Instructions to use REPA-E/sit-ldm-e2e-vavae with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use REPA-E/sit-ldm-e2e-vavae 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-ldm-e2e-vavae", 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 pipeline tag and library name
#1
by nielsr HF Staff - opened
This PR adds the pipeline_tag and library_name to the model card metadata. The pipeline_tag is set to image-to-image as the model generates images from images. The library_name is set to diffusers based on the training scripts and code examples provided.
xingjianleng changed pull request status to merged