_1.weight",
+            "encoder.block.19.layer.1.DenseReluDense.wo.weight",
+            "encoder.block.19.layer.1.layer_norm.weight",
+            "encoder.block.2.layer.0.SelfAttention.k.weight",
+            "encoder.block.2.layer.0.SelfAttention.o.weight",
+            "encoder.block.2.layer.0.SelfAttention.q.weight",
+            "encoder.block.2.layer.0.SelfAttention.v.weight",
+            "encoder.block.2.layer.0.layer_norm.weight",
+            "encoder.block.2.layer.1.DenseReluDense.wi_0.weight",
+            "encoder.block.2.layer.1.DenseReluDense.wi_1.weight",
+            "encoder.block.2.layer.1.DenseReluDense.wo.weight",
+            "encoder.block.2.layer.1.layer_norm.weight",
+            "encoder.block.20.layer.0.SelfAttention.k.weight",
+            "encoder.block.20.layer.0.SelfAttention.o.weight",
+            "encoder.block.20.layer.0.SelfAttention.q.weight",
+            "encoder.block.20.layer.0.SelfAttention.v.weight",
+            "encoder.block.20.layer.0.layer_norm.weight",
+            "encoder.block.20.layer.1.DenseReluDense.wi_0.weight",
+            "encoder.block.20.layer.1.DenseReluDense.wi_1.weight",
+            "encoder.block.20.layer.1.DenseReluDense.wo.weight",
+            "encoder.block.20.layer.1.layer_norm.weight",
+            "encoder.block.21.layer.0.SelfAttention.k.weight",
+            "encoder.block.21.layer.0.SelfAttention.o.weight",
+            "encoder.block.21.layer.0.SelfAttention.q.weight",
+            "encoder.block.21.layer.0.SelfAttention.v.weight",
+            "encoder.block.21.layer.0.layer_norm.weight",
+            "encoder.block.21.layer.1.DenseReluDense.wi_0.weight",
+            "encoder.block.21.layer.1.DenseReluDense.wi_1.weight",
+            "encoder.block.21.layer.1.DenseReluDense.wo.weight",
+            "encoder.block.21.layer.1.layer_norm.weight",
+            "encoder.block.22.layer.0.SelfAttention.k.weight",
+            "encoder.block.22.layer.0.SelfAttention.o.weight",
+            "encoder.block.22.layer.0.SelfAttention.q.weight",
+            "encoder.block.22.layer.0.SelfAttention.v.weight",
+            "encoder.block.22.layer.0.layer_norm.weight",
+            "encoder.block.22.layer.1.DenseReluDense.wi_0.weight",
+            "encoder.block.22.layer.1.DenseReluDense.wi_1.weight",
+            "encoder.block.22.layer.1.DenseReluDense.wo.weight",
+            "encoder.block.22.layer.1.layer_norm.weight",
+            "encoder.block.23.layer.0.SelfAttention.k.weight",
+            "encoder.block.23.layer.0.SelfAttention.o.weight",
+            "encoder.block.23.layer.0.SelfAttention.q.weight",
+            "encoder.block.23.layer.0.SelfAttention.v.weight",
+            "encoder.block.23.layer.0.layer_norm.weight",
+            "encoder.block.23.layer.1.DenseReluDense.wi_0.weight",
+            "encoder.block.23.layer.1.DenseReluDense.wi_1.weight",
+            "encoder.block.23.layer.1.DenseReluDense.wo.weight",
+            "encoder.block.23.layer.1.layer_norm.weight",
+            "encoder.block.3.layer.0.SelfAttention.k.weight",
+            "encoder.block.3.layer.0.SelfAttention.o.weight",
+            "encoder.block.3.layer.0.SelfAttention.q.weight",
+            "encoder.block.3.layer.0.SelfAttention.v.weight",
+            "encoder.block.3.layer.0.layer_norm.weight",
+            "encoder.block.3.layer.1.DenseReluDense.wi_0.weight",
+            "encoder.block.3.layer.1.DenseReluDense.wi_1.weight",
+            "encoder.block.3.layer.1.DenseReluDense.wo.weight",
+            "encoder.block.3.layer.1.layer_norm.weight",
+            "encoder.block.4.layer.0.SelfAttention.k.weight",
+            "encoder.block.4.layer.0.SelfAttention.o.weight",
+            "encoder.block.4.layer.0.SelfAttention.q.weight",
+            "encoder.block.4.layer.0.SelfAttention.v.weight",
+            "encoder.block.4.layer.0.layer_norm.weight",
+            "encoder.block.4.layer.1.DenseReluDense.wi_0.weight",
+            "encoder.block.4.layer.1.DenseReluDense.wi_1.weight",
+            "encoder.block.4.layer.1.DenseReluDense.wo.weight",
+            "encoder.block.4.layer.1.layer_norm.weight",
+            "encoder.block.5.layer.0.SelfAttention.k.weight",
+            "encoder.block.5.layer.0.SelfAttention.o.weight",
+            "encoder.block.5.layer.0.SelfAttention.q.weight",
+            "encoder.block.5.layer.0.SelfAttention.v.weight",
+            "encoder.block.5.layer.0.layer_norm.weight",
+            "encoder.block.5.layer.1.DenseReluDense.wi_0.weight",
+            "encoder.block.5.layer.1.DenseReluDense.wi_1.weight",
+            "encoder.block.5.layer.1.DenseReluDense.wo.weight",
+            "encoder.block.5.layer.1.layer_norm.weight",
+            "encoder.block.6.layer.0.SelfAttention.k.weight",
+            "encoder.block.6.layer.0.SelfAttention.o.weight",
+            "encoder.block.6.layer.0.SelfAttention.q.weight",
+            "encoder.block.6.layer.0.SelfAttention.v.weight",
+            "encoder.block.6.layer.0.layer_norm.weight",
+            "encoder.block.6.layer.1.DenseReluDense.wi_0.weight",
+            "encoder.block.6.layer.1.DenseReluDense.wi_1.weight",
+            "encoder.block.6.layer.1.DenseReluDense.wo.weight",
+            "encoder.block.6.layer.1.layer_norm.weight",
+            "encoder.block.7.layer.0.SelfAttention.k.weight",
+            "encoder.block.7.layer.0.SelfAttention.o.weight",
+            "encoder.block.7.layer.0.SelfAttention.q.weight",
+            "encoder.block.7.layer.0.SelfAttention.v.weight",
+            "encoder.block.7.layer.0.layer_norm.weight",
+            "encoder.block.7.layer.1.DenseReluDense.wi_0.weight",
+            "encoder.block.7.layer.1.DenseReluDense.wi_1.weight",
+            "encoder.block.7.layer.1.DenseReluDense.wo.weight",
+            "encoder.block.7.layer.1.layer_norm.weight",
+            "encoder.block.8.layer.0.SelfAttention.k.weight",
+            "encoder.block.8.layer.0.SelfAttention.o.weight",
+            "encoder.block.8.layer.0.SelfAttention.q.weight",
+            "encoder.block.8.layer.0.SelfAttention.v.weight",
+            "encoder.block.8.layer.0.layer_norm.weight",
+            "encoder.block.8.layer.1.DenseReluDense.wi_0.weight",
+            "encoder.block.8.layer.1.DenseReluDense.wi_1.weight",
+            "encoder.block.8.layer.1.DenseReluDense.wo.weight",
+            "encoder.block.8.layer.1.layer_norm.weight",
+            "encoder.block.9.layer.0.SelfAttention.k.weight",
+            "encoder.block.9.layer.0.SelfAttention.o.weight",
+            "encoder.block.9.layer.0.SelfAttention.q.weight",
+            "encoder.block.9.layer.0.SelfAttention.v.weight",
+            "encoder.block.9.layer.0.layer_norm.weight",
+            "encoder.block.9.layer.1.DenseReluDense.wi_0.weight",
+            "encoder.block.9.layer.1.DenseReluDense.wi_1.weight",
+            "encoder.block.9.layer.1.DenseReluDense.wo.weight",
+            "encoder.block.9.layer.1.layer_norm.weight",
+            "encoder.embed_tokens.weight",
+            "encoder.final_layer_norm.weight",
+            "shared.weight",
+        ]
+        state_dict_ = {}
+        for name, param in state_dict.items():
+            if name in name_list:
+                state_dict_[name] = param
+        return state_dict_
+
diff --git a/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/sd3_vae_decoder.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/sd3_vae_decoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..55fd9c05bdb3b6efe4da417f77b7c7b6e1ef949b
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/sd3_vae_decoder.py
@@ -0,0 +1,81 @@
+import torch
+from .sd_vae_decoder import VAEAttentionBlock, SDVAEDecoderStateDictConverter
+from .sd_unet import ResnetBlock, UpSampler
+from .tiler import TileWorker
+
+
+
+class SD3VAEDecoder(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.scaling_factor = 1.5305 # Different from SD 1.x
+        self.shift_factor = 0.0609 # Different from SD 1.x
+        self.conv_in = torch.nn.Conv2d(16, 512, kernel_size=3, padding=1) # Different from SD 1.x
+
+        self.blocks = torch.nn.ModuleList([
+            # UNetMidBlock2D
+            ResnetBlock(512, 512, eps=1e-6),
+            VAEAttentionBlock(1, 512, 512, 1, eps=1e-6),
+            ResnetBlock(512, 512, eps=1e-6),
+            # UpDecoderBlock2D
+            ResnetBlock(512, 512, eps=1e-6),
+            ResnetBlock(512, 512, eps=1e-6),
+            ResnetBlock(512, 512, eps=1e-6),
+            UpSampler(512),
+            # UpDecoderBlock2D
+            ResnetBlock(512, 512, eps=1e-6),
+            ResnetBlock(512, 512, eps=1e-6),
+            ResnetBlock(512, 512, eps=1e-6),
+            UpSampler(512),
+            # UpDecoderBlock2D
+            ResnetBlock(512, 256, eps=1e-6),
+            ResnetBlock(256, 256, eps=1e-6),
+            ResnetBlock(256, 256, eps=1e-6),
+            UpSampler(256),
+            # UpDecoderBlock2D
+            ResnetBlock(256, 128, eps=1e-6),
+            ResnetBlock(128, 128, eps=1e-6),
+            ResnetBlock(128, 128, eps=1e-6),
+        ])
+
+        self.conv_norm_out = torch.nn.GroupNorm(num_channels=128, num_groups=32, eps=1e-6)
+        self.conv_act = torch.nn.SiLU()
+        self.conv_out = torch.nn.Conv2d(128, 3, kernel_size=3, padding=1)
+    
+    def tiled_forward(self, sample, tile_size=64, tile_stride=32):
+        hidden_states = TileWorker().tiled_forward(
+            lambda x: self.forward(x),
+            sample,
+            tile_size,
+            tile_stride,
+            tile_device=sample.device,
+            tile_dtype=sample.dtype
+        )
+        return hidden_states
+
+    def forward(self, sample, tiled=False, tile_size=64, tile_stride=32, **kwargs):
+        # For VAE Decoder, we do not need to apply the tiler on each layer.
+        if tiled:
+            return self.tiled_forward(sample, tile_size=tile_size, tile_stride=tile_stride)
+
+        # 1. pre-process
+        hidden_states = sample / self.scaling_factor + self.shift_factor
+        hidden_states = self.conv_in(hidden_states)
+        time_emb = None
+        text_emb = None
+        res_stack = None
+
+        # 2. blocks
+        for i, block in enumerate(self.blocks):
+            hidden_states, time_emb, text_emb, res_stack = block(hidden_states, time_emb, text_emb, res_stack)
+        
+        # 3. output
+        hidden_states = self.conv_norm_out(hidden_states)
+        hidden_states = self.conv_act(hidden_states)
+        hidden_states = self.conv_out(hidden_states)
+
+        return hidden_states
+    
+    @staticmethod
+    def state_dict_converter():
+        return SDVAEDecoderStateDictConverter()
\ No newline at end of file
diff --git a/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/sd3_vae_encoder.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/sd3_vae_encoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..c486866b889093ac501c54e224f67c5428cf81c8
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/sd3_vae_encoder.py
@@ -0,0 +1,95 @@
+import torch
+from .sd_unet import ResnetBlock, DownSampler
+from .sd_vae_encoder import VAEAttentionBlock, SDVAEEncoderStateDictConverter
+from .tiler import TileWorker
+from einops import rearrange
+
+
+class SD3VAEEncoder(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.scaling_factor = 1.5305 # Different from SD 1.x
+        self.shift_factor = 0.0609 # Different from SD 1.x
+        self.conv_in = torch.nn.Conv2d(3, 128, kernel_size=3, padding=1)
+
+        self.blocks = torch.nn.ModuleList([
+            # DownEncoderBlock2D
+            ResnetBlock(128, 128, eps=1e-6),
+            ResnetBlock(128, 128, eps=1e-6),
+            DownSampler(128, padding=0, extra_padding=True),
+            # DownEncoderBlock2D
+            ResnetBlock(128, 256, eps=1e-6),
+            ResnetBlock(256, 256, eps=1e-6),
+            DownSampler(256, padding=0, extra_padding=True),
+            # DownEncoderBlock2D
+            ResnetBlock(256, 512, eps=1e-6),
+            ResnetBlock(512, 512, eps=1e-6),
+            DownSampler(512, padding=0, extra_padding=True),
+            # DownEncoderBlock2D
+            ResnetBlock(512, 512, eps=1e-6),
+            ResnetBlock(512, 512, eps=1e-6),
+            # UNetMidBlock2D
+            ResnetBlock(512, 512, eps=1e-6),
+            VAEAttentionBlock(1, 512, 512, 1, eps=1e-6),
+            ResnetBlock(512, 512, eps=1e-6),
+        ])
+
+        self.conv_norm_out = torch.nn.GroupNorm(num_channels=512, num_groups=32, eps=1e-6)
+        self.conv_act = torch.nn.SiLU()
+        self.conv_out = torch.nn.Conv2d(512, 32, kernel_size=3, padding=1)
+
+    def tiled_forward(self, sample, tile_size=64, tile_stride=32):
+        hidden_states = TileWorker().tiled_forward(
+            lambda x: self.forward(x),
+            sample,
+            tile_size,
+            tile_stride,
+            tile_device=sample.device,
+            tile_dtype=sample.dtype
+        )
+        return hidden_states
+
+    def forward(self, sample, tiled=False, tile_size=64, tile_stride=32, **kwargs):
+        # For VAE Decoder, we do not need to apply the tiler on each layer.
+        if tiled:
+            return self.tiled_forward(sample, tile_size=tile_size, tile_stride=tile_stride)
+        
+        # 1. pre-process
+        hidden_states = self.conv_in(sample)
+        time_emb = None
+        text_emb = None
+        res_stack = None
+
+        # 2. blocks
+        for i, block in enumerate(self.blocks):
+            hidden_states, time_emb, text_emb, res_stack = block(hidden_states, time_emb, text_emb, res_stack)
+        
+        # 3. output
+        hidden_states = self.conv_norm_out(hidden_states)
+        hidden_states = self.conv_act(hidden_states)
+        hidden_states = self.conv_out(hidden_states)
+        hidden_states = hidden_states[:, :16]
+        hidden_states = (hidden_states - self.shift_factor) * self.scaling_factor
+
+        return hidden_states
+    
+    def encode_video(self, sample, batch_size=8):
+        B = sample.shape[0]
+        hidden_states = []
+
+        for i in range(0, sample.shape[2], batch_size):
+
+            j = min(i + batch_size, sample.shape[2])
+            sample_batch = rearrange(sample[:,:,i:j], "B C T H W -> (B T) C H W")
+
+            hidden_states_batch = self(sample_batch)
+            hidden_states_batch = rearrange(hidden_states_batch, "(B T) C H W -> B C T H W", B=B)
+
+            hidden_states.append(hidden_states_batch)
+        
+        hidden_states = torch.concat(hidden_states, dim=2)
+        return hidden_states
+    
+    @staticmethod
+    def state_dict_converter():
+        return SDVAEEncoderStateDictConverter()
diff --git a/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/sd_controlnet.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/sd_controlnet.py
new file mode 100644
index 0000000000000000000000000000000000000000..910e0dbae8dc6647e6e478f79c239450cefb2027
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/sd_controlnet.py
@@ -0,0 +1,589 @@
+import torch
+from .sd_unet import Timesteps, ResnetBlock, AttentionBlock, PushBlock, DownSampler
+from .tiler import TileWorker
+
+
+class ControlNetConditioningLayer(torch.nn.Module):
+    def __init__(self, channels = (3, 16, 32, 96, 256, 320)):
+        super().__init__()
+        self.blocks = torch.nn.ModuleList([])
+        self.blocks.append(torch.nn.Conv2d(channels[0], channels[1], kernel_size=3, padding=1))
+        self.blocks.append(torch.nn.SiLU())
+        for i in range(1, len(channels) - 2):
+            self.blocks.append(torch.nn.Conv2d(channels[i], channels[i], kernel_size=3, padding=1))
+            self.blocks.append(torch.nn.SiLU())
+            self.blocks.append(torch.nn.Conv2d(channels[i], channels[i+1], kernel_size=3, padding=1, stride=2))
+            self.blocks.append(torch.nn.SiLU())
+        self.blocks.append(torch.nn.Conv2d(channels[-2], channels[-1], kernel_size=3, padding=1))
+
+    def forward(self, conditioning):
+        for block in self.blocks:
+            conditioning = block(conditioning)
+        return conditioning
+
+
+class SDControlNet(torch.nn.Module):
+    def __init__(self, global_pool=False):
+        super().__init__()
+        self.time_proj = Timesteps(320)
+        self.time_embedding = torch.nn.Sequential(
+            torch.nn.Linear(320, 1280),
+            torch.nn.SiLU(),
+            torch.nn.Linear(1280, 1280)
+        )
+        self.conv_in = torch.nn.Conv2d(4, 320, kernel_size=3, padding=1)
+
+        self.controlnet_conv_in = ControlNetConditioningLayer(channels=(3, 16, 32, 96, 256, 320))
+
+        self.blocks = torch.nn.ModuleList([
+            # CrossAttnDownBlock2D
+            ResnetBlock(320, 320, 1280),
+            AttentionBlock(8, 40, 320, 1, 768),
+            PushBlock(),
+            ResnetBlock(320, 320, 1280),
+            AttentionBlock(8, 40, 320, 1, 768),
+            PushBlock(),
+            DownSampler(320),
+            PushBlock(),
+            # CrossAttnDownBlock2D
+            ResnetBlock(320, 640, 1280),
+            AttentionBlock(8, 80, 640, 1, 768),
+            PushBlock(),
+            ResnetBlock(640, 640, 1280),
+            AttentionBlock(8, 80, 640, 1, 768),
+            PushBlock(),
+            DownSampler(640),
+            PushBlock(),
+            # CrossAttnDownBlock2D
+            ResnetBlock(640, 1280, 1280),
+            AttentionBlock(8, 160, 1280, 1, 768),
+            PushBlock(),
+            ResnetBlock(1280, 1280, 1280),
+            AttentionBlock(8, 160, 1280, 1, 768),
+            PushBlock(),
+            DownSampler(1280),
+            PushBlock(),
+            # DownBlock2D
+            ResnetBlock(1280, 1280, 1280),
+            PushBlock(),
+            ResnetBlock(1280, 1280, 1280),
+            PushBlock(),
+            # UNetMidBlock2DCrossAttn
+            ResnetBlock(1280, 1280, 1280),
+            AttentionBlock(8, 160, 1280, 1, 768),
+            ResnetBlock(1280, 1280, 1280),
+            PushBlock()
+        ])
+
+        self.controlnet_blocks = torch.nn.ModuleList([
+            torch.nn.Conv2d(320, 320, kernel_size=(1, 1)),
+            torch.nn.Conv2d(320, 320, kernel_size=(1, 1), bias=False),
+            torch.nn.Conv2d(320, 320, kernel_size=(1, 1), bias=False),
+            torch.nn.Conv2d(320, 320, kernel_size=(1, 1), bias=False),
+            torch.nn.Conv2d(640, 640, kernel_size=(1, 1)),
+            torch.nn.Conv2d(640, 640, kernel_size=(1, 1), bias=False),
+            torch.nn.Conv2d(640, 640, kernel_size=(1, 1), bias=False),
+            torch.nn.Conv2d(1280, 1280, kernel_size=(1, 1)),
+            torch.nn.Conv2d(1280, 1280, kernel_size=(1, 1), bias=False),
+            torch.nn.Conv2d(1280, 1280, kernel_size=(1, 1), bias=False),
+            torch.nn.Conv2d(1280, 1280, kernel_size=(1, 1), bias=False),
+            torch.nn.Conv2d(1280, 1280, kernel_size=(1, 1), bias=False),
+            torch.nn.Conv2d(1280, 1280, kernel_size=(1, 1), bias=False),
+        ])
+
+        self.global_pool = global_pool
+
+    def forward(
+        self,
+        sample, timestep, encoder_hidden_states, conditioning,
+        tiled=False, tile_size=64, tile_stride=32,
+        **kwargs
+    ):
+        # 1. time
+        time_emb = self.time_proj(timestep).to(sample.dtype)
+        time_emb = self.time_embedding(time_emb)
+        time_emb = time_emb.repeat(sample.shape[0], 1)
+
+        # 2. pre-process
+        height, width = sample.shape[2], sample.shape[3]
+        hidden_states = self.conv_in(sample) + self.controlnet_conv_in(conditioning)
+        text_emb = encoder_hidden_states
+        res_stack = [hidden_states]
+
+        # 3. blocks
+        for i, block in enumerate(self.blocks):
+            if tiled and not isinstance(block, PushBlock):
+                _, _, inter_height, _ = hidden_states.shape
+                resize_scale = inter_height / height
+                hidden_states = TileWorker().tiled_forward(
+                    lambda x: block(x, time_emb, text_emb, res_stack)[0],
+                    hidden_states,
+                    int(tile_size * resize_scale),
+                    int(tile_stride * resize_scale),
+                    tile_device=hidden_states.device,
+                    tile_dtype=hidden_states.dtype
+                )
+            else:
+                hidden_states, _, _, _ = block(hidden_states, time_emb, text_emb, res_stack)
+
+        # 4. ControlNet blocks
+        controlnet_res_stack = [block(res) for block, res in zip(self.controlnet_blocks, res_stack)]
+
+        # pool
+        if self.global_pool:
+            controlnet_res_stack = [res.mean(dim=(2, 3), keepdim=True) for res in controlnet_res_stack]
+
+        return controlnet_res_stack
+
+    @staticmethod
+    def state_dict_converter():
+        return SDControlNetStateDictConverter()
+
+
+class SDControlNetStateDictConverter:
+    def __init__(self):
+        pass
+
+    def from_diffusers(self, state_dict):
+        # architecture
+        block_types = [
+            'ResnetBlock', 'AttentionBlock', 'PushBlock', 'ResnetBlock', 'AttentionBlock', 'PushBlock', 'DownSampler', 'PushBlock',
+            'ResnetBlock', 'AttentionBlock', 'PushBlock', 'ResnetBlock', 'AttentionBlock', 'PushBlock', 'DownSampler', 'PushBlock',
+            'ResnetBlock', 'AttentionBlock', 'PushBlock', 'ResnetBlock', 'AttentionBlock', 'PushBlock', 'DownSampler', 'PushBlock',
+            'ResnetBlock', 'PushBlock', 'ResnetBlock', 'PushBlock', 
+            'ResnetBlock', 'AttentionBlock', 'ResnetBlock',
+            'PopBlock', 'ResnetBlock', 'PopBlock', 'ResnetBlock', 'PopBlock', 'ResnetBlock', 'UpSampler',
+            'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'UpSampler',
+            'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'UpSampler',
+            'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock'
+        ]
+
+        # controlnet_rename_dict
+        controlnet_rename_dict = {
+            "controlnet_cond_embedding.conv_in.weight": "controlnet_conv_in.blocks.0.weight",
+            "controlnet_cond_embedding.conv_in.bias": "controlnet_conv_in.blocks.0.bias",
+            "controlnet_cond_embedding.blocks.0.weight": "controlnet_conv_in.blocks.2.weight",
+            "controlnet_cond_embedding.blocks.0.bias": "controlnet_conv_in.blocks.2.bias",
+            "controlnet_cond_embedding.blocks.1.weight": "controlnet_conv_in.blocks.4.weight",
+            "controlnet_cond_embedding.blocks.1.bias": "controlnet_conv_in.blocks.4.bias",
+            "controlnet_cond_embedding.blocks.2.weight": "controlnet_conv_in.blocks.6.weight",
+            "controlnet_cond_embedding.blocks.2.bias": "controlnet_conv_in.blocks.6.bias",
+            "controlnet_cond_embedding.blocks.3.weight": "controlnet_conv_in.blocks.8.weight",
+            "controlnet_cond_embedding.blocks.3.bias": "controlnet_conv_in.blocks.8.bias",
+            "controlnet_cond_embedding.blocks.4.weight": "controlnet_conv_in.blocks.10.weight",
+            "controlnet_cond_embedding.blocks.4.bias": "controlnet_conv_in.blocks.10.bias",
+            "controlnet_cond_embedding.blocks.5.weight": "controlnet_conv_in.blocks.12.weight",
+            "controlnet_cond_embedding.blocks.5.bias": "controlnet_conv_in.blocks.12.bias",
+            "controlnet_cond_embedding.conv_out.weight": "controlnet_conv_in.blocks.14.weight",
+            "controlnet_cond_embedding.conv_out.bias": "controlnet_conv_in.blocks.14.bias",
+        }
+
+        # Rename each parameter
+        name_list = sorted([name for name in state_dict])
+        rename_dict = {}
+        block_id = {"ResnetBlock": -1, "AttentionBlock": -1, "DownSampler": -1, "UpSampler": -1}
+        last_block_type_with_id = {"ResnetBlock": "", "AttentionBlock": "", "DownSampler": "", "UpSampler": ""}
+        for name in name_list:
+            names = name.split(".")
+            if names[0] in ["conv_in", "conv_norm_out", "conv_out"]:
+                pass
+            elif name in controlnet_rename_dict:
+                names = controlnet_rename_dict[name].split(".")
+            elif names[0] == "controlnet_down_blocks":
+                names[0] = "controlnet_blocks"
+            elif names[0] == "controlnet_mid_block":
+                names = ["controlnet_blocks", "12", names[-1]]
+            elif names[0] in ["time_embedding", "add_embedding"]:
+                if names[0] == "add_embedding":
+                    names[0] = "add_time_embedding"
+                names[1] = {"linear_1": "0", "linear_2": "2"}[names[1]]
+            elif names[0] in ["down_blocks", "mid_block", "up_blocks"]:
+                if names[0] == "mid_block":
+                    names.insert(1, "0")
+                block_type = {"resnets": "ResnetBlock", "attentions": "AttentionBlock", "downsamplers": "DownSampler", "upsamplers": "UpSampler"}[names[2]]
+                block_type_with_id = ".".join(names[:4])
+                if block_type_with_id != last_block_type_with_id[block_type]:
+                    block_id[block_type] += 1
+                last_block_type_with_id[block_type] = block_type_with_id
+                while block_id[block_type] < len(block_types) and block_types[block_id[block_type]] != block_type:
+                    block_id[block_type] += 1
+                block_type_with_id = ".".join(names[:4])
+                names = ["blocks", str(block_id[block_type])] + names[4:]
+                if "ff" in names:
+                    ff_index = names.index("ff")
+                    component = ".".join(names[ff_index:ff_index+3])
+                    component = {"ff.net.0": "act_fn", "ff.net.2": "ff"}[component]
+                    names = names[:ff_index] + [component] + names[ff_index+3:]
+                if "to_out" in names:
+                    names.pop(names.index("to_out") + 1)
+            else:
+                raise ValueError(f"Unknown parameters: {name}")
+            rename_dict[name] = ".".join(names)
+
+        # Convert state_dict
+        state_dict_ = {}
+        for name, param in state_dict.items():
+            if ".proj_in." in name or ".proj_out." in name:
+                param = param.squeeze()
+            if rename_dict[name] in [
+                "controlnet_blocks.1.bias", "controlnet_blocks.2.bias", "controlnet_blocks.3.bias", "controlnet_blocks.5.bias", "controlnet_blocks.6.bias",
+                "controlnet_blocks.8.bias", "controlnet_blocks.9.bias", "controlnet_blocks.10.bias", "controlnet_blocks.11.bias", "controlnet_blocks.12.bias"
+            ]:
+                continue
+            state_dict_[rename_dict[name]] = param
+        return state_dict_
+    
+    def from_civitai(self, state_dict):
+        if "mid_block.resnets.1.time_emb_proj.weight" in state_dict:
+            # For controlnets in diffusers format
+            return self.from_diffusers(state_dict)
+        rename_dict = {
+            "control_model.time_embed.0.weight": "time_embedding.0.weight",
+            "control_model.time_embed.0.bias": "time_embedding.0.bias",
+            "control_model.time_embed.2.weight": "time_embedding.2.weight",
+            "control_model.time_embed.2.bias": "time_embedding.2.bias",
+            "control_model.input_blocks.0.0.weight": "conv_in.weight",
+            "control_model.input_blocks.0.0.bias": "conv_in.bias",
+            "control_model.input_blocks.1.0.in_layers.0.weight": "blocks.0.norm1.weight",
+            "control_model.input_blocks.1.0.in_layers.0.bias": "blocks.0.norm1.bias",
+            "control_model.input_blocks.1.0.in_layers.2.weight": "blocks.0.conv1.weight",
+            "control_model.input_blocks.1.0.in_layers.2.bias": "blocks.0.conv1.bias",
+            "control_model.input_blocks.1.0.emb_layers.1.weight": "blocks.0.time_emb_proj.weight",
+            "control_model.input_blocks.1.0.emb_layers.1.bias": "blocks.0.time_emb_proj.bias",
+            "control_model.input_blocks.1.0.out_layers.0.weight": "blocks.0.norm2.weight",
+            "control_model.input_blocks.1.0.out_layers.0.bias": "blocks.0.norm2.bias",
+            "control_model.input_blocks.1.0.out_layers.3.weight": "blocks.0.conv2.weight",
+            "control_model.input_blocks.1.0.out_layers.3.bias": "blocks.0.conv2.bias",
+            "control_model.input_blocks.1.1.norm.weight": "blocks.1.norm.weight",
+            "control_model.input_blocks.1.1.norm.bias": "blocks.1.norm.bias",
+            "control_model.input_blocks.1.1.proj_in.weight": "blocks.1.proj_in.weight",
+            "control_model.input_blocks.1.1.proj_in.bias": "blocks.1.proj_in.bias",
+            "control_model.input_blocks.1.1.transformer_blocks.0.attn1.to_q.weight": "blocks.1.transformer_blocks.0.attn1.to_q.weight",
+            "control_model.input_blocks.1.1.transformer_blocks.0.attn1.to_k.weight": "blocks.1.transformer_blocks.0.attn1.to_k.weight",
+            "control_model.input_blocks.1.1.transformer_blocks.0.attn1.to_v.weight": "blocks.1.transformer_blocks.0.attn1.to_v.weight",
+            "control_model.input_blocks.1.1.transformer_blocks.0.attn1.to_out.0.weight": "blocks.1.transformer_blocks.0.attn1.to_out.weight",
+            "control_model.input_blocks.1.1.transformer_blocks.0.attn1.to_out.0.bias": "blocks.1.transformer_blocks.0.attn1.to_out.bias",
+            "control_model.input_blocks.1.1.transformer_blocks.0.ff.net.0.proj.weight": "blocks.1.transformer_blocks.0.act_fn.proj.weight",
+            "control_model.input_blocks.1.1.transformer_blocks.0.ff.net.0.proj.bias": "blocks.1.transformer_blocks.0.act_fn.proj.bias",
+            "control_model.input_blocks.1.1.transformer_blocks.0.ff.net.2.weight": "blocks.1.transformer_blocks.0.ff.weight",
+            "control_model.input_blocks.1.1.transformer_blocks.0.ff.net.2.bias": "blocks.1.transformer_blocks.0.ff.bias",
+            "control_model.input_blocks.1.1.transformer_blocks.0.attn2.to_q.weight": "blocks.1.transformer_blocks.0.attn2.to_q.weight",
+            "control_model.input_blocks.1.1.transformer_blocks.0.attn2.to_k.weight": "blocks.1.transformer_blocks.0.attn2.to_k.weight",
+            "control_model.input_blocks.1.1.transformer_blocks.0.attn2.to_v.weight": "blocks.1.transformer_blocks.0.attn2.to_v.weight",
+            "control_model.input_blocks.1.1.transformer_blocks.0.attn2.to_out.0.weight": "blocks.1.transformer_blocks.0.attn2.to_out.weight",
+            "control_model.input_blocks.1.1.transformer_blocks.0.attn2.to_out.0.bias": "blocks.1.transformer_blocks.0.attn2.to_out.bias",
+            "control_model.input_blocks.1.1.transformer_blocks.0.norm1.weight": "blocks.1.transformer_blocks.0.norm1.weight",
+            "control_model.input_blocks.1.1.transformer_blocks.0.norm1.bias": "blocks.1.transformer_blocks.0.norm1.bias",
+            "control_model.input_blocks.1.1.transformer_blocks.0.norm2.weight": "blocks.1.transformer_blocks.0.norm2.weight",
+            "control_model.input_blocks.1.1.transformer_blocks.0.norm2.bias": "blocks.1.transformer_blocks.0.norm2.bias",
+            "control_model.input_blocks.1.1.transformer_blocks.0.norm3.weight": "blocks.1.transformer_blocks.0.norm3.weight",
+            "control_model.input_blocks.1.1.transformer_blocks.0.norm3.bias": "blocks.1.transformer_blocks.0.norm3.bias",
+            "control_model.input_blocks.1.1.proj_out.weight": "blocks.1.proj_out.weight",
+            "control_model.input_blocks.1.1.proj_out.bias": "blocks.1.proj_out.bias",
+            "control_model.input_blocks.2.0.in_layers.0.weight": "blocks.3.norm1.weight",
+            "control_model.input_blocks.2.0.in_layers.0.bias": "blocks.3.norm1.bias",
+            "control_model.input_blocks.2.0.in_layers.2.weight": "blocks.3.conv1.weight",
+            "control_model.input_blocks.2.0.in_layers.2.bias": "blocks.3.conv1.bias",
+            "control_model.input_blocks.2.0.emb_layers.1.weight": "blocks.3.time_emb_proj.weight",
+            "control_model.input_blocks.2.0.emb_layers.1.bias": "blocks.3.time_emb_proj.bias",
+            "control_model.input_blocks.2.0.out_layers.0.weight": "blocks.3.norm2.weight",
+            "control_model.input_blocks.2.0.out_layers.0.bias": "blocks.3.norm2.bias",
+            "control_model.input_blocks.2.0.out_layers.3.weight": "blocks.3.conv2.weight",
+            "control_model.input_blocks.2.0.out_layers.3.bias": "blocks.3.conv2.bias",
+            "control_model.input_blocks.2.1.norm.weight": "blocks.4.norm.weight",
+            "control_model.input_blocks.2.1.norm.bias": "blocks.4.norm.bias",
+            "control_model.input_blocks.2.1.proj_in.weight": "blocks.4.proj_in.weight",
+            "control_model.input_blocks.2.1.proj_in.bias": "blocks.4.proj_in.bias",
+            "control_model.input_blocks.2.1.transformer_blocks.0.attn1.to_q.weight": "blocks.4.transformer_blocks.0.attn1.to_q.weight",
+            "control_model.input_blocks.2.1.transformer_blocks.0.attn1.to_k.weight": "blocks.4.transformer_blocks.0.attn1.to_k.weight",
+            "control_model.input_blocks.2.1.transformer_blocks.0.attn1.to_v.weight": "blocks.4.transformer_blocks.0.attn1.to_v.weight",
+            "control_model.input_blocks.2.1.transformer_blocks.0.attn1.to_out.0.weight": "blocks.4.transformer_blocks.0.attn1.to_out.weight",
+            "control_model.input_blocks.2.1.transformer_blocks.0.attn1.to_out.0.bias": "blocks.4.transformer_blocks.0.attn1.to_out.bias",
+            "control_model.input_blocks.2.1.transformer_blocks.0.ff.net.0.proj.weight": "blocks.4.transformer_blocks.0.act_fn.proj.weight",
+            "control_model.input_blocks.2.1.transformer_blocks.0.ff.net.0.proj.bias": "blocks.4.transformer_blocks.0.act_fn.proj.bias",
+            "control_model.input_blocks.2.1.transformer_blocks.0.ff.net.2.weight": "blocks.4.transformer_blocks.0.ff.weight",
+            "control_model.input_blocks.2.1.transformer_blocks.0.ff.net.2.bias": "blocks.4.transformer_blocks.0.ff.bias",
+            "control_model.input_blocks.2.1.transformer_blocks.0.attn2.to_q.weight": "blocks.4.transformer_blocks.0.attn2.to_q.weight",
+            "control_model.input_blocks.2.1.transformer_blocks.0.attn2.to_k.weight": "blocks.4.transformer_blocks.0.attn2.to_k.weight",
+            "control_model.input_blocks.2.1.transformer_blocks.0.attn2.to_v.weight": "blocks.4.transformer_blocks.0.attn2.to_v.weight",
+            "control_model.input_blocks.2.1.transformer_blocks.0.attn2.to_out.0.weight": "blocks.4.transformer_blocks.0.attn2.to_out.weight",
+            "control_model.input_blocks.2.1.transformer_blocks.0.attn2.to_out.0.bias": "blocks.4.transformer_blocks.0.attn2.to_out.bias",
+            "control_model.input_blocks.2.1.transformer_blocks.0.norm1.weight": "blocks.4.transformer_blocks.0.norm1.weight",
+            "control_model.input_blocks.2.1.transformer_blocks.0.norm1.bias": "blocks.4.transformer_blocks.0.norm1.bias",
+            "control_model.input_blocks.2.1.transformer_blocks.0.norm2.weight": "blocks.4.transformer_blocks.0.norm2.weight",
+            "control_model.input_blocks.2.1.transformer_blocks.0.norm2.bias": "blocks.4.transformer_blocks.0.norm2.bias",
+            "control_model.input_blocks.2.1.transformer_blocks.0.norm3.weight": "blocks.4.transformer_blocks.0.norm3.weight",
+            "control_model.input_blocks.2.1.transformer_blocks.0.norm3.bias": "blocks.4.transformer_blocks.0.norm3.bias",
+            "control_model.input_blocks.2.1.proj_out.weight": "blocks.4.proj_out.weight",
+            "control_model.input_blocks.2.1.proj_out.bias": "blocks.4.proj_out.bias",
+            "control_model.input_blocks.3.0.op.weight": "blocks.6.conv.weight",
+            "control_model.input_blocks.3.0.op.bias": "blocks.6.conv.bias",
+            "control_model.input_blocks.4.0.in_layers.0.weight": "blocks.8.norm1.weight",
+            "control_model.input_blocks.4.0.in_layers.0.bias": "blocks.8.norm1.bias",
+            "control_model.input_blocks.4.0.in_layers.2.weight": "blocks.8.conv1.weight",
+            "control_model.input_blocks.4.0.in_layers.2.bias": "blocks.8.conv1.bias",
+            "control_model.input_blocks.4.0.emb_layers.1.weight": "blocks.8.time_emb_proj.weight",
+            "control_model.input_blocks.4.0.emb_layers.1.bias": "blocks.8.time_emb_proj.bias",
+            "control_model.input_blocks.4.0.out_layers.0.weight": "blocks.8.norm2.weight",
+            "control_model.input_blocks.4.0.out_layers.0.bias": "blocks.8.norm2.bias",
+            "control_model.input_blocks.4.0.out_layers.3.weight": "blocks.8.conv2.weight",
+            "control_model.input_blocks.4.0.out_layers.3.bias": "blocks.8.conv2.bias",
+            "control_model.input_blocks.4.0.skip_connection.weight": "blocks.8.conv_shortcut.weight",
+            "control_model.input_blocks.4.0.skip_connection.bias": "blocks.8.conv_shortcut.bias",
+            "control_model.input_blocks.4.1.norm.weight": "blocks.9.norm.weight",
+            "control_model.input_blocks.4.1.norm.bias": "blocks.9.norm.bias",
+            "control_model.input_blocks.4.1.proj_in.weight": "blocks.9.proj_in.weight",
+            "control_model.input_blocks.4.1.proj_in.bias": "blocks.9.proj_in.bias",
+            "control_model.input_blocks.4.1.transformer_blocks.0.attn1.to_q.weight": "blocks.9.transformer_blocks.0.attn1.to_q.weight",
+            "control_model.input_blocks.4.1.transformer_blocks.0.attn1.to_k.weight": "blocks.9.transformer_blocks.0.attn1.to_k.weight",
+            "control_model.input_blocks.4.1.transformer_blocks.0.attn1.to_v.weight": "blocks.9.transformer_blocks.0.attn1.to_v.weight",
+            "control_model.input_blocks.4.1.transformer_blocks.0.attn1.to_out.0.weight": "blocks.9.transformer_blocks.0.attn1.to_out.weight",
+            "control_model.input_blocks.4.1.transformer_blocks.0.attn1.to_out.0.bias": "blocks.9.transformer_blocks.0.attn1.to_out.bias",
+            "control_model.input_blocks.4.1.transformer_blocks.0.ff.net.0.proj.weight": "blocks.9.transformer_blocks.0.act_fn.proj.weight",
+            "control_model.input_blocks.4.1.transformer_blocks.0.ff.net.0.proj.bias": "blocks.9.transformer_blocks.0.act_fn.proj.bias",
+            "control_model.input_blocks.4.1.transformer_blocks.0.ff.net.2.weight": "blocks.9.transformer_blocks.0.ff.weight",
+            "control_model.input_blocks.4.1.transformer_blocks.0.ff.net.2.bias": "blocks.9.transformer_blocks.0.ff.bias",
+            "control_model.input_blocks.4.1.transformer_blocks.0.attn2.to_q.weight": "blocks.9.transformer_blocks.0.attn2.to_q.weight",
+            "control_model.input_blocks.4.1.transformer_blocks.0.attn2.to_k.weight": "blocks.9.transformer_blocks.0.attn2.to_k.weight",
+            "control_model.input_blocks.4.1.transformer_blocks.0.attn2.to_v.weight": "blocks.9.transformer_blocks.0.attn2.to_v.weight",
+            "control_model.input_blocks.4.1.transformer_blocks.0.attn2.to_out.0.weight": "blocks.9.transformer_blocks.0.attn2.to_out.weight",
+            "control_model.input_blocks.4.1.transformer_blocks.0.attn2.to_out.0.bias": "blocks.9.transformer_blocks.0.attn2.to_out.bias",
+            "control_model.input_blocks.4.1.transformer_blocks.0.norm1.weight": "blocks.9.transformer_blocks.0.norm1.weight",
+            "control_model.input_blocks.4.1.transformer_blocks.0.norm1.bias": "blocks.9.transformer_blocks.0.norm1.bias",
+            "control_model.input_blocks.4.1.transformer_blocks.0.norm2.weight": "blocks.9.transformer_blocks.0.norm2.weight",
+            "control_model.input_blocks.4.1.transformer_blocks.0.norm2.bias": "blocks.9.transformer_blocks.0.norm2.bias",
+            "control_model.input_blocks.4.1.transformer_blocks.0.norm3.weight": "blocks.9.transformer_blocks.0.norm3.weight",
+            "control_model.input_blocks.4.1.transformer_blocks.0.norm3.bias": "blocks.9.transformer_blocks.0.norm3.bias",
+            "control_model.input_blocks.4.1.proj_out.weight": "blocks.9.proj_out.weight",
+            "control_model.input_blocks.4.1.proj_out.bias": "blocks.9.proj_out.bias",
+            "control_model.input_blocks.5.0.in_layers.0.weight": "blocks.11.norm1.weight",
+            "control_model.input_blocks.5.0.in_layers.0.bias": "blocks.11.norm1.bias",
+            "control_model.input_blocks.5.0.in_layers.2.weight": "blocks.11.conv1.weight",
+            "control_model.input_blocks.5.0.in_layers.2.bias": "blocks.11.conv1.bias",
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+            "control_model.middle_block.1.transformer_blocks.0.norm2.bias": "blocks.29.transformer_blocks.0.norm2.bias",
+            "control_model.middle_block.1.transformer_blocks.0.norm3.weight": "blocks.29.transformer_blocks.0.norm3.weight",
+            "control_model.middle_block.1.transformer_blocks.0.norm3.bias": "blocks.29.transformer_blocks.0.norm3.bias",
+            "control_model.middle_block.1.proj_out.weight": "blocks.29.proj_out.weight",
+            "control_model.middle_block.1.proj_out.bias": "blocks.29.proj_out.bias",
+            "control_model.middle_block.2.in_layers.0.weight": "blocks.30.norm1.weight",
+            "control_model.middle_block.2.in_layers.0.bias": "blocks.30.norm1.bias",
+            "control_model.middle_block.2.in_layers.2.weight": "blocks.30.conv1.weight",
+            "control_model.middle_block.2.in_layers.2.bias": "blocks.30.conv1.bias",
+            "control_model.middle_block.2.emb_layers.1.weight": "blocks.30.time_emb_proj.weight",
+            "control_model.middle_block.2.emb_layers.1.bias": "blocks.30.time_emb_proj.bias",
+            "control_model.middle_block.2.out_layers.0.weight": "blocks.30.norm2.weight",
+            "control_model.middle_block.2.out_layers.0.bias": "blocks.30.norm2.bias",
+            "control_model.middle_block.2.out_layers.3.weight": "blocks.30.conv2.weight",
+            "control_model.middle_block.2.out_layers.3.bias": "blocks.30.conv2.bias",
+            "control_model.middle_block_out.0.weight": "controlnet_blocks.12.weight",
+            "control_model.middle_block_out.0.bias": "controlnet_blocks.7.bias",
+        }
+        state_dict_ = {}
+        for name in state_dict:
+            if name in rename_dict:
+                param = state_dict[name]
+                if ".proj_in." in name or ".proj_out." in name:
+                    param = param.squeeze()
+                state_dict_[rename_dict[name]] = param
+        return state_dict_
diff --git a/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/sd_ipadapter.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/sd_ipadapter.py
new file mode 100644
index 0000000000000000000000000000000000000000..8d6ebd7d5e79ccd534aab11d22e046111562ccde
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/sd_ipadapter.py
@@ -0,0 +1,57 @@
+from .svd_image_encoder import SVDImageEncoder
+from .sdxl_ipadapter import IpAdapterImageProjModel, IpAdapterModule, SDXLIpAdapterStateDictConverter
+from transformers import CLIPImageProcessor
+import torch
+
+
+class IpAdapterCLIPImageEmbedder(SVDImageEncoder):
+    def __init__(self):
+        super().__init__()
+        self.image_processor = CLIPImageProcessor()
+
+    def forward(self, image):
+        pixel_values = self.image_processor(images=image, return_tensors="pt").pixel_values
+        pixel_values = pixel_values.to(device=self.embeddings.class_embedding.device, dtype=self.embeddings.class_embedding.dtype)
+        return super().forward(pixel_values)
+
+
+class SDIpAdapter(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        shape_list = [(768, 320)] * 2 + [(768, 640)] * 2 + [(768, 1280)] * 5 + [(768, 640)] * 3  + [(768, 320)] * 3 + [(768, 1280)] * 1
+        self.ipadapter_modules = torch.nn.ModuleList([IpAdapterModule(*shape) for shape in shape_list])
+        self.image_proj = IpAdapterImageProjModel(cross_attention_dim=768, clip_embeddings_dim=1024, clip_extra_context_tokens=4)
+        self.set_full_adapter()
+
+    def set_full_adapter(self):
+        block_ids = [1, 4, 9, 12, 17, 20, 40, 43, 46, 50, 53, 56, 60, 63, 66, 29]
+        self.call_block_id = {(i, 0): j for j, i in enumerate(block_ids)}
+
+    def set_less_adapter(self):
+        # IP-Adapter for SD v1.5 doesn't support this feature.
+        self.set_full_adapter()
+
+    def forward(self, hidden_states, scale=1.0):
+        hidden_states = self.image_proj(hidden_states)
+        hidden_states = hidden_states.view(1, -1, hidden_states.shape[-1])
+        ip_kv_dict = {}
+        for (block_id, transformer_id) in self.call_block_id:
+            ipadapter_id = self.call_block_id[(block_id, transformer_id)]
+            ip_k, ip_v = self.ipadapter_modules[ipadapter_id](hidden_states)
+            if block_id not in ip_kv_dict:
+                ip_kv_dict[block_id] = {}
+            ip_kv_dict[block_id][transformer_id] = {
+                "ip_k": ip_k,
+                "ip_v": ip_v,
+                "scale": scale
+            }
+        return ip_kv_dict
+
+    @staticmethod
+    def state_dict_converter():
+        return SDIpAdapterStateDictConverter()
+
+
+class SDIpAdapterStateDictConverter(SDXLIpAdapterStateDictConverter):
+    def __init__(self):
+        pass
diff --git a/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/sd_motion.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/sd_motion.py
new file mode 100644
index 0000000000000000000000000000000000000000..fb49138e147538537a60fb4a3e2d12a175da4a50
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/sd_motion.py
@@ -0,0 +1,199 @@
+from .sd_unet import SDUNet, Attention, GEGLU
+import torch
+from einops import rearrange, repeat
+
+
+class TemporalTransformerBlock(torch.nn.Module):
+
+    def __init__(self, dim, num_attention_heads, attention_head_dim, max_position_embeddings=32):
+        super().__init__()
+
+        # 1. Self-Attn
+        self.pe1 = torch.nn.Parameter(torch.zeros(1, max_position_embeddings, dim))
+        self.norm1 = torch.nn.LayerNorm(dim, elementwise_affine=True)
+        self.attn1 = Attention(q_dim=dim, num_heads=num_attention_heads, head_dim=attention_head_dim, bias_out=True)
+
+        # 2. Cross-Attn
+        self.pe2 = torch.nn.Parameter(torch.zeros(1, max_position_embeddings, dim))
+        self.norm2 = torch.nn.LayerNorm(dim, elementwise_affine=True)
+        self.attn2 = Attention(q_dim=dim, num_heads=num_attention_heads, head_dim=attention_head_dim, bias_out=True)
+
+        # 3. Feed-forward
+        self.norm3 = torch.nn.LayerNorm(dim, elementwise_affine=True)
+        self.act_fn = GEGLU(dim, dim * 4)
+        self.ff = torch.nn.Linear(dim * 4, dim)
+
+
+    def forward(self, hidden_states, batch_size=1):
+
+        # 1. Self-Attention
+        norm_hidden_states = self.norm1(hidden_states)
+        norm_hidden_states = rearrange(norm_hidden_states, "(b f) h c -> (b h) f c", b=batch_size)
+        attn_output = self.attn1(norm_hidden_states + self.pe1[:, :norm_hidden_states.shape[1]])
+        attn_output = rearrange(attn_output, "(b h) f c -> (b f) h c", b=batch_size)
+        hidden_states = attn_output + hidden_states
+
+        # 2. Cross-Attention
+        norm_hidden_states = self.norm2(hidden_states)
+        norm_hidden_states = rearrange(norm_hidden_states, "(b f) h c -> (b h) f c", b=batch_size)
+        attn_output = self.attn2(norm_hidden_states + self.pe2[:, :norm_hidden_states.shape[1]])
+        attn_output = rearrange(attn_output, "(b h) f c -> (b f) h c", b=batch_size)
+        hidden_states = attn_output + hidden_states
+
+        # 3. Feed-forward
+        norm_hidden_states = self.norm3(hidden_states)
+        ff_output = self.act_fn(norm_hidden_states)
+        ff_output = self.ff(ff_output)
+        hidden_states = ff_output + hidden_states
+
+        return hidden_states
+
+
+class TemporalBlock(torch.nn.Module):
+    
+    def __init__(self, num_attention_heads, attention_head_dim, in_channels, num_layers=1, norm_num_groups=32, eps=1e-5):
+        super().__init__()
+        inner_dim = num_attention_heads * attention_head_dim
+
+        self.norm = torch.nn.GroupNorm(num_groups=norm_num_groups, num_channels=in_channels, eps=eps, affine=True)
+        self.proj_in = torch.nn.Linear(in_channels, inner_dim)
+
+        self.transformer_blocks = torch.nn.ModuleList([
+            TemporalTransformerBlock(
+                inner_dim,
+                num_attention_heads,
+                attention_head_dim
+            )
+            for d in range(num_layers)
+        ])
+
+        self.proj_out = torch.nn.Linear(inner_dim, in_channels)
+
+    def forward(self, hidden_states, time_emb, text_emb, res_stack, batch_size=1):
+        batch, _, height, width = hidden_states.shape
+        residual = hidden_states
+
+        hidden_states = self.norm(hidden_states)
+        inner_dim = hidden_states.shape[1]
+        hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * width, inner_dim)
+        hidden_states = self.proj_in(hidden_states)
+
+        for block in self.transformer_blocks:
+            hidden_states = block(
+                hidden_states,
+                batch_size=batch_size
+            )
+
+        hidden_states = self.proj_out(hidden_states)
+        hidden_states = hidden_states.reshape(batch, height, width, inner_dim).permute(0, 3, 1, 2).contiguous()
+        hidden_states = hidden_states + residual
+
+        return hidden_states, time_emb, text_emb, res_stack
+
+
+class SDMotionModel(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.motion_modules = torch.nn.ModuleList([
+            TemporalBlock(8, 40, 320, eps=1e-6),
+            TemporalBlock(8, 40, 320, eps=1e-6),
+            TemporalBlock(8, 80, 640, eps=1e-6),
+            TemporalBlock(8, 80, 640, eps=1e-6),
+            TemporalBlock(8, 160, 1280, eps=1e-6),
+            TemporalBlock(8, 160, 1280, eps=1e-6),
+            TemporalBlock(8, 160, 1280, eps=1e-6),
+            TemporalBlock(8, 160, 1280, eps=1e-6),
+            TemporalBlock(8, 160, 1280, eps=1e-6),
+            TemporalBlock(8, 160, 1280, eps=1e-6),
+            TemporalBlock(8, 160, 1280, eps=1e-6),
+            TemporalBlock(8, 160, 1280, eps=1e-6),
+            TemporalBlock(8, 160, 1280, eps=1e-6),
+            TemporalBlock(8, 160, 1280, eps=1e-6),
+            TemporalBlock(8, 160, 1280, eps=1e-6),
+            TemporalBlock(8, 80, 640, eps=1e-6),
+            TemporalBlock(8, 80, 640, eps=1e-6),
+            TemporalBlock(8, 80, 640, eps=1e-6),
+            TemporalBlock(8, 40, 320, eps=1e-6),
+            TemporalBlock(8, 40, 320, eps=1e-6),
+            TemporalBlock(8, 40, 320, eps=1e-6),
+        ])
+        self.call_block_id = {
+            1: 0,
+            4: 1,
+            9: 2,
+            12: 3,
+            17: 4,
+            20: 5,
+            24: 6,
+            26: 7,
+            29: 8,
+            32: 9,
+            34: 10,
+            36: 11,
+            40: 12,
+            43: 13,
+            46: 14,
+            50: 15,
+            53: 16,
+            56: 17,
+            60: 18,
+            63: 19,
+            66: 20
+        }
+        
+    def forward(self):
+        pass
+
+    @staticmethod
+    def state_dict_converter():
+        return SDMotionModelStateDictConverter()
+
+
+class SDMotionModelStateDictConverter:
+    def __init__(self):
+        pass
+
+    def from_diffusers(self, state_dict):
+        rename_dict = {
+            "norm": "norm",
+            "proj_in": "proj_in",
+            "transformer_blocks.0.attention_blocks.0.to_q": "transformer_blocks.0.attn1.to_q",
+            "transformer_blocks.0.attention_blocks.0.to_k": "transformer_blocks.0.attn1.to_k",
+            "transformer_blocks.0.attention_blocks.0.to_v": "transformer_blocks.0.attn1.to_v",
+            "transformer_blocks.0.attention_blocks.0.to_out.0": "transformer_blocks.0.attn1.to_out",
+            "transformer_blocks.0.attention_blocks.0.pos_encoder": "transformer_blocks.0.pe1",
+            "transformer_blocks.0.attention_blocks.1.to_q": "transformer_blocks.0.attn2.to_q",
+            "transformer_blocks.0.attention_blocks.1.to_k": "transformer_blocks.0.attn2.to_k",
+            "transformer_blocks.0.attention_blocks.1.to_v": "transformer_blocks.0.attn2.to_v",
+            "transformer_blocks.0.attention_blocks.1.to_out.0": "transformer_blocks.0.attn2.to_out",
+            "transformer_blocks.0.attention_blocks.1.pos_encoder": "transformer_blocks.0.pe2",
+            "transformer_blocks.0.norms.0": "transformer_blocks.0.norm1",
+            "transformer_blocks.0.norms.1": "transformer_blocks.0.norm2",
+            "transformer_blocks.0.ff.net.0.proj": "transformer_blocks.0.act_fn.proj",
+            "transformer_blocks.0.ff.net.2": "transformer_blocks.0.ff",
+            "transformer_blocks.0.ff_norm": "transformer_blocks.0.norm3",
+            "proj_out": "proj_out",
+        }
+        name_list = sorted([i for i in state_dict if i.startswith("down_blocks.")])
+        name_list += sorted([i for i in state_dict if i.startswith("mid_block.")])
+        name_list += sorted([i for i in state_dict if i.startswith("up_blocks.")])
+        state_dict_ = {}
+        last_prefix, module_id = "", -1
+        for name in name_list:
+            names = name.split(".")
+            prefix_index = names.index("temporal_transformer") + 1
+            prefix = ".".join(names[:prefix_index])
+            if prefix != last_prefix:
+                last_prefix = prefix
+                module_id += 1
+            middle_name = ".".join(names[prefix_index:-1])
+            suffix = names[-1]
+            if "pos_encoder" in names:
+                rename = ".".join(["motion_modules", str(module_id), rename_dict[middle_name]])
+            else:
+                rename = ".".join(["motion_modules", str(module_id), rename_dict[middle_name], suffix])
+            state_dict_[rename] = state_dict[name]
+        return state_dict_
+    
+    def from_civitai(self, state_dict):
+        return self.from_diffusers(state_dict)
diff --git a/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/sd_text_encoder.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/sd_text_encoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..8fe8994a10bc998b8778cae2cbd57b95545166ba
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/sd_text_encoder.py
@@ -0,0 +1,321 @@
+import torch
+from .attention import Attention
+
+
+class CLIPEncoderLayer(torch.nn.Module):
+    def __init__(self, embed_dim, intermediate_size, num_heads=12, head_dim=64, use_quick_gelu=True):
+        super().__init__()
+        self.attn = Attention(q_dim=embed_dim, num_heads=num_heads, head_dim=head_dim, bias_q=True, bias_kv=True, bias_out=True)
+        self.layer_norm1 = torch.nn.LayerNorm(embed_dim)
+        self.layer_norm2 = torch.nn.LayerNorm(embed_dim)
+        self.fc1 = torch.nn.Linear(embed_dim, intermediate_size)
+        self.fc2 = torch.nn.Linear(intermediate_size, embed_dim)
+
+        self.use_quick_gelu = use_quick_gelu
+
+    def quickGELU(self, x):
+        return x * torch.sigmoid(1.702 * x)
+    
+    def forward(self, hidden_states, attn_mask=None):
+        residual = hidden_states
+
+        hidden_states = self.layer_norm1(hidden_states)
+        hidden_states = self.attn(hidden_states, attn_mask=attn_mask)
+        hidden_states = residual + hidden_states
+
+        residual = hidden_states
+        hidden_states = self.layer_norm2(hidden_states)
+        hidden_states = self.fc1(hidden_states)
+        if self.use_quick_gelu:
+            hidden_states = self.quickGELU(hidden_states)
+        else:
+            hidden_states = torch.nn.functional.gelu(hidden_states)
+        hidden_states = self.fc2(hidden_states)
+        hidden_states = residual + hidden_states
+
+        return hidden_states
+    
+
+class SDTextEncoder(torch.nn.Module):
+    def __init__(self, embed_dim=768, vocab_size=49408, max_position_embeddings=77, num_encoder_layers=12, encoder_intermediate_size=3072):
+        super().__init__()
+
+        # token_embedding
+        self.token_embedding = torch.nn.Embedding(vocab_size, embed_dim)
+
+        # position_embeds (This is a fixed tensor)
+        self.position_embeds = torch.nn.Parameter(torch.zeros(1, max_position_embeddings, embed_dim))
+
+        # encoders
+        self.encoders = torch.nn.ModuleList([CLIPEncoderLayer(embed_dim, encoder_intermediate_size) for _ in range(num_encoder_layers)])
+
+        # attn_mask
+        self.attn_mask = self.attention_mask(max_position_embeddings)
+
+        # final_layer_norm
+        self.final_layer_norm = torch.nn.LayerNorm(embed_dim)
+
+    def attention_mask(self, length):
+        mask = torch.empty(length, length)
+        mask.fill_(float("-inf"))
+        mask.triu_(1)
+        return mask
+
+    def forward(self, input_ids, clip_skip=1):
+        embeds = self.token_embedding(input_ids) + self.position_embeds
+        attn_mask = self.attn_mask.to(device=embeds.device, dtype=embeds.dtype)
+        for encoder_id, encoder in enumerate(self.encoders):
+            embeds = encoder(embeds, attn_mask=attn_mask)
+            if encoder_id + clip_skip == len(self.encoders):
+                break
+        embeds = self.final_layer_norm(embeds)
+        return embeds
+    
+    @staticmethod
+    def state_dict_converter():
+        return SDTextEncoderStateDictConverter()
+
+
+class SDTextEncoderStateDictConverter:
+    def __init__(self):
+        pass
+
+    def from_diffusers(self, state_dict):
+        rename_dict = {
+            "text_model.embeddings.token_embedding.weight": "token_embedding.weight",
+            "text_model.embeddings.position_embedding.weight": "position_embeds",
+            "text_model.final_layer_norm.weight": "final_layer_norm.weight",
+            "text_model.final_layer_norm.bias": "final_layer_norm.bias"
+        }
+        attn_rename_dict = {
+            "self_attn.q_proj": "attn.to_q",
+            "self_attn.k_proj": "attn.to_k",
+            "self_attn.v_proj": "attn.to_v",
+            "self_attn.out_proj": "attn.to_out",
+            "layer_norm1": "layer_norm1",
+            "layer_norm2": "layer_norm2",
+            "mlp.fc1": "fc1",
+            "mlp.fc2": "fc2",
+        }
+        state_dict_ = {}
+        for name in state_dict:
+            if name in rename_dict:
+                param = state_dict[name]
+                if name == "text_model.embeddings.position_embedding.weight":
+                    param = param.reshape((1, param.shape[0], param.shape[1]))
+                state_dict_[rename_dict[name]] = param
+            elif name.startswith("text_model.encoder.layers."):
+                param = state_dict[name]
+                names = name.split(".")
+                layer_id, layer_type, tail = names[3], ".".join(names[4:-1]), names[-1]
+                name_ = ".".join(["encoders", layer_id, attn_rename_dict[layer_type], tail])
+                state_dict_[name_] = param
+        return state_dict_
+    
+    def from_civitai(self, state_dict):
+        rename_dict = {
+            "cond_stage_model.transformer.text_model.embeddings.token_embedding.weight": "token_embedding.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.0.layer_norm1.bias": "encoders.0.layer_norm1.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.0.layer_norm1.weight": "encoders.0.layer_norm1.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.0.layer_norm2.bias": "encoders.0.layer_norm2.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.0.layer_norm2.weight": "encoders.0.layer_norm2.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.0.mlp.fc1.bias": "encoders.0.fc1.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.0.mlp.fc1.weight": "encoders.0.fc1.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.0.mlp.fc2.bias": "encoders.0.fc2.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.0.mlp.fc2.weight": "encoders.0.fc2.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.0.self_attn.k_proj.bias": "encoders.0.attn.to_k.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.0.self_attn.k_proj.weight": "encoders.0.attn.to_k.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.0.self_attn.out_proj.bias": "encoders.0.attn.to_out.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.0.self_attn.out_proj.weight": "encoders.0.attn.to_out.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.0.self_attn.q_proj.bias": "encoders.0.attn.to_q.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.0.self_attn.q_proj.weight": "encoders.0.attn.to_q.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.0.self_attn.v_proj.bias": "encoders.0.attn.to_v.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.0.self_attn.v_proj.weight": "encoders.0.attn.to_v.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.1.layer_norm1.bias": "encoders.1.layer_norm1.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.1.layer_norm1.weight": "encoders.1.layer_norm1.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.1.layer_norm2.bias": "encoders.1.layer_norm2.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.1.layer_norm2.weight": "encoders.1.layer_norm2.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.1.mlp.fc1.bias": "encoders.1.fc1.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.1.mlp.fc1.weight": "encoders.1.fc1.weight",
+            "cond_stage_model.transformer.text_model.encoder.layers.1.mlp.fc2.bias": "encoders.1.fc2.bias",
+            "cond_stage_model.transformer.text_model.encoder.layers.1.mlp.fc2.weight": "encoders.1.fc2.weight",
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+            "cond_stage_model.transformer.text_model.encoder.layers.9.self_attn.v_proj.weight": "encoders.9.attn.to_v.weight",
+            "cond_stage_model.transformer.text_model.final_layer_norm.bias": "final_layer_norm.bias",
+            "cond_stage_model.transformer.text_model.final_layer_norm.weight": "final_layer_norm.weight",
+            "cond_stage_model.transformer.text_model.embeddings.position_embedding.weight": "position_embeds"
+        }
+        state_dict_ = {}
+        for name in state_dict:
+            if name in rename_dict:
+                param = state_dict[name]
+                if name == "cond_stage_model.transformer.text_model.embeddings.position_embedding.weight":
+                    param = param.reshape((1, param.shape[0], param.shape[1]))
+                state_dict_[rename_dict[name]] = param
+        return state_dict_
diff --git a/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/sd_unet.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/sd_unet.py
new file mode 100644
index 0000000000000000000000000000000000000000..33363909e4968292fe22e1953dc0c4a12e41d921
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/sd_unet.py
@@ -0,0 +1,1108 @@
+import torch, math
+from .attention import Attention
+from .tiler import TileWorker
+
+
+class Timesteps(torch.nn.Module):
+    def __init__(self, num_channels):
+        super().__init__()
+        self.num_channels = num_channels
+
+    def forward(self, timesteps):
+        half_dim = self.num_channels // 2
+        exponent = -math.log(10000) * torch.arange(start=0, end=half_dim, dtype=torch.float32, device=timesteps.device) / half_dim
+        timesteps = timesteps.unsqueeze(-1)
+        emb = timesteps.float() * torch.exp(exponent)
+        emb = torch.cat([torch.cos(emb), torch.sin(emb)], dim=-1)
+        return emb
+
+
+class GEGLU(torch.nn.Module):
+
+    def __init__(self, dim_in, dim_out):
+        super().__init__()
+        self.proj = torch.nn.Linear(dim_in, dim_out * 2)
+
+    def forward(self, hidden_states):
+        hidden_states, gate = self.proj(hidden_states).chunk(2, dim=-1)
+        return hidden_states * torch.nn.functional.gelu(gate)
+
+
+class BasicTransformerBlock(torch.nn.Module):
+
+    def __init__(self, dim, num_attention_heads, attention_head_dim, cross_attention_dim):
+        super().__init__()
+
+        # 1. Self-Attn
+        self.norm1 = torch.nn.LayerNorm(dim, elementwise_affine=True)
+        self.attn1 = Attention(q_dim=dim, num_heads=num_attention_heads, head_dim=attention_head_dim, bias_out=True)
+
+        # 2. Cross-Attn
+        self.norm2 = torch.nn.LayerNorm(dim, elementwise_affine=True)
+        self.attn2 = Attention(q_dim=dim, kv_dim=cross_attention_dim, num_heads=num_attention_heads, head_dim=attention_head_dim, bias_out=True)
+
+        # 3. Feed-forward
+        self.norm3 = torch.nn.LayerNorm(dim, elementwise_affine=True)
+        self.act_fn = GEGLU(dim, dim * 4)
+        self.ff = torch.nn.Linear(dim * 4, dim)
+
+
+    def forward(self, hidden_states, encoder_hidden_states, ipadapter_kwargs=None):
+        # 1. Self-Attention
+        norm_hidden_states = self.norm1(hidden_states)
+        attn_output = self.attn1(norm_hidden_states, encoder_hidden_states=None)
+        hidden_states = attn_output + hidden_states
+
+        # 2. Cross-Attention
+        norm_hidden_states = self.norm2(hidden_states)
+        attn_output = self.attn2(norm_hidden_states, encoder_hidden_states=encoder_hidden_states, ipadapter_kwargs=ipadapter_kwargs)
+        hidden_states = attn_output + hidden_states
+
+        # 3. Feed-forward
+        norm_hidden_states = self.norm3(hidden_states)
+        ff_output = self.act_fn(norm_hidden_states)
+        ff_output = self.ff(ff_output)
+        hidden_states = ff_output + hidden_states
+
+        return hidden_states
+
+
+class DownSampler(torch.nn.Module):
+    def __init__(self, channels, padding=1, extra_padding=False):
+        super().__init__()
+        self.conv = torch.nn.Conv2d(channels, channels, 3, stride=2, padding=padding)
+        self.extra_padding = extra_padding
+
+    def forward(self, hidden_states, time_emb, text_emb, res_stack, **kwargs):
+        if self.extra_padding:
+            hidden_states = torch.nn.functional.pad(hidden_states, (0, 1, 0, 1), mode="constant", value=0)
+        hidden_states = self.conv(hidden_states)
+        return hidden_states, time_emb, text_emb, res_stack
+
+
+class UpSampler(torch.nn.Module):
+    def __init__(self, channels):
+        super().__init__()
+        self.conv = torch.nn.Conv2d(channels, channels, 3, padding=1)
+
+    def forward(self, hidden_states, time_emb, text_emb, res_stack, **kwargs):
+        hidden_states = torch.nn.functional.interpolate(hidden_states, scale_factor=2.0, mode="nearest")
+        hidden_states = self.conv(hidden_states)
+        return hidden_states, time_emb, text_emb, res_stack
+
+
+class ResnetBlock(torch.nn.Module):
+    def __init__(self, in_channels, out_channels, temb_channels=None, groups=32, eps=1e-5):
+        super().__init__()
+        self.norm1 = torch.nn.GroupNorm(num_groups=groups, num_channels=in_channels, eps=eps, affine=True)
+        self.conv1 = torch.nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
+        if temb_channels is not None:
+            self.time_emb_proj = torch.nn.Linear(temb_channels, out_channels)
+        self.norm2 = torch.nn.GroupNorm(num_groups=groups, num_channels=out_channels, eps=eps, affine=True)
+        self.conv2 = torch.nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1)
+        self.nonlinearity = torch.nn.SiLU()
+        self.conv_shortcut = None
+        if in_channels != out_channels:
+            self.conv_shortcut = torch.nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0, bias=True)
+
+    def forward(self, hidden_states, time_emb, text_emb, res_stack, **kwargs):
+        x = hidden_states
+        x = self.norm1(x)
+        x = self.nonlinearity(x)
+        x = self.conv1(x)
+        if time_emb is not None:
+            emb = self.nonlinearity(time_emb)
+            emb = self.time_emb_proj(emb)[:, :, None, None]
+            x = x + emb
+        x = self.norm2(x)
+        x = self.nonlinearity(x)
+        x = self.conv2(x)
+        if self.conv_shortcut is not None:
+            hidden_states = self.conv_shortcut(hidden_states)
+        hidden_states = hidden_states + x
+        return hidden_states, time_emb, text_emb, res_stack
+
+
+class AttentionBlock(torch.nn.Module):
+
+    def __init__(self, num_attention_heads, attention_head_dim, in_channels, num_layers=1, cross_attention_dim=None, norm_num_groups=32, eps=1e-5, need_proj_out=True):
+        super().__init__()
+        inner_dim = num_attention_heads * attention_head_dim
+
+        self.norm = torch.nn.GroupNorm(num_groups=norm_num_groups, num_channels=in_channels, eps=eps, affine=True)
+        self.proj_in = torch.nn.Linear(in_channels, inner_dim)
+
+        self.transformer_blocks = torch.nn.ModuleList([
+            BasicTransformerBlock(
+                inner_dim,
+                num_attention_heads,
+                attention_head_dim,
+                cross_attention_dim=cross_attention_dim
+            )
+            for d in range(num_layers)
+        ])
+        self.need_proj_out = need_proj_out
+        if need_proj_out:
+            self.proj_out = torch.nn.Linear(inner_dim, in_channels)
+
+    def forward(
+        self,
+        hidden_states, time_emb, text_emb, res_stack,
+        cross_frame_attention=False,
+        tiled=False, tile_size=64, tile_stride=32,
+        ipadapter_kwargs_list={},
+        **kwargs
+    ):
+        batch, _, height, width = hidden_states.shape
+        residual = hidden_states
+
+        hidden_states = self.norm(hidden_states)
+        inner_dim = hidden_states.shape[1]
+        hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * width, inner_dim)
+        hidden_states = self.proj_in(hidden_states)
+
+        if cross_frame_attention:
+            hidden_states = hidden_states.reshape(1, batch * height * width, inner_dim)
+            encoder_hidden_states = text_emb.mean(dim=0, keepdim=True)
+        else:
+            encoder_hidden_states = text_emb
+            if encoder_hidden_states.shape[0] != hidden_states.shape[0]:
+                encoder_hidden_states = encoder_hidden_states.repeat(hidden_states.shape[0], 1, 1)
+
+        if tiled:
+            tile_size = min(tile_size, min(height, width))
+            hidden_states = hidden_states.permute(0, 2, 1).reshape(batch, inner_dim, height, width)
+            def block_tile_forward(x):
+                b, c, h, w = x.shape
+                x = x.permute(0, 2, 3, 1).reshape(b, h*w, c)
+                x = block(x, encoder_hidden_states)
+                x = x.reshape(b, h, w, c).permute(0, 3, 1, 2)
+                return x
+            for block in self.transformer_blocks:
+                hidden_states = TileWorker().tiled_forward(
+                    block_tile_forward,
+                    hidden_states,
+                    tile_size,
+                    tile_stride,
+                    tile_device=hidden_states.device,
+                    tile_dtype=hidden_states.dtype
+                )
+            hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * width, inner_dim)
+        else:
+            for block_id, block in enumerate(self.transformer_blocks):
+                hidden_states = block(
+                    hidden_states,
+                    encoder_hidden_states=encoder_hidden_states,
+                    ipadapter_kwargs=ipadapter_kwargs_list.get(block_id, None)
+                )
+        if cross_frame_attention:
+            hidden_states = hidden_states.reshape(batch, height * width, inner_dim)
+
+        if self.need_proj_out:
+            hidden_states = self.proj_out(hidden_states)
+            hidden_states = hidden_states.reshape(batch, height, width, inner_dim).permute(0, 3, 1, 2).contiguous()
+            hidden_states = hidden_states + residual
+        else:
+            hidden_states = hidden_states.reshape(batch, height, width, inner_dim).permute(0, 3, 1, 2).contiguous()
+
+        return hidden_states, time_emb, text_emb, res_stack
+
+
+class PushBlock(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    def forward(self, hidden_states, time_emb, text_emb, res_stack, **kwargs):
+        res_stack.append(hidden_states)
+        return hidden_states, time_emb, text_emb, res_stack
+
+
+class PopBlock(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    def forward(self, hidden_states, time_emb, text_emb, res_stack, **kwargs):
+        res_hidden_states = res_stack.pop()
+        hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
+        return hidden_states, time_emb, text_emb, res_stack
+
+
+class SDUNet(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.time_proj = Timesteps(320)
+        self.time_embedding = torch.nn.Sequential(
+            torch.nn.Linear(320, 1280),
+            torch.nn.SiLU(),
+            torch.nn.Linear(1280, 1280)
+        )
+        self.conv_in = torch.nn.Conv2d(4, 320, kernel_size=3, padding=1)
+
+        self.blocks = torch.nn.ModuleList([
+            # CrossAttnDownBlock2D
+            ResnetBlock(320, 320, 1280),
+            AttentionBlock(8, 40, 320, 1, 768, eps=1e-6),
+            PushBlock(),
+            ResnetBlock(320, 320, 1280),
+            AttentionBlock(8, 40, 320, 1, 768, eps=1e-6),
+            PushBlock(),
+            DownSampler(320),
+            PushBlock(),
+            # CrossAttnDownBlock2D
+            ResnetBlock(320, 640, 1280),
+            AttentionBlock(8, 80, 640, 1, 768, eps=1e-6),
+            PushBlock(),
+            ResnetBlock(640, 640, 1280),
+            AttentionBlock(8, 80, 640, 1, 768, eps=1e-6),
+            PushBlock(),
+            DownSampler(640),
+            PushBlock(),
+            # CrossAttnDownBlock2D
+            ResnetBlock(640, 1280, 1280),
+            AttentionBlock(8, 160, 1280, 1, 768, eps=1e-6),
+            PushBlock(),
+            ResnetBlock(1280, 1280, 1280),
+            AttentionBlock(8, 160, 1280, 1, 768, eps=1e-6),
+            PushBlock(),
+            DownSampler(1280),
+            PushBlock(),
+            # DownBlock2D
+            ResnetBlock(1280, 1280, 1280),
+            PushBlock(),
+            ResnetBlock(1280, 1280, 1280),
+            PushBlock(),
+            # UNetMidBlock2DCrossAttn
+            ResnetBlock(1280, 1280, 1280),
+            AttentionBlock(8, 160, 1280, 1, 768, eps=1e-6),
+            ResnetBlock(1280, 1280, 1280),
+            # UpBlock2D
+            PopBlock(),
+            ResnetBlock(2560, 1280, 1280),
+            PopBlock(),
+            ResnetBlock(2560, 1280, 1280),
+            PopBlock(),
+            ResnetBlock(2560, 1280, 1280),
+            UpSampler(1280),
+            # CrossAttnUpBlock2D
+            PopBlock(),
+            ResnetBlock(2560, 1280, 1280),
+            AttentionBlock(8, 160, 1280, 1, 768, eps=1e-6),
+            PopBlock(),
+            ResnetBlock(2560, 1280, 1280),
+            AttentionBlock(8, 160, 1280, 1, 768, eps=1e-6),
+            PopBlock(),
+            ResnetBlock(1920, 1280, 1280),
+            AttentionBlock(8, 160, 1280, 1, 768, eps=1e-6),
+            UpSampler(1280),
+            # CrossAttnUpBlock2D
+            PopBlock(),
+            ResnetBlock(1920, 640, 1280),
+            AttentionBlock(8, 80, 640, 1, 768, eps=1e-6),
+            PopBlock(),
+            ResnetBlock(1280, 640, 1280),
+            AttentionBlock(8, 80, 640, 1, 768, eps=1e-6),
+            PopBlock(),
+            ResnetBlock(960, 640, 1280),
+            AttentionBlock(8, 80, 640, 1, 768, eps=1e-6),
+            UpSampler(640),
+            # CrossAttnUpBlock2D
+            PopBlock(),
+            ResnetBlock(960, 320, 1280),
+            AttentionBlock(8, 40, 320, 1, 768, eps=1e-6),
+            PopBlock(),
+            ResnetBlock(640, 320, 1280),
+            AttentionBlock(8, 40, 320, 1, 768, eps=1e-6),
+            PopBlock(),
+            ResnetBlock(640, 320, 1280),
+            AttentionBlock(8, 40, 320, 1, 768, eps=1e-6),
+        ])
+
+        self.conv_norm_out = torch.nn.GroupNorm(num_channels=320, num_groups=32, eps=1e-5)
+        self.conv_act = torch.nn.SiLU()
+        self.conv_out = torch.nn.Conv2d(320, 4, kernel_size=3, padding=1)
+
+    def forward(self, sample, timestep, encoder_hidden_states, **kwargs):
+        # 1. time
+        time_emb = self.time_proj(timestep).to(sample.dtype)
+        time_emb = self.time_embedding(time_emb)
+
+        # 2. pre-process
+        hidden_states = self.conv_in(sample)
+        text_emb = encoder_hidden_states
+        res_stack = [hidden_states]
+
+        # 3. blocks
+        for i, block in enumerate(self.blocks):
+            hidden_states, time_emb, text_emb, res_stack = block(hidden_states, time_emb, text_emb, res_stack)
+        
+        # 4. output
+        hidden_states = self.conv_norm_out(hidden_states)
+        hidden_states = self.conv_act(hidden_states)
+        hidden_states = self.conv_out(hidden_states)
+
+        return hidden_states
+    
+    @staticmethod
+    def state_dict_converter():
+        return SDUNetStateDictConverter()
+
+
+class SDUNetStateDictConverter:
+    def __init__(self):
+        pass
+
+    def from_diffusers(self, state_dict):
+        # architecture
+        block_types = [
+            'ResnetBlock', 'AttentionBlock', 'PushBlock', 'ResnetBlock', 'AttentionBlock', 'PushBlock', 'DownSampler', 'PushBlock',
+            'ResnetBlock', 'AttentionBlock', 'PushBlock', 'ResnetBlock', 'AttentionBlock', 'PushBlock', 'DownSampler', 'PushBlock',
+            'ResnetBlock', 'AttentionBlock', 'PushBlock', 'ResnetBlock', 'AttentionBlock', 'PushBlock', 'DownSampler', 'PushBlock',
+            'ResnetBlock', 'PushBlock', 'ResnetBlock', 'PushBlock', 
+            'ResnetBlock', 'AttentionBlock', 'ResnetBlock',
+            'PopBlock', 'ResnetBlock', 'PopBlock', 'ResnetBlock', 'PopBlock', 'ResnetBlock', 'UpSampler',
+            'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'UpSampler',
+            'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'UpSampler',
+            'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock'
+        ]
+
+        # Rename each parameter
+        name_list = sorted([name for name in state_dict])
+        rename_dict = {}
+        block_id = {"ResnetBlock": -1, "AttentionBlock": -1, "DownSampler": -1, "UpSampler": -1}
+        last_block_type_with_id = {"ResnetBlock": "", "AttentionBlock": "", "DownSampler": "", "UpSampler": ""}
+        for name in name_list:
+            names = name.split(".")
