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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README.md
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@@ -56,7 +56,7 @@ controlnet_model = "InstantX/Qwen-Image-ControlNet-Inpainting"
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controlnet = QwenImageControlNetModel.from_pretrained(controlnet_model, torch_dtype=torch.bfloat16)
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pipe =
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base_model, controlnet=controlnet, torch_dtype=torch.bfloat16
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)
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pipe.to("cuda")
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controlnet = QwenImageControlNetModel.from_pretrained(controlnet_model, torch_dtype=torch.bfloat16)
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pipe = QwenImageControlNetInpaintPipeline.from_pretrained(
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base_model, controlnet=controlnet, torch_dtype=torch.bfloat16
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)
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pipe.to("cuda")
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