Instructions to use REPA-E/e2e-invae with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use REPA-E/e2e-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/e2e-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 a config.json to easily use this checkpoint with diffusers
#3
by SwayStar123 - opened
Good example here:
https://huggingface.co/KBlueLeaf/EQ-SDXL-VAE/blob/main/config.json
This would allow people to use this vae easily by just doing
vae = AutoencoderKL.from_pretrained("REPA-E/e2e-invae")
@SwayStar123 Might be a little late to the party but
https://huggingface.co/Shio-Koube/REPA-E-INVAE-diffusers