Image-to-Image
Diffusers
QwenImageEditPipeline
qwen-image
image-editing
quantized
fp8
e4m3
lora
lightning
Instructions to use wavespeed/Qwen-Image-Edit-l8v1.1-e4m3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use wavespeed/Qwen-Image-Edit-l8v1.1-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("Qwen/Qwen-Image-Edit,lightx2v/Qwen-Image-Lightning", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("wavespeed/Qwen-Image-Edit-l8v1.1-e4m3") 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
- Local Apps Settings
- Draw Things
- Xet hash:
- 8278d561d811212fc23d0505fc9829d593e8c4dfd2c8babc17d7677c25f36bb0
- Size of remote file:
- 1.69 GB
- SHA256:
- 6e119c8ab13b04d11f3a91826c0f6ad78ae57a0b053334807abc332bd793cfe3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.