+            if names[0] in ["conv_in", "conv_norm_out", "conv_out"]:
+                pass
+            elif names[0] in ["time_embedding", "add_embedding"]:
+                if names[0] == "add_embedding":
+                    names[0] = "add_time_embedding"
+                names[1] = {"linear_1": "0", "linear_2": "2"}[names[1]]
+            elif names[0] in ["down_blocks", "mid_block", "up_blocks"]:
+                if names[0] == "mid_block":
+                    names.insert(1, "0")
+                block_type = {"resnets": "ResnetBlock", "attentions": "AttentionBlock", "downsamplers": "DownSampler", "upsamplers": "UpSampler"}[names[2]]
+                block_type_with_id = ".".join(names[:4])
+                if block_type_with_id != last_block_type_with_id[block_type]:
+                    block_id[block_type] += 1
+                last_block_type_with_id[block_type] = block_type_with_id
+                while block_id[block_type] < len(block_types) and block_types[block_id[block_type]] != block_type:
+                    block_id[block_type] += 1
+                block_type_with_id = ".".join(names[:4])
+                names = ["blocks", str(block_id[block_type])] + names[4:]
+                if "ff" in names:
+                    ff_index = names.index("ff")
+                    component = ".".join(names[ff_index:ff_index+3])
+                    component = {"ff.net.0": "act_fn", "ff.net.2": "ff"}[component]
+                    names = names[:ff_index] + [component] + names[ff_index+3:]
+                if "to_out" in names:
+                    names.pop(names.index("to_out") + 1)
+            else:
+                raise ValueError(f"Unknown parameters: {name}")
+            rename_dict[name] = ".".join(names)
+
+        # Convert state_dict
+        state_dict_ = {}
+        for name, param in state_dict.items():
+            if ".proj_in." in name or ".proj_out." in name:
+                param = param.squeeze()
+            state_dict_[rename_dict[name]] = param
+        return state_dict_
+    
+    def from_civitai(self, state_dict):
+        rename_dict = {
+            "model.diffusion_model.input_blocks.0.0.bias": "conv_in.bias",
+            "model.diffusion_model.input_blocks.0.0.weight": "conv_in.weight",
+            "model.diffusion_model.input_blocks.1.0.emb_layers.1.bias": "blocks.0.time_emb_proj.bias",
+            "model.diffusion_model.input_blocks.1.0.emb_layers.1.weight": "blocks.0.time_emb_proj.weight",
+            "model.diffusion_model.input_blocks.1.0.in_layers.0.bias": "blocks.0.norm1.bias",
+            "model.diffusion_model.input_blocks.1.0.in_layers.0.weight": "blocks.0.norm1.weight",
+            "model.diffusion_model.input_blocks.1.0.in_layers.2.bias": "blocks.0.conv1.bias",
+            "model.diffusion_model.input_blocks.1.0.in_layers.2.weight": "blocks.0.conv1.weight",
+            "model.diffusion_model.input_blocks.1.0.out_layers.0.bias": "blocks.0.norm2.bias",
+            "model.diffusion_model.input_blocks.1.0.out_layers.0.weight": "blocks.0.norm2.weight",
+            "model.diffusion_model.input_blocks.1.0.out_layers.3.bias": "blocks.0.conv2.bias",
+            "model.diffusion_model.input_blocks.1.0.out_layers.3.weight": "blocks.0.conv2.weight",
+            "model.diffusion_model.input_blocks.1.1.norm.bias": "blocks.1.norm.bias",
+            "model.diffusion_model.input_blocks.1.1.norm.weight": "blocks.1.norm.weight",
+            "model.diffusion_model.input_blocks.1.1.proj_in.bias": "blocks.1.proj_in.bias",
+            "model.diffusion_model.input_blocks.1.1.proj_in.weight": "blocks.1.proj_in.weight",
+            "model.diffusion_model.input_blocks.1.1.proj_out.bias": "blocks.1.proj_out.bias",
+            "model.diffusion_model.input_blocks.1.1.proj_out.weight": "blocks.1.proj_out.weight",
+            "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.attn1.to_k.weight": "blocks.1.transformer_blocks.0.attn1.to_k.weight",
+            "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.attn1.to_out.0.bias": "blocks.1.transformer_blocks.0.attn1.to_out.bias",
+            "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.attn1.to_out.0.weight": "blocks.1.transformer_blocks.0.attn1.to_out.weight",
+            "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.attn1.to_q.weight": "blocks.1.transformer_blocks.0.attn1.to_q.weight",
+            "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.attn1.to_v.weight": "blocks.1.transformer_blocks.0.attn1.to_v.weight",
+            "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.attn2.to_k.weight": "blocks.1.transformer_blocks.0.attn2.to_k.weight",
+            "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.attn2.to_out.0.bias": "blocks.1.transformer_blocks.0.attn2.to_out.bias",
+            "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.attn2.to_out.0.weight": "blocks.1.transformer_blocks.0.attn2.to_out.weight",
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+            "model.diffusion_model.output_blocks.7.1.transformer_blocks.0.norm2.weight": "blocks.53.transformer_blocks.0.norm2.weight",
+            "model.diffusion_model.output_blocks.7.1.transformer_blocks.0.norm3.bias": "blocks.53.transformer_blocks.0.norm3.bias",
+            "model.diffusion_model.output_blocks.7.1.transformer_blocks.0.norm3.weight": "blocks.53.transformer_blocks.0.norm3.weight",
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+            "model.diffusion_model.output_blocks.8.0.emb_layers.1.weight": "blocks.55.time_emb_proj.weight",
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+            "model.diffusion_model.output_blocks.8.0.in_layers.0.weight": "blocks.55.norm1.weight",
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+            "model.diffusion_model.output_blocks.9.1.norm.bias": "blocks.60.norm.bias",
+            "model.diffusion_model.output_blocks.9.1.norm.weight": "blocks.60.norm.weight",
+            "model.diffusion_model.output_blocks.9.1.proj_in.bias": "blocks.60.proj_in.bias",
+            "model.diffusion_model.output_blocks.9.1.proj_in.weight": "blocks.60.proj_in.weight",
+            "model.diffusion_model.output_blocks.9.1.proj_out.bias": "blocks.60.proj_out.bias",
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+            "model.diffusion_model.output_blocks.9.1.transformer_blocks.0.attn1.to_q.weight": "blocks.60.transformer_blocks.0.attn1.to_q.weight",
+            "model.diffusion_model.output_blocks.9.1.transformer_blocks.0.attn1.to_v.weight": "blocks.60.transformer_blocks.0.attn1.to_v.weight",
+            "model.diffusion_model.output_blocks.9.1.transformer_blocks.0.attn2.to_k.weight": "blocks.60.transformer_blocks.0.attn2.to_k.weight",
+            "model.diffusion_model.output_blocks.9.1.transformer_blocks.0.attn2.to_out.0.bias": "blocks.60.transformer_blocks.0.attn2.to_out.bias",
+            "model.diffusion_model.output_blocks.9.1.transformer_blocks.0.attn2.to_out.0.weight": "blocks.60.transformer_blocks.0.attn2.to_out.weight",
+            "model.diffusion_model.output_blocks.9.1.transformer_blocks.0.attn2.to_q.weight": "blocks.60.transformer_blocks.0.attn2.to_q.weight",
+            "model.diffusion_model.output_blocks.9.1.transformer_blocks.0.attn2.to_v.weight": "blocks.60.transformer_blocks.0.attn2.to_v.weight",
+            "model.diffusion_model.output_blocks.9.1.transformer_blocks.0.ff.net.0.proj.bias": "blocks.60.transformer_blocks.0.act_fn.proj.bias",
+            "model.diffusion_model.output_blocks.9.1.transformer_blocks.0.ff.net.0.proj.weight": "blocks.60.transformer_blocks.0.act_fn.proj.weight",
+            "model.diffusion_model.output_blocks.9.1.transformer_blocks.0.ff.net.2.bias": "blocks.60.transformer_blocks.0.ff.bias",
+            "model.diffusion_model.output_blocks.9.1.transformer_blocks.0.ff.net.2.weight": "blocks.60.transformer_blocks.0.ff.weight",
+            "model.diffusion_model.output_blocks.9.1.transformer_blocks.0.norm1.bias": "blocks.60.transformer_blocks.0.norm1.bias",
+            "model.diffusion_model.output_blocks.9.1.transformer_blocks.0.norm1.weight": "blocks.60.transformer_blocks.0.norm1.weight",
+            "model.diffusion_model.output_blocks.9.1.transformer_blocks.0.norm2.bias": "blocks.60.transformer_blocks.0.norm2.bias",
+            "model.diffusion_model.output_blocks.9.1.transformer_blocks.0.norm2.weight": "blocks.60.transformer_blocks.0.norm2.weight",
+            "model.diffusion_model.output_blocks.9.1.transformer_blocks.0.norm3.bias": "blocks.60.transformer_blocks.0.norm3.bias",
+            "model.diffusion_model.output_blocks.9.1.transformer_blocks.0.norm3.weight": "blocks.60.transformer_blocks.0.norm3.weight",
+            "model.diffusion_model.time_embed.0.bias": "time_embedding.0.bias",
+            "model.diffusion_model.time_embed.0.weight": "time_embedding.0.weight",
+            "model.diffusion_model.time_embed.2.bias": "time_embedding.2.bias",
+            "model.diffusion_model.time_embed.2.weight": "time_embedding.2.weight",
+        }
+        state_dict_ = {}
+        for name in state_dict:
+            if name in rename_dict:
+                param = state_dict[name]
+                if ".proj_in." in name or ".proj_out." in name:
+                    param = param.squeeze()
+                state_dict_[rename_dict[name]] = param
+        return state_dict_
\ No newline at end of file
diff --git a/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/sd_vae_decoder.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/sd_vae_decoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..93f015a63ededec53507ea73d34e2d904f5bed06
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/sd_vae_decoder.py
@@ -0,0 +1,336 @@
+import torch
+from .attention import Attention
+from .sd_unet import ResnetBlock, UpSampler
+from .tiler import TileWorker
+
+
+class VAEAttentionBlock(torch.nn.Module):
+
+    def __init__(self, num_attention_heads, attention_head_dim, in_channels, num_layers=1, norm_num_groups=32, eps=1e-5):
+        super().__init__()
+        inner_dim = num_attention_heads * attention_head_dim
+
+        self.norm = torch.nn.GroupNorm(num_groups=norm_num_groups, num_channels=in_channels, eps=eps, affine=True)
+
+        self.transformer_blocks = torch.nn.ModuleList([
+            Attention(
+                inner_dim,
+                num_attention_heads,
+                attention_head_dim,
+                bias_q=True,
+                bias_kv=True,
+                bias_out=True
+            )
+            for d in range(num_layers)
+        ])
+
+    def forward(self, hidden_states, time_emb, text_emb, res_stack):
+        batch, _, height, width = hidden_states.shape
+        residual = hidden_states
+
+        hidden_states = self.norm(hidden_states)
+        inner_dim = hidden_states.shape[1]
+        hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * width, inner_dim)
+
+        for block in self.transformer_blocks:
+            hidden_states = block(hidden_states)
+
+        hidden_states = hidden_states.reshape(batch, height, width, inner_dim).permute(0, 3, 1, 2).contiguous()
+        hidden_states = hidden_states + residual
+
+        return hidden_states, time_emb, text_emb, res_stack
+
+
+class SDVAEDecoder(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.scaling_factor = 0.18215
+        self.post_quant_conv = torch.nn.Conv2d(4, 4, kernel_size=1)
+        self.conv_in = torch.nn.Conv2d(4, 512, kernel_size=3, padding=1)
+
+        self.blocks = torch.nn.ModuleList([
+            # UNetMidBlock2D
+            ResnetBlock(512, 512, eps=1e-6),
+            VAEAttentionBlock(1, 512, 512, 1, eps=1e-6),
+            ResnetBlock(512, 512, eps=1e-6),
+            # UpDecoderBlock2D
+            ResnetBlock(512, 512, eps=1e-6),
+            ResnetBlock(512, 512, eps=1e-6),
+            ResnetBlock(512, 512, eps=1e-6),
+            UpSampler(512),
+            # UpDecoderBlock2D
+            ResnetBlock(512, 512, eps=1e-6),
+            ResnetBlock(512, 512, eps=1e-6),
+            ResnetBlock(512, 512, eps=1e-6),
+            UpSampler(512),
+            # UpDecoderBlock2D
+            ResnetBlock(512, 256, eps=1e-6),
+            ResnetBlock(256, 256, eps=1e-6),
+            ResnetBlock(256, 256, eps=1e-6),
+            UpSampler(256),
+            # UpDecoderBlock2D
+            ResnetBlock(256, 128, eps=1e-6),
+            ResnetBlock(128, 128, eps=1e-6),
+            ResnetBlock(128, 128, eps=1e-6),
+        ])
+
+        self.conv_norm_out = torch.nn.GroupNorm(num_channels=128, num_groups=32, eps=1e-5)
+        self.conv_act = torch.nn.SiLU()
+        self.conv_out = torch.nn.Conv2d(128, 3, kernel_size=3, padding=1)
+    
+    def tiled_forward(self, sample, tile_size=64, tile_stride=32):
+        hidden_states = TileWorker().tiled_forward(
+            lambda x: self.forward(x),
+            sample,
+            tile_size,
+            tile_stride,
+            tile_device=sample.device,
+            tile_dtype=sample.dtype
+        )
+        return hidden_states
+
+    def forward(self, sample, tiled=False, tile_size=64, tile_stride=32, **kwargs):
+        original_dtype = sample.dtype
+        sample = sample.to(dtype=next(iter(self.parameters())).dtype)
+        # For VAE Decoder, we do not need to apply the tiler on each layer.
+        if tiled:
+            return self.tiled_forward(sample, tile_size=tile_size, tile_stride=tile_stride)
+
+        # 1. pre-process
+        sample = sample / self.scaling_factor
+        hidden_states = self.post_quant_conv(sample)
+        hidden_states = self.conv_in(hidden_states)
+        time_emb = None
+        text_emb = None
+        res_stack = None
+
+        # 2. blocks
+        for i, block in enumerate(self.blocks):
+            hidden_states, time_emb, text_emb, res_stack = block(hidden_states, time_emb, text_emb, res_stack)
+        
+        # 3. output
+        hidden_states = self.conv_norm_out(hidden_states)
+        hidden_states = self.conv_act(hidden_states)
+        hidden_states = self.conv_out(hidden_states)
+        hidden_states = hidden_states.to(original_dtype)
+
+        return hidden_states
+    
+    @staticmethod
+    def state_dict_converter():
+        return SDVAEDecoderStateDictConverter()
+    
+
+class SDVAEDecoderStateDictConverter:
+    def __init__(self):
+        pass
+
+    def from_diffusers(self, state_dict):
+        # architecture
+        block_types = [
+            'ResnetBlock', 'VAEAttentionBlock', 'ResnetBlock',
+            'ResnetBlock', 'ResnetBlock', 'ResnetBlock', 'UpSampler',
+            'ResnetBlock', 'ResnetBlock', 'ResnetBlock', 'UpSampler',
+            'ResnetBlock', 'ResnetBlock', 'ResnetBlock', 'UpSampler',
+            'ResnetBlock', 'ResnetBlock', 'ResnetBlock'
+        ]
+
+        # Rename each parameter
+        local_rename_dict = {
+            "post_quant_conv": "post_quant_conv",
+            "decoder.conv_in": "conv_in",
+            "decoder.mid_block.attentions.0.group_norm": "blocks.1.norm",
+            "decoder.mid_block.attentions.0.to_q": "blocks.1.transformer_blocks.0.to_q",
+            "decoder.mid_block.attentions.0.to_k": "blocks.1.transformer_blocks.0.to_k",
+            "decoder.mid_block.attentions.0.to_v": "blocks.1.transformer_blocks.0.to_v",
+            "decoder.mid_block.attentions.0.to_out.0": "blocks.1.transformer_blocks.0.to_out",
+            "decoder.mid_block.resnets.0.norm1": "blocks.0.norm1",
+            "decoder.mid_block.resnets.0.conv1": "blocks.0.conv1",
+            "decoder.mid_block.resnets.0.norm2": "blocks.0.norm2",
+            "decoder.mid_block.resnets.0.conv2": "blocks.0.conv2",
+            "decoder.mid_block.resnets.1.norm1": "blocks.2.norm1",
+            "decoder.mid_block.resnets.1.conv1": "blocks.2.conv1",
+            "decoder.mid_block.resnets.1.norm2": "blocks.2.norm2",
+            "decoder.mid_block.resnets.1.conv2": "blocks.2.conv2",
+            "decoder.conv_norm_out": "conv_norm_out",
+            "decoder.conv_out": "conv_out",
+        }
+        name_list = sorted([name for name in state_dict])
+        rename_dict = {}
+        block_id = {"ResnetBlock": 2, "DownSampler": 2, "UpSampler": 2}
+        last_block_type_with_id = {"ResnetBlock": "", "DownSampler": "", "UpSampler": ""}
+        for name in name_list:
+            names = name.split(".")
+            name_prefix = ".".join(names[:-1])
+            if name_prefix in local_rename_dict:
+                rename_dict[name] = local_rename_dict[name_prefix] + "." + names[-1]
+            elif name.startswith("decoder.up_blocks"):
+                block_type = {"resnets": "ResnetBlock", "downsamplers": "DownSampler", "upsamplers": "UpSampler"}[names[3]]
+                block_type_with_id = ".".join(names[:5])
+                if block_type_with_id != last_block_type_with_id[block_type]:
+                    block_id[block_type] += 1
+                last_block_type_with_id[block_type] = block_type_with_id
+                while block_id[block_type] < len(block_types) and block_types[block_id[block_type]] != block_type:
+                    block_id[block_type] += 1
+                block_type_with_id = ".".join(names[:5])
+                names = ["blocks", str(block_id[block_type])] + names[5:]
+                rename_dict[name] = ".".join(names)
+
+        # Convert state_dict
+        state_dict_ = {}
+        for name, param in state_dict.items():
+            if name in rename_dict:
+                state_dict_[rename_dict[name]] = param
+        return state_dict_
+    
+    def from_civitai(self, state_dict):
+        rename_dict = {
+            "first_stage_model.decoder.conv_in.bias": "conv_in.bias",
+            "first_stage_model.decoder.conv_in.weight": "conv_in.weight",
+            "first_stage_model.decoder.conv_out.bias": "conv_out.bias",
+            "first_stage_model.decoder.conv_out.weight": "conv_out.weight",
+            "first_stage_model.decoder.mid.attn_1.k.bias": "blocks.1.transformer_blocks.0.to_k.bias",
+            "first_stage_model.decoder.mid.attn_1.k.weight": "blocks.1.transformer_blocks.0.to_k.weight",
+            "first_stage_model.decoder.mid.attn_1.norm.bias": "blocks.1.norm.bias",
+            "first_stage_model.decoder.mid.attn_1.norm.weight": "blocks.1.norm.weight",
+            "first_stage_model.decoder.mid.attn_1.proj_out.bias": "blocks.1.transformer_blocks.0.to_out.bias",    
+            "first_stage_model.decoder.mid.attn_1.proj_out.weight": "blocks.1.transformer_blocks.0.to_out.weight",
+            "first_stage_model.decoder.mid.attn_1.q.bias": "blocks.1.transformer_blocks.0.to_q.bias",
+            "first_stage_model.decoder.mid.attn_1.q.weight": "blocks.1.transformer_blocks.0.to_q.weight",
+            "first_stage_model.decoder.mid.attn_1.v.bias": "blocks.1.transformer_blocks.0.to_v.bias",
+            "first_stage_model.decoder.mid.attn_1.v.weight": "blocks.1.transformer_blocks.0.to_v.weight",
+            "first_stage_model.decoder.mid.block_1.conv1.bias": "blocks.0.conv1.bias",
+            "first_stage_model.decoder.mid.block_1.conv1.weight": "blocks.0.conv1.weight",
+            "first_stage_model.decoder.mid.block_1.conv2.bias": "blocks.0.conv2.bias",
+            "first_stage_model.decoder.mid.block_1.conv2.weight": "blocks.0.conv2.weight",
+            "first_stage_model.decoder.mid.block_1.norm1.bias": "blocks.0.norm1.bias",
+            "first_stage_model.decoder.mid.block_1.norm1.weight": "blocks.0.norm1.weight",
+            "first_stage_model.decoder.mid.block_1.norm2.bias": "blocks.0.norm2.bias",
+            "first_stage_model.decoder.mid.block_1.norm2.weight": "blocks.0.norm2.weight",
+            "first_stage_model.decoder.mid.block_2.conv1.bias": "blocks.2.conv1.bias",
+            "first_stage_model.decoder.mid.block_2.conv1.weight": "blocks.2.conv1.weight",
+            "first_stage_model.decoder.mid.block_2.conv2.bias": "blocks.2.conv2.bias",
+            "first_stage_model.decoder.mid.block_2.conv2.weight": "blocks.2.conv2.weight",
+            "first_stage_model.decoder.mid.block_2.norm1.bias": "blocks.2.norm1.bias",
+            "first_stage_model.decoder.mid.block_2.norm1.weight": "blocks.2.norm1.weight",
+            "first_stage_model.decoder.mid.block_2.norm2.bias": "blocks.2.norm2.bias",
+            "first_stage_model.decoder.mid.block_2.norm2.weight": "blocks.2.norm2.weight",
+            "first_stage_model.decoder.norm_out.bias": "conv_norm_out.bias",
+            "first_stage_model.decoder.norm_out.weight": "conv_norm_out.weight",
+            "first_stage_model.decoder.up.0.block.0.conv1.bias": "blocks.15.conv1.bias",
+            "first_stage_model.decoder.up.0.block.0.conv1.weight": "blocks.15.conv1.weight",
+            "first_stage_model.decoder.up.0.block.0.conv2.bias": "blocks.15.conv2.bias",
+            "first_stage_model.decoder.up.0.block.0.conv2.weight": "blocks.15.conv2.weight",
+            "first_stage_model.decoder.up.0.block.0.nin_shortcut.bias": "blocks.15.conv_shortcut.bias",
+            "first_stage_model.decoder.up.0.block.0.nin_shortcut.weight": "blocks.15.conv_shortcut.weight",       
+            "first_stage_model.decoder.up.0.block.0.norm1.bias": "blocks.15.norm1.bias",
+            "first_stage_model.decoder.up.0.block.0.norm1.weight": "blocks.15.norm1.weight",
+            "first_stage_model.decoder.up.0.block.0.norm2.bias": "blocks.15.norm2.bias",
+            "first_stage_model.decoder.up.0.block.0.norm2.weight": "blocks.15.norm2.weight",
+            "first_stage_model.decoder.up.0.block.1.conv1.bias": "blocks.16.conv1.bias",
+            "first_stage_model.decoder.up.0.block.1.conv1.weight": "blocks.16.conv1.weight",
+            "first_stage_model.decoder.up.0.block.1.conv2.bias": "blocks.16.conv2.bias",
+            "first_stage_model.decoder.up.0.block.1.conv2.weight": "blocks.16.conv2.weight",
+            "first_stage_model.decoder.up.0.block.1.norm1.bias": "blocks.16.norm1.bias",
+            "first_stage_model.decoder.up.0.block.1.norm1.weight": "blocks.16.norm1.weight",
+            "first_stage_model.decoder.up.0.block.1.norm2.bias": "blocks.16.norm2.bias",
+            "first_stage_model.decoder.up.0.block.1.norm2.weight": "blocks.16.norm2.weight",
+            "first_stage_model.decoder.up.0.block.2.conv1.bias": "blocks.17.conv1.bias",
+            "first_stage_model.decoder.up.0.block.2.conv1.weight": "blocks.17.conv1.weight",
+            "first_stage_model.decoder.up.0.block.2.conv2.bias": "blocks.17.conv2.bias",
+            "first_stage_model.decoder.up.0.block.2.conv2.weight": "blocks.17.conv2.weight",
+            "first_stage_model.decoder.up.0.block.2.norm1.bias": "blocks.17.norm1.bias",
+            "first_stage_model.decoder.up.0.block.2.norm1.weight": "blocks.17.norm1.weight",
+            "first_stage_model.decoder.up.0.block.2.norm2.bias": "blocks.17.norm2.bias",
+            "first_stage_model.decoder.up.0.block.2.norm2.weight": "blocks.17.norm2.weight",
+            "first_stage_model.decoder.up.1.block.0.conv1.bias": "blocks.11.conv1.bias",
+            "first_stage_model.decoder.up.1.block.0.conv1.weight": "blocks.11.conv1.weight",
+            "first_stage_model.decoder.up.1.block.0.conv2.bias": "blocks.11.conv2.bias",
+            "first_stage_model.decoder.up.1.block.0.conv2.weight": "blocks.11.conv2.weight",
+            "first_stage_model.decoder.up.1.block.0.nin_shortcut.bias": "blocks.11.conv_shortcut.bias",
+            "first_stage_model.decoder.up.1.block.0.nin_shortcut.weight": "blocks.11.conv_shortcut.weight",       
+            "first_stage_model.decoder.up.1.block.0.norm1.bias": "blocks.11.norm1.bias",
+            "first_stage_model.decoder.up.1.block.0.norm1.weight": "blocks.11.norm1.weight",
+            "first_stage_model.decoder.up.1.block.0.norm2.bias": "blocks.11.norm2.bias",
+            "first_stage_model.decoder.up.1.block.0.norm2.weight": "blocks.11.norm2.weight",
+            "first_stage_model.decoder.up.1.block.1.conv1.bias": "blocks.12.conv1.bias",
+            "first_stage_model.decoder.up.1.block.1.conv1.weight": "blocks.12.conv1.weight",
+            "first_stage_model.decoder.up.1.block.1.conv2.bias": "blocks.12.conv2.bias",
+            "first_stage_model.decoder.up.1.block.1.conv2.weight": "blocks.12.conv2.weight",
+            "first_stage_model.decoder.up.1.block.1.norm1.bias": "blocks.12.norm1.bias",
+            "first_stage_model.decoder.up.1.block.1.norm1.weight": "blocks.12.norm1.weight",
+            "first_stage_model.decoder.up.1.block.1.norm2.bias": "blocks.12.norm2.bias",
+            "first_stage_model.decoder.up.1.block.1.norm2.weight": "blocks.12.norm2.weight",
+            "first_stage_model.decoder.up.1.block.2.conv1.bias": "blocks.13.conv1.bias",
+            "first_stage_model.decoder.up.1.block.2.conv1.weight": "blocks.13.conv1.weight",
+            "first_stage_model.decoder.up.1.block.2.conv2.bias": "blocks.13.conv2.bias",
+            "first_stage_model.decoder.up.1.block.2.conv2.weight": "blocks.13.conv2.weight",
+            "first_stage_model.decoder.up.1.block.2.norm1.bias": "blocks.13.norm1.bias",
+            "first_stage_model.decoder.up.1.block.2.norm1.weight": "blocks.13.norm1.weight",
+            "first_stage_model.decoder.up.1.block.2.norm2.bias": "blocks.13.norm2.bias",
+            "first_stage_model.decoder.up.1.block.2.norm2.weight": "blocks.13.norm2.weight",
+            "first_stage_model.decoder.up.1.upsample.conv.bias": "blocks.14.conv.bias",
+            "first_stage_model.decoder.up.1.upsample.conv.weight": "blocks.14.conv.weight",
+            "first_stage_model.decoder.up.2.block.0.conv1.bias": "blocks.7.conv1.bias",
+            "first_stage_model.decoder.up.2.block.0.conv1.weight": "blocks.7.conv1.weight",
+            "first_stage_model.decoder.up.2.block.0.conv2.bias": "blocks.7.conv2.bias",
+            "first_stage_model.decoder.up.2.block.0.conv2.weight": "blocks.7.conv2.weight",
+            "first_stage_model.decoder.up.2.block.0.norm1.bias": "blocks.7.norm1.bias",
+            "first_stage_model.decoder.up.2.block.0.norm1.weight": "blocks.7.norm1.weight",
+            "first_stage_model.decoder.up.2.block.0.norm2.bias": "blocks.7.norm2.bias",
+            "first_stage_model.decoder.up.2.block.0.norm2.weight": "blocks.7.norm2.weight",
+            "first_stage_model.decoder.up.2.block.1.conv1.bias": "blocks.8.conv1.bias",
+            "first_stage_model.decoder.up.2.block.1.conv1.weight": "blocks.8.conv1.weight",
+            "first_stage_model.decoder.up.2.block.1.conv2.bias": "blocks.8.conv2.bias",
+            "first_stage_model.decoder.up.2.block.1.conv2.weight": "blocks.8.conv2.weight",
+            "first_stage_model.decoder.up.2.block.1.norm1.bias": "blocks.8.norm1.bias",
+            "first_stage_model.decoder.up.2.block.1.norm1.weight": "blocks.8.norm1.weight",
+            "first_stage_model.decoder.up.2.block.1.norm2.bias": "blocks.8.norm2.bias",
+            "first_stage_model.decoder.up.2.block.1.norm2.weight": "blocks.8.norm2.weight",
+            "first_stage_model.decoder.up.2.block.2.conv1.bias": "blocks.9.conv1.bias",
+            "first_stage_model.decoder.up.2.block.2.conv1.weight": "blocks.9.conv1.weight",
+            "first_stage_model.decoder.up.2.block.2.conv2.bias": "blocks.9.conv2.bias",
+            "first_stage_model.decoder.up.2.block.2.conv2.weight": "blocks.9.conv2.weight",
+            "first_stage_model.decoder.up.2.block.2.norm1.bias": "blocks.9.norm1.bias",
+            "first_stage_model.decoder.up.2.block.2.norm1.weight": "blocks.9.norm1.weight",
+            "first_stage_model.decoder.up.2.block.2.norm2.bias": "blocks.9.norm2.bias",
+            "first_stage_model.decoder.up.2.block.2.norm2.weight": "blocks.9.norm2.weight",
+            "first_stage_model.decoder.up.2.upsample.conv.bias": "blocks.10.conv.bias",
+            "first_stage_model.decoder.up.2.upsample.conv.weight": "blocks.10.conv.weight",
+            "first_stage_model.decoder.up.3.block.0.conv1.bias": "blocks.3.conv1.bias",
+            "first_stage_model.decoder.up.3.block.0.conv1.weight": "blocks.3.conv1.weight",
+            "first_stage_model.decoder.up.3.block.0.conv2.bias": "blocks.3.conv2.bias",
+            "first_stage_model.decoder.up.3.block.0.conv2.weight": "blocks.3.conv2.weight",
+            "first_stage_model.decoder.up.3.block.0.norm1.bias": "blocks.3.norm1.bias",
+            "first_stage_model.decoder.up.3.block.0.norm1.weight": "blocks.3.norm1.weight",
+            "first_stage_model.decoder.up.3.block.0.norm2.bias": "blocks.3.norm2.bias",
+            "first_stage_model.decoder.up.3.block.0.norm2.weight": "blocks.3.norm2.weight",
+            "first_stage_model.decoder.up.3.block.1.conv1.bias": "blocks.4.conv1.bias",
+            "first_stage_model.decoder.up.3.block.1.conv1.weight": "blocks.4.conv1.weight",
+            "first_stage_model.decoder.up.3.block.1.conv2.bias": "blocks.4.conv2.bias",
+            "first_stage_model.decoder.up.3.block.1.conv2.weight": "blocks.4.conv2.weight",
+            "first_stage_model.decoder.up.3.block.1.norm1.bias": "blocks.4.norm1.bias",
+            "first_stage_model.decoder.up.3.block.1.norm1.weight": "blocks.4.norm1.weight",
+            "first_stage_model.decoder.up.3.block.1.norm2.bias": "blocks.4.norm2.bias",
+            "first_stage_model.decoder.up.3.block.1.norm2.weight": "blocks.4.norm2.weight",
+            "first_stage_model.decoder.up.3.block.2.conv1.bias": "blocks.5.conv1.bias",
+            "first_stage_model.decoder.up.3.block.2.conv1.weight": "blocks.5.conv1.weight",
+            "first_stage_model.decoder.up.3.block.2.conv2.bias": "blocks.5.conv2.bias",
+            "first_stage_model.decoder.up.3.block.2.conv2.weight": "blocks.5.conv2.weight",
+            "first_stage_model.decoder.up.3.block.2.norm1.bias": "blocks.5.norm1.bias",
+            "first_stage_model.decoder.up.3.block.2.norm1.weight": "blocks.5.norm1.weight",
+            "first_stage_model.decoder.up.3.block.2.norm2.bias": "blocks.5.norm2.bias",
+            "first_stage_model.decoder.up.3.block.2.norm2.weight": "blocks.5.norm2.weight",
+            "first_stage_model.decoder.up.3.upsample.conv.bias": "blocks.6.conv.bias",
+            "first_stage_model.decoder.up.3.upsample.conv.weight": "blocks.6.conv.weight",
+            "first_stage_model.post_quant_conv.bias": "post_quant_conv.bias",
+            "first_stage_model.post_quant_conv.weight": "post_quant_conv.weight",
+        }
+        state_dict_ = {}
+        for name in state_dict:
+            if name in rename_dict:
+                param = state_dict[name]
+                if "transformer_blocks" in rename_dict[name]:
+                    param = param.squeeze()
+                state_dict_[rename_dict[name]] = param
+        return state_dict_
diff --git a/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/sd_vae_encoder.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/sd_vae_encoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..60965c591c01bd12dce5f0abdbfd121c033c47c6
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/sd_vae_encoder.py
@@ -0,0 +1,282 @@
+import torch
+from .sd_unet import ResnetBlock, DownSampler
+from .sd_vae_decoder import VAEAttentionBlock
+from .tiler import TileWorker
+from einops import rearrange
+
+
+class SDVAEEncoder(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.scaling_factor = 0.18215
+        self.quant_conv = torch.nn.Conv2d(8, 8, kernel_size=1)
+        self.conv_in = torch.nn.Conv2d(3, 128, kernel_size=3, padding=1)
+
+        self.blocks = torch.nn.ModuleList([
+            # DownEncoderBlock2D
+            ResnetBlock(128, 128, eps=1e-6),
+            ResnetBlock(128, 128, eps=1e-6),
+            DownSampler(128, padding=0, extra_padding=True),
+            # DownEncoderBlock2D
+            ResnetBlock(128, 256, eps=1e-6),
+            ResnetBlock(256, 256, eps=1e-6),
+            DownSampler(256, padding=0, extra_padding=True),
+            # DownEncoderBlock2D
+            ResnetBlock(256, 512, eps=1e-6),
+            ResnetBlock(512, 512, eps=1e-6),
+            DownSampler(512, padding=0, extra_padding=True),
+            # DownEncoderBlock2D
+            ResnetBlock(512, 512, eps=1e-6),
+            ResnetBlock(512, 512, eps=1e-6),
+            # UNetMidBlock2D
+            ResnetBlock(512, 512, eps=1e-6),
+            VAEAttentionBlock(1, 512, 512, 1, eps=1e-6),
+            ResnetBlock(512, 512, eps=1e-6),
+        ])
+
+        self.conv_norm_out = torch.nn.GroupNorm(num_channels=512, num_groups=32, eps=1e-6)
+        self.conv_act = torch.nn.SiLU()
+        self.conv_out = torch.nn.Conv2d(512, 8, kernel_size=3, padding=1)
+
+    def tiled_forward(self, sample, tile_size=64, tile_stride=32):
+        hidden_states = TileWorker().tiled_forward(
+            lambda x: self.forward(x),
+            sample,
+            tile_size,
+            tile_stride,
+            tile_device=sample.device,
+            tile_dtype=sample.dtype
+        )
+        return hidden_states
+
+    def forward(self, sample, tiled=False, tile_size=64, tile_stride=32, **kwargs):
+        original_dtype = sample.dtype
+        sample = sample.to(dtype=next(iter(self.parameters())).dtype)
+        # For VAE Decoder, we do not need to apply the tiler on each layer.
+        if tiled:
+            return self.tiled_forward(sample, tile_size=tile_size, tile_stride=tile_stride)
+        
+        # 1. pre-process
+        hidden_states = self.conv_in(sample)
+        time_emb = None
+        text_emb = None
+        res_stack = None
+
+        # 2. blocks
+        for i, block in enumerate(self.blocks):
+            hidden_states, time_emb, text_emb, res_stack = block(hidden_states, time_emb, text_emb, res_stack)
+        
+        # 3. output
+        hidden_states = self.conv_norm_out(hidden_states)
+        hidden_states = self.conv_act(hidden_states)
+        hidden_states = self.conv_out(hidden_states)
+        hidden_states = self.quant_conv(hidden_states)
+        hidden_states = hidden_states[:, :4]
+        hidden_states *= self.scaling_factor
+        hidden_states = hidden_states.to(original_dtype)
+
+        return hidden_states
+    
+    def encode_video(self, sample, batch_size=8):
+        B = sample.shape[0]
+        hidden_states = []
+
+        for i in range(0, sample.shape[2], batch_size):
+
+            j = min(i + batch_size, sample.shape[2])
+            sample_batch = rearrange(sample[:,:,i:j], "B C T H W -> (B T) C H W")
+
+            hidden_states_batch = self(sample_batch)
+            hidden_states_batch = rearrange(hidden_states_batch, "(B T) C H W -> B C T H W", B=B)
+
+            hidden_states.append(hidden_states_batch)
+        
+        hidden_states = torch.concat(hidden_states, dim=2)
+        return hidden_states
+    
+    @staticmethod
+    def state_dict_converter():
+        return SDVAEEncoderStateDictConverter()
+    
+
+class SDVAEEncoderStateDictConverter:
+    def __init__(self):
+        pass
+
+    def from_diffusers(self, state_dict):
+        # architecture
+        block_types = [
+            'ResnetBlock', 'ResnetBlock', 'DownSampler',
+            'ResnetBlock', 'ResnetBlock', 'DownSampler',
+            'ResnetBlock', 'ResnetBlock', 'DownSampler',
+            'ResnetBlock', 'ResnetBlock',
+            'ResnetBlock', 'VAEAttentionBlock', 'ResnetBlock'
+        ]
+
+        # Rename each parameter
+        local_rename_dict = {
+            "quant_conv": "quant_conv",
+            "encoder.conv_in": "conv_in",
+            "encoder.mid_block.attentions.0.group_norm": "blocks.12.norm",
+            "encoder.mid_block.attentions.0.to_q": "blocks.12.transformer_blocks.0.to_q",
+            "encoder.mid_block.attentions.0.to_k": "blocks.12.transformer_blocks.0.to_k",
+            "encoder.mid_block.attentions.0.to_v": "blocks.12.transformer_blocks.0.to_v",
+            "encoder.mid_block.attentions.0.to_out.0": "blocks.12.transformer_blocks.0.to_out",
+            "encoder.mid_block.resnets.0.norm1": "blocks.11.norm1",
+            "encoder.mid_block.resnets.0.conv1": "blocks.11.conv1",
+            "encoder.mid_block.resnets.0.norm2": "blocks.11.norm2",
+            "encoder.mid_block.resnets.0.conv2": "blocks.11.conv2",
+            "encoder.mid_block.resnets.1.norm1": "blocks.13.norm1",
+            "encoder.mid_block.resnets.1.conv1": "blocks.13.conv1",
+            "encoder.mid_block.resnets.1.norm2": "blocks.13.norm2",
+            "encoder.mid_block.resnets.1.conv2": "blocks.13.conv2",
+            "encoder.conv_norm_out": "conv_norm_out",
+            "encoder.conv_out": "conv_out",
+        }
+        name_list = sorted([name for name in state_dict])
+        rename_dict = {}
+        block_id = {"ResnetBlock": -1, "DownSampler": -1, "UpSampler": -1}
+        last_block_type_with_id = {"ResnetBlock": "", "DownSampler": "", "UpSampler": ""}
+        for name in name_list:
+            names = name.split(".")
+            name_prefix = ".".join(names[:-1])
+            if name_prefix in local_rename_dict:
+                rename_dict[name] = local_rename_dict[name_prefix] + "." + names[-1]
+            elif name.startswith("encoder.down_blocks"):
+                block_type = {"resnets": "ResnetBlock", "downsamplers": "DownSampler", "upsamplers": "UpSampler"}[names[3]]
+                block_type_with_id = ".".join(names[:5])
+                if block_type_with_id != last_block_type_with_id[block_type]:
+                    block_id[block_type] += 1
+                last_block_type_with_id[block_type] = block_type_with_id
+                while block_id[block_type] < len(block_types) and block_types[block_id[block_type]] != block_type:
+                    block_id[block_type] += 1
+                block_type_with_id = ".".join(names[:5])
+                names = ["blocks", str(block_id[block_type])] + names[5:]
+                rename_dict[name] = ".".join(names)
+
+        # Convert state_dict
+        state_dict_ = {}
+        for name, param in state_dict.items():
+            if name in rename_dict:
+                state_dict_[rename_dict[name]] = param
+        return state_dict_
+    
+    def from_civitai(self, state_dict):
+        rename_dict = {
+            "first_stage_model.encoder.conv_in.bias": "conv_in.bias",
+            "first_stage_model.encoder.conv_in.weight": "conv_in.weight",
+            "first_stage_model.encoder.conv_out.bias": "conv_out.bias",
+            "first_stage_model.encoder.conv_out.weight": "conv_out.weight",
+            "first_stage_model.encoder.down.0.block.0.conv1.bias": "blocks.0.conv1.bias",
+            "first_stage_model.encoder.down.0.block.0.conv1.weight": "blocks.0.conv1.weight",
+            "first_stage_model.encoder.down.0.block.0.conv2.bias": "blocks.0.conv2.bias",
+            "first_stage_model.encoder.down.0.block.0.conv2.weight": "blocks.0.conv2.weight",
+            "first_stage_model.encoder.down.0.block.0.norm1.bias": "blocks.0.norm1.bias",
+            "first_stage_model.encoder.down.0.block.0.norm1.weight": "blocks.0.norm1.weight",
+            "first_stage_model.encoder.down.0.block.0.norm2.bias": "blocks.0.norm2.bias",
+            "first_stage_model.encoder.down.0.block.0.norm2.weight": "blocks.0.norm2.weight",
+            "first_stage_model.encoder.down.0.block.1.conv1.bias": "blocks.1.conv1.bias",
+            "first_stage_model.encoder.down.0.block.1.conv1.weight": "blocks.1.conv1.weight",
+            "first_stage_model.encoder.down.0.block.1.conv2.bias": "blocks.1.conv2.bias",
+            "first_stage_model.encoder.down.0.block.1.conv2.weight": "blocks.1.conv2.weight",
+            "first_stage_model.encoder.down.0.block.1.norm1.bias": "blocks.1.norm1.bias",
+            "first_stage_model.encoder.down.0.block.1.norm1.weight": "blocks.1.norm1.weight",
+            "first_stage_model.encoder.down.0.block.1.norm2.bias": "blocks.1.norm2.bias",
+            "first_stage_model.encoder.down.0.block.1.norm2.weight": "blocks.1.norm2.weight",
+            "first_stage_model.encoder.down.0.downsample.conv.bias": "blocks.2.conv.bias",
+            "first_stage_model.encoder.down.0.downsample.conv.weight": "blocks.2.conv.weight",
+            "first_stage_model.encoder.down.1.block.0.conv1.bias": "blocks.3.conv1.bias",
+            "first_stage_model.encoder.down.1.block.0.conv1.weight": "blocks.3.conv1.weight",
+            "first_stage_model.encoder.down.1.block.0.conv2.bias": "blocks.3.conv2.bias",
+            "first_stage_model.encoder.down.1.block.0.conv2.weight": "blocks.3.conv2.weight",
+            "first_stage_model.encoder.down.1.block.0.nin_shortcut.bias": "blocks.3.conv_shortcut.bias",
+            "first_stage_model.encoder.down.1.block.0.nin_shortcut.weight": "blocks.3.conv_shortcut.weight",
+            "first_stage_model.encoder.down.1.block.0.norm1.bias": "blocks.3.norm1.bias",
+            "first_stage_model.encoder.down.1.block.0.norm1.weight": "blocks.3.norm1.weight",
+            "first_stage_model.encoder.down.1.block.0.norm2.bias": "blocks.3.norm2.bias",
+            "first_stage_model.encoder.down.1.block.0.norm2.weight": "blocks.3.norm2.weight",
+            "first_stage_model.encoder.down.1.block.1.conv1.bias": "blocks.4.conv1.bias",
+            "first_stage_model.encoder.down.1.block.1.conv1.weight": "blocks.4.conv1.weight",
+            "first_stage_model.encoder.down.1.block.1.conv2.bias": "blocks.4.conv2.bias",
+            "first_stage_model.encoder.down.1.block.1.conv2.weight": "blocks.4.conv2.weight",
+            "first_stage_model.encoder.down.1.block.1.norm1.bias": "blocks.4.norm1.bias",
+            "first_stage_model.encoder.down.1.block.1.norm1.weight": "blocks.4.norm1.weight",
+            "first_stage_model.encoder.down.1.block.1.norm2.bias": "blocks.4.norm2.bias",
+            "first_stage_model.encoder.down.1.block.1.norm2.weight": "blocks.4.norm2.weight",
+            "first_stage_model.encoder.down.1.downsample.conv.bias": "blocks.5.conv.bias",
+            "first_stage_model.encoder.down.1.downsample.conv.weight": "blocks.5.conv.weight",
+            "first_stage_model.encoder.down.2.block.0.conv1.bias": "blocks.6.conv1.bias",
+            "first_stage_model.encoder.down.2.block.0.conv1.weight": "blocks.6.conv1.weight",
+            "first_stage_model.encoder.down.2.block.0.conv2.bias": "blocks.6.conv2.bias",
+            "first_stage_model.encoder.down.2.block.0.conv2.weight": "blocks.6.conv2.weight",
+            "first_stage_model.encoder.down.2.block.0.nin_shortcut.bias": "blocks.6.conv_shortcut.bias",
+            "first_stage_model.encoder.down.2.block.0.nin_shortcut.weight": "blocks.6.conv_shortcut.weight",
+            "first_stage_model.encoder.down.2.block.0.norm1.bias": "blocks.6.norm1.bias",
+            "first_stage_model.encoder.down.2.block.0.norm1.weight": "blocks.6.norm1.weight",
+            "first_stage_model.encoder.down.2.block.0.norm2.bias": "blocks.6.norm2.bias",
+            "first_stage_model.encoder.down.2.block.0.norm2.weight": "blocks.6.norm2.weight",
+            "first_stage_model.encoder.down.2.block.1.conv1.bias": "blocks.7.conv1.bias",
+            "first_stage_model.encoder.down.2.block.1.conv1.weight": "blocks.7.conv1.weight",
+            "first_stage_model.encoder.down.2.block.1.conv2.bias": "blocks.7.conv2.bias",
+            "first_stage_model.encoder.down.2.block.1.conv2.weight": "blocks.7.conv2.weight",
+            "first_stage_model.encoder.down.2.block.1.norm1.bias": "blocks.7.norm1.bias",
+            "first_stage_model.encoder.down.2.block.1.norm1.weight": "blocks.7.norm1.weight",
+            "first_stage_model.encoder.down.2.block.1.norm2.bias": "blocks.7.norm2.bias",
+            "first_stage_model.encoder.down.2.block.1.norm2.weight": "blocks.7.norm2.weight",
+            "first_stage_model.encoder.down.2.downsample.conv.bias": "blocks.8.conv.bias",
+            "first_stage_model.encoder.down.2.downsample.conv.weight": "blocks.8.conv.weight",
+            "first_stage_model.encoder.down.3.block.0.conv1.bias": "blocks.9.conv1.bias",
+            "first_stage_model.encoder.down.3.block.0.conv1.weight": "blocks.9.conv1.weight",
+            "first_stage_model.encoder.down.3.block.0.conv2.bias": "blocks.9.conv2.bias",
+            "first_stage_model.encoder.down.3.block.0.conv2.weight": "blocks.9.conv2.weight",
+            "first_stage_model.encoder.down.3.block.0.norm1.bias": "blocks.9.norm1.bias",
+            "first_stage_model.encoder.down.3.block.0.norm1.weight": "blocks.9.norm1.weight",
+            "first_stage_model.encoder.down.3.block.0.norm2.bias": "blocks.9.norm2.bias",
+            "first_stage_model.encoder.down.3.block.0.norm2.weight": "blocks.9.norm2.weight",
+            "first_stage_model.encoder.down.3.block.1.conv1.bias": "blocks.10.conv1.bias",
+            "first_stage_model.encoder.down.3.block.1.conv1.weight": "blocks.10.conv1.weight",
+            "first_stage_model.encoder.down.3.block.1.conv2.bias": "blocks.10.conv2.bias",
+            "first_stage_model.encoder.down.3.block.1.conv2.weight": "blocks.10.conv2.weight",
+            "first_stage_model.encoder.down.3.block.1.norm1.bias": "blocks.10.norm1.bias",
+            "first_stage_model.encoder.down.3.block.1.norm1.weight": "blocks.10.norm1.weight",
+            "first_stage_model.encoder.down.3.block.1.norm2.bias": "blocks.10.norm2.bias",
+            "first_stage_model.encoder.down.3.block.1.norm2.weight": "blocks.10.norm2.weight",
+            "first_stage_model.encoder.mid.attn_1.k.bias": "blocks.12.transformer_blocks.0.to_k.bias",
+            "first_stage_model.encoder.mid.attn_1.k.weight": "blocks.12.transformer_blocks.0.to_k.weight",
+            "first_stage_model.encoder.mid.attn_1.norm.bias": "blocks.12.norm.bias",
+            "first_stage_model.encoder.mid.attn_1.norm.weight": "blocks.12.norm.weight",
+            "first_stage_model.encoder.mid.attn_1.proj_out.bias": "blocks.12.transformer_blocks.0.to_out.bias",       
+            "first_stage_model.encoder.mid.attn_1.proj_out.weight": "blocks.12.transformer_blocks.0.to_out.weight",   
+            "first_stage_model.encoder.mid.attn_1.q.bias": "blocks.12.transformer_blocks.0.to_q.bias",
+            "first_stage_model.encoder.mid.attn_1.q.weight": "blocks.12.transformer_blocks.0.to_q.weight",
+            "first_stage_model.encoder.mid.attn_1.v.bias": "blocks.12.transformer_blocks.0.to_v.bias",
+            "first_stage_model.encoder.mid.attn_1.v.weight": "blocks.12.transformer_blocks.0.to_v.weight",
+            "first_stage_model.encoder.mid.block_1.conv1.bias": "blocks.11.conv1.bias",
+            "first_stage_model.encoder.mid.block_1.conv1.weight": "blocks.11.conv1.weight",
+            "first_stage_model.encoder.mid.block_1.conv2.bias": "blocks.11.conv2.bias",
+            "first_stage_model.encoder.mid.block_1.conv2.weight": "blocks.11.conv2.weight",
+            "first_stage_model.encoder.mid.block_1.norm1.bias": "blocks.11.norm1.bias",
+            "first_stage_model.encoder.mid.block_1.norm1.weight": "blocks.11.norm1.weight",
+            "first_stage_model.encoder.mid.block_1.norm2.bias": "blocks.11.norm2.bias",
+            "first_stage_model.encoder.mid.block_1.norm2.weight": "blocks.11.norm2.weight",
+            "first_stage_model.encoder.mid.block_2.conv1.bias": "blocks.13.conv1.bias",
+            "first_stage_model.encoder.mid.block_2.conv1.weight": "blocks.13.conv1.weight",
+            "first_stage_model.encoder.mid.block_2.conv2.bias": "blocks.13.conv2.bias",
+            "first_stage_model.encoder.mid.block_2.conv2.weight": "blocks.13.conv2.weight",
+            "first_stage_model.encoder.mid.block_2.norm1.bias": "blocks.13.norm1.bias",
+            "first_stage_model.encoder.mid.block_2.norm1.weight": "blocks.13.norm1.weight",
+            "first_stage_model.encoder.mid.block_2.norm2.bias": "blocks.13.norm2.bias",
+            "first_stage_model.encoder.mid.block_2.norm2.weight": "blocks.13.norm2.weight",
+            "first_stage_model.encoder.norm_out.bias": "conv_norm_out.bias",
+            "first_stage_model.encoder.norm_out.weight": "conv_norm_out.weight",
+            "first_stage_model.quant_conv.bias": "quant_conv.bias",
+            "first_stage_model.quant_conv.weight": "quant_conv.weight",
+        }
+        state_dict_ = {}
+        for name in state_dict:
+            if name in rename_dict:
+                param = state_dict[name]
+                if "transformer_blocks" in rename_dict[name]:
+                    param = param.squeeze()
+                state_dict_[rename_dict[name]] = param
+        return state_dict_
diff --git a/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/sdxl_controlnet.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/sdxl_controlnet.py
new file mode 100644
index 0000000000000000000000000000000000000000..acddf1cc4109af01bd9c06121f6cd4d8604ce945
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/sdxl_controlnet.py
@@ -0,0 +1,318 @@
+import torch
+from .sd_unet import Timesteps, ResnetBlock, AttentionBlock, PushBlock, DownSampler
+from .sdxl_unet import SDXLUNet
+from .tiler import TileWorker
+from .sd_controlnet import ControlNetConditioningLayer
+from collections import OrderedDict
+
+
+
+class QuickGELU(torch.nn.Module):
+
+    def forward(self, x: torch.Tensor):
+        return x * torch.sigmoid(1.702 * x)
+
+
+
+class ResidualAttentionBlock(torch.nn.Module):
+
+    def __init__(self, d_model: int, n_head: int, attn_mask: torch.Tensor = None):
+        super().__init__()
+
+        self.attn = torch.nn.MultiheadAttention(d_model, n_head)
+        self.ln_1 = torch.nn.LayerNorm(d_model)
+        self.mlp = torch.nn.Sequential(OrderedDict([
+            ("c_fc", torch.nn.Linear(d_model, d_model * 4)),
+            ("gelu", QuickGELU()),
+            ("c_proj", torch.nn.Linear(d_model * 4, d_model))
+        ]))
+        self.ln_2 = torch.nn.LayerNorm(d_model)
+        self.attn_mask = attn_mask
+
+    def attention(self, x: torch.Tensor):
+        self.attn_mask = self.attn_mask.to(dtype=x.dtype, device=x.device) if self.attn_mask is not None else None
+        return self.attn(x, x, x, need_weights=False, attn_mask=self.attn_mask)[0]
+
+    def forward(self, x: torch.Tensor):
+        x = x + self.attention(self.ln_1(x))
+        x = x + self.mlp(self.ln_2(x))
+        return x
+
+
+
+class SDXLControlNetUnion(torch.nn.Module):
+    def __init__(self, global_pool=False):
+        super().__init__()
+        self.time_proj = Timesteps(320)
+        self.time_embedding = torch.nn.Sequential(
+            torch.nn.Linear(320, 1280),
+            torch.nn.SiLU(),
+            torch.nn.Linear(1280, 1280)
+        )
+        self.add_time_proj = Timesteps(256)
+        self.add_time_embedding = torch.nn.Sequential(
+            torch.nn.Linear(2816, 1280),
+            torch.nn.SiLU(),
+            torch.nn.Linear(1280, 1280)
+        )
+        self.control_type_proj = Timesteps(256)
+        self.control_type_embedding = torch.nn.Sequential(
+            torch.nn.Linear(256 * 8, 1280),
+            torch.nn.SiLU(),
+            torch.nn.Linear(1280, 1280)
+        )
+        self.conv_in = torch.nn.Conv2d(4, 320, kernel_size=3, padding=1)
+
+        self.controlnet_conv_in = ControlNetConditioningLayer(channels=(3, 16, 32, 96, 256, 320))
+        self.controlnet_transformer = ResidualAttentionBlock(320, 8)
+        self.task_embedding = torch.nn.Parameter(torch.randn(8, 320))
+        self.spatial_ch_projs = torch.nn.Linear(320, 320)
+
+        self.blocks = torch.nn.ModuleList([
+            # DownBlock2D
+            ResnetBlock(320, 320, 1280),
+            PushBlock(),
+            ResnetBlock(320, 320, 1280),
+            PushBlock(),
+            DownSampler(320),
+            PushBlock(),
+            # CrossAttnDownBlock2D
+            ResnetBlock(320, 640, 1280),
+            AttentionBlock(10, 64, 640, 2, 2048),
+            PushBlock(),
+            ResnetBlock(640, 640, 1280),
+            AttentionBlock(10, 64, 640, 2, 2048),
+            PushBlock(),
+            DownSampler(640),
+            PushBlock(),
+            # CrossAttnDownBlock2D
+            ResnetBlock(640, 1280, 1280),
+            AttentionBlock(20, 64, 1280, 10, 2048),
+            PushBlock(),
+            ResnetBlock(1280, 1280, 1280),
+            AttentionBlock(20, 64, 1280, 10, 2048),
+            PushBlock(),
+            # UNetMidBlock2DCrossAttn
+            ResnetBlock(1280, 1280, 1280),
+            AttentionBlock(20, 64, 1280, 10, 2048),
+            ResnetBlock(1280, 1280, 1280),
+            PushBlock()
+        ])
+
+        self.controlnet_blocks = torch.nn.ModuleList([
+            torch.nn.Conv2d(320, 320, kernel_size=(1, 1)),
+            torch.nn.Conv2d(320, 320, kernel_size=(1, 1)),
+            torch.nn.Conv2d(320, 320, kernel_size=(1, 1)),
+            torch.nn.Conv2d(320, 320, kernel_size=(1, 1)),
+            torch.nn.Conv2d(640, 640, kernel_size=(1, 1)),
+            torch.nn.Conv2d(640, 640, kernel_size=(1, 1)),
+            torch.nn.Conv2d(640, 640, kernel_size=(1, 1)),
+            torch.nn.Conv2d(1280, 1280, kernel_size=(1, 1)),
+            torch.nn.Conv2d(1280, 1280, kernel_size=(1, 1)),
+            torch.nn.Conv2d(1280, 1280, kernel_size=(1, 1)),
+        ])
+
+        self.global_pool = global_pool
+
+        # 0 -- openpose
+        # 1 -- depth
+        # 2 -- hed/pidi/scribble/ted
+        # 3 -- canny/lineart/anime_lineart/mlsd
+        # 4 -- normal
+        # 5 -- segment
+        # 6 -- tile
+        # 7 -- repaint
+        self.task_id = {
+            "openpose": 0,
+            "depth": 1,
+            "softedge": 2,
+            "canny": 3,
+            "lineart": 3,
+            "lineart_anime": 3,
+            "tile": 6,
+            "inpaint": 7
+        }
+
+
+    def fuse_condition_to_input(self, hidden_states, task_id, conditioning):
+        controlnet_cond = self.controlnet_conv_in(conditioning)
+        feat_seq = torch.mean(controlnet_cond, dim=(2, 3))
+        feat_seq = feat_seq + self.task_embedding[task_id]
+        x = torch.stack([feat_seq, torch.mean(hidden_states, dim=(2, 3))], dim=1)
+        x = self.controlnet_transformer(x)
+
+        alpha = self.spatial_ch_projs(x[:,0]).unsqueeze(-1).unsqueeze(-1)
+        controlnet_cond_fuser = controlnet_cond + alpha
+
+        hidden_states = hidden_states + controlnet_cond_fuser
+        return hidden_states
+    
+
+    def forward(
+        self,
+        sample, timestep, encoder_hidden_states,
+        conditioning, processor_id, add_time_id, add_text_embeds,
+        tiled=False, tile_size=64, tile_stride=32,
+        unet:SDXLUNet=None,
+        **kwargs
+    ):
+        task_id = self.task_id[processor_id]
+
+        # 1. time
+        t_emb = self.time_proj(timestep).to(sample.dtype)
+        t_emb = self.time_embedding(t_emb)
+        
+        time_embeds = self.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)
+        if unet is not None and unet.is_kolors:
+            add_embeds = unet.add_time_embedding(add_embeds)
+        else:
+            add_embeds = self.add_time_embedding(add_embeds)
+
+        control_type = torch.zeros((sample.shape[0], 8), dtype=sample.dtype, device=sample.device)
+        control_type[:, task_id] = 1
+        control_embeds = self.control_type_proj(control_type.flatten())
+        control_embeds = control_embeds.reshape((sample.shape[0], -1))
+        control_embeds = control_embeds.to(sample.dtype)
+        control_embeds = self.control_type_embedding(control_embeds)
+        time_emb = t_emb + add_embeds + control_embeds
+
+        # 2. pre-process
+        height, width = sample.shape[2], sample.shape[3]
+        hidden_states = self.conv_in(sample)
+        hidden_states = self.fuse_condition_to_input(hidden_states, task_id, conditioning)
+        text_emb = encoder_hidden_states
+        if unet is not None and unet.is_kolors:
+            text_emb = unet.text_intermediate_proj(text_emb)
+        res_stack = [hidden_states]
+
+        # 3. blocks
+        for i, block in enumerate(self.blocks):
+            if tiled and not isinstance(block, PushBlock):
+                _, _, inter_height, _ = hidden_states.shape
+                resize_scale = inter_height / height
+                hidden_states = TileWorker().tiled_forward(
+                    lambda x: block(x, time_emb, text_emb, res_stack)[0],
+                    hidden_states,
+                    int(tile_size * resize_scale),
+                    int(tile_stride * resize_scale),
+                    tile_device=hidden_states.device,
+                    tile_dtype=hidden_states.dtype
+                )
+            else:
+                hidden_states, _, _, _ = block(hidden_states, time_emb, text_emb, res_stack)
+
+        # 4. ControlNet blocks
+        controlnet_res_stack = [block(res) for block, res in zip(self.controlnet_blocks, res_stack)]
+
+        # pool
+        if self.global_pool:
+            controlnet_res_stack = [res.mean(dim=(2, 3), keepdim=True) for res in controlnet_res_stack]
+
+        return controlnet_res_stack
+
+    @staticmethod
+    def state_dict_converter():
+        return SDXLControlNetUnionStateDictConverter()
+
+
+
+class SDXLControlNetUnionStateDictConverter:
+    def __init__(self):
+        pass
+
+    def from_diffusers(self, state_dict):
+        # architecture
+        block_types = [
+            "ResnetBlock", "PushBlock", "ResnetBlock", "PushBlock", "DownSampler", "PushBlock",
+            "ResnetBlock", "AttentionBlock", "PushBlock", "ResnetBlock", "AttentionBlock", "PushBlock", "DownSampler", "PushBlock",
+            "ResnetBlock", "AttentionBlock", "PushBlock", "ResnetBlock", "AttentionBlock", "PushBlock",
+            "ResnetBlock", "AttentionBlock", "ResnetBlock", "PushBlock"
+        ]
+
+        # controlnet_rename_dict
+        controlnet_rename_dict = {
+            "controlnet_cond_embedding.conv_in.weight": "controlnet_conv_in.blocks.0.weight",
+            "controlnet_cond_embedding.conv_in.bias": "controlnet_conv_in.blocks.0.bias",
+            "controlnet_cond_embedding.blocks.0.weight": "controlnet_conv_in.blocks.2.weight",
+            "controlnet_cond_embedding.blocks.0.bias": "controlnet_conv_in.blocks.2.bias",
+            "controlnet_cond_embedding.blocks.1.weight": "controlnet_conv_in.blocks.4.weight",
+            "controlnet_cond_embedding.blocks.1.bias": "controlnet_conv_in.blocks.4.bias",
+            "controlnet_cond_embedding.blocks.2.weight": "controlnet_conv_in.blocks.6.weight",
+            "controlnet_cond_embedding.blocks.2.bias": "controlnet_conv_in.blocks.6.bias",
+            "controlnet_cond_embedding.blocks.3.weight": "controlnet_conv_in.blocks.8.weight",
+            "controlnet_cond_embedding.blocks.3.bias": "controlnet_conv_in.blocks.8.bias",
+            "controlnet_cond_embedding.blocks.4.weight": "controlnet_conv_in.blocks.10.weight",
+            "controlnet_cond_embedding.blocks.4.bias": "controlnet_conv_in.blocks.10.bias",
+            "controlnet_cond_embedding.blocks.5.weight": "controlnet_conv_in.blocks.12.weight",
+            "controlnet_cond_embedding.blocks.5.bias": "controlnet_conv_in.blocks.12.bias",
+            "controlnet_cond_embedding.conv_out.weight": "controlnet_conv_in.blocks.14.weight",
+            "controlnet_cond_embedding.conv_out.bias": "controlnet_conv_in.blocks.14.bias",
+            "control_add_embedding.linear_1.weight": "control_type_embedding.0.weight",
+            "control_add_embedding.linear_1.bias": "control_type_embedding.0.bias",
+            "control_add_embedding.linear_2.weight": "control_type_embedding.2.weight",
+            "control_add_embedding.linear_2.bias": "control_type_embedding.2.bias",
+        }
+
+        # Rename each parameter
+        name_list = sorted([name for name in state_dict])
+        rename_dict = {}
+        block_id = {"ResnetBlock": -1, "AttentionBlock": -1, "DownSampler": -1, "UpSampler": -1}
+        last_block_type_with_id = {"ResnetBlock": "", "AttentionBlock": "", "DownSampler": "", "UpSampler": ""}
+        for name in name_list:
+            names = name.split(".")
+            if names[0] in ["conv_in", "conv_norm_out", "conv_out", "task_embedding", "spatial_ch_projs"]:
+                pass
+            elif name in controlnet_rename_dict:
+                names = controlnet_rename_dict[name].split(".")
+            elif names[0] == "controlnet_down_blocks":
+                names[0] = "controlnet_blocks"
+            elif names[0] == "controlnet_mid_block":
+                names = ["controlnet_blocks", "9", names[-1]]
+            elif names[0] in ["time_embedding", "add_embedding"]:
+                if names[0] == "add_embedding":
+                    names[0] = "add_time_embedding"
+                names[1] = {"linear_1": "0", "linear_2": "2"}[names[1]]
+            elif names[0] == "control_add_embedding":
+                names[0] = "control_type_embedding"
+            elif names[0] == "transformer_layes":
+                names[0] = "controlnet_transformer"
+                names.pop(1)
+            elif names[0] in ["down_blocks", "mid_block", "up_blocks"]:
+                if names[0] == "mid_block":
+                    names.insert(1, "0")
+                block_type = {"resnets": "ResnetBlock", "attentions": "AttentionBlock", "downsamplers": "DownSampler", "upsamplers": "UpSampler"}[names[2]]
+                block_type_with_id = ".".join(names[:4])
+                if block_type_with_id != last_block_type_with_id[block_type]:
+                    block_id[block_type] += 1
+                last_block_type_with_id[block_type] = block_type_with_id
+                while block_id[block_type] < len(block_types) and block_types[block_id[block_type]] != block_type:
+                    block_id[block_type] += 1
+                block_type_with_id = ".".join(names[:4])
+                names = ["blocks", str(block_id[block_type])] + names[4:]
+                if "ff" in names:
+                    ff_index = names.index("ff")
+                    component = ".".join(names[ff_index:ff_index+3])
+                    component = {"ff.net.0": "act_fn", "ff.net.2": "ff"}[component]
+                    names = names[:ff_index] + [component] + names[ff_index+3:]
+                if "to_out" in names:
+                    names.pop(names.index("to_out") + 1)
+            else:
+                print(name, state_dict[name].shape)
+                # raise ValueError(f"Unknown parameters: {name}")
+            rename_dict[name] = ".".join(names)
+
+        # Convert state_dict
+        state_dict_ = {}
+        for name, param in state_dict.items():
+            if name not in rename_dict:
+                continue
+            if ".proj_in." in name or ".proj_out." in name:
+                param = param.squeeze()
+            state_dict_[rename_dict[name]] = param
+        return state_dict_
+    
+    def from_civitai(self, state_dict):
+        return self.from_diffusers(state_dict)
\ No newline at end of file
diff --git a/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/sdxl_ipadapter.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/sdxl_ipadapter.py
new file mode 100644
index 0000000000000000000000000000000000000000..d959d3b9249e690c6240670fc191e11e27deaaa2
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/sdxl_ipadapter.py
@@ -0,0 +1,122 @@
+from .svd_image_encoder import SVDImageEncoder
+from transformers import CLIPImageProcessor
+import torch
+
+
+class IpAdapterXLCLIPImageEmbedder(SVDImageEncoder):
+    def __init__(self):
+        super().__init__(embed_dim=1664, encoder_intermediate_size=8192, projection_dim=1280, num_encoder_layers=48, num_heads=16, head_dim=104)
+        self.image_processor = CLIPImageProcessor()
+
+    def forward(self, image):
+        pixel_values = self.image_processor(images=image, return_tensors="pt").pixel_values
+        pixel_values = pixel_values.to(device=self.embeddings.class_embedding.device, dtype=self.embeddings.class_embedding.dtype)
+        return super().forward(pixel_values)
+
+
+class IpAdapterImageProjModel(torch.nn.Module):
+    def __init__(self, cross_attention_dim=2048, clip_embeddings_dim=1280, clip_extra_context_tokens=4):
+        super().__init__()
+        self.cross_attention_dim = cross_attention_dim
+        self.clip_extra_context_tokens = clip_extra_context_tokens
+        self.proj = torch.nn.Linear(clip_embeddings_dim, self.clip_extra_context_tokens * cross_attention_dim)
+        self.norm = torch.nn.LayerNorm(cross_attention_dim)
+
+    def forward(self, image_embeds):
+        clip_extra_context_tokens = self.proj(image_embeds).reshape(-1, self.clip_extra_context_tokens, self.cross_attention_dim)
+        clip_extra_context_tokens = self.norm(clip_extra_context_tokens)
+        return clip_extra_context_tokens
+
+
+class IpAdapterModule(torch.nn.Module):
+    def __init__(self, input_dim, output_dim):
+        super().__init__()
+        self.to_k_ip = torch.nn.Linear(input_dim, output_dim, bias=False)
+        self.to_v_ip = torch.nn.Linear(input_dim, output_dim, bias=False)
+
+    def forward(self, hidden_states):
+        ip_k = self.to_k_ip(hidden_states)
+        ip_v = self.to_v_ip(hidden_states)
+        return ip_k, ip_v
+
+
+class SDXLIpAdapter(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        shape_list = [(2048, 640)] * 4 + [(2048, 1280)] * 50 + [(2048, 640)] * 6 + [(2048, 1280)] * 10
+        self.ipadapter_modules = torch.nn.ModuleList([IpAdapterModule(*shape) for shape in shape_list])
+        self.image_proj = IpAdapterImageProjModel()
+        self.set_full_adapter()
+
+    def set_full_adapter(self):
+        map_list = sum([
+            [(7, i) for i in range(2)],
+            [(10, i) for i in range(2)],
+            [(15, i) for i in range(10)],
+            [(18, i) for i in range(10)],
+            [(25, i) for i in range(10)],
+            [(28, i) for i in range(10)],
+            [(31, i) for i in range(10)],
+            [(35, i) for i in range(2)],
+            [(38, i) for i in range(2)],
+            [(41, i) for i in range(2)],
+            [(21, i) for i in range(10)],
+        ], [])
+        self.call_block_id = {i: j for j, i in enumerate(map_list)}
+
+    def set_less_adapter(self):
+        map_list = sum([
+            [(7, i) for i in range(2)],
+            [(10, i) for i in range(2)],
+            [(15, i) for i in range(10)],
+            [(18, i) for i in range(10)],
+            [(25, i) for i in range(10)],
+            [(28, i) for i in range(10)],
+            [(31, i) for i in range(10)],
+            [(35, i) for i in range(2)],
+            [(38, i) for i in range(2)],
+            [(41, i) for i in range(2)],
+            [(21, i) for i in range(10)],
+        ], [])
+        self.call_block_id = {i: j for j, i in enumerate(map_list) if j>=34 and j<44}
+
+    def forward(self, hidden_states, scale=1.0):
+        hidden_states = self.image_proj(hidden_states)
+        hidden_states = hidden_states.view(1, -1, hidden_states.shape[-1])
+        ip_kv_dict = {}
+        for (block_id, transformer_id) in self.call_block_id:
+            ipadapter_id = self.call_block_id[(block_id, transformer_id)]
+            ip_k, ip_v = self.ipadapter_modules[ipadapter_id](hidden_states)
+            if block_id not in ip_kv_dict:
+                ip_kv_dict[block_id] = {}
+            ip_kv_dict[block_id][transformer_id] = {
+                "ip_k": ip_k,
+                "ip_v": ip_v,
+                "scale": scale
+            }
+        return ip_kv_dict
+
+    @staticmethod
+    def state_dict_converter():
+        return SDXLIpAdapterStateDictConverter()
+
+
+class SDXLIpAdapterStateDictConverter:
+    def __init__(self):
+        pass
+
+    def from_diffusers(self, state_dict):
+        state_dict_ = {}
+        for name in state_dict["ip_adapter"]:
+            names = name.split(".")
+            layer_id = str(int(names[0]) // 2)
+            name_ = ".".join(["ipadapter_modules"] + [layer_id] + names[1:])
+            state_dict_[name_] = state_dict["ip_adapter"][name]
+        for name in state_dict["image_proj"]:
+            name_ = "image_proj." + name
+            state_dict_[name_] = state_dict["image_proj"][name]
+        return state_dict_
+    
+    def from_civitai(self, state_dict):
+        return self.from_diffusers(state_dict)
+
diff --git a/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/sdxl_motion.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/sdxl_motion.py
new file mode 100644
index 0000000000000000000000000000000000000000..268c3e96f006e697eed5ac03fad3f5c995cfa319
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/sdxl_motion.py
@@ -0,0 +1,104 @@
+from .sd_motion import TemporalBlock
+import torch
+
+
+
+class SDXLMotionModel(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.motion_modules = torch.nn.ModuleList([
+            TemporalBlock(8, 320//8, 320, eps=1e-6),
+            TemporalBlock(8, 320//8, 320, eps=1e-6),
+
+            TemporalBlock(8, 640//8, 640, eps=1e-6),
+            TemporalBlock(8, 640//8, 640, eps=1e-6),
+
+            TemporalBlock(8, 1280//8, 1280, eps=1e-6),
+            TemporalBlock(8, 1280//8, 1280, eps=1e-6),
+
+            TemporalBlock(8, 1280//8, 1280, eps=1e-6),
+            TemporalBlock(8, 1280//8, 1280, eps=1e-6),
+            TemporalBlock(8, 1280//8, 1280, eps=1e-6),
+
+            TemporalBlock(8, 640//8, 640, eps=1e-6),
+            TemporalBlock(8, 640//8, 640, eps=1e-6),
+            TemporalBlock(8, 640//8, 640, eps=1e-6),
+
+            TemporalBlock(8, 320//8, 320, eps=1e-6),
+            TemporalBlock(8, 320//8, 320, eps=1e-6),
+            TemporalBlock(8, 320//8, 320, eps=1e-6),
+        ])
+        self.call_block_id = {
+            0: 0,
+            2: 1,
+            7: 2,
+            10: 3,
+            15: 4,
+            18: 5,
+            25: 6,
+            28: 7,
+            31: 8,
+            35: 9,
+            38: 10,
+            41: 11,
+            44: 12,
+            46: 13,
+            48: 14,
+        }
+        
+    def forward(self):
+        pass
+
+    @staticmethod
+    def state_dict_converter():
+        return SDMotionModelStateDictConverter()
+
+
+class SDMotionModelStateDictConverter:
+    def __init__(self):
+        pass
+
+    def from_diffusers(self, state_dict):
+        rename_dict = {
+            "norm": "norm",
+            "proj_in": "proj_in",
+            "transformer_blocks.0.attention_blocks.0.to_q": "transformer_blocks.0.attn1.to_q",
+            "transformer_blocks.0.attention_blocks.0.to_k": "transformer_blocks.0.attn1.to_k",
+            "transformer_blocks.0.attention_blocks.0.to_v": "transformer_blocks.0.attn1.to_v",
+            "transformer_blocks.0.attention_blocks.0.to_out.0": "transformer_blocks.0.attn1.to_out",
+            "transformer_blocks.0.attention_blocks.0.pos_encoder": "transformer_blocks.0.pe1",
+            "transformer_blocks.0.attention_blocks.1.to_q": "transformer_blocks.0.attn2.to_q",
+            "transformer_blocks.0.attention_blocks.1.to_k": "transformer_blocks.0.attn2.to_k",
+            "transformer_blocks.0.attention_blocks.1.to_v": "transformer_blocks.0.attn2.to_v",
+            "transformer_blocks.0.attention_blocks.1.to_out.0": "transformer_blocks.0.attn2.to_out",
+            "transformer_blocks.0.attention_blocks.1.pos_encoder": "transformer_blocks.0.pe2",
+            "transformer_blocks.0.norms.0": "transformer_blocks.0.norm1",
+            "transformer_blocks.0.norms.1": "transformer_blocks.0.norm2",
+            "transformer_blocks.0.ff.net.0.proj": "transformer_blocks.0.act_fn.proj",
+            "transformer_blocks.0.ff.net.2": "transformer_blocks.0.ff",
+            "transformer_blocks.0.ff_norm": "transformer_blocks.0.norm3",
+            "proj_out": "proj_out",
+        }
+        name_list = sorted([i for i in state_dict if i.startswith("down_blocks.")])
+        name_list += sorted([i for i in state_dict if i.startswith("mid_block.")])
+        name_list += sorted([i for i in state_dict if i.startswith("up_blocks.")])
+        state_dict_ = {}
+        last_prefix, module_id = "", -1
+        for name in name_list:
+            names = name.split(".")
+            prefix_index = names.index("temporal_transformer") + 1
+            prefix = ".".join(names[:prefix_index])
+            if prefix != last_prefix:
+                last_prefix = prefix
+                module_id += 1
+            middle_name = ".".join(names[prefix_index:-1])
+            suffix = names[-1]
+            if "pos_encoder" in names:
+                rename = ".".join(["motion_modules", str(module_id), rename_dict[middle_name]])
+            else:
+                rename = ".".join(["motion_modules", str(module_id), rename_dict[middle_name], suffix])
+            state_dict_[rename] = state_dict[name]
+        return state_dict_
+    
+    def from_civitai(self, state_dict):
+        return self.from_diffusers(state_dict)
diff --git a/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/sdxl_text_encoder.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/sdxl_text_encoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..5d30c7d4056bf37abbb341b2807aa47a67785023
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/sdxl_text_encoder.py
@@ -0,0 +1,759 @@
+import torch
+from .sd_text_encoder import CLIPEncoderLayer
+    
+
+class SDXLTextEncoder(torch.nn.Module):
+    def __init__(self, embed_dim=768, vocab_size=49408, max_position_embeddings=77, num_encoder_layers=11, encoder_intermediate_size=3072):
+        super().__init__()
+
+        # token_embedding
+        self.token_embedding = torch.nn.Embedding(vocab_size, embed_dim)
+
+        # position_embeds (This is a fixed tensor)
+        self.position_embeds = torch.nn.Parameter(torch.zeros(1, max_position_embeddings, embed_dim))
+
+        # encoders
+        self.encoders = torch.nn.ModuleList([CLIPEncoderLayer(embed_dim, encoder_intermediate_size) for _ in range(num_encoder_layers)])
+
+        # attn_mask
+        self.attn_mask = self.attention_mask(max_position_embeddings)
+
+        # The text encoder is different to that in Stable Diffusion 1.x.
+        # It does not include final_layer_norm.
