Image-to-Image
Diffusers
Safetensors
English
Image-to-Image
ControlNet
Diffusers
QwenImageControlNetPipeline
Qwen-Image
Instructions to use InstantX/Qwen-Image-ControlNet-Union with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use InstantX/Qwen-Image-ControlNet-Union 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-Union", 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
Download outputs/canny.png from InstantX/Qwen-Image-ControlNet-Union: direct link, hf CLI and curl.
- Browser
- Download file 1.48 MB
-
https://huggingface.co/InstantX/Qwen-Image-ControlNet-Union/resolve/main/outputs/canny.png
- Command line
-
hf download hf://InstantX/Qwen-Image-ControlNet-Union/outputs/canny.png
-
curl -L -o canny.png https://huggingface.co/InstantX/Qwen-Image-ControlNet-Union/resolve/main/outputs/canny.png
1.48 MB

- Xet hash:
- 268a8c64e86e4cc313b991717c4b9742a8490573ce9564bdd19ba73b3e51b426
- Size of remote file:
- 1.48 MB
- SHA256:
- 2fba6c4c8e46e71522131143bcafde63051a42227911cdaa07f11dbbbbb0909e
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