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

- Xet hash:
- f9310e3587b00b9c9f043ae7fabc0703a42925b4f6e4eb2aef51b9dd00700948
- Size of remote file:
- 106 kB
- SHA256:
- 6e2ba1022bb71d026c764b12e7d6c67a233cfa4c6836616f618a878764fe7a7c
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