+
+    def attention_mask(self, length):
+        mask = torch.empty(length, length)
+        mask.fill_(float("-inf"))
+        mask.triu_(1)
+        return mask
+
+    def forward(self, input_ids, clip_skip=1):
+        embeds = self.token_embedding(input_ids) + self.position_embeds
+        attn_mask = self.attn_mask.to(device=embeds.device, dtype=embeds.dtype)
+        for encoder_id, encoder in enumerate(self.encoders):
+            embeds = encoder(embeds, attn_mask=attn_mask)
+            if encoder_id + clip_skip == len(self.encoders):
+                break
+        return embeds
+    
+    @staticmethod
+    def state_dict_converter():
+        return SDXLTextEncoderStateDictConverter()
+    
+
+class SDXLTextEncoder2(torch.nn.Module):
+    def __init__(self, embed_dim=1280, vocab_size=49408, max_position_embeddings=77, num_encoder_layers=32, encoder_intermediate_size=5120):
+        super().__init__()
+
+        # token_embedding
+        self.token_embedding = torch.nn.Embedding(vocab_size, embed_dim)
+
+        # position_embeds (This is a fixed tensor)
+        self.position_embeds = torch.nn.Parameter(torch.zeros(1, max_position_embeddings, embed_dim))
+
+        # encoders
+        self.encoders = torch.nn.ModuleList([CLIPEncoderLayer(embed_dim, encoder_intermediate_size, num_heads=20, head_dim=64, use_quick_gelu=False) for _ in range(num_encoder_layers)])
+
+        # attn_mask
+        self.attn_mask = self.attention_mask(max_position_embeddings)
+
+        # final_layer_norm
+        self.final_layer_norm = torch.nn.LayerNorm(embed_dim)
+
+        # text_projection
+        self.text_projection = torch.nn.Linear(embed_dim, embed_dim, bias=False)
+
+    def attention_mask(self, length):
+        mask = torch.empty(length, length)
+        mask.fill_(float("-inf"))
+        mask.triu_(1)
+        return mask
+
+    def forward(self, input_ids, clip_skip=2):
+        embeds = self.token_embedding(input_ids) + self.position_embeds
+        attn_mask = self.attn_mask.to(device=embeds.device, dtype=embeds.dtype)
+        for encoder_id, encoder in enumerate(self.encoders):
+            embeds = encoder(embeds, attn_mask=attn_mask)
+            if encoder_id + clip_skip == len(self.encoders):
+                hidden_states = embeds
+        embeds = self.final_layer_norm(embeds)
+        pooled_embeds = embeds[torch.arange(embeds.shape[0]), input_ids.to(dtype=torch.int).argmax(dim=-1)]
+        pooled_embeds = self.text_projection(pooled_embeds)
+        return pooled_embeds, hidden_states
+    
+    @staticmethod
+    def state_dict_converter():
+        return SDXLTextEncoder2StateDictConverter()
+
+
+class SDXLTextEncoderStateDictConverter:
+    def __init__(self):
+        pass
+
+    def from_diffusers(self, state_dict):
+        rename_dict = {
+            "text_model.embeddings.token_embedding.weight": "token_embedding.weight",
+            "text_model.embeddings.position_embedding.weight": "position_embeds",
+            "text_model.final_layer_norm.weight": "final_layer_norm.weight",
+            "text_model.final_layer_norm.bias": "final_layer_norm.bias"
+        }
+        attn_rename_dict = {
+            "self_attn.q_proj": "attn.to_q",
+            "self_attn.k_proj": "attn.to_k",
+            "self_attn.v_proj": "attn.to_v",
+            "self_attn.out_proj": "attn.to_out",
+            "layer_norm1": "layer_norm1",
+            "layer_norm2": "layer_norm2",
+            "mlp.fc1": "fc1",
+            "mlp.fc2": "fc2",
+        }
+        state_dict_ = {}
+        for name in state_dict:
+            if name in rename_dict:
+                param = state_dict[name]
+                if name == "text_model.embeddings.position_embedding.weight":
+                    param = param.reshape((1, param.shape[0], param.shape[1]))
+                state_dict_[rename_dict[name]] = param
+            elif name.startswith("text_model.encoder.layers."):
+                param = state_dict[name]
+                names = name.split(".")
+                layer_id, layer_type, tail = names[3], ".".join(names[4:-1]), names[-1]
+                name_ = ".".join(["encoders", layer_id, attn_rename_dict[layer_type], tail])
+                state_dict_[name_] = param
+        return state_dict_
+    
+    def from_civitai(self, state_dict):
+        rename_dict = {
+            "conditioner.embedders.0.transformer.text_model.embeddings.position_embedding.weight": "position_embeds",
+            "conditioner.embedders.0.transformer.text_model.embeddings.token_embedding.weight": "token_embedding.weight",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.0.layer_norm1.bias": "encoders.0.layer_norm1.bias",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.0.layer_norm1.weight": "encoders.0.layer_norm1.weight",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.0.layer_norm2.bias": "encoders.0.layer_norm2.bias",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.0.layer_norm2.weight": "encoders.0.layer_norm2.weight",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.0.mlp.fc1.bias": "encoders.0.fc1.bias",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.0.mlp.fc1.weight": "encoders.0.fc1.weight",
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+            "conditioner.embedders.0.transformer.text_model.encoder.layers.9.self_attn.k_proj.bias": "encoders.9.attn.to_k.bias",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.9.self_attn.k_proj.weight": "encoders.9.attn.to_k.weight",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.9.self_attn.out_proj.bias": "encoders.9.attn.to_out.bias",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.9.self_attn.out_proj.weight": "encoders.9.attn.to_out.weight",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.9.self_attn.q_proj.bias": "encoders.9.attn.to_q.bias",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.9.self_attn.q_proj.weight": "encoders.9.attn.to_q.weight",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.9.self_attn.v_proj.bias": "encoders.9.attn.to_v.bias",
+            "conditioner.embedders.0.transformer.text_model.encoder.layers.9.self_attn.v_proj.weight": "encoders.9.attn.to_v.weight",
+        }
+        state_dict_ = {}
+        for name in state_dict:
+            if name in rename_dict:
+                param = state_dict[name]
+                if name == "conditioner.embedders.0.transformer.text_model.embeddings.position_embedding.weight":
+                    param = param.reshape((1, param.shape[0], param.shape[1]))
+                state_dict_[rename_dict[name]] = param
+        return state_dict_
+
+
+class SDXLTextEncoder2StateDictConverter:
+    def __init__(self):
+        pass
+
+    def from_diffusers(self, state_dict):
+        rename_dict = {
+            "text_model.embeddings.token_embedding.weight": "token_embedding.weight",
+            "text_model.embeddings.position_embedding.weight": "position_embeds",
+            "text_model.final_layer_norm.weight": "final_layer_norm.weight",
+            "text_model.final_layer_norm.bias": "final_layer_norm.bias",
+            "text_projection.weight": "text_projection.weight"
+        }
+        attn_rename_dict = {
+            "self_attn.q_proj": "attn.to_q",
+            "self_attn.k_proj": "attn.to_k",
+            "self_attn.v_proj": "attn.to_v",
+            "self_attn.out_proj": "attn.to_out",
+            "layer_norm1": "layer_norm1",
+            "layer_norm2": "layer_norm2",
+            "mlp.fc1": "fc1",
+            "mlp.fc2": "fc2",
+        }
+        state_dict_ = {}
+        for name in state_dict:
+            if name in rename_dict:
+                param = state_dict[name]
+                if name == "text_model.embeddings.position_embedding.weight":
+                    param = param.reshape((1, param.shape[0], param.shape[1]))
+                state_dict_[rename_dict[name]] = param
+            elif name.startswith("text_model.encoder.layers."):
+                param = state_dict[name]
+                names = name.split(".")
+                layer_id, layer_type, tail = names[3], ".".join(names[4:-1]), names[-1]
+                name_ = ".".join(["encoders", layer_id, attn_rename_dict[layer_type], tail])
+                state_dict_[name_] = param
+        return state_dict_
+    
+    def from_civitai(self, state_dict):
+        rename_dict = {
+            "conditioner.embedders.1.model.ln_final.bias": "final_layer_norm.bias",
+            "conditioner.embedders.1.model.ln_final.weight": "final_layer_norm.weight",
+            "conditioner.embedders.1.model.positional_embedding": "position_embeds",
+            "conditioner.embedders.1.model.token_embedding.weight": "token_embedding.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.0.attn.in_proj_bias": ['encoders.0.attn.to_q.bias', 'encoders.0.attn.to_k.bias', 'encoders.0.attn.to_v.bias'],
+            "conditioner.embedders.1.model.transformer.resblocks.0.attn.in_proj_weight": ['encoders.0.attn.to_q.weight', 'encoders.0.attn.to_k.weight', 'encoders.0.attn.to_v.weight'],
+            "conditioner.embedders.1.model.transformer.resblocks.0.attn.out_proj.bias": "encoders.0.attn.to_out.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.0.attn.out_proj.weight": "encoders.0.attn.to_out.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.0.ln_1.bias": "encoders.0.layer_norm1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.0.ln_1.weight": "encoders.0.layer_norm1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.0.ln_2.bias": "encoders.0.layer_norm2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.0.ln_2.weight": "encoders.0.layer_norm2.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.0.mlp.c_fc.bias": "encoders.0.fc1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.0.mlp.c_fc.weight": "encoders.0.fc1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.0.mlp.c_proj.bias": "encoders.0.fc2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.0.mlp.c_proj.weight": "encoders.0.fc2.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.1.attn.in_proj_bias": ['encoders.1.attn.to_q.bias', 'encoders.1.attn.to_k.bias', 'encoders.1.attn.to_v.bias'],
+            "conditioner.embedders.1.model.transformer.resblocks.1.attn.in_proj_weight": ['encoders.1.attn.to_q.weight', 'encoders.1.attn.to_k.weight', 'encoders.1.attn.to_v.weight'],
+            "conditioner.embedders.1.model.transformer.resblocks.1.attn.out_proj.bias": "encoders.1.attn.to_out.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.1.attn.out_proj.weight": "encoders.1.attn.to_out.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.1.ln_1.bias": "encoders.1.layer_norm1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.1.ln_1.weight": "encoders.1.layer_norm1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.1.ln_2.bias": "encoders.1.layer_norm2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.1.ln_2.weight": "encoders.1.layer_norm2.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.1.mlp.c_fc.bias": "encoders.1.fc1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.1.mlp.c_fc.weight": "encoders.1.fc1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.1.mlp.c_proj.bias": "encoders.1.fc2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.1.mlp.c_proj.weight": "encoders.1.fc2.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.10.attn.in_proj_bias": ['encoders.10.attn.to_q.bias', 'encoders.10.attn.to_k.bias', 'encoders.10.attn.to_v.bias'],
+            "conditioner.embedders.1.model.transformer.resblocks.10.attn.in_proj_weight": ['encoders.10.attn.to_q.weight', 'encoders.10.attn.to_k.weight', 'encoders.10.attn.to_v.weight'],
+            "conditioner.embedders.1.model.transformer.resblocks.10.attn.out_proj.bias": "encoders.10.attn.to_out.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.10.attn.out_proj.weight": "encoders.10.attn.to_out.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.10.ln_1.bias": "encoders.10.layer_norm1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.10.ln_1.weight": "encoders.10.layer_norm1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.10.ln_2.bias": "encoders.10.layer_norm2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.10.ln_2.weight": "encoders.10.layer_norm2.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.10.mlp.c_fc.bias": "encoders.10.fc1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.10.mlp.c_fc.weight": "encoders.10.fc1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.10.mlp.c_proj.bias": "encoders.10.fc2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.10.mlp.c_proj.weight": "encoders.10.fc2.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.11.attn.in_proj_bias": ['encoders.11.attn.to_q.bias', 'encoders.11.attn.to_k.bias', 'encoders.11.attn.to_v.bias'],
+            "conditioner.embedders.1.model.transformer.resblocks.11.attn.in_proj_weight": ['encoders.11.attn.to_q.weight', 'encoders.11.attn.to_k.weight', 'encoders.11.attn.to_v.weight'],
+            "conditioner.embedders.1.model.transformer.resblocks.11.attn.out_proj.bias": "encoders.11.attn.to_out.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.11.attn.out_proj.weight": "encoders.11.attn.to_out.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.11.ln_1.bias": "encoders.11.layer_norm1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.11.ln_1.weight": "encoders.11.layer_norm1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.11.ln_2.bias": "encoders.11.layer_norm2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.11.ln_2.weight": "encoders.11.layer_norm2.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.11.mlp.c_fc.bias": "encoders.11.fc1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.11.mlp.c_fc.weight": "encoders.11.fc1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.11.mlp.c_proj.bias": "encoders.11.fc2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.11.mlp.c_proj.weight": "encoders.11.fc2.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.12.attn.in_proj_bias": ['encoders.12.attn.to_q.bias', 'encoders.12.attn.to_k.bias', 'encoders.12.attn.to_v.bias'],
+            "conditioner.embedders.1.model.transformer.resblocks.12.attn.in_proj_weight": ['encoders.12.attn.to_q.weight', 'encoders.12.attn.to_k.weight', 'encoders.12.attn.to_v.weight'],
+            "conditioner.embedders.1.model.transformer.resblocks.12.attn.out_proj.bias": "encoders.12.attn.to_out.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.12.attn.out_proj.weight": "encoders.12.attn.to_out.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.12.ln_1.bias": "encoders.12.layer_norm1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.12.ln_1.weight": "encoders.12.layer_norm1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.12.ln_2.bias": "encoders.12.layer_norm2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.12.ln_2.weight": "encoders.12.layer_norm2.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.12.mlp.c_fc.bias": "encoders.12.fc1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.12.mlp.c_fc.weight": "encoders.12.fc1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.12.mlp.c_proj.bias": "encoders.12.fc2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.12.mlp.c_proj.weight": "encoders.12.fc2.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.13.attn.in_proj_bias": ['encoders.13.attn.to_q.bias', 'encoders.13.attn.to_k.bias', 'encoders.13.attn.to_v.bias'],
+            "conditioner.embedders.1.model.transformer.resblocks.13.attn.in_proj_weight": ['encoders.13.attn.to_q.weight', 'encoders.13.attn.to_k.weight', 'encoders.13.attn.to_v.weight'],
+            "conditioner.embedders.1.model.transformer.resblocks.13.attn.out_proj.bias": "encoders.13.attn.to_out.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.13.attn.out_proj.weight": "encoders.13.attn.to_out.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.13.ln_1.bias": "encoders.13.layer_norm1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.13.ln_1.weight": "encoders.13.layer_norm1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.13.ln_2.bias": "encoders.13.layer_norm2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.13.ln_2.weight": "encoders.13.layer_norm2.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.13.mlp.c_fc.bias": "encoders.13.fc1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.13.mlp.c_fc.weight": "encoders.13.fc1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.13.mlp.c_proj.bias": "encoders.13.fc2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.13.mlp.c_proj.weight": "encoders.13.fc2.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.14.attn.in_proj_bias": ['encoders.14.attn.to_q.bias', 'encoders.14.attn.to_k.bias', 'encoders.14.attn.to_v.bias'],
+            "conditioner.embedders.1.model.transformer.resblocks.14.attn.in_proj_weight": ['encoders.14.attn.to_q.weight', 'encoders.14.attn.to_k.weight', 'encoders.14.attn.to_v.weight'],
+            "conditioner.embedders.1.model.transformer.resblocks.14.attn.out_proj.bias": "encoders.14.attn.to_out.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.14.attn.out_proj.weight": "encoders.14.attn.to_out.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.14.ln_1.bias": "encoders.14.layer_norm1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.14.ln_1.weight": "encoders.14.layer_norm1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.14.ln_2.bias": "encoders.14.layer_norm2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.14.ln_2.weight": "encoders.14.layer_norm2.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.14.mlp.c_fc.bias": "encoders.14.fc1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.14.mlp.c_fc.weight": "encoders.14.fc1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.14.mlp.c_proj.bias": "encoders.14.fc2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.14.mlp.c_proj.weight": "encoders.14.fc2.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.15.attn.in_proj_bias": ['encoders.15.attn.to_q.bias', 'encoders.15.attn.to_k.bias', 'encoders.15.attn.to_v.bias'],
+            "conditioner.embedders.1.model.transformer.resblocks.15.attn.in_proj_weight": ['encoders.15.attn.to_q.weight', 'encoders.15.attn.to_k.weight', 'encoders.15.attn.to_v.weight'],
+            "conditioner.embedders.1.model.transformer.resblocks.15.attn.out_proj.bias": "encoders.15.attn.to_out.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.15.attn.out_proj.weight": "encoders.15.attn.to_out.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.15.ln_1.bias": "encoders.15.layer_norm1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.15.ln_1.weight": "encoders.15.layer_norm1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.15.ln_2.bias": "encoders.15.layer_norm2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.15.ln_2.weight": "encoders.15.layer_norm2.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.15.mlp.c_fc.bias": "encoders.15.fc1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.15.mlp.c_fc.weight": "encoders.15.fc1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.15.mlp.c_proj.bias": "encoders.15.fc2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.15.mlp.c_proj.weight": "encoders.15.fc2.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.16.attn.in_proj_bias": ['encoders.16.attn.to_q.bias', 'encoders.16.attn.to_k.bias', 'encoders.16.attn.to_v.bias'],
+            "conditioner.embedders.1.model.transformer.resblocks.16.attn.in_proj_weight": ['encoders.16.attn.to_q.weight', 'encoders.16.attn.to_k.weight', 'encoders.16.attn.to_v.weight'],
+            "conditioner.embedders.1.model.transformer.resblocks.16.attn.out_proj.bias": "encoders.16.attn.to_out.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.16.attn.out_proj.weight": "encoders.16.attn.to_out.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.16.ln_1.bias": "encoders.16.layer_norm1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.16.ln_1.weight": "encoders.16.layer_norm1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.16.ln_2.bias": "encoders.16.layer_norm2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.16.ln_2.weight": "encoders.16.layer_norm2.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.16.mlp.c_fc.bias": "encoders.16.fc1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.16.mlp.c_fc.weight": "encoders.16.fc1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.16.mlp.c_proj.bias": "encoders.16.fc2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.16.mlp.c_proj.weight": "encoders.16.fc2.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.17.attn.in_proj_bias": ['encoders.17.attn.to_q.bias', 'encoders.17.attn.to_k.bias', 'encoders.17.attn.to_v.bias'],
+            "conditioner.embedders.1.model.transformer.resblocks.17.attn.in_proj_weight": ['encoders.17.attn.to_q.weight', 'encoders.17.attn.to_k.weight', 'encoders.17.attn.to_v.weight'],
+            "conditioner.embedders.1.model.transformer.resblocks.17.attn.out_proj.bias": "encoders.17.attn.to_out.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.17.attn.out_proj.weight": "encoders.17.attn.to_out.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.17.ln_1.bias": "encoders.17.layer_norm1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.17.ln_1.weight": "encoders.17.layer_norm1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.17.ln_2.bias": "encoders.17.layer_norm2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.17.ln_2.weight": "encoders.17.layer_norm2.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.17.mlp.c_fc.bias": "encoders.17.fc1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.17.mlp.c_fc.weight": "encoders.17.fc1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.17.mlp.c_proj.bias": "encoders.17.fc2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.17.mlp.c_proj.weight": "encoders.17.fc2.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.18.attn.in_proj_bias": ['encoders.18.attn.to_q.bias', 'encoders.18.attn.to_k.bias', 'encoders.18.attn.to_v.bias'],
+            "conditioner.embedders.1.model.transformer.resblocks.18.attn.in_proj_weight": ['encoders.18.attn.to_q.weight', 'encoders.18.attn.to_k.weight', 'encoders.18.attn.to_v.weight'],
+            "conditioner.embedders.1.model.transformer.resblocks.18.attn.out_proj.bias": "encoders.18.attn.to_out.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.18.attn.out_proj.weight": "encoders.18.attn.to_out.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.18.ln_1.bias": "encoders.18.layer_norm1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.18.ln_1.weight": "encoders.18.layer_norm1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.18.ln_2.bias": "encoders.18.layer_norm2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.18.ln_2.weight": "encoders.18.layer_norm2.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.18.mlp.c_fc.bias": "encoders.18.fc1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.18.mlp.c_fc.weight": "encoders.18.fc1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.18.mlp.c_proj.bias": "encoders.18.fc2.bias",
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+            "conditioner.embedders.1.model.transformer.resblocks.30.ln_1.bias": "encoders.30.layer_norm1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.30.ln_1.weight": "encoders.30.layer_norm1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.30.ln_2.bias": "encoders.30.layer_norm2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.30.ln_2.weight": "encoders.30.layer_norm2.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.30.mlp.c_fc.bias": "encoders.30.fc1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.30.mlp.c_fc.weight": "encoders.30.fc1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.30.mlp.c_proj.bias": "encoders.30.fc2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.30.mlp.c_proj.weight": "encoders.30.fc2.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.31.attn.in_proj_bias": ['encoders.31.attn.to_q.bias', 'encoders.31.attn.to_k.bias', 'encoders.31.attn.to_v.bias'],
+            "conditioner.embedders.1.model.transformer.resblocks.31.attn.in_proj_weight": ['encoders.31.attn.to_q.weight', 'encoders.31.attn.to_k.weight', 'encoders.31.attn.to_v.weight'],
+            "conditioner.embedders.1.model.transformer.resblocks.31.attn.out_proj.bias": "encoders.31.attn.to_out.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.31.attn.out_proj.weight": "encoders.31.attn.to_out.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.31.ln_1.bias": "encoders.31.layer_norm1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.31.ln_1.weight": "encoders.31.layer_norm1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.31.ln_2.bias": "encoders.31.layer_norm2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.31.ln_2.weight": "encoders.31.layer_norm2.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.31.mlp.c_fc.bias": "encoders.31.fc1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.31.mlp.c_fc.weight": "encoders.31.fc1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.31.mlp.c_proj.bias": "encoders.31.fc2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.31.mlp.c_proj.weight": "encoders.31.fc2.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.4.attn.in_proj_bias": ['encoders.4.attn.to_q.bias', 'encoders.4.attn.to_k.bias', 'encoders.4.attn.to_v.bias'],
+            "conditioner.embedders.1.model.transformer.resblocks.4.attn.in_proj_weight": ['encoders.4.attn.to_q.weight', 'encoders.4.attn.to_k.weight', 'encoders.4.attn.to_v.weight'],
+            "conditioner.embedders.1.model.transformer.resblocks.4.attn.out_proj.bias": "encoders.4.attn.to_out.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.4.attn.out_proj.weight": "encoders.4.attn.to_out.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.4.ln_1.bias": "encoders.4.layer_norm1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.4.ln_1.weight": "encoders.4.layer_norm1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.4.ln_2.bias": "encoders.4.layer_norm2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.4.ln_2.weight": "encoders.4.layer_norm2.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.4.mlp.c_fc.bias": "encoders.4.fc1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.4.mlp.c_fc.weight": "encoders.4.fc1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.4.mlp.c_proj.bias": "encoders.4.fc2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.4.mlp.c_proj.weight": "encoders.4.fc2.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.5.attn.in_proj_bias": ['encoders.5.attn.to_q.bias', 'encoders.5.attn.to_k.bias', 'encoders.5.attn.to_v.bias'],
+            "conditioner.embedders.1.model.transformer.resblocks.5.attn.in_proj_weight": ['encoders.5.attn.to_q.weight', 'encoders.5.attn.to_k.weight', 'encoders.5.attn.to_v.weight'],
+            "conditioner.embedders.1.model.transformer.resblocks.5.attn.out_proj.bias": "encoders.5.attn.to_out.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.5.attn.out_proj.weight": "encoders.5.attn.to_out.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.5.ln_1.bias": "encoders.5.layer_norm1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.5.ln_1.weight": "encoders.5.layer_norm1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.5.ln_2.bias": "encoders.5.layer_norm2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.5.ln_2.weight": "encoders.5.layer_norm2.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.5.mlp.c_fc.bias": "encoders.5.fc1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.5.mlp.c_fc.weight": "encoders.5.fc1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.5.mlp.c_proj.bias": "encoders.5.fc2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.5.mlp.c_proj.weight": "encoders.5.fc2.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.6.attn.in_proj_bias": ['encoders.6.attn.to_q.bias', 'encoders.6.attn.to_k.bias', 'encoders.6.attn.to_v.bias'],
+            "conditioner.embedders.1.model.transformer.resblocks.6.attn.in_proj_weight": ['encoders.6.attn.to_q.weight', 'encoders.6.attn.to_k.weight', 'encoders.6.attn.to_v.weight'],
+            "conditioner.embedders.1.model.transformer.resblocks.6.attn.out_proj.bias": "encoders.6.attn.to_out.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.6.attn.out_proj.weight": "encoders.6.attn.to_out.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.6.ln_1.bias": "encoders.6.layer_norm1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.6.ln_1.weight": "encoders.6.layer_norm1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.6.ln_2.bias": "encoders.6.layer_norm2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.6.ln_2.weight": "encoders.6.layer_norm2.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.6.mlp.c_fc.bias": "encoders.6.fc1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.6.mlp.c_fc.weight": "encoders.6.fc1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.6.mlp.c_proj.bias": "encoders.6.fc2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.6.mlp.c_proj.weight": "encoders.6.fc2.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.7.attn.in_proj_bias": ['encoders.7.attn.to_q.bias', 'encoders.7.attn.to_k.bias', 'encoders.7.attn.to_v.bias'],
+            "conditioner.embedders.1.model.transformer.resblocks.7.attn.in_proj_weight": ['encoders.7.attn.to_q.weight', 'encoders.7.attn.to_k.weight', 'encoders.7.attn.to_v.weight'],
+            "conditioner.embedders.1.model.transformer.resblocks.7.attn.out_proj.bias": "encoders.7.attn.to_out.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.7.attn.out_proj.weight": "encoders.7.attn.to_out.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.7.ln_1.bias": "encoders.7.layer_norm1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.7.ln_1.weight": "encoders.7.layer_norm1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.7.ln_2.bias": "encoders.7.layer_norm2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.7.ln_2.weight": "encoders.7.layer_norm2.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.7.mlp.c_fc.bias": "encoders.7.fc1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.7.mlp.c_fc.weight": "encoders.7.fc1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.7.mlp.c_proj.bias": "encoders.7.fc2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.7.mlp.c_proj.weight": "encoders.7.fc2.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.8.attn.in_proj_bias": ['encoders.8.attn.to_q.bias', 'encoders.8.attn.to_k.bias', 'encoders.8.attn.to_v.bias'],
+            "conditioner.embedders.1.model.transformer.resblocks.8.attn.in_proj_weight": ['encoders.8.attn.to_q.weight', 'encoders.8.attn.to_k.weight', 'encoders.8.attn.to_v.weight'],
+            "conditioner.embedders.1.model.transformer.resblocks.8.attn.out_proj.bias": "encoders.8.attn.to_out.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.8.attn.out_proj.weight": "encoders.8.attn.to_out.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.8.ln_1.bias": "encoders.8.layer_norm1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.8.ln_1.weight": "encoders.8.layer_norm1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.8.ln_2.bias": "encoders.8.layer_norm2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.8.ln_2.weight": "encoders.8.layer_norm2.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.8.mlp.c_fc.bias": "encoders.8.fc1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.8.mlp.c_fc.weight": "encoders.8.fc1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.8.mlp.c_proj.bias": "encoders.8.fc2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.8.mlp.c_proj.weight": "encoders.8.fc2.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.9.attn.in_proj_bias": ['encoders.9.attn.to_q.bias', 'encoders.9.attn.to_k.bias', 'encoders.9.attn.to_v.bias'],
+            "conditioner.embedders.1.model.transformer.resblocks.9.attn.in_proj_weight": ['encoders.9.attn.to_q.weight', 'encoders.9.attn.to_k.weight', 'encoders.9.attn.to_v.weight'],
+            "conditioner.embedders.1.model.transformer.resblocks.9.attn.out_proj.bias": "encoders.9.attn.to_out.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.9.attn.out_proj.weight": "encoders.9.attn.to_out.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.9.ln_1.bias": "encoders.9.layer_norm1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.9.ln_1.weight": "encoders.9.layer_norm1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.9.ln_2.bias": "encoders.9.layer_norm2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.9.ln_2.weight": "encoders.9.layer_norm2.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.9.mlp.c_fc.bias": "encoders.9.fc1.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.9.mlp.c_fc.weight": "encoders.9.fc1.weight",
+            "conditioner.embedders.1.model.transformer.resblocks.9.mlp.c_proj.bias": "encoders.9.fc2.bias",
+            "conditioner.embedders.1.model.transformer.resblocks.9.mlp.c_proj.weight": "encoders.9.fc2.weight",
+            "conditioner.embedders.1.model.text_projection": "text_projection.weight",
+        }
+        state_dict_ = {}
+        for name in state_dict:
+            if name in rename_dict:
+                param = state_dict[name]
+                if name == "conditioner.embedders.1.model.positional_embedding":
+                    param = param.reshape((1, param.shape[0], param.shape[1]))
+                elif name == "conditioner.embedders.1.model.text_projection":
+                    param = param.T
+                if isinstance(rename_dict[name], str):
+                    state_dict_[rename_dict[name]] = param
+                else:
+                    length = param.shape[0] // 3
+                    for i, rename in enumerate(rename_dict[name]):
+                        state_dict_[rename] = param[i*length: i*length+length]
+        return state_dict_
\ No newline at end of file
diff --git a/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/sdxl_unet.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/sdxl_unet.py
new file mode 100644
index 0000000000000000000000000000000000000000..9bc63e63181c999f94421b72843c7b9e03b31d4a
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/sdxl_unet.py
@@ -0,0 +1,1901 @@
+import torch
+from .sd_unet import Timesteps, ResnetBlock, AttentionBlock, PushBlock, PopBlock, DownSampler, UpSampler
+
+
+class SDXLUNet(torch.nn.Module):
+    def __init__(self, is_kolors=False):
+        super().__init__()
+        self.time_proj = Timesteps(320)
+        self.time_embedding = torch.nn.Sequential(
+            torch.nn.Linear(320, 1280),
+            torch.nn.SiLU(),
+            torch.nn.Linear(1280, 1280)
+        )
+        self.add_time_proj = Timesteps(256)
+        self.add_time_embedding = torch.nn.Sequential(
+            torch.nn.Linear(5632 if is_kolors else 2816, 1280),
+            torch.nn.SiLU(),
+            torch.nn.Linear(1280, 1280)
+        )
+        self.conv_in = torch.nn.Conv2d(4, 320, kernel_size=3, padding=1)
+        self.text_intermediate_proj = torch.nn.Linear(4096, 2048) if is_kolors else None
+
+        self.blocks = torch.nn.ModuleList([
+            # DownBlock2D
+            ResnetBlock(320, 320, 1280),
+            PushBlock(),
+            ResnetBlock(320, 320, 1280),
+            PushBlock(),
+            DownSampler(320),
+            PushBlock(),
+            # CrossAttnDownBlock2D
+            ResnetBlock(320, 640, 1280),
+            AttentionBlock(10, 64, 640, 2, 2048),
+            PushBlock(),
+            ResnetBlock(640, 640, 1280),
+            AttentionBlock(10, 64, 640, 2, 2048),
+            PushBlock(),
+            DownSampler(640),
+            PushBlock(),
+            # CrossAttnDownBlock2D
+            ResnetBlock(640, 1280, 1280),
+            AttentionBlock(20, 64, 1280, 10, 2048),
+            PushBlock(),
+            ResnetBlock(1280, 1280, 1280),
+            AttentionBlock(20, 64, 1280, 10, 2048),
+            PushBlock(),
+            # UNetMidBlock2DCrossAttn
+            ResnetBlock(1280, 1280, 1280),
+            AttentionBlock(20, 64, 1280, 10, 2048),
+            ResnetBlock(1280, 1280, 1280),
+            # CrossAttnUpBlock2D
+            PopBlock(),
+            ResnetBlock(2560, 1280, 1280),
+            AttentionBlock(20, 64, 1280, 10, 2048),
+            PopBlock(),
+            ResnetBlock(2560, 1280, 1280),
+            AttentionBlock(20, 64, 1280, 10, 2048),
+            PopBlock(),
+            ResnetBlock(1920, 1280, 1280),
+            AttentionBlock(20, 64, 1280, 10, 2048),
+            UpSampler(1280),
+            # CrossAttnUpBlock2D
+            PopBlock(),
+            ResnetBlock(1920, 640, 1280),
+            AttentionBlock(10, 64, 640, 2, 2048),
+            PopBlock(),
+            ResnetBlock(1280, 640, 1280),
+            AttentionBlock(10, 64, 640, 2, 2048),
+            PopBlock(),
+            ResnetBlock(960, 640, 1280),
+            AttentionBlock(10, 64, 640, 2, 2048),
+            UpSampler(640),
+            # UpBlock2D
+            PopBlock(),
+            ResnetBlock(960, 320, 1280),
+            PopBlock(),
+            ResnetBlock(640, 320, 1280),
+            PopBlock(),
+            ResnetBlock(640, 320, 1280)
+        ])
+
+        self.conv_norm_out = torch.nn.GroupNorm(num_channels=320, num_groups=32, eps=1e-5)
+        self.conv_act = torch.nn.SiLU()
+        self.conv_out = torch.nn.Conv2d(320, 4, kernel_size=3, padding=1)
+
+        self.is_kolors = is_kolors
+
+    def forward(
+        self,
+        sample, timestep, encoder_hidden_states, add_time_id, add_text_embeds,
+        tiled=False, tile_size=64, tile_stride=8,
+        use_gradient_checkpointing=False,
+        **kwargs
+    ):
+        # 1. time
+        t_emb = self.time_proj(timestep).to(sample.dtype)
+        t_emb = self.time_embedding(t_emb)
+        
+        time_embeds = self.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 = self.add_time_embedding(add_embeds)
+
+        time_emb = t_emb + add_embeds
+        
+        # 2. pre-process
+        height, width = sample.shape[2], sample.shape[3]
+        hidden_states = self.conv_in(sample)
+        text_emb = encoder_hidden_states if self.text_intermediate_proj is None else self.text_intermediate_proj(encoder_hidden_states)
+        res_stack = [hidden_states]
+        
+        # 3. blocks
+        def create_custom_forward(module):
+            def custom_forward(*inputs):
+                return module(*inputs)
+            return custom_forward
+        for i, block in enumerate(self.blocks):
+            if self.training and use_gradient_checkpointing and not (isinstance(block, PushBlock) or isinstance(block, PopBlock)):
+                hidden_states, time_emb, text_emb, res_stack = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(block),
+                    hidden_states, time_emb, text_emb, res_stack,
+                    use_reentrant=False,
+                )
+            else:
+                hidden_states, time_emb, text_emb, res_stack = block(
+                    hidden_states, time_emb, text_emb, res_stack,
+                    tiled=tiled, tile_size=tile_size, tile_stride=tile_stride
+                )
+        
+        # 4. output
+        hidden_states = self.conv_norm_out(hidden_states)
+        hidden_states = self.conv_act(hidden_states)
+        hidden_states = self.conv_out(hidden_states)
+
+        return hidden_states
+    
+    @staticmethod
+    def state_dict_converter():
+        return SDXLUNetStateDictConverter()
+
+
+class SDXLUNetStateDictConverter:
+    def __init__(self):
+        pass
+
+    def from_diffusers(self, state_dict):
+        # architecture
+        block_types = [
+            'ResnetBlock', 'PushBlock', 'ResnetBlock', 'PushBlock', 'DownSampler', 'PushBlock',
+            'ResnetBlock', 'AttentionBlock', 'PushBlock', 'ResnetBlock', 'AttentionBlock', 'PushBlock', 'DownSampler', 'PushBlock',
+            'ResnetBlock', 'AttentionBlock', 'PushBlock', 'ResnetBlock', 'AttentionBlock', 'PushBlock',
+            'ResnetBlock', 'AttentionBlock', 'ResnetBlock',
+            'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'UpSampler',
+            'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'UpSampler',
+            'PopBlock', 'ResnetBlock', 'PopBlock', 'ResnetBlock', 'PopBlock', 'ResnetBlock'
+        ]
+
+        # Rename each parameter
+        name_list = sorted([name for name in state_dict])
+        rename_dict = {}
+        block_id = {"ResnetBlock": -1, "AttentionBlock": -1, "DownSampler": -1, "UpSampler": -1}
+        last_block_type_with_id = {"ResnetBlock": "", "AttentionBlock": "", "DownSampler": "", "UpSampler": ""}
+        for name in name_list:
+            names = name.split(".")
+            if names[0] in ["conv_in", "conv_norm_out", "conv_out"]:
+                pass
+            elif names[0] in ["encoder_hid_proj"]:
+                names[0] = "text_intermediate_proj"
+            elif names[0] in ["time_embedding", "add_embedding"]:
+                if names[0] == "add_embedding":
+                    names[0] = "add_time_embedding"
+                names[1] = {"linear_1": "0", "linear_2": "2"}[names[1]]
+            elif names[0] in ["down_blocks", "mid_block", "up_blocks"]:
+                if names[0] == "mid_block":
+                    names.insert(1, "0")
+                block_type = {"resnets": "ResnetBlock", "attentions": "AttentionBlock", "downsamplers": "DownSampler", "upsamplers": "UpSampler"}[names[2]]
+                block_type_with_id = ".".join(names[:4])
+                if block_type_with_id != last_block_type_with_id[block_type]:
+                    block_id[block_type] += 1
+                last_block_type_with_id[block_type] = block_type_with_id
+                while block_id[block_type] < len(block_types) and block_types[block_id[block_type]] != block_type:
+                    block_id[block_type] += 1
+                block_type_with_id = ".".join(names[:4])
+                names = ["blocks", str(block_id[block_type])] + names[4:]
+                if "ff" in names:
+                    ff_index = names.index("ff")
+                    component = ".".join(names[ff_index:ff_index+3])
+                    component = {"ff.net.0": "act_fn", "ff.net.2": "ff"}[component]
+                    names = names[:ff_index] + [component] + names[ff_index+3:]
+                if "to_out" in names:
+                    names.pop(names.index("to_out") + 1)
+            else:
+                raise ValueError(f"Unknown parameters: {name}")
+            rename_dict[name] = ".".join(names)
+
+        # Convert state_dict
+        state_dict_ = {}
+        for name, param in state_dict.items():
+            if ".proj_in." in name or ".proj_out." in name:
+                param = param.squeeze()
+            state_dict_[rename_dict[name]] = param
+        if "text_intermediate_proj.weight" in state_dict_:
+            return state_dict_, {"is_kolors": True}
+        else:
+            return state_dict_
+    
+    def from_civitai(self, state_dict):
+        rename_dict = {
+            "model.diffusion_model.input_blocks.0.0.bias": "conv_in.bias",
+            "model.diffusion_model.input_blocks.0.0.weight": "conv_in.weight",
+            "model.diffusion_model.input_blocks.1.0.emb_layers.1.bias": "blocks.0.time_emb_proj.bias",
+            "model.diffusion_model.input_blocks.1.0.emb_layers.1.weight": "blocks.0.time_emb_proj.weight",
+            "model.diffusion_model.input_blocks.1.0.in_layers.0.bias": "blocks.0.norm1.bias",
+            "model.diffusion_model.input_blocks.1.0.in_layers.0.weight": "blocks.0.norm1.weight",
+            "model.diffusion_model.input_blocks.1.0.in_layers.2.bias": "blocks.0.conv1.bias",
+            "model.diffusion_model.input_blocks.1.0.in_layers.2.weight": "blocks.0.conv1.weight",
+            "model.diffusion_model.input_blocks.1.0.out_layers.0.bias": "blocks.0.norm2.bias",
+            "model.diffusion_model.input_blocks.1.0.out_layers.0.weight": "blocks.0.norm2.weight",
+            "model.diffusion_model.input_blocks.1.0.out_layers.3.bias": "blocks.0.conv2.bias",
+            "model.diffusion_model.input_blocks.1.0.out_layers.3.weight": "blocks.0.conv2.weight",
+            "model.diffusion_model.input_blocks.2.0.emb_layers.1.bias": "blocks.2.time_emb_proj.bias",
+            "model.diffusion_model.input_blocks.2.0.emb_layers.1.weight": "blocks.2.time_emb_proj.weight",
+            "model.diffusion_model.input_blocks.2.0.in_layers.0.bias": "blocks.2.norm1.bias",
+            "model.diffusion_model.input_blocks.2.0.in_layers.0.weight": "blocks.2.norm1.weight",
+            "model.diffusion_model.input_blocks.2.0.in_layers.2.bias": "blocks.2.conv1.bias",
+            "model.diffusion_model.input_blocks.2.0.in_layers.2.weight": "blocks.2.conv1.weight",
+            "model.diffusion_model.input_blocks.2.0.out_layers.0.bias": "blocks.2.norm2.bias",
+            "model.diffusion_model.input_blocks.2.0.out_layers.0.weight": "blocks.2.norm2.weight",
+            "model.diffusion_model.input_blocks.2.0.out_layers.3.bias": "blocks.2.conv2.bias",
+            "model.diffusion_model.input_blocks.2.0.out_layers.3.weight": "blocks.2.conv2.weight",
+            "model.diffusion_model.input_blocks.3.0.op.bias": "blocks.4.conv.bias",
+            "model.diffusion_model.input_blocks.3.0.op.weight": "blocks.4.conv.weight",
+            "model.diffusion_model.input_blocks.4.0.emb_layers.1.bias": "blocks.6.time_emb_proj.bias",
+            "model.diffusion_model.input_blocks.4.0.emb_layers.1.weight": "blocks.6.time_emb_proj.weight",
+            "model.diffusion_model.input_blocks.4.0.in_layers.0.bias": "blocks.6.norm1.bias",
+            "model.diffusion_model.input_blocks.4.0.in_layers.0.weight": "blocks.6.norm1.weight",
+            "model.diffusion_model.input_blocks.4.0.in_layers.2.bias": "blocks.6.conv1.bias",
+            "model.diffusion_model.input_blocks.4.0.in_layers.2.weight": "blocks.6.conv1.weight",
+            "model.diffusion_model.input_blocks.4.0.out_layers.0.bias": "blocks.6.norm2.bias",
+            "model.diffusion_model.input_blocks.4.0.out_layers.0.weight": "blocks.6.norm2.weight",
+            "model.diffusion_model.input_blocks.4.0.out_layers.3.bias": "blocks.6.conv2.bias",
+            "model.diffusion_model.input_blocks.4.0.out_layers.3.weight": "blocks.6.conv2.weight",
+            "model.diffusion_model.input_blocks.4.0.skip_connection.bias": "blocks.6.conv_shortcut.bias",
+            "model.diffusion_model.input_blocks.4.0.skip_connection.weight": "blocks.6.conv_shortcut.weight",
+            "model.diffusion_model.input_blocks.4.1.norm.bias": "blocks.7.norm.bias",
+            "model.diffusion_model.input_blocks.4.1.norm.weight": "blocks.7.norm.weight",
+            "model.diffusion_model.input_blocks.4.1.proj_in.bias": "blocks.7.proj_in.bias",
+            "model.diffusion_model.input_blocks.4.1.proj_in.weight": "blocks.7.proj_in.weight",
+            "model.diffusion_model.input_blocks.4.1.proj_out.bias": "blocks.7.proj_out.bias",
+            "model.diffusion_model.input_blocks.4.1.proj_out.weight": "blocks.7.proj_out.weight",
+            "model.diffusion_model.input_blocks.4.1.transformer_blocks.0.attn1.to_k.weight": "blocks.7.transformer_blocks.0.attn1.to_k.weight",
+            "model.diffusion_model.input_blocks.4.1.transformer_blocks.0.attn1.to_out.0.bias": "blocks.7.transformer_blocks.0.attn1.to_out.bias",
+            "model.diffusion_model.input_blocks.4.1.transformer_blocks.0.attn1.to_out.0.weight": "blocks.7.transformer_blocks.0.attn1.to_out.weight",
+            "model.diffusion_model.input_blocks.4.1.transformer_blocks.0.attn1.to_q.weight": "blocks.7.transformer_blocks.0.attn1.to_q.weight",
+            "model.diffusion_model.input_blocks.4.1.transformer_blocks.0.attn1.to_v.weight": "blocks.7.transformer_blocks.0.attn1.to_v.weight",
+            "model.diffusion_model.input_blocks.4.1.transformer_blocks.0.attn2.to_k.weight": "blocks.7.transformer_blocks.0.attn2.to_k.weight",
+            "model.diffusion_model.input_blocks.4.1.transformer_blocks.0.attn2.to_out.0.bias": "blocks.7.transformer_blocks.0.attn2.to_out.bias",
+            "model.diffusion_model.input_blocks.4.1.transformer_blocks.0.attn2.to_out.0.weight": "blocks.7.transformer_blocks.0.attn2.to_out.weight",
+            "model.diffusion_model.input_blocks.4.1.transformer_blocks.0.attn2.to_q.weight": "blocks.7.transformer_blocks.0.attn2.to_q.weight",
+            "model.diffusion_model.input_blocks.4.1.transformer_blocks.0.attn2.to_v.weight": "blocks.7.transformer_blocks.0.attn2.to_v.weight",
+            "model.diffusion_model.input_blocks.4.1.transformer_blocks.0.ff.net.0.proj.bias": "blocks.7.transformer_blocks.0.act_fn.proj.bias",
+            "model.diffusion_model.input_blocks.4.1.transformer_blocks.0.ff.net.0.proj.weight": "blocks.7.transformer_blocks.0.act_fn.proj.weight",
+            "model.diffusion_model.input_blocks.4.1.transformer_blocks.0.ff.net.2.bias": "blocks.7.transformer_blocks.0.ff.bias",
+            "model.diffusion_model.input_blocks.4.1.transformer_blocks.0.ff.net.2.weight": "blocks.7.transformer_blocks.0.ff.weight",
+            "model.diffusion_model.input_blocks.4.1.transformer_blocks.0.norm1.bias": "blocks.7.transformer_blocks.0.norm1.bias",
+            "model.diffusion_model.input_blocks.4.1.transformer_blocks.0.norm1.weight": "blocks.7.transformer_blocks.0.norm1.weight",
+            "model.diffusion_model.input_blocks.4.1.transformer_blocks.0.norm2.bias": "blocks.7.transformer_blocks.0.norm2.bias",
+            "model.diffusion_model.input_blocks.4.1.transformer_blocks.0.norm2.weight": "blocks.7.transformer_blocks.0.norm2.weight",
+            "model.diffusion_model.input_blocks.4.1.transformer_blocks.0.norm3.bias": "blocks.7.transformer_blocks.0.norm3.bias",
+            "model.diffusion_model.input_blocks.4.1.transformer_blocks.0.norm3.weight": "blocks.7.transformer_blocks.0.norm3.weight",
+            "model.diffusion_model.input_blocks.4.1.transformer_blocks.1.attn1.to_k.weight": "blocks.7.transformer_blocks.1.attn1.to_k.weight",
+            "model.diffusion_model.input_blocks.4.1.transformer_blocks.1.attn1.to_out.0.bias": "blocks.7.transformer_blocks.1.attn1.to_out.bias",
+            "model.diffusion_model.input_blocks.4.1.transformer_blocks.1.attn1.to_out.0.weight": "blocks.7.transformer_blocks.1.attn1.to_out.weight",
+            "model.diffusion_model.input_blocks.4.1.transformer_blocks.1.attn1.to_q.weight": "blocks.7.transformer_blocks.1.attn1.to_q.weight",
+            "model.diffusion_model.input_blocks.4.1.transformer_blocks.1.attn1.to_v.weight": "blocks.7.transformer_blocks.1.attn1.to_v.weight",
+            "model.diffusion_model.input_blocks.4.1.transformer_blocks.1.attn2.to_k.weight": "blocks.7.transformer_blocks.1.attn2.to_k.weight",
+            "model.diffusion_model.input_blocks.4.1.transformer_blocks.1.attn2.to_out.0.bias": "blocks.7.transformer_blocks.1.attn2.to_out.bias",
+            "model.diffusion_model.input_blocks.4.1.transformer_blocks.1.attn2.to_out.0.weight": "blocks.7.transformer_blocks.1.attn2.to_out.weight",
+            "model.diffusion_model.input_blocks.4.1.transformer_blocks.1.attn2.to_q.weight": "blocks.7.transformer_blocks.1.attn2.to_q.weight",
+            "model.diffusion_model.input_blocks.4.1.transformer_blocks.1.attn2.to_v.weight": "blocks.7.transformer_blocks.1.attn2.to_v.weight",
+            "model.diffusion_model.input_blocks.4.1.transformer_blocks.1.ff.net.0.proj.bias": "blocks.7.transformer_blocks.1.act_fn.proj.bias",
+            "model.diffusion_model.input_blocks.4.1.transformer_blocks.1.ff.net.0.proj.weight": "blocks.7.transformer_blocks.1.act_fn.proj.weight",
+            "model.diffusion_model.input_blocks.4.1.transformer_blocks.1.ff.net.2.bias": "blocks.7.transformer_blocks.1.ff.bias",
+            "model.diffusion_model.input_blocks.4.1.transformer_blocks.1.ff.net.2.weight": "blocks.7.transformer_blocks.1.ff.weight",
+            "model.diffusion_model.input_blocks.4.1.transformer_blocks.1.norm1.bias": "blocks.7.transformer_blocks.1.norm1.bias",
+            "model.diffusion_model.input_blocks.4.1.transformer_blocks.1.norm1.weight": "blocks.7.transformer_blocks.1.norm1.weight",
+            "model.diffusion_model.input_blocks.4.1.transformer_blocks.1.norm2.bias": "blocks.7.transformer_blocks.1.norm2.bias",
+            "model.diffusion_model.input_blocks.4.1.transformer_blocks.1.norm2.weight": "blocks.7.transformer_blocks.1.norm2.weight",
+            "model.diffusion_model.input_blocks.4.1.transformer_blocks.1.norm3.bias": "blocks.7.transformer_blocks.1.norm3.bias",
+            "model.diffusion_model.input_blocks.4.1.transformer_blocks.1.norm3.weight": "blocks.7.transformer_blocks.1.norm3.weight",
+            "model.diffusion_model.input_blocks.5.0.emb_layers.1.bias": "blocks.9.time_emb_proj.bias",
+            "model.diffusion_model.input_blocks.5.0.emb_layers.1.weight": "blocks.9.time_emb_proj.weight",
+            "model.diffusion_model.input_blocks.5.0.in_layers.0.bias": "blocks.9.norm1.bias",
+            "model.diffusion_model.input_blocks.5.0.in_layers.0.weight": "blocks.9.norm1.weight",
+            "model.diffusion_model.input_blocks.5.0.in_layers.2.bias": "blocks.9.conv1.bias",
+            "model.diffusion_model.input_blocks.5.0.in_layers.2.weight": "blocks.9.conv1.weight",
+            "model.diffusion_model.input_blocks.5.0.out_layers.0.bias": "blocks.9.norm2.bias",
+            "model.diffusion_model.input_blocks.5.0.out_layers.0.weight": "blocks.9.norm2.weight",
+            "model.diffusion_model.input_blocks.5.0.out_layers.3.bias": "blocks.9.conv2.bias",
+            "model.diffusion_model.input_blocks.5.0.out_layers.3.weight": "blocks.9.conv2.weight",
+            "model.diffusion_model.input_blocks.5.1.norm.bias": "blocks.10.norm.bias",
+            "model.diffusion_model.input_blocks.5.1.norm.weight": "blocks.10.norm.weight",
+            "model.diffusion_model.input_blocks.5.1.proj_in.bias": "blocks.10.proj_in.bias",
+            "model.diffusion_model.input_blocks.5.1.proj_in.weight": "blocks.10.proj_in.weight",
+            "model.diffusion_model.input_blocks.5.1.proj_out.bias": "blocks.10.proj_out.bias",
+            "model.diffusion_model.input_blocks.5.1.proj_out.weight": "blocks.10.proj_out.weight",
+            "model.diffusion_model.input_blocks.5.1.transformer_blocks.0.attn1.to_k.weight": "blocks.10.transformer_blocks.0.attn1.to_k.weight",
+            "model.diffusion_model.input_blocks.5.1.transformer_blocks.0.attn1.to_out.0.bias": "blocks.10.transformer_blocks.0.attn1.to_out.bias",
+            "model.diffusion_model.input_blocks.5.1.transformer_blocks.0.attn1.to_out.0.weight": "blocks.10.transformer_blocks.0.attn1.to_out.weight",
+            "model.diffusion_model.input_blocks.5.1.transformer_blocks.0.attn1.to_q.weight": "blocks.10.transformer_blocks.0.attn1.to_q.weight",
+            "model.diffusion_model.input_blocks.5.1.transformer_blocks.0.attn1.to_v.weight": "blocks.10.transformer_blocks.0.attn1.to_v.weight",
+            "model.diffusion_model.input_blocks.5.1.transformer_blocks.0.attn2.to_k.weight": "blocks.10.transformer_blocks.0.attn2.to_k.weight",
+            "model.diffusion_model.input_blocks.5.1.transformer_blocks.0.attn2.to_out.0.bias": "blocks.10.transformer_blocks.0.attn2.to_out.bias",
+            "model.diffusion_model.input_blocks.5.1.transformer_blocks.0.attn2.to_out.0.weight": "blocks.10.transformer_blocks.0.attn2.to_out.weight",
+            "model.diffusion_model.input_blocks.5.1.transformer_blocks.0.attn2.to_q.weight": "blocks.10.transformer_blocks.0.attn2.to_q.weight",
+            "model.diffusion_model.input_blocks.5.1.transformer_blocks.0.attn2.to_v.weight": "blocks.10.transformer_blocks.0.attn2.to_v.weight",
+            "model.diffusion_model.input_blocks.5.1.transformer_blocks.0.ff.net.0.proj.bias": "blocks.10.transformer_blocks.0.act_fn.proj.bias",
+            "model.diffusion_model.input_blocks.5.1.transformer_blocks.0.ff.net.0.proj.weight": "blocks.10.transformer_blocks.0.act_fn.proj.weight",
+            "model.diffusion_model.input_blocks.5.1.transformer_blocks.0.ff.net.2.bias": "blocks.10.transformer_blocks.0.ff.bias",
+            "model.diffusion_model.input_blocks.5.1.transformer_blocks.0.ff.net.2.weight": "blocks.10.transformer_blocks.0.ff.weight",
+            "model.diffusion_model.input_blocks.5.1.transformer_blocks.0.norm1.bias": "blocks.10.transformer_blocks.0.norm1.bias",
+            "model.diffusion_model.input_blocks.5.1.transformer_blocks.0.norm1.weight": "blocks.10.transformer_blocks.0.norm1.weight",
+            "model.diffusion_model.input_blocks.5.1.transformer_blocks.0.norm2.bias": "blocks.10.transformer_blocks.0.norm2.bias",
+            "model.diffusion_model.input_blocks.5.1.transformer_blocks.0.norm2.weight": "blocks.10.transformer_blocks.0.norm2.weight",
+            "model.diffusion_model.input_blocks.5.1.transformer_blocks.0.norm3.bias": "blocks.10.transformer_blocks.0.norm3.bias",
+            "model.diffusion_model.input_blocks.5.1.transformer_blocks.0.norm3.weight": "blocks.10.transformer_blocks.0.norm3.weight",
+            "model.diffusion_model.input_blocks.5.1.transformer_blocks.1.attn1.to_k.weight": "blocks.10.transformer_blocks.1.attn1.to_k.weight",
+            "model.diffusion_model.input_blocks.5.1.transformer_blocks.1.attn1.to_out.0.bias": "blocks.10.transformer_blocks.1.attn1.to_out.bias",
+            "model.diffusion_model.input_blocks.5.1.transformer_blocks.1.attn1.to_out.0.weight": "blocks.10.transformer_blocks.1.attn1.to_out.weight",
+            "model.diffusion_model.input_blocks.5.1.transformer_blocks.1.attn1.to_q.weight": "blocks.10.transformer_blocks.1.attn1.to_q.weight",
+            "model.diffusion_model.input_blocks.5.1.transformer_blocks.1.attn1.to_v.weight": "blocks.10.transformer_blocks.1.attn1.to_v.weight",
+            "model.diffusion_model.input_blocks.5.1.transformer_blocks.1.attn2.to_k.weight": "blocks.10.transformer_blocks.1.attn2.to_k.weight",
+            "model.diffusion_model.input_blocks.5.1.transformer_blocks.1.attn2.to_out.0.bias": "blocks.10.transformer_blocks.1.attn2.to_out.bias",
+            "model.diffusion_model.input_blocks.5.1.transformer_blocks.1.attn2.to_out.0.weight": "blocks.10.transformer_blocks.1.attn2.to_out.weight",
+            "model.diffusion_model.input_blocks.5.1.transformer_blocks.1.attn2.to_q.weight": "blocks.10.transformer_blocks.1.attn2.to_q.weight",
+            "model.diffusion_model.input_blocks.5.1.transformer_blocks.1.attn2.to_v.weight": "blocks.10.transformer_blocks.1.attn2.to_v.weight",
+            "model.diffusion_model.input_blocks.5.1.transformer_blocks.1.ff.net.0.proj.bias": "blocks.10.transformer_blocks.1.act_fn.proj.bias",
+            "model.diffusion_model.input_blocks.5.1.transformer_blocks.1.ff.net.0.proj.weight": "blocks.10.transformer_blocks.1.act_fn.proj.weight",
+            "model.diffusion_model.input_blocks.5.1.transformer_blocks.1.ff.net.2.bias": "blocks.10.transformer_blocks.1.ff.bias",
+            "model.diffusion_model.input_blocks.5.1.transformer_blocks.1.ff.net.2.weight": "blocks.10.transformer_blocks.1.ff.weight",
+            "model.diffusion_model.input_blocks.5.1.transformer_blocks.1.norm1.bias": "blocks.10.transformer_blocks.1.norm1.bias",
+            "model.diffusion_model.input_blocks.5.1.transformer_blocks.1.norm1.weight": "blocks.10.transformer_blocks.1.norm1.weight",
+            "model.diffusion_model.input_blocks.5.1.transformer_blocks.1.norm2.bias": "blocks.10.transformer_blocks.1.norm2.bias",
+            "model.diffusion_model.input_blocks.5.1.transformer_blocks.1.norm2.weight": "blocks.10.transformer_blocks.1.norm2.weight",
+            "model.diffusion_model.input_blocks.5.1.transformer_blocks.1.norm3.bias": "blocks.10.transformer_blocks.1.norm3.bias",
+            "model.diffusion_model.input_blocks.5.1.transformer_blocks.1.norm3.weight": "blocks.10.transformer_blocks.1.norm3.weight",
+            "model.diffusion_model.input_blocks.6.0.op.bias": "blocks.12.conv.bias",
+            "model.diffusion_model.input_blocks.6.0.op.weight": "blocks.12.conv.weight",
+            "model.diffusion_model.input_blocks.7.0.emb_layers.1.bias": "blocks.14.time_emb_proj.bias",
+            "model.diffusion_model.input_blocks.7.0.emb_layers.1.weight": "blocks.14.time_emb_proj.weight",
+            "model.diffusion_model.input_blocks.7.0.in_layers.0.bias": "blocks.14.norm1.bias",
+            "model.diffusion_model.input_blocks.7.0.in_layers.0.weight": "blocks.14.norm1.weight",
+            "model.diffusion_model.input_blocks.7.0.in_layers.2.bias": "blocks.14.conv1.bias",
+            "model.diffusion_model.input_blocks.7.0.in_layers.2.weight": "blocks.14.conv1.weight",
+            "model.diffusion_model.input_blocks.7.0.out_layers.0.bias": "blocks.14.norm2.bias",
+            "model.diffusion_model.input_blocks.7.0.out_layers.0.weight": "blocks.14.norm2.weight",
+            "model.diffusion_model.input_blocks.7.0.out_layers.3.bias": "blocks.14.conv2.bias",
+            "model.diffusion_model.input_blocks.7.0.out_layers.3.weight": "blocks.14.conv2.weight",
+            "model.diffusion_model.input_blocks.7.0.skip_connection.bias": "blocks.14.conv_shortcut.bias",
+            "model.diffusion_model.input_blocks.7.0.skip_connection.weight": "blocks.14.conv_shortcut.weight",
+            "model.diffusion_model.input_blocks.7.1.norm.bias": "blocks.15.norm.bias",
+            "model.diffusion_model.input_blocks.7.1.norm.weight": "blocks.15.norm.weight",
+            "model.diffusion_model.input_blocks.7.1.proj_in.bias": "blocks.15.proj_in.bias",
+            "model.diffusion_model.input_blocks.7.1.proj_in.weight": "blocks.15.proj_in.weight",
+            "model.diffusion_model.input_blocks.7.1.proj_out.bias": "blocks.15.proj_out.bias",
+            "model.diffusion_model.input_blocks.7.1.proj_out.weight": "blocks.15.proj_out.weight",
+            "model.diffusion_model.input_blocks.7.1.transformer_blocks.0.attn1.to_k.weight": "blocks.15.transformer_blocks.0.attn1.to_k.weight",
+            "model.diffusion_model.input_blocks.7.1.transformer_blocks.0.attn1.to_out.0.bias": "blocks.15.transformer_blocks.0.attn1.to_out.bias",
+            "model.diffusion_model.input_blocks.7.1.transformer_blocks.0.attn1.to_out.0.weight": "blocks.15.transformer_blocks.0.attn1.to_out.weight",
+            "model.diffusion_model.input_blocks.7.1.transformer_blocks.0.attn1.to_q.weight": "blocks.15.transformer_blocks.0.attn1.to_q.weight",
+            "model.diffusion_model.input_blocks.7.1.transformer_blocks.0.attn1.to_v.weight": "blocks.15.transformer_blocks.0.attn1.to_v.weight",
+            "model.diffusion_model.input_blocks.7.1.transformer_blocks.0.attn2.to_k.weight": "blocks.15.transformer_blocks.0.attn2.to_k.weight",
+            "model.diffusion_model.input_blocks.7.1.transformer_blocks.0.attn2.to_out.0.bias": "blocks.15.transformer_blocks.0.attn2.to_out.bias",
+            "model.diffusion_model.input_blocks.7.1.transformer_blocks.0.attn2.to_out.0.weight": "blocks.15.transformer_blocks.0.attn2.to_out.weight",
+            "model.diffusion_model.input_blocks.7.1.transformer_blocks.0.attn2.to_q.weight": "blocks.15.transformer_blocks.0.attn2.to_q.weight",
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+            "model.diffusion_model.output_blocks.8.0.in_layers.0.bias": "blocks.48.norm1.bias",
+            "model.diffusion_model.output_blocks.8.0.in_layers.0.weight": "blocks.48.norm1.weight",
+            "model.diffusion_model.output_blocks.8.0.in_layers.2.bias": "blocks.48.conv1.bias",
+            "model.diffusion_model.output_blocks.8.0.in_layers.2.weight": "blocks.48.conv1.weight",
+            "model.diffusion_model.output_blocks.8.0.out_layers.0.bias": "blocks.48.norm2.bias",
+            "model.diffusion_model.output_blocks.8.0.out_layers.0.weight": "blocks.48.norm2.weight",
+            "model.diffusion_model.output_blocks.8.0.out_layers.3.bias": "blocks.48.conv2.bias",
+            "model.diffusion_model.output_blocks.8.0.out_layers.3.weight": "blocks.48.conv2.weight",
+            "model.diffusion_model.output_blocks.8.0.skip_connection.bias": "blocks.48.conv_shortcut.bias",
+            "model.diffusion_model.output_blocks.8.0.skip_connection.weight": "blocks.48.conv_shortcut.weight",
+            "model.diffusion_model.time_embed.0.bias": "time_embedding.0.bias",
+            "model.diffusion_model.time_embed.0.weight": "time_embedding.0.weight",
+            "model.diffusion_model.time_embed.2.bias": "time_embedding.2.bias",
+            "model.diffusion_model.time_embed.2.weight": "time_embedding.2.weight",
+        }
+        state_dict_ = {}
+        for name in state_dict:
+            if name in rename_dict:
+                param = state_dict[name]
+                if ".proj_in." in name or ".proj_out." in name:
+                    param = param.squeeze()
+                state_dict_[rename_dict[name]] = param
+        if "text_intermediate_proj.weight" in state_dict_:
+            return state_dict_, {"is_kolors": True}
+        else:
+            return state_dict_
diff --git a/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/sdxl_vae_decoder.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/sdxl_vae_decoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..290c7851e3619f61a1baa2ddb3ad77180809297d
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/sdxl_vae_decoder.py
@@ -0,0 +1,24 @@
+from .sd_vae_decoder import SDVAEDecoder, SDVAEDecoderStateDictConverter
+
+
+class SDXLVAEDecoder(SDVAEDecoder):
+    def __init__(self, upcast_to_float32=True):
+        super().__init__()
+        self.scaling_factor = 0.13025
+
+    @staticmethod
+    def state_dict_converter():
+        return SDXLVAEDecoderStateDictConverter()
+    
+
+class SDXLVAEDecoderStateDictConverter(SDVAEDecoderStateDictConverter):
+    def __init__(self):
+        super().__init__()
+
+    def from_diffusers(self, state_dict):
+        state_dict = super().from_diffusers(state_dict)
+        return state_dict, {"upcast_to_float32": True}
+    
+    def from_civitai(self, state_dict):
+        state_dict = super().from_civitai(state_dict)
+        return state_dict, {"upcast_to_float32": True}
diff --git a/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/sdxl_vae_encoder.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/sdxl_vae_encoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..14af09cd33c41452b9777daa6819115eb900b788
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/sdxl_vae_encoder.py
@@ -0,0 +1,24 @@
+from .sd_vae_encoder import SDVAEEncoderStateDictConverter, SDVAEEncoder
+
+
+class SDXLVAEEncoder(SDVAEEncoder):
+    def __init__(self, upcast_to_float32=True):
+        super().__init__()
+        self.scaling_factor = 0.13025
+    
+    @staticmethod
+    def state_dict_converter():
+        return SDXLVAEEncoderStateDictConverter()
+
+
+class SDXLVAEEncoderStateDictConverter(SDVAEEncoderStateDictConverter):
+    def __init__(self):
+        super().__init__()
+
+    def from_diffusers(self, state_dict):
+        state_dict = super().from_diffusers(state_dict)
+        return state_dict, {"upcast_to_float32": True}
+    
+    def from_civitai(self, state_dict):
+        state_dict = super().from_civitai(state_dict)
+        return state_dict, {"upcast_to_float32": True}
diff --git a/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/step1x_connector.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/step1x_connector.py
new file mode 100644
index 0000000000000000000000000000000000000000..9d5f0d90f4c3627124e487474e60d32549efc4ce
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/step1x_connector.py
@@ -0,0 +1,683 @@
+from typing import Optional
+
+import torch, math
+import torch.nn
+from einops import rearrange
+from torch import nn
+from functools import partial
+from einops import rearrange
+
+
+
+def attention(q, k, v, attn_mask, mode="torch"):
+    q = q.transpose(1, 2)
+    k = k.transpose(1, 2)
+    v = v.transpose(1, 2)
+    x = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=attn_mask)
+    x = rearrange(x, "b n s d -> b s (n d)")
+    return x
+    
+
+
+class MLP(nn.Module):
+    """MLP as used in Vision Transformer, MLP-Mixer and related networks"""
+
+    def __init__(
+        self,
+        in_channels,
+        hidden_channels=None,
+        out_features=None,
+        act_layer=nn.GELU,
+        norm_layer=None,
+        bias=True,
+        drop=0.0,
+        use_conv=False,
+        device=None,
+        dtype=None,
+    ):
+        super().__init__()
+        out_features = out_features or in_channels
+        hidden_channels = hidden_channels or in_channels
+        bias = (bias, bias)
+        drop_probs = (drop, drop)
+        linear_layer = partial(nn.Conv2d, kernel_size=1) if use_conv else nn.Linear
+
+        self.fc1 = linear_layer(
+            in_channels, hidden_channels, bias=bias[0], device=device, dtype=dtype
+        )
+        self.act = act_layer()
+        self.drop1 = nn.Dropout(drop_probs[0])
+        self.norm = (
+            norm_layer(hidden_channels, device=device, dtype=dtype)
+            if norm_layer is not None
+            else nn.Identity()
+        )
+        self.fc2 = linear_layer(
+            hidden_channels, out_features, bias=bias[1], device=device, dtype=dtype
+        )
+        self.drop2 = nn.Dropout(drop_probs[1])
+
+    def forward(self, x):
+        x = self.fc1(x)
+        x = self.act(x)
+        x = self.drop1(x)
+        x = self.norm(x)
+        x = self.fc2(x)
+        x = self.drop2(x)
+        return x
+    
+    
+class TextProjection(nn.Module):
+    """
+    Projects text embeddings. Also handles dropout for classifier-free guidance.
+
+    Adapted from https://github.com/PixArt-alpha/PixArt-alpha/blob/master/diffusion/model/nets/PixArt_blocks.py
+    """
+
+    def __init__(self, in_channels, hidden_size, act_layer, dtype=None, device=None):
+        factory_kwargs = {"dtype": dtype, "device": device}
+        super().__init__()
+        self.linear_1 = nn.Linear(
+            in_features=in_channels,
+            out_features=hidden_size,
+            bias=True,
+            **factory_kwargs,
+        )
+        self.act_1 = act_layer()
+        self.linear_2 = nn.Linear(
+            in_features=hidden_size,
+            out_features=hidden_size,
+            bias=True,
+            **factory_kwargs,
+        )
+
+    def forward(self, caption):
+        hidden_states = self.linear_1(caption)
+        hidden_states = self.act_1(hidden_states)
+        hidden_states = self.linear_2(hidden_states)
+        return hidden_states
+    
+    
+class TimestepEmbedder(nn.Module):
+    """
+    Embeds scalar timesteps into vector representations.
+    """
+
+    def __init__(
+        self,
+        hidden_size,
+        act_layer,
+        frequency_embedding_size=256,
+        max_period=10000,
+        out_size=None,
+        dtype=None,
+        device=None,
+    ):
+        factory_kwargs = {"dtype": dtype, "device": device}
+        super().__init__()
+        self.frequency_embedding_size = frequency_embedding_size
+        self.max_period = max_period
+        if out_size is None:
+            out_size = hidden_size
+
+        self.mlp = nn.Sequential(
+            nn.Linear(
+                frequency_embedding_size, hidden_size, bias=True, **factory_kwargs
+            ),
+            act_layer(),
+            nn.Linear(hidden_size, out_size, bias=True, **factory_kwargs),
+        )
+        nn.init.normal_(self.mlp[0].weight, std=0.02)  # type: ignore
+        nn.init.normal_(self.mlp[2].weight, std=0.02)  # type: ignore
+
+    @staticmethod
+    def timestep_embedding(t, dim, max_period=10000):
+        """
+        Create sinusoidal timestep embeddings.
+
+        Args:
+            t (torch.Tensor): a 1-D Tensor of N indices, one per batch element. These may be fractional.
+            dim (int): the dimension of the output.
+            max_period (int): controls the minimum frequency of the embeddings.
+
+        Returns:
+            embedding (torch.Tensor): An (N, D) Tensor of positional embeddings.
+
+        .. ref_link: https://github.com/openai/glide-text2im/blob/main/glide_text2im/nn.py
+        """
+        half = dim // 2
+        freqs = torch.exp(
+            -math.log(max_period)
+            * torch.arange(start=0, end=half, dtype=torch.float32)
+            / half
+        ).to(device=t.device)
+        args = t[:, None].float() * freqs[None]
+        embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
+        if dim % 2:
+            embedding = torch.cat(
+                [embedding, torch.zeros_like(embedding[:, :1])], dim=-1
+            )
+        return embedding
+
+    def forward(self, t):
+        t_freq = self.timestep_embedding(
+            t, self.frequency_embedding_size, self.max_period
+        ).type(t.dtype)  # type: ignore
+        t_emb = self.mlp(t_freq)
+        return t_emb
+    
+    
+def apply_gate(x, gate=None, tanh=False):
+    """AI is creating summary for apply_gate
+
+    Args:
+        x (torch.Tensor): input tensor.
+        gate (torch.Tensor, optional): gate tensor. Defaults to None.
+        tanh (bool, optional): whether to use tanh function. Defaults to False.
+
+    Returns:
+        torch.Tensor: the output tensor after apply gate.
+    """
+    if gate is None:
+        return x
+    if tanh:
+        return x * gate.unsqueeze(1).tanh()
+    else:
+        return x * gate.unsqueeze(1)
+
+
+class RMSNorm(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        elementwise_affine=True,
+        eps: float = 1e-6,
+        device=None,
+        dtype=None,
+    ):
+        """
+        Initialize the RMSNorm normalization layer.
+
+        Args:
+            dim (int): The dimension of the input tensor.
+            eps (float, optional): A small value added to the denominator for numerical stability. Default is 1e-6.
+
+        Attributes:
+            eps (float): A small value added to the denominator for numerical stability.
+            weight (nn.Parameter): Learnable scaling parameter.
+
+        """
+        factory_kwargs = {"device": device, "dtype": dtype}
+        super().__init__()
+        self.eps = eps
+        if elementwise_affine:
+            self.weight = nn.Parameter(torch.ones(dim, **factory_kwargs))
+
+    def _norm(self, x):
+        """
+        Apply the RMSNorm normalization to the input tensor.
+
+        Args:
+            x (torch.Tensor): The input tensor.
+
+        Returns:
+            torch.Tensor: The normalized tensor.
+
+        """
+        return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
+
+    def forward(self, x):
+        """
+        Forward pass through the RMSNorm layer.
+
+        Args:
+            x (torch.Tensor): The input tensor.
+
+        Returns:
+            torch.Tensor: The output tensor after applying RMSNorm.
+
+        """
+        output = self._norm(x.float()).type_as(x)
+        if hasattr(self, "weight"):
+            output = output * self.weight
+        return output
+
+
+def get_norm_layer(norm_layer):
+    """
+    Get the normalization layer.
+
+    Args:
+        norm_layer (str): The type of normalization layer.
+
+    Returns:
+        norm_layer (nn.Module): The normalization layer.
+    """
+    if norm_layer == "layer":
+        return nn.LayerNorm
+    elif norm_layer == "rms":
+        return RMSNorm
+    else:
+        raise NotImplementedError(f"Norm layer {norm_layer} is not implemented")
+
+
+def get_activation_layer(act_type):
+    """get activation layer
+
+    Args:
+        act_type (str): the activation type
+
+    Returns:
+        torch.nn.functional: the activation layer
+    """
+    if act_type == "gelu":
+        return lambda: nn.GELU()
+    elif act_type == "gelu_tanh":
+        return lambda: nn.GELU(approximate="tanh")
+    elif act_type == "relu":
+        return nn.ReLU
+    elif act_type == "silu":
+        return nn.SiLU
+    else:
+        raise ValueError(f"Unknown activation type: {act_type}")
+
+class IndividualTokenRefinerBlock(torch.nn.Module):
+    def __init__(
+        self,
+        hidden_size,
+        heads_num,
+        mlp_width_ratio: str = 4.0,
+        mlp_drop_rate: float = 0.0,
+        act_type: str = "silu",
+        qk_norm: bool = False,
+        qk_norm_type: str = "layer",
+        qkv_bias: bool = True,
+        need_CA: bool = False,
+        dtype: Optional[torch.dtype] = None,
+        device: Optional[torch.device] = None,
+    ):
+        factory_kwargs = {"device": device, "dtype": dtype}
+        super().__init__()
+        self.need_CA = need_CA
+        self.heads_num = heads_num
+        head_dim = hidden_size // heads_num
+        mlp_hidden_dim = int(hidden_size * mlp_width_ratio)
+
+        self.norm1 = nn.LayerNorm(
+            hidden_size, elementwise_affine=True, eps=1e-6, **factory_kwargs
+        )
+        self.self_attn_qkv = nn.Linear(
+            hidden_size, hidden_size * 3, bias=qkv_bias, **factory_kwargs
+        )
+        qk_norm_layer = get_norm_layer(qk_norm_type)
+        self.self_attn_q_norm = (
+            qk_norm_layer(head_dim, elementwise_affine=True, eps=1e-6, **factory_kwargs)
+            if qk_norm
+            else nn.Identity()
+        )
+        self.self_attn_k_norm = (
+            qk_norm_layer(head_dim, elementwise_affine=True, eps=1e-6, **factory_kwargs)
+            if qk_norm
+            else nn.Identity()
+        )
+        self.self_attn_proj = nn.Linear(
+            hidden_size, hidden_size, bias=qkv_bias, **factory_kwargs
+        )
+
+        self.norm2 = nn.LayerNorm(
+            hidden_size, elementwise_affine=True, eps=1e-6, **factory_kwargs
+        )
+        act_layer = get_activation_layer(act_type)
+        self.mlp = MLP(
+            in_channels=hidden_size,
+            hidden_channels=mlp_hidden_dim,
+            act_layer=act_layer,
+            drop=mlp_drop_rate,
+            **factory_kwargs,
+        )
+
+        self.adaLN_modulation = nn.Sequential(
+            act_layer(),
+            nn.Linear(hidden_size, 2 * hidden_size, bias=True, **factory_kwargs),
+        )
+
+        if self.need_CA:
+            self.cross_attnblock=CrossAttnBlock(hidden_size=hidden_size,
+                        heads_num=heads_num,
+                        mlp_width_ratio=mlp_width_ratio,
+                        mlp_drop_rate=mlp_drop_rate,
+                        act_type=act_type,
+                        qk_norm=qk_norm,
+                        qk_norm_type=qk_norm_type,
+                        qkv_bias=qkv_bias,
+                        **factory_kwargs,)
+        # Zero-initialize the modulation
+        nn.init.zeros_(self.adaLN_modulation[1].weight)
+        nn.init.zeros_(self.adaLN_modulation[1].bias)
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        c: torch.Tensor,  # timestep_aware_representations + context_aware_representations
+        attn_mask: torch.Tensor = None,
+        y: torch.Tensor = None,
+    ):
+        gate_msa, gate_mlp = self.adaLN_modulation(c).chunk(2, dim=1)
+
+        norm_x = self.norm1(x)
+        qkv = self.self_attn_qkv(norm_x)
+        q, k, v = rearrange(qkv, "B L (K H D) -> K B L H D", K=3, H=self.heads_num)
+        # Apply QK-Norm if needed
+        q = self.self_attn_q_norm(q).to(v)
+        k = self.self_attn_k_norm(k).to(v)
+
+        # Self-Attention
+        attn = attention(q, k, v, mode="torch", attn_mask=attn_mask)
+
+        x = x + apply_gate(self.self_attn_proj(attn), gate_msa)
+        
+        if self.need_CA:
+            x = self.cross_attnblock(x, c, attn_mask, y)
+
+        # FFN Layer
+        x = x + apply_gate(self.mlp(self.norm2(x)), gate_mlp)
+
+        return x
+
+
+
+
+class CrossAttnBlock(torch.nn.Module):
+    def __init__(
+        self,
+        hidden_size,
+        heads_num,
+        mlp_width_ratio: str = 4.0,
+        mlp_drop_rate: float = 0.0,
+        act_type: str = "silu",
+        qk_norm: bool = False,
+        qk_norm_type: str = "layer",
+        qkv_bias: bool = True,
+        dtype: Optional[torch.dtype] = None,
+        device: Optional[torch.device] = None,
+    ):
+        factory_kwargs = {"device": device, "dtype": dtype}
+        super().__init__()
+        self.heads_num = heads_num
+        head_dim = hidden_size // heads_num
+
+        self.norm1 = nn.LayerNorm(
+            hidden_size, elementwise_affine=True, eps=1e-6, **factory_kwargs
+        )
+        self.norm1_2 = nn.LayerNorm(
+            hidden_size, elementwise_affine=True, eps=1e-6, **factory_kwargs
+        )
+        self.self_attn_q = nn.Linear(
+            hidden_size, hidden_size, bias=qkv_bias, **factory_kwargs
+        )
+        self.self_attn_kv = nn.Linear(
+            hidden_size, hidden_size*2, bias=qkv_bias, **factory_kwargs
+        )
+        qk_norm_layer = get_norm_layer(qk_norm_type)
+        self.self_attn_q_norm = (
+            qk_norm_layer(head_dim, elementwise_affine=True, eps=1e-6, **factory_kwargs)
+            if qk_norm
+            else nn.Identity()
+        )
+        self.self_attn_k_norm = (
+            qk_norm_layer(head_dim, elementwise_affine=True, eps=1e-6, **factory_kwargs)
+            if qk_norm
+            else nn.Identity()
+        )
+        self.self_attn_proj = nn.Linear(
+            hidden_size, hidden_size, bias=qkv_bias, **factory_kwargs
+        )
+
+        self.norm2 = nn.LayerNorm(
+            hidden_size, elementwise_affine=True, eps=1e-6, **factory_kwargs
+        )
+        act_layer = get_activation_layer(act_type)
+
+        self.adaLN_modulation = nn.Sequential(
+            act_layer(),
+            nn.Linear(hidden_size, 2 * hidden_size, bias=True, **factory_kwargs),
+        )
+        # Zero-initialize the modulation
+        nn.init.zeros_(self.adaLN_modulation[1].weight)
+        nn.init.zeros_(self.adaLN_modulation[1].bias)
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        c: torch.Tensor,  # timestep_aware_representations + context_aware_representations
+        attn_mask: torch.Tensor = None,
+        y: torch.Tensor=None,
+        
+    ):
+        gate_msa, gate_mlp = self.adaLN_modulation(c).chunk(2, dim=1)
+
+        norm_x = self.norm1(x)
+        norm_y = self.norm1_2(y)
+        q = self.self_attn_q(norm_x)
+        q = rearrange(q, "B L (H D) -> B L H D",  H=self.heads_num)
+        kv = self.self_attn_kv(norm_y)
+        k, v = rearrange(kv, "B L (K H D) -> K B L H D", K=2, H=self.heads_num)
+        # Apply QK-Norm if needed
+        q = self.self_attn_q_norm(q).to(v)
+        k = self.self_attn_k_norm(k).to(v)
+
+        # Self-Attention
+        attn = attention(q, k, v, mode="torch", attn_mask=attn_mask)
+
+        x = x + apply_gate(self.self_attn_proj(attn), gate_msa)
+
+        return x
+
+
+
+class IndividualTokenRefiner(torch.nn.Module):
+    def __init__(
+        self,
+        hidden_size,
+        heads_num,
+        depth,
+        mlp_width_ratio: float = 4.0,
+        mlp_drop_rate: float = 0.0,
+        act_type: str = "silu",
+        qk_norm: bool = False,
+        qk_norm_type: str = "layer",
+        qkv_bias: bool = True,
+        need_CA:bool=False,
+        dtype: Optional[torch.dtype] = None,
+        device: Optional[torch.device] = None,
+    ):  
+        
+        factory_kwargs = {"device": device, "dtype": dtype}
+        super().__init__()
+        self.need_CA = need_CA
+        self.blocks = nn.ModuleList(
+            [
+                IndividualTokenRefinerBlock(
+                    hidden_size=hidden_size,
+                    heads_num=heads_num,
+                    mlp_width_ratio=mlp_width_ratio,
+                    mlp_drop_rate=mlp_drop_rate,
+                    act_type=act_type,
+                    qk_norm=qk_norm,
+                    qk_norm_type=qk_norm_type,
+                    qkv_bias=qkv_bias,
+                    need_CA=self.need_CA,
+                    **factory_kwargs,
+                )
+                for _ in range(depth)
+            ]
+        )
+
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        c: torch.LongTensor,
+        mask: Optional[torch.Tensor] = None,
+        y:torch.Tensor=None,
+    ):
+        self_attn_mask = None
+        if mask is not None:
+            batch_size = mask.shape[0]
+            seq_len = mask.shape[1]
+            mask = mask.to(x.device)
+            # batch_size x 1 x seq_len x seq_len
+            self_attn_mask_1 = mask.view(batch_size, 1, 1, seq_len).repeat(
+                1, 1, seq_len, 1
+            )
+            # batch_size x 1 x seq_len x seq_len
+            self_attn_mask_2 = self_attn_mask_1.transpose(2, 3)
+            # batch_size x 1 x seq_len x seq_len, 1 for broadcasting of heads_num
+            self_attn_mask = (self_attn_mask_1 & self_attn_mask_2).bool()
+            # avoids self-attention weight being NaN for padding tokens
+            self_attn_mask[:, :, :, 0] = True
+        
+        
+        for block in self.blocks:
+            x = block(x, c, self_attn_mask,y)
+
+        return x
+
+
+class SingleTokenRefiner(torch.nn.Module):
+    """
+    A single token refiner block for llm text embedding refine.
