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
Upload 2 files
Browse files- config.json +20 -0
- diffusion_pytorch_model.safetensors +3 -0
config.json
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{
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"_class_name": "QwenImageControlNetModel",
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"_diffusers_version": "0.35.0.dev0",
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"_name_or_path": "qwenimage-controlnet-inpaint-v2/checkpoint-25000/controlnet",
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"attention_head_dim": 128,
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"axes_dims_rope": [
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16,
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56,
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56
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],
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"extra_condition_channels": 4,
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"guidance_embeds": false,
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"in_channels": 64,
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"joint_attention_dim": 3584,
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"num_attention_heads": 24,
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"num_layers": 6,
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"out_channels": 16,
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"patch_size": 2,
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"pooled_projection_dim": 768
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}
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diffusion_pytorch_model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:49c01aafe6545c1f6e7627a724c8fb13357fe74efee235622158f0a8f30e5458
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size 4234599432
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