Image-to-Image
Diffusers
Safetensors
English
Image-to-Image
ControlNet
Diffusers
QwenImageControlNetInpaintPipeline
Qwen-Image
Instructions to use InstantX/Qwen-Image-ControlNet-Inpainting with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use InstantX/Qwen-Image-ControlNet-Inpainting with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("InstantX/Qwen-Image-ControlNet-Inpainting", dtype=torch.bfloat16, device_map="cuda") prompt = "Turn this cat into a dog" input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png") image = pipe(image=input_image, prompt=prompt).images[0] - Notebooks
- Google Colab
- Kaggle
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image.save(f"qwenimage_cn_inpaint_result.png")
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```
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# Limitations
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This model is slightly sensitive to user prompts. Using detailed prompts that describe the entire image (both the inpainted area and the background) is highly recommended. Please use descriptive prompt instead of instructive prompt.
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image.save(f"qwenimage_cn_inpaint_result.png")
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```
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# ComfyUI Support
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[ComfyUI](https://www.comfy.org/) offers native support for Qwen-Image-ControlNet-Inpainting.
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# Community Support
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[Liblib AI](https://www.liblib.art/) offers native support for Qwen-Image-ControlNet-Inpainting. [Visit](https://www.liblib.art) for online WebUI or ComfyUI inference.
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# Limitations
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This model is slightly sensitive to user prompts. Using detailed prompts that describe the entire image (both the inpainted area and the background) is highly recommended. Please use descriptive prompt instead of instructive prompt.
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