Qwen Image Blockwise ControlNet Inpaint

Qwen-Image Image Structure Control Model

Model Introduction

This model is a local image redraw model trained based on Qwen-Image , with a model structure of ControlNet, capable of redrawing local areas of an image. The training framework is built on DiffSynth-Studio , and the dataset used is Qwen-Image-Self-Generated-Dataset。

This model is compatible with both Qwen-Image and Qwen-Image-Edit,It can perform local redrawing on Qwen-Image and edit specified areas on Qwen-Image-Edit.

Effect Demonstration

Input Prompt	Input Image	Redrawn Image
A robot with wings and a hat standing in a colorful garden with flowers and butterflies.
A girl in a school uniform stands gracefully in front of a vibrant stained glass window with colorful geometric patterns.
A small wooden boat battles against towering, crashing waves in a stormy sea.

Limitations

• Inpaint models based on the ControlNet structure may result in disharmonious boundaries between the redrawn and non-redrawn areas.

• The model is trained on rectangular area redraw data, so its generalization to non-rectangular areas might not be optimal.

Inference Code

git clone https://github.com/modelscope/DiffSynth-Studio.git  
cd DiffSynth-Studio
pip install -e .

Qwen-Image:

import torch
from PIL import Image
from modelscope import dataset_snapshot_download
from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig, ControlNetInput


pipe = QwenImagePipeline.from_pretrained(
    torch_dtype=torch.bfloat16,
    device="cuda",
    model_configs=[
        ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"),
        ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"),
        ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
        ModelConfig(model_id="DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Inpaint", origin_file_pattern="model.safetensors"),
    ],
    tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"),
)

dataset_snapshot_download(
    dataset_id="DiffSynth-Studio/example_image_dataset",
    local_dir="./data/example_image_dataset",
    allow_file_pattern="inpaint/*.jpg"
)
prompt = "a cat with sunglasses"
controlnet_image = Image.open("./data/example_image_dataset/inpaint/image_1.jpg").convert("RGB").resize((1328, 1328))
inpaint_mask = Image.open("./data/example_image_dataset/inpaint/mask.jpg").convert("RGB").resize((1328, 1328))
image = pipe(
    prompt, seed=0,
    input_image=controlnet_image, inpaint_mask=inpaint_mask,
    blockwise_controlnet_inputs=[ControlNetInput(image=controlnet_image, inpaint_mask=inpaint_mask)],
    num_inference_steps=40,
)
image.save("image.jpg")

Qwen-Image-Edit:

import torch
from PIL import Image
from modelscope import dataset_snapshot_download
from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig, ControlNetInput


pipe = QwenImagePipeline.from_pretrained(
    torch_dtype=torch.bfloat16,
    device="cuda",
    model_configs=[
        ModelConfig(model_id="Qwen/Qwen-Image-Edit", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"),
        ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"),
        ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
        ModelConfig(model_id="DiffSynth-Studio/Qwen-Image-Blockwise-ControlNet-Inpaint", origin_file_pattern="model.safetensors"),
    ],
    tokenizer_config=None,
    processor_config=ModelConfig(model_id="Qwen/Qwen-Image-Edit", origin_file_pattern="processor/"),
)

dataset_snapshot_download(
    dataset_id="DiffSynth-Studio/example_image_dataset",
    local_dir="./data/example_image_dataset",
    allow_file_pattern="inpaint/*.jpg"
)
prompt = "Put sunglasses on this cat"
controlnet_image = Image.open("./data/example_image_dataset/inpaint/image_1.jpg").convert("RGB").resize((1328, 1328))
inpaint_mask = Image.open("./data/example_image_dataset/inpaint/mask.jpg").convert("RGB").resize((1328, 1328))
image = pipe(
    prompt, seed=0,
    input_image=controlnet_image, inpaint_mask=inpaint_mask,
    blockwise_controlnet_inputs=[ControlNetInput(image=controlnet_image, inpaint_mask=inpaint_mask)],
    num_inference_steps=40,
    edit_image=controlnet_image, # add edit_image here.
)
image.save("image.jpg")

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license: apache-2.0