+    """
+    def __init__(
+        self,
+        in_channels,
+        hidden_size,
+        heads_num,
+        depth,
+        mlp_width_ratio: float = 4.0,
+        mlp_drop_rate: float = 0.0,
+        act_type: str = "silu",
+        qk_norm: bool = False,
+        qk_norm_type: str = "layer",
+        qkv_bias: bool = True,
+        need_CA:bool=False,
+        attn_mode: str = "torch",
+        dtype: Optional[torch.dtype] = None,
+        device: Optional[torch.device] = None,
+    ):
+        factory_kwargs = {"device": device, "dtype": dtype}
+        super().__init__()
+        self.attn_mode = attn_mode
+        self.need_CA = need_CA
+        assert self.attn_mode == "torch", "Only support 'torch' mode for token refiner."
+
+        self.input_embedder = nn.Linear(
+            in_channels, hidden_size, bias=True, **factory_kwargs
+        )
+        if self.need_CA:
+            self.input_embedder_CA = nn.Linear(
+            in_channels, hidden_size, bias=True, **factory_kwargs
+        )
+
+        act_layer = get_activation_layer(act_type)
+        # Build timestep embedding layer
+        self.t_embedder = TimestepEmbedder(hidden_size, act_layer, **factory_kwargs)
+        # Build context embedding layer
+        self.c_embedder = TextProjection(
+            in_channels, hidden_size, act_layer, **factory_kwargs
+        )
+
+        self.individual_token_refiner = IndividualTokenRefiner(
+            hidden_size=hidden_size,
+            heads_num=heads_num,
+            depth=depth,
+            mlp_width_ratio=mlp_width_ratio,
+            mlp_drop_rate=mlp_drop_rate,
+            act_type=act_type,
+            qk_norm=qk_norm,
+            qk_norm_type=qk_norm_type,
+            qkv_bias=qkv_bias,
+            need_CA=need_CA,
+            **factory_kwargs,
+        )
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        t: torch.LongTensor,
+        mask: Optional[torch.LongTensor] = None,
+        y: torch.LongTensor=None,
+    ):
+        timestep_aware_representations = self.t_embedder(t)
+
+        if mask is None:
+            context_aware_representations = x.mean(dim=1)
+        else:
+            mask_float = mask.unsqueeze(-1)  # [b, s1, 1]
+            context_aware_representations = (x * mask_float).sum(
+                dim=1
+            ) / mask_float.sum(dim=1)
+        context_aware_representations = self.c_embedder(context_aware_representations)
+        c = timestep_aware_representations + context_aware_representations
+
+        x = self.input_embedder(x)
+        if self.need_CA:
+            y = self.input_embedder_CA(y)
+            x = self.individual_token_refiner(x, c, mask, y)
+        else:
+            x = self.individual_token_refiner(x, c, mask)
+
+        return x
+
+
+class Qwen2Connector(torch.nn.Module):
+    def __init__(
+        self,
+        # biclip_dim=1024,
+        in_channels=3584,
+        hidden_size=4096,
+        heads_num=32,
+        depth=2,
+        need_CA=False,
+        device=None,
+        dtype=torch.bfloat16,
+    ):
+        super().__init__()
+        factory_kwargs = {"device": device, "dtype":dtype}
+
+        self.S =SingleTokenRefiner(in_channels=in_channels,hidden_size=hidden_size,heads_num=heads_num,depth=depth,need_CA=need_CA,**factory_kwargs)
+        self.global_proj_out=nn.Linear(in_channels,768)
+
+        self.scale_factor = nn.Parameter(torch.zeros(1))
+        with torch.no_grad():
+            self.scale_factor.data += -(1 - 0.09)
+
+    def forward(self, x,t,mask):
+        mask_float = mask.unsqueeze(-1)  # [b, s1, 1]
+        x_mean = (x * mask_float).sum(
+                dim=1
+            ) / mask_float.sum(dim=1) * (1 + self.scale_factor.to(dtype=x.dtype, device=x.device))
+
+        global_out=self.global_proj_out(x_mean)
+        encoder_hidden_states = self.S(x,t,mask)
+        return encoder_hidden_states,global_out
+    
+    @staticmethod
+    def state_dict_converter():
+        return Qwen2ConnectorStateDictConverter()
+    
+    
+class Qwen2ConnectorStateDictConverter:
+    def __init__(self):
+        pass
+
+    def from_diffusers(self, state_dict):
+        return state_dict
+    
+    def from_civitai(self, state_dict):
+        state_dict_ = {}
+        for name, param in state_dict.items():
+            if name.startswith("connector."):
+                name_ = name[len("connector."):]
+                state_dict_[name_] = param
+        return state_dict_
diff --git a/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/stepvideo_dit.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/stepvideo_dit.py
new file mode 100644
index 0000000000000000000000000000000000000000..10576e77dbbcd9ac10e1e5b4ed8d52362e6ad82a
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/stepvideo_dit.py
@@ -0,0 +1,940 @@
+# Copyright 2025 StepFun Inc. All Rights Reserved.
+# 
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+# ==============================================================================
+from typing import Dict, Optional, Tuple, Union, List
+import torch, math
+from torch import nn
+from einops import rearrange, repeat
+from tqdm import tqdm
+
+
+class RMSNorm(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        elementwise_affine=True,
+        eps: float = 1e-6,
+        device=None,
+        dtype=None,
+    ):
+        """
+        Initialize the RMSNorm normalization layer.
+
+        Args:
+            dim (int): The dimension of the input tensor.
+            eps (float, optional): A small value added to the denominator for numerical stability. Default is 1e-6.
+
+        Attributes:
+            eps (float): A small value added to the denominator for numerical stability.
+            weight (nn.Parameter): Learnable scaling parameter.
+
+        """
+        factory_kwargs = {"device": device, "dtype": dtype}
+        super().__init__()
+        self.eps = eps
+        if elementwise_affine:
+            self.weight = nn.Parameter(torch.ones(dim, **factory_kwargs))
+
+    def _norm(self, x):
+        """
+        Apply the RMSNorm normalization to the input tensor.
+
+        Args:
+            x (torch.Tensor): The input tensor.
+
+        Returns:
+            torch.Tensor: The normalized tensor.
+
+        """
+        return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
+
+    def forward(self, x):
+        """
+        Forward pass through the RMSNorm layer.
+
+        Args:
+            x (torch.Tensor): The input tensor.
+
+        Returns:
+            torch.Tensor: The output tensor after applying RMSNorm.
+
+        """
+        output = self._norm(x.float()).type_as(x)
+        if hasattr(self, "weight"):
+            output = output * self.weight
+        return output
+    
+
+ACTIVATION_FUNCTIONS = {
+    "swish": nn.SiLU(),
+    "silu": nn.SiLU(),
+    "mish": nn.Mish(),
+    "gelu": nn.GELU(),
+    "relu": nn.ReLU(),
+}
+
+
+def get_activation(act_fn: str) -> nn.Module:
+    """Helper function to get activation function from string.
+
+    Args:
+        act_fn (str): Name of activation function.
+
+    Returns:
+        nn.Module: Activation function.
+    """
+
+    act_fn = act_fn.lower()
+    if act_fn in ACTIVATION_FUNCTIONS:
+        return ACTIVATION_FUNCTIONS[act_fn]
+    else:
+        raise ValueError(f"Unsupported activation function: {act_fn}")
+
+
+def get_timestep_embedding(
+    timesteps: torch.Tensor,
+    embedding_dim: int,
+    flip_sin_to_cos: bool = False,
+    downscale_freq_shift: float = 1,
+    scale: float = 1,
+    max_period: int = 10000,
+):
+    """
+    This matches the implementation in Denoising Diffusion Probabilistic Models: Create sinusoidal timestep embeddings.
+
+    :param timesteps: a 1-D Tensor of N indices, one per batch element.
+                      These may be fractional.
+    :param embedding_dim: the dimension of the output. :param max_period: controls the minimum frequency of the
+    embeddings. :return: an [N x dim] Tensor of positional embeddings.
+    """
+    assert len(timesteps.shape) == 1, "Timesteps should be a 1d-array"
+
+    half_dim = embedding_dim // 2
+    exponent = -math.log(max_period) * torch.arange(
+        start=0, end=half_dim, dtype=torch.float32, device=timesteps.device
+    )
+    exponent = exponent / (half_dim - downscale_freq_shift)
+
+    emb = torch.exp(exponent)
+    emb = timesteps[:, None].float() * emb[None, :]
+
+    # scale embeddings
+    emb = scale * emb
+
+    # concat sine and cosine embeddings
+    emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=-1)
+
+    # flip sine and cosine embeddings
+    if flip_sin_to_cos:
+        emb = torch.cat([emb[:, half_dim:], emb[:, :half_dim]], dim=-1)
+
+    # zero pad
+    if embedding_dim % 2 == 1:
+        emb = torch.nn.functional.pad(emb, (0, 1, 0, 0))
+    return emb
+
+
+class Timesteps(nn.Module):
+    def __init__(self, num_channels: int, flip_sin_to_cos: bool, downscale_freq_shift: float):
+        super().__init__()
+        self.num_channels = num_channels
+        self.flip_sin_to_cos = flip_sin_to_cos
+        self.downscale_freq_shift = downscale_freq_shift
+
+    def forward(self, timesteps):
+        t_emb = get_timestep_embedding(
+            timesteps,
+            self.num_channels,
+            flip_sin_to_cos=self.flip_sin_to_cos,
+            downscale_freq_shift=self.downscale_freq_shift,
+        )
+        return t_emb
+
+
+class TimestepEmbedding(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        time_embed_dim: int,
+        act_fn: str = "silu",
+        out_dim: int = None,
+        post_act_fn: Optional[str] = None,
+        cond_proj_dim=None,
+        sample_proj_bias=True
+    ):
+        super().__init__()
+        linear_cls = nn.Linear
+
+        self.linear_1 = linear_cls(
+                in_channels, 
+                time_embed_dim, 
+                bias=sample_proj_bias,
+            )
+
+        if cond_proj_dim is not None:
+            self.cond_proj = linear_cls(
+                    cond_proj_dim, 
+                    in_channels, 
+                    bias=False,
+                )
+        else:
+            self.cond_proj = None
+
+        self.act = get_activation(act_fn)
+
+        if out_dim is not None:
+            time_embed_dim_out = out_dim
+        else:
+            time_embed_dim_out = time_embed_dim
+            
+        self.linear_2 = linear_cls(
+                time_embed_dim, 
+                time_embed_dim_out, 
+                bias=sample_proj_bias, 
+            )
+
+        if post_act_fn is None:
+            self.post_act = None
+        else:
+            self.post_act = get_activation(post_act_fn)
+
+    def forward(self, sample, condition=None):
+        if condition is not None:
+            sample = sample + self.cond_proj(condition)
+        sample = self.linear_1(sample)
+
+        if self.act is not None:
+            sample = self.act(sample)
+
+        sample = self.linear_2(sample)
+
+        if self.post_act is not None:
+            sample = self.post_act(sample)
+        return sample
+
+
+class PixArtAlphaCombinedTimestepSizeEmbeddings(nn.Module):
+    def __init__(self, embedding_dim, size_emb_dim, use_additional_conditions: bool = False):
+        super().__init__()
+
+        self.outdim = size_emb_dim
+        self.time_proj = Timesteps(num_channels=256, flip_sin_to_cos=True, downscale_freq_shift=0)
+        self.timestep_embedder = TimestepEmbedding(in_channels=256, time_embed_dim=embedding_dim)
+
+        self.use_additional_conditions = use_additional_conditions
+        if self.use_additional_conditions:
+            self.additional_condition_proj = Timesteps(num_channels=256, flip_sin_to_cos=True, downscale_freq_shift=0)
+            self.resolution_embedder = TimestepEmbedding(in_channels=256, time_embed_dim=size_emb_dim)
+            self.nframe_embedder = TimestepEmbedding(in_channels=256, time_embed_dim=embedding_dim)
+            self.fps_embedder = TimestepEmbedding(in_channels=256, time_embed_dim=embedding_dim)
+
+    def forward(self, timestep, resolution=None, nframe=None, fps=None):
+        hidden_dtype = timestep.dtype
+
+        timesteps_proj = self.time_proj(timestep)
+        timesteps_emb = self.timestep_embedder(timesteps_proj.to(dtype=hidden_dtype))  # (N, D)
+
+        if self.use_additional_conditions:
+            batch_size = timestep.shape[0]
+            resolution_emb = self.additional_condition_proj(resolution.flatten()).to(hidden_dtype)
+            resolution_emb = self.resolution_embedder(resolution_emb).reshape(batch_size, -1)
+            nframe_emb = self.additional_condition_proj(nframe.flatten()).to(hidden_dtype)
+            nframe_emb = self.nframe_embedder(nframe_emb).reshape(batch_size, -1)
+            conditioning = timesteps_emb + resolution_emb + nframe_emb
+
+            if fps is not None:
+                fps_emb = self.additional_condition_proj(fps.flatten()).to(hidden_dtype)
+                fps_emb = self.fps_embedder(fps_emb).reshape(batch_size, -1)
+                conditioning = conditioning + fps_emb
+        else:
+            conditioning = timesteps_emb
+
+        return conditioning
+
+
+class AdaLayerNormSingle(nn.Module):
+    r"""
+        Norm layer adaptive layer norm single (adaLN-single).
+
+        As proposed in PixArt-Alpha (see: https://arxiv.org/abs/2310.00426; Section 2.3).
+
+        Parameters:
+            embedding_dim (`int`): The size of each embedding vector.
+            use_additional_conditions (`bool`): To use additional conditions for normalization or not.
+    """
+    def __init__(self, embedding_dim: int, use_additional_conditions: bool = False, time_step_rescale=1000):
+        super().__init__()
+
+        self.emb = PixArtAlphaCombinedTimestepSizeEmbeddings(
+            embedding_dim, size_emb_dim=embedding_dim // 2, use_additional_conditions=use_additional_conditions
+        )
+
+        self.silu = nn.SiLU()
+        self.linear = nn.Linear(embedding_dim, 6 * embedding_dim, bias=True)
+
+        self.time_step_rescale = time_step_rescale  ## timestep usually in [0, 1], we rescale it to [0,1000] for stability
+
+    def forward(
+        self,
+        timestep: torch.Tensor,
+        added_cond_kwargs: Dict[str, torch.Tensor] = None,
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+        embedded_timestep = self.emb(timestep*self.time_step_rescale, **added_cond_kwargs)
+
+        out = self.linear(self.silu(embedded_timestep))
+
+        return out, embedded_timestep
+    
+
+class PixArtAlphaTextProjection(nn.Module):
+    """
+    Projects caption embeddings. Also handles dropout for classifier-free guidance.
+
+    Adapted from https://github.com/PixArt-alpha/PixArt-alpha/blob/master/diffusion/model/nets/PixArt_blocks.py
+    """
+
+    def __init__(self, in_features, hidden_size):
+        super().__init__()
+        self.linear_1 = nn.Linear(
+                in_features, 
+                hidden_size, 
+                bias=True, 
+            )        
+        self.act_1 = nn.GELU(approximate="tanh")
+        self.linear_2 = nn.Linear(
+                hidden_size, 
+                hidden_size, 
+                bias=True, 
+            )
+
+    def forward(self, caption):
+        hidden_states = self.linear_1(caption)
+        hidden_states = self.act_1(hidden_states)
+        hidden_states = self.linear_2(hidden_states)
+        return hidden_states
+
+
+class Attention(nn.Module):
+    def __init__(self):
+        super().__init__()
+    
+    def attn_processor(self, attn_type):
+        if attn_type == 'torch':
+            return self.torch_attn_func
+        elif attn_type == 'parallel':
+            return self.parallel_attn_func
+        else:
+            raise Exception('Not supported attention type...')
+
+    def torch_attn_func(
+        self,
+        q,
+        k,
+        v,
+        attn_mask=None,
+        causal=False,
+        drop_rate=0.0,
+        **kwargs
+    ):
+
+        if attn_mask is not None and attn_mask.dtype != torch.bool:
+            attn_mask = attn_mask.to(q.dtype)
+            
+        if attn_mask is not None and attn_mask.ndim == 3:   ## no head
+            n_heads = q.shape[2]
+            attn_mask = attn_mask.unsqueeze(1).repeat(1, n_heads, 1, 1)
+        
+        q, k, v = map(lambda x: rearrange(x, 'b s h d -> b h s d'), (q, k, v))
+        if attn_mask is not None:
+            attn_mask = attn_mask.to(q.device)
+        x = torch.nn.functional.scaled_dot_product_attention(
+            q, k, v, attn_mask=attn_mask, dropout_p=drop_rate, is_causal=causal
+        )
+        x = rearrange(x, 'b h s d -> b s h d')
+        return x        
+
+
+class RoPE1D:
+    def __init__(self, freq=1e4, F0=1.0, scaling_factor=1.0):
+        self.base = freq
+        self.F0 = F0
+        self.scaling_factor = scaling_factor
+        self.cache = {}
+
+    def get_cos_sin(self, D, seq_len, device, dtype):
+        if (D, seq_len, device, dtype) not in self.cache:
+            inv_freq = 1.0 / (self.base ** (torch.arange(0, D, 2).float().to(device) / D))
+            t = torch.arange(seq_len, device=device, dtype=inv_freq.dtype)
+            freqs = torch.einsum("i,j->ij", t, inv_freq).to(dtype)
+            freqs = torch.cat((freqs, freqs), dim=-1)
+            cos = freqs.cos()  # (Seq, Dim)
+            sin = freqs.sin()
+            self.cache[D, seq_len, device, dtype] = (cos, sin)
+        return self.cache[D, seq_len, device, dtype]
+
+    @staticmethod
+    def rotate_half(x):
+        x1, x2 = x[..., : x.shape[-1] // 2], x[..., x.shape[-1] // 2:]
+        return torch.cat((-x2, x1), dim=-1)
+
+    def apply_rope1d(self, tokens, pos1d, cos, sin):
+        assert pos1d.ndim == 2
+        cos = torch.nn.functional.embedding(pos1d, cos)[:, :, None, :]
+        sin = torch.nn.functional.embedding(pos1d, sin)[:, :, None, :]
+        return (tokens * cos) + (self.rotate_half(tokens) * sin)
+
+    def __call__(self, tokens, positions):
+        """
+        input:
+            * tokens: batch_size x ntokens x nheads x dim
+            * positions: batch_size x ntokens (t position of each token)
+        output:
+            * tokens after applying RoPE2D (batch_size x ntokens x nheads x dim)
+        """
+        D = tokens.size(3)
+        assert positions.ndim == 2  # Batch, Seq
+        cos, sin = self.get_cos_sin(D, int(positions.max()) + 1, tokens.device, tokens.dtype)
+        tokens = self.apply_rope1d(tokens, positions, cos, sin)
+        return tokens
+
+
+class RoPE3D(RoPE1D):
+    def __init__(self, freq=1e4, F0=1.0, scaling_factor=1.0):
+        super(RoPE3D, self).__init__(freq, F0, scaling_factor)
+        self.position_cache = {}
+
+    def get_mesh_3d(self, rope_positions, bsz):
+        f, h, w = rope_positions
+
+        if f"{f}-{h}-{w}" not in self.position_cache:
+            x = torch.arange(f, device='cpu')
+            y = torch.arange(h, device='cpu')
+            z = torch.arange(w, device='cpu')
+            self.position_cache[f"{f}-{h}-{w}"] = torch.cartesian_prod(x, y, z).view(1, f*h*w, 3).expand(bsz, -1, 3)
+        return self.position_cache[f"{f}-{h}-{w}"]
+     
+    def __call__(self, tokens, rope_positions, ch_split, parallel=False):
+        """
+        input:
+            * tokens: batch_size x ntokens x nheads x dim
+            * rope_positions: list of (f, h, w)
+        output:
+            * tokens after applying RoPE2D (batch_size x ntokens x nheads x dim)
+        """
+        assert sum(ch_split) == tokens.size(-1); 
+
+        mesh_grid = self.get_mesh_3d(rope_positions, bsz=tokens.shape[0])
+        out = []
+        for i, (D, x) in enumerate(zip(ch_split, torch.split(tokens, ch_split, dim=-1))):
+            cos, sin = self.get_cos_sin(D, int(mesh_grid.max()) + 1, tokens.device, tokens.dtype)
+            
+            if parallel:
+                pass
+            else:
+                mesh = mesh_grid[:, :, i].clone()
+            x = self.apply_rope1d(x, mesh.to(tokens.device), cos, sin)
+            out.append(x)
+            
+        tokens = torch.cat(out, dim=-1)
+        return tokens
+
+
+class SelfAttention(Attention):
+    def __init__(self, hidden_dim, head_dim, bias=False, with_rope=True, with_qk_norm=True, attn_type='torch'):
+        super().__init__()
+        self.head_dim = head_dim
+        self.n_heads = hidden_dim // head_dim
+        
+        self.wqkv = nn.Linear(hidden_dim, hidden_dim*3, bias=bias)
+        self.wo = nn.Linear(hidden_dim, hidden_dim, bias=bias)
+        
+        self.with_rope = with_rope
+        self.with_qk_norm = with_qk_norm
+        if self.with_qk_norm:
+            self.q_norm = RMSNorm(head_dim, elementwise_affine=True)
+            self.k_norm = RMSNorm(head_dim, elementwise_affine=True)
+        
+        if self.with_rope:
+            self.rope_3d = RoPE3D(freq=1e4, F0=1.0, scaling_factor=1.0)
+            self.rope_ch_split = [64, 32, 32]
+        
+        self.core_attention = self.attn_processor(attn_type=attn_type)
+        self.parallel = attn_type=='parallel'
+        
+    def apply_rope3d(self, x, fhw_positions, rope_ch_split, parallel=True):
+        x = self.rope_3d(x, fhw_positions, rope_ch_split, parallel)
+        return x
+        
+    def forward(
+        self, 
+        x,
+        cu_seqlens=None,
+        max_seqlen=None,
+        rope_positions=None,
+        attn_mask=None
+    ):
+        xqkv = self.wqkv(x) 
+        xqkv = xqkv.view(*x.shape[:-1], self.n_heads, 3*self.head_dim)
+
+        xq, xk, xv = torch.split(xqkv, [self.head_dim]*3, dim=-1)  ## seq_len, n, dim
+    
+        if self.with_qk_norm:
+            xq = self.q_norm(xq)
+            xk = self.k_norm(xk)
+    
+        if self.with_rope:
+            xq = self.apply_rope3d(xq, rope_positions, self.rope_ch_split, parallel=self.parallel)
+            xk = self.apply_rope3d(xk, rope_positions, self.rope_ch_split, parallel=self.parallel)
+            
+        output = self.core_attention(
+                    xq,
+                    xk,
+                    xv,
+                    cu_seqlens=cu_seqlens,
+                    max_seqlen=max_seqlen,
+                    attn_mask=attn_mask
+                )
+        output = rearrange(output, 'b s h d -> b s (h d)')
+        output = self.wo(output)
+        
+        return output
+    
+    
+class CrossAttention(Attention):
+    def __init__(self, hidden_dim, head_dim, bias=False, with_qk_norm=True, attn_type='torch'):
+        super().__init__()
+        self.head_dim = head_dim
+        self.n_heads = hidden_dim // head_dim
+        
+        self.wq = nn.Linear(hidden_dim, hidden_dim, bias=bias)
+        self.wkv = nn.Linear(hidden_dim, hidden_dim*2, bias=bias)
+        self.wo = nn.Linear(hidden_dim, hidden_dim, bias=bias)
+        
+        self.with_qk_norm = with_qk_norm
+        if self.with_qk_norm:
+            self.q_norm = RMSNorm(head_dim, elementwise_affine=True)
+            self.k_norm = RMSNorm(head_dim, elementwise_affine=True)
+        
+        self.core_attention = self.attn_processor(attn_type=attn_type)
+
+    def forward(
+            self, 
+            x: torch.Tensor,
+            encoder_hidden_states: torch.Tensor,
+            attn_mask=None
+        ):
+        xq = self.wq(x) 
+        xq = xq.view(*xq.shape[:-1], self.n_heads, self.head_dim)
+        
+        xkv = self.wkv(encoder_hidden_states)
+        xkv = xkv.view(*xkv.shape[:-1], self.n_heads, 2*self.head_dim)
+
+        xk, xv = torch.split(xkv, [self.head_dim]*2, dim=-1)  ## seq_len, n, dim
+    
+        if self.with_qk_norm:
+            xq = self.q_norm(xq)
+            xk = self.k_norm(xk)
+
+        output = self.core_attention(
+                    xq,
+                    xk,
+                    xv,
+                    attn_mask=attn_mask
+                )
+        
+        output = rearrange(output, 'b s h d -> b s (h d)')
+        output = self.wo(output)
+        
+        return output
+
+    
+class GELU(nn.Module):
+    r"""
+    GELU activation function with tanh approximation support with `approximate="tanh"`.
+
+    Parameters:
+        dim_in (`int`): The number of channels in the input.
+        dim_out (`int`): The number of channels in the output.
+        approximate (`str`, *optional*, defaults to `"none"`): If `"tanh"`, use tanh approximation.
+        bias (`bool`, defaults to True): Whether to use a bias in the linear layer.
+    """
+
+    def __init__(self, dim_in: int, dim_out: int, approximate: str = "none", bias: bool = True):
+        super().__init__()
+        self.proj = nn.Linear(dim_in, dim_out, bias=bias)
+        self.approximate = approximate
+
+    def gelu(self, gate: torch.Tensor) -> torch.Tensor:
+        return torch.nn.functional.gelu(gate, approximate=self.approximate)
+
+    def forward(self, hidden_states):
+        hidden_states = self.proj(hidden_states)
+        hidden_states = self.gelu(hidden_states)
+        return hidden_states
+    
+    
+class FeedForward(nn.Module):
+    def __init__(
+        self, 
+        dim: int,
+        inner_dim: Optional[int] = None,
+        dim_out: Optional[int] = None,
+        mult: int = 4,
+        bias: bool = False,
+    ):
+        super().__init__()
+        inner_dim = dim*mult if inner_dim is None else inner_dim
+        dim_out = dim if dim_out is None else dim_out
+        self.net = nn.ModuleList([
+            GELU(dim, inner_dim, approximate="tanh", bias=bias),
+            nn.Identity(),
+            nn.Linear(inner_dim, dim_out, bias=bias)
+        ])
+        
+        
+    def forward(self, hidden_states: torch.Tensor, *args, **kwargs) -> torch.Tensor:
+        for module in self.net:
+            hidden_states = module(hidden_states)
+        return hidden_states
+    
+
+def modulate(x, scale, shift):
+    x = x * (1 + scale) + shift
+    return x
+
+
+def gate(x, gate):
+    x = gate * x
+    return x
+
+
+class StepVideoTransformerBlock(nn.Module):
+    r"""
+    A basic Transformer block.
+
+    Parameters:
+        dim (`int`): The number of channels in the input and output.
+        num_attention_heads (`int`): The number of heads to use for multi-head attention.
+        attention_head_dim (`int`): The number of channels in each head.
+        dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
+        cross_attention_dim (`int`, *optional*): The size of the encoder_hidden_states vector for cross attention.
+        activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to be used in feed-forward.
+        num_embeds_ada_norm (:
+            obj: `int`, *optional*): The number of diffusion steps used during training. See `Transformer2DModel`.
+        attention_bias (:
+            obj: `bool`, *optional*, defaults to `False`): Configure if the attentions should contain a bias parameter.
+        only_cross_attention (`bool`, *optional*):
+            Whether to use only cross-attention layers. In this case two cross attention layers are used.
+        double_self_attention (`bool`, *optional*):
+            Whether to use two self-attention layers. In this case no cross attention layers are used.
+        upcast_attention (`bool`, *optional*):
+            Whether to upcast the attention computation to float32. This is useful for mixed precision training.
+        norm_elementwise_affine (`bool`, *optional*, defaults to `True`):
+            Whether to use learnable elementwise affine parameters for normalization.
+        norm_type (`str`, *optional*, defaults to `"layer_norm"`):
+            The normalization layer to use. Can be `"layer_norm"`, `"ada_norm"` or `"ada_norm_zero"`.
+        final_dropout (`bool` *optional*, defaults to False):
+            Whether to apply a final dropout after the last feed-forward layer.
+        attention_type (`str`, *optional*, defaults to `"default"`):
+            The type of attention to use. Can be `"default"` or `"gated"` or `"gated-text-image"`.
+        positional_embeddings (`str`, *optional*, defaults to `None`):
+            The type of positional embeddings to apply to.
+        num_positional_embeddings (`int`, *optional*, defaults to `None`):
+            The maximum number of positional embeddings to apply.
+    """
+
+    def __init__(
+        self,
+        dim: int,
+        attention_head_dim: int,
+        norm_eps: float = 1e-5,
+        ff_inner_dim: Optional[int] = None,
+        ff_bias: bool = False,
+        attention_type: str = 'parallel'
+    ):
+        super().__init__()
+        self.dim = dim
+        self.norm1 = nn.LayerNorm(dim, eps=norm_eps)
+        self.attn1 = SelfAttention(dim, attention_head_dim, bias=False, with_rope=True, with_qk_norm=True, attn_type=attention_type)
+        
+        self.norm2 = nn.LayerNorm(dim, eps=norm_eps)
+        self.attn2 = CrossAttention(dim, attention_head_dim, bias=False, with_qk_norm=True, attn_type='torch')
+
+        self.ff = FeedForward(dim=dim, inner_dim=ff_inner_dim, dim_out=dim, bias=ff_bias)
+
+        self.scale_shift_table = nn.Parameter(torch.randn(6, dim) /dim**0.5)
+
+    @torch.no_grad()
+    def forward(
+        self,
+        q: torch.Tensor,
+        kv: Optional[torch.Tensor] = None,
+        timestep: Optional[torch.LongTensor] =  None,
+        attn_mask = None,
+        rope_positions: list = None, 
+    ) -> torch.Tensor:
+        shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (
+            torch.clone(chunk) for chunk in (self.scale_shift_table[None].to(dtype=q.dtype, device=q.device) + timestep.reshape(-1, 6, self.dim)).chunk(6, dim=1)
+        )
+        
+        scale_shift_q = modulate(self.norm1(q), scale_msa, shift_msa)
+
+        attn_q = self.attn1(
+            scale_shift_q,
+            rope_positions=rope_positions
+        )
+
+        q = gate(attn_q, gate_msa) + q
+        
+        attn_q = self.attn2(
+                q,
+                kv,
+                attn_mask
+            )
+
+        q = attn_q + q
+
+        scale_shift_q = modulate(self.norm2(q), scale_mlp, shift_mlp)
+
+        ff_output = self.ff(scale_shift_q)
+        
+        q = gate(ff_output, gate_mlp) + q
+        
+        return q
+    
+    
+class PatchEmbed(nn.Module):
+    """2D Image to Patch Embedding"""
+
+    def __init__(
+        self,
+        patch_size=64,
+        in_channels=3,
+        embed_dim=768,
+        layer_norm=False,
+        flatten=True,
+        bias=True,
+    ):
+        super().__init__()
+
+        self.flatten = flatten
+        self.layer_norm = layer_norm
+
+        self.proj = nn.Conv2d(
+            in_channels, embed_dim, kernel_size=(patch_size, patch_size), stride=patch_size, bias=bias
+        )
+
+    def forward(self, latent):
+        latent = self.proj(latent).to(latent.dtype)   
+        if self.flatten:
+            latent = latent.flatten(2).transpose(1, 2)  # BCHW -> BNC
+        if self.layer_norm:
+            latent = self.norm(latent)
+
+        return latent
+
+
+class StepVideoModel(torch.nn.Module):
+    def __init__(
+        self,
+        num_attention_heads: int = 48,
+        attention_head_dim: int = 128,
+        in_channels: int = 64,
+        out_channels: Optional[int] = 64,
+        num_layers: int = 48,
+        dropout: float = 0.0,
+        patch_size: int = 1,
+        norm_type: str = "ada_norm_single",
+        norm_elementwise_affine: bool = False,
+        norm_eps: float = 1e-6,
+        use_additional_conditions: Optional[bool] = False,
+        caption_channels: Optional[Union[int, List, Tuple]] = [6144, 1024],
+        attention_type: Optional[str] = "torch",
+    ):
+        super().__init__()
+
+        # Set some common variables used across the board.
+        self.inner_dim = num_attention_heads * attention_head_dim
+        self.out_channels = in_channels if out_channels is None else out_channels
+
+        self.use_additional_conditions = use_additional_conditions
+
+        self.pos_embed = PatchEmbed(
+            patch_size=patch_size,
+            in_channels=in_channels,
+            embed_dim=self.inner_dim,
+        )
+
+        self.transformer_blocks = nn.ModuleList(
+            [
+                StepVideoTransformerBlock(
+                    dim=self.inner_dim,
+                    attention_head_dim=attention_head_dim,
+                    attention_type=attention_type
+                )
+                for _ in range(num_layers)
+            ]
+        )
+
+        # 3. Output blocks.
+        self.norm_out = nn.LayerNorm(self.inner_dim, eps=norm_eps, elementwise_affine=norm_elementwise_affine)
+        self.scale_shift_table = nn.Parameter(torch.randn(2, self.inner_dim) / self.inner_dim**0.5)
+        self.proj_out = nn.Linear(self.inner_dim, patch_size * patch_size * self.out_channels)
+        self.patch_size = patch_size
+
+        self.adaln_single = AdaLayerNormSingle(
+            self.inner_dim, use_additional_conditions=self.use_additional_conditions
+        )
+
+        if isinstance(caption_channels, int):
+            caption_channel = caption_channels
+        else:
+            caption_channel, clip_channel = caption_channels
+            self.clip_projection = nn.Linear(clip_channel, self.inner_dim) 
+
+        self.caption_norm = nn.LayerNorm(caption_channel,  eps=norm_eps, elementwise_affine=norm_elementwise_affine)
+        
+        self.caption_projection = PixArtAlphaTextProjection(
+            in_features=caption_channel, hidden_size=self.inner_dim
+        )
+        
+        self.parallel = attention_type=='parallel'
+
+    def patchfy(self, hidden_states):
+        hidden_states = rearrange(hidden_states, 'b f c h w -> (b f) c h w')
+        hidden_states = self.pos_embed(hidden_states)
+        return hidden_states
+
+    def prepare_attn_mask(self, encoder_attention_mask, encoder_hidden_states, q_seqlen):
+        kv_seqlens = encoder_attention_mask.sum(dim=1).int()
+        mask = torch.zeros([len(kv_seqlens), q_seqlen, max(kv_seqlens)], dtype=torch.bool, device=encoder_attention_mask.device)
+        encoder_hidden_states = encoder_hidden_states[:,: max(kv_seqlens)]
+        for i, kv_len in enumerate(kv_seqlens):
+            mask[i, :, :kv_len] = 1
+        return encoder_hidden_states, mask
+        
+        
+    def block_forward(
+        self,
+        hidden_states,
+        encoder_hidden_states=None,
+        timestep=None,
+        rope_positions=None,
+        attn_mask=None,
+        parallel=True
+    ):
+        for block in tqdm(self.transformer_blocks, desc="Transformer blocks"):
+            hidden_states = block(
+                hidden_states,
+                encoder_hidden_states,
+                timestep=timestep,
+                attn_mask=attn_mask,
+                rope_positions=rope_positions
+            )
+
+        return hidden_states
+        
+
+    @torch.inference_mode()
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: Optional[torch.Tensor] = None,
+        encoder_hidden_states_2: Optional[torch.Tensor] = None,
+        timestep: Optional[torch.LongTensor] = None,
+        added_cond_kwargs: Dict[str, torch.Tensor] = None,
+        encoder_attention_mask: Optional[torch.Tensor] = None,
+        fps: torch.Tensor=None,
+        return_dict: bool = False,
+    ):
+        assert hidden_states.ndim==5; "hidden_states's shape should be (bsz, f, ch, h ,w)"
+
+        bsz, frame, _, height, width = hidden_states.shape
+        height, width = height // self.patch_size, width // self.patch_size
+                
+        hidden_states = self.patchfy(hidden_states) 
+        len_frame = hidden_states.shape[1]
+                
+        if self.use_additional_conditions:
+            added_cond_kwargs = {
+                "resolution": torch.tensor([(height, width)]*bsz, device=hidden_states.device, dtype=hidden_states.dtype),
+                "nframe": torch.tensor([frame]*bsz, device=hidden_states.device, dtype=hidden_states.dtype),
+                "fps": fps
+            }    
+        else:
+            added_cond_kwargs = {}
+        
+        timestep, embedded_timestep = self.adaln_single(
+            timestep, added_cond_kwargs=added_cond_kwargs
+        )
+
+        encoder_hidden_states = self.caption_projection(self.caption_norm(encoder_hidden_states))
+        
+        if encoder_hidden_states_2 is not None and hasattr(self, 'clip_projection'):
+            clip_embedding = self.clip_projection(encoder_hidden_states_2)
+            encoder_hidden_states = torch.cat([clip_embedding, encoder_hidden_states], dim=1)
+
+        hidden_states = rearrange(hidden_states, '(b f) l d->  b (f l) d', b=bsz, f=frame, l=len_frame).contiguous()
+        encoder_hidden_states, attn_mask = self.prepare_attn_mask(encoder_attention_mask, encoder_hidden_states, q_seqlen=frame*len_frame)
+        
+        hidden_states = self.block_forward(
+            hidden_states,
+            encoder_hidden_states,
+            timestep=timestep,
+            rope_positions=[frame, height, width],
+            attn_mask=attn_mask,
+            parallel=self.parallel
+        )
+        
+        hidden_states = rearrange(hidden_states, 'b (f l) d -> (b f) l d', b=bsz, f=frame, l=len_frame)
+        
+        embedded_timestep = repeat(embedded_timestep, 'b d -> (b f) d', f=frame).contiguous()
+        
+        shift, scale = (self.scale_shift_table[None].to(dtype=embedded_timestep.dtype, device=embedded_timestep.device) + embedded_timestep[:, None]).chunk(2, dim=1)
+        hidden_states = self.norm_out(hidden_states)
+        # Modulation
+        hidden_states = hidden_states * (1 + scale) + shift
+        hidden_states = self.proj_out(hidden_states)
+        
+        # unpatchify
+        hidden_states = hidden_states.reshape(
+            shape=(-1, height, width, self.patch_size, self.patch_size, self.out_channels)
+        )
+        
+        hidden_states = rearrange(hidden_states, 'n h w p q c -> n c h p w q')
+        output = hidden_states.reshape(
+            shape=(-1, self.out_channels, height * self.patch_size, width * self.patch_size)
+        )
+
+        output = rearrange(output, '(b f) c h w -> b f c h w', f=frame)
+
+        if return_dict:
+            return {'x': output}
+        return output
+    
+    @staticmethod
+    def state_dict_converter():
+        return StepVideoDiTStateDictConverter()
+
+
+class StepVideoDiTStateDictConverter:
+    def __init__(self):
+        super().__init__()
+
+    def from_diffusers(self, state_dict):
+        return state_dict
+    
+    def from_civitai(self, state_dict):
+        return state_dict
+
+    
+    
\ No newline at end of file
diff --git a/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/stepvideo_text_encoder.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/stepvideo_text_encoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..598825a9402ea15183c9ff1488943f3bb4e5a548
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/stepvideo_text_encoder.py
@@ -0,0 +1,553 @@
+# Copyright 2025 StepFun Inc. All Rights Reserved.
+# 
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+# ==============================================================================
+import os
+from typing import Optional
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from .stepvideo_dit import RMSNorm
+from safetensors.torch import load_file
+from transformers.modeling_utils import PretrainedConfig, PreTrainedModel
+from einops import rearrange
+import json
+from typing import List
+from functools import wraps
+import warnings
+
+
+
+class EmptyInitOnDevice(torch.overrides.TorchFunctionMode):
+    def __init__(self, device=None):
+        self.device = device
+
+    def __torch_function__(self, func, types, args=(), kwargs=None):
+        kwargs = kwargs or {}
+        if getattr(func, '__module__', None) == 'torch.nn.init':
+            if 'tensor' in kwargs:
+                return kwargs['tensor']
+            else:
+                return args[0]
+        if self.device is not None and func in torch.utils._device._device_constructors() and kwargs.get('device') is None:
+            kwargs['device'] = self.device
+        return func(*args, **kwargs)
+    
+
+def with_empty_init(func):
+    @wraps(func)
+    def wrapper(*args, **kwargs):
+        with EmptyInitOnDevice('cpu'):
+            return func(*args, **kwargs)
+    return wrapper
+
+
+
+class LLaMaEmbedding(nn.Module):
+    """Language model embeddings.
+
+    Arguments:
+        hidden_size: hidden size
+        vocab_size: vocabulary size
+        max_sequence_length: maximum size of sequence. This
+                             is used for positional embedding
+        embedding_dropout_prob: dropout probability for embeddings
+        init_method: weight initialization method
+        num_tokentypes: size of the token-type embeddings. 0 value
+                        will ignore this embedding
+    """
+
+    def __init__(self,
+                 cfg,
+                 ):
+        super().__init__()
+        self.hidden_size = cfg.hidden_size
+        self.params_dtype = cfg.params_dtype
+        self.fp32_residual_connection = cfg.fp32_residual_connection 
+        self.embedding_weights_in_fp32 = cfg.embedding_weights_in_fp32
+        self.word_embeddings = torch.nn.Embedding(
+            cfg.padded_vocab_size, self.hidden_size,
+        )
+        self.embedding_dropout = torch.nn.Dropout(cfg.hidden_dropout)
+
+    def forward(self, input_ids):
+        # Embeddings.
+        if self.embedding_weights_in_fp32:
+            self.word_embeddings = self.word_embeddings.to(torch.float32)
+        embeddings = self.word_embeddings(input_ids)
+        if self.embedding_weights_in_fp32:
+            embeddings = embeddings.to(self.params_dtype)
+            self.word_embeddings = self.word_embeddings.to(self.params_dtype)
+
+        # Data format change to avoid explicit transposes : [b s h] --> [s b h].
+        embeddings = embeddings.transpose(0, 1).contiguous()
+
+        # If the input flag for fp32 residual connection is set, convert for float.
+        if self.fp32_residual_connection:
+            embeddings = embeddings.float()
+
+        # Dropout.
+        embeddings = self.embedding_dropout(embeddings)
+
+        return embeddings
+
+
+
+class StepChatTokenizer:
+    """Step Chat Tokenizer"""
+
+    def __init__(
+        self, model_file, name="StepChatTokenizer",
+        bot_token="<|BOT|>",  # Begin of Turn
+        eot_token="<|EOT|>",  # End of Turn
+        call_start_token="<|CALL_START|>",      # Call Start
+        call_end_token="<|CALL_END|>",          # Call End
+        think_start_token="<|THINK_START|>",    # Think Start
+        think_end_token="<|THINK_END|>",        # Think End
+        mask_start_token="<|MASK_1e69f|>",      # Mask start
+        mask_end_token="<|UNMASK_1e69f|>",      # Mask end
+    ):
+        import sentencepiece
+
+        self._tokenizer = sentencepiece.SentencePieceProcessor(model_file=model_file)
+
+        self._vocab = {}
+        self._inv_vocab = {}
+
+        self._special_tokens = {}
+        self._inv_special_tokens = {}
+
+        self._t5_tokens = []
+
+        for idx in range(self._tokenizer.get_piece_size()):
+            text = self._tokenizer.id_to_piece(idx)
+            self._inv_vocab[idx] = text
+            self._vocab[text] = idx
+
+            if self._tokenizer.is_control(idx) or self._tokenizer.is_unknown(idx):
+                self._special_tokens[text] = idx
+                self._inv_special_tokens[idx] = text
+
+        self._unk_id = self._tokenizer.unk_id()
+        self._bos_id = self._tokenizer.bos_id()
+        self._eos_id = self._tokenizer.eos_id()
+
+        for token in [
+            bot_token, eot_token, call_start_token, call_end_token,
+            think_start_token, think_end_token
+        ]:
+            assert token in self._vocab, f"Token '{token}' not found in tokenizer"
+            assert token in self._special_tokens, f"Token '{token}' is not a special token"
+
+        for token in [mask_start_token, mask_end_token]:
+            assert token in self._vocab, f"Token '{token}' not found in tokenizer"
+
+        self._bot_id = self._tokenizer.piece_to_id(bot_token)
+        self._eot_id = self._tokenizer.piece_to_id(eot_token)
+        self._call_start_id = self._tokenizer.piece_to_id(call_start_token)
+        self._call_end_id = self._tokenizer.piece_to_id(call_end_token)
+        self._think_start_id = self._tokenizer.piece_to_id(think_start_token)
+        self._think_end_id = self._tokenizer.piece_to_id(think_end_token)
+        self._mask_start_id = self._tokenizer.piece_to_id(mask_start_token)
+        self._mask_end_id = self._tokenizer.piece_to_id(mask_end_token)
+
+        self._underline_id = self._tokenizer.piece_to_id("\u2581")
+        
+    @property
+    def vocab(self):
+        return self._vocab
+
+    @property
+    def inv_vocab(self):
+        return self._inv_vocab
+
+    @property
+    def vocab_size(self):
+        return self._tokenizer.vocab_size()
+
+    def tokenize(self, text: str) -> List[int]:
+        return self._tokenizer.encode_as_ids(text)
+
+    def detokenize(self, token_ids: List[int]) -> str:
+        return self._tokenizer.decode_ids(token_ids)
+
+    
+class Tokens:
+    def __init__(self, input_ids, cu_input_ids, attention_mask, cu_seqlens, max_seq_len) -> None:
+        self.input_ids = input_ids
+        self.attention_mask = attention_mask
+        self.cu_input_ids = cu_input_ids
+        self.cu_seqlens = cu_seqlens
+        self.max_seq_len = max_seq_len
+    def to(self, device):
+        self.input_ids = self.input_ids.to(device)
+        self.attention_mask = self.attention_mask.to(device)
+        self.cu_input_ids = self.cu_input_ids.to(device)
+        self.cu_seqlens = self.cu_seqlens.to(device)
+        return self
+    
+class Wrapped_StepChatTokenizer(StepChatTokenizer):
+    def __call__(self, text, max_length=320, padding="max_length", truncation=True, return_tensors="pt"):
+        # [bos, ..., eos, pad, pad, ..., pad]
+        self.BOS = 1
+        self.EOS = 2
+        self.PAD = 2
+        out_tokens = []
+        attn_mask = []
+        if len(text) == 0:
+            part_tokens = [self.BOS] + [self.EOS]
+            valid_size = len(part_tokens)
+            if len(part_tokens) < max_length:
+                part_tokens += [self.PAD] * (max_length - valid_size)
+            out_tokens.append(part_tokens)
+            attn_mask.append([1]*valid_size+[0]*(max_length-valid_size))
+        else:
+            for part in text:
+                part_tokens = self.tokenize(part)
+                part_tokens = part_tokens[:(max_length - 2)] # leave 2 space for bos and eos
+                part_tokens = [self.BOS] + part_tokens + [self.EOS]
+                valid_size = len(part_tokens)
+                if len(part_tokens) < max_length:
+                    part_tokens += [self.PAD] * (max_length - valid_size)
+                out_tokens.append(part_tokens)
+                attn_mask.append([1]*valid_size+[0]*(max_length-valid_size))
+
+        out_tokens = torch.tensor(out_tokens, dtype=torch.long)
+        attn_mask = torch.tensor(attn_mask, dtype=torch.long)
+
+        # padding y based on tp size
+        padded_len = 0
+        padded_flag = True if padded_len > 0 else False
+        if padded_flag:
+            pad_tokens = torch.tensor([[self.PAD] * max_length], device=out_tokens.device)
+            pad_attn_mask = torch.tensor([[1]*padded_len+[0]*(max_length-padded_len)], device=attn_mask.device)
+            out_tokens = torch.cat([out_tokens, pad_tokens], dim=0)
+            attn_mask = torch.cat([attn_mask, pad_attn_mask], dim=0)
+        
+        # cu_seqlens
+        cu_out_tokens = out_tokens.masked_select(attn_mask != 0).unsqueeze(0)
+        seqlen = attn_mask.sum(dim=1).tolist()
+        cu_seqlens = torch.cumsum(torch.tensor([0]+seqlen), 0).to(device=out_tokens.device,dtype=torch.int32)
+        max_seq_len = max(seqlen)
+        return Tokens(out_tokens, cu_out_tokens, attn_mask, cu_seqlens, max_seq_len)
+
+
+
+def flash_attn_func(q, k, v, dropout_p=0.0, softmax_scale=None, causal=True,
+                    return_attn_probs=False, tp_group_rank=0, tp_group_size=1):
+    softmax_scale = q.size(-1) ** (-0.5) if softmax_scale is None else softmax_scale
+    if hasattr(torch.ops.Optimus, "fwd"):
+        results = torch.ops.Optimus.fwd(q, k, v, None, dropout_p, softmax_scale, causal, return_attn_probs, None, tp_group_rank, tp_group_size)[0]
+    else:
+        warnings.warn("Cannot load `torch.ops.Optimus.fwd`. Using `torch.nn.functional.scaled_dot_product_attention` instead.")
+        results = torch.nn.functional.scaled_dot_product_attention(q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2), is_causal=True, scale=softmax_scale).transpose(1, 2)
+    return results
+
+
+class FlashSelfAttention(torch.nn.Module):
+    def __init__(
+        self,
+        attention_dropout=0.0,
+    ):
+        super().__init__()
+        self.dropout_p = attention_dropout
+
+
+    def forward(self, q, k, v, cu_seqlens=None, max_seq_len=None):
+        if cu_seqlens is None:
+            output = flash_attn_func(q, k, v, dropout_p=self.dropout_p)
+        else:
+            raise ValueError('cu_seqlens is not supported!')
+
+        return output
+
+
+    
+def safediv(n, d):
+    q, r = divmod(n, d)
+    assert r == 0
+    return q
+
+
+class MultiQueryAttention(nn.Module):
+    def __init__(self, cfg, layer_id=None):
+        super().__init__()
+
+        self.head_dim = cfg.hidden_size // cfg.num_attention_heads
+        self.max_seq_len = cfg.seq_length
+        self.use_flash_attention = cfg.use_flash_attn
+        assert self.use_flash_attention, 'FlashAttention is required!'
+
+        self.n_groups = cfg.num_attention_groups
+        self.tp_size = 1
+        self.n_local_heads = cfg.num_attention_heads
+        self.n_local_groups = self.n_groups
+
+        self.wqkv = nn.Linear(
+            cfg.hidden_size,
+            cfg.hidden_size + self.head_dim * 2 * self.n_groups,
+            bias=False,
+        )
+        self.wo = nn.Linear(
+            cfg.hidden_size,
+            cfg.hidden_size,
+            bias=False,
+        )
+
+        assert self.use_flash_attention, 'non-Flash attention not supported yet.'
+        self.core_attention = FlashSelfAttention(attention_dropout=cfg.attention_dropout)
+        
+        self.layer_id = layer_id
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        mask: Optional[torch.Tensor],
+        cu_seqlens: Optional[torch.Tensor],
+        max_seq_len: Optional[torch.Tensor],
+    ):
+        seqlen, bsz, dim = x.shape
+        xqkv = self.wqkv(x)
+
+        xq, xkv = torch.split(
+            xqkv,
+            (dim // self.tp_size,
+             self.head_dim*2*self.n_groups // self.tp_size
+            ),
+            dim=-1,
+        )
+
+        # gather on 1st dimension
+        xq = xq.view(seqlen, bsz, self.n_local_heads, self.head_dim)
+        xkv = xkv.view(seqlen, bsz, self.n_local_groups, 2 * self.head_dim)
+        xk, xv = xkv.chunk(2, -1)
+
+        # rotary embedding + flash attn
+        xq = rearrange(xq, "s b h d -> b s h d")
+        xk = rearrange(xk, "s b h d -> b s h d")
+        xv = rearrange(xv, "s b h d -> b s h d")
+
+        q_per_kv = self.n_local_heads // self.n_local_groups
+        if q_per_kv > 1:
+            b, s, h, d = xk.size()
+            if h == 1:
+                xk = xk.expand(b, s, q_per_kv, d)
+                xv = xv.expand(b, s, q_per_kv, d)
+            else:
+                ''' To cover the cases where h > 1, we have
+                    the following implementation, which is equivalent to:
+                        xk = xk.repeat_interleave(q_per_kv, dim=-2)
+                        xv = xv.repeat_interleave(q_per_kv, dim=-2)
+                    but can avoid calling aten::item() that involves cpu.
+                '''
+                idx = torch.arange(q_per_kv * h, device=xk.device).reshape(q_per_kv, -1).permute(1, 0).flatten()
+                xk = torch.index_select(xk.repeat(1, 1, q_per_kv, 1), 2, idx).contiguous()
+                xv = torch.index_select(xv.repeat(1, 1, q_per_kv, 1), 2, idx).contiguous()
+
+        if self.use_flash_attention:
+            output = self.core_attention(xq, xk, xv,
+                                      cu_seqlens=cu_seqlens,
+                                      max_seq_len=max_seq_len)
+            # reduce-scatter only support first dimension now
+            output = rearrange(output, "b s h d -> s b (h d)").contiguous()
+        else:
+            xq, xk, xv = [
+                rearrange(x, "b s ... -> s b ...").contiguous()
+                for x in (xq, xk, xv)
+            ]
+            output = self.core_attention(xq, xk, xv, mask)
+        output = self.wo(output)
+        return output
+
+
+
+class FeedForward(nn.Module):
+    def __init__(
+        self,
+        cfg,
+        dim: int,
+        hidden_dim: int,
+        layer_id: int,
+        multiple_of: int=256,
+    ):
+        super().__init__()
+
+        hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of)
+        def swiglu(x):
+            x = torch.chunk(x, 2, dim=-1)
+            return F.silu(x[0]) * x[1]
+        self.swiglu = swiglu
+            
+        self.w1 = nn.Linear(
+            dim,
+            2 * hidden_dim,
+            bias=False,
+        )
+        self.w2 = nn.Linear(
+            hidden_dim,
+            dim,
+            bias=False,
+        )
+
+    def forward(self, x):
+        x = self.swiglu(self.w1(x))
+        output = self.w2(x)
+        return output
+
+
+
+class TransformerBlock(nn.Module):
+    def __init__(
+        self, cfg, layer_id: int
+    ):
+        super().__init__()
+
+        self.n_heads = cfg.num_attention_heads
+        self.dim = cfg.hidden_size
+        self.head_dim = cfg.hidden_size // cfg.num_attention_heads
+        self.attention = MultiQueryAttention(
+            cfg,
+            layer_id=layer_id,
+        )
+
+        self.feed_forward = FeedForward(
+            cfg,
+            dim=cfg.hidden_size,
+            hidden_dim=cfg.ffn_hidden_size,
+            layer_id=layer_id,
+        )
+        self.layer_id = layer_id
+        self.attention_norm = RMSNorm(
+            cfg.hidden_size,
+            eps=cfg.layernorm_epsilon,
+        )
+        self.ffn_norm = RMSNorm(
+            cfg.hidden_size,
+            eps=cfg.layernorm_epsilon,
+        )
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        mask: Optional[torch.Tensor],
+        cu_seqlens: Optional[torch.Tensor],
+        max_seq_len: Optional[torch.Tensor],
+    ):
+        residual = self.attention.forward(
+            self.attention_norm(x), mask,
+            cu_seqlens, max_seq_len
+        )
+        h = x + residual
+        ffn_res = self.feed_forward.forward(self.ffn_norm(h))
+        out = h + ffn_res
+        return out
+
+
+class Transformer(nn.Module):
+    def __init__(
+        self,
+        config,
+        max_seq_size=8192,
+    ):
+        super().__init__()
+        self.num_layers = config.num_layers
+        self.layers = self._build_layers(config)
+
+    def _build_layers(self, config):
+        layers = torch.nn.ModuleList()
+        for layer_id in range(self.num_layers):
+            layers.append(
+                TransformerBlock(
+                    config,
+                    layer_id=layer_id + 1 ,
+                )
+            )
+        return layers
+
+    def forward(
+        self,
+        hidden_states,
+        attention_mask,
+        cu_seqlens=None,
+        max_seq_len=None,
+    ):
+
+        if max_seq_len is not None and not isinstance(max_seq_len, torch.Tensor):
+            max_seq_len = torch.tensor(max_seq_len, dtype=torch.int32, device="cpu")
+
+        for lid, layer in enumerate(self.layers):
+            hidden_states = layer(
+                                    hidden_states,
+                                    attention_mask,
+                                    cu_seqlens,
+                                    max_seq_len,
+                                )
+        return hidden_states
+
+
+class Step1Model(PreTrainedModel):
+    config_class=PretrainedConfig
+    @with_empty_init
+    def __init__(
+        self,
+        config,
+    ):
+        super().__init__(config)
+        self.tok_embeddings = LLaMaEmbedding(config)
+        self.transformer = Transformer(config)
+
+    def forward(
+        self,
+        input_ids=None,
+        attention_mask=None,
+    ):
+
+        hidden_states = self.tok_embeddings(input_ids)
+
+        hidden_states = self.transformer(
+            hidden_states,
+            attention_mask,
+        )
+        return hidden_states
+    
+    
+
+class STEP1TextEncoder(torch.nn.Module):
+    def __init__(self, model_dir, max_length=320):
+        super(STEP1TextEncoder, self).__init__()
+        self.max_length = max_length
+        self.text_tokenizer = Wrapped_StepChatTokenizer(os.path.join(model_dir, 'step1_chat_tokenizer.model'))
+        text_encoder = Step1Model.from_pretrained(model_dir)
+        self.text_encoder = text_encoder.eval().to(torch.bfloat16)
+
+    @staticmethod
+    def from_pretrained(path, torch_dtype=torch.bfloat16):
+        model = STEP1TextEncoder(path).to(torch_dtype)
+        return model
+        
+    @torch.no_grad
+    def forward(self, prompts, with_mask=True, max_length=None, device="cuda"):
+        self.device = device
+        with torch.no_grad(), torch.amp.autocast(dtype=torch.bfloat16, device_type=device):
+            if type(prompts) is str:
+                prompts = [prompts]
+            
+            txt_tokens = self.text_tokenizer(
+                prompts, max_length=max_length or self.max_length, padding="max_length", truncation=True, return_tensors="pt"
+            )
+            y = self.text_encoder(
+                txt_tokens.input_ids.to(self.device), 
+                attention_mask=txt_tokens.attention_mask.to(self.device) if with_mask else None
+            )
+            y_mask = txt_tokens.attention_mask
+        return y.transpose(0,1), y_mask
+
diff --git a/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/stepvideo_vae.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/stepvideo_vae.py
new file mode 100644
index 0000000000000000000000000000000000000000..db244c00de53c29f87de67956e472fabad17256b
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/stepvideo_vae.py
@@ -0,0 +1,1132 @@
+# Copyright 2025 StepFun Inc. All Rights Reserved.
+# 
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+# ==============================================================================
+import torch
+from einops import rearrange
+from torch import nn
+from torch.nn import functional as F
+from tqdm import tqdm
+from einops import repeat
+
+
+class BaseGroupNorm(nn.GroupNorm):
+    def __init__(self, num_groups, num_channels):
+        super().__init__(num_groups=num_groups, num_channels=num_channels)
+
+    def forward(self, x, zero_pad=False, **kwargs):
+        if zero_pad:
+            return base_group_norm_with_zero_pad(x, self, **kwargs)
+        else:
+            return base_group_norm(x, self, **kwargs)
+
+
+def base_group_norm(x, norm_layer, act_silu=False, channel_last=False):
+    if hasattr(base_group_norm, 'spatial') and base_group_norm.spatial:
+        assert channel_last == True
+        x_shape = x.shape
+        x = x.flatten(0, 1)
+        if channel_last:
+            # Permute to NCHW format
+            x = x.permute(0, 3, 1, 2)
+
+        out = F.group_norm(x.contiguous(), norm_layer.num_groups, norm_layer.weight, norm_layer.bias, norm_layer.eps)
+        if act_silu:
+            out = F.silu(out)
+        
+        if channel_last:
+            # Permute back to NHWC format
+            out = out.permute(0, 2, 3, 1)
+
+        out = out.view(x_shape)
+    else:
+        if channel_last:
+            # Permute to NCHW format
+            x = x.permute(0, 3, 1, 2)
+        out = F.group_norm(x.contiguous(), norm_layer.num_groups, norm_layer.weight, norm_layer.bias, norm_layer.eps)
+        if act_silu:
+            out = F.silu(out)
+        if channel_last:
+            # Permute back to NHWC format
+            out = out.permute(0, 2, 3, 1)
+    return out
+
+def base_conv2d(x, conv_layer, channel_last=False, residual=None):
+    if channel_last:
+        x = x.permute(0, 3, 1, 2)  # NHWC to NCHW
+    out = F.conv2d(x, conv_layer.weight, conv_layer.bias, stride=conv_layer.stride, padding=conv_layer.padding)
+    if residual is not None:
+        if channel_last:
+            residual = residual.permute(0, 3, 1, 2)  # NHWC to NCHW
+        out += residual
+    if channel_last:
+        out = out.permute(0, 2, 3, 1)  # NCHW to NHWC
+    return out
+
+def base_conv3d(x, conv_layer, channel_last=False, residual=None, only_return_output=False):
+    if only_return_output:
+        size = cal_outsize(x.shape, conv_layer.weight.shape, conv_layer.stride, conv_layer.padding)
+        return torch.empty(size, device=x.device, dtype=x.dtype)
+    if channel_last:
+        x = x.permute(0, 4, 1, 2, 3)  # NDHWC to NCDHW
+    out = F.conv3d(x, conv_layer.weight, conv_layer.bias, stride=conv_layer.stride, padding=conv_layer.padding)
+    if residual is not None:
+        if channel_last:
+            residual = residual.permute(0, 4, 1, 2, 3)  # NDHWC to NCDHW
+        out += residual
+    if channel_last:
+        out = out.permute(0, 2, 3, 4, 1)  # NCDHW to NDHWC
+    return out
+
+
+def cal_outsize(input_sizes, kernel_sizes, stride, padding):
+    stride_d, stride_h, stride_w = stride
+    padding_d, padding_h, padding_w = padding 
+    dilation_d, dilation_h, dilation_w = 1, 1, 1
+
+    in_d = input_sizes[1]
+    in_h = input_sizes[2]
+    in_w = input_sizes[3]
+    in_channel = input_sizes[4]
+
+
+    kernel_d = kernel_sizes[2]
+    kernel_h = kernel_sizes[3]
+    kernel_w = kernel_sizes[4]
+    out_channels = kernel_sizes[0]
+
+    out_d = calc_out_(in_d, padding_d, dilation_d, kernel_d, stride_d)
+    out_h = calc_out_(in_h, padding_h, dilation_h, kernel_h, stride_h)
+    out_w = calc_out_(in_w, padding_w, dilation_w, kernel_w, stride_w)
+    size = [input_sizes[0], out_d, out_h, out_w, out_channels]
+    return size
+
+
+
+
+def calc_out_(in_size, padding, dilation, kernel, stride):
+    return (in_size + 2 * padding - dilation * (kernel - 1) - 1) // stride + 1
+
+
+
+def base_conv3d_channel_last(x, conv_layer, residual=None):
+    in_numel = x.numel()
+    out_numel = int(x.numel() * conv_layer.out_channels / conv_layer.in_channels)
+    if (in_numel >= 2**30) or (out_numel >= 2**30):
+        assert conv_layer.stride[0] == 1, "time split asks time stride = 1"
+
+        B,T,H,W,C = x.shape
+        K = conv_layer.kernel_size[0]
+
+        chunks = 4
+        chunk_size = T // chunks
+
+        if residual is None:
+            out_nhwc = base_conv3d(x, conv_layer, channel_last=True, residual=residual, only_return_output=True)
+        else:
+            out_nhwc = residual
+
+        assert B == 1
+        outs = []
+        for i in range(chunks):
+            if i == chunks-1:
+                xi = x[:1,chunk_size*i:]
+                out_nhwci = out_nhwc[:1,chunk_size*i:]
+            else:
+                xi = x[:1,chunk_size*i:chunk_size*(i+1)+K-1]
+                out_nhwci = out_nhwc[:1,chunk_size*i:chunk_size*(i+1)]
+            if residual is not None:
+                if i == chunks-1:
+                    ri = residual[:1,chunk_size*i:]
+                else:
+                    ri = residual[:1,chunk_size*i:chunk_size*(i+1)]
+            else:
+                ri = None
+            out_nhwci.copy_(base_conv3d(xi, conv_layer, channel_last=True, residual=ri))
+    else:
+        out_nhwc = base_conv3d(x, conv_layer, channel_last=True, residual=residual)
+    return out_nhwc
+
+
+
+class Upsample2D(nn.Module):
+    def __init__(self,
+                 channels,
+                 use_conv=False,
+                 use_conv_transpose=False,
+                 out_channels=None):
+        super().__init__()
+        self.channels = channels
+        self.out_channels = out_channels or channels
+        self.use_conv = use_conv
+        self.use_conv_transpose = use_conv_transpose
+
+        if use_conv:
+            self.conv = nn.Conv2d(self.channels, self.out_channels, 3, padding=1)
+        else:
+            assert "Not Supported"
+            self.conv = nn.ConvTranspose2d(channels, self.out_channels, 4, 2, 1)
+
+    def forward(self, x, output_size=None):
+        assert x.shape[-1] == self.channels
+
+        if self.use_conv_transpose:
+            return self.conv(x)
+
+        if output_size is None:
+            x = F.interpolate(
+                x.permute(0,3,1,2).to(memory_format=torch.channels_last),
+                scale_factor=2.0, mode='nearest').permute(0,2,3,1).contiguous()
+        else:
+            x = F.interpolate(
+                x.permute(0,3,1,2).to(memory_format=torch.channels_last),
+                size=output_size, mode='nearest').permute(0,2,3,1).contiguous()
+
+        # x = self.conv(x)
+        x = base_conv2d(x, self.conv, channel_last=True)
+        return x
+
+
+class Downsample2D(nn.Module):
+    def __init__(self, channels, use_conv=False, out_channels=None, padding=1):
+        super().__init__()
+        self.channels = channels
+        self.out_channels = out_channels or channels
+        self.use_conv = use_conv
+        self.padding = padding
+        stride = 2
+
+        if use_conv:
+            self.conv = nn.Conv2d(self.channels, self.out_channels, 3, stride=stride, padding=padding)
+        else:
+            assert self.channels == self.out_channels
+            self.conv = nn.AvgPool2d(kernel_size=stride, stride=stride)
+
+    def forward(self, x):
+        assert x.shape[-1] == self.channels
+        if self.use_conv and self.padding == 0:
+            pad = (0, 0, 0, 1, 0, 1)
+            x = F.pad(x, pad, mode="constant", value=0)
+
+        assert x.shape[-1] == self.channels
+        # x = self.conv(x)
+        x = base_conv2d(x, self.conv, channel_last=True)
+        return x
+
+
+
+class CausalConv(nn.Module):
+    def __init__(self,
+        chan_in,
+        chan_out,
+        kernel_size,
+        **kwargs
+    ):
+        super().__init__()
+
+        if isinstance(kernel_size, int):
+            kernel_size = kernel_size if isinstance(kernel_size, tuple) else ((kernel_size,) * 3)
+        time_kernel_size, height_kernel_size, width_kernel_size = kernel_size
+
+        self.dilation = kwargs.pop('dilation', 1)
+        self.stride = kwargs.pop('stride', 1)
+        if isinstance(self.stride, int):
+            self.stride = (self.stride, 1, 1)
+        time_pad = self.dilation * (time_kernel_size - 1) + max((1 - self.stride[0]), 0)
+        height_pad = height_kernel_size // 2
+        width_pad = width_kernel_size // 2
+        self.time_causal_padding = (width_pad, width_pad, height_pad, height_pad, time_pad, 0)
+        self.time_uncausal_padding = (width_pad, width_pad, height_pad, height_pad, 0, 0)
+
+        self.conv = nn.Conv3d(chan_in, chan_out, kernel_size, stride=self.stride, dilation=self.dilation, **kwargs)
+        self.is_first_run = True
+
+    def forward(self, x, is_init=True, residual=None):
+        x = nn.functional.pad(x,
+            self.time_causal_padding if is_init else self.time_uncausal_padding)
+
+        x = self.conv(x)
+        if residual is not None:
+            x.add_(residual)
+        return x
+
+
+class ChannelDuplicatingPixelUnshuffleUpSampleLayer3D(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        factor: int,
+    ):
+        super().__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.factor = factor
+        assert out_channels * factor**3 % in_channels == 0
+        self.repeats = out_channels * factor**3 // in_channels
+
+    def forward(self, x: torch.Tensor, is_init=True) -> torch.Tensor:
+        x = x.repeat_interleave(self.repeats, dim=1)
+        x = x.view(x.size(0), self.out_channels, self.factor, self.factor, self.factor, x.size(2), x.size(3), x.size(4))
+        x = x.permute(0, 1, 5, 2, 6, 3, 7, 4).contiguous()
+        x = x.view(x.size(0), self.out_channels, x.size(2)*self.factor, x.size(4)*self.factor, x.size(6)*self.factor)
+        x = x[:, :, self.factor - 1:, :, :]
+        return x
+
+class ConvPixelShuffleUpSampleLayer3D(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        kernel_size: int,
+        factor: int,
+    ):
+        super().__init__()
+        self.factor = factor
+        out_ratio = factor**3
+        self.conv = CausalConv(
+            in_channels,
+            out_channels * out_ratio,
+            kernel_size=kernel_size
+        )
+
+    def forward(self, x: torch.Tensor, is_init=True) -> torch.Tensor:
+        x = self.conv(x, is_init)
+        x = self.pixel_shuffle_3d(x, self.factor)
+        return x
+
+    @staticmethod
+    def pixel_shuffle_3d(x: torch.Tensor, factor: int) -> torch.Tensor:
+        batch_size, channels, depth, height, width = x.size()
+        new_channels = channels // (factor ** 3)
+        new_depth = depth * factor
+        new_height = height * factor
+        new_width = width * factor
+
+        x = x.view(batch_size, new_channels, factor, factor, factor, depth, height, width)
+        x = x.permute(0, 1, 5, 2, 6, 3, 7, 4).contiguous()
+        x = x.view(batch_size, new_channels, new_depth, new_height, new_width)
+        x = x[:, :, factor - 1:, :, :]
+        return x
+
+class ConvPixelUnshuffleDownSampleLayer3D(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        kernel_size: int,
+        factor: int,
+    ):
+        super().__init__()
+        self.factor = factor
+        out_ratio = factor**3
+        assert out_channels % out_ratio == 0
+        self.conv = CausalConv(
+            in_channels,
+            out_channels // out_ratio,
+            kernel_size=kernel_size
+        )
+
+    def forward(self, x: torch.Tensor, is_init=True) -> torch.Tensor:
+        x = self.conv(x, is_init)
+        x = self.pixel_unshuffle_3d(x, self.factor)
+        return x
+
+    @staticmethod
+    def pixel_unshuffle_3d(x: torch.Tensor, factor: int) -> torch.Tensor:
+        pad = (0, 0, 0, 0, factor-1, 0)  # (left, right, top, bottom, front, back)
+        x = F.pad(x, pad)
+        B, C, D, H, W = x.shape
+        x = x.view(B, C, D // factor, factor, H // factor, factor, W // factor, factor)
+        x = x.permute(0, 1, 3, 5, 7, 2, 4, 6).contiguous()
+        x = x.view(B, C * factor**3, D // factor, H // factor, W // factor)
+        return x
+
+class PixelUnshuffleChannelAveragingDownSampleLayer3D(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        factor: int,
+    ):
+        super().__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.factor = factor
+        assert in_channels * factor**3 % out_channels == 0
+        self.group_size = in_channels * factor**3 // out_channels
+
+    def forward(self, x: torch.Tensor, is_init=True) -> torch.Tensor:
+        pad = (0, 0, 0, 0, self.factor-1, 0)  # (left, right, top, bottom, front, back)
+        x = F.pad(x, pad)
+        B, C, D, H, W = x.shape
+        x = x.view(B, C, D // self.factor, self.factor, H // self.factor, self.factor, W // self.factor, self.factor)
+        x = x.permute(0, 1, 3, 5, 7, 2, 4, 6).contiguous()
+        x = x.view(B, C * self.factor**3, D // self.factor, H // self.factor, W // self.factor)
+        x = x.view(B, self.out_channels, self.group_size, D // self.factor, H // self.factor, W // self.factor)
+        x = x.mean(dim=2)
+        return x
+
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        factor: int,
+    ):
+        super().__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.factor = factor
+        assert in_channels * factor**3 % out_channels == 0
+        self.group_size = in_channels * factor**3 // out_channels
+
+    def forward(self, x: torch.Tensor, is_init=True) -> torch.Tensor:
+        pad = (0, 0, 0, 0, self.factor-1, 0)  # (left, right, top, bottom, front, back)
+        x = F.pad(x, pad)
+        B, C, D, H, W = x.shape
+        x = x.view(B, C, D // self.factor, self.factor, H // self.factor, self.factor, W // self.factor, self.factor)
+        x = x.permute(0, 1, 3, 5, 7, 2, 4, 6).contiguous()
+        x = x.view(B, C * self.factor**3, D // self.factor, H // self.factor, W // self.factor)
+        x = x.view(B, self.out_channels, self.group_size, D // self.factor, H // self.factor, W // self.factor)
+        x = x.mean(dim=2)
+        return x
+
+
+
+
+def base_group_norm_with_zero_pad(x, norm_layer, act_silu=True, pad_size=2):
+    out_shape = list(x.shape)
+    out_shape[1] += pad_size
+    out = torch.empty(out_shape, dtype=x.dtype, device=x.device)
+    out[:, pad_size:] = base_group_norm(x, norm_layer, act_silu=act_silu, channel_last=True)
+    out[:, :pad_size] = 0
+    return out
+
+
+class CausalConvChannelLast(CausalConv):
+    def __init__(self,
+        chan_in,
+        chan_out,
+        kernel_size,
+        **kwargs
+    ):
+        super().__init__(
+            chan_in, chan_out, kernel_size, **kwargs)
+
+        self.time_causal_padding = (0, 0) + self.time_causal_padding
+        self.time_uncausal_padding = (0, 0) + self.time_uncausal_padding
+
+    def forward(self, x, is_init=True, residual=None):
+        if self.is_first_run:
+            self.is_first_run = False
+            # self.conv.weight = nn.Parameter(self.conv.weight.permute(0,2,3,4,1).contiguous())
+
+        x = nn.functional.pad(x,
+            self.time_causal_padding if is_init else self.time_uncausal_padding)
+
+        x = base_conv3d_channel_last(x, self.conv, residual=residual)
+        return x
+
+class CausalConvAfterNorm(CausalConv):
+    def __init__(self,
+        chan_in,
+        chan_out,
+        kernel_size,
+        **kwargs
+    ):
+        super().__init__(
+            chan_in, chan_out, kernel_size, **kwargs)
+
+        if self.time_causal_padding == (1, 1, 1, 1, 2, 0):
+            self.conv = nn.Conv3d(chan_in, chan_out, kernel_size, stride=self.stride, dilation=self.dilation, padding=(0, 1, 1), **kwargs)
+        else:
+            self.conv = nn.Conv3d(chan_in, chan_out, kernel_size, stride=self.stride, dilation=self.dilation, **kwargs)
+        self.is_first_run = True
+
+    def forward(self, x, is_init=True, residual=None):
+        if self.is_first_run:
+            self.is_first_run = False
+
+        if self.time_causal_padding == (1, 1, 1, 1, 2, 0):
+            pass
+        else:
+            x = nn.functional.pad(x, self.time_causal_padding).contiguous()
+
+        x = base_conv3d_channel_last(x, self.conv, residual=residual)
+        return x
+
+class AttnBlock(nn.Module):
+    def __init__(self,
+        in_channels
+    ):
+        super().__init__()
+
+        self.norm = BaseGroupNorm(num_groups=32, num_channels=in_channels)
+        self.q        = CausalConvChannelLast(in_channels, in_channels, kernel_size=1)
+        self.k        = CausalConvChannelLast(in_channels, in_channels, kernel_size=1)
+        self.v        = CausalConvChannelLast(in_channels, in_channels, kernel_size=1)
+        self.proj_out = CausalConvChannelLast(in_channels, in_channels, kernel_size=1)
+
+    def attention(self, x, is_init=True):
+        x = self.norm(x, act_silu=False, channel_last=True)
+        q = self.q(x, is_init)
+        k = self.k(x, is_init)
+        v = self.v(x, is_init)
+
+        b, t, h, w, c = q.shape
+        q, k, v = map(lambda x: rearrange(x, "b t h w c -> b 1 (t h w) c"), (q, k, v))
+        x = nn.functional.scaled_dot_product_attention(q, k, v, is_causal=True)
+        x = rearrange(x, "b 1 (t h w) c -> b t h w c", t=t, h=h, w=w)
+
+        return x
+
+    def forward(self, x):
+        x = x.permute(0,2,3,4,1).contiguous()
+        h = self.attention(x)
+        x = self.proj_out(h, residual=x)
+        x = x.permute(0,4,1,2,3)
+        return x
+
+class Resnet3DBlock(nn.Module):
+    def __init__(self,
+        in_channels,
+        out_channels=None,
+        temb_channels=512,
+        conv_shortcut=False,
+    ):
+        super().__init__()
+
+        self.in_channels = in_channels
+        out_channels = in_channels if out_channels is None else out_channels
+        self.out_channels = out_channels
+
+        self.norm1 = BaseGroupNorm(num_groups=32, num_channels=in_channels)
+        self.conv1 = CausalConvAfterNorm(in_channels, out_channels, kernel_size=3)
+        if temb_channels > 0:
+            self.temb_proj = nn.Linear(temb_channels, out_channels)
+
+        self.norm2 = BaseGroupNorm(num_groups=32, num_channels=out_channels)
+        self.conv2 = CausalConvAfterNorm(out_channels, out_channels, kernel_size=3)
+
+        assert conv_shortcut is False
+        self.use_conv_shortcut = conv_shortcut
+        if self.in_channels != self.out_channels:
+            if self.use_conv_shortcut:
+                self.conv_shortcut = CausalConvAfterNorm(in_channels, out_channels, kernel_size=3)
+            else:
+                self.nin_shortcut = CausalConvAfterNorm(in_channels, out_channels, kernel_size=1)
+
+    def forward(self, x, temb=None, is_init=True):
+        x = x.permute(0,2,3,4,1).contiguous()
+
+        h = self.norm1(x, zero_pad=True, act_silu=True, pad_size=2)
+        h = self.conv1(h)
+        if temb is not None:
+            h = h + self.temb_proj(nn.functional.silu(temb))[:, :, None, None]
+
+        x = self.nin_shortcut(x) if self.in_channels != self.out_channels else x
+
+        h = self.norm2(h, zero_pad=True, act_silu=True, pad_size=2)
+        x = self.conv2(h, residual=x)
+
+        x = x.permute(0,4,1,2,3)
+        return x
+
+
+class Downsample3D(nn.Module):
+    def __init__(self,
+        in_channels,
+        with_conv,
+        stride
+    ):
+        super().__init__()
+
+        self.with_conv = with_conv
+        if with_conv:
+            self.conv = CausalConv(in_channels, in_channels, kernel_size=3, stride=stride)
+
+    def forward(self, x, is_init=True):
+        if self.with_conv:
+            x = self.conv(x, is_init)
+        else:
+            x = nn.functional.avg_pool3d(x, kernel_size=2, stride=2)
+        return x
+
+class VideoEncoder(nn.Module):
+    def __init__(self,
+        ch=32,
+        ch_mult=(4, 8, 16, 16),
+        num_res_blocks=2,
+        in_channels=3,
+        z_channels=16,
+        double_z=True,
+        down_sampling_layer=[1, 2],
+        resamp_with_conv=True,
+        version=1,
+    ):
+        super().__init__()
+
+        temb_ch = 0
+
+        self.num_resolutions = len(ch_mult)
+        self.num_res_blocks = num_res_blocks
+
+        # downsampling
+        self.conv_in = CausalConv(in_channels, ch, kernel_size=3)
+        self.down_sampling_layer = down_sampling_layer
+
+        in_ch_mult = (1,) + tuple(ch_mult)
+        self.down = nn.ModuleList()
+        for i_level in range(self.num_resolutions):
+            block = nn.ModuleList()
+            attn = nn.ModuleList()
+            block_in = ch * in_ch_mult[i_level]
+            block_out = ch * ch_mult[i_level]
+            for i_block in range(self.num_res_blocks):
+                block.append(
+                    Resnet3DBlock(in_channels=block_in, out_channels=block_out, temb_channels=temb_ch))
+                block_in = block_out
+            down = nn.Module()
+            down.block = block
+            down.attn = attn
+            if i_level != self.num_resolutions - 1:
+                if i_level in self.down_sampling_layer:
+                    down.downsample = Downsample3D(block_in, resamp_with_conv, stride=(2, 2, 2))
+                else:
+                    down.downsample = Downsample2D(block_in, resamp_with_conv, padding=0) #DIFF
+            self.down.append(down)
+
+        # middle
+        self.mid = nn.Module()
+        self.mid.block_1 = Resnet3DBlock(in_channels=block_in, out_channels=block_in, temb_channels=temb_ch)
+        self.mid.attn_1 = AttnBlock(block_in)
+        self.mid.block_2 = Resnet3DBlock(in_channels=block_in, out_channels=block_in, temb_channels=temb_ch)
+
+        # end
+        self.norm_out = nn.GroupNorm(num_groups=32, num_channels=block_in)
+        self.version = version
+        if version == 2:
+            channels = 4 * z_channels * 2 ** 3
+            self.conv_patchify = ConvPixelUnshuffleDownSampleLayer3D(block_in, channels, kernel_size=3, factor=2)
+            self.shortcut_pathify = PixelUnshuffleChannelAveragingDownSampleLayer3D(block_in, channels, 2)
+            self.shortcut_out = PixelUnshuffleChannelAveragingDownSampleLayer3D(channels, 2 * z_channels if double_z else z_channels, 1)
+            self.conv_out = CausalConvChannelLast(channels, 2 * z_channels if double_z else z_channels, kernel_size=3)
+        else:
+            self.conv_out = CausalConvAfterNorm(block_in, 2 * z_channels if double_z else z_channels, kernel_size=3)
+
+    @torch.inference_mode()
+    def forward(self, x, video_frame_num, is_init=True):
+        # timestep embedding
+        temb = None
+
+        t = video_frame_num
+
+        # downsampling
+        h = self.conv_in(x, is_init)
+
+        # make it real channel last, but behave like normal layout
+        h = h.permute(0,2,3,4,1).contiguous().permute(0,4,1,2,3)
+
+        for i_level in range(self.num_resolutions):
+            for i_block in range(self.num_res_blocks):
+                h = self.down[i_level].block[i_block](h, temb, is_init)
+                if len(self.down[i_level].attn) > 0:
+                    h = self.down[i_level].attn[i_block](h)
+
+            if i_level != self.num_resolutions - 1:
+                if isinstance(self.down[i_level].downsample, Downsample2D):
+                    _, _, t, _, _ = h.shape
+                    h = rearrange(h, "b c t h w -> (b t) h w c", t=t)
+                    h = self.down[i_level].downsample(h)
+                    h = rearrange(h, "(b t) h w c -> b c t h w", t=t)
+                else:
+                    h = self.down[i_level].downsample(h, is_init)
+
+        h = self.mid.block_1(h, temb, is_init)
+        h = self.mid.attn_1(h)
+        h = self.mid.block_2(h, temb, is_init)
+
+        h = h.permute(0,2,3,4,1).contiguous() # b c l h w -> b l h w c
+        if self.version == 2:
+            h = base_group_norm(h, self.norm_out, act_silu=True, channel_last=True)
+            h = h.permute(0,4,1,2,3).contiguous()
+            shortcut = self.shortcut_pathify(h, is_init)
+            h = self.conv_patchify(h, is_init)
+            h = h.add_(shortcut)
+            shortcut = self.shortcut_out(h, is_init).permute(0,2,3,4,1)
+            h = self.conv_out(h.permute(0,2,3,4,1).contiguous(), is_init)
+            h = h.add_(shortcut)
+        else:
+            h = base_group_norm_with_zero_pad(h, self.norm_out, act_silu=True, pad_size=2)
+            h = self.conv_out(h, is_init)
+        h = h.permute(0,4,1,2,3) # b l h w c -> b c l h w
+
+        h = rearrange(h, "b c t h w -> b t c h w")
+        return h
+
+
+class Res3DBlockUpsample(nn.Module):
+    def __init__(self,
+        input_filters,
+        num_filters,
+        down_sampling_stride,
+        down_sampling=False
+    ):
+        super().__init__()
+
+        self.input_filters = input_filters
+        self.num_filters = num_filters
+
+        self.act_ = nn.SiLU(inplace=True)
+
+        self.conv1 = CausalConvChannelLast(num_filters, num_filters, kernel_size=[3, 3, 3])
+        self.norm1 = BaseGroupNorm(32, num_filters)
+
+        self.conv2 = CausalConvChannelLast(num_filters, num_filters, kernel_size=[3, 3, 3])
+        self.norm2 = BaseGroupNorm(32, num_filters)
+
+        self.down_sampling = down_sampling
+        if down_sampling:
+            self.down_sampling_stride = down_sampling_stride
+        else:
+            self.down_sampling_stride = [1, 1, 1]
+
+        if num_filters != input_filters or down_sampling:
+            self.conv3 = CausalConvChannelLast(input_filters, num_filters, kernel_size=[1, 1, 1], stride=self.down_sampling_stride)
+            self.norm3 = BaseGroupNorm(32, num_filters)
+
+    def forward(self, x, is_init=False):
+        x = x.permute(0,2,3,4,1).contiguous()
+
+        residual = x
+
+        h = self.conv1(x, is_init)
+        h = self.norm1(h, act_silu=True, channel_last=True)
+
+        h = self.conv2(h, is_init)
+        h = self.norm2(h, act_silu=False, channel_last=True)
+
+        if self.down_sampling or self.num_filters != self.input_filters:
+            x = self.conv3(x, is_init)
+            x = self.norm3(x, act_silu=False, channel_last=True)
+
+        h.add_(x)
+        h = self.act_(h)
+        if residual is not None:
+            h.add_(residual)
+
+        h = h.permute(0,4,1,2,3)
+        return h
+
+class Upsample3D(nn.Module):
+    def __init__(self,
+        in_channels,
+        scale_factor=2
+    ):
+        super().__init__()
+
+        self.scale_factor = scale_factor
+        self.conv3d = Res3DBlockUpsample(input_filters=in_channels,
+                                         num_filters=in_channels,
+                                         down_sampling_stride=(1, 1, 1),
+                                         down_sampling=False)
+
+    def forward(self, x, is_init=True, is_split=True):
+        b, c, t, h, w = x.shape
+
+        # x = x.permute(0,2,3,4,1).contiguous().permute(0,4,1,2,3).to(memory_format=torch.channels_last_3d)
+        if is_split:
+            split_size = c // 8
+            x_slices = torch.split(x, split_size, dim=1)
+            x = [nn.functional.interpolate(x, scale_factor=self.scale_factor) for x in x_slices]
+            x = torch.cat(x, dim=1)
+        else:
+            x = nn.functional.interpolate(x, scale_factor=self.scale_factor)
+
+        x = self.conv3d(x, is_init)
+        return x
+
+class VideoDecoder(nn.Module):
+    def __init__(self,
+        ch=128,
+        z_channels=16,
+        out_channels=3,
+        ch_mult=(1, 2, 4, 4),
+        num_res_blocks=2,
+        temporal_up_layers=[2, 3],
+        temporal_downsample=4,
+        resamp_with_conv=True,
+        version=1,
+    ):
+        super().__init__()
+
+        temb_ch = 0
+
+        self.num_resolutions = len(ch_mult)
+        self.num_res_blocks = num_res_blocks
+        self.temporal_downsample = temporal_downsample
+
+        block_in = ch * ch_mult[self.num_resolutions - 1]
+        self.version = version
+        if version == 2:
+            channels = 4 * z_channels * 2 ** 3
+            self.conv_in = CausalConv(z_channels, channels, kernel_size=3)
+            self.shortcut_in = ChannelDuplicatingPixelUnshuffleUpSampleLayer3D(z_channels, channels, 1)
+            self.conv_unpatchify = ConvPixelShuffleUpSampleLayer3D(channels, block_in, kernel_size=3, factor=2)
+            self.shortcut_unpathify = ChannelDuplicatingPixelUnshuffleUpSampleLayer3D(channels, block_in, 2)
+        else:
+            self.conv_in = CausalConv(z_channels, block_in, kernel_size=3)
+
+        # middle
+        self.mid = nn.Module()
+        self.mid.block_1 = Resnet3DBlock(in_channels=block_in, out_channels=block_in, temb_channels=temb_ch)
+        self.mid.attn_1 = AttnBlock(block_in)
+        self.mid.block_2 = Resnet3DBlock(in_channels=block_in, out_channels=block_in, temb_channels=temb_ch)
+
+        # upsampling
+        self.up_id = len(temporal_up_layers)
+        self.video_frame_num = 1
+        self.cur_video_frame_num = self.video_frame_num // 2 ** self.up_id + 1
+        self.up = nn.ModuleList()
+        for i_level in reversed(range(self.num_resolutions)):
+            block = nn.ModuleList()
+            attn = nn.ModuleList()
+            block_out = ch * ch_mult[i_level]
+            for i_block in range(self.num_res_blocks + 1):
+                block.append(
+                    Resnet3DBlock(in_channels=block_in, out_channels=block_out, temb_channels=temb_ch))
+                block_in = block_out
+            up = nn.Module()
+            up.block = block
+            up.attn = attn
+            if i_level != 0:
+                if i_level in temporal_up_layers:
+                    up.upsample = Upsample3D(block_in)
+                    self.cur_video_frame_num = self.cur_video_frame_num * 2
+                else:
+                    up.upsample = Upsample2D(block_in, resamp_with_conv)
+            self.up.insert(0, up)  # prepend to get consistent order
+
+        # end
+        self.norm_out = nn.GroupNorm(num_groups=32, num_channels=block_in)
+        self.conv_out = CausalConvAfterNorm(block_in, out_channels, kernel_size=3)
+
+    @torch.inference_mode()
+    def forward(self, z, is_init=True):
+        z = rearrange(z, "b t c h w -> b c t h w")
+
+        h = self.conv_in(z, is_init=is_init)
+        if self.version == 2:
+            shortcut = self.shortcut_in(z, is_init=is_init)
+            h = h.add_(shortcut)
+            shortcut = self.shortcut_unpathify(h, is_init=is_init)
+            h = self.conv_unpatchify(h, is_init=is_init)
+            h = h.add_(shortcut)
+
+        temb = None
+
+        h = h.permute(0,2,3,4,1).contiguous().permute(0,4,1,2,3)
+        h = self.mid.block_1(h, temb, is_init=is_init)
+        h = self.mid.attn_1(h)
+        h = h.permute(0,2,3,4,1).contiguous().permute(0,4,1,2,3)
+        h = self.mid.block_2(h, temb, is_init=is_init)
+
+        # upsampling
+        for i_level in reversed(range(self.num_resolutions)):
+            for i_block in range(self.num_res_blocks + 1):
+                h = h.permute(0,2,3,4,1).contiguous().permute(0,4,1,2,3)
+                h = self.up[i_level].block[i_block](h, temb, is_init=is_init)
+                if len(self.up[i_level].attn) > 0:
+                    h = self.up[i_level].attn[i_block](h)
+            if i_level != 0:
+                if isinstance(self.up[i_level].upsample, Upsample2D) or (hasattr(self.up[i_level].upsample, "module") and isinstance(self.up[i_level].upsample.module, Upsample2D)):
+                    B = h.size(0)
+                    h = h.permute(0,2,3,4,1).flatten(0,1)
+                    h = self.up[i_level].upsample(h)
+                    h = h.unflatten(0, (B, -1)).permute(0,4,1,2,3)
+                else:
+                    h = self.up[i_level].upsample(h, is_init=is_init)
+
+        # end
+        h = h.permute(0,2,3,4,1) # b c l h w -> b l h w c
+        self.norm_out.to(dtype=h.dtype, device=h.device) # To be updated
+        h = base_group_norm_with_zero_pad(h, self.norm_out, act_silu=True, pad_size=2)
+        h = self.conv_out(h)
+        h = h.permute(0,4,1,2,3)
+
+        if is_init:
+            h = h[:, :, (self.temporal_downsample - 1):]
+        return h
+
+
+
+def rms_norm(input, normalized_shape, eps=1e-6):
+    dtype = input.dtype
+    input = input.to(torch.float32)
+    variance = input.pow(2).flatten(-len(normalized_shape)).mean(-1)[(...,) + (None,) * len(normalized_shape)]
+    input = input * torch.rsqrt(variance + eps)
+    return input.to(dtype)
+
+class DiagonalGaussianDistribution(object):
+    def __init__(self, parameters, deterministic=False, rms_norm_mean=False, only_return_mean=False):
+        self.parameters = parameters
+        self.mean, self.logvar = torch.chunk(parameters, 2, dim=-3) #N,[X],C,H,W
+        self.logvar = torch.clamp(self.logvar, -30.0, 20.0)
+        self.std = torch.exp(0.5 * self.logvar)
+        self.var = torch.exp(self.logvar)
+        self.deterministic = deterministic
+        if self.deterministic:
+            self.var = self.std = torch.zeros_like(
+                self.mean,
+                device=self.parameters.device,
+                dtype=self.parameters.dtype)
+        if rms_norm_mean:
+            self.mean = rms_norm(self.mean, self.mean.size()[1:])
+        self.only_return_mean = only_return_mean
+
+    def sample(self, generator=None):
+        # make sure sample is on the same device
+        # as the parameters and has same dtype
+        sample = torch.randn(
+            self.mean.shape, generator=generator, device=self.parameters.device)
+        sample = sample.to(dtype=self.parameters.dtype)
+        x = self.mean + self.std * sample
+        if self.only_return_mean:
+            return self.mean
+        else:
+            return x
+
+
+class StepVideoVAE(nn.Module):
+    def __init__(self,
+        in_channels=3,
+        out_channels=3,
+        z_channels=64,
+        num_res_blocks=2,
+        model_path=None,
+        weight_dict={},
+        world_size=1,
+        version=2,
+    ):
+        super().__init__()
+
+        self.frame_len = 17
+        self.latent_len = 3 if version == 2 else 5
+
+        base_group_norm.spatial = True if version == 2 else False
+
+        self.encoder = VideoEncoder(
+            in_channels=in_channels,
+            z_channels=z_channels,
+            num_res_blocks=num_res_blocks,
+            version=version,
+        )
+
+        self.decoder = VideoDecoder(
+            z_channels=z_channels,
+            out_channels=out_channels,
+            num_res_blocks=num_res_blocks,
+            version=version,
+        )
+
+        if model_path is not None:
+            weight_dict = self.init_from_ckpt(model_path)
+        if len(weight_dict) != 0:
+            self.load_from_dict(weight_dict)
+        self.convert_channel_last()
+
+        self.world_size = world_size
+
+    def init_from_ckpt(self, model_path):
+        from safetensors import safe_open
+        p = {}
+        with safe_open(model_path, framework="pt", device="cpu") as f:
+            for k in f.keys():
+                tensor = f.get_tensor(k)
+                if k.startswith("decoder.conv_out."):
+                    k = k.replace("decoder.conv_out.", "decoder.conv_out.conv.")
