Image-to-Image
Diffusers
Safetensors
English
Image-to-Image
ControlNet
Diffusers
QwenImageControlNetPipeline
Qwen-Image
Instructions to use Runware/Qwen-Image-ControlNet-Union with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Runware/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("Runware/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:
- aa1f01840406a9dca73a225232acc9e1d94288771f1c908aba03dbea00f52932
- Size of remote file:
- 3.54 GB
- SHA256:
- d51dca0073366a675108d5b83c3b7ef941cf2214c9a1c95c23f1e9a228ddbdb0
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