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
File size: 482 Bytes
02b8280 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | {
"_class_name": "QwenImageControlNetModel",
"_diffusers_version": "0.35.0.dev0",
"_name_or_path": "qwenimage-controlnet-inpaint-v2/checkpoint-25000/controlnet",
"attention_head_dim": 128,
"axes_dims_rope": [
16,
56,
56
],
"extra_condition_channels": 4,
"guidance_embeds": false,
"in_channels": 64,
"joint_attention_dim": 3584,
"num_attention_heads": 24,
"num_layers": 6,
"out_channels": 16,
"patch_size": 2,
"pooled_projection_dim": 768
}
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