+                p[k] = tensor
+        return p
+
+    def load_from_dict(self, p):
+        self.load_state_dict(p)
+
+    def convert_channel_last(self):
+        #Conv2d NCHW->NHWC
+        pass
+
+    def naive_encode(self, x, is_init_image=True):
+        b, l, c, h, w = x.size()
+        x = rearrange(x, 'b l c h w -> b c l h w').contiguous()
+        z = self.encoder(x, l, True) # 下采样[1, 4, 8, 16, 16]
+        return z
+
+    @torch.inference_mode()
+    def encode(self, x):
+        # b (nc cf) c h w -> (b nc) cf c h w -> encode -> (b nc) cf c h w -> b (nc cf) c h w
+        chunks = list(x.split(self.frame_len, dim=1))
+        for i in range(len(chunks)):
+            chunks[i] = self.naive_encode(chunks[i], True)
+        z = torch.cat(chunks, dim=1)
+
+        posterior = DiagonalGaussianDistribution(z)
+        return posterior.sample()
+
+    def decode_naive(self, z, is_init=True):
+        z = z.to(next(self.decoder.parameters()).dtype)
+        dec = self.decoder(z, is_init)
+        return dec
+
+    @torch.inference_mode()
+    def decode_original(self, z):
+        # b (nc cf) c h w -> (b nc) cf c h w -> decode -> (b nc) c cf h w -> b (nc cf) c h w
+        chunks = list(z.split(self.latent_len, dim=1))
+
+        if self.world_size > 1:
+            chunks_total_num = len(chunks)
+            max_num_per_rank = (chunks_total_num + self.world_size - 1) // self.world_size
+            rank = torch.distributed.get_rank()
+            chunks_ = chunks[max_num_per_rank * rank : max_num_per_rank * (rank + 1)]
+            if len(chunks_) < max_num_per_rank:
+                chunks_.extend(chunks[:max_num_per_rank-len(chunks_)])
+            chunks = chunks_
+
+        for i in range(len(chunks)):
+            chunks[i] = self.decode_naive(chunks[i], True).permute(0,2,1,3,4)
+        x = torch.cat(chunks, dim=1)
+
+        if self.world_size > 1:
+            x_ = torch.empty([x.size(0), (self.world_size * max_num_per_rank) * self.frame_len, *x.shape[2:]], dtype=x.dtype, device=x.device)
+            torch.distributed.all_gather_into_tensor(x_, x)
+            x = x_[:, : chunks_total_num * self.frame_len]
+
+        x = self.mix(x)
+        return x
+
+    def mix(self, x, smooth_scale = 0.6):
+        remain_scale = smooth_scale
+        mix_scale = 1. - remain_scale
+        front = slice(self.frame_len - 1, x.size(1) - 1, self.frame_len)
+        back = slice(self.frame_len, x.size(1), self.frame_len)
+        x[:, front], x[:, back] = (
+            x[:, front] * remain_scale + x[:, back] * mix_scale,
+            x[:, back] * remain_scale + x[:, front] * mix_scale
+        )
+        return x
+    
+    def single_decode(self, hidden_states, device):
+        chunks = list(hidden_states.split(self.latent_len, dim=1))
+        for i in range(len(chunks)):
+            chunks[i] = self.decode_naive(chunks[i].to(device), True).permute(0,2,1,3,4).cpu()
+        x = torch.cat(chunks, dim=1)
+        return x
+    
+    def build_1d_mask(self, length, left_bound, right_bound, border_width):
+        x = torch.ones((length,))
+        if not left_bound:
+            x[:border_width] = (torch.arange(border_width) + 1) / border_width
+        if not right_bound:
+            x[-border_width:] = torch.flip((torch.arange(border_width) + 1) / border_width, dims=(0,))
+        return x
+    
+    def build_mask(self, data, is_bound, border_width):
+        _, _, _, H, W = data.shape
+        h = self.build_1d_mask(H, is_bound[0], is_bound[1], border_width[0])
+        w = self.build_1d_mask(W, is_bound[2], is_bound[3], border_width[1])
+
+        h = repeat(h, "H -> H W", H=H, W=W)
+        w = repeat(w, "W -> H W", H=H, W=W)
+
+        mask = torch.stack([h, w]).min(dim=0).values
+        mask = rearrange(mask, "H W -> 1 1 1 H W")
+        return mask
+    
+    def tiled_decode(self, hidden_states, device, tile_size=(34, 34), tile_stride=(16, 16)):
+        B, T, C, H, W = hidden_states.shape
+        size_h, size_w = tile_size
+        stride_h, stride_w = tile_stride
+
+        # Split tasks
+        tasks = []
+        for t in range(0, T, 3):
+            for h in range(0, H, stride_h):
+                if (h-stride_h >= 0 and h-stride_h+size_h >= H): continue
+                for w in range(0, W, stride_w):
+                    if (w-stride_w >= 0 and w-stride_w+size_w >= W): continue
+                    t_, h_, w_ = t + 3, h + size_h, w + size_w
+                    tasks.append((t, t_, h, h_, w, w_))
+
+        # Run
+        data_device = "cpu"
+        computation_device = device
+
+        weight = torch.zeros((1, 1, T//3*17, H * 16, W * 16), dtype=hidden_states.dtype, device=data_device)
+        values = torch.zeros((B, 3, T//3*17, H * 16, W * 16), dtype=hidden_states.dtype, device=data_device)
+
+        for t, t_, h, h_, w, w_ in tqdm(tasks, desc="VAE decoding"):
+            hidden_states_batch = hidden_states[:, t:t_, :, h:h_, w:w_].to(computation_device)
+            hidden_states_batch = self.decode_naive(hidden_states_batch, True).to(data_device)
+
+            mask = self.build_mask(
+                hidden_states_batch,
+                is_bound=(h==0, h_>=H, w==0, w_>=W),
+                border_width=((size_h - stride_h) * 16, (size_w - stride_w) * 16)
+            ).to(dtype=hidden_states.dtype, device=data_device)
+
+            target_t = t // 3 * 17
+            target_h = h * 16
+            target_w = w * 16
+            values[
+                :,
+                :,
+                target_t: target_t + hidden_states_batch.shape[2],
+                target_h: target_h + hidden_states_batch.shape[3],
+                target_w: target_w + hidden_states_batch.shape[4],
+            ] += hidden_states_batch * mask
+            weight[
+                :,
+                :,
+                target_t: target_t + hidden_states_batch.shape[2],
+                target_h: target_h + hidden_states_batch.shape[3],
+                target_w: target_w + hidden_states_batch.shape[4],
+            ] += mask
+        return values / weight
+    
+    def decode(self, hidden_states, device, tiled=False, tile_size=(34, 34), tile_stride=(16, 16), smooth_scale=0.6):
+        hidden_states = hidden_states.to("cpu")
+        if tiled:
+            video = self.tiled_decode(hidden_states, device, tile_size, tile_stride)
+        else:
+            video = self.single_decode(hidden_states, device)
+        video = self.mix(video, smooth_scale=smooth_scale)
+        return video
+
+    @staticmethod
+    def state_dict_converter():
+        return StepVideoVAEStateDictConverter()
+
+
+class StepVideoVAEStateDictConverter:
+    def __init__(self):
+        super().__init__()
+
+    def from_diffusers(self, state_dict):
+        return self.from_civitai(state_dict)
+    
+    def from_civitai(self, state_dict):
+        state_dict_ = {}
+        for name, param in state_dict.items():
+            if name.startswith("decoder.conv_out."):
+                name_ = name.replace("decoder.conv_out.", "decoder.conv_out.conv.")
+            else:
+                name_ = name
+            state_dict_[name_] = param
+        return state_dict_
diff --git a/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/svd_image_encoder.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/svd_image_encoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..4ee79c863ec8da0185bf0e57ac0a286495b049ed
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/svd_image_encoder.py
@@ -0,0 +1,505 @@
+import torch
+from .sd_text_encoder import CLIPEncoderLayer
+
+
+class CLIPVisionEmbeddings(torch.nn.Module):
+    def __init__(self, embed_dim=1280, image_size=224, patch_size=14, num_channels=3):
+        super().__init__()
+
+        # class_embeds (This is a fixed tensor)
+        self.class_embedding = torch.nn.Parameter(torch.randn(1, 1, embed_dim))
+
+        # position_embeds
+        self.patch_embedding = torch.nn.Conv2d(in_channels=num_channels, out_channels=embed_dim, kernel_size=patch_size, stride=patch_size, bias=False)
+
+        # position_embeds (This is a fixed tensor)
+        self.position_embeds = torch.nn.Parameter(torch.zeros(1, (image_size // patch_size) ** 2 + 1, embed_dim))
+
+    def forward(self, pixel_values):
+        batch_size = pixel_values.shape[0]
+        patch_embeds = self.patch_embedding(pixel_values)
+        patch_embeds = patch_embeds.flatten(2).transpose(1, 2)
+        class_embeds = self.class_embedding.repeat(batch_size, 1, 1)
+        embeddings = torch.cat([class_embeds, patch_embeds], dim=1) + self.position_embeds
+        return embeddings
+
+
+class SVDImageEncoder(torch.nn.Module):
+    def __init__(self, embed_dim=1280, layer_norm_eps=1e-5, num_encoder_layers=32, encoder_intermediate_size=5120, projection_dim=1024, num_heads=16, head_dim=80):
+        super().__init__()
+        self.embeddings = CLIPVisionEmbeddings(embed_dim=embed_dim)
+        self.pre_layernorm = torch.nn.LayerNorm(embed_dim, eps=layer_norm_eps)
+        self.encoders = torch.nn.ModuleList([
+            CLIPEncoderLayer(embed_dim, encoder_intermediate_size, num_heads=num_heads, head_dim=head_dim, use_quick_gelu=False)
+            for _ in range(num_encoder_layers)])
+        self.post_layernorm = torch.nn.LayerNorm(embed_dim, eps=layer_norm_eps)
+        self.visual_projection = torch.nn.Linear(embed_dim, projection_dim, bias=False)
+
+    def forward(self, pixel_values):
+        embeds = self.embeddings(pixel_values)
+        embeds = self.pre_layernorm(embeds)
+        for encoder_id, encoder in enumerate(self.encoders):
+            embeds = encoder(embeds)
+        embeds = self.post_layernorm(embeds[:, 0, :])
+        embeds = self.visual_projection(embeds)
+        return embeds
+
+    @staticmethod
+    def state_dict_converter():
+        return SVDImageEncoderStateDictConverter()
+
+
+class SVDImageEncoderStateDictConverter:
+    def __init__(self):
+        pass
+
+    def from_diffusers(self, state_dict):
+        rename_dict = {
+            "vision_model.embeddings.patch_embedding.weight": "embeddings.patch_embedding.weight",
+            "vision_model.embeddings.class_embedding": "embeddings.class_embedding",
+            "vision_model.embeddings.position_embedding.weight": "embeddings.position_embeds",
+            "vision_model.pre_layrnorm.weight": "pre_layernorm.weight",
+            "vision_model.pre_layrnorm.bias": "pre_layernorm.bias",
+            "vision_model.post_layernorm.weight": "post_layernorm.weight",
+            "vision_model.post_layernorm.bias": "post_layernorm.bias",
+            "visual_projection.weight": "visual_projection.weight"
+        }
+        attn_rename_dict = {
+            "self_attn.q_proj": "attn.to_q",
+            "self_attn.k_proj": "attn.to_k",
+            "self_attn.v_proj": "attn.to_v",
+            "self_attn.out_proj": "attn.to_out",
+            "layer_norm1": "layer_norm1",
+            "layer_norm2": "layer_norm2",
+            "mlp.fc1": "fc1",
+            "mlp.fc2": "fc2",
+        }
+        state_dict_ = {}
+        for name in state_dict:
+            if name in rename_dict:
+                param = state_dict[name]
+                if name == "vision_model.embeddings.class_embedding":
+                    param = state_dict[name].view(1, 1, -1)
+                elif name == "vision_model.embeddings.position_embedding.weight":
+                    param = state_dict[name].unsqueeze(0)
+                state_dict_[rename_dict[name]] = param
+            elif name.startswith("vision_model.encoder.layers."):
+                param = state_dict[name]
+                names = name.split(".")
+                layer_id, layer_type, tail = names[3], ".".join(names[4:-1]), names[-1]
+                name_ = ".".join(["encoders", layer_id, attn_rename_dict[layer_type], tail])
+                state_dict_[name_] = param
+        return state_dict_
+    
+    def from_civitai(self, state_dict):
+        rename_dict = {
+            "conditioner.embedders.0.open_clip.model.visual.class_embedding": "embeddings.class_embedding",
+            "conditioner.embedders.0.open_clip.model.visual.conv1.weight": "embeddings.patch_embedding.weight",
+            "conditioner.embedders.0.open_clip.model.visual.ln_post.bias": "post_layernorm.bias",
+            "conditioner.embedders.0.open_clip.model.visual.ln_post.weight": "post_layernorm.weight",
+            "conditioner.embedders.0.open_clip.model.visual.ln_pre.bias": "pre_layernorm.bias",
+            "conditioner.embedders.0.open_clip.model.visual.ln_pre.weight": "pre_layernorm.weight",
+            "conditioner.embedders.0.open_clip.model.visual.positional_embedding": "embeddings.position_embeds",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.0.attn.in_proj_bias": ['encoders.0.attn.to_q.bias', 'encoders.0.attn.to_k.bias', 'encoders.0.attn.to_v.bias'],
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.0.attn.in_proj_weight": ['encoders.0.attn.to_q.weight', 'encoders.0.attn.to_k.weight', 'encoders.0.attn.to_v.weight'],
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.0.attn.out_proj.bias": "encoders.0.attn.to_out.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.0.attn.out_proj.weight": "encoders.0.attn.to_out.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.0.ln_1.bias": "encoders.0.layer_norm1.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.0.ln_1.weight": "encoders.0.layer_norm1.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.0.ln_2.bias": "encoders.0.layer_norm2.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.0.ln_2.weight": "encoders.0.layer_norm2.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.0.mlp.c_fc.bias": "encoders.0.fc1.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.0.mlp.c_fc.weight": "encoders.0.fc1.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.0.mlp.c_proj.bias": "encoders.0.fc2.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.0.mlp.c_proj.weight": "encoders.0.fc2.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.1.attn.in_proj_bias": ['encoders.1.attn.to_q.bias', 'encoders.1.attn.to_k.bias', 'encoders.1.attn.to_v.bias'],
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.1.attn.in_proj_weight": ['encoders.1.attn.to_q.weight', 'encoders.1.attn.to_k.weight', 'encoders.1.attn.to_v.weight'],
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.1.attn.out_proj.bias": "encoders.1.attn.to_out.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.1.attn.out_proj.weight": "encoders.1.attn.to_out.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.1.ln_1.bias": "encoders.1.layer_norm1.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.1.ln_1.weight": "encoders.1.layer_norm1.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.1.ln_2.bias": "encoders.1.layer_norm2.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.1.ln_2.weight": "encoders.1.layer_norm2.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.1.mlp.c_fc.bias": "encoders.1.fc1.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.1.mlp.c_fc.weight": "encoders.1.fc1.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.1.mlp.c_proj.bias": "encoders.1.fc2.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.1.mlp.c_proj.weight": "encoders.1.fc2.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.10.attn.in_proj_bias": ['encoders.10.attn.to_q.bias', 'encoders.10.attn.to_k.bias', 'encoders.10.attn.to_v.bias'],
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.10.attn.in_proj_weight": ['encoders.10.attn.to_q.weight', 'encoders.10.attn.to_k.weight', 'encoders.10.attn.to_v.weight'],
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+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.4.attn.in_proj_weight": ['encoders.4.attn.to_q.weight', 'encoders.4.attn.to_k.weight', 'encoders.4.attn.to_v.weight'],
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.4.attn.out_proj.bias": "encoders.4.attn.to_out.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.4.attn.out_proj.weight": "encoders.4.attn.to_out.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.4.ln_1.bias": "encoders.4.layer_norm1.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.4.ln_1.weight": "encoders.4.layer_norm1.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.4.ln_2.bias": "encoders.4.layer_norm2.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.4.ln_2.weight": "encoders.4.layer_norm2.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.4.mlp.c_fc.bias": "encoders.4.fc1.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.4.mlp.c_fc.weight": "encoders.4.fc1.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.4.mlp.c_proj.bias": "encoders.4.fc2.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.4.mlp.c_proj.weight": "encoders.4.fc2.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.5.attn.in_proj_bias": ['encoders.5.attn.to_q.bias', 'encoders.5.attn.to_k.bias', 'encoders.5.attn.to_v.bias'],
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.5.attn.in_proj_weight": ['encoders.5.attn.to_q.weight', 'encoders.5.attn.to_k.weight', 'encoders.5.attn.to_v.weight'],
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.5.attn.out_proj.bias": "encoders.5.attn.to_out.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.5.attn.out_proj.weight": "encoders.5.attn.to_out.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.5.ln_1.bias": "encoders.5.layer_norm1.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.5.ln_1.weight": "encoders.5.layer_norm1.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.5.ln_2.bias": "encoders.5.layer_norm2.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.5.ln_2.weight": "encoders.5.layer_norm2.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.5.mlp.c_fc.bias": "encoders.5.fc1.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.5.mlp.c_fc.weight": "encoders.5.fc1.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.5.mlp.c_proj.bias": "encoders.5.fc2.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.5.mlp.c_proj.weight": "encoders.5.fc2.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.6.attn.in_proj_bias": ['encoders.6.attn.to_q.bias', 'encoders.6.attn.to_k.bias', 'encoders.6.attn.to_v.bias'],
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.6.attn.in_proj_weight": ['encoders.6.attn.to_q.weight', 'encoders.6.attn.to_k.weight', 'encoders.6.attn.to_v.weight'],
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.6.attn.out_proj.bias": "encoders.6.attn.to_out.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.6.attn.out_proj.weight": "encoders.6.attn.to_out.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.6.ln_1.bias": "encoders.6.layer_norm1.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.6.ln_1.weight": "encoders.6.layer_norm1.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.6.ln_2.bias": "encoders.6.layer_norm2.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.6.ln_2.weight": "encoders.6.layer_norm2.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.6.mlp.c_fc.bias": "encoders.6.fc1.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.6.mlp.c_fc.weight": "encoders.6.fc1.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.6.mlp.c_proj.bias": "encoders.6.fc2.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.6.mlp.c_proj.weight": "encoders.6.fc2.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.7.attn.in_proj_bias": ['encoders.7.attn.to_q.bias', 'encoders.7.attn.to_k.bias', 'encoders.7.attn.to_v.bias'],
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.7.attn.in_proj_weight": ['encoders.7.attn.to_q.weight', 'encoders.7.attn.to_k.weight', 'encoders.7.attn.to_v.weight'],
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.7.attn.out_proj.bias": "encoders.7.attn.to_out.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.7.attn.out_proj.weight": "encoders.7.attn.to_out.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.7.ln_1.bias": "encoders.7.layer_norm1.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.7.ln_1.weight": "encoders.7.layer_norm1.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.7.ln_2.bias": "encoders.7.layer_norm2.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.7.ln_2.weight": "encoders.7.layer_norm2.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.7.mlp.c_fc.bias": "encoders.7.fc1.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.7.mlp.c_fc.weight": "encoders.7.fc1.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.7.mlp.c_proj.bias": "encoders.7.fc2.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.7.mlp.c_proj.weight": "encoders.7.fc2.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.8.attn.in_proj_bias": ['encoders.8.attn.to_q.bias', 'encoders.8.attn.to_k.bias', 'encoders.8.attn.to_v.bias'],
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.8.attn.in_proj_weight": ['encoders.8.attn.to_q.weight', 'encoders.8.attn.to_k.weight', 'encoders.8.attn.to_v.weight'],
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.8.attn.out_proj.bias": "encoders.8.attn.to_out.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.8.attn.out_proj.weight": "encoders.8.attn.to_out.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.8.ln_1.bias": "encoders.8.layer_norm1.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.8.ln_1.weight": "encoders.8.layer_norm1.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.8.ln_2.bias": "encoders.8.layer_norm2.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.8.ln_2.weight": "encoders.8.layer_norm2.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.8.mlp.c_fc.bias": "encoders.8.fc1.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.8.mlp.c_fc.weight": "encoders.8.fc1.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.8.mlp.c_proj.bias": "encoders.8.fc2.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.8.mlp.c_proj.weight": "encoders.8.fc2.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.9.attn.in_proj_bias": ['encoders.9.attn.to_q.bias', 'encoders.9.attn.to_k.bias', 'encoders.9.attn.to_v.bias'],
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.9.attn.in_proj_weight": ['encoders.9.attn.to_q.weight', 'encoders.9.attn.to_k.weight', 'encoders.9.attn.to_v.weight'],
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.9.attn.out_proj.bias": "encoders.9.attn.to_out.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.9.attn.out_proj.weight": "encoders.9.attn.to_out.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.9.ln_1.bias": "encoders.9.layer_norm1.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.9.ln_1.weight": "encoders.9.layer_norm1.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.9.ln_2.bias": "encoders.9.layer_norm2.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.9.ln_2.weight": "encoders.9.layer_norm2.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.9.mlp.c_fc.bias": "encoders.9.fc1.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.9.mlp.c_fc.weight": "encoders.9.fc1.weight",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.9.mlp.c_proj.bias": "encoders.9.fc2.bias",
+            "conditioner.embedders.0.open_clip.model.visual.transformer.resblocks.9.mlp.c_proj.weight": "encoders.9.fc2.weight",
+            "conditioner.embedders.0.open_clip.model.visual.proj": "visual_projection.weight",
+        }
+        state_dict_ = {}
+        for name in state_dict:
+            if name in rename_dict:
+                param = state_dict[name]
+                if name == "conditioner.embedders.0.open_clip.model.visual.class_embedding":
+                    param = param.reshape((1, 1, param.shape[0]))
+                elif name == "conditioner.embedders.0.open_clip.model.visual.positional_embedding":
+                    param = param.reshape((1, param.shape[0], param.shape[1]))
+                elif name == "conditioner.embedders.0.open_clip.model.visual.proj":
+                    param = param.T
+                if isinstance(rename_dict[name], str):
+                    state_dict_[rename_dict[name]] = param
+                else:
+                    length = param.shape[0] // 3
+                    for i, rename in enumerate(rename_dict[name]):
+                        state_dict_[rename] = param[i*length: i*length+length]
+        return state_dict_
diff --git a/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/svd_unet.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/svd_unet.py
new file mode 100644
index 0000000000000000000000000000000000000000..f50eb0d8791ea2d7a6b46d1aeb81cf0d1cba5a6d
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/svd_unet.py
@@ -0,0 +1,2014 @@
+import torch, math
+from einops import rearrange, repeat
+from .sd_unet import Timesteps, PushBlock, PopBlock, Attention, GEGLU, ResnetBlock, AttentionBlock, DownSampler, UpSampler
+
+
+class TemporalResnetBlock(torch.nn.Module):
+    def __init__(self, in_channels, out_channels, temb_channels=None, groups=32, eps=1e-5):
+        super().__init__()
+        self.norm1 = torch.nn.GroupNorm(num_groups=groups, num_channels=in_channels, eps=eps, affine=True)
+        self.conv1 = torch.nn.Conv3d(in_channels, out_channels, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0))
+        if temb_channels is not None:
+            self.time_emb_proj = torch.nn.Linear(temb_channels, out_channels)
+        self.norm2 = torch.nn.GroupNorm(num_groups=groups, num_channels=out_channels, eps=eps, affine=True)
+        self.conv2 = torch.nn.Conv3d(out_channels, out_channels, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0))
+        self.nonlinearity = torch.nn.SiLU()
+        self.conv_shortcut = None
+        if in_channels != out_channels:
+            self.conv_shortcut = torch.nn.Conv3d(in_channels, out_channels, kernel_size=1, stride=1, padding=0, bias=True)
+
+    def forward(self, hidden_states, time_emb, text_emb, res_stack, **kwargs):
+        x = rearrange(hidden_states, "f c h w -> 1 c f h w")
+        x = self.norm1(x)
+        x = self.nonlinearity(x)
+        x = self.conv1(x)
+        if time_emb is not None:
+            emb = self.nonlinearity(time_emb)
+            emb = self.time_emb_proj(emb)
+            emb = repeat(emb, "b c -> b c f 1 1", f=hidden_states.shape[0])
+            x = x + emb
+        x = self.norm2(x)
+        x = self.nonlinearity(x)
+        x = self.conv2(x)
+        if self.conv_shortcut is not None:
+            hidden_states = self.conv_shortcut(hidden_states)
+        x = rearrange(x[0], "c f h w -> f c h w")
+        hidden_states = hidden_states + x
+        return hidden_states, time_emb, text_emb, res_stack
+
+
+def get_timestep_embedding(
+    timesteps: torch.Tensor,
+    embedding_dim: int,
+    flip_sin_to_cos: bool = False,
+    downscale_freq_shift: float = 1,
+    scale: float = 1,
+    max_period: int = 10000,
+    computation_device = None,
+    align_dtype_to_timestep = False,
+):
+    """
+    This matches the implementation in Denoising Diffusion Probabilistic Models: Create sinusoidal timestep embeddings.
+
+    :param timesteps: a 1-D Tensor of N indices, one per batch element.
+                      These may be fractional.
+    :param embedding_dim: the dimension of the output. :param max_period: controls the minimum frequency of the
+    embeddings. :return: an [N x dim] Tensor of positional embeddings.
+    """
+    assert len(timesteps.shape) == 1, "Timesteps should be a 1d-array"
+
+    half_dim = embedding_dim // 2
+    exponent = -math.log(max_period) * torch.arange(
+        start=0, end=half_dim, dtype=torch.float32, device=timesteps.device if computation_device is None else computation_device
+    )
+    exponent = exponent / (half_dim - downscale_freq_shift)
+
+    emb = torch.exp(exponent).to(timesteps.device)
+    if align_dtype_to_timestep:
+        emb = emb.to(timesteps.dtype)
+    emb = timesteps[:, None].float() * emb[None, :]
+
+    # scale embeddings
+    emb = scale * emb
+
+    # concat sine and cosine embeddings
+    emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=-1)
+
+    # flip sine and cosine embeddings
+    if flip_sin_to_cos:
+        emb = torch.cat([emb[:, half_dim:], emb[:, :half_dim]], dim=-1)
+
+    # zero pad
+    if embedding_dim % 2 == 1:
+        emb = torch.nn.functional.pad(emb, (0, 1, 0, 0))
+    return emb
+
+
+class TemporalTimesteps(torch.nn.Module):
+    def __init__(self, num_channels: int, flip_sin_to_cos: bool, downscale_freq_shift: float, computation_device = None, scale=1, align_dtype_to_timestep=False):
+        super().__init__()
+        self.num_channels = num_channels
+        self.flip_sin_to_cos = flip_sin_to_cos
+        self.downscale_freq_shift = downscale_freq_shift
+        self.computation_device = computation_device
+        self.scale = scale
+        self.align_dtype_to_timestep = align_dtype_to_timestep
+
+    def forward(self, timesteps):
+        t_emb = get_timestep_embedding(
+            timesteps,
+            self.num_channels,
+            flip_sin_to_cos=self.flip_sin_to_cos,
+            downscale_freq_shift=self.downscale_freq_shift,
+            computation_device=self.computation_device,
+            scale=self.scale,
+            align_dtype_to_timestep=self.align_dtype_to_timestep,
+        )
+        return t_emb
+    
+
+class TrainableTemporalTimesteps(torch.nn.Module):
+    def __init__(self, num_channels: int, flip_sin_to_cos: bool, downscale_freq_shift: float, num_frames: int):
+        super().__init__()
+        timesteps = PositionalID()(num_frames)
+        embeddings = get_timestep_embedding(timesteps, num_channels, flip_sin_to_cos, downscale_freq_shift)
+        self.embeddings = torch.nn.Parameter(embeddings)
+
+    def forward(self, timesteps):
+        t_emb = self.embeddings[timesteps]
+        return t_emb
+
+
+class PositionalID(torch.nn.Module):
+    def __init__(self, max_id=25, repeat_length=20):
+        super().__init__()
+        self.max_id = max_id
+        self.repeat_length = repeat_length
+
+    def frame_id_to_position_id(self, frame_id):
+        if frame_id < self.max_id:
+            position_id = frame_id
+        else:
+            position_id = (frame_id - self.max_id) % (self.repeat_length * 2)
+            if position_id < self.repeat_length:
+                position_id = self.max_id - 2 - position_id
+            else:
+                position_id = self.max_id - 2 * self.repeat_length + position_id
+        return position_id
+
+    def forward(self, num_frames, pivot_frame_id=0):
+        position_ids = [self.frame_id_to_position_id(abs(i-pivot_frame_id)) for i in range(num_frames)]
+        position_ids = torch.IntTensor(position_ids)
+        return position_ids
+
+
+class TemporalAttentionBlock(torch.nn.Module):
+
+    def __init__(self, num_attention_heads, attention_head_dim, in_channels, cross_attention_dim=None, add_positional_conv=None):
+        super().__init__()
+
+        self.positional_embedding_proj = torch.nn.Sequential(
+            torch.nn.Linear(in_channels, in_channels * 4),
+            torch.nn.SiLU(),
+            torch.nn.Linear(in_channels * 4, in_channels)
+        )
+        if add_positional_conv is not None:
+            self.positional_embedding = TrainableTemporalTimesteps(in_channels, True, 0, add_positional_conv)
+            self.positional_conv = torch.nn.Conv3d(in_channels, in_channels, kernel_size=3, padding=1, padding_mode="reflect")
+        else:
+            self.positional_embedding = TemporalTimesteps(in_channels, True, 0)
+            self.positional_conv = None
+
+        self.norm_in = torch.nn.LayerNorm(in_channels)
+        self.act_fn_in = GEGLU(in_channels, in_channels * 4)
+        self.ff_in = torch.nn.Linear(in_channels * 4, in_channels)
+
+        self.norm1 = torch.nn.LayerNorm(in_channels)
+        self.attn1 = Attention(
+            q_dim=in_channels,
+            num_heads=num_attention_heads,
+            head_dim=attention_head_dim,
+            bias_out=True
+        )
+
+        self.norm2 = torch.nn.LayerNorm(in_channels)
+        self.attn2 = Attention(
+            q_dim=in_channels,
+            kv_dim=cross_attention_dim,
+            num_heads=num_attention_heads,
+            head_dim=attention_head_dim,
+            bias_out=True
+        )
+
+        self.norm_out = torch.nn.LayerNorm(in_channels)
+        self.act_fn_out = GEGLU(in_channels, in_channels * 4)
+        self.ff_out = torch.nn.Linear(in_channels * 4, in_channels)
+
+    def forward(self, hidden_states, time_emb, text_emb, res_stack, **kwargs):
+
+        batch, inner_dim, height, width = hidden_states.shape
+        pos_emb = torch.arange(batch)
+        pos_emb = self.positional_embedding(pos_emb).to(dtype=hidden_states.dtype, device=hidden_states.device)
+        pos_emb = self.positional_embedding_proj(pos_emb)
+        
+        hidden_states = rearrange(hidden_states, "T C H W -> 1 C T H W") + rearrange(pos_emb, "T C -> 1 C T 1 1")
+        if self.positional_conv is not None:
+            hidden_states = self.positional_conv(hidden_states)
+        hidden_states = rearrange(hidden_states[0], "C T H W -> (H W) T C")
+
+        residual = hidden_states
+        hidden_states = self.norm_in(hidden_states)
+        hidden_states = self.act_fn_in(hidden_states)
+        hidden_states = self.ff_in(hidden_states)
+        hidden_states = hidden_states + residual
+
+        norm_hidden_states = self.norm1(hidden_states)
+        attn_output = self.attn1(norm_hidden_states, encoder_hidden_states=None)
+        hidden_states = attn_output + hidden_states
+
+        norm_hidden_states = self.norm2(hidden_states)
+        attn_output = self.attn2(norm_hidden_states, encoder_hidden_states=text_emb.repeat(height * width, 1))
+        hidden_states = attn_output + hidden_states
+
+        residual = hidden_states
+        hidden_states = self.norm_out(hidden_states)
+        hidden_states = self.act_fn_out(hidden_states)
+        hidden_states = self.ff_out(hidden_states)
+        hidden_states = hidden_states + residual
+
+        hidden_states = hidden_states.reshape(height, width, batch, inner_dim).permute(2, 3, 0, 1)
+
+        return hidden_states, time_emb, text_emb, res_stack
+    
+
+class PopMixBlock(torch.nn.Module):
+    def __init__(self, in_channels=None):
+        super().__init__()
+        self.mix_factor = torch.nn.Parameter(torch.Tensor([0.5]))
+        self.need_proj = in_channels is not None
+        if self.need_proj:
+            self.proj = torch.nn.Linear(in_channels, in_channels)
+    
+    def forward(self, hidden_states, time_emb, text_emb, res_stack, **kwargs):
+        res_hidden_states = res_stack.pop()
+        alpha = torch.sigmoid(self.mix_factor)
+        hidden_states = alpha * res_hidden_states + (1 - alpha) * hidden_states
+        if self.need_proj:
+            hidden_states = hidden_states.permute(0, 2, 3, 1)
+            hidden_states = self.proj(hidden_states)
+            hidden_states = hidden_states.permute(0, 3, 1, 2)
+            res_hidden_states = res_stack.pop()
+            hidden_states = hidden_states + res_hidden_states
+        return hidden_states, time_emb, text_emb, res_stack
+
+
+class SVDUNet(torch.nn.Module):
+    def __init__(self, add_positional_conv=None):
+        super().__init__()
+        self.time_proj = Timesteps(320)
+        self.time_embedding = torch.nn.Sequential(
+            torch.nn.Linear(320, 1280),
+            torch.nn.SiLU(),
+            torch.nn.Linear(1280, 1280)
+        )
+        self.add_time_proj = Timesteps(256)
+        self.add_time_embedding = torch.nn.Sequential(
+            torch.nn.Linear(768, 1280),
+            torch.nn.SiLU(),
+            torch.nn.Linear(1280, 1280)
+        )
+        self.conv_in = torch.nn.Conv2d(8, 320, kernel_size=3, padding=1)
+
+        self.blocks = torch.nn.ModuleList([
+            # CrossAttnDownBlockSpatioTemporal
+            ResnetBlock(320, 320, 1280, eps=1e-6),                      PushBlock(), TemporalResnetBlock(320, 320, 1280, eps=1e-6),   PopMixBlock(),  PushBlock(),
+            AttentionBlock(5, 64, 320, 1, 1024, need_proj_out=False),   PushBlock(), TemporalAttentionBlock(5, 64, 320, 1024, add_positional_conv),   PopMixBlock(320),  PushBlock(),
+            ResnetBlock(320, 320, 1280, eps=1e-6),                      PushBlock(), TemporalResnetBlock(320, 320, 1280, eps=1e-6),   PopMixBlock(),  PushBlock(),
+            AttentionBlock(5, 64, 320, 1, 1024, need_proj_out=False),   PushBlock(), TemporalAttentionBlock(5, 64, 320, 1024, add_positional_conv),   PopMixBlock(320),  PushBlock(),
+            DownSampler(320), PushBlock(),
+            # CrossAttnDownBlockSpatioTemporal
+            ResnetBlock(320, 640, 1280, eps=1e-6),                      PushBlock(), TemporalResnetBlock(640, 640, 1280, eps=1e-6),   PopMixBlock(),  PushBlock(),
+            AttentionBlock(10, 64, 640, 1, 1024, need_proj_out=False),  PushBlock(), TemporalAttentionBlock(10, 64, 640, 1024, add_positional_conv),  PopMixBlock(640),  PushBlock(),
+            ResnetBlock(640, 640, 1280, eps=1e-6),                      PushBlock(), TemporalResnetBlock(640, 640, 1280, eps=1e-6),   PopMixBlock(),  PushBlock(),
+            AttentionBlock(10, 64, 640, 1, 1024, need_proj_out=False),  PushBlock(), TemporalAttentionBlock(10, 64, 640, 1024, add_positional_conv),  PopMixBlock(640),  PushBlock(),
+            DownSampler(640), PushBlock(),
+            # CrossAttnDownBlockSpatioTemporal
+            ResnetBlock(640, 1280, 1280, eps=1e-6),                     PushBlock(), TemporalResnetBlock(1280, 1280, 1280, eps=1e-6), PopMixBlock(),  PushBlock(),
+            AttentionBlock(20, 64, 1280, 1, 1024, need_proj_out=False), PushBlock(), TemporalAttentionBlock(20, 64, 1280, 1024, add_positional_conv), PopMixBlock(1280), PushBlock(),
+            ResnetBlock(1280, 1280, 1280, eps=1e-6),                    PushBlock(), TemporalResnetBlock(1280, 1280, 1280, eps=1e-6), PopMixBlock(),  PushBlock(),
+            AttentionBlock(20, 64, 1280, 1, 1024, need_proj_out=False), PushBlock(), TemporalAttentionBlock(20, 64, 1280, 1024, add_positional_conv), PopMixBlock(1280), PushBlock(),
+            DownSampler(1280), PushBlock(),
+            # DownBlockSpatioTemporal
+            ResnetBlock(1280, 1280, 1280, eps=1e-5),                    PushBlock(), TemporalResnetBlock(1280, 1280, 1280, eps=1e-5), PopMixBlock(),  PushBlock(),
+            ResnetBlock(1280, 1280, 1280, eps=1e-5),                    PushBlock(), TemporalResnetBlock(1280, 1280, 1280, eps=1e-5), PopMixBlock(),  PushBlock(),
+            # UNetMidBlockSpatioTemporal
+            ResnetBlock(1280, 1280, 1280, eps=1e-5),                    PushBlock(), TemporalResnetBlock(1280, 1280, 1280, eps=1e-5), PopMixBlock(),  PushBlock(),
+            AttentionBlock(20, 64, 1280, 1, 1024, need_proj_out=False), PushBlock(), TemporalAttentionBlock(20, 64, 1280, 1024, add_positional_conv), PopMixBlock(1280),
+            ResnetBlock(1280, 1280, 1280, eps=1e-5),                    PushBlock(), TemporalResnetBlock(1280, 1280, 1280, eps=1e-5), PopMixBlock(),
+            # UpBlockSpatioTemporal
+            PopBlock(), ResnetBlock(2560, 1280, 1280, eps=1e-6),        PushBlock(), TemporalResnetBlock(1280, 1280, 1280, eps=1e-5), PopMixBlock(),
+            PopBlock(), ResnetBlock(2560, 1280, 1280, eps=1e-6),        PushBlock(), TemporalResnetBlock(1280, 1280, 1280, eps=1e-5), PopMixBlock(),
+            PopBlock(), ResnetBlock(2560, 1280, 1280, eps=1e-6),        PushBlock(), TemporalResnetBlock(1280, 1280, 1280, eps=1e-5), PopMixBlock(),
+            UpSampler(1280),
+            # CrossAttnUpBlockSpatioTemporal
+            PopBlock(),        ResnetBlock(2560, 1280, 1280, eps=1e-6), PushBlock(), TemporalResnetBlock(1280, 1280, 1280, eps=1e-6), PopMixBlock(),  PushBlock(),
+            AttentionBlock(20, 64, 1280, 1, 1024, need_proj_out=False), PushBlock(), TemporalAttentionBlock(20, 64, 1280, 1024, add_positional_conv), PopMixBlock(1280),
+            PopBlock(),        ResnetBlock(2560, 1280, 1280, eps=1e-6), PushBlock(), TemporalResnetBlock(1280, 1280, 1280, eps=1e-6), PopMixBlock(),  PushBlock(),
+            AttentionBlock(20, 64, 1280, 1, 1024, need_proj_out=False), PushBlock(), TemporalAttentionBlock(20, 64, 1280, 1024, add_positional_conv), PopMixBlock(1280),
+            PopBlock(),        ResnetBlock(1920, 1280, 1280, eps=1e-6), PushBlock(), TemporalResnetBlock(1280, 1280, 1280, eps=1e-6), PopMixBlock(),  PushBlock(),
+            AttentionBlock(20, 64, 1280, 1, 1024, need_proj_out=False), PushBlock(), TemporalAttentionBlock(20, 64, 1280, 1024, add_positional_conv), PopMixBlock(1280),
+            UpSampler(1280),
+            # CrossAttnUpBlockSpatioTemporal
+            PopBlock(),        ResnetBlock(1920, 640, 1280, eps=1e-6),  PushBlock(), TemporalResnetBlock(640, 640, 1280, eps=1e-6),   PopMixBlock(),  PushBlock(),
+            AttentionBlock(10, 64, 640, 1, 1024, need_proj_out=False),  PushBlock(), TemporalAttentionBlock(10, 64, 640, 1024, add_positional_conv),  PopMixBlock(640),
+            PopBlock(),        ResnetBlock(1280, 640, 1280, eps=1e-6),  PushBlock(), TemporalResnetBlock(640, 640, 1280, eps=1e-6),   PopMixBlock(),  PushBlock(),
+            AttentionBlock(10, 64, 640, 1, 1024, need_proj_out=False),  PushBlock(), TemporalAttentionBlock(10, 64, 640, 1024, add_positional_conv),  PopMixBlock(640),
+            PopBlock(),        ResnetBlock(960, 640, 1280, eps=1e-6),   PushBlock(), TemporalResnetBlock(640, 640, 1280, eps=1e-6),   PopMixBlock(),  PushBlock(),
+            AttentionBlock(10, 64, 640, 1, 1024, need_proj_out=False),  PushBlock(), TemporalAttentionBlock(10, 64, 640, 1024, add_positional_conv),  PopMixBlock(640),
+            UpSampler(640),
+            # CrossAttnUpBlockSpatioTemporal
+            PopBlock(),        ResnetBlock(960, 320, 1280, eps=1e-6),   PushBlock(), TemporalResnetBlock(320, 320, 1280, eps=1e-6),   PopMixBlock(),  PushBlock(),
+            AttentionBlock(5, 64, 320, 1, 1024, need_proj_out=False),   PushBlock(), TemporalAttentionBlock(5, 64, 320, 1024, add_positional_conv),   PopMixBlock(320),
+            PopBlock(),        ResnetBlock(640, 320, 1280, eps=1e-6),   PushBlock(), TemporalResnetBlock(320, 320, 1280, eps=1e-6),   PopMixBlock(),  PushBlock(),
+            AttentionBlock(5, 64, 320, 1, 1024, need_proj_out=False),   PushBlock(), TemporalAttentionBlock(5, 64, 320, 1024, add_positional_conv),   PopMixBlock(320),
+            PopBlock(),        ResnetBlock(640, 320, 1280, eps=1e-6),   PushBlock(), TemporalResnetBlock(320, 320, 1280, eps=1e-6),   PopMixBlock(),  PushBlock(),
+            AttentionBlock(5, 64, 320, 1, 1024, need_proj_out=False),   PushBlock(), TemporalAttentionBlock(5, 64, 320, 1024, add_positional_conv),   PopMixBlock(320),
+        ])
+
+        self.conv_norm_out = torch.nn.GroupNorm(32, 320, eps=1e-05, affine=True)
+        self.conv_act = torch.nn.SiLU()
+        self.conv_out = torch.nn.Conv2d(320, 4, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
+        
+
+    def build_mask(self, data, is_bound):
+        T, C, H, W = data.shape
+        t = repeat(torch.arange(T), "T -> T H W", T=T, H=H, W=W)
+        h = repeat(torch.arange(H), "H -> T H W", T=T, H=H, W=W)
+        w = repeat(torch.arange(W), "W -> T H W", T=T, H=H, W=W)
+        border_width = (T + H + W) // 6
+        pad = torch.ones_like(t) * border_width
+        mask = torch.stack([
+            pad if is_bound[0] else t + 1,
+            pad if is_bound[1] else T - t,
+            pad if is_bound[2] else h + 1,
+            pad if is_bound[3] else H - h,
+            pad if is_bound[4] else w + 1,
+            pad if is_bound[5] else W - w
+        ]).min(dim=0).values
+        mask = mask.clip(1, border_width)
+        mask = (mask / border_width).to(dtype=data.dtype, device=data.device)
+        mask = rearrange(mask, "T H W -> T 1 H W")
+        return mask
+    
+
+    def tiled_forward(
+        self, sample, timestep, encoder_hidden_states, add_time_id,
+        batch_time=25, batch_height=128, batch_width=128,
+        stride_time=5, stride_height=64, stride_width=64,
+        progress_bar=lambda x:x
+    ):
+        data_device = sample.device
+        computation_device = self.conv_in.weight.device
+        torch_dtype = sample.dtype
+        T, C, H, W = sample.shape
+
+        weight = torch.zeros((T, 1, H, W), dtype=torch_dtype, device=data_device)
+        values = torch.zeros((T, 4, H, W), dtype=torch_dtype, device=data_device)
+
+        # Split tasks
+        tasks = []
+        for t in range(0, T, stride_time):
+            for h in range(0, H, stride_height):
+                for w in range(0, W, stride_width):
+                    if (t-stride_time >= 0 and t-stride_time+batch_time >= T)\
+                        or (h-stride_height >= 0 and h-stride_height+batch_height >= H)\
+                        or (w-stride_width >= 0 and w-stride_width+batch_width >= W):
+                        continue
+                    tasks.append((t, t+batch_time, h, h+batch_height, w, w+batch_width))
+        
+        # Run
+        for tl, tr, hl, hr, wl, wr in progress_bar(tasks):
+            sample_batch = sample[tl:tr, :, hl:hr, wl:wr].to(computation_device)
+            sample_batch = self.forward(sample_batch, timestep, encoder_hidden_states, add_time_id).to(data_device)
+            mask = self.build_mask(sample_batch, is_bound=(tl==0, tr>=T, hl==0, hr>=H, wl==0, wr>=W))
+            values[tl:tr, :, hl:hr, wl:wr] += sample_batch * mask
+            weight[tl:tr, :, hl:hr, wl:wr] += mask
+        values /= weight
+        return values
+    
+
+    def forward(self, sample, timestep, encoder_hidden_states, add_time_id, use_gradient_checkpointing=False, **kwargs):
+        # 1. time
+        timestep = torch.tensor((timestep,)).to(sample.device)
+        t_emb = self.time_proj(timestep).to(sample.dtype)
+        t_emb = self.time_embedding(t_emb)
+
+        add_embeds = self.add_time_proj(add_time_id.flatten()).to(sample.dtype)
+        add_embeds = add_embeds.reshape((-1, 768))
+        add_embeds = self.add_time_embedding(add_embeds)
+
+        time_emb = t_emb + add_embeds
+
+        # 2. pre-process
+        height, width = sample.shape[2], sample.shape[3]
+        hidden_states = self.conv_in(sample)
+        text_emb = encoder_hidden_states
+        res_stack = [hidden_states]
+
+        # 3. blocks
+        def create_custom_forward(module):
+            def custom_forward(*inputs):
+                return module(*inputs)
+            return custom_forward
+        for i, block in enumerate(self.blocks):
+            if self.training and use_gradient_checkpointing and not (isinstance(block, PushBlock) or isinstance(block, PopBlock) or isinstance(block, PopMixBlock)):
+                hidden_states, time_emb, text_emb, res_stack = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(block),
+                    hidden_states, time_emb, text_emb, res_stack,
+                    use_reentrant=False,
+                )
+            else:
+                hidden_states, time_emb, text_emb, res_stack = block(hidden_states, time_emb, text_emb, res_stack)
+
+        # 4. output
+        hidden_states = self.conv_norm_out(hidden_states)
+        hidden_states = self.conv_act(hidden_states)
+        hidden_states = self.conv_out(hidden_states)
+
+        return hidden_states
+    
+    @staticmethod
+    def state_dict_converter():
+        return SVDUNetStateDictConverter()
+    
+
+
+class SVDUNetStateDictConverter:
+    def __init__(self):
+        pass
+
+    def get_block_name(self, names):
+        if names[0] in ["down_blocks", "mid_block", "up_blocks"]:
+            if names[4] in ["norm", "proj_in"]:
+                return ".".join(names[:4] + ["transformer_blocks"])
+            elif names[4] in ["time_pos_embed"]:
+                return ".".join(names[:4] + ["temporal_transformer_blocks"])
+            elif names[4] in ["proj_out"]:
+                return ".".join(names[:4] + ["time_mixer"])
+            else:
+                return ".".join(names[:5])
+        return ""
+
+    def from_diffusers(self, state_dict):
+        rename_dict = {
+            "time_embedding.linear_1": "time_embedding.0",
+            "time_embedding.linear_2": "time_embedding.2",
+            "add_embedding.linear_1": "add_time_embedding.0",
+            "add_embedding.linear_2": "add_time_embedding.2",
+            "conv_in": "conv_in",
+            "conv_norm_out": "conv_norm_out",
+            "conv_out": "conv_out",
+        }
+        blocks_rename_dict = [
+            "down_blocks.0.resnets.0.spatial_res_block", None, "down_blocks.0.resnets.0.temporal_res_block", "down_blocks.0.resnets.0.time_mixer", None,
+            "down_blocks.0.attentions.0.transformer_blocks", None, "down_blocks.0.attentions.0.temporal_transformer_blocks", "down_blocks.0.attentions.0.time_mixer", None,
+            "down_blocks.0.resnets.1.spatial_res_block", None, "down_blocks.0.resnets.1.temporal_res_block", "down_blocks.0.resnets.1.time_mixer", None,
+            "down_blocks.0.attentions.1.transformer_blocks", None, "down_blocks.0.attentions.1.temporal_transformer_blocks", "down_blocks.0.attentions.1.time_mixer", None,
+            "down_blocks.0.downsamplers.0.conv", None,
+            "down_blocks.1.resnets.0.spatial_res_block", None, "down_blocks.1.resnets.0.temporal_res_block", "down_blocks.1.resnets.0.time_mixer", None,
+            "down_blocks.1.attentions.0.transformer_blocks", None, "down_blocks.1.attentions.0.temporal_transformer_blocks", "down_blocks.1.attentions.0.time_mixer", None,
+            "down_blocks.1.resnets.1.spatial_res_block", None, "down_blocks.1.resnets.1.temporal_res_block", "down_blocks.1.resnets.1.time_mixer", None,
+            "down_blocks.1.attentions.1.transformer_blocks", None, "down_blocks.1.attentions.1.temporal_transformer_blocks", "down_blocks.1.attentions.1.time_mixer", None,
+            "down_blocks.1.downsamplers.0.conv", None,
+            "down_blocks.2.resnets.0.spatial_res_block", None, "down_blocks.2.resnets.0.temporal_res_block", "down_blocks.2.resnets.0.time_mixer", None,
+            "down_blocks.2.attentions.0.transformer_blocks", None, "down_blocks.2.attentions.0.temporal_transformer_blocks", "down_blocks.2.attentions.0.time_mixer", None,
+            "down_blocks.2.resnets.1.spatial_res_block", None, "down_blocks.2.resnets.1.temporal_res_block", "down_blocks.2.resnets.1.time_mixer", None,
+            "down_blocks.2.attentions.1.transformer_blocks", None, "down_blocks.2.attentions.1.temporal_transformer_blocks", "down_blocks.2.attentions.1.time_mixer", None,
+            "down_blocks.2.downsamplers.0.conv", None,
+            "down_blocks.3.resnets.0.spatial_res_block", None, "down_blocks.3.resnets.0.temporal_res_block", "down_blocks.3.resnets.0.time_mixer", None,
+            "down_blocks.3.resnets.1.spatial_res_block", None, "down_blocks.3.resnets.1.temporal_res_block", "down_blocks.3.resnets.1.time_mixer", None,
+            "mid_block.mid_block.resnets.0.spatial_res_block", None, "mid_block.mid_block.resnets.0.temporal_res_block", "mid_block.mid_block.resnets.0.time_mixer", None,
+            "mid_block.mid_block.attentions.0.transformer_blocks", None, "mid_block.mid_block.attentions.0.temporal_transformer_blocks", "mid_block.mid_block.attentions.0.time_mixer",
+            "mid_block.mid_block.resnets.1.spatial_res_block", None, "mid_block.mid_block.resnets.1.temporal_res_block", "mid_block.mid_block.resnets.1.time_mixer",
+            None, "up_blocks.0.resnets.0.spatial_res_block", None, "up_blocks.0.resnets.0.temporal_res_block", "up_blocks.0.resnets.0.time_mixer",
+            None, "up_blocks.0.resnets.1.spatial_res_block", None, "up_blocks.0.resnets.1.temporal_res_block", "up_blocks.0.resnets.1.time_mixer",
+            None, "up_blocks.0.resnets.2.spatial_res_block", None, "up_blocks.0.resnets.2.temporal_res_block", "up_blocks.0.resnets.2.time_mixer",
+            "up_blocks.0.upsamplers.0.conv",
+            None, "up_blocks.1.resnets.0.spatial_res_block", None, "up_blocks.1.resnets.0.temporal_res_block", "up_blocks.1.resnets.0.time_mixer", None,
+            "up_blocks.1.attentions.0.transformer_blocks", None, "up_blocks.1.attentions.0.temporal_transformer_blocks", "up_blocks.1.attentions.0.time_mixer",
+            None, "up_blocks.1.resnets.1.spatial_res_block", None, "up_blocks.1.resnets.1.temporal_res_block", "up_blocks.1.resnets.1.time_mixer", None,
+            "up_blocks.1.attentions.1.transformer_blocks", None, "up_blocks.1.attentions.1.temporal_transformer_blocks", "up_blocks.1.attentions.1.time_mixer",
+            None, "up_blocks.1.resnets.2.spatial_res_block", None, "up_blocks.1.resnets.2.temporal_res_block", "up_blocks.1.resnets.2.time_mixer", None,
+            "up_blocks.1.attentions.2.transformer_blocks", None, "up_blocks.1.attentions.2.temporal_transformer_blocks", "up_blocks.1.attentions.2.time_mixer",
+            "up_blocks.1.upsamplers.0.conv",
+            None, "up_blocks.2.resnets.0.spatial_res_block", None, "up_blocks.2.resnets.0.temporal_res_block", "up_blocks.2.resnets.0.time_mixer", None,
+            "up_blocks.2.attentions.0.transformer_blocks", None, "up_blocks.2.attentions.0.temporal_transformer_blocks", "up_blocks.2.attentions.0.time_mixer",
+            None, "up_blocks.2.resnets.1.spatial_res_block", None, "up_blocks.2.resnets.1.temporal_res_block", "up_blocks.2.resnets.1.time_mixer", None,
+            "up_blocks.2.attentions.1.transformer_blocks", None, "up_blocks.2.attentions.1.temporal_transformer_blocks", "up_blocks.2.attentions.1.time_mixer",
+            None, "up_blocks.2.resnets.2.spatial_res_block", None, "up_blocks.2.resnets.2.temporal_res_block", "up_blocks.2.resnets.2.time_mixer", None,
+            "up_blocks.2.attentions.2.transformer_blocks", None, "up_blocks.2.attentions.2.temporal_transformer_blocks", "up_blocks.2.attentions.2.time_mixer",
+            "up_blocks.2.upsamplers.0.conv",
+            None, "up_blocks.3.resnets.0.spatial_res_block", None, "up_blocks.3.resnets.0.temporal_res_block", "up_blocks.3.resnets.0.time_mixer", None,
+            "up_blocks.3.attentions.0.transformer_blocks", None, "up_blocks.3.attentions.0.temporal_transformer_blocks", "up_blocks.3.attentions.0.time_mixer",
+            None, "up_blocks.3.resnets.1.spatial_res_block", None, "up_blocks.3.resnets.1.temporal_res_block", "up_blocks.3.resnets.1.time_mixer", None,
+            "up_blocks.3.attentions.1.transformer_blocks", None, "up_blocks.3.attentions.1.temporal_transformer_blocks", "up_blocks.3.attentions.1.time_mixer",
+            None, "up_blocks.3.resnets.2.spatial_res_block", None, "up_blocks.3.resnets.2.temporal_res_block", "up_blocks.3.resnets.2.time_mixer", None,
+            "up_blocks.3.attentions.2.transformer_blocks", None, "up_blocks.3.attentions.2.temporal_transformer_blocks", "up_blocks.3.attentions.2.time_mixer",
+        ]
+        blocks_rename_dict = {i:j for j,i in enumerate(blocks_rename_dict) if i is not None}
+        state_dict_ = {}
+        for name, param in sorted(state_dict.items()):
+            names = name.split(".")
