metadata
frameworks:
- Pytorch
license: Apache License 2.0
tasks:
- text-to-image-synthesis
base_model:
- Qwen/Qwen-Image
base_model_relation: adapter
Qwen-Image 图像结构控制模型 - Depth ControlNet
模型介绍
本模型是基于 Qwen-Image 训练的图像结构控制模型，模型结构为 ControlNet，可根据深度(Depth)图控制生成的图像结构。训练框架基于 DiffSynth-Studio 构建，采用的数据集是 BLIP3o。
效果展示
| 结构图 | 生成图1 | 生成图2 |
|---|---|---|
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推理代码
git clone https://github.com/modelscope/DiffSynth-Studio.git
cd DiffSynth-Studio
pip install -e .
from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig, ControlNetInput
from PIL import Image
import torch
from modelscope import dataset_snapshot_download
pipe = QwenImagePipeline.from_pretrained(
torch_dtype=torch.bfloat16,
device="cuda",
model_configs=[
ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"),
ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"),
ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
ModelConfig(model_id="DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Depth", origin_file_pattern="model.safetensors"),
],
tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"),
)
dataset_snapshot_download(
dataset_id="DiffSynth-Studio/example_image_dataset",
local_dir="./data/example_image_dataset",
allow_file_pattern="depth/image_1.jpg"
)
controlnet_image = Image.open("data/example_image_dataset/depth/image_1.jpg").resize((1328, 1328))
prompt = "精致肖像，水下少女，蓝裙飘逸，发丝轻扬，光影透澈，气泡环绕，面容恬静，细节精致，梦幻唯美。"
image = pipe(
prompt, seed=0,
blockwise_controlnet_inputs=[ControlNetInput(image=controlnet_image)]
)
image.save("image.jpg")