Instructions to use BiliSakura/RSEdit-UNet-text-ablation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use BiliSakura/RSEdit-UNet-text-ablation 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("BiliSakura/RSEdit-UNet-text-ablation", 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
Improve model card: add metadata, paper links, and usage guide
#1
by nielsr HF Staff - opened
Hi! I'm Niels, part of the community science team at Hugging Face. I'm opening this PR to improve the model card for RSEdit-UNet.
This PR adds:
- Metadata including
library_name: diffusersandpipeline_tag: image-to-image. - Direct links to the paper, GitHub repository, and project page.
- A cleaned-up "Quick Start" guide for inference using the
diffuserslibrary. - Relevant tags for better discoverability.
These updates make it easier for researchers to find, use, and cite your work!
BiliSakura changed pull request status to merged