+            if names[0] == "mid_block":
+                names = ["mid_block"] + names
+            if names[-1] in ["weight", "bias"]:
+                name_prefix = ".".join(names[:-1])
+                if name_prefix in rename_dict:
+                    state_dict_[rename_dict[name_prefix] + "." + names[-1]] = param
+                else:
+                    block_name = self.get_block_name(names)
+                    if "resnets" in block_name and block_name in blocks_rename_dict:
+                        rename = ".".join(["blocks", str(blocks_rename_dict[block_name])] + names[5:])
+                        state_dict_[rename] = param
+                    elif ("downsamplers" in block_name or "upsamplers" in block_name) and block_name in blocks_rename_dict:
+                        rename = ".".join(["blocks", str(blocks_rename_dict[block_name])] + names[-2:])
+                        state_dict_[rename] = param
+                    elif "attentions" in block_name and block_name in blocks_rename_dict:
+                        attention_id = names[5]
+                        if "transformer_blocks" in names:
+                            suffix_dict = {
+                                "attn1.to_out.0": "attn1.to_out",
+                                "attn2.to_out.0": "attn2.to_out",
+                                "ff.net.0.proj": "act_fn.proj",
+                                "ff.net.2": "ff",
+                            }
+                            suffix = ".".join(names[6:-1])
+                            suffix = suffix_dict.get(suffix, suffix)
+                            rename = ".".join(["blocks", str(blocks_rename_dict[block_name]), "transformer_blocks", attention_id, suffix, names[-1]])
+                        elif "temporal_transformer_blocks" in names:
+                            suffix_dict = {
+                                "attn1.to_out.0": "attn1.to_out",
+                                "attn2.to_out.0": "attn2.to_out",
+                                "ff_in.net.0.proj": "act_fn_in.proj",
+                                "ff_in.net.2": "ff_in",
+                                "ff.net.0.proj": "act_fn_out.proj",
+                                "ff.net.2": "ff_out",
+                                "norm3": "norm_out",
+                            }
+                            suffix = ".".join(names[6:-1])
+                            suffix = suffix_dict.get(suffix, suffix)
+                            rename = ".".join(["blocks", str(blocks_rename_dict[block_name]), suffix, names[-1]])
+                        elif "time_mixer" in block_name:
+                            rename = ".".join(["blocks", str(blocks_rename_dict[block_name]), "proj", names[-1]])
+                        else:
+                            suffix_dict = {
+                                "linear_1": "positional_embedding_proj.0",
+                                "linear_2": "positional_embedding_proj.2",
+                            }
+                            suffix = names[-2]
+                            suffix = suffix_dict.get(suffix, suffix)
+                            rename = ".".join(["blocks", str(blocks_rename_dict[block_name]), suffix, names[-1]])
+                        state_dict_[rename] = param
+                    else:
+                        print(name)
+            else:
+                block_name = self.get_block_name(names)
+                if len(block_name)>0 and block_name in blocks_rename_dict:
+                    rename = ".".join(["blocks", str(blocks_rename_dict[block_name]), names[-1]])
+                    state_dict_[rename] = param
+        return state_dict_
+    
+
+    def from_civitai(self, state_dict, add_positional_conv=None):
+        rename_dict = {
+            "model.diffusion_model.input_blocks.0.0.bias": "conv_in.bias",
+            "model.diffusion_model.input_blocks.0.0.weight": "conv_in.weight",
+            "model.diffusion_model.input_blocks.1.0.emb_layers.1.bias": "blocks.0.time_emb_proj.bias",
+            "model.diffusion_model.input_blocks.1.0.emb_layers.1.weight": "blocks.0.time_emb_proj.weight",
+            "model.diffusion_model.input_blocks.1.0.in_layers.0.bias": "blocks.0.norm1.bias",
+            "model.diffusion_model.input_blocks.1.0.in_layers.0.weight": "blocks.0.norm1.weight",
+            "model.diffusion_model.input_blocks.1.0.in_layers.2.bias": "blocks.0.conv1.bias",
+            "model.diffusion_model.input_blocks.1.0.in_layers.2.weight": "blocks.0.conv1.weight",
+            "model.diffusion_model.input_blocks.1.0.out_layers.0.bias": "blocks.0.norm2.bias",
+            "model.diffusion_model.input_blocks.1.0.out_layers.0.weight": "blocks.0.norm2.weight",
+            "model.diffusion_model.input_blocks.1.0.out_layers.3.bias": "blocks.0.conv2.bias",
+            "model.diffusion_model.input_blocks.1.0.out_layers.3.weight": "blocks.0.conv2.weight",
+            "model.diffusion_model.input_blocks.1.0.time_mixer.mix_factor": "blocks.3.mix_factor",
+            "model.diffusion_model.input_blocks.1.0.time_stack.emb_layers.1.bias": "blocks.2.time_emb_proj.bias",
+            "model.diffusion_model.input_blocks.1.0.time_stack.emb_layers.1.weight": "blocks.2.time_emb_proj.weight",
+            "model.diffusion_model.input_blocks.1.0.time_stack.in_layers.0.bias": "blocks.2.norm1.bias",
+            "model.diffusion_model.input_blocks.1.0.time_stack.in_layers.0.weight": "blocks.2.norm1.weight",
+            "model.diffusion_model.input_blocks.1.0.time_stack.in_layers.2.bias": "blocks.2.conv1.bias",
+            "model.diffusion_model.input_blocks.1.0.time_stack.in_layers.2.weight": "blocks.2.conv1.weight",
+            "model.diffusion_model.input_blocks.1.0.time_stack.out_layers.0.bias": "blocks.2.norm2.bias",
+            "model.diffusion_model.input_blocks.1.0.time_stack.out_layers.0.weight": "blocks.2.norm2.weight",
+            "model.diffusion_model.input_blocks.1.0.time_stack.out_layers.3.bias": "blocks.2.conv2.bias",
+            "model.diffusion_model.input_blocks.1.0.time_stack.out_layers.3.weight": "blocks.2.conv2.weight",
+            "model.diffusion_model.input_blocks.1.1.norm.bias": "blocks.5.norm.bias",
+            "model.diffusion_model.input_blocks.1.1.norm.weight": "blocks.5.norm.weight",
+            "model.diffusion_model.input_blocks.1.1.proj_in.bias": "blocks.5.proj_in.bias",
+            "model.diffusion_model.input_blocks.1.1.proj_in.weight": "blocks.5.proj_in.weight",
+            "model.diffusion_model.input_blocks.1.1.proj_out.bias": "blocks.8.proj.bias",
+            "model.diffusion_model.input_blocks.1.1.proj_out.weight": "blocks.8.proj.weight",
+            "model.diffusion_model.input_blocks.1.1.time_mixer.mix_factor": "blocks.8.mix_factor",
+            "model.diffusion_model.input_blocks.1.1.time_pos_embed.0.bias": "blocks.7.positional_embedding_proj.0.bias",
+            "model.diffusion_model.input_blocks.1.1.time_pos_embed.0.weight": "blocks.7.positional_embedding_proj.0.weight",
+            "model.diffusion_model.input_blocks.1.1.time_pos_embed.2.bias": "blocks.7.positional_embedding_proj.2.bias",
+            "model.diffusion_model.input_blocks.1.1.time_pos_embed.2.weight": "blocks.7.positional_embedding_proj.2.weight",
+            "model.diffusion_model.input_blocks.1.1.time_stack.0.attn1.to_k.weight": "blocks.7.attn1.to_k.weight",
+            "model.diffusion_model.input_blocks.1.1.time_stack.0.attn1.to_out.0.bias": "blocks.7.attn1.to_out.bias",
+            "model.diffusion_model.input_blocks.1.1.time_stack.0.attn1.to_out.0.weight": "blocks.7.attn1.to_out.weight",
+            "model.diffusion_model.input_blocks.1.1.time_stack.0.attn1.to_q.weight": "blocks.7.attn1.to_q.weight",
+            "model.diffusion_model.input_blocks.1.1.time_stack.0.attn1.to_v.weight": "blocks.7.attn1.to_v.weight",
+            "model.diffusion_model.input_blocks.1.1.time_stack.0.attn2.to_k.weight": "blocks.7.attn2.to_k.weight",
+            "model.diffusion_model.input_blocks.1.1.time_stack.0.attn2.to_out.0.bias": "blocks.7.attn2.to_out.bias",
+            "model.diffusion_model.input_blocks.1.1.time_stack.0.attn2.to_out.0.weight": "blocks.7.attn2.to_out.weight",
+            "model.diffusion_model.input_blocks.1.1.time_stack.0.attn2.to_q.weight": "blocks.7.attn2.to_q.weight",
+            "model.diffusion_model.input_blocks.1.1.time_stack.0.attn2.to_v.weight": "blocks.7.attn2.to_v.weight",
+            "model.diffusion_model.input_blocks.1.1.time_stack.0.ff.net.0.proj.bias": "blocks.7.act_fn_out.proj.bias",
+            "model.diffusion_model.input_blocks.1.1.time_stack.0.ff.net.0.proj.weight": "blocks.7.act_fn_out.proj.weight",
+            "model.diffusion_model.input_blocks.1.1.time_stack.0.ff.net.2.bias": "blocks.7.ff_out.bias",
+            "model.diffusion_model.input_blocks.1.1.time_stack.0.ff.net.2.weight": "blocks.7.ff_out.weight",
+            "model.diffusion_model.input_blocks.1.1.time_stack.0.ff_in.net.0.proj.bias": "blocks.7.act_fn_in.proj.bias",
+            "model.diffusion_model.input_blocks.1.1.time_stack.0.ff_in.net.0.proj.weight": "blocks.7.act_fn_in.proj.weight",
+            "model.diffusion_model.input_blocks.1.1.time_stack.0.ff_in.net.2.bias": "blocks.7.ff_in.bias",
+            "model.diffusion_model.input_blocks.1.1.time_stack.0.ff_in.net.2.weight": "blocks.7.ff_in.weight",
+            "model.diffusion_model.input_blocks.1.1.time_stack.0.norm1.bias": "blocks.7.norm1.bias",
+            "model.diffusion_model.input_blocks.1.1.time_stack.0.norm1.weight": "blocks.7.norm1.weight",
+            "model.diffusion_model.input_blocks.1.1.time_stack.0.norm2.bias": "blocks.7.norm2.bias",
+            "model.diffusion_model.input_blocks.1.1.time_stack.0.norm2.weight": "blocks.7.norm2.weight",
+            "model.diffusion_model.input_blocks.1.1.time_stack.0.norm3.bias": "blocks.7.norm_out.bias",
+            "model.diffusion_model.input_blocks.1.1.time_stack.0.norm3.weight": "blocks.7.norm_out.weight",
+            "model.diffusion_model.input_blocks.1.1.time_stack.0.norm_in.bias": "blocks.7.norm_in.bias",
+            "model.diffusion_model.input_blocks.1.1.time_stack.0.norm_in.weight": "blocks.7.norm_in.weight",
+            "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.attn1.to_k.weight": "blocks.5.transformer_blocks.0.attn1.to_k.weight",
+            "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.attn1.to_out.0.bias": "blocks.5.transformer_blocks.0.attn1.to_out.bias",
+            "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.attn1.to_out.0.weight": "blocks.5.transformer_blocks.0.attn1.to_out.weight",
+            "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.attn1.to_q.weight": "blocks.5.transformer_blocks.0.attn1.to_q.weight",
+            "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.attn1.to_v.weight": "blocks.5.transformer_blocks.0.attn1.to_v.weight",
+            "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.attn2.to_k.weight": "blocks.5.transformer_blocks.0.attn2.to_k.weight",
+            "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.attn2.to_out.0.bias": "blocks.5.transformer_blocks.0.attn2.to_out.bias",
+            "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.attn2.to_out.0.weight": "blocks.5.transformer_blocks.0.attn2.to_out.weight",
+            "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.attn2.to_q.weight": "blocks.5.transformer_blocks.0.attn2.to_q.weight",
+            "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.attn2.to_v.weight": "blocks.5.transformer_blocks.0.attn2.to_v.weight",
+            "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.ff.net.0.proj.bias": "blocks.5.transformer_blocks.0.act_fn.proj.bias",
+            "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.ff.net.0.proj.weight": "blocks.5.transformer_blocks.0.act_fn.proj.weight",
+            "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.ff.net.2.bias": "blocks.5.transformer_blocks.0.ff.bias",
+            "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.ff.net.2.weight": "blocks.5.transformer_blocks.0.ff.weight",
+            "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.norm1.bias": "blocks.5.transformer_blocks.0.norm1.bias",
+            "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.norm1.weight": "blocks.5.transformer_blocks.0.norm1.weight",
+            "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.norm2.bias": "blocks.5.transformer_blocks.0.norm2.bias",
+            "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.norm2.weight": "blocks.5.transformer_blocks.0.norm2.weight",
+            "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.norm3.bias": "blocks.5.transformer_blocks.0.norm3.bias",
+            "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.norm3.weight": "blocks.5.transformer_blocks.0.norm3.weight",
+            "model.diffusion_model.input_blocks.10.0.emb_layers.1.bias": "blocks.66.time_emb_proj.bias",
+            "model.diffusion_model.input_blocks.10.0.emb_layers.1.weight": "blocks.66.time_emb_proj.weight",
+            "model.diffusion_model.input_blocks.10.0.in_layers.0.bias": "blocks.66.norm1.bias",
+            "model.diffusion_model.input_blocks.10.0.in_layers.0.weight": "blocks.66.norm1.weight",
+            "model.diffusion_model.input_blocks.10.0.in_layers.2.bias": "blocks.66.conv1.bias",
+            "model.diffusion_model.input_blocks.10.0.in_layers.2.weight": "blocks.66.conv1.weight",
+            "model.diffusion_model.input_blocks.10.0.out_layers.0.bias": "blocks.66.norm2.bias",
+            "model.diffusion_model.input_blocks.10.0.out_layers.0.weight": "blocks.66.norm2.weight",
+            "model.diffusion_model.input_blocks.10.0.out_layers.3.bias": "blocks.66.conv2.bias",
+            "model.diffusion_model.input_blocks.10.0.out_layers.3.weight": "blocks.66.conv2.weight",
+            "model.diffusion_model.input_blocks.10.0.time_mixer.mix_factor": "blocks.69.mix_factor",
+            "model.diffusion_model.input_blocks.10.0.time_stack.emb_layers.1.bias": "blocks.68.time_emb_proj.bias",
+            "model.diffusion_model.input_blocks.10.0.time_stack.emb_layers.1.weight": "blocks.68.time_emb_proj.weight",
+            "model.diffusion_model.input_blocks.10.0.time_stack.in_layers.0.bias": "blocks.68.norm1.bias",
+            "model.diffusion_model.input_blocks.10.0.time_stack.in_layers.0.weight": "blocks.68.norm1.weight",
+            "model.diffusion_model.input_blocks.10.0.time_stack.in_layers.2.bias": "blocks.68.conv1.bias",
+            "model.diffusion_model.input_blocks.10.0.time_stack.in_layers.2.weight": "blocks.68.conv1.weight",
+            "model.diffusion_model.input_blocks.10.0.time_stack.out_layers.0.bias": "blocks.68.norm2.bias",
+            "model.diffusion_model.input_blocks.10.0.time_stack.out_layers.0.weight": "blocks.68.norm2.weight",
+            "model.diffusion_model.input_blocks.10.0.time_stack.out_layers.3.bias": "blocks.68.conv2.bias",
+            "model.diffusion_model.input_blocks.10.0.time_stack.out_layers.3.weight": "blocks.68.conv2.weight",
+            "model.diffusion_model.input_blocks.11.0.emb_layers.1.bias": "blocks.71.time_emb_proj.bias",
+            "model.diffusion_model.input_blocks.11.0.emb_layers.1.weight": "blocks.71.time_emb_proj.weight",
+            "model.diffusion_model.input_blocks.11.0.in_layers.0.bias": "blocks.71.norm1.bias",
+            "model.diffusion_model.input_blocks.11.0.in_layers.0.weight": "blocks.71.norm1.weight",
+            "model.diffusion_model.input_blocks.11.0.in_layers.2.bias": "blocks.71.conv1.bias",
+            "model.diffusion_model.input_blocks.11.0.in_layers.2.weight": "blocks.71.conv1.weight",
+            "model.diffusion_model.input_blocks.11.0.out_layers.0.bias": "blocks.71.norm2.bias",
+            "model.diffusion_model.input_blocks.11.0.out_layers.0.weight": "blocks.71.norm2.weight",
+            "model.diffusion_model.input_blocks.11.0.out_layers.3.bias": "blocks.71.conv2.bias",
+            "model.diffusion_model.input_blocks.11.0.out_layers.3.weight": "blocks.71.conv2.weight",
+            "model.diffusion_model.input_blocks.11.0.time_mixer.mix_factor": "blocks.74.mix_factor",
+            "model.diffusion_model.input_blocks.11.0.time_stack.emb_layers.1.bias": "blocks.73.time_emb_proj.bias",
+            "model.diffusion_model.input_blocks.11.0.time_stack.emb_layers.1.weight": "blocks.73.time_emb_proj.weight",
+            "model.diffusion_model.input_blocks.11.0.time_stack.in_layers.0.bias": "blocks.73.norm1.bias",
+            "model.diffusion_model.input_blocks.11.0.time_stack.in_layers.0.weight": "blocks.73.norm1.weight",
+            "model.diffusion_model.input_blocks.11.0.time_stack.in_layers.2.bias": "blocks.73.conv1.bias",
+            "model.diffusion_model.input_blocks.11.0.time_stack.in_layers.2.weight": "blocks.73.conv1.weight",
+            "model.diffusion_model.input_blocks.11.0.time_stack.out_layers.0.bias": "blocks.73.norm2.bias",
+            "model.diffusion_model.input_blocks.11.0.time_stack.out_layers.0.weight": "blocks.73.norm2.weight",
+            "model.diffusion_model.input_blocks.11.0.time_stack.out_layers.3.bias": "blocks.73.conv2.bias",
+            "model.diffusion_model.input_blocks.11.0.time_stack.out_layers.3.weight": "blocks.73.conv2.weight",
+            "model.diffusion_model.input_blocks.2.0.emb_layers.1.bias": "blocks.10.time_emb_proj.bias",
+            "model.diffusion_model.input_blocks.2.0.emb_layers.1.weight": "blocks.10.time_emb_proj.weight",
+            "model.diffusion_model.input_blocks.2.0.in_layers.0.bias": "blocks.10.norm1.bias",
+            "model.diffusion_model.input_blocks.2.0.in_layers.0.weight": "blocks.10.norm1.weight",
+            "model.diffusion_model.input_blocks.2.0.in_layers.2.bias": "blocks.10.conv1.bias",
+            "model.diffusion_model.input_blocks.2.0.in_layers.2.weight": "blocks.10.conv1.weight",
+            "model.diffusion_model.input_blocks.2.0.out_layers.0.bias": "blocks.10.norm2.bias",
+            "model.diffusion_model.input_blocks.2.0.out_layers.0.weight": "blocks.10.norm2.weight",
+            "model.diffusion_model.input_blocks.2.0.out_layers.3.bias": "blocks.10.conv2.bias",
+            "model.diffusion_model.input_blocks.2.0.out_layers.3.weight": "blocks.10.conv2.weight",
+            "model.diffusion_model.input_blocks.2.0.time_mixer.mix_factor": "blocks.13.mix_factor",
+            "model.diffusion_model.input_blocks.2.0.time_stack.emb_layers.1.bias": "blocks.12.time_emb_proj.bias",
+            "model.diffusion_model.input_blocks.2.0.time_stack.emb_layers.1.weight": "blocks.12.time_emb_proj.weight",
+            "model.diffusion_model.input_blocks.2.0.time_stack.in_layers.0.bias": "blocks.12.norm1.bias",
+            "model.diffusion_model.input_blocks.2.0.time_stack.in_layers.0.weight": "blocks.12.norm1.weight",
+            "model.diffusion_model.input_blocks.2.0.time_stack.in_layers.2.bias": "blocks.12.conv1.bias",
+            "model.diffusion_model.input_blocks.2.0.time_stack.in_layers.2.weight": "blocks.12.conv1.weight",
+            "model.diffusion_model.input_blocks.2.0.time_stack.out_layers.0.bias": "blocks.12.norm2.bias",
+            "model.diffusion_model.input_blocks.2.0.time_stack.out_layers.0.weight": "blocks.12.norm2.weight",
+            "model.diffusion_model.input_blocks.2.0.time_stack.out_layers.3.bias": "blocks.12.conv2.bias",
+            "model.diffusion_model.input_blocks.2.0.time_stack.out_layers.3.weight": "blocks.12.conv2.weight",
+            "model.diffusion_model.input_blocks.2.1.norm.bias": "blocks.15.norm.bias",
+            "model.diffusion_model.input_blocks.2.1.norm.weight": "blocks.15.norm.weight",
+            "model.diffusion_model.input_blocks.2.1.proj_in.bias": "blocks.15.proj_in.bias",
+            "model.diffusion_model.input_blocks.2.1.proj_in.weight": "blocks.15.proj_in.weight",
+            "model.diffusion_model.input_blocks.2.1.proj_out.bias": "blocks.18.proj.bias",
+            "model.diffusion_model.input_blocks.2.1.proj_out.weight": "blocks.18.proj.weight",
+            "model.diffusion_model.input_blocks.2.1.time_mixer.mix_factor": "blocks.18.mix_factor",
+            "model.diffusion_model.input_blocks.2.1.time_pos_embed.0.bias": "blocks.17.positional_embedding_proj.0.bias",
+            "model.diffusion_model.input_blocks.2.1.time_pos_embed.0.weight": "blocks.17.positional_embedding_proj.0.weight",
+            "model.diffusion_model.input_blocks.2.1.time_pos_embed.2.bias": "blocks.17.positional_embedding_proj.2.bias",
+            "model.diffusion_model.input_blocks.2.1.time_pos_embed.2.weight": "blocks.17.positional_embedding_proj.2.weight",
+            "model.diffusion_model.input_blocks.2.1.time_stack.0.attn1.to_k.weight": "blocks.17.attn1.to_k.weight",
+            "model.diffusion_model.input_blocks.2.1.time_stack.0.attn1.to_out.0.bias": "blocks.17.attn1.to_out.bias",
+            "model.diffusion_model.input_blocks.2.1.time_stack.0.attn1.to_out.0.weight": "blocks.17.attn1.to_out.weight",
+            "model.diffusion_model.input_blocks.2.1.time_stack.0.attn1.to_q.weight": "blocks.17.attn1.to_q.weight",
+            "model.diffusion_model.input_blocks.2.1.time_stack.0.attn1.to_v.weight": "blocks.17.attn1.to_v.weight",
+            "model.diffusion_model.input_blocks.2.1.time_stack.0.attn2.to_k.weight": "blocks.17.attn2.to_k.weight",
+            "model.diffusion_model.input_blocks.2.1.time_stack.0.attn2.to_out.0.bias": "blocks.17.attn2.to_out.bias",
+            "model.diffusion_model.input_blocks.2.1.time_stack.0.attn2.to_out.0.weight": "blocks.17.attn2.to_out.weight",
+            "model.diffusion_model.input_blocks.2.1.time_stack.0.attn2.to_q.weight": "blocks.17.attn2.to_q.weight",
+            "model.diffusion_model.input_blocks.2.1.time_stack.0.attn2.to_v.weight": "blocks.17.attn2.to_v.weight",
+            "model.diffusion_model.input_blocks.2.1.time_stack.0.ff.net.0.proj.bias": "blocks.17.act_fn_out.proj.bias",
+            "model.diffusion_model.input_blocks.2.1.time_stack.0.ff.net.0.proj.weight": "blocks.17.act_fn_out.proj.weight",
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+            "model.diffusion_model.output_blocks.9.1.transformer_blocks.0.attn1.to_out.0.bias": "blocks.173.transformer_blocks.0.attn1.to_out.bias",
+            "model.diffusion_model.output_blocks.9.1.transformer_blocks.0.attn1.to_out.0.weight": "blocks.173.transformer_blocks.0.attn1.to_out.weight",
+            "model.diffusion_model.output_blocks.9.1.transformer_blocks.0.attn1.to_q.weight": "blocks.173.transformer_blocks.0.attn1.to_q.weight",
+            "model.diffusion_model.output_blocks.9.1.transformer_blocks.0.attn1.to_v.weight": "blocks.173.transformer_blocks.0.attn1.to_v.weight",
+            "model.diffusion_model.output_blocks.9.1.transformer_blocks.0.attn2.to_k.weight": "blocks.173.transformer_blocks.0.attn2.to_k.weight",
+            "model.diffusion_model.output_blocks.9.1.transformer_blocks.0.attn2.to_out.0.bias": "blocks.173.transformer_blocks.0.attn2.to_out.bias",
+            "model.diffusion_model.output_blocks.9.1.transformer_blocks.0.attn2.to_out.0.weight": "blocks.173.transformer_blocks.0.attn2.to_out.weight",
+            "model.diffusion_model.output_blocks.9.1.transformer_blocks.0.attn2.to_q.weight": "blocks.173.transformer_blocks.0.attn2.to_q.weight",
+            "model.diffusion_model.output_blocks.9.1.transformer_blocks.0.attn2.to_v.weight": "blocks.173.transformer_blocks.0.attn2.to_v.weight",
+            "model.diffusion_model.output_blocks.9.1.transformer_blocks.0.ff.net.0.proj.bias": "blocks.173.transformer_blocks.0.act_fn.proj.bias",
+            "model.diffusion_model.output_blocks.9.1.transformer_blocks.0.ff.net.0.proj.weight": "blocks.173.transformer_blocks.0.act_fn.proj.weight",
+            "model.diffusion_model.output_blocks.9.1.transformer_blocks.0.ff.net.2.bias": "blocks.173.transformer_blocks.0.ff.bias",
+            "model.diffusion_model.output_blocks.9.1.transformer_blocks.0.ff.net.2.weight": "blocks.173.transformer_blocks.0.ff.weight",
+            "model.diffusion_model.output_blocks.9.1.transformer_blocks.0.norm1.bias": "blocks.173.transformer_blocks.0.norm1.bias",
+            "model.diffusion_model.output_blocks.9.1.transformer_blocks.0.norm1.weight": "blocks.173.transformer_blocks.0.norm1.weight",
+            "model.diffusion_model.output_blocks.9.1.transformer_blocks.0.norm2.bias": "blocks.173.transformer_blocks.0.norm2.bias",
+            "model.diffusion_model.output_blocks.9.1.transformer_blocks.0.norm2.weight": "blocks.173.transformer_blocks.0.norm2.weight",
+            "model.diffusion_model.output_blocks.9.1.transformer_blocks.0.norm3.bias": "blocks.173.transformer_blocks.0.norm3.bias",
+            "model.diffusion_model.output_blocks.9.1.transformer_blocks.0.norm3.weight": "blocks.173.transformer_blocks.0.norm3.weight",
+            "model.diffusion_model.time_embed.0.bias": "time_embedding.0.bias",
+            "model.diffusion_model.time_embed.0.weight": "time_embedding.0.weight",
+            "model.diffusion_model.time_embed.2.bias": "time_embedding.2.bias",
+            "model.diffusion_model.time_embed.2.weight": "time_embedding.2.weight",
+        }
+        state_dict_ = {}
+        for name in state_dict:
+            if name in rename_dict:
+                param = state_dict[name]
+                if ".proj_in." in name or ".proj_out." in name:
+                    param = param.squeeze()
+                state_dict_[rename_dict[name]] = param
+        if add_positional_conv is not None:
+            extra_names = [
+                "blocks.7.positional_conv", "blocks.17.positional_conv", "blocks.29.positional_conv", "blocks.39.positional_conv",
+                "blocks.51.positional_conv", "blocks.61.positional_conv", "blocks.83.positional_conv", "blocks.113.positional_conv",
+                "blocks.123.positional_conv", "blocks.133.positional_conv", "blocks.144.positional_conv", "blocks.154.positional_conv",
+                "blocks.164.positional_conv", "blocks.175.positional_conv", "blocks.185.positional_conv", "blocks.195.positional_conv",
+            ]
+            extra_channels = [320, 320, 640, 640, 1280, 1280, 1280, 1280, 1280, 1280, 640, 640, 640, 320, 320, 320]
+            for name, channels in zip(extra_names, extra_channels):
+                weight = torch.zeros((channels, channels, 3, 3, 3))
+                weight[:,:,1,1,1] = torch.eye(channels, channels)
+                bias = torch.zeros((channels,))
+                state_dict_[name + ".weight"] = weight
+                state_dict_[name + ".bias"] = bias
+        return state_dict_
diff --git a/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/svd_vae_decoder.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/svd_vae_decoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..a4815961136820dc1b863573a559076b68fd785a
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/svd_vae_decoder.py
@@ -0,0 +1,578 @@
+import torch
+from .attention import Attention
+from .sd_unet import ResnetBlock, UpSampler
+from .tiler import TileWorker
+from einops import rearrange, repeat
+
+
+class VAEAttentionBlock(torch.nn.Module):
+
+    def __init__(self, num_attention_heads, attention_head_dim, in_channels, num_layers=1, norm_num_groups=32, eps=1e-5):
+        super().__init__()
+        inner_dim = num_attention_heads * attention_head_dim
+
+        self.norm = torch.nn.GroupNorm(num_groups=norm_num_groups, num_channels=in_channels, eps=eps, affine=True)
+
+        self.transformer_blocks = torch.nn.ModuleList([
+            Attention(
+                inner_dim,
+                num_attention_heads,
+                attention_head_dim,
+                bias_q=True,
+                bias_kv=True,
+                bias_out=True
+            )
+            for d in range(num_layers)
+        ])
+
+    def forward(self, hidden_states, time_emb, text_emb, res_stack):
+        batch, _, height, width = hidden_states.shape
+        residual = hidden_states
+
+        hidden_states = self.norm(hidden_states)
+        inner_dim = hidden_states.shape[1]
+        hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * width, inner_dim)
+
+        for block in self.transformer_blocks:
+            hidden_states = block(hidden_states)
+
+        hidden_states = hidden_states.reshape(batch, height, width, inner_dim).permute(0, 3, 1, 2).contiguous()
+        hidden_states = hidden_states + residual
+
+        return hidden_states, time_emb, text_emb, res_stack
+    
+
+class TemporalResnetBlock(torch.nn.Module):
+
+    def __init__(self, in_channels, out_channels, groups=32, eps=1e-5):
+        super().__init__()
+        self.norm1 = torch.nn.GroupNorm(num_groups=groups, num_channels=in_channels, eps=eps, affine=True)
+        self.conv1 = torch.nn.Conv3d(in_channels, out_channels, kernel_size=(3, 1, 1), stride=1, padding=(1, 0, 0))
+        self.norm2 = torch.nn.GroupNorm(num_groups=groups, num_channels=out_channels, eps=eps, affine=True)
+        self.conv2 = torch.nn.Conv3d(out_channels, out_channels, kernel_size=(3, 1, 1), stride=1, padding=(1, 0, 0))
+        self.nonlinearity = torch.nn.SiLU()
+        self.mix_factor = torch.nn.Parameter(torch.Tensor([0.5]))
+
+    def forward(self, hidden_states, time_emb, text_emb, res_stack, **kwargs):
+        x_spatial = hidden_states
+        x = rearrange(hidden_states, "T C H W -> 1 C T H W")
+        x = self.norm1(x)
+        x = self.nonlinearity(x)
+        x = self.conv1(x)
+        x = self.norm2(x)
+        x = self.nonlinearity(x)
+        x = self.conv2(x)
+        x_temporal = hidden_states + x[0].permute(1, 0, 2, 3)
+        alpha = torch.sigmoid(self.mix_factor)
+        hidden_states = alpha * x_temporal + (1 - alpha) * x_spatial
+        return hidden_states, time_emb, text_emb, res_stack
+    
+
+class SVDVAEDecoder(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.scaling_factor = 0.18215
+        self.conv_in = torch.nn.Conv2d(4, 512, kernel_size=3, padding=1)
+
+        self.blocks = torch.nn.ModuleList([
+            # UNetMidBlock
+            ResnetBlock(512, 512, eps=1e-6),
+            TemporalResnetBlock(512, 512, eps=1e-6),
+            VAEAttentionBlock(1, 512, 512, 1, eps=1e-6),
+            ResnetBlock(512, 512, eps=1e-6),
+            TemporalResnetBlock(512, 512, eps=1e-6),
+            # UpDecoderBlock
+            ResnetBlock(512, 512, eps=1e-6),
+            TemporalResnetBlock(512, 512, eps=1e-6),
+            ResnetBlock(512, 512, eps=1e-6),
+            TemporalResnetBlock(512, 512, eps=1e-6),
+            ResnetBlock(512, 512, eps=1e-6),
+            TemporalResnetBlock(512, 512, eps=1e-6),
+            UpSampler(512),
+            # UpDecoderBlock
+            ResnetBlock(512, 512, eps=1e-6),
+            TemporalResnetBlock(512, 512, eps=1e-6),
+            ResnetBlock(512, 512, eps=1e-6),
+            TemporalResnetBlock(512, 512, eps=1e-6),
+            ResnetBlock(512, 512, eps=1e-6),
+            TemporalResnetBlock(512, 512, eps=1e-6),
+            UpSampler(512),
+            # UpDecoderBlock
+            ResnetBlock(512, 256, eps=1e-6),
+            TemporalResnetBlock(256, 256, eps=1e-6),
+            ResnetBlock(256, 256, eps=1e-6),
+            TemporalResnetBlock(256, 256, eps=1e-6),
+            ResnetBlock(256, 256, eps=1e-6),
+            TemporalResnetBlock(256, 256, eps=1e-6),
+            UpSampler(256),
+            # UpDecoderBlock
+            ResnetBlock(256, 128, eps=1e-6),
+            TemporalResnetBlock(128, 128, eps=1e-6),
+            ResnetBlock(128, 128, eps=1e-6),
+            TemporalResnetBlock(128, 128, eps=1e-6),
+            ResnetBlock(128, 128, eps=1e-6),
+            TemporalResnetBlock(128, 128, eps=1e-6),
+        ])
+
+        self.conv_norm_out = torch.nn.GroupNorm(num_channels=128, num_groups=32, eps=1e-5)
+        self.conv_act = torch.nn.SiLU()
+        self.conv_out = torch.nn.Conv2d(128, 3, kernel_size=3, padding=1)
+        self.time_conv_out = torch.nn.Conv3d(3, 3, kernel_size=(3, 1, 1), padding=(1, 0, 0))
+
+
+    def forward(self, sample):
+        # 1. pre-process
+        hidden_states = rearrange(sample, "C T H W -> T C H W")
+        hidden_states = hidden_states / self.scaling_factor
+        hidden_states = self.conv_in(hidden_states)
+        time_emb, text_emb, res_stack = None, None, None
+
+        # 2. blocks
+        for i, block in enumerate(self.blocks):
+            hidden_states, time_emb, text_emb, res_stack = block(hidden_states, time_emb, text_emb, res_stack)
+
+        # 3. output
+        hidden_states = self.conv_norm_out(hidden_states)
+        hidden_states = self.conv_act(hidden_states)
+        hidden_states = self.conv_out(hidden_states)
+        hidden_states = rearrange(hidden_states, "T C H W -> C T H W")
+        hidden_states = self.time_conv_out(hidden_states)
+
+        return hidden_states
+    
+    
+    def build_mask(self, data, is_bound):
+        _, T, H, W = data.shape
+        t = repeat(torch.arange(T), "T -> T H W", T=T, H=H, W=W)
+        h = repeat(torch.arange(H), "H -> T H W", T=T, H=H, W=W)
+        w = repeat(torch.arange(W), "W -> T H W", T=T, H=H, W=W)
+        border_width = (T + H + W) // 6
+        pad = torch.ones_like(t) * border_width
+        mask = torch.stack([
+            pad if is_bound[0] else t + 1,
+            pad if is_bound[1] else T - t,
+            pad if is_bound[2] else h + 1,
+            pad if is_bound[3] else H - h,
+            pad if is_bound[4] else w + 1,
+            pad if is_bound[5] else W - w
+        ]).min(dim=0).values
+        mask = mask.clip(1, border_width)
+        mask = (mask / border_width).to(dtype=data.dtype, device=data.device)
+        mask = rearrange(mask, "T H W -> 1 T H W")
+        return mask
+    
+
+    def decode_video(
+        self, sample,
+        batch_time=8, batch_height=128, batch_width=128,
+        stride_time=4, stride_height=32, stride_width=32,
+        progress_bar=lambda x:x
+    ):
+        sample = sample.permute(1, 0, 2, 3)
+        data_device = sample.device
+        computation_device = self.conv_in.weight.device
+        torch_dtype = sample.dtype
+        _, T, H, W = sample.shape
+
+        weight = torch.zeros((1, T, H*8, W*8), dtype=torch_dtype, device=data_device)
+        values = torch.zeros((3, T, H*8, W*8), dtype=torch_dtype, device=data_device)
+
+        # Split tasks
+        tasks = []
+        for t in range(0, T, stride_time):
+            for h in range(0, H, stride_height):
+                for w in range(0, W, stride_width):
+                    if (t-stride_time >= 0 and t-stride_time+batch_time >= T)\
+                        or (h-stride_height >= 0 and h-stride_height+batch_height >= H)\
+                        or (w-stride_width >= 0 and w-stride_width+batch_width >= W):
+                        continue
+                    tasks.append((t, t+batch_time, h, h+batch_height, w, w+batch_width))
+        
+        # Run
+        for tl, tr, hl, hr, wl, wr in progress_bar(tasks):
+            sample_batch = sample[:, tl:tr, hl:hr, wl:wr].to(computation_device)
+            sample_batch = self.forward(sample_batch).to(data_device)
+            mask = self.build_mask(sample_batch, is_bound=(tl==0, tr>=T, hl==0, hr>=H, wl==0, wr>=W))
+            values[:, tl:tr, hl*8:hr*8, wl*8:wr*8] += sample_batch * mask
+            weight[:, tl:tr, hl*8:hr*8, wl*8:wr*8] += mask
+        values /= weight
+        return values
+    
+    
+    @staticmethod
+    def state_dict_converter():
+        return SVDVAEDecoderStateDictConverter()
+    
+
+class SVDVAEDecoderStateDictConverter:
+    def __init__(self):
+        pass
+
+    def from_diffusers(self, state_dict):
+        static_rename_dict = {
+            "decoder.conv_in":  "conv_in",
+            "decoder.mid_block.attentions.0.group_norm": "blocks.2.norm",
+            "decoder.mid_block.attentions.0.to_q": "blocks.2.transformer_blocks.0.to_q",
+            "decoder.mid_block.attentions.0.to_k": "blocks.2.transformer_blocks.0.to_k",
+            "decoder.mid_block.attentions.0.to_v": "blocks.2.transformer_blocks.0.to_v",
+            "decoder.mid_block.attentions.0.to_out.0": "blocks.2.transformer_blocks.0.to_out",
+            "decoder.up_blocks.0.upsamplers.0.conv": "blocks.11.conv",
+            "decoder.up_blocks.1.upsamplers.0.conv": "blocks.18.conv",
+            "decoder.up_blocks.2.upsamplers.0.conv": "blocks.25.conv",
+            "decoder.conv_norm_out": "conv_norm_out",
+            "decoder.conv_out": "conv_out",
+            "decoder.time_conv_out": "time_conv_out"
+        }
+        prefix_rename_dict = {
+            "decoder.mid_block.resnets.0.spatial_res_block": "blocks.0",
+            "decoder.mid_block.resnets.0.temporal_res_block": "blocks.1",
+            "decoder.mid_block.resnets.0.time_mixer": "blocks.1",
+            "decoder.mid_block.resnets.1.spatial_res_block": "blocks.3",
+            "decoder.mid_block.resnets.1.temporal_res_block": "blocks.4",
+            "decoder.mid_block.resnets.1.time_mixer": "blocks.4",
+
+            "decoder.up_blocks.0.resnets.0.spatial_res_block": "blocks.5",
+            "decoder.up_blocks.0.resnets.0.temporal_res_block": "blocks.6",
+            "decoder.up_blocks.0.resnets.0.time_mixer": "blocks.6",
+            "decoder.up_blocks.0.resnets.1.spatial_res_block": "blocks.7",
+            "decoder.up_blocks.0.resnets.1.temporal_res_block": "blocks.8",
+            "decoder.up_blocks.0.resnets.1.time_mixer": "blocks.8",
+            "decoder.up_blocks.0.resnets.2.spatial_res_block": "blocks.9",
+            "decoder.up_blocks.0.resnets.2.temporal_res_block": "blocks.10",
+            "decoder.up_blocks.0.resnets.2.time_mixer": "blocks.10",
+
+            "decoder.up_blocks.1.resnets.0.spatial_res_block": "blocks.12",
+            "decoder.up_blocks.1.resnets.0.temporal_res_block": "blocks.13",
+            "decoder.up_blocks.1.resnets.0.time_mixer": "blocks.13",
+            "decoder.up_blocks.1.resnets.1.spatial_res_block": "blocks.14",
+            "decoder.up_blocks.1.resnets.1.temporal_res_block": "blocks.15",
+            "decoder.up_blocks.1.resnets.1.time_mixer": "blocks.15",
+            "decoder.up_blocks.1.resnets.2.spatial_res_block": "blocks.16",
+            "decoder.up_blocks.1.resnets.2.temporal_res_block": "blocks.17",
+            "decoder.up_blocks.1.resnets.2.time_mixer": "blocks.17",
+
+            "decoder.up_blocks.2.resnets.0.spatial_res_block": "blocks.19",
+            "decoder.up_blocks.2.resnets.0.temporal_res_block": "blocks.20",
+            "decoder.up_blocks.2.resnets.0.time_mixer": "blocks.20",
+            "decoder.up_blocks.2.resnets.1.spatial_res_block": "blocks.21",
+            "decoder.up_blocks.2.resnets.1.temporal_res_block": "blocks.22",
+            "decoder.up_blocks.2.resnets.1.time_mixer": "blocks.22",
+            "decoder.up_blocks.2.resnets.2.spatial_res_block": "blocks.23",
+            "decoder.up_blocks.2.resnets.2.temporal_res_block": "blocks.24",
+            "decoder.up_blocks.2.resnets.2.time_mixer": "blocks.24",
+
+            "decoder.up_blocks.3.resnets.0.spatial_res_block": "blocks.26",
+            "decoder.up_blocks.3.resnets.0.temporal_res_block": "blocks.27",
+            "decoder.up_blocks.3.resnets.0.time_mixer": "blocks.27",
+            "decoder.up_blocks.3.resnets.1.spatial_res_block": "blocks.28",
+            "decoder.up_blocks.3.resnets.1.temporal_res_block": "blocks.29",
+            "decoder.up_blocks.3.resnets.1.time_mixer": "blocks.29",
+            "decoder.up_blocks.3.resnets.2.spatial_res_block": "blocks.30",
+            "decoder.up_blocks.3.resnets.2.temporal_res_block": "blocks.31",
+            "decoder.up_blocks.3.resnets.2.time_mixer": "blocks.31",
+        }
+        suffix_rename_dict = {
+            "norm1.weight": "norm1.weight",
+            "conv1.weight": "conv1.weight",
+            "norm2.weight": "norm2.weight",
+            "conv2.weight": "conv2.weight",
+            "conv_shortcut.weight": "conv_shortcut.weight",
+            "norm1.bias": "norm1.bias",
+            "conv1.bias": "conv1.bias",
+            "norm2.bias": "norm2.bias",
+            "conv2.bias": "conv2.bias",
+            "conv_shortcut.bias": "conv_shortcut.bias",
+            "mix_factor": "mix_factor",
+        }
+
+        state_dict_ = {}
+        for name in static_rename_dict:
+            state_dict_[static_rename_dict[name] + ".weight"] = state_dict[name + ".weight"]
+            state_dict_[static_rename_dict[name] + ".bias"] = state_dict[name + ".bias"]
+        for prefix_name in prefix_rename_dict:
+            for suffix_name in suffix_rename_dict:
+                name = prefix_name + "." + suffix_name
+                name_ = prefix_rename_dict[prefix_name] + "." + suffix_rename_dict[suffix_name]
+                if name in state_dict:
+                    state_dict_[name_] = state_dict[name]
+        
+        return state_dict_
+    
+
+    def from_civitai(self, state_dict):
+        rename_dict = {
+            "first_stage_model.decoder.conv_in.bias": "conv_in.bias",
+            "first_stage_model.decoder.conv_in.weight": "conv_in.weight",
+            "first_stage_model.decoder.conv_out.bias": "conv_out.bias",
+            "first_stage_model.decoder.conv_out.time_mix_conv.bias": "time_conv_out.bias",
+            "first_stage_model.decoder.conv_out.time_mix_conv.weight": "time_conv_out.weight",
+            "first_stage_model.decoder.conv_out.weight": "conv_out.weight",
+            "first_stage_model.decoder.mid.attn_1.k.bias": "blocks.2.transformer_blocks.0.to_k.bias",
+            "first_stage_model.decoder.mid.attn_1.k.weight": "blocks.2.transformer_blocks.0.to_k.weight",
+            "first_stage_model.decoder.mid.attn_1.norm.bias": "blocks.2.norm.bias",
+            "first_stage_model.decoder.mid.attn_1.norm.weight": "blocks.2.norm.weight",
+            "first_stage_model.decoder.mid.attn_1.proj_out.bias": "blocks.2.transformer_blocks.0.to_out.bias",
+            "first_stage_model.decoder.mid.attn_1.proj_out.weight": "blocks.2.transformer_blocks.0.to_out.weight",
+            "first_stage_model.decoder.mid.attn_1.q.bias": "blocks.2.transformer_blocks.0.to_q.bias",
+            "first_stage_model.decoder.mid.attn_1.q.weight": "blocks.2.transformer_blocks.0.to_q.weight",
+            "first_stage_model.decoder.mid.attn_1.v.bias": "blocks.2.transformer_blocks.0.to_v.bias",
+            "first_stage_model.decoder.mid.attn_1.v.weight": "blocks.2.transformer_blocks.0.to_v.weight",
+            "first_stage_model.decoder.mid.block_1.conv1.bias": "blocks.0.conv1.bias",
+            "first_stage_model.decoder.mid.block_1.conv1.weight": "blocks.0.conv1.weight",
+            "first_stage_model.decoder.mid.block_1.conv2.bias": "blocks.0.conv2.bias",
+            "first_stage_model.decoder.mid.block_1.conv2.weight": "blocks.0.conv2.weight",
+            "first_stage_model.decoder.mid.block_1.mix_factor": "blocks.1.mix_factor",
+            "first_stage_model.decoder.mid.block_1.norm1.bias": "blocks.0.norm1.bias",
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+            "first_stage_model.decoder.up.3.block.1.conv2.weight": "blocks.7.conv2.weight",
+            "first_stage_model.decoder.up.3.block.1.mix_factor": "blocks.8.mix_factor",
+            "first_stage_model.decoder.up.3.block.1.norm1.bias": "blocks.7.norm1.bias",
+            "first_stage_model.decoder.up.3.block.1.norm1.weight": "blocks.7.norm1.weight",
+            "first_stage_model.decoder.up.3.block.1.norm2.bias": "blocks.7.norm2.bias",
+            "first_stage_model.decoder.up.3.block.1.norm2.weight": "blocks.7.norm2.weight",
+            "first_stage_model.decoder.up.3.block.1.time_stack.in_layers.0.bias": "blocks.8.norm1.bias",
+            "first_stage_model.decoder.up.3.block.1.time_stack.in_layers.0.weight": "blocks.8.norm1.weight",
+            "first_stage_model.decoder.up.3.block.1.time_stack.in_layers.2.bias": "blocks.8.conv1.bias",
+            "first_stage_model.decoder.up.3.block.1.time_stack.in_layers.2.weight": "blocks.8.conv1.weight",
+            "first_stage_model.decoder.up.3.block.1.time_stack.out_layers.0.bias": "blocks.8.norm2.bias",
+            "first_stage_model.decoder.up.3.block.1.time_stack.out_layers.0.weight": "blocks.8.norm2.weight",
+            "first_stage_model.decoder.up.3.block.1.time_stack.out_layers.3.bias": "blocks.8.conv2.bias",
+            "first_stage_model.decoder.up.3.block.1.time_stack.out_layers.3.weight": "blocks.8.conv2.weight",
+            "first_stage_model.decoder.up.3.block.2.conv1.bias": "blocks.9.conv1.bias",
+            "first_stage_model.decoder.up.3.block.2.conv1.weight": "blocks.9.conv1.weight",
+            "first_stage_model.decoder.up.3.block.2.conv2.bias": "blocks.9.conv2.bias",
+            "first_stage_model.decoder.up.3.block.2.conv2.weight": "blocks.9.conv2.weight",
+            "first_stage_model.decoder.up.3.block.2.mix_factor": "blocks.10.mix_factor",
+            "first_stage_model.decoder.up.3.block.2.norm1.bias": "blocks.9.norm1.bias",
+            "first_stage_model.decoder.up.3.block.2.norm1.weight": "blocks.9.norm1.weight",
+            "first_stage_model.decoder.up.3.block.2.norm2.bias": "blocks.9.norm2.bias",
+            "first_stage_model.decoder.up.3.block.2.norm2.weight": "blocks.9.norm2.weight",
+            "first_stage_model.decoder.up.3.block.2.time_stack.in_layers.0.bias": "blocks.10.norm1.bias",
+            "first_stage_model.decoder.up.3.block.2.time_stack.in_layers.0.weight": "blocks.10.norm1.weight",
+            "first_stage_model.decoder.up.3.block.2.time_stack.in_layers.2.bias": "blocks.10.conv1.bias",
+            "first_stage_model.decoder.up.3.block.2.time_stack.in_layers.2.weight": "blocks.10.conv1.weight",
+            "first_stage_model.decoder.up.3.block.2.time_stack.out_layers.0.bias": "blocks.10.norm2.bias",
+            "first_stage_model.decoder.up.3.block.2.time_stack.out_layers.0.weight": "blocks.10.norm2.weight",
+            "first_stage_model.decoder.up.3.block.2.time_stack.out_layers.3.bias": "blocks.10.conv2.bias",
+            "first_stage_model.decoder.up.3.block.2.time_stack.out_layers.3.weight": "blocks.10.conv2.weight",
+            "first_stage_model.decoder.up.3.upsample.conv.bias": "blocks.11.conv.bias",
+            "first_stage_model.decoder.up.3.upsample.conv.weight": "blocks.11.conv.weight",
+        }
+        state_dict_ = {}
+        for name in state_dict:
+            if name in rename_dict:
+                param = state_dict[name]
+                if "blocks.2.transformer_blocks.0" in rename_dict[name]:
+                    param = param.squeeze()
+                state_dict_[rename_dict[name]] = param
+        return state_dict_
diff --git a/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/svd_vae_encoder.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/svd_vae_encoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..04a543a22c2794095d5f56089b2ca60d445fbc4e
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/svd_vae_encoder.py
@@ -0,0 +1,139 @@
+from .sd_vae_encoder import SDVAEEncoderStateDictConverter, SDVAEEncoder
+
+
+class SVDVAEEncoder(SDVAEEncoder):
+    def __init__(self):
+        super().__init__()
+        self.scaling_factor = 0.13025
+    
+    @staticmethod
+    def state_dict_converter():
+        return SVDVAEEncoderStateDictConverter()
+
+
+class SVDVAEEncoderStateDictConverter(SDVAEEncoderStateDictConverter):
+    def __init__(self):
+        super().__init__()
+
+    def from_diffusers(self, state_dict):
+        return super().from_diffusers(state_dict)
+    
+    def from_civitai(self, state_dict):
+        rename_dict = {
+            "conditioner.embedders.3.encoder.encoder.conv_in.bias": "conv_in.bias",
+            "conditioner.embedders.3.encoder.encoder.conv_in.weight": "conv_in.weight",
+            "conditioner.embedders.3.encoder.encoder.conv_out.bias": "conv_out.bias",
+            "conditioner.embedders.3.encoder.encoder.conv_out.weight": "conv_out.weight",
+            "conditioner.embedders.3.encoder.encoder.down.0.block.0.conv1.bias": "blocks.0.conv1.bias",
+            "conditioner.embedders.3.encoder.encoder.down.0.block.0.conv1.weight": "blocks.0.conv1.weight",
+            "conditioner.embedders.3.encoder.encoder.down.0.block.0.conv2.bias": "blocks.0.conv2.bias",
+            "conditioner.embedders.3.encoder.encoder.down.0.block.0.conv2.weight": "blocks.0.conv2.weight",
+            "conditioner.embedders.3.encoder.encoder.down.0.block.0.norm1.bias": "blocks.0.norm1.bias",
+            "conditioner.embedders.3.encoder.encoder.down.0.block.0.norm1.weight": "blocks.0.norm1.weight",
+            "conditioner.embedders.3.encoder.encoder.down.0.block.0.norm2.bias": "blocks.0.norm2.bias",
+            "conditioner.embedders.3.encoder.encoder.down.0.block.0.norm2.weight": "blocks.0.norm2.weight",
+            "conditioner.embedders.3.encoder.encoder.down.0.block.1.conv1.bias": "blocks.1.conv1.bias",
+            "conditioner.embedders.3.encoder.encoder.down.0.block.1.conv1.weight": "blocks.1.conv1.weight",
+            "conditioner.embedders.3.encoder.encoder.down.0.block.1.conv2.bias": "blocks.1.conv2.bias",
+            "conditioner.embedders.3.encoder.encoder.down.0.block.1.conv2.weight": "blocks.1.conv2.weight",
+            "conditioner.embedders.3.encoder.encoder.down.0.block.1.norm1.bias": "blocks.1.norm1.bias",
+            "conditioner.embedders.3.encoder.encoder.down.0.block.1.norm1.weight": "blocks.1.norm1.weight",
+            "conditioner.embedders.3.encoder.encoder.down.0.block.1.norm2.bias": "blocks.1.norm2.bias",
+            "conditioner.embedders.3.encoder.encoder.down.0.block.1.norm2.weight": "blocks.1.norm2.weight",
+            "conditioner.embedders.3.encoder.encoder.down.0.downsample.conv.bias": "blocks.2.conv.bias",
+            "conditioner.embedders.3.encoder.encoder.down.0.downsample.conv.weight": "blocks.2.conv.weight",
+            "conditioner.embedders.3.encoder.encoder.down.1.block.0.conv1.bias": "blocks.3.conv1.bias",
+            "conditioner.embedders.3.encoder.encoder.down.1.block.0.conv1.weight": "blocks.3.conv1.weight",
+            "conditioner.embedders.3.encoder.encoder.down.1.block.0.conv2.bias": "blocks.3.conv2.bias",
+            "conditioner.embedders.3.encoder.encoder.down.1.block.0.conv2.weight": "blocks.3.conv2.weight",
+            "conditioner.embedders.3.encoder.encoder.down.1.block.0.nin_shortcut.bias": "blocks.3.conv_shortcut.bias",
+            "conditioner.embedders.3.encoder.encoder.down.1.block.0.nin_shortcut.weight": "blocks.3.conv_shortcut.weight",
+            "conditioner.embedders.3.encoder.encoder.down.1.block.0.norm1.bias": "blocks.3.norm1.bias",
+            "conditioner.embedders.3.encoder.encoder.down.1.block.0.norm1.weight": "blocks.3.norm1.weight",
+            "conditioner.embedders.3.encoder.encoder.down.1.block.0.norm2.bias": "blocks.3.norm2.bias",
+            "conditioner.embedders.3.encoder.encoder.down.1.block.0.norm2.weight": "blocks.3.norm2.weight",
+            "conditioner.embedders.3.encoder.encoder.down.1.block.1.conv1.bias": "blocks.4.conv1.bias",
+            "conditioner.embedders.3.encoder.encoder.down.1.block.1.conv1.weight": "blocks.4.conv1.weight",
+            "conditioner.embedders.3.encoder.encoder.down.1.block.1.conv2.bias": "blocks.4.conv2.bias",
+            "conditioner.embedders.3.encoder.encoder.down.1.block.1.conv2.weight": "blocks.4.conv2.weight",
+            "conditioner.embedders.3.encoder.encoder.down.1.block.1.norm1.bias": "blocks.4.norm1.bias",
+            "conditioner.embedders.3.encoder.encoder.down.1.block.1.norm1.weight": "blocks.4.norm1.weight",
+            "conditioner.embedders.3.encoder.encoder.down.1.block.1.norm2.bias": "blocks.4.norm2.bias",
+            "conditioner.embedders.3.encoder.encoder.down.1.block.1.norm2.weight": "blocks.4.norm2.weight",
+            "conditioner.embedders.3.encoder.encoder.down.1.downsample.conv.bias": "blocks.5.conv.bias",
+            "conditioner.embedders.3.encoder.encoder.down.1.downsample.conv.weight": "blocks.5.conv.weight",
+            "conditioner.embedders.3.encoder.encoder.down.2.block.0.conv1.bias": "blocks.6.conv1.bias",
+            "conditioner.embedders.3.encoder.encoder.down.2.block.0.conv1.weight": "blocks.6.conv1.weight",
+            "conditioner.embedders.3.encoder.encoder.down.2.block.0.conv2.bias": "blocks.6.conv2.bias",
+            "conditioner.embedders.3.encoder.encoder.down.2.block.0.conv2.weight": "blocks.6.conv2.weight",
+            "conditioner.embedders.3.encoder.encoder.down.2.block.0.nin_shortcut.bias": "blocks.6.conv_shortcut.bias",
+            "conditioner.embedders.3.encoder.encoder.down.2.block.0.nin_shortcut.weight": "blocks.6.conv_shortcut.weight",
+            "conditioner.embedders.3.encoder.encoder.down.2.block.0.norm1.bias": "blocks.6.norm1.bias",
+            "conditioner.embedders.3.encoder.encoder.down.2.block.0.norm1.weight": "blocks.6.norm1.weight",
+            "conditioner.embedders.3.encoder.encoder.down.2.block.0.norm2.bias": "blocks.6.norm2.bias",
+            "conditioner.embedders.3.encoder.encoder.down.2.block.0.norm2.weight": "blocks.6.norm2.weight",
+            "conditioner.embedders.3.encoder.encoder.down.2.block.1.conv1.bias": "blocks.7.conv1.bias",
+            "conditioner.embedders.3.encoder.encoder.down.2.block.1.conv1.weight": "blocks.7.conv1.weight",
+            "conditioner.embedders.3.encoder.encoder.down.2.block.1.conv2.bias": "blocks.7.conv2.bias",
+            "conditioner.embedders.3.encoder.encoder.down.2.block.1.conv2.weight": "blocks.7.conv2.weight",
+            "conditioner.embedders.3.encoder.encoder.down.2.block.1.norm1.bias": "blocks.7.norm1.bias",
+            "conditioner.embedders.3.encoder.encoder.down.2.block.1.norm1.weight": "blocks.7.norm1.weight",
+            "conditioner.embedders.3.encoder.encoder.down.2.block.1.norm2.bias": "blocks.7.norm2.bias",
+            "conditioner.embedders.3.encoder.encoder.down.2.block.1.norm2.weight": "blocks.7.norm2.weight",
+            "conditioner.embedders.3.encoder.encoder.down.2.downsample.conv.bias": "blocks.8.conv.bias",
+            "conditioner.embedders.3.encoder.encoder.down.2.downsample.conv.weight": "blocks.8.conv.weight",
+            "conditioner.embedders.3.encoder.encoder.down.3.block.0.conv1.bias": "blocks.9.conv1.bias",
+            "conditioner.embedders.3.encoder.encoder.down.3.block.0.conv1.weight": "blocks.9.conv1.weight",
+            "conditioner.embedders.3.encoder.encoder.down.3.block.0.conv2.bias": "blocks.9.conv2.bias",
+            "conditioner.embedders.3.encoder.encoder.down.3.block.0.conv2.weight": "blocks.9.conv2.weight",
+            "conditioner.embedders.3.encoder.encoder.down.3.block.0.norm1.bias": "blocks.9.norm1.bias",
+            "conditioner.embedders.3.encoder.encoder.down.3.block.0.norm1.weight": "blocks.9.norm1.weight",
+            "conditioner.embedders.3.encoder.encoder.down.3.block.0.norm2.bias": "blocks.9.norm2.bias",
+            "conditioner.embedders.3.encoder.encoder.down.3.block.0.norm2.weight": "blocks.9.norm2.weight",
+            "conditioner.embedders.3.encoder.encoder.down.3.block.1.conv1.bias": "blocks.10.conv1.bias",
+            "conditioner.embedders.3.encoder.encoder.down.3.block.1.conv1.weight": "blocks.10.conv1.weight",
+            "conditioner.embedders.3.encoder.encoder.down.3.block.1.conv2.bias": "blocks.10.conv2.bias",
+            "conditioner.embedders.3.encoder.encoder.down.3.block.1.conv2.weight": "blocks.10.conv2.weight",
+            "conditioner.embedders.3.encoder.encoder.down.3.block.1.norm1.bias": "blocks.10.norm1.bias",
+            "conditioner.embedders.3.encoder.encoder.down.3.block.1.norm1.weight": "blocks.10.norm1.weight",
+            "conditioner.embedders.3.encoder.encoder.down.3.block.1.norm2.bias": "blocks.10.norm2.bias",
+            "conditioner.embedders.3.encoder.encoder.down.3.block.1.norm2.weight": "blocks.10.norm2.weight",
+            "conditioner.embedders.3.encoder.encoder.mid.attn_1.k.bias": "blocks.12.transformer_blocks.0.to_k.bias",
+            "conditioner.embedders.3.encoder.encoder.mid.attn_1.k.weight": "blocks.12.transformer_blocks.0.to_k.weight",
+            "conditioner.embedders.3.encoder.encoder.mid.attn_1.norm.bias": "blocks.12.norm.bias",
+            "conditioner.embedders.3.encoder.encoder.mid.attn_1.norm.weight": "blocks.12.norm.weight",
+            "conditioner.embedders.3.encoder.encoder.mid.attn_1.proj_out.bias": "blocks.12.transformer_blocks.0.to_out.bias",
+            "conditioner.embedders.3.encoder.encoder.mid.attn_1.proj_out.weight": "blocks.12.transformer_blocks.0.to_out.weight",
+            "conditioner.embedders.3.encoder.encoder.mid.attn_1.q.bias": "blocks.12.transformer_blocks.0.to_q.bias",
+            "conditioner.embedders.3.encoder.encoder.mid.attn_1.q.weight": "blocks.12.transformer_blocks.0.to_q.weight",
+            "conditioner.embedders.3.encoder.encoder.mid.attn_1.v.bias": "blocks.12.transformer_blocks.0.to_v.bias",
+            "conditioner.embedders.3.encoder.encoder.mid.attn_1.v.weight": "blocks.12.transformer_blocks.0.to_v.weight",
+            "conditioner.embedders.3.encoder.encoder.mid.block_1.conv1.bias": "blocks.11.conv1.bias",
+            "conditioner.embedders.3.encoder.encoder.mid.block_1.conv1.weight": "blocks.11.conv1.weight",
+            "conditioner.embedders.3.encoder.encoder.mid.block_1.conv2.bias": "blocks.11.conv2.bias",
+            "conditioner.embedders.3.encoder.encoder.mid.block_1.conv2.weight": "blocks.11.conv2.weight",
+            "conditioner.embedders.3.encoder.encoder.mid.block_1.norm1.bias": "blocks.11.norm1.bias",
+            "conditioner.embedders.3.encoder.encoder.mid.block_1.norm1.weight": "blocks.11.norm1.weight",
+            "conditioner.embedders.3.encoder.encoder.mid.block_1.norm2.bias": "blocks.11.norm2.bias",
+            "conditioner.embedders.3.encoder.encoder.mid.block_1.norm2.weight": "blocks.11.norm2.weight",
+            "conditioner.embedders.3.encoder.encoder.mid.block_2.conv1.bias": "blocks.13.conv1.bias",
+            "conditioner.embedders.3.encoder.encoder.mid.block_2.conv1.weight": "blocks.13.conv1.weight",
+            "conditioner.embedders.3.encoder.encoder.mid.block_2.conv2.bias": "blocks.13.conv2.bias",
+            "conditioner.embedders.3.encoder.encoder.mid.block_2.conv2.weight": "blocks.13.conv2.weight",
+            "conditioner.embedders.3.encoder.encoder.mid.block_2.norm1.bias": "blocks.13.norm1.bias",
+            "conditioner.embedders.3.encoder.encoder.mid.block_2.norm1.weight": "blocks.13.norm1.weight",
+            "conditioner.embedders.3.encoder.encoder.mid.block_2.norm2.bias": "blocks.13.norm2.bias",
+            "conditioner.embedders.3.encoder.encoder.mid.block_2.norm2.weight": "blocks.13.norm2.weight",
+            "conditioner.embedders.3.encoder.encoder.norm_out.bias": "conv_norm_out.bias",
+            "conditioner.embedders.3.encoder.encoder.norm_out.weight": "conv_norm_out.weight",
+            "conditioner.embedders.3.encoder.quant_conv.bias": "quant_conv.bias",
+            "conditioner.embedders.3.encoder.quant_conv.weight": "quant_conv.weight",
+        }
+        state_dict_ = {}
+        for name in state_dict:
+            if name in rename_dict:
+                param = state_dict[name]
+                if "transformer_blocks" in rename_dict[name]:
+                    param = param.squeeze()
+                state_dict_[rename_dict[name]] = param
+        return state_dict_
diff --git a/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/tiler.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/tiler.py
new file mode 100644
index 0000000000000000000000000000000000000000..dff5ebf2674b504f0b66a6ba7aba800e048f5099
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/tiler.py
@@ -0,0 +1,234 @@
+import torch
+from einops import rearrange, repeat
+
+
+class TileWorker:
+    def __init__(self):
+        pass
+
+
+    def mask(self, height, width, border_width):
+        # Create a mask with shape (height, width).
+        # The centre area is filled with 1, and the border line is filled with values in range (0, 1].
+        x = torch.arange(height).repeat(width, 1).T
+        y = torch.arange(width).repeat(height, 1)
+        mask = torch.stack([x + 1, height - x, y + 1, width - y]).min(dim=0).values
+        mask = (mask / border_width).clip(0, 1)
+        return mask
+
+
+    def tile(self, model_input, tile_size, tile_stride, tile_device, tile_dtype):
+        # Convert a tensor (b, c, h, w) to (b, c, tile_size, tile_size, tile_num)
+        batch_size, channel, _, _ = model_input.shape
+        model_input = model_input.to(device=tile_device, dtype=tile_dtype)
+        unfold_operator = torch.nn.Unfold(
+            kernel_size=(tile_size, tile_size),
+            stride=(tile_stride, tile_stride)
+        )
+        model_input = unfold_operator(model_input)
+        model_input = model_input.view((batch_size, channel, tile_size, tile_size, -1))
+
+        return model_input
+
+
+    def tiled_inference(self, forward_fn, model_input, tile_batch_size, inference_device, inference_dtype, tile_device, tile_dtype):
+        # Call y=forward_fn(x) for each tile
+        tile_num = model_input.shape[-1]
+        model_output_stack = []
+
+        for tile_id in range(0, tile_num, tile_batch_size):
+
+            # process input
+            tile_id_ = min(tile_id + tile_batch_size, tile_num)
+            x = model_input[:, :, :, :, tile_id: tile_id_]
+            x = x.to(device=inference_device, dtype=inference_dtype)
+            x = rearrange(x, "b c h w n -> (n b) c h w")
+
+            # process output
+            y = forward_fn(x)
+            y = rearrange(y, "(n b) c h w -> b c h w n", n=tile_id_-tile_id)
+            y = y.to(device=tile_device, dtype=tile_dtype)
+            model_output_stack.append(y)
+
+        model_output = torch.concat(model_output_stack, dim=-1)
+        return model_output
+
+
+    def io_scale(self, model_output, tile_size):
+        # Determine the size modification happened in forward_fn
+        # We only consider the same scale on height and width.
+        io_scale = model_output.shape[2] / tile_size
+        return io_scale
+    
+
+    def untile(self, model_output, height, width, tile_size, tile_stride, border_width, tile_device, tile_dtype):
+        # The reversed function of tile
+        mask = self.mask(tile_size, tile_size, border_width)
+        mask = mask.to(device=tile_device, dtype=tile_dtype)
+        mask = rearrange(mask, "h w -> 1 1 h w 1")
+        model_output = model_output * mask
+
+        fold_operator = torch.nn.Fold(
+            output_size=(height, width),
+            kernel_size=(tile_size, tile_size),
+            stride=(tile_stride, tile_stride)
+        )
+        mask = repeat(mask[0, 0, :, :, 0], "h w -> 1 (h w) n", n=model_output.shape[-1])
+        model_output = rearrange(model_output, "b c h w n -> b (c h w) n")
+        model_output = fold_operator(model_output) / fold_operator(mask)
+
+        return model_output
+
+
+    def tiled_forward(self, forward_fn, model_input, tile_size, tile_stride, tile_batch_size=1, tile_device="cpu", tile_dtype=torch.float32, border_width=None):
+        # Prepare
+        inference_device, inference_dtype = model_input.device, model_input.dtype
+        height, width = model_input.shape[2], model_input.shape[3]
+        border_width = int(tile_stride*0.5) if border_width is None else border_width
+
+        # tile
+        model_input = self.tile(model_input, tile_size, tile_stride, tile_device, tile_dtype)
+
+        # inference
+        model_output = self.tiled_inference(forward_fn, model_input, tile_batch_size, inference_device, inference_dtype, tile_device, tile_dtype)
+
+        # resize
+        io_scale = self.io_scale(model_output, tile_size)
+        height, width = int(height*io_scale), int(width*io_scale)
+        tile_size, tile_stride = int(tile_size*io_scale), int(tile_stride*io_scale)
+        border_width = int(border_width*io_scale)
+
+        # untile
+        model_output = self.untile(model_output, height, width, tile_size, tile_stride, border_width, tile_device, tile_dtype)
+        
+        # Done!
+        model_output = model_output.to(device=inference_device, dtype=inference_dtype)
+        return model_output
+    
+
+
+class FastTileWorker:
+    def __init__(self):
+        pass
+
+
+    def build_mask(self, data, is_bound):
+        _, _, H, W = data.shape
+        h = repeat(torch.arange(H), "H -> H W", H=H, W=W)
+        w = repeat(torch.arange(W), "W -> H W", H=H, W=W)
+        border_width = (H + W) // 4
+        pad = torch.ones_like(h) * border_width
+        mask = torch.stack([
+            pad if is_bound[0] else h + 1,
+            pad if is_bound[1] else H - h,
+            pad if is_bound[2] else w + 1,
+            pad if is_bound[3] else W - w
+        ]).min(dim=0).values
+        mask = mask.clip(1, border_width)
+        mask = (mask / border_width).to(dtype=data.dtype, device=data.device)
+        mask = rearrange(mask, "H W -> 1 H W")
+        return mask
+
+
+    def tiled_forward(self, forward_fn, model_input, tile_size, tile_stride, tile_device="cpu", tile_dtype=torch.float32, border_width=None):
+        # Prepare
+        B, C, H, W = model_input.shape
+        border_width = int(tile_stride*0.5) if border_width is None else border_width
+        weight = torch.zeros((1, 1, H, W), dtype=tile_dtype, device=tile_device)
+        values = torch.zeros((B, C, H, W), dtype=tile_dtype, device=tile_device)
+
+        # Split tasks
+        tasks = []
+        for h in range(0, H, tile_stride):
+            for w in range(0, W, tile_stride):
+                if (h-tile_stride >= 0 and h-tile_stride+tile_size >= H) or (w-tile_stride >= 0 and w-tile_stride+tile_size >= W):
+                    continue
+                h_, w_ = h + tile_size, w + tile_size
+                if h_ > H: h, h_ = H - tile_size, H
+                if w_ > W: w, w_ = W - tile_size, W
+                tasks.append((h, h_, w, w_))
+        
+        # Run
+        for hl, hr, wl, wr in tasks:
+            # Forward
+            hidden_states_batch = forward_fn(hl, hr, wl, wr).to(dtype=tile_dtype, device=tile_device)
+
+            mask = self.build_mask(hidden_states_batch, is_bound=(hl==0, hr>=H, wl==0, wr>=W))
+            values[:, :, hl:hr, wl:wr] += hidden_states_batch * mask
+            weight[:, :, hl:hr, wl:wr] += mask
+        values /= weight
+        return values
+
+
+
+class TileWorker2Dto3D:
+    """
+    Process 3D tensors, but only enable TileWorker on 2D.
