Instructions to use wavespeed/Qwen-Image-Edit-e4m3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use wavespeed/Qwen-Image-Edit-e4m3 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("wavespeed/Qwen-Image-Edit-e4m3", 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:
- a84c7a8f00f4903c05eb8aad1a02b1f478523921dbeb9f839d453a59e20df433
- Size of remote file:
- 254 MB
- SHA256:
- 988e2ce6729cd1a7c98de5d24ab718f2b5b9abf371436a5770c873bfbab002ff
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.