Instructions to use peteromallet/Qwen-Image-Edit-InScene with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use peteromallet/Qwen-Image-Edit-InScene 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", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("peteromallet/Qwen-Image-Edit-InScene") 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:
- abcf88a6a8e41324c11ad0545888cb66cd0fd9873c4cc57a967ee099d6324e41
- Size of remote file:
- 590 MB
- SHA256:
- 24215dd60fb4533d264235b300e308a2f1021e5dec2c4e28ec4e5e388c8d0c15
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.