+    """
+    def __init__(self):
+        pass
+
+
+    def build_mask(self, T, H, W, dtype, device, is_bound, border_width):
+        t = repeat(torch.arange(T), "T -> T H W", T=T, H=H, W=W)
+        h = repeat(torch.arange(H), "H -> T H W", T=T, H=H, W=W)
+        w = repeat(torch.arange(W), "W -> T H W", T=T, H=H, W=W)
+        border_width = (H + W) // 4 if border_width is None else border_width
+        pad = torch.ones_like(h) * border_width
+        mask = torch.stack([
+            pad if is_bound[0] else t + 1,
+            pad if is_bound[1] else T - t,
+            pad if is_bound[2] else h + 1,
+            pad if is_bound[3] else H - h,
+            pad if is_bound[4] else w + 1,
+            pad if is_bound[5] else W - w
+        ]).min(dim=0).values
+        mask = mask.clip(1, border_width)
+        mask = (mask / border_width).to(dtype=dtype, device=device)
+        mask = rearrange(mask, "T H W -> 1 1 T H W")
+        return mask
+
+
+    def tiled_forward(
+        self,
+        forward_fn,
+        model_input,
+        tile_size, tile_stride,
+        tile_device="cpu", tile_dtype=torch.float32,
+        computation_device="cuda", computation_dtype=torch.float32,
+        border_width=None, scales=[1, 1, 1, 1],
+        progress_bar=lambda x:x
+    ):
+        B, C, T, H, W = model_input.shape
+        scale_C, scale_T, scale_H, scale_W = scales
+        tile_size_H, tile_size_W = tile_size
+        tile_stride_H, tile_stride_W = tile_stride
+
+        value = torch.zeros((B, int(C*scale_C), int(T*scale_T), int(H*scale_H), int(W*scale_W)), dtype=tile_dtype, device=tile_device)
+        weight = torch.zeros((1, 1, int(T*scale_T), int(H*scale_H), int(W*scale_W)), dtype=tile_dtype, device=tile_device)
+
+        # Split tasks
+        tasks = []
+        for h in range(0, H, tile_stride_H):
+            for w in range(0, W, tile_stride_W):
+                if (h-tile_stride_H >= 0 and h-tile_stride_H+tile_size_H >= H) or (w-tile_stride_W >= 0 and w-tile_stride_W+tile_size_W >= W):
+                    continue
+                h_, w_ = h + tile_size_H, w + tile_size_W
+                if h_ > H: h, h_ = max(H - tile_size_H, 0), H
+                if w_ > W: w, w_ = max(W - tile_size_W, 0), W
+                tasks.append((h, h_, w, w_))
+
+        # Run
+        for hl, hr, wl, wr in progress_bar(tasks):
+            mask = self.build_mask(
+                int(T*scale_T), int((hr-hl)*scale_H), int((wr-wl)*scale_W),
+                tile_dtype, tile_device,
+                is_bound=(True, True, hl==0, hr>=H, wl==0, wr>=W),
+                border_width=border_width
+            )
+            grid_input = model_input[:, :, :, hl:hr, wl:wr].to(dtype=computation_dtype, device=computation_device)
+            grid_output = forward_fn(grid_input).to(dtype=tile_dtype, device=tile_device)
+            value[:, :, :, int(hl*scale_H):int(hr*scale_H), int(wl*scale_W):int(wr*scale_W)] += grid_output * mask
+            weight[:, :, :, int(hl*scale_H):int(hr*scale_H), int(wl*scale_W):int(wr*scale_W)] += mask
+        value = value / weight
+        return value
\ No newline at end of file
diff --git a/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/utils.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..34a123f84abe3b1c26f3506665bd64ff163040dd
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/utils.py
@@ -0,0 +1,182 @@
+import torch, os
+from safetensors import safe_open
+from contextlib import contextmanager
+import hashlib
+
+@contextmanager
+def init_weights_on_device(device = torch.device("meta"), include_buffers :bool = False):
+    
+    old_register_parameter = torch.nn.Module.register_parameter
+    if include_buffers:
+        old_register_buffer = torch.nn.Module.register_buffer
+    
+    def register_empty_parameter(module, name, param):
+        old_register_parameter(module, name, param)
+        if param is not None:
+            param_cls = type(module._parameters[name])
+            kwargs = module._parameters[name].__dict__
+            kwargs["requires_grad"] = param.requires_grad
+            module._parameters[name] = param_cls(module._parameters[name].to(device), **kwargs)
+
+    def register_empty_buffer(module, name, buffer, persistent=True):
+        old_register_buffer(module, name, buffer, persistent=persistent)
+        if buffer is not None:
+            module._buffers[name] = module._buffers[name].to(device)
+            
+    def patch_tensor_constructor(fn):
+        def wrapper(*args, **kwargs):
+            kwargs["device"] = device
+            return fn(*args, **kwargs)
+
+        return wrapper
+    
+    if include_buffers:
+        tensor_constructors_to_patch = {
+            torch_function_name: getattr(torch, torch_function_name)
+            for torch_function_name in ["empty", "zeros", "ones", "full"]
+        }
+    else:
+        tensor_constructors_to_patch = {}
+    
+    try:
+        torch.nn.Module.register_parameter = register_empty_parameter
+        if include_buffers:
+            torch.nn.Module.register_buffer = register_empty_buffer
+        for torch_function_name in tensor_constructors_to_patch.keys():
+            setattr(torch, torch_function_name, patch_tensor_constructor(getattr(torch, torch_function_name)))
+        yield
+    finally:
+        torch.nn.Module.register_parameter = old_register_parameter
+        if include_buffers:
+            torch.nn.Module.register_buffer = old_register_buffer
+        for torch_function_name, old_torch_function in tensor_constructors_to_patch.items():
+            setattr(torch, torch_function_name, old_torch_function)
+
+def load_state_dict_from_folder(file_path, torch_dtype=None):
+    state_dict = {}
+    for file_name in os.listdir(file_path):
+        if "." in file_name and file_name.split(".")[-1] in [
+            "safetensors", "bin", "ckpt", "pth", "pt"
+        ]:
+            state_dict.update(load_state_dict(os.path.join(file_path, file_name), torch_dtype=torch_dtype))
+    return state_dict
+
+
+def load_state_dict(file_path, torch_dtype=None, device="cpu"):
+    if file_path.endswith(".safetensors"):
+        return load_state_dict_from_safetensors(file_path, torch_dtype=torch_dtype, device=device)
+    else:
+        return load_state_dict_from_bin(file_path, torch_dtype=torch_dtype, device=device)
+
+
+def load_state_dict_from_safetensors(file_path, torch_dtype=None, device="cpu"):
+    state_dict = {}
+    with safe_open(file_path,framework="pt",device=str(device)) as f:
+        for k in f.keys():
+            state_dict[k] = f.get_tensor(k)
+            if torch_dtype is not None:
+                state_dict[k] = state_dict[k].to(torch_dtype)
+    return state_dict
+
+
+def load_state_dict_from_bin(file_path, torch_dtype=None, device="cpu"):
+    state_dict = torch.load(file_path, map_location=device, weights_only=True)
+    if torch_dtype is not None:
+        for i in state_dict:
+            if isinstance(state_dict[i], torch.Tensor):
+                state_dict[i] = state_dict[i].to(torch_dtype)
+    return state_dict
+
+
+def search_for_embeddings(state_dict):
+    embeddings = []
+    for k in state_dict:
+        if isinstance(state_dict[k], torch.Tensor):
+            embeddings.append(state_dict[k])
+        elif isinstance(state_dict[k], dict):
+            embeddings += search_for_embeddings(state_dict[k])
+    return embeddings
+
+
+def search_parameter(param, state_dict):
+    for name, param_ in state_dict.items():
+        if param.numel() == param_.numel():
+            if param.shape == param_.shape:
+                if torch.dist(param, param_) < 1e-3:
+                    return name
+            else:
+                if torch.dist(param.flatten(), param_.flatten()) < 1e-3:
+                    return name
+    return None
+
+
+def build_rename_dict(source_state_dict, target_state_dict, split_qkv=False):
+    matched_keys = set()
+    with torch.no_grad():
+        for name in source_state_dict:
+            rename = search_parameter(source_state_dict[name], target_state_dict)
+            if rename is not None:
+                print(f'"{name}": "{rename}",')
+                matched_keys.add(rename)
+            elif split_qkv and len(source_state_dict[name].shape)>=1 and source_state_dict[name].shape[0]%3==0:
+                length = source_state_dict[name].shape[0] // 3
+                rename = []
+                for i in range(3):
+                    rename.append(search_parameter(source_state_dict[name][i*length: i*length+length], target_state_dict))
+                if None not in rename:
+                    print(f'"{name}": {rename},')
+                    for rename_ in rename:
+                        matched_keys.add(rename_)
+    for name in target_state_dict:
+        if name not in matched_keys:
+            print("Cannot find", name, target_state_dict[name].shape)
+
+
+def search_for_files(folder, extensions):
+    files = []
+    if os.path.isdir(folder):
+        for file in sorted(os.listdir(folder)):
+            files += search_for_files(os.path.join(folder, file), extensions)
+    elif os.path.isfile(folder):
+        for extension in extensions:
+            if folder.endswith(extension):
+                files.append(folder)
+                break
+    return files
+
+
+def convert_state_dict_keys_to_single_str(state_dict, with_shape=True):
+    keys = []
+    for key, value in state_dict.items():
+        if isinstance(key, str):
+            if isinstance(value, torch.Tensor):
+                if with_shape:
+                    shape = "_".join(map(str, list(value.shape)))
+                    keys.append(key + ":" + shape)
+                keys.append(key)
+            elif isinstance(value, dict):
+                keys.append(key + "|" + convert_state_dict_keys_to_single_str(value, with_shape=with_shape))
+    keys.sort()
+    keys_str = ",".join(keys)
+    return keys_str
+
+
+def split_state_dict_with_prefix(state_dict):
+    keys = sorted([key for key in state_dict if isinstance(key, str)])
+    prefix_dict = {}
+    for key in  keys:
+        prefix = key if "." not in key else key.split(".")[0]
+        if prefix not in prefix_dict:
+            prefix_dict[prefix] = []
+        prefix_dict[prefix].append(key)
+    state_dicts = []
+    for prefix, keys in prefix_dict.items():
+        sub_state_dict = {key: state_dict[key] for key in keys}
+        state_dicts.append(sub_state_dict)
+    return state_dicts
+
+
+def hash_state_dict_keys(state_dict, with_shape=True):
+    keys_str = convert_state_dict_keys_to_single_str(state_dict, with_shape=with_shape)
+    keys_str = keys_str.encode(encoding="UTF-8")
+    return hashlib.md5(keys_str).hexdigest()
\ No newline at end of file
diff --git a/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/wan_video_camera_controller.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/wan_video_camera_controller.py
new file mode 100644
index 0000000000000000000000000000000000000000..45a44ee6bcd408d7ee9d18653f933151ce351a72
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/wan_video_camera_controller.py
@@ -0,0 +1,206 @@
+import torch
+import torch.nn as nn
+import numpy as np
+from einops import rearrange
+import os
+from typing_extensions import Literal
+
+class SimpleAdapter(nn.Module):
+    def __init__(self, in_dim, out_dim, kernel_size, stride, num_residual_blocks=1):
+        super(SimpleAdapter, self).__init__()
+
+        # Pixel Unshuffle: reduce spatial dimensions by a factor of 8
+        self.pixel_unshuffle = nn.PixelUnshuffle(downscale_factor=8)
+
+        # Convolution: reduce spatial dimensions by a factor
+        #  of 2 (without overlap)
+        self.conv = nn.Conv2d(in_dim * 64, out_dim, kernel_size=kernel_size, stride=stride, padding=0)
+
+        # Residual blocks for feature extraction
+        self.residual_blocks = nn.Sequential(
+            *[ResidualBlock(out_dim) for _ in range(num_residual_blocks)]
+        )
+
+    def forward(self, x):
+        # Reshape to merge the frame dimension into batch
+        bs, c, f, h, w = x.size()
+        x = x.permute(0, 2, 1, 3, 4).contiguous().view(bs * f, c, h, w)
+
+        # Pixel Unshuffle operation
+        x_unshuffled = self.pixel_unshuffle(x)
+
+        # Convolution operation
+        x_conv = self.conv(x_unshuffled)
+
+        # Feature extraction with residual blocks
+        out = self.residual_blocks(x_conv)
+
+        # Reshape to restore original bf dimension
+        out = out.view(bs, f, out.size(1), out.size(2), out.size(3))
+
+        # Permute dimensions to reorder (if needed), e.g., swap channels and feature frames
+        out = out.permute(0, 2, 1, 3, 4)
+
+        return out
+    
+    def process_camera_coordinates(
+        self,
+        direction: Literal["Left", "Right", "Up", "Down", "LeftUp", "LeftDown", "RightUp", "RightDown"],
+        length: int,
+        height: int,
+        width: int,
+        speed: float = 1/54,
+        origin=(0, 0.532139961, 0.946026558, 0.5, 0.5, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0)
+    ):
+        if origin is None:
+            origin = (0, 0.532139961, 0.946026558, 0.5, 0.5, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0)
+        coordinates = generate_camera_coordinates(direction, length, speed, origin)
+        plucker_embedding = process_pose_file(coordinates, width, height)
+        return plucker_embedding
+        
+    
+
+class ResidualBlock(nn.Module):
+    def __init__(self, dim):
+        super(ResidualBlock, self).__init__()
+        self.conv1 = nn.Conv2d(dim, dim, kernel_size=3, padding=1)
+        self.relu = nn.ReLU(inplace=True)
+        self.conv2 = nn.Conv2d(dim, dim, kernel_size=3, padding=1)
+
+    def forward(self, x):
+        residual = x
+        out = self.relu(self.conv1(x))
+        out = self.conv2(out)
+        out += residual
+        return out
+    
+class Camera(object):
+    """Copied from https://github.com/hehao13/CameraCtrl/blob/main/inference.py
+    """
+    def __init__(self, entry):
+        fx, fy, cx, cy = entry[1:5]
+        self.fx = fx
+        self.fy = fy
+        self.cx = cx
+        self.cy = cy
+        w2c_mat = np.array(entry[7:]).reshape(3, 4)
+        w2c_mat_4x4 = np.eye(4)
+        w2c_mat_4x4[:3, :] = w2c_mat
+        self.w2c_mat = w2c_mat_4x4
+        self.c2w_mat = np.linalg.inv(w2c_mat_4x4)
+
+def get_relative_pose(cam_params):
+    """Copied from https://github.com/hehao13/CameraCtrl/blob/main/inference.py
+    """
+    abs_w2cs = [cam_param.w2c_mat for cam_param in cam_params]
+    abs_c2ws = [cam_param.c2w_mat for cam_param in cam_params]
+    cam_to_origin = 0
+    target_cam_c2w = np.array([
+        [1, 0, 0, 0],
+        [0, 1, 0, -cam_to_origin],
+        [0, 0, 1, 0],
+        [0, 0, 0, 1]
+    ])
+    abs2rel = target_cam_c2w @ abs_w2cs[0]
+    ret_poses = [target_cam_c2w, ] + [abs2rel @ abs_c2w for abs_c2w in abs_c2ws[1:]]
+    ret_poses = np.array(ret_poses, dtype=np.float32)
+    return ret_poses
+
+def custom_meshgrid(*args):
+    # torch>=2.0.0 only
+    return torch.meshgrid(*args, indexing='ij')
+
+
+def ray_condition(K, c2w, H, W, device):
+    """Copied from https://github.com/hehao13/CameraCtrl/blob/main/inference.py
+    """
+    # c2w: B, V, 4, 4
+    # K: B, V, 4
+
+    B = K.shape[0]
+
+    j, i = custom_meshgrid(
+        torch.linspace(0, H - 1, H, device=device, dtype=c2w.dtype),
+        torch.linspace(0, W - 1, W, device=device, dtype=c2w.dtype),
+    )
+    i = i.reshape([1, 1, H * W]).expand([B, 1, H * W]) + 0.5  # [B, HxW]
+    j = j.reshape([1, 1, H * W]).expand([B, 1, H * W]) + 0.5  # [B, HxW]
+
+    fx, fy, cx, cy = K.chunk(4, dim=-1)  # B,V, 1
+
+    zs = torch.ones_like(i)  # [B, HxW]
+    xs = (i - cx) / fx * zs
+    ys = (j - cy) / fy * zs
+    zs = zs.expand_as(ys)
+
+    directions = torch.stack((xs, ys, zs), dim=-1)  # B, V, HW, 3
+    directions = directions / directions.norm(dim=-1, keepdim=True)  # B, V, HW, 3
+
+    rays_d = directions @ c2w[..., :3, :3].transpose(-1, -2)  # B, V, 3, HW
+    rays_o = c2w[..., :3, 3]  # B, V, 3
+    rays_o = rays_o[:, :, None].expand_as(rays_d)  # B, V, 3, HW
+    # c2w @ dirctions
+    rays_dxo = torch.linalg.cross(rays_o, rays_d)
+    plucker = torch.cat([rays_dxo, rays_d], dim=-1)
+    plucker = plucker.reshape(B, c2w.shape[1], H, W, 6)  # B, V, H, W, 6
+    # plucker = plucker.permute(0, 1, 4, 2, 3)
+    return plucker
+
+
+def process_pose_file(cam_params, width=672, height=384, original_pose_width=1280, original_pose_height=720, device='cpu', return_poses=False):
+    if return_poses:
+        return cam_params
+    else:
+        cam_params = [Camera(cam_param) for cam_param in cam_params]
+
+        sample_wh_ratio = width / height
+        pose_wh_ratio = original_pose_width / original_pose_height  # Assuming placeholder ratios, change as needed
+
+        if pose_wh_ratio > sample_wh_ratio:
+            resized_ori_w = height * pose_wh_ratio
+            for cam_param in cam_params:
+                cam_param.fx = resized_ori_w * cam_param.fx / width
+        else:
+            resized_ori_h = width / pose_wh_ratio
+            for cam_param in cam_params:
+                cam_param.fy = resized_ori_h * cam_param.fy / height
+
+        intrinsic = np.asarray([[cam_param.fx * width,
+                                cam_param.fy * height,
+                                cam_param.cx * width,
+                                cam_param.cy * height]
+                                for cam_param in cam_params], dtype=np.float32)
+
+        K = torch.as_tensor(intrinsic)[None]  # [1, 1, 4]
+        c2ws = get_relative_pose(cam_params)  # Assuming this function is defined elsewhere
+        c2ws = torch.as_tensor(c2ws)[None]  # [1, n_frame, 4, 4]
+        plucker_embedding = ray_condition(K, c2ws, height, width, device=device)[0].permute(0, 3, 1, 2).contiguous()  # V, 6, H, W
+        plucker_embedding = plucker_embedding[None]
+        plucker_embedding = rearrange(plucker_embedding, "b f c h w -> b f h w c")[0]
+        return plucker_embedding
+
+
+
+def generate_camera_coordinates(
+    direction: Literal["Left", "Right", "Up", "Down", "LeftUp", "LeftDown", "RightUp", "RightDown", "In", "Out"],
+    length: int,
+    speed: float = 1/54,
+    origin=(0, 0.532139961, 0.946026558, 0.5, 0.5, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0)
+):
+    coordinates = [list(origin)]
+    while len(coordinates) < length:
+        coor = coordinates[-1].copy()
+        if "Left" in direction:
+            coor[9] += speed
+        if "Right" in direction:
+            coor[9] -= speed
+        if "Up" in direction:
+            coor[13] += speed
+        if "Down" in direction:
+            coor[13] -= speed
+        if "In" in direction:
+            coor[18] -= speed
+        if "Out" in direction:
+            coor[18] += speed
+        coordinates.append(coor)
+    return coordinates
diff --git a/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/wan_video_dit.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/wan_video_dit.py
new file mode 100644
index 0000000000000000000000000000000000000000..1a54728fab7f99c024b361074977b7d1a1ec2ece
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/wan_video_dit.py
@@ -0,0 +1,755 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import math
+from typing import Tuple, Optional
+from einops import rearrange
+from .utils import hash_state_dict_keys
+from .wan_video_camera_controller import SimpleAdapter
+try:
+    import flash_attn_interface
+    FLASH_ATTN_3_AVAILABLE = True
+except ModuleNotFoundError:
+    FLASH_ATTN_3_AVAILABLE = False
+
+try:
+    import flash_attn
+    FLASH_ATTN_2_AVAILABLE = True
+except ModuleNotFoundError:
+    FLASH_ATTN_2_AVAILABLE = False
+
+try:
+    from sageattention import sageattn
+    SAGE_ATTN_AVAILABLE = True
+except ModuleNotFoundError:
+    SAGE_ATTN_AVAILABLE = False
+    
+    
+def flash_attention(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, num_heads: int, compatibility_mode=False):
+    if compatibility_mode:
+        q = rearrange(q, "b s (n d) -> b n s d", n=num_heads)
+        k = rearrange(k, "b s (n d) -> b n s d", n=num_heads)
+        v = rearrange(v, "b s (n d) -> b n s d", n=num_heads)
+        x = F.scaled_dot_product_attention(q, k, v)
+        x = rearrange(x, "b n s d -> b s (n d)", n=num_heads)
+    elif FLASH_ATTN_3_AVAILABLE:
+        q = rearrange(q, "b s (n d) -> b s n d", n=num_heads)
+        k = rearrange(k, "b s (n d) -> b s n d", n=num_heads)
+        v = rearrange(v, "b s (n d) -> b s n d", n=num_heads)
+        x = flash_attn_interface.flash_attn_func(q, k, v)
+        if isinstance(x,tuple):
+            x = x[0]
+        x = rearrange(x, "b s n d -> b s (n d)", n=num_heads)
+    elif FLASH_ATTN_2_AVAILABLE:
+        q = rearrange(q, "b s (n d) -> b s n d", n=num_heads)
+        k = rearrange(k, "b s (n d) -> b s n d", n=num_heads)
+        v = rearrange(v, "b s (n d) -> b s n d", n=num_heads)
+        x = flash_attn.flash_attn_func(q, k, v)
+        x = rearrange(x, "b s n d -> b s (n d)", n=num_heads)
+    elif SAGE_ATTN_AVAILABLE:
+        q = rearrange(q, "b s (n d) -> b n s d", n=num_heads)
+        k = rearrange(k, "b s (n d) -> b n s d", n=num_heads)
+        v = rearrange(v, "b s (n d) -> b n s d", n=num_heads)
+        x = sageattn(q, k, v)
+        x = rearrange(x, "b n s d -> b s (n d)", n=num_heads)
+    else:
+        q = rearrange(q, "b s (n d) -> b n s d", n=num_heads)
+        k = rearrange(k, "b s (n d) -> b n s d", n=num_heads)
+        v = rearrange(v, "b s (n d) -> b n s d", n=num_heads)
+        x = F.scaled_dot_product_attention(q, k, v)
+        x = rearrange(x, "b n s d -> b s (n d)", n=num_heads)
+    return x
+
+
+def modulate(x: torch.Tensor, shift: torch.Tensor, scale: torch.Tensor):
+    return (x * (1 + scale) + shift)
+
+
+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 precompute_freqs_cis_3d(dim: int, end: int = 1024, theta: float = 10000.0):
+    # 3d rope precompute
+    f_freqs_cis = precompute_freqs_cis(dim - 2 * (dim // 3), end, theta)
+    h_freqs_cis = precompute_freqs_cis(dim // 3, end, theta)
+    w_freqs_cis = precompute_freqs_cis(dim // 3, end, theta)
+    return f_freqs_cis, h_freqs_cis, w_freqs_cis
+
+
+def precompute_freqs_cis(dim: int, end: int = 1024, theta: float = 10000.0):
+    # 1d rope precompute
+    freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)
+                   [: (dim // 2)].double() / dim))
+    freqs = torch.outer(torch.arange(end, device=freqs.device), freqs)
+    freqs_cis = torch.polar(torch.ones_like(freqs), freqs)  # complex64
+    return freqs_cis
+
+
+def rope_apply(x, freqs, num_heads):
+    x = rearrange(x, "b s (n d) -> b s n d", n=num_heads)
+    x_out = torch.view_as_complex(x.to(torch.float64).reshape(
+        x.shape[0], x.shape[1], x.shape[2], -1, 2))
+    x_out = torch.view_as_real(x_out * freqs).flatten(2)
+    return x_out.to(x.dtype)
+
+
+class RMSNorm(nn.Module):
+    def __init__(self, dim, eps=1e-5):
+        super().__init__()
+        self.eps = eps
+        self.weight = nn.Parameter(torch.ones(dim))
+
+    def norm(self, x):
+        return x * torch.rsqrt(x.pow(2).mean(dim=-1, keepdim=True) + self.eps)
+
+    def forward(self, x):
+        dtype = x.dtype
+        return self.norm(x.float()).to(dtype) * self.weight
+
+
+class AttentionModule(nn.Module):
+    def __init__(self, num_heads):
+        super().__init__()
+        self.num_heads = num_heads
+        
+    def forward(self, q, k, v):
+        x = flash_attention(q=q, k=k, v=v, num_heads=self.num_heads)
+        return x
+
+
+class SelfAttention(nn.Module):
+    def __init__(self, dim: int, num_heads: int, eps: float = 1e-6):
+        super().__init__()
+        self.dim = dim
+        self.num_heads = num_heads
+        self.head_dim = dim // num_heads
+
+        self.q = nn.Linear(dim, dim)
+        self.k = nn.Linear(dim, dim)
+        self.v = nn.Linear(dim, dim)
+        self.o = nn.Linear(dim, dim)
+        self.norm_q = RMSNorm(dim, eps=eps)
+        self.norm_k = RMSNorm(dim, eps=eps)
+        
+        self.attn = AttentionModule(self.num_heads)
+
+    def 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)
+        x = self.attn(q, k, v)
+        return self.o(x)
+
+
+class CrossAttention(nn.Module):
+    def __init__(self, dim: int, num_heads: int, eps: float = 1e-6, has_image_input: bool = False):
+        super().__init__()
+        self.dim = dim
+        self.num_heads = num_heads
+        self.head_dim = dim // num_heads
+
+        self.q = nn.Linear(dim, dim)
+        self.k = nn.Linear(dim, dim)
+        self.v = nn.Linear(dim, dim)
+        self.o = nn.Linear(dim, dim)
+        self.norm_q = RMSNorm(dim, eps=eps)
+        self.norm_k = RMSNorm(dim, eps=eps)
+        self.has_image_input = has_image_input
+        if has_image_input:
+            self.k_img = nn.Linear(dim, dim)
+            self.v_img = nn.Linear(dim, dim)
+            self.norm_k_img = RMSNorm(dim, eps=eps)
+            
+        self.attn = AttentionModule(self.num_heads)
+
+    def forward(self, x: torch.Tensor, y: torch.Tensor):
+        if self.has_image_input:
+            img = y[:, :257]
+            ctx = y[:, 257:]
+        else:
+            ctx = y
+        q = self.norm_q(self.q(x))
+        k = self.norm_k(self.k(ctx))
+        v = self.v(ctx)
+        x = self.attn(q, k, v)
+        if self.has_image_input:
+            k_img = self.norm_k_img(self.k_img(img))
+            v_img = self.v_img(img)
+            y = flash_attention(q, k_img, v_img, num_heads=self.num_heads)
+            x = x + y
+        return self.o(x)
+
+
+class GateModule(nn.Module):
+    def __init__(self,):
+        super().__init__()
+
+    def forward(self, x, gate, residual):
+        return x + gate * residual
+
+class DiTBlock(nn.Module):
+    def __init__(self, has_image_input: bool, dim: int, num_heads: int, ffn_dim: int, eps: float = 1e-6):
+        super().__init__()
+        self.dim = dim
+        self.num_heads = num_heads
+        self.ffn_dim = ffn_dim
+
+        self.self_attn = SelfAttention(dim, num_heads, eps)
+        self.cross_attn = CrossAttention(
+            dim, num_heads, eps, has_image_input=has_image_input)
+        self.norm1 = nn.LayerNorm(dim, eps=eps, elementwise_affine=False)
+        self.norm2 = nn.LayerNorm(dim, eps=eps, elementwise_affine=False)
+        self.norm3 = nn.LayerNorm(dim, eps=eps)
+        self.ffn = nn.Sequential(nn.Linear(dim, ffn_dim), nn.GELU(
+            approximate='tanh'), nn.Linear(ffn_dim, dim))
+        self.modulation = nn.Parameter(torch.randn(1, 6, dim) / dim**0.5)
+        self.gate = GateModule()
+
+    def forward(self, x, context, t_mod, freqs):
+        has_seq = len(t_mod.shape) == 4
+        chunk_dim = 2 if has_seq else 1
+        # msa: multi-head self-attention  mlp: multi-layer perceptron
+        shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (
+            self.modulation.to(dtype=t_mod.dtype, device=t_mod.device) + t_mod).chunk(6, dim=chunk_dim)
+        if has_seq:
+            shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (
+                shift_msa.squeeze(2), scale_msa.squeeze(2), gate_msa.squeeze(2),
+                shift_mlp.squeeze(2), scale_mlp.squeeze(2), gate_mlp.squeeze(2),
+            )
+        input_x = modulate(self.norm1(x), shift_msa, scale_msa)
+        x = self.gate(x, gate_msa, self.self_attn(input_x, freqs))
+        x = x + self.cross_attn(self.norm3(x), context)
+        input_x = modulate(self.norm2(x), shift_mlp, scale_mlp)
+        x = self.gate(x, gate_mlp, self.ffn(input_x))
+        return x
+
+
+class MLP(torch.nn.Module):
+    def __init__(self, in_dim, out_dim, has_pos_emb=False):
+        super().__init__()
+        self.proj = torch.nn.Sequential(
+            nn.LayerNorm(in_dim),
+            nn.Linear(in_dim, in_dim),
+            nn.GELU(),
+            nn.Linear(in_dim, out_dim),
+            nn.LayerNorm(out_dim)
+        )
+        self.has_pos_emb = has_pos_emb
+        if has_pos_emb:
+            self.emb_pos = torch.nn.Parameter(torch.zeros((1, 514, 1280)))
+
+    def forward(self, x):
+        if self.has_pos_emb:
+            x = x + self.emb_pos.to(dtype=x.dtype, device=x.device)
+        return self.proj(x)
+
+
+class Head(nn.Module):
+    def __init__(self, dim: int, out_dim: int, patch_size: Tuple[int, int, int], eps: float):
+        super().__init__()
+        self.dim = dim
+        self.patch_size = patch_size
+        self.norm = nn.LayerNorm(dim, eps=eps, elementwise_affine=False)
+        self.head = nn.Linear(dim, out_dim * math.prod(patch_size))
+        self.modulation = nn.Parameter(torch.randn(1, 2, dim) / dim**0.5)
+
+    def forward(self, x, t_mod):
+        if len(t_mod.shape) == 3:
+            shift, scale = (self.modulation.unsqueeze(0).to(dtype=t_mod.dtype, device=t_mod.device) + t_mod.unsqueeze(2)).chunk(2, dim=2)
+            x = (self.head(self.norm(x) * (1 + scale.squeeze(2)) + shift.squeeze(2)))
+        else:
+            shift, scale = (self.modulation.to(dtype=t_mod.dtype, device=t_mod.device) + t_mod).chunk(2, dim=1)
+            x = (self.head(self.norm(x) * (1 + scale) + shift))
+        return x
+
+
+class WanModel(torch.nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        in_dim: int,
+        ffn_dim: int,
+        out_dim: int,
+        text_dim: int,
+        freq_dim: int,
+        eps: float,
+        patch_size: Tuple[int, int, int],
+        num_heads: int,
+        num_layers: int,
+        has_image_input: bool,
+        has_image_pos_emb: bool = False,
+        has_ref_conv: bool = False,
+        add_control_adapter: bool = False,
+        in_dim_control_adapter: int = 24,
+        seperated_timestep: bool = False,
+        require_vae_embedding: bool = True,
+        require_clip_embedding: bool = True,
+        fuse_vae_embedding_in_latents: bool = False,
+    ):
+        super().__init__()
+        self.dim = dim
+        self.in_dim = in_dim
+        self.freq_dim = freq_dim
+        self.has_image_input = has_image_input
+        self.patch_size = patch_size
+        self.seperated_timestep = seperated_timestep
+        self.require_vae_embedding = require_vae_embedding
+        self.require_clip_embedding = require_clip_embedding
+        self.fuse_vae_embedding_in_latents = fuse_vae_embedding_in_latents
+
+        self.patch_embedding = nn.Conv3d(
+            in_dim, dim, kernel_size=patch_size, stride=patch_size)
+        self.text_embedding = nn.Sequential(
+            nn.Linear(text_dim, dim),
+            nn.GELU(approximate='tanh'),
+            nn.Linear(dim, dim)
+        )
+        self.time_embedding = nn.Sequential(
+            nn.Linear(freq_dim, dim),
+            nn.SiLU(),
+            nn.Linear(dim, dim)
+        )
+        self.time_projection = nn.Sequential(
+            nn.SiLU(), nn.Linear(dim, dim * 6))
+        self.blocks = nn.ModuleList([
+            DiTBlock(has_image_input, dim, num_heads, ffn_dim, eps)
+            for _ in range(num_layers)
+        ])
+        self.head = Head(dim, out_dim, patch_size, eps)
+        head_dim = dim // num_heads
+        self.freqs = precompute_freqs_cis_3d(head_dim)
+
+        if has_image_input:
+            self.img_emb = MLP(1280, dim, has_pos_emb=has_image_pos_emb)  # clip_feature_dim = 1280
+        if has_ref_conv:
+            self.ref_conv = nn.Conv2d(16, dim, kernel_size=(2, 2), stride=(2, 2))
+        self.has_image_pos_emb = has_image_pos_emb
+        self.has_ref_conv = has_ref_conv
+        if add_control_adapter:
+            self.control_adapter = SimpleAdapter(in_dim_control_adapter, dim, kernel_size=patch_size[1:], stride=patch_size[1:])
+        else:
+            self.control_adapter = None
+
+    def patchify(self, x: torch.Tensor, control_camera_latents_input: Optional[torch.Tensor] = None):
+        x = self.patch_embedding(x)
+        if self.control_adapter is not None and control_camera_latents_input is not None:
+            y_camera = self.control_adapter(control_camera_latents_input)
+            x = [u + v for u, v in zip(x, y_camera)]
+            x = x[0].unsqueeze(0)
+        grid_size = x.shape[2:]
+        x = rearrange(x, 'b c f h w -> b (f h w) c').contiguous()
+        return x, grid_size  # x, grid_size: (f, h, w)
+
+    def unpatchify(self, x: torch.Tensor, grid_size: torch.Tensor):
+        return rearrange(
+            x, 'b (f h w) (x y z c) -> b c (f x) (h y) (w z)',
+            f=grid_size[0], h=grid_size[1], w=grid_size[2], 
+            x=self.patch_size[0], y=self.patch_size[1], z=self.patch_size[2]
+        )
+
+    def 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
+
+        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)
+        x = self.unpatchify(x, (f, h, w))
+        return x
+
+    @staticmethod
+    def state_dict_converter():
+        return WanModelStateDictConverter()
+    
+    
+class WanModelStateDictConverter:
+    def __init__(self):
+        pass
+
+    def from_diffusers(self, state_dict):
+        rename_dict = {
+            "blocks.0.attn1.norm_k.weight": "blocks.0.self_attn.norm_k.weight",
+            "blocks.0.attn1.norm_q.weight": "blocks.0.self_attn.norm_q.weight",
+            "blocks.0.attn1.to_k.bias": "blocks.0.self_attn.k.bias",
+            "blocks.0.attn1.to_k.weight": "blocks.0.self_attn.k.weight",
+            "blocks.0.attn1.to_out.0.bias": "blocks.0.self_attn.o.bias",
+            "blocks.0.attn1.to_out.0.weight": "blocks.0.self_attn.o.weight",
+            "blocks.0.attn1.to_q.bias": "blocks.0.self_attn.q.bias",
+            "blocks.0.attn1.to_q.weight": "blocks.0.self_attn.q.weight",
+            "blocks.0.attn1.to_v.bias": "blocks.0.self_attn.v.bias",
+            "blocks.0.attn1.to_v.weight": "blocks.0.self_attn.v.weight",
+            "blocks.0.attn2.norm_k.weight": "blocks.0.cross_attn.norm_k.weight",
+            "blocks.0.attn2.norm_q.weight": "blocks.0.cross_attn.norm_q.weight",
+            "blocks.0.attn2.to_k.bias": "blocks.0.cross_attn.k.bias",
+            "blocks.0.attn2.to_k.weight": "blocks.0.cross_attn.k.weight",
+            "blocks.0.attn2.to_out.0.bias": "blocks.0.cross_attn.o.bias",
+            "blocks.0.attn2.to_out.0.weight": "blocks.0.cross_attn.o.weight",
+            "blocks.0.attn2.to_q.bias": "blocks.0.cross_attn.q.bias",
+            "blocks.0.attn2.to_q.weight": "blocks.0.cross_attn.q.weight",
+            "blocks.0.attn2.to_v.bias": "blocks.0.cross_attn.v.bias",
+            "blocks.0.attn2.to_v.weight": "blocks.0.cross_attn.v.weight",
+            "blocks.0.ffn.net.0.proj.bias": "blocks.0.ffn.0.bias",
+            "blocks.0.ffn.net.0.proj.weight": "blocks.0.ffn.0.weight",
+            "blocks.0.ffn.net.2.bias": "blocks.0.ffn.2.bias",
+            "blocks.0.ffn.net.2.weight": "blocks.0.ffn.2.weight",
+            "blocks.0.norm2.bias": "blocks.0.norm3.bias",
+            "blocks.0.norm2.weight": "blocks.0.norm3.weight",
+            "blocks.0.scale_shift_table": "blocks.0.modulation",
+            "condition_embedder.text_embedder.linear_1.bias": "text_embedding.0.bias",
+            "condition_embedder.text_embedder.linear_1.weight": "text_embedding.0.weight",
+            "condition_embedder.text_embedder.linear_2.bias": "text_embedding.2.bias",
+            "condition_embedder.text_embedder.linear_2.weight": "text_embedding.2.weight",
+            "condition_embedder.time_embedder.linear_1.bias": "time_embedding.0.bias",
+            "condition_embedder.time_embedder.linear_1.weight": "time_embedding.0.weight",
+            "condition_embedder.time_embedder.linear_2.bias": "time_embedding.2.bias",
+            "condition_embedder.time_embedder.linear_2.weight": "time_embedding.2.weight",
+            "condition_embedder.time_proj.bias": "time_projection.1.bias",
+            "condition_embedder.time_proj.weight": "time_projection.1.weight",
+            "patch_embedding.bias": "patch_embedding.bias",
+            "patch_embedding.weight": "patch_embedding.weight",
+            "scale_shift_table": "head.modulation",
+            "proj_out.bias": "head.head.bias",
+            "proj_out.weight": "head.head.weight",
+        }
+        state_dict_ = {}
+        for name, param in state_dict.items():
+            if name in rename_dict:
+                state_dict_[rename_dict[name]] = param
+            else:
+                name_ = ".".join(name.split(".")[:1] + ["0"] + name.split(".")[2:])
+                if name_ in rename_dict:
+                    name_ = rename_dict[name_]
+                    name_ = ".".join(name_.split(".")[:1] + [name.split(".")[1]] + name_.split(".")[2:])
+                    state_dict_[name_] = param
+        if hash_state_dict_keys(state_dict) == "cb104773c6c2cb6df4f9529ad5c60d0b":
+            config = {
+                "model_type": "t2v",
+                "patch_size": (1, 2, 2),
+                "text_len": 512,
+                "in_dim": 16,
+                "dim": 5120,
+                "ffn_dim": 13824,
+                "freq_dim": 256,
+                "text_dim": 4096,
+                "out_dim": 16,
+                "num_heads": 40,
+                "num_layers": 40,
+                "window_size": (-1, -1),
+                "qk_norm": True,
+                "cross_attn_norm": True,
+                "eps": 1e-6,
+            }
+        else:
+            config = {}
+        return state_dict_, config
+    
+    def from_civitai(self, state_dict):
+        state_dict = {name: param for name, param in state_dict.items() if not name.startswith("vace")}
+        if hash_state_dict_keys(state_dict) == "9269f8db9040a9d860eaca435be61814":
+            config = {
+                "has_image_input": False,
+                "patch_size": [1, 2, 2],
+                "in_dim": 16,
+                "dim": 1536,
+                "ffn_dim": 8960,
+                "freq_dim": 256,
+                "text_dim": 4096,
+                "out_dim": 16,
+                "num_heads": 12,
+                "num_layers": 30,
+                "eps": 1e-6
+            }
+        elif hash_state_dict_keys(state_dict) == "aafcfd9672c3a2456dc46e1cb6e52c70":
+            config = {
+                "has_image_input": False,
+                "patch_size": [1, 2, 2],
+                "in_dim": 16,
+                "dim": 5120,
+                "ffn_dim": 13824,
+                "freq_dim": 256,
+                "text_dim": 4096,
+                "out_dim": 16,
+                "num_heads": 40,
+                "num_layers": 40,
+                "eps": 1e-6
+            }
+        elif hash_state_dict_keys(state_dict) == "6bfcfb3b342cb286ce886889d519a77e":
+            config = {
+                "has_image_input": True,
+                "patch_size": [1, 2, 2],
+                "in_dim": 36,
+                "dim": 5120,
+                "ffn_dim": 13824,
+                "freq_dim": 256,
+                "text_dim": 4096,
+                "out_dim": 16,
+                "num_heads": 40,
+                "num_layers": 40,
+                "eps": 1e-6
+            }
+        elif hash_state_dict_keys(state_dict) == "6d6ccde6845b95ad9114ab993d917893":
+            config = {
+                "has_image_input": True,
+                "patch_size": [1, 2, 2],
+                "in_dim": 36,
+                "dim": 1536,
+                "ffn_dim": 8960,
+                "freq_dim": 256,
+                "text_dim": 4096,
+                "out_dim": 16,
+                "num_heads": 12,
+                "num_layers": 30,
+                "eps": 1e-6
+            }
+        elif hash_state_dict_keys(state_dict) == "6bfcfb3b342cb286ce886889d519a77e":
+            config = {
+                "has_image_input": True,
+                "patch_size": [1, 2, 2],
+                "in_dim": 36,
+                "dim": 5120,
+                "ffn_dim": 13824,
+                "freq_dim": 256,
+                "text_dim": 4096,
+                "out_dim": 16,
+                "num_heads": 40,
+                "num_layers": 40,
+                "eps": 1e-6
+            }
+        elif hash_state_dict_keys(state_dict) == "349723183fc063b2bfc10bb2835cf677":
+            # 1.3B PAI control
+            config = {
+                "has_image_input": True,
+                "patch_size": [1, 2, 2],
+                "in_dim": 48,
+                "dim": 1536,
+                "ffn_dim": 8960,
+                "freq_dim": 256,
+                "text_dim": 4096,
+                "out_dim": 16,
+                "num_heads": 12,
+                "num_layers": 30,
+                "eps": 1e-6
+            }
+        elif hash_state_dict_keys(state_dict) == "efa44cddf936c70abd0ea28b6cbe946c":
+            # 14B PAI control
+            config = {
+                "has_image_input": True,
+                "patch_size": [1, 2, 2],
+                "in_dim": 48,
+                "dim": 5120,
+                "ffn_dim": 13824,
+                "freq_dim": 256,
+                "text_dim": 4096,
+                "out_dim": 16,
+                "num_heads": 40,
+                "num_layers": 40,
+                "eps": 1e-6
+            }
+        elif hash_state_dict_keys(state_dict) == "3ef3b1f8e1dab83d5b71fd7b617f859f":
+            config = {
+                "has_image_input": True,
+                "patch_size": [1, 2, 2],
+                "in_dim": 36,
+                "dim": 5120,
+                "ffn_dim": 13824,
+                "freq_dim": 256,
+                "text_dim": 4096,
+                "out_dim": 16,
+                "num_heads": 40,
+                "num_layers": 40,
+                "eps": 1e-6,
+                "has_image_pos_emb": True
+            }
+        elif hash_state_dict_keys(state_dict) == "70ddad9d3a133785da5ea371aae09504":
+            # 1.3B PAI control v1.1
+            config = {
+                "has_image_input": True,
+                "patch_size": [1, 2, 2],
+                "in_dim": 48,
+                "dim": 1536,
+                "ffn_dim": 8960,
+                "freq_dim": 256,
+                "text_dim": 4096,
+                "out_dim": 16,
+                "num_heads": 12,
+                "num_layers": 30,
+                "eps": 1e-6,
+                "has_ref_conv": True
+            }
+        elif hash_state_dict_keys(state_dict) == "26bde73488a92e64cc20b0a7485b9e5b":
+            # 14B PAI control v1.1
+            config = {
+                "has_image_input": True,
+                "patch_size": [1, 2, 2],
+                "in_dim": 48,
+                "dim": 5120,
+                "ffn_dim": 13824,
+                "freq_dim": 256,
+                "text_dim": 4096,
+                "out_dim": 16,
+                "num_heads": 40,
+                "num_layers": 40,
+                "eps": 1e-6,
+                "has_ref_conv": True
+            }
+        elif hash_state_dict_keys(state_dict) == "ac6a5aa74f4a0aab6f64eb9a72f19901":
+            # 1.3B PAI control-camera v1.1
+            config = {
+                "has_image_input": True,
+                "patch_size": [1, 2, 2],
+                "in_dim": 32,
+                "dim": 1536,
+                "ffn_dim": 8960,
+                "freq_dim": 256,
+                "text_dim": 4096,
+                "out_dim": 16,
+                "num_heads": 12,
+                "num_layers": 30,
+                "eps": 1e-6,
+                "has_ref_conv": False,
+                "add_control_adapter": True,
+                "in_dim_control_adapter": 24,
+            }
+        elif hash_state_dict_keys(state_dict) == "b61c605c2adbd23124d152ed28e049ae":
+            # 14B PAI control-camera v1.1
+            config = {
+                "has_image_input": True,
+                "patch_size": [1, 2, 2],
+                "in_dim": 32,
+                "dim": 5120,
+                "ffn_dim": 13824,
+                "freq_dim": 256,
+                "text_dim": 4096,
+                "out_dim": 16,
+                "num_heads": 40,
+                "num_layers": 40,
+                "eps": 1e-6,
+                "has_ref_conv": False,
+                "add_control_adapter": True,
+                "in_dim_control_adapter": 24,
+            }
+        elif hash_state_dict_keys(state_dict) == "1f5ab7703c6fc803fdded85ff040c316":
+            # Wan-AI/Wan2.2-TI2V-5B
+            config = {
+                "has_image_input": False,
+                "patch_size": [1, 2, 2],
+                "in_dim": 48,
+                "dim": 3072,
+                "ffn_dim": 14336,
+                "freq_dim": 256,
+                "text_dim": 4096,
+                "out_dim": 48,
+                "num_heads": 24,
+                "num_layers": 30,
+                "eps": 1e-6,
+                "seperated_timestep": True,
+                "require_clip_embedding": False,
+                "require_vae_embedding": False,
+                "fuse_vae_embedding_in_latents": True,
+            }
+        elif hash_state_dict_keys(state_dict) == "5b013604280dd715f8457c6ed6d6a626":
+            # Wan-AI/Wan2.2-I2V-A14B
+            config = {
+                "has_image_input": False,
+                "patch_size": [1, 2, 2],
+                "in_dim": 36,
+                "dim": 5120,
+                "ffn_dim": 13824,
+                "freq_dim": 256,
+                "text_dim": 4096,
+                "out_dim": 16,
+                "num_heads": 40,
+                "num_layers": 40,
+                "eps": 1e-6,
+                "require_clip_embedding": False,
+            }
+        elif hash_state_dict_keys(state_dict) == "2267d489f0ceb9f21836532952852ee5":
+            # Wan2.2-Fun-A14B-Control
+            config = {
+                "has_image_input": False,
+                "patch_size": [1, 2, 2],
+                "in_dim": 52,
+                "dim": 5120,
+                "ffn_dim": 13824,
+                "freq_dim": 256,
+                "text_dim": 4096,
+                "out_dim": 16,
+                "num_heads": 40,
+                "num_layers": 40,
+                "eps": 1e-6,
+                "has_ref_conv": True,
+                "require_clip_embedding": False,
+            }
+        elif hash_state_dict_keys(state_dict) == "47dbeab5e560db3180adf51dc0232fb1":
+            # Wan2.2-Fun-A14B-Control-Camera
+            config = {
+                "has_image_input": False,
+                "patch_size": [1, 2, 2],
+                "in_dim": 36,
+                "dim": 5120,
+                "ffn_dim": 13824,
+                "freq_dim": 256,
+                "text_dim": 4096,
+                "out_dim": 16,
+                "num_heads": 40,
+                "num_layers": 40,
+                "eps": 1e-6,
+                "has_ref_conv": False,
+                "add_control_adapter": True,
+                "in_dim_control_adapter": 24,
+                "require_clip_embedding": False,
+            }
+        else:
+            config = {}
+        return state_dict, config
diff --git a/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/wan_video_dit_s2v.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/wan_video_dit_s2v.py
new file mode 100644
index 0000000000000000000000000000000000000000..fa5459180be9c2b699a4ce0c20164fc2a5b30748
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/wan_video_dit_s2v.py
@@ -0,0 +1,627 @@
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from typing import Tuple
+from .utils import hash_state_dict_keys
+from .wan_video_dit import rearrange, precompute_freqs_cis_3d, DiTBlock, Head, CrossAttention, modulate, sinusoidal_embedding_1d
+
+
+def torch_dfs(model: nn.Module, parent_name='root'):
+    module_names, modules = [], []
+    current_name = parent_name if parent_name else 'root'
+    module_names.append(current_name)
+    modules.append(model)
+
+    for name, child in model.named_children():
+        if parent_name:
+            child_name = f'{parent_name}.{name}'
+        else:
+            child_name = name
+        child_modules, child_names = torch_dfs(child, child_name)
+        module_names += child_names
+        modules += child_modules
+    return modules, module_names
+
+
+def rope_precompute(x, grid_sizes, freqs, start=None):
+    b, s, n, c = x.size(0), x.size(1), x.size(2), x.size(3) // 2
+
+    # split freqs
+    if type(freqs) is list:
+        trainable_freqs = freqs[1]
+        freqs = freqs[0]
+    freqs = freqs.split([c - 2 * (c // 3), c // 3, c // 3], dim=1)
+
+    # loop over samples
+    output = torch.view_as_complex(x.detach().reshape(b, s, n, -1, 2).to(torch.float64))
+    seq_bucket = [0]
+    if not type(grid_sizes) is list:
+        grid_sizes = [grid_sizes]
+    for g in grid_sizes:
+        if not type(g) is list:
+            g = [torch.zeros_like(g), g]
+        batch_size = g[0].shape[0]
+        for i in range(batch_size):
+            if start is None:
+                f_o, h_o, w_o = g[0][i]
+            else:
+                f_o, h_o, w_o = start[i]
+
+            f, h, w = g[1][i]
+            t_f, t_h, t_w = g[2][i]
+            seq_f, seq_h, seq_w = f - f_o, h - h_o, w - w_o
+            seq_len = int(seq_f * seq_h * seq_w)
+            if seq_len > 0:
+                if t_f > 0:
+                    factor_f, factor_h, factor_w = (t_f / seq_f).item(), (t_h / seq_h).item(), (t_w / seq_w).item()
+                    # Generate a list of seq_f integers starting from f_o and ending at math.ceil(factor_f * seq_f.item() + f_o.item())
+                    if f_o >= 0:
+                        f_sam = np.linspace(f_o.item(), (t_f + f_o).item() - 1, seq_f).astype(int).tolist()
+                    else:
+                        f_sam = np.linspace(-f_o.item(), (-t_f - f_o).item() + 1, seq_f).astype(int).tolist()
+                    h_sam = np.linspace(h_o.item(), (t_h + h_o).item() - 1, seq_h).astype(int).tolist()
+                    w_sam = np.linspace(w_o.item(), (t_w + w_o).item() - 1, seq_w).astype(int).tolist()
+
+                    assert f_o * f >= 0 and h_o * h >= 0 and w_o * w >= 0
+                    freqs_0 = freqs[0][f_sam] if f_o >= 0 else freqs[0][f_sam].conj()
+                    freqs_0 = freqs_0.view(seq_f, 1, 1, -1)
+
+                    freqs_i = torch.cat(
+                        [
+                            freqs_0.expand(seq_f, seq_h, seq_w, -1),
+                            freqs[1][h_sam].view(1, seq_h, 1, -1).expand(seq_f, seq_h, seq_w, -1),
+                            freqs[2][w_sam].view(1, 1, seq_w, -1).expand(seq_f, seq_h, seq_w, -1),
+                        ],
+                        dim=-1
+                    ).reshape(seq_len, 1, -1)
+                elif t_f < 0:
+                    freqs_i = trainable_freqs.unsqueeze(1)
+                # apply rotary embedding
+                output[i, seq_bucket[-1]:seq_bucket[-1] + seq_len] = freqs_i
+        seq_bucket.append(seq_bucket[-1] + seq_len)
+    return output
+
+
+class CausalConv1d(nn.Module):
+
+    def __init__(self, chan_in, chan_out, kernel_size=3, stride=1, dilation=1, pad_mode='replicate', **kwargs):
+        super().__init__()
+
+        self.pad_mode = pad_mode
+        padding = (kernel_size - 1, 0)  # T
+        self.time_causal_padding = padding
+
+        self.conv = nn.Conv1d(chan_in, chan_out, kernel_size, stride=stride, dilation=dilation, **kwargs)
+
+    def forward(self, x):
+        x = F.pad(x, self.time_causal_padding, mode=self.pad_mode)
+        return self.conv(x)
+
+
+class MotionEncoder_tc(nn.Module):
+
+    def __init__(self, in_dim: int, hidden_dim: int, num_heads=int, need_global=True, dtype=None, device=None):
+        factory_kwargs = {"dtype": dtype, "device": device}
+        super().__init__()
+
+        self.num_heads = num_heads
+        self.need_global = need_global
+        self.conv1_local = CausalConv1d(in_dim, hidden_dim // 4 * num_heads, 3, stride=1)
+        if need_global:
+            self.conv1_global = CausalConv1d(in_dim, hidden_dim // 4, 3, stride=1)
+        self.norm1 = nn.LayerNorm(hidden_dim // 4, elementwise_affine=False, eps=1e-6, **factory_kwargs)
+        self.act = nn.SiLU()
+        self.conv2 = CausalConv1d(hidden_dim // 4, hidden_dim // 2, 3, stride=2)
+        self.conv3 = CausalConv1d(hidden_dim // 2, hidden_dim, 3, stride=2)
+
+        if need_global:
+            self.final_linear = nn.Linear(hidden_dim, hidden_dim, **factory_kwargs)
+
+        self.norm1 = nn.LayerNorm(hidden_dim // 4, elementwise_affine=False, eps=1e-6, **factory_kwargs)
+        self.norm2 = nn.LayerNorm(hidden_dim // 2, elementwise_affine=False, eps=1e-6, **factory_kwargs)
+        self.norm3 = nn.LayerNorm(hidden_dim, elementwise_affine=False, eps=1e-6, **factory_kwargs)
+        self.padding_tokens = nn.Parameter(torch.zeros(1, 1, 1, hidden_dim))
+
+    def forward(self, x):
+        x = rearrange(x, 'b t c -> b c t')
+        x_ori = x.clone()
+        b, c, t = x.shape
+        x = self.conv1_local(x)
+        x = rearrange(x, 'b (n c) t -> (b n) t c', n=self.num_heads)
+        x = self.norm1(x)
+        x = self.act(x)
+        x = rearrange(x, 'b t c -> b c t')
+        x = self.conv2(x)
+        x = rearrange(x, 'b c t -> b t c')
+        x = self.norm2(x)
+        x = self.act(x)
+        x = rearrange(x, 'b t c -> b c t')
+        x = self.conv3(x)
+        x = rearrange(x, 'b c t -> b t c')
+        x = self.norm3(x)
+        x = self.act(x)
+        x = rearrange(x, '(b n) t c -> b t n c', b=b)
+        padding = self.padding_tokens.repeat(b, x.shape[1], 1, 1).to(device=x.device, dtype=x.dtype)
+        x = torch.cat([x, padding], dim=-2)
+        x_local = x.clone()
+
+        if not self.need_global:
+            return x_local
+
+        x = self.conv1_global(x_ori)
+        x = rearrange(x, 'b c t -> b t c')
+        x = self.norm1(x)
+        x = self.act(x)
+        x = rearrange(x, 'b t c -> b c t')
+        x = self.conv2(x)
+        x = rearrange(x, 'b c t -> b t c')
+        x = self.norm2(x)
+        x = self.act(x)
+        x = rearrange(x, 'b t c -> b c t')
+        x = self.conv3(x)
+        x = rearrange(x, 'b c t -> b t c')
+        x = self.norm3(x)
+        x = self.act(x)
+        x = self.final_linear(x)
+        x = rearrange(x, '(b n) t c -> b t n c', b=b)
+
+        return x, x_local
+
+
+class FramePackMotioner(nn.Module):
+
+    def __init__(self, inner_dim=1024, num_heads=16, zip_frame_buckets=[1, 2, 16], drop_mode="drop", *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.proj = nn.Conv3d(16, inner_dim, kernel_size=(1, 2, 2), stride=(1, 2, 2))
+        self.proj_2x = nn.Conv3d(16, inner_dim, kernel_size=(2, 4, 4), stride=(2, 4, 4))
+        self.proj_4x = nn.Conv3d(16, inner_dim, kernel_size=(4, 8, 8), stride=(4, 8, 8))
+        self.zip_frame_buckets = torch.tensor(zip_frame_buckets, dtype=torch.long)
+
+        self.inner_dim = inner_dim
+        self.num_heads = num_heads
+        self.freqs = torch.cat(precompute_freqs_cis_3d(inner_dim // num_heads), dim=1)
+        self.drop_mode = drop_mode
+
+    def forward(self, motion_latents, add_last_motion=2):
+        motion_frames = motion_latents[0].shape[1]
+        mot = []
+        mot_remb = []
+        for m in motion_latents:
+            lat_height, lat_width = m.shape[2], m.shape[3]
+            padd_lat = torch.zeros(16, self.zip_frame_buckets.sum(), lat_height, lat_width).to(device=m.device, dtype=m.dtype)
+            overlap_frame = min(padd_lat.shape[1], m.shape[1])
+            if overlap_frame > 0:
+                padd_lat[:, -overlap_frame:] = m[:, -overlap_frame:]
+
+            if add_last_motion < 2 and self.drop_mode != "drop":
+                zero_end_frame = self.zip_frame_buckets[:self.zip_frame_buckets.__len__() - add_last_motion - 1].sum()
+                padd_lat[:, -zero_end_frame:] = 0
+
+            padd_lat = padd_lat.unsqueeze(0)
+            clean_latents_4x, clean_latents_2x, clean_latents_post = padd_lat[:, :, -self.zip_frame_buckets.sum():, :, :].split(
+                list(self.zip_frame_buckets)[::-1], dim=2
+            )  # 16, 2 ,1
+
+            # patchfy
+            clean_latents_post = self.proj(clean_latents_post).flatten(2).transpose(1, 2)
+            clean_latents_2x = self.proj_2x(clean_latents_2x).flatten(2).transpose(1, 2)
+            clean_latents_4x = self.proj_4x(clean_latents_4x).flatten(2).transpose(1, 2)
+
+            if add_last_motion < 2 and self.drop_mode == "drop":
+                clean_latents_post = clean_latents_post[:, :0] if add_last_motion < 2 else clean_latents_post
+                clean_latents_2x = clean_latents_2x[:, :0] if add_last_motion < 1 else clean_latents_2x
+
+            motion_lat = torch.cat([clean_latents_post, clean_latents_2x, clean_latents_4x], dim=1)
+
+            # rope
+            start_time_id = -(self.zip_frame_buckets[:1].sum())
+            end_time_id = start_time_id + self.zip_frame_buckets[0]
+            grid_sizes = [] if add_last_motion < 2 and self.drop_mode == "drop" else \
+                        [
+                            [torch.tensor([start_time_id, 0, 0]).unsqueeze(0).repeat(1, 1),
+                            torch.tensor([end_time_id, lat_height // 2, lat_width // 2]).unsqueeze(0).repeat(1, 1),
+                            torch.tensor([self.zip_frame_buckets[0], lat_height // 2, lat_width // 2]).unsqueeze(0).repeat(1, 1), ]
+                        ]
+
+            start_time_id = -(self.zip_frame_buckets[:2].sum())
+            end_time_id = start_time_id + self.zip_frame_buckets[1] // 2
+            grid_sizes_2x = [] if add_last_motion < 1 and self.drop_mode == "drop" else \
+            [
+                [torch.tensor([start_time_id, 0, 0]).unsqueeze(0).repeat(1, 1),
+                torch.tensor([end_time_id, lat_height // 4, lat_width // 4]).unsqueeze(0).repeat(1, 1),
+                torch.tensor([self.zip_frame_buckets[1], lat_height // 2, lat_width // 2]).unsqueeze(0).repeat(1, 1), ]
+            ]
+
+            start_time_id = -(self.zip_frame_buckets[:3].sum())
+            end_time_id = start_time_id + self.zip_frame_buckets[2] // 4
+            grid_sizes_4x = [
+                [
+                    torch.tensor([start_time_id, 0, 0]).unsqueeze(0).repeat(1, 1),
+                    torch.tensor([end_time_id, lat_height // 8, lat_width // 8]).unsqueeze(0).repeat(1, 1),
+                    torch.tensor([self.zip_frame_buckets[2], lat_height // 2, lat_width // 2]).unsqueeze(0).repeat(1, 1),
+                ]
+            ]
+
+            grid_sizes = grid_sizes + grid_sizes_2x + grid_sizes_4x
+
+            motion_rope_emb = rope_precompute(
+                motion_lat.detach().view(1, motion_lat.shape[1], self.num_heads, self.inner_dim // self.num_heads),
+                grid_sizes,
+                self.freqs,
+                start=None
+            )
+
+            mot.append(motion_lat)
+            mot_remb.append(motion_rope_emb)
+        return mot, mot_remb
+
+
+class AdaLayerNorm(nn.Module):
+
+    def __init__(
+        self,
+        embedding_dim: int,
+        output_dim: int,
+        norm_eps: float = 1e-5,
+    ):
+        super().__init__()
+        self.silu = nn.SiLU()
+        self.linear = nn.Linear(embedding_dim, output_dim)
+        self.norm = nn.LayerNorm(output_dim // 2, norm_eps, elementwise_affine=False)
+
+    def forward(self, x, temb):
+        temb = self.linear(F.silu(temb))
+        shift, scale = temb.chunk(2, dim=1)
+        shift = shift[:, None, :]
+        scale = scale[:, None, :]
+        x = self.norm(x) * (1 + scale) + shift
+        return x
+
+
+class AudioInjector_WAN(nn.Module):
+
+    def __init__(
+        self,
+        all_modules,
+        all_modules_names,
+        dim=2048,
+        num_heads=32,
+        inject_layer=[0, 27],
+        enable_adain=False,
+        adain_dim=2048,
+    ):
+        super().__init__()
+        self.injected_block_id = {}
+        audio_injector_id = 0
+        for mod_name, mod in zip(all_modules_names, all_modules):
+            if isinstance(mod, DiTBlock):
+                for inject_id in inject_layer:
+                    if f'transformer_blocks.{inject_id}' in mod_name:
+                        self.injected_block_id[inject_id] = audio_injector_id
+                        audio_injector_id += 1
+
+        self.injector = nn.ModuleList([CrossAttention(
+            dim=dim,
+            num_heads=num_heads,
+        ) for _ in range(audio_injector_id)])
+        self.injector_pre_norm_feat = nn.ModuleList([nn.LayerNorm(
+            dim,
+            elementwise_affine=False,
+            eps=1e-6,
+        ) for _ in range(audio_injector_id)])
+        self.injector_pre_norm_vec = nn.ModuleList([nn.LayerNorm(
+            dim,
+            elementwise_affine=False,
+            eps=1e-6,
+        ) for _ in range(audio_injector_id)])
+        if enable_adain:
+            self.injector_adain_layers = nn.ModuleList([AdaLayerNorm(output_dim=dim * 2, embedding_dim=adain_dim) for _ in range(audio_injector_id)])
+
+
+class CausalAudioEncoder(nn.Module):
+
+    def __init__(self, dim=5120, num_layers=25, out_dim=2048, num_token=4, need_global=False):
+        super().__init__()
+        self.encoder = MotionEncoder_tc(in_dim=dim, hidden_dim=out_dim, num_heads=num_token, need_global=need_global)
+        weight = torch.ones((1, num_layers, 1, 1)) * 0.01
+
+        self.weights = torch.nn.Parameter(weight)
+        self.act = torch.nn.SiLU()
+
+    def forward(self, features):
+        # features B * num_layers * dim * video_length
+        weights = self.act(self.weights.to(device=features.device, dtype=features.dtype))
+        weights_sum = weights.sum(dim=1, keepdims=True)
+        weighted_feat = ((features * weights) / weights_sum).sum(dim=1)  # b dim f
+        weighted_feat = weighted_feat.permute(0, 2, 1)  # b f dim
+        res = self.encoder(weighted_feat)  # b f n dim
+        return res  # b f n dim
+
+
+class WanS2VDiTBlock(DiTBlock):
+
+    def forward(self, x, context, t_mod, seq_len_x, freqs):
+        t_mod = (self.modulation.unsqueeze(2).to(dtype=t_mod.dtype, device=t_mod.device) + t_mod).chunk(6, dim=1)
+        # t_mod[:, :, 0] for x, t_mod[:, :, 1] for other like ref, motion, etc.
+        t_mod = [
+            torch.cat([element[:, :, 0].expand(1, seq_len_x, x.shape[-1]), element[:, :, 1].expand(1, x.shape[1] - seq_len_x, x.shape[-1])], dim=1)
+            for element in t_mod
+        ]
+        shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = t_mod
+        input_x = modulate(self.norm1(x), shift_msa, scale_msa)
+        x = self.gate(x, gate_msa, self.self_attn(input_x, freqs))
+        x = x + self.cross_attn(self.norm3(x), context)
+        input_x = modulate(self.norm2(x), shift_mlp, scale_mlp)
+        x = self.gate(x, gate_mlp, self.ffn(input_x))
+        return x
+
+
+class WanS2VModel(torch.nn.Module):
+
+    def __init__(
+        self,
+        dim: int,
+        in_dim: int,
+        ffn_dim: int,
+        out_dim: int,
+        text_dim: int,
+        freq_dim: int,
+        eps: float,
+        patch_size: Tuple[int, int, int],
+        num_heads: int,
+        num_layers: int,
+        cond_dim: int,
+        audio_dim: int,
+        num_audio_token: int,
+        enable_adain: bool = True,
+        audio_inject_layers: list = [0, 4, 8, 12, 16, 20, 24, 27, 30, 33, 36, 39],
+        zero_timestep: bool = True,
+        add_last_motion: bool = True,
+        framepack_drop_mode: str = "padd",
+        fuse_vae_embedding_in_latents: bool = True,
+        require_vae_embedding: bool = False,
+        seperated_timestep: bool = False,
+        require_clip_embedding: bool = False,
+    ):
+        super().__init__()
+        self.dim = dim
+        self.in_dim = in_dim
+        self.freq_dim = freq_dim
+        self.patch_size = patch_size
+        self.num_heads = num_heads
+        self.enbale_adain = enable_adain
+        self.add_last_motion = add_last_motion
+        self.zero_timestep = zero_timestep
+        self.fuse_vae_embedding_in_latents = fuse_vae_embedding_in_latents
+        self.require_vae_embedding = require_vae_embedding
+        self.seperated_timestep = seperated_timestep
+        self.require_clip_embedding = require_clip_embedding
+
+        self.patch_embedding = nn.Conv3d(in_dim, dim, kernel_size=patch_size, stride=patch_size)
+        self.text_embedding = nn.Sequential(nn.Linear(text_dim, dim), nn.GELU(approximate='tanh'), nn.Linear(dim, dim))
+        self.time_embedding = nn.Sequential(nn.Linear(freq_dim, dim), nn.SiLU(), nn.Linear(dim, dim))
+        self.time_projection = nn.Sequential(nn.SiLU(), nn.Linear(dim, dim * 6))
+
+        self.blocks = nn.ModuleList([WanS2VDiTBlock(False, dim, num_heads, ffn_dim, eps) for _ in range(num_layers)])
+        self.head = Head(dim, out_dim, patch_size, eps)
+        self.freqs = torch.cat(precompute_freqs_cis_3d(dim // num_heads), dim=1)
+
+        self.cond_encoder = nn.Conv3d(cond_dim, dim, kernel_size=patch_size, stride=patch_size)
+        self.casual_audio_encoder = CausalAudioEncoder(dim=audio_dim, out_dim=dim, num_token=num_audio_token, need_global=enable_adain)
+        all_modules, all_modules_names = torch_dfs(self.blocks, parent_name="root.transformer_blocks")
+        # TODO: refactor dfs
+        self.audio_injector = AudioInjector_WAN(
+            all_modules,
+            all_modules_names,
+            dim=dim,
+            num_heads=num_heads,
+            inject_layer=audio_inject_layers,
+            enable_adain=enable_adain,
+            adain_dim=dim,
+        )
+        self.trainable_cond_mask = nn.Embedding(3, dim)
+        self.frame_packer = FramePackMotioner(inner_dim=dim, num_heads=num_heads, zip_frame_buckets=[1, 2, 16], drop_mode=framepack_drop_mode)
+
+    def patchify(self, x: torch.Tensor):
+        grid_size = x.shape[2:]
+        x = rearrange(x, 'b c f h w -> b (f h w) c').contiguous()
+        return x, grid_size  # x, grid_size: (f, h, w)
+
+    def unpatchify(self, x: torch.Tensor, grid_size: torch.Tensor):
+        return rearrange(
+            x,
+            'b (f h w) (x y z c) -> b c (f x) (h y) (w z)',
+            f=grid_size[0],
+            h=grid_size[1],
+            w=grid_size[2],
+            x=self.patch_size[0],
+            y=self.patch_size[1],
+            z=self.patch_size[2]
+        )
+
+    def process_motion_frame_pack(self, motion_latents, drop_motion_frames=False, add_last_motion=2):
+        flattern_mot, mot_remb = self.frame_packer(motion_latents, add_last_motion)
+        if drop_motion_frames:
+            return [m[:, :0] for m in flattern_mot], [m[:, :0] for m in mot_remb]
+        else:
+            return flattern_mot, mot_remb
+
+    def inject_motion(self, x, rope_embs, mask_input, motion_latents, drop_motion_frames=True, add_last_motion=2):
+        # inject the motion frames token to the hidden states
+        # TODO: check drop_motion_frames = False
+        mot, mot_remb = self.process_motion_frame_pack(motion_latents, drop_motion_frames=drop_motion_frames, add_last_motion=add_last_motion)
+        if len(mot) > 0:
+            x = torch.cat([x, mot[0]], dim=1)
+            rope_embs = torch.cat([rope_embs, mot_remb[0]], dim=1)
+            mask_input = torch.cat(
+                [mask_input, 2 * torch.ones([1, x.shape[1] - mask_input.shape[1]], device=mask_input.device, dtype=mask_input.dtype)], dim=1
+            )
+        return x, rope_embs, mask_input
+
+    def after_transformer_block(self, block_idx, hidden_states, audio_emb_global, audio_emb, original_seq_len, use_unified_sequence_parallel=False):
+        if block_idx in self.audio_injector.injected_block_id.keys():
+            audio_attn_id = self.audio_injector.injected_block_id[block_idx]
+            num_frames = audio_emb.shape[1]
+            if use_unified_sequence_parallel:
+                from xfuser.core.distributed import get_sp_group
+                hidden_states = get_sp_group().all_gather(hidden_states, dim=1)
+
+            input_hidden_states = hidden_states[:, :original_seq_len].clone()  # b (f h w) c
+            input_hidden_states = rearrange(input_hidden_states, "b (t n) c -> (b t) n c", t=num_frames)
+
+            audio_emb_global = rearrange(audio_emb_global, "b t n c -> (b t) n c")
+            adain_hidden_states = self.audio_injector.injector_adain_layers[audio_attn_id](input_hidden_states, temb=audio_emb_global[:, 0])
+            attn_hidden_states = adain_hidden_states
+
+            audio_emb = rearrange(audio_emb, "b t n c -> (b t) n c", t=num_frames)
+            attn_audio_emb = audio_emb
+            residual_out = self.audio_injector.injector[audio_attn_id](attn_hidden_states, attn_audio_emb)
+            residual_out = rearrange(residual_out, "(b t) n c -> b (t n) c", t=num_frames)
+            hidden_states[:, :original_seq_len] = hidden_states[:, :original_seq_len] + residual_out
+            if use_unified_sequence_parallel:
+                from xfuser.core.distributed import get_sequence_parallel_world_size, get_sequence_parallel_rank
+                hidden_states = torch.chunk(hidden_states, get_sequence_parallel_world_size(), dim=1)[get_sequence_parallel_rank()]
+        return hidden_states
+
+    def cal_audio_emb(self, audio_input, motion_frames=[73, 19]):
+        audio_input = torch.cat([audio_input[..., 0:1].repeat(1, 1, 1, motion_frames[0]), audio_input], dim=-1)
+        audio_emb_global, audio_emb = self.casual_audio_encoder(audio_input)
+        audio_emb_global = audio_emb_global[:, motion_frames[1]:].clone()
+        merged_audio_emb = audio_emb[:, motion_frames[1]:, :]
+        return audio_emb_global, merged_audio_emb
+
+    def get_grid_sizes(self, grid_size_x, grid_size_ref):
+        f, h, w = grid_size_x
+        rf, rh, rw = grid_size_ref
+        grid_sizes_x = torch.tensor([f, h, w], dtype=torch.long).unsqueeze(0)
+        grid_sizes_x = [[torch.zeros_like(grid_sizes_x), grid_sizes_x, grid_sizes_x]]
+        grid_sizes_ref = [[
+            torch.tensor([30, 0, 0]).unsqueeze(0),
+            torch.tensor([31, rh, rw]).unsqueeze(0),
+            torch.tensor([1, rh, rw]).unsqueeze(0),
+        ]]
+        return grid_sizes_x + grid_sizes_ref
+
+    def forward(
+        self,
+        latents,
+        timestep,
+        context,
+        audio_input,
+        motion_latents,
+        pose_cond,
+        use_gradient_checkpointing_offload=False,
+        use_gradient_checkpointing=False
+    ):
+        origin_ref_latents = latents[:, :, 0:1]
+        x = latents[:, :, 1:]
+
+        # context embedding
+        context = self.text_embedding(context)
+
+        # audio encode
+        audio_emb_global, merged_audio_emb = self.cal_audio_emb(audio_input)
+
+        # x and pose_cond
+        pose_cond = torch.zeros_like(x) if pose_cond is None else pose_cond
+        x, (f, h, w) = self.patchify(self.patch_embedding(x) + self.cond_encoder(pose_cond))  # torch.Size([1, 29120, 5120])
+        seq_len_x = x.shape[1]
+
+        # reference image
+        ref_latents, (rf, rh, rw) = self.patchify(self.patch_embedding(origin_ref_latents))  # torch.Size([1, 1456, 5120])
+        grid_sizes = self.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), self.num_heads, self.dim // self.num_heads), grid_sizes, self.freqs, start=None
+        )
+        # motion
+        x, pre_compute_freqs, mask = self.inject_motion(x, pre_compute_freqs, mask, motion_latents, add_last_motion=2)
+
+        x = x + self.trainable_cond_mask(mask).to(x.dtype)
+
+        # t_mod
+        timestep = torch.cat([timestep, torch.zeros([1], dtype=timestep.dtype, device=timestep.device)])
+        t = self.time_embedding(sinusoidal_embedding_1d(self.freq_dim, timestep))
+        t_mod = self.time_projection(t).unflatten(1, (6, self.dim)).unsqueeze(2).transpose(0, 2)
+
+        def create_custom_forward(module):
+            def custom_forward(*inputs):
+                return module(*inputs)
+            return custom_forward
+
+        for block_id, block in enumerate(self.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: self.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: self.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 = self.after_transformer_block(block_id, x, audio_emb_global, merged_audio_emb, seq_len_x)
+
+        x = x[:, :seq_len_x]
+        x = self.head(x, t[:-1])
+        x = self.unpatchify(x, (f, h, w))
+        # make compatible with wan video
+        x = torch.cat([origin_ref_latents, x], dim=2)
+        return x
+
+    @staticmethod
+    def state_dict_converter():
+        return WanS2VModelStateDictConverter()
+
+
+class WanS2VModelStateDictConverter:
+
+    def __init__(self):
+        pass
+
+    def from_civitai(self, state_dict):
+        config = {}
+        if hash_state_dict_keys(state_dict) == "966cffdcc52f9c46c391768b27637614":
+            config = {
+                "dim": 5120,
+                "in_dim": 16,
+                "ffn_dim": 13824,
+                "out_dim": 16,
+                "text_dim": 4096,
+                "freq_dim": 256,
+                "eps": 1e-06,
+                "patch_size": (1, 2, 2),
+                "num_heads": 40,
+                "num_layers": 40,
+                "cond_dim": 16,
+                "audio_dim": 1024,
+                "num_audio_token": 4,
+            }
+        return state_dict, config
diff --git a/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/wan_video_image_encoder.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/wan_video_image_encoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..5ca878b1fd6ed6dc00420f092f87479fb65ef63a
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/wan_video_image_encoder.py
@@ -0,0 +1,902 @@
+"""
+Concise re-implementation of
+``https://github.com/openai/CLIP'' and
+``https://github.com/mlfoundations/open_clip''.
+"""
+import math
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torchvision.transforms as T
+from .wan_video_dit import flash_attention
+
+
+class SelfAttention(nn.Module):
+
+    def __init__(self, dim, num_heads, dropout=0.1, eps=1e-5):
+        assert dim % num_heads == 0
+        super().__init__()
+        self.dim = dim
+        self.num_heads = num_heads
+        self.head_dim = dim // num_heads
+        self.eps = eps
+
+        # layers
+        self.q = nn.Linear(dim, dim)
+        self.k = nn.Linear(dim, dim)
+        self.v = nn.Linear(dim, dim)
+        self.o = nn.Linear(dim, dim)
+        self.dropout = nn.Dropout(dropout)
+
+    def forward(self, x, mask):
+        """
+        x:   [B, L, C].
+        """
+        b, s, c, n, d = *x.size(), self.num_heads, self.head_dim
+
+        # compute query, key, value
+        q = self.q(x).reshape(b, s, n, d).permute(0, 2, 1, 3)
+        k = self.k(x).reshape(b, s, n, d).permute(0, 2, 1, 3)
+        v = self.v(x).reshape(b, s, n, d).permute(0, 2, 1, 3)
+
+        # compute attention
+        p = self.dropout.p if self.training else 0.0
+        x = F.scaled_dot_product_attention(q, k, v, mask, p)
+        x = x.permute(0, 2, 1, 3).reshape(b, s, c)
+
+        # output
+        x = self.o(x)
+        x = self.dropout(x)
+        return x
+
+
+class AttentionBlock(nn.Module):
+
+    def __init__(self, dim, num_heads, post_norm, dropout=0.1, eps=1e-5):
+        super().__init__()
+        self.dim = dim
+        self.num_heads = num_heads
+        self.post_norm = post_norm
+        self.eps = eps
+
+        # layers
+        self.attn = SelfAttention(dim, num_heads, dropout, eps)
+        self.norm1 = nn.LayerNorm(dim, eps=eps)
+        self.ffn = nn.Sequential(
+            nn.Linear(dim, dim * 4), nn.GELU(), nn.Linear(dim * 4, dim),
+            nn.Dropout(dropout))
+        self.norm2 = nn.LayerNorm(dim, eps=eps)
+
+    def forward(self, x, mask):
+        if self.post_norm:
+            x = self.norm1(x + self.attn(x, mask))
+            x = self.norm2(x + self.ffn(x))
+        else:
+            x = x + self.attn(self.norm1(x), mask)
+            x = x + self.ffn(self.norm2(x))
+        return x
+
+
+class XLMRoberta(nn.Module):
+    """
+    XLMRobertaModel with no pooler and no LM head.
+    """
+
+    def __init__(self,
+                 vocab_size=250002,
+                 max_seq_len=514,
+                 type_size=1,
+                 pad_id=1,
+                 dim=1024,
+                 num_heads=16,
+                 num_layers=24,
+                 post_norm=True,
+                 dropout=0.1,
+                 eps=1e-5):
+        super().__init__()
+        self.vocab_size = vocab_size
+        self.max_seq_len = max_seq_len
+        self.type_size = type_size
+        self.pad_id = pad_id
+        self.dim = dim
+        self.num_heads = num_heads
+        self.num_layers = num_layers
+        self.post_norm = post_norm
+        self.eps = eps
+
+        # embeddings
+        self.token_embedding = nn.Embedding(vocab_size, dim, padding_idx=pad_id)
+        self.type_embedding = nn.Embedding(type_size, dim)
+        self.pos_embedding = nn.Embedding(max_seq_len, dim, padding_idx=pad_id)
+        self.dropout = nn.Dropout(dropout)
+
+        # blocks
+        self.blocks = nn.ModuleList([
+            AttentionBlock(dim, num_heads, post_norm, dropout, eps)
+            for _ in range(num_layers)
+        ])
+
+        # norm layer
+        self.norm = nn.LayerNorm(dim, eps=eps)
+
+    def forward(self, ids):
+        """
+        ids: [B, L] of torch.LongTensor.
+        """
+        b, s = ids.shape
+        mask = ids.ne(self.pad_id).long()
+
+        # embeddings
+        x = self.token_embedding(ids) + \
+            self.type_embedding(torch.zeros_like(ids)) + \
+            self.pos_embedding(self.pad_id + torch.cumsum(mask, dim=1) * mask)
+        if self.post_norm:
+            x = self.norm(x)
+        x = self.dropout(x)
+
+        # blocks
+        mask = torch.where(
+            mask.view(b, 1, 1, s).gt(0), 0.0,
+            torch.finfo(x.dtype).min)
+        for block in self.blocks:
+            x = block(x, mask)
+
+        # output
+        if not self.post_norm:
+            x = self.norm(x)
+        return x
+
+
+def xlm_roberta_large(pretrained=False,
+                      return_tokenizer=False,
+                      device='cpu',
+                      **kwargs):
+    """
+    XLMRobertaLarge adapted from Huggingface.
+    """
+    # params
+    cfg = dict(
+        vocab_size=250002,
+        max_seq_len=514,
+        type_size=1,
+        pad_id=1,
+        dim=1024,
+        num_heads=16,
+        num_layers=24,
+        post_norm=True,
+        dropout=0.1,
+        eps=1e-5)
+    cfg.update(**kwargs)
+
+    # init model
+    if pretrained:
+        from sora import DOWNLOAD_TO_CACHE
+
+        # init a meta model
+        with torch.device('meta'):
+            model = XLMRoberta(**cfg)
+
+        # load checkpoint
+        model.load_state_dict(
+            torch.load(
+                DOWNLOAD_TO_CACHE('models/xlm_roberta/xlm_roberta_large.pth'),
+                map_location=device),
+            assign=True)
+    else:
+        # init a model on device
+        with torch.device(device):
+            model = XLMRoberta(**cfg)
+
+    # init tokenizer
+    if return_tokenizer:
+        from sora.data import HuggingfaceTokenizer
+        tokenizer = HuggingfaceTokenizer(
+            name='xlm-roberta-large',
+            seq_len=model.text_len,
+            clean='whitespace')
+        return model, tokenizer
+    else:
+        return model
+
+
+
+def pos_interpolate(pos, seq_len):
+    if pos.size(1) == seq_len:
+        return pos
+    else:
+        src_grid = int(math.sqrt(pos.size(1)))
+        tar_grid = int(math.sqrt(seq_len))
+        n = pos.size(1) - src_grid * src_grid
+        return torch.cat([
+            pos[:, :n],
+            F.interpolate(
+                pos[:, n:].float().reshape(1, src_grid, src_grid, -1).permute(
+                    0, 3, 1, 2),
+                size=(tar_grid, tar_grid),
+                mode='bicubic',
+                align_corners=False).flatten(2).transpose(1, 2)
+        ],
+                         dim=1)
+
+
+class QuickGELU(nn.Module):
+
+    def forward(self, x):
+        return x * torch.sigmoid(1.702 * x)
+
+
+class LayerNorm(nn.LayerNorm):
+
+    def forward(self, x):
+        return super().forward(x).type_as(x)
+
+
+class SelfAttention(nn.Module):
+
+    def __init__(self,
+                 dim,
+                 num_heads,
+                 causal=False,
+                 attn_dropout=0.0,
+                 proj_dropout=0.0):
+        assert dim % num_heads == 0
+        super().__init__()
+        self.dim = dim
+        self.num_heads = num_heads
+        self.head_dim = dim // num_heads
+        self.causal = causal
+        self.attn_dropout = attn_dropout
+        self.proj_dropout = proj_dropout
+
+        # layers
+        self.to_qkv = nn.Linear(dim, dim * 3)
+        self.proj = nn.Linear(dim, dim)
+
+    def forward(self, x):
+        """
+        x:   [B, L, C].
+        """
+        # compute query, key, value
+        q, k, v = self.to_qkv(x).chunk(3, dim=-1)
+
+        # compute attention
+        x = flash_attention(q, k, v, num_heads=self.num_heads, compatibility_mode=True)
+
+        # output
+        x = self.proj(x)
+        x = F.dropout(x, self.proj_dropout, self.training)
+        return x
+
+
+class SwiGLU(nn.Module):
+
+    def __init__(self, dim, mid_dim):
+        super().__init__()
+        self.dim = dim
+        self.mid_dim = mid_dim
+
+        # layers
+        self.fc1 = nn.Linear(dim, mid_dim)
+        self.fc2 = nn.Linear(dim, mid_dim)
+        self.fc3 = nn.Linear(mid_dim, dim)
+
+    def forward(self, x):
+        x = F.silu(self.fc1(x)) * self.fc2(x)
+        x = self.fc3(x)
+        return x
+
+
+class AttentionBlock(nn.Module):
+
+    def __init__(self,
+                 dim,
+                 mlp_ratio,
+                 num_heads,
+                 post_norm=False,
+                 causal=False,
+                 activation='quick_gelu',
+                 attn_dropout=0.0,
+                 proj_dropout=0.0,
+                 norm_eps=1e-5):
+        assert activation in ['quick_gelu', 'gelu', 'swi_glu']
+        super().__init__()
+        self.dim = dim
+        self.mlp_ratio = mlp_ratio
+        self.num_heads = num_heads
+        self.post_norm = post_norm
+        self.causal = causal
+        self.norm_eps = norm_eps
+
+        # layers
+        self.norm1 = LayerNorm(dim, eps=norm_eps)
+        self.attn = SelfAttention(dim, num_heads, causal, attn_dropout,
+                                  proj_dropout)
+        self.norm2 = LayerNorm(dim, eps=norm_eps)
+        if activation == 'swi_glu':
+            self.mlp = SwiGLU(dim, int(dim * mlp_ratio))
+        else:
+            self.mlp = nn.Sequential(
+                nn.Linear(dim, int(dim * mlp_ratio)),
+                QuickGELU() if activation == 'quick_gelu' else nn.GELU(),
+                nn.Linear(int(dim * mlp_ratio), dim), nn.Dropout(proj_dropout))
+
+    def forward(self, x):
+        if self.post_norm:
+            x = x + self.norm1(self.attn(x))
+            x = x + self.norm2(self.mlp(x))
+        else:
+            x = x + self.attn(self.norm1(x))
+            x = x + self.mlp(self.norm2(x))
+        return x
+
+
+class AttentionPool(nn.Module):
+
+    def __init__(self,
+                 dim,
+                 mlp_ratio,
+                 num_heads,
+                 activation='gelu',
+                 proj_dropout=0.0,
+                 norm_eps=1e-5):
+        assert dim % num_heads == 0
+        super().__init__()
+        self.dim = dim
+        self.mlp_ratio = mlp_ratio
+        self.num_heads = num_heads
+        self.head_dim = dim // num_heads
+        self.proj_dropout = proj_dropout
+        self.norm_eps = norm_eps
+
+        # layers
+        gain = 1.0 / math.sqrt(dim)
+        self.cls_embedding = nn.Parameter(gain * torch.randn(1, 1, dim))
+        self.to_q = nn.Linear(dim, dim)
+        self.to_kv = nn.Linear(dim, dim * 2)
+        self.proj = nn.Linear(dim, dim)
+        self.norm = LayerNorm(dim, eps=norm_eps)
+        self.mlp = nn.Sequential(
+            nn.Linear(dim, int(dim * mlp_ratio)),
+            QuickGELU() if activation == 'quick_gelu' else nn.GELU(),
+            nn.Linear(int(dim * mlp_ratio), dim), nn.Dropout(proj_dropout))
+
+    def forward(self, x):
+        """
+        x:  [B, L, C].
+        """
+        b, s, c, n, d = *x.size(), self.num_heads, self.head_dim
+
+        # compute query, key, value
+        q = self.to_q(self.cls_embedding).view(1, 1, n*d).expand(b, -1, -1)
+        k, v = self.to_kv(x).chunk(2, dim=-1)
+
+        # compute attention
+        x = flash_attention(q, k, v, num_heads=self.num_heads, compatibility_mode=True)
+        x = x.reshape(b, 1, c)
+
+        # output
+        x = self.proj(x)
+        x = F.dropout(x, self.proj_dropout, self.training)
+
+        # mlp
+        x = x + self.mlp(self.norm(x))
+        return x[:, 0]
+
+
+class VisionTransformer(nn.Module):
+
+    def __init__(self,
+                 image_size=224,
+                 patch_size=16,
+                 dim=768,
+                 mlp_ratio=4,
+                 out_dim=512,
+                 num_heads=12,
+                 num_layers=12,
+                 pool_type='token',
+                 pre_norm=True,
+                 post_norm=False,
+                 activation='quick_gelu',
+                 attn_dropout=0.0,
+                 proj_dropout=0.0,
+                 embedding_dropout=0.0,
+                 norm_eps=1e-5):
+        if image_size % patch_size != 0:
+            print(
+                '[WARNING] image_size is not divisible by patch_size',
+                flush=True)
+        assert pool_type in ('token', 'token_fc', 'attn_pool')
+        out_dim = out_dim or dim
+        super().__init__()
+        self.image_size = image_size
+        self.patch_size = patch_size
+        self.num_patches = (image_size // patch_size)**2
+        self.dim = dim
+        self.mlp_ratio = mlp_ratio
+        self.out_dim = out_dim
+        self.num_heads = num_heads
+        self.num_layers = num_layers
+        self.pool_type = pool_type
+        self.post_norm = post_norm
+        self.norm_eps = norm_eps
+
+        # embeddings
+        gain = 1.0 / math.sqrt(dim)
+        self.patch_embedding = nn.Conv2d(
+            3,
+            dim,
+            kernel_size=patch_size,
+            stride=patch_size,
+            bias=not pre_norm)
+        if pool_type in ('token', 'token_fc'):
+            self.cls_embedding = nn.Parameter(gain * torch.randn(1, 1, dim))
+        self.pos_embedding = nn.Parameter(gain * torch.randn(
+            1, self.num_patches +
+            (1 if pool_type in ('token', 'token_fc') else 0), dim))
+        self.dropout = nn.Dropout(embedding_dropout)
+
+        # transformer
+        self.pre_norm = LayerNorm(dim, eps=norm_eps) if pre_norm else None
+        self.transformer = nn.Sequential(*[
+            AttentionBlock(dim, mlp_ratio, num_heads, post_norm, False,
+                           activation, attn_dropout, proj_dropout, norm_eps)
+            for _ in range(num_layers)
+        ])
+        self.post_norm = LayerNorm(dim, eps=norm_eps)
+
+        # head
+        if pool_type == 'token':
+            self.head = nn.Parameter(gain * torch.randn(dim, out_dim))
+        elif pool_type == 'token_fc':
+            self.head = nn.Linear(dim, out_dim)
+        elif pool_type == 'attn_pool':
+            self.head = AttentionPool(dim, mlp_ratio, num_heads, activation,
+                                      proj_dropout, norm_eps)
+
+    def forward(self, x, interpolation=False, use_31_block=False):
+        b = x.size(0)
+
+        # embeddings
+        x = self.patch_embedding(x).flatten(2).permute(0, 2, 1)
+        if self.pool_type in ('token', 'token_fc'):
+            x = torch.cat([self.cls_embedding.expand(b, -1, -1).to(dtype=x.dtype, device=x.device), x], dim=1)
+        if interpolation:
+            e = pos_interpolate(self.pos_embedding, x.size(1))
+        else:
+            e = self.pos_embedding
+        e = e.to(dtype=x.dtype, device=x.device)
+        x = self.dropout(x + e)
+        if self.pre_norm is not None:
+            x = self.pre_norm(x)
+
+        # transformer
+        if use_31_block:
+            x = self.transformer[:-1](x)
+            return x
+        else:
+            x = self.transformer(x)
+            return x
+
+
+class CLIP(nn.Module):
+
+    def __init__(self,
+                 embed_dim=512,
+                 image_size=224,
+                 patch_size=16,
+                 vision_dim=768,
+                 vision_mlp_ratio=4,
+                 vision_heads=12,
+                 vision_layers=12,
+                 vision_pool='token',
+                 vision_pre_norm=True,
+                 vision_post_norm=False,
+                 vocab_size=49408,
+                 text_len=77,
+                 text_dim=512,
+                 text_mlp_ratio=4,
+                 text_heads=8,
+                 text_layers=12,
+                 text_causal=True,
+                 text_pool='argmax',
+                 text_head_bias=False,
+                 logit_bias=None,
+                 activation='quick_gelu',
+                 attn_dropout=0.0,
+                 proj_dropout=0.0,
+                 embedding_dropout=0.0,
+                 norm_eps=1e-5):
+        super().__init__()
+        self.embed_dim = embed_dim
+        self.image_size = image_size
+        self.patch_size = patch_size
+        self.vision_dim = vision_dim
+        self.vision_mlp_ratio = vision_mlp_ratio
+        self.vision_heads = vision_heads
+        self.vision_layers = vision_layers
+        self.vision_pool = vision_pool
+        self.vision_pre_norm = vision_pre_norm
+        self.vision_post_norm = vision_post_norm
+        self.vocab_size = vocab_size
+        self.text_len = text_len
+        self.text_dim = text_dim
+        self.text_mlp_ratio = text_mlp_ratio
+        self.text_heads = text_heads
+        self.text_layers = text_layers
+        self.text_causal = text_causal
+        self.text_pool = text_pool
+        self.text_head_bias = text_head_bias
+        self.norm_eps = norm_eps
+
+        # models
+        self.visual = VisionTransformer(
+            image_size=image_size,
+            patch_size=patch_size,
+            dim=vision_dim,
+            mlp_ratio=vision_mlp_ratio,
+            out_dim=embed_dim,
+            num_heads=vision_heads,
+            num_layers=vision_layers,
+            pool_type=vision_pool,
+            pre_norm=vision_pre_norm,
+            post_norm=vision_post_norm,
+            activation=activation,
+            attn_dropout=attn_dropout,
+            proj_dropout=proj_dropout,
+            embedding_dropout=embedding_dropout,
+            norm_eps=norm_eps)
+        self.textual = TextTransformer(
+            vocab_size=vocab_size,
+            text_len=text_len,
+            dim=text_dim,
+            mlp_ratio=text_mlp_ratio,
+            out_dim=embed_dim,
+            num_heads=text_heads,
+            num_layers=text_layers,
+            causal=text_causal,
+            pool_type=text_pool,
+            head_bias=text_head_bias,
+            activation=activation,
+            attn_dropout=attn_dropout,
+            proj_dropout=proj_dropout,
+            embedding_dropout=embedding_dropout,
+            norm_eps=norm_eps)
+        self.log_scale = nn.Parameter(math.log(1 / 0.07) * torch.ones([]))
+        if logit_bias is not None:
+            self.logit_bias = nn.Parameter(logit_bias * torch.ones([]))
+
+        # initialize weights
+        self.init_weights()
+
+    def forward(self, imgs, txt_ids):
+        """
+        imgs:       [B, 3, H, W] of torch.float32.
+        - mean:     [0.48145466, 0.4578275, 0.40821073]
+        - std:      [0.26862954, 0.26130258, 0.27577711]
+        txt_ids:    [B, L] of torch.long. Encoded by data.CLIPTokenizer.
+        """
+        xi = self.visual(imgs)
+        xt = self.textual(txt_ids)
+        return xi, xt
+
+    def init_weights(self):
+        # embeddings
+        nn.init.normal_(self.textual.token_embedding.weight, std=0.02)
+        nn.init.normal_(self.visual.patch_embedding.weight, std=0.1)
+
+        # attentions
+        for modality in ['visual', 'textual']:
+            dim = self.vision_dim if modality == 'visual' else self.text_dim
+            transformer = getattr(self, modality).transformer
+            proj_gain = (1.0 / math.sqrt(dim)) * (
+                1.0 / math.sqrt(2 * len(transformer)))
+            attn_gain = 1.0 / math.sqrt(dim)
+            mlp_gain = 1.0 / math.sqrt(2.0 * dim)
+            for block in transformer:
+                nn.init.normal_(block.attn.to_qkv.weight, std=attn_gain)
+                nn.init.normal_(block.attn.proj.weight, std=proj_gain)
+                nn.init.normal_(block.mlp[0].weight, std=mlp_gain)
+                nn.init.normal_(block.mlp[2].weight, std=proj_gain)
+
+    def param_groups(self):
+        groups = [{
+            'params': [
+                p for n, p in self.named_parameters()
+                if 'norm' in n or n.endswith('bias')
+            ],
+            'weight_decay': 0.0
+        }, {
+            'params': [
+                p for n, p in self.named_parameters()
+                if not ('norm' in n or n.endswith('bias'))
+            ]
+        }]
+        return groups
+
+
+class XLMRobertaWithHead(XLMRoberta):
+
+    def __init__(self, **kwargs):
+        self.out_dim = kwargs.pop('out_dim')
+        super().__init__(**kwargs)
+
+        # head
+        mid_dim = (self.dim + self.out_dim) // 2
+        self.head = nn.Sequential(
+            nn.Linear(self.dim, mid_dim, bias=False), nn.GELU(),
+            nn.Linear(mid_dim, self.out_dim, bias=False))
+
+    def forward(self, ids):
+        # xlm-roberta
+        x = super().forward(ids)
+
+        # average pooling
+        mask = ids.ne(self.pad_id).unsqueeze(-1).to(x)
+        x = (x * mask).sum(dim=1) / mask.sum(dim=1)
+
+        # head
+        x = self.head(x)
+        return x
+
+
+class XLMRobertaCLIP(nn.Module):
+
+    def __init__(self,
+                 embed_dim=1024,
+                 image_size=224,
+                 patch_size=14,
+                 vision_dim=1280,
+                 vision_mlp_ratio=4,
+                 vision_heads=16,
+                 vision_layers=32,
+                 vision_pool='token',
+                 vision_pre_norm=True,
+                 vision_post_norm=False,
+                 activation='gelu',
+                 vocab_size=250002,
+                 max_text_len=514,
+                 type_size=1,
+                 pad_id=1,
+                 text_dim=1024,
+                 text_heads=16,
+                 text_layers=24,
+                 text_post_norm=True,
+                 text_dropout=0.1,
+                 attn_dropout=0.0,
+                 proj_dropout=0.0,
+                 embedding_dropout=0.0,
+                 norm_eps=1e-5):
+        super().__init__()
+        self.embed_dim = embed_dim
+        self.image_size = image_size
+        self.patch_size = patch_size
+        self.vision_dim = vision_dim
+        self.vision_mlp_ratio = vision_mlp_ratio
+        self.vision_heads = vision_heads
+        self.vision_layers = vision_layers
+        self.vision_pre_norm = vision_pre_norm
+        self.vision_post_norm = vision_post_norm
+        self.activation = activation
+        self.vocab_size = vocab_size
+        self.max_text_len = max_text_len
+        self.type_size = type_size
+        self.pad_id = pad_id
+        self.text_dim = text_dim
+        self.text_heads = text_heads
+        self.text_layers = text_layers
+        self.text_post_norm = text_post_norm
+        self.norm_eps = norm_eps
+
+        # models
+        self.visual = VisionTransformer(
+            image_size=image_size,
+            patch_size=patch_size,
+            dim=vision_dim,
+            mlp_ratio=vision_mlp_ratio,
+            out_dim=embed_dim,
+            num_heads=vision_heads,
+            num_layers=vision_layers,
+            pool_type=vision_pool,
+            pre_norm=vision_pre_norm,
+            post_norm=vision_post_norm,
+            activation=activation,
+            attn_dropout=attn_dropout,
+            proj_dropout=proj_dropout,
+            embedding_dropout=embedding_dropout,
+            norm_eps=norm_eps)
+        self.textual = None
+        self.log_scale = nn.Parameter(math.log(1 / 0.07) * torch.ones([]))
+
+    def forward(self, imgs, txt_ids):
+        """
+        imgs:       [B, 3, H, W] of torch.float32.
