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:
- 2d572be8576ce51b2169fbac961aea081ad6c5ce922c34cfba9cf2f361564d17
- Size of remote file:
- 4.97 GB
- SHA256:
- 131be13df494a10920bfb3defb85ff3069762e686e5bf4144346d557c0bb7a2b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.