metadata
frameworks:
- Pytorch
tasks:
- text-to-image-synthesis
base_model:
- Qwen/Qwen-Image
base_model_relation: adapter
Qwen-Image Image Structure Control Model
Model Introduction
This model is a structure control model for images, trained based on Qwen-Image. The model architecture is ControlNet, capable of controlling the generated image structure according to edge detection (Canny) maps. The training framework is built upon DiffSynth-Studio and the dataset used is BLIP3o。
Effect Demonstration
| Structure Map | Generated Image 1 | Generated Image 2 |
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Inference Code
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-Canny", origin_file_pattern="model.safetensors"),
],
tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"),
)
dataset_snapshot_download(
dataset_id="DiffSynth-Studio/example_image_dataset",
local_dir="./data/example_image_dataset",
allow_file_pattern="canny/image_1.jpg"
)
controlnet_image = Image.open("data/example_image_dataset/canny/image_1.jpg").resize((1328, 1328))
prompt = "A puppy with shiny, smooth fur and lively eyes, with a spring garden full of cherry blossoms as the background, beautiful and warm."
image = pipe(
prompt, seed=0,
blockwise_controlnet_inputs=[ControlNetInput(image=controlnet_image)]
)
image.save("image.jpg")