Instructions to use REPA-E/sit-repae-invae with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use REPA-E/sit-repae-invae 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("REPA-E/sit-repae-invae", 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
Add model card metadata and link to paper and project page
#1
by nielsr HF Staff - opened
This PR adds a model card by linking it to the paper REPA-E: Unlocking VAE for End-to-End Tuning of Latent Diffusion Transformers.
The PR improves the model card by adding the relevant pipeline_tag and library_name, ensuring people can find your model more easily on the Hub. It also adds a link to the project page.
Please review and merge this PR if everything looks good.
xingjianleng changed pull request status to merged