+        - mean:     [0.48145466, 0.4578275, 0.40821073]
+        - std:      [0.26862954, 0.26130258, 0.27577711]
+        txt_ids:    [B, L] of torch.long.
+                    Encoded by data.CLIPTokenizer.
+        """
+        xi = self.visual(imgs)
+        xt = self.textual(txt_ids)
+        return xi, xt
+
+    def param_groups(self):
+        groups = [{
+            'params': [
+                p for n, p in self.named_parameters()
+                if 'norm' in n or n.endswith('bias')
+            ],
+            'weight_decay': 0.0
+        }, {
+            'params': [
+                p for n, p in self.named_parameters()
+                if not ('norm' in n or n.endswith('bias'))
+            ]
+        }]
+        return groups
+
+
+def _clip(pretrained=False,
+          pretrained_name=None,
+          model_cls=CLIP,
+          return_transforms=False,
+          return_tokenizer=False,
+          tokenizer_padding='eos',
+          dtype=torch.float32,
+          device='cpu',
+          **kwargs):
+    # init model
+    if pretrained and pretrained_name:
+        from sora import BUCKET, DOWNLOAD_TO_CACHE
+
+        # init a meta model
+        with torch.device('meta'):
+            model = model_cls(**kwargs)
+
+        # checkpoint path
+        checkpoint = f'models/clip/{pretrained_name}'
+        if dtype in (torch.float16, torch.bfloat16):
+            suffix = '-' + {
+                torch.float16: 'fp16',
+                torch.bfloat16: 'bf16'
+            }[dtype]
+            if object_exists(BUCKET, f'{checkpoint}{suffix}.pth'):
+                checkpoint = f'{checkpoint}{suffix}'
+        checkpoint += '.pth'
+
+        # load
+        model.load_state_dict(
+            torch.load(DOWNLOAD_TO_CACHE(checkpoint), map_location=device),
+            assign=True,
+            strict=False)
+    else:
+        # init a model on device
+        with torch.device(device):
+            model = model_cls(**kwargs)
+
+    # set device
+    output = (model,)
+
+    # init transforms
+    if return_transforms:
+        # mean and std
+        if 'siglip' in pretrained_name.lower():
+            mean, std = [0.5, 0.5, 0.5], [0.5, 0.5, 0.5]
+        else:
+            mean = [0.48145466, 0.4578275, 0.40821073]
+            std = [0.26862954, 0.26130258, 0.27577711]
+
+        # transforms
+        transforms = T.Compose([
+            T.Resize((model.image_size, model.image_size),
+                     interpolation=T.InterpolationMode.BICUBIC),
+            T.ToTensor(),
+            T.Normalize(mean=mean, std=std)
+        ])
+        output += (transforms,)
+
+    # init tokenizer
+    if return_tokenizer:
+        from sora import data
+        if 'siglip' in pretrained_name.lower():
+            tokenizer = data.HuggingfaceTokenizer(
+                name=f'timm/{pretrained_name}',
+                seq_len=model.text_len,
+                clean='canonicalize')
+        elif 'xlm' in pretrained_name.lower():
+            tokenizer = data.HuggingfaceTokenizer(
+                name='xlm-roberta-large',
+                seq_len=model.max_text_len - 2,
+                clean='whitespace')
+        elif 'mba' in pretrained_name.lower():
+            tokenizer = data.HuggingfaceTokenizer(
+                name='facebook/xlm-roberta-xl',
+                seq_len=model.max_text_len - 2,
+                clean='whitespace')
+        else:
+            tokenizer = data.CLIPTokenizer(
+                seq_len=model.text_len, padding=tokenizer_padding)
+        output += (tokenizer,)
+    return output[0] if len(output) == 1 else output
+
+
+def clip_xlm_roberta_vit_h_14(
+        pretrained=False,
+        pretrained_name='open-clip-xlm-roberta-large-vit-huge-14',
+        **kwargs):
+    cfg = dict(
+        embed_dim=1024,
+        image_size=224,
+        patch_size=14,
+        vision_dim=1280,
+        vision_mlp_ratio=4,
+        vision_heads=16,
+        vision_layers=32,
+        vision_pool='token',
+        activation='gelu',
+        vocab_size=250002,
+        max_text_len=514,
+        type_size=1,
+        pad_id=1,
+        text_dim=1024,
+        text_heads=16,
+        text_layers=24,
+        text_post_norm=True,
+        text_dropout=0.1,
+        attn_dropout=0.0,
+        proj_dropout=0.0,
+        embedding_dropout=0.0)
+    cfg.update(**kwargs)
+    return _clip(pretrained, pretrained_name, XLMRobertaCLIP, **cfg)
+
+
+class WanImageEncoder(torch.nn.Module):
+
+    def __init__(self):
+        super().__init__()
+        # init model
+        self.model, self.transforms = clip_xlm_roberta_vit_h_14(
+            pretrained=False,
+            return_transforms=True,
+            return_tokenizer=False,
+            dtype=torch.float32,
+            device="cpu")
+
+    def encode_image(self, videos):
+        # preprocess
+        size = (self.model.image_size,) * 2
+        videos = torch.cat([
+            F.interpolate(
+                u,
+                size=size,
+                mode='bicubic',
+                align_corners=False) for u in videos
+        ])
+        videos = self.transforms.transforms[-1](videos.mul_(0.5).add_(0.5))
+
+        # forward
+        dtype = next(iter(self.model.visual.parameters())).dtype
+        videos = videos.to(dtype)
+        out = self.model.visual(videos, use_31_block=True)
+        return out
+        
+    @staticmethod
+    def state_dict_converter():
+        return WanImageEncoderStateDictConverter()
+    
+    
+class WanImageEncoderStateDictConverter:
+    def __init__(self):
+        pass
+
+    def from_diffusers(self, state_dict):
+        return state_dict
+    
+    def from_civitai(self, state_dict):
+        state_dict_ = {}
+        for name, param in state_dict.items():
+            if name.startswith("textual."):
+                continue
+            name = "model." + name
+            state_dict_[name] = param
+        return state_dict_
+
diff --git a/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/wan_video_motion_controller.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/wan_video_motion_controller.py
new file mode 100644
index 0000000000000000000000000000000000000000..518c1c66edca1cae11d5f3371af0455808b2a66a
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/wan_video_motion_controller.py
@@ -0,0 +1,44 @@
+import torch
+import torch.nn as nn
+from .wan_video_dit import sinusoidal_embedding_1d
+
+
+
+class WanMotionControllerModel(torch.nn.Module):
+    def __init__(self, freq_dim=256, dim=1536):
+        super().__init__()
+        self.freq_dim = freq_dim
+        self.linear = nn.Sequential(
+            nn.Linear(freq_dim, dim),
+            nn.SiLU(),
+            nn.Linear(dim, dim),
+            nn.SiLU(),
+            nn.Linear(dim, dim * 6),
+        )
+
+    def forward(self, motion_bucket_id):
+        emb = sinusoidal_embedding_1d(self.freq_dim, motion_bucket_id * 10)
+        emb = self.linear(emb)
+        return emb
+
+    def init(self):
+        state_dict = self.linear[-1].state_dict()
+        state_dict = {i: state_dict[i] * 0 for i in state_dict}
+        self.linear[-1].load_state_dict(state_dict)
+
+    @staticmethod
+    def state_dict_converter():
+        return WanMotionControllerModelDictConverter()
+    
+    
+
+class WanMotionControllerModelDictConverter:
+    def __init__(self):
+        pass
+
+    def from_diffusers(self, state_dict):
+        return state_dict
+    
+    def from_civitai(self, state_dict):
+        return state_dict
+
diff --git a/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/wan_video_text_encoder.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/wan_video_text_encoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..c28873722ee92f23914712c9d5b2c3a26fd2adb7
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/wan_video_text_encoder.py
@@ -0,0 +1,269 @@
+import math
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+def fp16_clamp(x):
+    if x.dtype == torch.float16 and torch.isinf(x).any():
+        clamp = torch.finfo(x.dtype).max - 1000
+        x = torch.clamp(x, min=-clamp, max=clamp)
+    return x
+
+
+class GELU(nn.Module):
+
+    def forward(self, x):
+        return 0.5 * x * (1.0 + torch.tanh(
+            math.sqrt(2.0 / math.pi) * (x + 0.044715 * torch.pow(x, 3.0))))
+
+
+class T5LayerNorm(nn.Module):
+
+    def __init__(self, dim, eps=1e-6):
+        super(T5LayerNorm, self).__init__()
+        self.dim = dim
+        self.eps = eps
+        self.weight = nn.Parameter(torch.ones(dim))
+
+    def forward(self, x):
+        x = x * torch.rsqrt(x.float().pow(2).mean(dim=-1, keepdim=True) +
+                            self.eps)
+        if self.weight.dtype in [torch.float16, torch.bfloat16]:
+            x = x.type_as(self.weight)
+        return self.weight * x
+
+
+class T5Attention(nn.Module):
+
+    def __init__(self, dim, dim_attn, num_heads, dropout=0.1):
+        assert dim_attn % num_heads == 0
+        super(T5Attention, self).__init__()
+        self.dim = dim
+        self.dim_attn = dim_attn
+        self.num_heads = num_heads
+        self.head_dim = dim_attn // num_heads
+
+        # layers
+        self.q = nn.Linear(dim, dim_attn, bias=False)
+        self.k = nn.Linear(dim, dim_attn, bias=False)
+        self.v = nn.Linear(dim, dim_attn, bias=False)
+        self.o = nn.Linear(dim_attn, dim, bias=False)
+        self.dropout = nn.Dropout(dropout)
+
+    def forward(self, x, context=None, mask=None, pos_bias=None):
+        """
+        x:          [B, L1, C].
+        context:    [B, L2, C] or None.
+        mask:       [B, L2] or [B, L1, L2] or None.
+        """
+        # check inputs
+        context = x if context is None else context
+        b, n, c = x.size(0), self.num_heads, self.head_dim
+
+        # compute query, key, value
+        q = self.q(x).view(b, -1, n, c)
+        k = self.k(context).view(b, -1, n, c)
+        v = self.v(context).view(b, -1, n, c)
+
+        # attention bias
+        attn_bias = x.new_zeros(b, n, q.size(1), k.size(1))
+        if pos_bias is not None:
+            attn_bias += pos_bias
+        if mask is not None:
+            assert mask.ndim in [2, 3]
+            mask = mask.view(b, 1, 1,
+                             -1) if mask.ndim == 2 else mask.unsqueeze(1)
+            attn_bias.masked_fill_(mask == 0, torch.finfo(x.dtype).min)
+
+        # compute attention (T5 does not use scaling)
+        attn = torch.einsum('binc,bjnc->bnij', q, k) + attn_bias
+        attn = F.softmax(attn.float(), dim=-1).type_as(attn)
+        x = torch.einsum('bnij,bjnc->binc', attn, v)
+
+        # output
+        x = x.reshape(b, -1, n * c)
+        x = self.o(x)
+        x = self.dropout(x)
+        return x
+
+
+class T5FeedForward(nn.Module):
+
+    def __init__(self, dim, dim_ffn, dropout=0.1):
+        super(T5FeedForward, self).__init__()
+        self.dim = dim
+        self.dim_ffn = dim_ffn
+
+        # layers
+        self.gate = nn.Sequential(nn.Linear(dim, dim_ffn, bias=False), GELU())
+        self.fc1 = nn.Linear(dim, dim_ffn, bias=False)
+        self.fc2 = nn.Linear(dim_ffn, dim, bias=False)
+        self.dropout = nn.Dropout(dropout)
+
+    def forward(self, x):
+        x = self.fc1(x) * self.gate(x)
+        x = self.dropout(x)
+        x = self.fc2(x)
+        x = self.dropout(x)
+        return x
+
+
+class T5SelfAttention(nn.Module):
+
+    def __init__(self,
+                 dim,
+                 dim_attn,
+                 dim_ffn,
+                 num_heads,
+                 num_buckets,
+                 shared_pos=True,
+                 dropout=0.1):
+        super(T5SelfAttention, self).__init__()
+        self.dim = dim
+        self.dim_attn = dim_attn
+        self.dim_ffn = dim_ffn
+        self.num_heads = num_heads
+        self.num_buckets = num_buckets
+        self.shared_pos = shared_pos
+
+        # layers
+        self.norm1 = T5LayerNorm(dim)
+        self.attn = T5Attention(dim, dim_attn, num_heads, dropout)
+        self.norm2 = T5LayerNorm(dim)
+        self.ffn = T5FeedForward(dim, dim_ffn, dropout)
+        self.pos_embedding = None if shared_pos else T5RelativeEmbedding(
+            num_buckets, num_heads, bidirectional=True)
+
+    def forward(self, x, mask=None, pos_bias=None):
+        e = pos_bias if self.shared_pos else self.pos_embedding(
+            x.size(1), x.size(1))
+        x = fp16_clamp(x + self.attn(self.norm1(x), mask=mask, pos_bias=e))
+        x = fp16_clamp(x + self.ffn(self.norm2(x)))
+        return x
+
+
+class T5RelativeEmbedding(nn.Module):
+
+    def __init__(self, num_buckets, num_heads, bidirectional, max_dist=128):
+        super(T5RelativeEmbedding, self).__init__()
+        self.num_buckets = num_buckets
+        self.num_heads = num_heads
+        self.bidirectional = bidirectional
+        self.max_dist = max_dist
+
+        # layers
+        self.embedding = nn.Embedding(num_buckets, num_heads)
+
+    def forward(self, lq, lk):
+        device = self.embedding.weight.device
+        # rel_pos = torch.arange(lk).unsqueeze(0).to(device) - \
+        #     torch.arange(lq).unsqueeze(1).to(device)
+        rel_pos = torch.arange(lk, device=device).unsqueeze(0) - \
+            torch.arange(lq, device=device).unsqueeze(1)
+        rel_pos = self._relative_position_bucket(rel_pos)
+        rel_pos_embeds = self.embedding(rel_pos)
+        rel_pos_embeds = rel_pos_embeds.permute(2, 0, 1).unsqueeze(
+            0)  # [1, N, Lq, Lk]
+        return rel_pos_embeds.contiguous()
+
+    def _relative_position_bucket(self, rel_pos):
+        # preprocess
+        if self.bidirectional:
+            num_buckets = self.num_buckets // 2
+            rel_buckets = (rel_pos > 0).long() * num_buckets
+            rel_pos = torch.abs(rel_pos)
+        else:
+            num_buckets = self.num_buckets
+            rel_buckets = 0
+            rel_pos = -torch.min(rel_pos, torch.zeros_like(rel_pos))
+
+        # embeddings for small and large positions
+        max_exact = num_buckets // 2
+        rel_pos_large = max_exact + (torch.log(rel_pos.float() / max_exact) /
+                                     math.log(self.max_dist / max_exact) *
+                                     (num_buckets - max_exact)).long()
+        rel_pos_large = torch.min(
+            rel_pos_large, torch.full_like(rel_pos_large, num_buckets - 1))
+        rel_buckets += torch.where(rel_pos < max_exact, rel_pos, rel_pos_large)
+        return rel_buckets
+
+def init_weights(m):
+    if isinstance(m, T5LayerNorm):
+        nn.init.ones_(m.weight)
+    elif isinstance(m, T5FeedForward):
+        nn.init.normal_(m.gate[0].weight, std=m.dim**-0.5)
+        nn.init.normal_(m.fc1.weight, std=m.dim**-0.5)
+        nn.init.normal_(m.fc2.weight, std=m.dim_ffn**-0.5)
+    elif isinstance(m, T5Attention):
+        nn.init.normal_(m.q.weight, std=(m.dim * m.dim_attn)**-0.5)
+        nn.init.normal_(m.k.weight, std=m.dim**-0.5)
+        nn.init.normal_(m.v.weight, std=m.dim**-0.5)
+        nn.init.normal_(m.o.weight, std=(m.num_heads * m.dim_attn)**-0.5)
+    elif isinstance(m, T5RelativeEmbedding):
+        nn.init.normal_(
+            m.embedding.weight, std=(2 * m.num_buckets * m.num_heads)**-0.5)
+
+
+class WanTextEncoder(torch.nn.Module):
+
+    def __init__(self,
+                 vocab=256384,
+                 dim=4096,
+                 dim_attn=4096,
+                 dim_ffn=10240,
+                 num_heads=64,
+                 num_layers=24,
+                 num_buckets=32,
+                 shared_pos=False,
+                 dropout=0.1):
+        super(WanTextEncoder, self).__init__()
+        self.dim = dim
+        self.dim_attn = dim_attn
+        self.dim_ffn = dim_ffn
+        self.num_heads = num_heads
+        self.num_layers = num_layers
+        self.num_buckets = num_buckets
+        self.shared_pos = shared_pos
+
+        # layers
+        self.token_embedding = vocab if isinstance(vocab, nn.Embedding) \
+            else nn.Embedding(vocab, dim)
+        self.pos_embedding = T5RelativeEmbedding(
+            num_buckets, num_heads, bidirectional=True) if shared_pos else None
+        self.dropout = nn.Dropout(dropout)
+        self.blocks = nn.ModuleList([
+            T5SelfAttention(dim, dim_attn, dim_ffn, num_heads, num_buckets,
+                            shared_pos, dropout) for _ in range(num_layers)
+        ])
+        self.norm = T5LayerNorm(dim)
+
+        # initialize weights
+        self.apply(init_weights)
+
+    def forward(self, ids, mask=None):
+        x = self.token_embedding(ids)
+        x = self.dropout(x)
+        e = self.pos_embedding(x.size(1),
+                               x.size(1)) if self.shared_pos else None
+        for block in self.blocks:
+            x = block(x, mask, pos_bias=e)
+        x = self.norm(x)
+        x = self.dropout(x)
+        return x
+    
+    @staticmethod
+    def state_dict_converter():
+        return WanTextEncoderStateDictConverter()
+    
+    
+class WanTextEncoderStateDictConverter:
+    def __init__(self):
+        pass
+
+    def from_diffusers(self, state_dict):
+        return state_dict
+    
+    def from_civitai(self, state_dict):
+        return state_dict
diff --git a/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/wan_video_vace.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/wan_video_vace.py
new file mode 100644
index 0000000000000000000000000000000000000000..40f38048fe07184084c76e3b1a5abac062bb0ab8
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/wan_video_vace.py
@@ -0,0 +1,113 @@
+import torch
+from .wan_video_dit import DiTBlock
+from .utils import hash_state_dict_keys
+
+class VaceWanAttentionBlock(DiTBlock):
+    def __init__(self, has_image_input, dim, num_heads, ffn_dim, eps=1e-6, block_id=0):
+        super().__init__(has_image_input, dim, num_heads, ffn_dim, eps=eps)
+        self.block_id = block_id
+        if block_id == 0:
+            self.before_proj = torch.nn.Linear(self.dim, self.dim)
+        self.after_proj = torch.nn.Linear(self.dim, self.dim)
+
+    def forward(self, c, x, context, t_mod, freqs):
+        if self.block_id == 0:
+            c = self.before_proj(c) + x
+            all_c = []
+        else:
+            all_c = list(torch.unbind(c))
+            c = all_c.pop(-1)
+        c = super().forward(c, context, t_mod, freqs)
+        c_skip = self.after_proj(c)
+        all_c += [c_skip, c]
+        c = torch.stack(all_c)
+        return c
+
+
+class VaceWanModel(torch.nn.Module):
+    def __init__(
+        self,
+        vace_layers=(0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28),
+        vace_in_dim=96,
+        patch_size=(1, 2, 2),
+        has_image_input=False,
+        dim=1536,
+        num_heads=12,
+        ffn_dim=8960,
+        eps=1e-6,
+    ):
+        super().__init__()
+        self.vace_layers = vace_layers
+        self.vace_in_dim = vace_in_dim
+        self.vace_layers_mapping = {i: n for n, i in enumerate(self.vace_layers)}
+
+        # vace blocks
+        self.vace_blocks = torch.nn.ModuleList([
+            VaceWanAttentionBlock(has_image_input, dim, num_heads, ffn_dim, eps, block_id=i)
+            for i in self.vace_layers
+        ])
+
+        # vace patch embeddings
+        self.vace_patch_embedding = torch.nn.Conv3d(vace_in_dim, dim, kernel_size=patch_size, stride=patch_size)
+
+    def forward(
+        self, x, vace_context, context, t_mod, freqs,
+        use_gradient_checkpointing: bool = False,
+        use_gradient_checkpointing_offload: bool = False,
+    ):
+        c = [self.vace_patch_embedding(u.unsqueeze(0)) for u in vace_context]
+        c = [u.flatten(2).transpose(1, 2) for u in c]
+        c = torch.cat([
+            torch.cat([u, u.new_zeros(1, x.shape[1] - u.size(1), u.size(2))],
+                      dim=1) for u in c
+        ])
+        
+        def create_custom_forward(module):
+            def custom_forward(*inputs):
+                return module(*inputs)
+            return custom_forward
+        
+        for block in self.vace_blocks:
+            if use_gradient_checkpointing_offload:
+                with torch.autograd.graph.save_on_cpu():
+                    c = torch.utils.checkpoint.checkpoint(
+                        create_custom_forward(block),
+                        c, x, context, t_mod, freqs,
+                        use_reentrant=False,
+                    )
+            elif use_gradient_checkpointing:
+                c = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(block),
+                    c, x, context, t_mod, freqs,
+                    use_reentrant=False,
+                )
+            else:
+                c = block(c, x, context, t_mod, freqs)
+        hints = torch.unbind(c)[:-1]
+        return hints
+    
+    @staticmethod
+    def state_dict_converter():
+        return VaceWanModelDictConverter()
+    
+    
+class VaceWanModelDictConverter:
+    def __init__(self):
+        pass
+    
+    def from_civitai(self, state_dict):
+        state_dict_ = {name: param for name, param in state_dict.items() if name.startswith("vace")}
+        if hash_state_dict_keys(state_dict_) == '3b2726384e4f64837bdf216eea3f310d': # vace 14B
+            config = {
+                "vace_layers": (0, 5, 10, 15, 20, 25, 30, 35),
+                "vace_in_dim": 96,
+                "patch_size": (1, 2, 2),
+                "has_image_input": False,
+                "dim": 5120,
+                "num_heads": 40,
+                "ffn_dim": 13824,
+                "eps": 1e-06,                
+            }
+        else:
+            config = {}
+        return state_dict_, config
diff --git a/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/wan_video_vae.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/wan_video_vae.py
new file mode 100644
index 0000000000000000000000000000000000000000..397a2e7b66258159d84ac299b7798c52bc7e038a
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/wan_video_vae.py
@@ -0,0 +1,1376 @@
+from einops import rearrange, repeat
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from tqdm import tqdm
+
+CACHE_T = 2
+
+
+def check_is_instance(model, module_class):
+    if isinstance(model, module_class):
+        return True
+    if hasattr(model, "module") and isinstance(model.module, module_class):
+        return True
+    return False
+
+
+def block_causal_mask(x, block_size):
+    # params
+    b, n, s, _, device = *x.size(), x.device
+    assert s % block_size == 0
+    num_blocks = s // block_size
+
+    # build mask
+    mask = torch.zeros(b, n, s, s, dtype=torch.bool, device=device)
+    for i in range(num_blocks):
+        mask[:, :,
+             i * block_size:(i + 1) * block_size, :(i + 1) * block_size] = 1
+    return mask
+
+
+class CausalConv3d(nn.Conv3d):
+    """
+    Causal 3d convolusion.
+    """
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self._padding = (self.padding[2], self.padding[2], self.padding[1],
+                         self.padding[1], 2 * self.padding[0], 0)
+        self.padding = (0, 0, 0)
+
+    def forward(self, x, cache_x=None):
+        padding = list(self._padding)
+        if cache_x is not None and self._padding[4] > 0:
+            cache_x = cache_x.to(x.device)
+            x = torch.cat([cache_x, x], dim=2)
+            padding[4] -= cache_x.shape[2]
+        x = F.pad(x, padding)
+
+        return super().forward(x)
+
+
+class RMS_norm(nn.Module):
+
+    def __init__(self, dim, channel_first=True, images=True, bias=False):
+        super().__init__()
+        broadcastable_dims = (1, 1, 1) if not images else (1, 1)
+        shape = (dim, *broadcastable_dims) if channel_first else (dim,)
+
+        self.channel_first = channel_first
+        self.scale = dim**0.5
+        self.gamma = nn.Parameter(torch.ones(shape))
+        self.bias = nn.Parameter(torch.zeros(shape)) if bias else 0.
+
+    def forward(self, x):
+        return F.normalize(
+            x, dim=(1 if self.channel_first else
+                    -1)) * self.scale * self.gamma + self.bias
+
+
+class Upsample(nn.Upsample):
+
+    def forward(self, x):
+        """
+        Fix bfloat16 support for nearest neighbor interpolation.
+        """
+        return super().forward(x.float()).type_as(x)
+
+
+class Resample(nn.Module):
+
+    def __init__(self, dim, mode):
+        assert mode in ('none', 'upsample2d', 'upsample3d', 'downsample2d',
+                        'downsample3d')
+        super().__init__()
+        self.dim = dim
+        self.mode = mode
+
+        # layers
+        if mode == 'upsample2d':
+            self.resample = nn.Sequential(
+                Upsample(scale_factor=(2., 2.), mode='nearest-exact'),
+                nn.Conv2d(dim, dim // 2, 3, padding=1))
+        elif mode == 'upsample3d':
+            self.resample = nn.Sequential(
+                Upsample(scale_factor=(2., 2.), mode='nearest-exact'),
+                nn.Conv2d(dim, dim // 2, 3, padding=1))
+            self.time_conv = CausalConv3d(dim,
+                                          dim * 2, (3, 1, 1),
+                                          padding=(1, 0, 0))
+
+        elif mode == 'downsample2d':
+            self.resample = nn.Sequential(
+                nn.ZeroPad2d((0, 1, 0, 1)),
+                nn.Conv2d(dim, dim, 3, stride=(2, 2)))
+        elif mode == 'downsample3d':
+            self.resample = nn.Sequential(
+                nn.ZeroPad2d((0, 1, 0, 1)),
+                nn.Conv2d(dim, dim, 3, stride=(2, 2)))
+            self.time_conv = CausalConv3d(dim,
+                                          dim, (3, 1, 1),
+                                          stride=(2, 1, 1),
+                                          padding=(0, 0, 0))
+
+        else:
+            self.resample = nn.Identity()
+
+    def forward(self, x, feat_cache=None, feat_idx=[0]):
+        b, c, t, h, w = x.size()
+        if self.mode == 'upsample3d':
+            if feat_cache is not None:
+                idx = feat_idx[0]
+                if feat_cache[idx] is None:
+                    feat_cache[idx] = 'Rep'
+                    feat_idx[0] += 1
+                else:
+
+                    cache_x = x[:, :, -CACHE_T:, :, :].clone()
+                    if cache_x.shape[2] < 2 and feat_cache[
+                            idx] is not None and feat_cache[idx] != 'Rep':
+                        # cache last frame of last two chunk
+                        cache_x = torch.cat([
+                            feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(
+                                cache_x.device), cache_x
+                        ],
+                                            dim=2)
+                    if cache_x.shape[2] < 2 and feat_cache[
+                            idx] is not None and feat_cache[idx] == 'Rep':
+                        cache_x = torch.cat([
+                            torch.zeros_like(cache_x).to(cache_x.device),
+                            cache_x
+                        ],
+                                            dim=2)
+                    if feat_cache[idx] == 'Rep':
+                        x = self.time_conv(x)
+                    else:
+                        x = self.time_conv(x, feat_cache[idx])
+                    feat_cache[idx] = cache_x
+                    feat_idx[0] += 1
+
+                    x = x.reshape(b, 2, c, t, h, w)
+                    x = torch.stack((x[:, 0, :, :, :, :], x[:, 1, :, :, :, :]),
+                                    3)
+                    x = x.reshape(b, c, t * 2, h, w)
+        t = x.shape[2]
+        x = rearrange(x, 'b c t h w -> (b t) c h w')
+        x = self.resample(x)
+        x = rearrange(x, '(b t) c h w -> b c t h w', t=t)
+
+        if self.mode == 'downsample3d':
+            if feat_cache is not None:
+                idx = feat_idx[0]
+                if feat_cache[idx] is None:
+                    feat_cache[idx] = x.clone()
+                    feat_idx[0] += 1
+                else:
+                    cache_x = x[:, :, -1:, :, :].clone()
+                    x = self.time_conv(
+                        torch.cat([feat_cache[idx][:, :, -1:, :, :], x], 2))
+                    feat_cache[idx] = cache_x
+                    feat_idx[0] += 1
+        return x
+
+    def init_weight(self, conv):
+        conv_weight = conv.weight
+        nn.init.zeros_(conv_weight)
+        c1, c2, t, h, w = conv_weight.size()
+        one_matrix = torch.eye(c1, c2)
+        init_matrix = one_matrix
+        nn.init.zeros_(conv_weight)
+        conv_weight.data[:, :, 1, 0, 0] = init_matrix
+        conv.weight.data.copy_(conv_weight)
+        nn.init.zeros_(conv.bias.data)
+
+    def init_weight2(self, conv):
+        conv_weight = conv.weight.data
+        nn.init.zeros_(conv_weight)
+        c1, c2, t, h, w = conv_weight.size()
+        init_matrix = torch.eye(c1 // 2, c2)
+        conv_weight[:c1 // 2, :, -1, 0, 0] = init_matrix
+        conv_weight[c1 // 2:, :, -1, 0, 0] = init_matrix
+        conv.weight.data.copy_(conv_weight)
+        nn.init.zeros_(conv.bias.data)
+
+
+
+def patchify(x, patch_size):
+    if patch_size == 1:
+        return x
+    if x.dim() == 4:
+        x = rearrange(x, "b c (h q) (w r) -> b (c r q) h w", q=patch_size, r=patch_size)
+    elif x.dim() == 5:
+        x = rearrange(x,
+                      "b c f (h q) (w r) -> b (c r q) f h w",
+                      q=patch_size,
+                      r=patch_size)
+    else:
+        raise ValueError(f"Invalid input shape: {x.shape}")
+    return x
+
+
+def unpatchify(x, patch_size):
+    if patch_size == 1:
+        return x
+    if x.dim() == 4:
+        x = rearrange(x, "b (c r q) h w -> b c (h q) (w r)", q=patch_size, r=patch_size)
+    elif x.dim() == 5:
+        x = rearrange(x,
+                      "b (c r q) f h w -> b c f (h q) (w r)",
+                      q=patch_size,
+                      r=patch_size)
+    return x
+
+
+class Resample38(Resample):
+
+    def __init__(self, dim, mode):
+        assert mode in (
+            "none",
+            "upsample2d",
+            "upsample3d",
+            "downsample2d",
+            "downsample3d",
+        )
+        super(Resample, self).__init__()
+        self.dim = dim
+        self.mode = mode
+
+        # layers
+        if mode == "upsample2d":
+            self.resample = nn.Sequential(
+                Upsample(scale_factor=(2.0, 2.0), mode="nearest-exact"),
+                nn.Conv2d(dim, dim, 3, padding=1),
+            )
+        elif mode == "upsample3d":
+            self.resample = nn.Sequential(
+                Upsample(scale_factor=(2.0, 2.0), mode="nearest-exact"),
+                nn.Conv2d(dim, dim, 3, padding=1),
+            )
+            self.time_conv = CausalConv3d(dim, dim * 2, (3, 1, 1), padding=(1, 0, 0))
+        elif mode == "downsample2d":
+            self.resample = nn.Sequential(
+                nn.ZeroPad2d((0, 1, 0, 1)), nn.Conv2d(dim, dim, 3, stride=(2, 2))
+            )
+        elif mode == "downsample3d":
+            self.resample = nn.Sequential(
+                nn.ZeroPad2d((0, 1, 0, 1)), nn.Conv2d(dim, dim, 3, stride=(2, 2))
+            )
+            self.time_conv = CausalConv3d(
+                dim, dim, (3, 1, 1), stride=(2, 1, 1), padding=(0, 0, 0)
+            )
+        else:
+            self.resample = nn.Identity()
+
+class ResidualBlock(nn.Module):
+
+    def __init__(self, in_dim, out_dim, dropout=0.0):
+        super().__init__()
+        self.in_dim = in_dim
+        self.out_dim = out_dim
+
+        # layers
+        self.residual = nn.Sequential(
+            RMS_norm(in_dim, images=False), nn.SiLU(),
+            CausalConv3d(in_dim, out_dim, 3, padding=1),
+            RMS_norm(out_dim, images=False), nn.SiLU(), nn.Dropout(dropout),
+            CausalConv3d(out_dim, out_dim, 3, padding=1))
+        self.shortcut = CausalConv3d(in_dim, out_dim, 1) \
+            if in_dim != out_dim else nn.Identity()
+
+    def forward(self, x, feat_cache=None, feat_idx=[0]):
+        h = self.shortcut(x)
+        for layer in self.residual:
+            if check_is_instance(layer, CausalConv3d) and feat_cache is not None:
+                idx = feat_idx[0]
+                cache_x = x[:, :, -CACHE_T:, :, :].clone()
+                if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
+                    # cache last frame of last two chunk
+                    cache_x = torch.cat([
+                        feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(
+                            cache_x.device), cache_x
+                    ],
+                                        dim=2)
+                x = layer(x, feat_cache[idx])
+                feat_cache[idx] = cache_x
+                feat_idx[0] += 1
+            else:
+                x = layer(x)
+        return x + h
+
+
+class AttentionBlock(nn.Module):
+    """
+    Causal self-attention with a single head.
+    """
+
+    def __init__(self, dim):
+        super().__init__()
+        self.dim = dim
+
+        # layers
+        self.norm = RMS_norm(dim)
+        self.to_qkv = nn.Conv2d(dim, dim * 3, 1)
+        self.proj = nn.Conv2d(dim, dim, 1)
+
+        # zero out the last layer params
+        nn.init.zeros_(self.proj.weight)
+
+    def forward(self, x):
+        identity = x
+        b, c, t, h, w = x.size()
+        x = rearrange(x, 'b c t h w -> (b t) c h w')
+        x = self.norm(x)
+        # compute query, key, value
+        q, k, v = self.to_qkv(x).reshape(b * t, 1, c * 3, -1).permute(
+            0, 1, 3, 2).contiguous().chunk(3, dim=-1)
+
+        # apply attention
+        x = F.scaled_dot_product_attention(
+            q,
+            k,
+            v,
+            #attn_mask=block_causal_mask(q, block_size=h * w)
+        )
+        x = x.squeeze(1).permute(0, 2, 1).reshape(b * t, c, h, w)
+
+        # output
+        x = self.proj(x)
+        x = rearrange(x, '(b t) c h w-> b c t h w', t=t)
+        return x + identity
+
+
+class AvgDown3D(nn.Module):
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        factor_t,
+        factor_s=1,
+    ):
+        super().__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.factor_t = factor_t
+        self.factor_s = factor_s
+        self.factor = self.factor_t * self.factor_s * self.factor_s
+
+        assert in_channels * self.factor % out_channels == 0
+        self.group_size = in_channels * self.factor // out_channels
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        pad_t = (self.factor_t - x.shape[2] % self.factor_t) % self.factor_t
+        pad = (0, 0, 0, 0, pad_t, 0)
+        x = F.pad(x, pad)
+        B, C, T, H, W = x.shape
+        x = x.view(
+            B,
+            C,
+            T // self.factor_t,
+            self.factor_t,
+            H // self.factor_s,
+            self.factor_s,
+            W // self.factor_s,
+            self.factor_s,
+        )
+        x = x.permute(0, 1, 3, 5, 7, 2, 4, 6).contiguous()
+        x = x.view(
+            B,
+            C * self.factor,
+            T // self.factor_t,
+            H // self.factor_s,
+            W // self.factor_s,
+        )
+        x = x.view(
+            B,
+            self.out_channels,
+            self.group_size,
+            T // self.factor_t,
+            H // self.factor_s,
+            W // self.factor_s,
+        )
+        x = x.mean(dim=2)
+        return x
+
+
+class DupUp3D(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        factor_t,
+        factor_s=1,
+    ):
+        super().__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+
+        self.factor_t = factor_t
+        self.factor_s = factor_s
+        self.factor = self.factor_t * self.factor_s * self.factor_s
+
+        assert out_channels * self.factor % in_channels == 0
+        self.repeats = out_channels * self.factor // in_channels
+
+    def forward(self, x: torch.Tensor, first_chunk=False) -> torch.Tensor:
+        x = x.repeat_interleave(self.repeats, dim=1)
+        x = x.view(
+            x.size(0),
+            self.out_channels,
+            self.factor_t,
+            self.factor_s,
+            self.factor_s,
+            x.size(2),
+            x.size(3),
+            x.size(4),
+        )
+        x = x.permute(0, 1, 5, 2, 6, 3, 7, 4).contiguous()
+        x = x.view(
+            x.size(0),
+            self.out_channels,
+            x.size(2) * self.factor_t,
+            x.size(4) * self.factor_s,
+            x.size(6) * self.factor_s,
+        )
+        if first_chunk:
+            x = x[:, :, self.factor_t - 1 :, :, :]
+        return x
+
+
+class Down_ResidualBlock(nn.Module):
+    def __init__(
+        self, in_dim, out_dim, dropout, mult, temperal_downsample=False, down_flag=False
+    ):
+        super().__init__()
+
+        # Shortcut path with downsample
+        self.avg_shortcut = AvgDown3D(
+            in_dim,
+            out_dim,
+            factor_t=2 if temperal_downsample else 1,
+            factor_s=2 if down_flag else 1,
+        )
+
+        # Main path with residual blocks and downsample
+        downsamples = []
+        for _ in range(mult):
+            downsamples.append(ResidualBlock(in_dim, out_dim, dropout))
+            in_dim = out_dim
+
+        # Add the final downsample block
+        if down_flag:
+            mode = "downsample3d" if temperal_downsample else "downsample2d"
+            downsamples.append(Resample38(out_dim, mode=mode))
+
+        self.downsamples = nn.Sequential(*downsamples)
+
+    def forward(self, x, feat_cache=None, feat_idx=[0]):
+        x_copy = x.clone()
+        for module in self.downsamples:
+            x = module(x, feat_cache, feat_idx)
+
+        return x + self.avg_shortcut(x_copy)
+
+
+class Up_ResidualBlock(nn.Module):
+    def __init__(
+        self, in_dim, out_dim, dropout, mult, temperal_upsample=False, up_flag=False
+    ):
+        super().__init__()
+        # Shortcut path with upsample
+        if up_flag:
+            self.avg_shortcut = DupUp3D(
+                in_dim,
+                out_dim,
+                factor_t=2 if temperal_upsample else 1,
+                factor_s=2 if up_flag else 1,
+            )
+        else:
+            self.avg_shortcut = None
+
+        # Main path with residual blocks and upsample
+        upsamples = []
+        for _ in range(mult):
+            upsamples.append(ResidualBlock(in_dim, out_dim, dropout))
+            in_dim = out_dim
+
+        # Add the final upsample block
+        if up_flag:
+            mode = "upsample3d" if temperal_upsample else "upsample2d"
+            upsamples.append(Resample38(out_dim, mode=mode))
+
+        self.upsamples = nn.Sequential(*upsamples)
+
+    def forward(self, x, feat_cache=None, feat_idx=[0], first_chunk=False):
+        x_main = x.clone()
+        for module in self.upsamples:
+            x_main = module(x_main, feat_cache, feat_idx)
+        if self.avg_shortcut is not None:
+            x_shortcut = self.avg_shortcut(x, first_chunk)
+            return x_main + x_shortcut
+        else:
+            return x_main
+
+
+class Encoder3d(nn.Module):
+
+    def __init__(self,
+                 dim=128,
+                 z_dim=4,
+                 dim_mult=[1, 2, 4, 4],
+                 num_res_blocks=2,
+                 attn_scales=[],
+                 temperal_downsample=[True, True, False],
+                 dropout=0.0):
+        super().__init__()
+        self.dim = dim
+        self.z_dim = z_dim
+        self.dim_mult = dim_mult
+        self.num_res_blocks = num_res_blocks
+        self.attn_scales = attn_scales
+        self.temperal_downsample = temperal_downsample
+
+        # dimensions
+        dims = [dim * u for u in [1] + dim_mult]
+        scale = 1.0
+
+        # init block
+        self.conv1 = CausalConv3d(3, dims[0], 3, padding=1)
+
+        # downsample blocks
+        downsamples = []
+        for i, (in_dim, out_dim) in enumerate(zip(dims[:-1], dims[1:])):
+            # residual (+attention) blocks
+            for _ in range(num_res_blocks):
+                downsamples.append(ResidualBlock(in_dim, out_dim, dropout))
+                if scale in attn_scales:
+                    downsamples.append(AttentionBlock(out_dim))
+                in_dim = out_dim
+
+            # downsample block
+            if i != len(dim_mult) - 1:
+                mode = 'downsample3d' if temperal_downsample[
+                    i] else 'downsample2d'
+                downsamples.append(Resample(out_dim, mode=mode))
+                scale /= 2.0
+        self.downsamples = nn.Sequential(*downsamples)
+
+        # middle blocks
+        self.middle = nn.Sequential(ResidualBlock(out_dim, out_dim, dropout),
+                                    AttentionBlock(out_dim),
+                                    ResidualBlock(out_dim, out_dim, dropout))
+
+        # output blocks
+        self.head = nn.Sequential(RMS_norm(out_dim, images=False), nn.SiLU(),
+                                  CausalConv3d(out_dim, z_dim, 3, padding=1))
+
+    def forward(self, x, feat_cache=None, feat_idx=[0]):
+        if feat_cache is not None:
+            idx = feat_idx[0]
+            cache_x = x[:, :, -CACHE_T:, :, :].clone()
+            if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
+                # cache last frame of last two chunk
+                cache_x = torch.cat([
+                    feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(
+                        cache_x.device), cache_x
+                ],
+                                    dim=2)
+            x = self.conv1(x, feat_cache[idx])
+            feat_cache[idx] = cache_x
+            feat_idx[0] += 1
+        else:
+            x = self.conv1(x)
+
+        ## downsamples
+        for layer in self.downsamples:
+            if feat_cache is not None:
+                x = layer(x, feat_cache, feat_idx)
+            else:
+                x = layer(x)
+
+        ## middle
+        for layer in self.middle:
+            if check_is_instance(layer, ResidualBlock) and feat_cache is not None:
+                x = layer(x, feat_cache, feat_idx)
+            else:
+                x = layer(x)
+
+        ## head
+        for layer in self.head:
+            if check_is_instance(layer, CausalConv3d) and feat_cache is not None:
+                idx = feat_idx[0]
+                cache_x = x[:, :, -CACHE_T:, :, :].clone()
+                if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
+                    # cache last frame of last two chunk
+                    cache_x = torch.cat([
+                        feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(
+                            cache_x.device), cache_x
+                    ],
+                                        dim=2)
+                x = layer(x, feat_cache[idx])
+                feat_cache[idx] = cache_x
+                feat_idx[0] += 1
+            else:
+                x = layer(x)
+        return x
+
+
+class Encoder3d_38(nn.Module):
+
+    def __init__(self,
+                 dim=128,
+                 z_dim=4,
+                 dim_mult=[1, 2, 4, 4],
+                 num_res_blocks=2,
+                 attn_scales=[],
+                 temperal_downsample=[False, True, True],
+                 dropout=0.0):
+        super().__init__()
+        self.dim = dim
+        self.z_dim = z_dim
+        self.dim_mult = dim_mult
+        self.num_res_blocks = num_res_blocks
+        self.attn_scales = attn_scales
+        self.temperal_downsample = temperal_downsample
+
+        # dimensions
+        dims = [dim * u for u in [1] + dim_mult]
+        scale = 1.0
+
+        # init block
+        self.conv1 = CausalConv3d(12, dims[0], 3, padding=1)
+
+        # downsample blocks
+        downsamples = []
+        for i, (in_dim, out_dim) in enumerate(zip(dims[:-1], dims[1:])):
+            t_down_flag = (
+                temperal_downsample[i] if i < len(temperal_downsample) else False
+            )
+            downsamples.append(
+                Down_ResidualBlock(
+                    in_dim=in_dim,
+                    out_dim=out_dim,
+                    dropout=dropout,
+                    mult=num_res_blocks,
+                    temperal_downsample=t_down_flag,
+                    down_flag=i != len(dim_mult) - 1,
+                )
+            )
+            scale /= 2.0
+        self.downsamples = nn.Sequential(*downsamples)
+
+        # middle blocks
+        self.middle = nn.Sequential(
+            ResidualBlock(out_dim, out_dim, dropout),
+            AttentionBlock(out_dim),
+            ResidualBlock(out_dim, out_dim, dropout),
+        )
+
+        # # output blocks
+        self.head = nn.Sequential(
+            RMS_norm(out_dim, images=False),
+            nn.SiLU(),
+            CausalConv3d(out_dim, z_dim, 3, padding=1),
+        )
+
+
+    def forward(self, x, feat_cache=None, feat_idx=[0]):
+
+        if feat_cache is not None:
+            idx = feat_idx[0]
+            cache_x = x[:, :, -CACHE_T:, :, :].clone()
+            if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
+                cache_x = torch.cat(
+                    [
+                        feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(cache_x.device),
+                        cache_x,
+                    ],
+                    dim=2,
+                )
+            x = self.conv1(x, feat_cache[idx])
+            feat_cache[idx] = cache_x
+            feat_idx[0] += 1
+        else:
+            x = self.conv1(x)
+
+        ## downsamples
+        for layer in self.downsamples:
+            if feat_cache is not None:
+                x = layer(x, feat_cache, feat_idx)
+            else:
+                x = layer(x)
+
+        ## middle
+        for layer in self.middle:
+            if isinstance(layer, ResidualBlock) and feat_cache is not None:
+                x = layer(x, feat_cache, feat_idx)
+            else:
+                x = layer(x)
+
+        ## head
+        for layer in self.head:
+            if isinstance(layer, CausalConv3d) and feat_cache is not None:
+                idx = feat_idx[0]
+                cache_x = x[:, :, -CACHE_T:, :, :].clone()
+                if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
+                    cache_x = torch.cat(
+                        [
+                            feat_cache[idx][:, :, -1, :, :]
+                            .unsqueeze(2)
+                            .to(cache_x.device),
+                            cache_x,
+                        ],
+                        dim=2,
+                    )
+                x = layer(x, feat_cache[idx])
+                feat_cache[idx] = cache_x
+                feat_idx[0] += 1
+            else:
+                x = layer(x)
+
+        return x
+
+
+class Decoder3d(nn.Module):
+
+    def __init__(self,
+                 dim=128,
+                 z_dim=4,
+                 dim_mult=[1, 2, 4, 4],
+                 num_res_blocks=2,
+                 attn_scales=[],
+                 temperal_upsample=[False, True, True],
+                 dropout=0.0):
+        super().__init__()
+        self.dim = dim
+        self.z_dim = z_dim
+        self.dim_mult = dim_mult
+        self.num_res_blocks = num_res_blocks
+        self.attn_scales = attn_scales
+        self.temperal_upsample = temperal_upsample
+
+        # dimensions
+        dims = [dim * u for u in [dim_mult[-1]] + dim_mult[::-1]]
+        scale = 1.0 / 2**(len(dim_mult) - 2)
+
+        # init block
+        self.conv1 = CausalConv3d(z_dim, dims[0], 3, padding=1)
+
+        # middle blocks
+        self.middle = nn.Sequential(ResidualBlock(dims[0], dims[0], dropout),
+                                    AttentionBlock(dims[0]),
+                                    ResidualBlock(dims[0], dims[0], dropout))
+
+        # upsample blocks
+        upsamples = []
+        for i, (in_dim, out_dim) in enumerate(zip(dims[:-1], dims[1:])):
+            # residual (+attention) blocks
+            if i == 1 or i == 2 or i == 3:
+                in_dim = in_dim // 2
+            for _ in range(num_res_blocks + 1):
+                upsamples.append(ResidualBlock(in_dim, out_dim, dropout))
+                if scale in attn_scales:
+                    upsamples.append(AttentionBlock(out_dim))
+                in_dim = out_dim
+
+            # upsample block
+            if i != len(dim_mult) - 1:
+                mode = 'upsample3d' if temperal_upsample[i] else 'upsample2d'
+                upsamples.append(Resample(out_dim, mode=mode))
+                scale *= 2.0
+        self.upsamples = nn.Sequential(*upsamples)
+
+        # output blocks
+        self.head = nn.Sequential(RMS_norm(out_dim, images=False), nn.SiLU(),
+                                  CausalConv3d(out_dim, 3, 3, padding=1))
+
+    def forward(self, x, feat_cache=None, feat_idx=[0]):
+        ## conv1
+        if feat_cache is not None:
+            idx = feat_idx[0]
+            cache_x = x[:, :, -CACHE_T:, :, :].clone()
+            if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
+                # cache last frame of last two chunk
+                cache_x = torch.cat([
+                    feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(
+                        cache_x.device), cache_x
+                ],
+                                    dim=2)
+            x = self.conv1(x, feat_cache[idx])
+            feat_cache[idx] = cache_x
+            feat_idx[0] += 1
+        else:
+            x = self.conv1(x)
+
+        ## middle
+        for layer in self.middle:
+            if check_is_instance(layer, ResidualBlock) and feat_cache is not None:
+                x = layer(x, feat_cache, feat_idx)
+            else:
+                x = layer(x)
+
+        ## upsamples
+        for layer in self.upsamples:
+            if feat_cache is not None:
+                x = layer(x, feat_cache, feat_idx)
+            else:
+                x = layer(x)
+
+        ## head
+        for layer in self.head:
+            if check_is_instance(layer, CausalConv3d) and feat_cache is not None:
+                idx = feat_idx[0]
+                cache_x = x[:, :, -CACHE_T:, :, :].clone()
+                if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
+                    # cache last frame of last two chunk
+                    cache_x = torch.cat([
+                        feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(
+                            cache_x.device), cache_x
+                    ],
+                                        dim=2)
+                x = layer(x, feat_cache[idx])
+                feat_cache[idx] = cache_x
+                feat_idx[0] += 1
+            else:
+                x = layer(x)
+        return x
+
+
+
+class Decoder3d_38(nn.Module):
+
+    def __init__(self,
+                 dim=128,
+                 z_dim=4,
+                 dim_mult=[1, 2, 4, 4],
+                 num_res_blocks=2,
+                 attn_scales=[],
+                 temperal_upsample=[False, True, True],
+                 dropout=0.0):
+        super().__init__()
+        self.dim = dim
+        self.z_dim = z_dim
+        self.dim_mult = dim_mult
+        self.num_res_blocks = num_res_blocks
+        self.attn_scales = attn_scales
+        self.temperal_upsample = temperal_upsample
+
+        # dimensions
+        dims = [dim * u for u in [dim_mult[-1]] + dim_mult[::-1]]
+        scale = 1.0 / 2 ** (len(dim_mult) - 2)
+        # init block
+        self.conv1 = CausalConv3d(z_dim, dims[0], 3, padding=1)
+
+        # middle blocks
+        self.middle = nn.Sequential(ResidualBlock(dims[0], dims[0], dropout),
+                                    AttentionBlock(dims[0]),
+                                    ResidualBlock(dims[0], dims[0], dropout))
+
+        # upsample blocks
+        upsamples = []
+        for i, (in_dim, out_dim) in enumerate(zip(dims[:-1], dims[1:])):
+            t_up_flag = temperal_upsample[i] if i < len(temperal_upsample) else False
+            upsamples.append(
+                Up_ResidualBlock(in_dim=in_dim,
+                                 out_dim=out_dim,
+                                 dropout=dropout,
+                                 mult=num_res_blocks + 1,
+                                 temperal_upsample=t_up_flag,
+                                 up_flag=i != len(dim_mult) - 1))
+        self.upsamples = nn.Sequential(*upsamples)
+
+        # output blocks
+        self.head = nn.Sequential(RMS_norm(out_dim, images=False), nn.SiLU(),
+                                  CausalConv3d(out_dim, 12, 3, padding=1))
+
+
+    def forward(self, x, feat_cache=None, feat_idx=[0], first_chunk=False):
+        if feat_cache is not None:
+            idx = feat_idx[0]
+            cache_x = x[:, :, -CACHE_T:, :, :].clone()
+            if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
+                cache_x = torch.cat(
+                    [
+                        feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(cache_x.device),
+                        cache_x,
+                    ],
+                    dim=2,
+                )
+            x = self.conv1(x, feat_cache[idx])
+            feat_cache[idx] = cache_x
+            feat_idx[0] += 1
+        else:
+            x = self.conv1(x)
+
+        for layer in self.middle:
+            if check_is_instance(layer, ResidualBlock) and feat_cache is not None:
+                x = layer(x, feat_cache, feat_idx)
+            else:
+                x = layer(x)
+
+        ## upsamples
+        for layer in self.upsamples:
+            if feat_cache is not None:
+                x = layer(x, feat_cache, feat_idx, first_chunk)
+            else:
+                x = layer(x)
+
+        ## head
+        for layer in self.head:
+            if check_is_instance(layer, CausalConv3d) and feat_cache is not None:
+                idx = feat_idx[0]
+                cache_x = x[:, :, -CACHE_T:, :, :].clone()
+                if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
+                    cache_x = torch.cat(
+                        [
+                            feat_cache[idx][:, :, -1, :, :]
+                            .unsqueeze(2)
+                            .to(cache_x.device),
+                            cache_x,
+                        ],
+                        dim=2,
+                    )
+                x = layer(x, feat_cache[idx])
+                feat_cache[idx] = cache_x
+                feat_idx[0] += 1
+            else:
+                x = layer(x)
+        return x
+
+
+def count_conv3d(model):
+    count = 0
+    for m in model.modules():
+        if isinstance(m, CausalConv3d):
+            count += 1
+    return count
+
+
+class VideoVAE_(nn.Module):
+
+    def __init__(self,
+                 dim=96,
+                 z_dim=16,
+                 dim_mult=[1, 2, 4, 4],
+                 num_res_blocks=2,
+                 attn_scales=[],
+                 temperal_downsample=[False, True, True],
+                 dropout=0.0):
+        super().__init__()
+        self.dim = dim
+        self.z_dim = z_dim
+        self.dim_mult = dim_mult
+        self.num_res_blocks = num_res_blocks
+        self.attn_scales = attn_scales
+        self.temperal_downsample = temperal_downsample
+        self.temperal_upsample = temperal_downsample[::-1]
+
+        # modules
+        self.encoder = Encoder3d(dim, z_dim * 2, dim_mult, num_res_blocks,
+                                 attn_scales, self.temperal_downsample, dropout)
+        self.conv1 = CausalConv3d(z_dim * 2, z_dim * 2, 1)
+        self.conv2 = CausalConv3d(z_dim, z_dim, 1)
+        self.decoder = Decoder3d(dim, z_dim, dim_mult, num_res_blocks,
+                                 attn_scales, self.temperal_upsample, dropout)
+
+    def forward(self, x):
+        mu, log_var = self.encode(x)
+        z = self.reparameterize(mu, log_var)
+        x_recon = self.decode(z)
+        return x_recon, mu, log_var
+
+    def encode(self, x, scale):
+        self.clear_cache()
+        ## cache
+        t = x.shape[2]
+        iter_ = 1 + (t - 1) // 4
+
+        for i in range(iter_):
+            self._enc_conv_idx = [0]
+            if i == 0:
+                out = self.encoder(x[:, :, :1, :, :],
+                                   feat_cache=self._enc_feat_map,
+                                   feat_idx=self._enc_conv_idx)
+            else:
+                out_ = self.encoder(x[:, :, 1 + 4 * (i - 1):1 + 4 * i, :, :],
+                                    feat_cache=self._enc_feat_map,
+                                    feat_idx=self._enc_conv_idx)
+                out = torch.cat([out, out_], 2)
+        mu, log_var = self.conv1(out).chunk(2, dim=1)
+        if isinstance(scale[0], torch.Tensor):
+            scale = [s.to(dtype=mu.dtype, device=mu.device) for s in scale]
+            mu = (mu - scale[0].view(1, self.z_dim, 1, 1, 1)) * scale[1].view(
+                1, self.z_dim, 1, 1, 1)
+        else:
+            scale = scale.to(dtype=mu.dtype, device=mu.device)
+            mu = (mu - scale[0]) * scale[1]
+        return mu
+
+    def decode(self, z, scale):
+        self.clear_cache()
+        # z: [b,c,t,h,w]
+        if isinstance(scale[0], torch.Tensor):
+            scale = [s.to(dtype=z.dtype, device=z.device) for s in scale]
+            z = z / scale[1].view(1, self.z_dim, 1, 1, 1) + scale[0].view(
+                1, self.z_dim, 1, 1, 1)
+        else:
+            scale = scale.to(dtype=z.dtype, device=z.device)
+            z = z / scale[1] + scale[0]
+        iter_ = z.shape[2]
+        x = self.conv2(z)
+        for i in range(iter_):
+            self._conv_idx = [0]
+            if i == 0:
+                out = self.decoder(x[:, :, i:i + 1, :, :],
+                                   feat_cache=self._feat_map,
+                                   feat_idx=self._conv_idx)
+            else:
+                out_ = self.decoder(x[:, :, i:i + 1, :, :],
+                                    feat_cache=self._feat_map,
+                                    feat_idx=self._conv_idx)
+                out = torch.cat([out, out_], 2) # may add tensor offload
+        return out
+
+    def reparameterize(self, mu, log_var):
+        std = torch.exp(0.5 * log_var)
+        eps = torch.randn_like(std)
+        return eps * std + mu
+
+    def sample(self, imgs, deterministic=False):
+        mu, log_var = self.encode(imgs)
+        if deterministic:
+            return mu
+        std = torch.exp(0.5 * log_var.clamp(-30.0, 20.0))
+        return mu + std * torch.randn_like(std)
+
+    def clear_cache(self):
+        self._conv_num = count_conv3d(self.decoder)
+        self._conv_idx = [0]
+        self._feat_map = [None] * self._conv_num
+        # cache encode
+        self._enc_conv_num = count_conv3d(self.encoder)
+        self._enc_conv_idx = [0]
+        self._enc_feat_map = [None] * self._enc_conv_num
+
+
+class WanVideoVAE(nn.Module):
+
+    def __init__(self, z_dim=16):
+        super().__init__()
+
+        mean = [
+            -0.7571, -0.7089, -0.9113, 0.1075, -0.1745, 0.9653, -0.1517, 1.5508,
+            0.4134, -0.0715, 0.5517, -0.3632, -0.1922, -0.9497, 0.2503, -0.2921
+        ]
+        std = [
+            2.8184, 1.4541, 2.3275, 2.6558, 1.2196, 1.7708, 2.6052, 2.0743,
+            3.2687, 2.1526, 2.8652, 1.5579, 1.6382, 1.1253, 2.8251, 1.9160
+        ]
+        self.mean = torch.tensor(mean)
+        self.std = torch.tensor(std)
+        self.scale = [self.mean, 1.0 / self.std]
+
+        # init model
+        self.model = VideoVAE_(z_dim=z_dim).eval().requires_grad_(False)
+        self.upsampling_factor = 8
+        self.z_dim = z_dim
+
+
+    def build_1d_mask(self, length, left_bound, right_bound, border_width):
+        x = torch.ones((length,))
+        if not left_bound:
+            x[:border_width] = (torch.arange(border_width) + 1) / border_width
+        if not right_bound:
+            x[-border_width:] = torch.flip((torch.arange(border_width) + 1) / border_width, dims=(0,))
+        return x
+
+
+    def build_mask(self, data, is_bound, border_width):
+        _, _, _, H, W = data.shape
+        h = self.build_1d_mask(H, is_bound[0], is_bound[1], border_width[0])
+        w = self.build_1d_mask(W, is_bound[2], is_bound[3], border_width[1])
+
+        h = repeat(h, "H -> H W", H=H, W=W)
+        w = repeat(w, "W -> H W", H=H, W=W)
+
+        mask = torch.stack([h, w]).min(dim=0).values
+        mask = rearrange(mask, "H W -> 1 1 1 H W")
+        return mask
+
+
+    def tiled_decode(self, hidden_states, device, tile_size, tile_stride):
+        _, _, T, H, W = hidden_states.shape
+        size_h, size_w = tile_size
+        stride_h, stride_w = tile_stride
+
+        # Split tasks
+        tasks = []
+        for h in range(0, H, stride_h):
+            if (h-stride_h >= 0 and h-stride_h+size_h >= H): continue
+            for w in range(0, W, stride_w):
+                if (w-stride_w >= 0 and w-stride_w+size_w >= W): continue
+                h_, w_ = h + size_h, w + size_w
+                tasks.append((h, h_, w, w_))
+
+        data_device = "cpu"
+        computation_device = device
+
+        out_T = T * 4 - 3
+        weight = torch.zeros((1, 1, out_T, H * self.upsampling_factor, W * self.upsampling_factor), dtype=hidden_states.dtype, device=data_device)
+        values = torch.zeros((1, 3, out_T, H * self.upsampling_factor, W * self.upsampling_factor), dtype=hidden_states.dtype, device=data_device)
+
+        for h, h_, w, w_ in tqdm(tasks, desc="VAE decoding"):
+            hidden_states_batch = hidden_states[:, :, :, h:h_, w:w_].to(computation_device)
+            hidden_states_batch = self.model.decode(hidden_states_batch, self.scale).to(data_device)
+
+            mask = self.build_mask(
+                hidden_states_batch,
+                is_bound=(h==0, h_>=H, w==0, w_>=W),
+                border_width=((size_h - stride_h) * self.upsampling_factor, (size_w - stride_w) * self.upsampling_factor)
+            ).to(dtype=hidden_states.dtype, device=data_device)
+
+            target_h = h * self.upsampling_factor
+            target_w = w * self.upsampling_factor
+            values[
+                :,
+                :,
+                :,
+                target_h:target_h + hidden_states_batch.shape[3],
+                target_w:target_w + hidden_states_batch.shape[4],
+            ] += hidden_states_batch * mask
+            weight[
+                :,
+                :,
+                :,
+                target_h: target_h + hidden_states_batch.shape[3],
+                target_w: target_w + hidden_states_batch.shape[4],
+            ] += mask
+        values = values / weight
+        values = values.clamp_(-1, 1)
+        return values
+
+
+    def tiled_encode(self, video, device, tile_size, tile_stride):
+        _, _, T, H, W = video.shape
+        size_h, size_w = tile_size
+        stride_h, stride_w = tile_stride
+
+        # Split tasks
+        tasks = []
+        for h in range(0, H, stride_h):
+            if (h-stride_h >= 0 and h-stride_h+size_h >= H): continue
+            for w in range(0, W, stride_w):
+                if (w-stride_w >= 0 and w-stride_w+size_w >= W): continue
+                h_, w_ = h + size_h, w + size_w
+                tasks.append((h, h_, w, w_))
+
+        data_device = "cpu"
+        computation_device = device
+
+        out_T = (T + 3) // 4
+        weight = torch.zeros((1, 1, out_T, H // self.upsampling_factor, W // self.upsampling_factor), dtype=video.dtype, device=data_device)
+        values = torch.zeros((1, self.z_dim, out_T, H // self.upsampling_factor, W // self.upsampling_factor), dtype=video.dtype, device=data_device)
+
+        for h, h_, w, w_ in tqdm(tasks, desc="VAE encoding"):
+            hidden_states_batch = video[:, :, :, h:h_, w:w_].to(computation_device)
+            hidden_states_batch = self.model.encode(hidden_states_batch, self.scale).to(data_device)
+
+            mask = self.build_mask(
+                hidden_states_batch,
+                is_bound=(h==0, h_>=H, w==0, w_>=W),
+                border_width=((size_h - stride_h) // self.upsampling_factor, (size_w - stride_w) // self.upsampling_factor)
+            ).to(dtype=video.dtype, device=data_device)
+
+            target_h = h // self.upsampling_factor
+            target_w = w // self.upsampling_factor
+            values[
+                :,
+                :,
+                :,
+                target_h:target_h + hidden_states_batch.shape[3],
+                target_w:target_w + hidden_states_batch.shape[4],
+            ] += hidden_states_batch * mask
+            weight[
+                :,
+                :,
+                :,
+                target_h: target_h + hidden_states_batch.shape[3],
+                target_w: target_w + hidden_states_batch.shape[4],
+            ] += mask
+        values = values / weight
+        return values
+
+
+    def single_encode(self, video, device):
+        video = video.to(device)
+        x = self.model.encode(video, self.scale)
+        return x
+
+
+    def single_decode(self, hidden_state, device):
+        hidden_state = hidden_state.to(device)
+        video = self.model.decode(hidden_state, self.scale)
+        return video.clamp_(-1, 1)
+
+
+    def encode(self, videos, device, tiled=False, tile_size=(34, 34), tile_stride=(18, 16)):
+
+        videos = [video.to("cpu") for video in videos]
+        hidden_states = []
+        for video in videos:
+            video = video.unsqueeze(0)
+            if tiled:
+                tile_size = (tile_size[0] * self.upsampling_factor, tile_size[1] * self.upsampling_factor)
+                tile_stride = (tile_stride[0] * self.upsampling_factor, tile_stride[1] * self.upsampling_factor)
+                hidden_state = self.tiled_encode(video, device, tile_size, tile_stride)
+            else:
+                hidden_state = self.single_encode(video, device)
+            hidden_state = hidden_state.squeeze(0)
+            hidden_states.append(hidden_state)
+        hidden_states = torch.stack(hidden_states)
+        return hidden_states
+
+
+    def decode(self, hidden_states, device, tiled=False, tile_size=(34, 34), tile_stride=(18, 16)):
+        if tiled:
+            video = self.tiled_decode(hidden_states, device, tile_size, tile_stride)
+        else:
+            video = self.single_decode(hidden_states, device)
+        return video
+
+
+    @staticmethod
+    def state_dict_converter():
+        return WanVideoVAEStateDictConverter()
+
+
+class WanVideoVAEStateDictConverter:
+
+    def __init__(self):
+        pass
+
+    def from_civitai(self, state_dict):
+        state_dict_ = {}
+        if 'model_state' in state_dict:
+            state_dict = state_dict['model_state']
+        for name in state_dict:
+            state_dict_['model.' + name] = state_dict[name]
+        return state_dict_
+
+
+class VideoVAE38_(VideoVAE_):
+
+    def __init__(self,
+                 dim=160,
+                 z_dim=48,
+                 dec_dim=256,
+                 dim_mult=[1, 2, 4, 4],
+                 num_res_blocks=2,
+                 attn_scales=[],
+                 temperal_downsample=[False, True, True],
+                 dropout=0.0):
+        super(VideoVAE_, self).__init__()
+        self.dim = dim
+        self.z_dim = z_dim
+        self.dim_mult = dim_mult
+        self.num_res_blocks = num_res_blocks
+        self.attn_scales = attn_scales
+        self.temperal_downsample = temperal_downsample
+        self.temperal_upsample = temperal_downsample[::-1]
+
+        # modules
+        self.encoder = Encoder3d_38(dim, z_dim * 2, dim_mult, num_res_blocks,
+                                    attn_scales, self.temperal_downsample, dropout)
+        self.conv1 = CausalConv3d(z_dim * 2, z_dim * 2, 1)
+        self.conv2 = CausalConv3d(z_dim, z_dim, 1)
+        self.decoder = Decoder3d_38(dec_dim, z_dim, dim_mult, num_res_blocks,
+                                    attn_scales, self.temperal_upsample, dropout)
+
+
+    def encode(self, x, scale):
+        self.clear_cache()
+        x = patchify(x, patch_size=2)
+        t = x.shape[2]
+        iter_ = 1 + (t - 1) // 4
+        for i in range(iter_):
+            self._enc_conv_idx = [0]
+            if i == 0:
+                out = self.encoder(x[:, :, :1, :, :],
+                                   feat_cache=self._enc_feat_map,
+                                   feat_idx=self._enc_conv_idx)
+            else:
+                out_ = self.encoder(x[:, :, 1 + 4 * (i - 1):1 + 4 * i, :, :],
+                                    feat_cache=self._enc_feat_map,
+                                    feat_idx=self._enc_conv_idx)
+                out = torch.cat([out, out_], 2)
+        mu, log_var = self.conv1(out).chunk(2, dim=1)
+        if isinstance(scale[0], torch.Tensor):
+            scale = [s.to(dtype=mu.dtype, device=mu.device) for s in scale]
+            mu = (mu - scale[0].view(1, self.z_dim, 1, 1, 1)) * scale[1].view(
+                1, self.z_dim, 1, 1, 1)
+        else:
+            scale = scale.to(dtype=mu.dtype, device=mu.device)
+            mu = (mu - scale[0]) * scale[1]
+        self.clear_cache()
+        return mu
+
+
+    def decode(self, z, scale):
+        self.clear_cache()
+        if isinstance(scale[0], torch.Tensor):
+            scale = [s.to(dtype=z.dtype, device=z.device) for s in scale]
+            z = z / scale[1].view(1, self.z_dim, 1, 1, 1) + scale[0].view(
+                1, self.z_dim, 1, 1, 1)
+        else:
+            scale = scale.to(dtype=z.dtype, device=z.device)
+            z = z / scale[1] + scale[0]
+        iter_ = z.shape[2]
+        x = self.conv2(z)
+        for i in range(iter_):
+            self._conv_idx = [0]
+            if i == 0:
+                out = self.decoder(x[:, :, i:i + 1, :, :],
+                                   feat_cache=self._feat_map,
+                                   feat_idx=self._conv_idx,
+                                   first_chunk=True)
+            else:
+                out_ = self.decoder(x[:, :, i:i + 1, :, :],
+                                    feat_cache=self._feat_map,
+                                    feat_idx=self._conv_idx)
+                out = torch.cat([out, out_], 2)
+        out = unpatchify(out, patch_size=2)
+        self.clear_cache()
+        return out
+
+
+class WanVideoVAE38(WanVideoVAE):
+
+    def __init__(self, z_dim=48, dim=160):
+        super(WanVideoVAE, self).__init__()
+
+        mean = [
+            -0.2289, -0.0052, -0.1323, -0.2339, -0.2799,  0.0174,  0.1838,  0.1557,
+            -0.1382,  0.0542,  0.2813,  0.0891,  0.1570, -0.0098,  0.0375, -0.1825,
+            -0.2246, -0.1207, -0.0698,  0.5109,  0.2665, -0.2108, -0.2158,  0.2502,
+            -0.2055, -0.0322,  0.1109,  0.1567, -0.0729,  0.0899, -0.2799, -0.1230,
+            -0.0313, -0.1649,  0.0117,  0.0723, -0.2839, -0.2083, -0.0520,  0.3748,
+            0.0152,  0.1957,  0.1433, -0.2944,  0.3573, -0.0548, -0.1681, -0.0667
+        ]
+        std = [
+            0.4765, 1.0364, 0.4514, 1.1677, 0.5313, 0.4990, 0.4818, 0.5013,
+            0.8158, 1.0344, 0.5894, 1.0901, 0.6885, 0.6165, 0.8454, 0.4978,
+            0.5759, 0.3523, 0.7135, 0.6804, 0.5833, 1.4146, 0.8986, 0.5659,
+            0.7069, 0.5338, 0.4889, 0.4917, 0.4069, 0.4999, 0.6866, 0.4093,
+            0.5709, 0.6065, 0.6415, 0.4944, 0.5726, 1.2042, 0.5458, 1.6887,
+            0.3971, 1.0600, 0.3943, 0.5537, 0.5444, 0.4089, 0.7468, 0.7744
+        ]
+        self.mean = torch.tensor(mean)
+        self.std = torch.tensor(std)
+        self.scale = [self.mean, 1.0 / self.std]
+
+        # init model
+        self.model = VideoVAE38_(z_dim=z_dim, dim=dim).eval().requires_grad_(False)
+        self.upsampling_factor = 16
+        self.z_dim = z_dim
diff --git a/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/wav2vec.py b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/wav2vec.py
new file mode 100644
index 0000000000000000000000000000000000000000..e17bd96ee9f5545e8bbf75fdc8b7f97e9cd00ffe
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/diffsynth/models/wav2vec.py
@@ -0,0 +1,197 @@
+import math
+import numpy as np
+import torch
+import torch.nn.functional as F
+
+
+def get_sample_indices(original_fps, total_frames, target_fps, num_sample, fixed_start=None):
+    required_duration = num_sample / target_fps
+    required_origin_frames = int(np.ceil(required_duration * original_fps))
+    if required_duration > total_frames / original_fps:
+        raise ValueError("required_duration must be less than video length")
+
+    if not fixed_start is None and fixed_start >= 0:
+        start_frame = fixed_start
+    else:
+        max_start = total_frames - required_origin_frames
+        if max_start < 0:
+            raise ValueError("video length is too short")
+        start_frame = np.random.randint(0, max_start + 1)
+    start_time = start_frame / original_fps
+
+    end_time = start_time + required_duration
+    time_points = np.linspace(start_time, end_time, num_sample, endpoint=False)
+
+    frame_indices = np.round(np.array(time_points) * original_fps).astype(int)
+    frame_indices = np.clip(frame_indices, 0, total_frames - 1)
+    return frame_indices
+
+
+def linear_interpolation(features, input_fps, output_fps, output_len=None):
+    """
+    features: shape=[1, T, 512]
+    input_fps: fps for audio, f_a
+    output_fps: fps for video, f_m
+    output_len: video length
+    """
+    features = features.transpose(1, 2)
+    seq_len = features.shape[2] / float(input_fps)
+    if output_len is None:
+        output_len = int(seq_len * output_fps)
+    output_features = F.interpolate(features, size=output_len, align_corners=True, mode='linear')  # [1, 512, output_len]
+    return output_features.transpose(1, 2)
+
+
+class WanS2VAudioEncoder(torch.nn.Module):
+
+    def __init__(self):
+        super().__init__()
+        from transformers import Wav2Vec2ForCTC, Wav2Vec2Config
+        config = {
+            "_name_or_path": "facebook/wav2vec2-large-xlsr-53",
+            "activation_dropout": 0.05,
+            "apply_spec_augment": True,
+            "architectures": ["Wav2Vec2ForCTC"],
+            "attention_dropout": 0.1,
+            "bos_token_id": 1,
+            "conv_bias": True,
+            "conv_dim": [512, 512, 512, 512, 512, 512, 512],
+            "conv_kernel": [10, 3, 3, 3, 3, 2, 2],
+            "conv_stride": [5, 2, 2, 2, 2, 2, 2],
+            "ctc_loss_reduction": "mean",
+            "ctc_zero_infinity": True,
+            "do_stable_layer_norm": True,
+            "eos_token_id": 2,
+            "feat_extract_activation": "gelu",
+            "feat_extract_dropout": 0.0,
+            "feat_extract_norm": "layer",
+            "feat_proj_dropout": 0.05,
+            "final_dropout": 0.0,
+            "hidden_act": "gelu",
+            "hidden_dropout": 0.05,
+            "hidden_size": 1024,
+            "initializer_range": 0.02,
+            "intermediate_size": 4096,
+            "layer_norm_eps": 1e-05,
+            "layerdrop": 0.05,
+            "mask_channel_length": 10,
+            "mask_channel_min_space": 1,
+            "mask_channel_other": 0.0,
+            "mask_channel_prob": 0.0,
+            "mask_channel_selection": "static",
+            "mask_feature_length": 10,
+            "mask_feature_prob": 0.0,
+            "mask_time_length": 10,
+            "mask_time_min_space": 1,
+            "mask_time_other": 0.0,
+            "mask_time_prob": 0.05,
+            "mask_time_selection": "static",
+            "model_type": "wav2vec2",
+            "num_attention_heads": 16,
+            "num_conv_pos_embedding_groups": 16,
+            "num_conv_pos_embeddings": 128,
+            "num_feat_extract_layers": 7,
+            "num_hidden_layers": 24,
+            "pad_token_id": 0,
+            "transformers_version": "4.7.0.dev0",
+            "vocab_size": 33
+        }
+        self.model = Wav2Vec2ForCTC(Wav2Vec2Config(**config))
+        self.video_rate = 30
+
+    def extract_audio_feat(self, input_audio, sample_rate, processor, return_all_layers=False, dtype=torch.float32, device='cpu'):
+        input_values = processor(input_audio, sampling_rate=sample_rate, return_tensors="pt").input_values.to(dtype=dtype, device=device)
+
+        # retrieve logits & take argmax
+        res = self.model(input_values, output_hidden_states=True)
+        if return_all_layers:
+            feat = torch.cat(res.hidden_states)
+        else:
+            feat = res.hidden_states[-1]
+        feat = linear_interpolation(feat, input_fps=50, output_fps=self.video_rate)
+        return feat
+
+    def get_audio_embed_bucket(self, audio_embed, stride=2, batch_frames=12, m=2):
+        num_layers, audio_frame_num, audio_dim = audio_embed.shape
+
+        if num_layers > 1:
+            return_all_layers = True
+        else:
+            return_all_layers = False
+
+        min_batch_num = int(audio_frame_num / (batch_frames * stride)) + 1
+
+        bucket_num = min_batch_num * batch_frames
+        batch_idx = [stride * i for i in range(bucket_num)]
+        batch_audio_eb = []
+        for bi in batch_idx:
+            if bi < audio_frame_num:
+                audio_sample_stride = 2
+                chosen_idx = list(range(bi - m * audio_sample_stride, bi + (m + 1) * audio_sample_stride, audio_sample_stride))
+                chosen_idx = [0 if c < 0 else c for c in chosen_idx]
+                chosen_idx = [audio_frame_num - 1 if c >= audio_frame_num else c for c in chosen_idx]
+
+                if return_all_layers:
+                    frame_audio_embed = audio_embed[:, chosen_idx].flatten(start_dim=-2, end_dim=-1)
+                else:
+                    frame_audio_embed = audio_embed[0][chosen_idx].flatten()
+            else:
+                frame_audio_embed = \
+                torch.zeros([audio_dim * (2 * m + 1)], device=audio_embed.device) if not return_all_layers \
+                    else torch.zeros([num_layers, audio_dim * (2 * m + 1)], device=audio_embed.device)
+            batch_audio_eb.append(frame_audio_embed)
+        batch_audio_eb = torch.cat([c.unsqueeze(0) for c in batch_audio_eb], dim=0)
+
+        return batch_audio_eb, min_batch_num
+
+    def get_audio_embed_bucket_fps(self, audio_embed, fps=16, batch_frames=81, m=0):
+        num_layers, audio_frame_num, audio_dim = audio_embed.shape
+
+        if num_layers > 1:
+            return_all_layers = True
+        else:
+            return_all_layers = False
+
+        scale = self.video_rate / fps
+
+        min_batch_num = int(audio_frame_num / (batch_frames * scale)) + 1
+
+        bucket_num = min_batch_num * batch_frames
+        padd_audio_num = math.ceil(min_batch_num * batch_frames / fps * self.video_rate) - audio_frame_num
+        batch_idx = get_sample_indices(
+            original_fps=self.video_rate, total_frames=audio_frame_num + padd_audio_num, target_fps=fps, num_sample=bucket_num, fixed_start=0
+        )
+        batch_audio_eb = []
+        audio_sample_stride = int(self.video_rate / fps)
+        for bi in batch_idx:
+            if bi < audio_frame_num:
+
+                chosen_idx = list(range(bi - m * audio_sample_stride, bi + (m + 1) * audio_sample_stride, audio_sample_stride))
+                chosen_idx = [0 if c < 0 else c for c in chosen_idx]
+                chosen_idx = [audio_frame_num - 1 if c >= audio_frame_num else c for c in chosen_idx]
+
+                if return_all_layers:
+                    frame_audio_embed = audio_embed[:, chosen_idx].flatten(start_dim=-2, end_dim=-1)
+                else:
+                    frame_audio_embed = audio_embed[0][chosen_idx].flatten()
+            else:
+                frame_audio_embed = \
+                torch.zeros([audio_dim * (2 * m + 1)], device=audio_embed.device) if not return_all_layers \
+                    else torch.zeros([num_layers, audio_dim * (2 * m + 1)], device=audio_embed.device)
+            batch_audio_eb.append(frame_audio_embed)
+        batch_audio_eb = torch.cat([c.unsqueeze(0) for c in batch_audio_eb], dim=0)
+
+        return batch_audio_eb, min_batch_num
+
+    @staticmethod
+    def state_dict_converter():
+        return WanS2VAudioEncoderStateDictConverter()
+
+
+class WanS2VAudioEncoderStateDictConverter():
+    def __init__(self):
+        pass
+
+    def from_civitai(self, state_dict):
+        state_dict = {'model.' + k: v for k, v in state_dict.items()}
+        return state_dict
diff --git a/exp_code/1_benchmark/DiffSynth-Studio/requirements.txt b/exp_code/1_benchmark/DiffSynth-Studio/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..889b7fa7fa8741d80452bc202ffffe9a55f7ba67
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/requirements.txt
@@ -0,0 +1,16 @@
+torch>=2.0.0
+torchvision
+cupy-cuda12x
+transformers
+controlnet-aux==0.0.7
+imageio
+imageio[ffmpeg]
+safetensors
+einops
+sentencepiece
+protobuf
+modelscope
+ftfy
+pynvml
+pandas
+accelerate
diff --git a/exp_code/1_benchmark/DiffSynth-Studio/setup.py b/exp_code/1_benchmark/DiffSynth-Studio/setup.py
new file mode 100644
index 0000000000000000000000000000000000000000..483a72990f1da9a2e339d24ef17b4a75e55cdfe1
--- /dev/null
+++ b/exp_code/1_benchmark/DiffSynth-Studio/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="1.1.7",
+    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',
+)