Instructions to use nvidia/Qwen-Image-Edit-NVPCB-OVSL2SL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use nvidia/Qwen-Image-Edit-NVPCB-OVSL2SL 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("nvidia/Qwen-Image-Edit-NVPCB-OVSL2SL", 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:
- 2f127f266c3f7219baaeb5f54dc636c79952102d7cdbe3e6fb298d9363934610
- Size of remote file:
- 11.4 MB
- SHA256:
- 6b4360dd6a184650ffc48056c2569bc603f896c5adfe94b10f1c79f809638aa5
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.