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
- Draw Things

- Xet hash:
- fa2f7b2a3ce5de09a248a650f6fd26b61380df2cd930d9b139bfbe8acf3967f8
- Size of remote file:
- 6.45 MB
- SHA256:
- 51bf15e0b8da224d7fd20a86740e2b817f7dffd7337e2ab4a40c5c9f6b6cd7b8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.