Instructions to use madebyollin/taef1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use madebyollin/taef1 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("madebyollin/taef1", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
Plans to support video models (Hunyuan, Wan, etc)
#3
by Wi-zz - opened
Currently VAE decoding of these models is especially slow, so this could be extremely useful. Thanks for your hard work so far.
I started on a small Hunyuan VAE this weekend https://github.com/madebyollin/taehv, will consider making one for Wan as well (are the input/output shapes the same as Hunyuan?)
Added Wan weights to the TAEHV repo as well. The quality is still a bit iffy but it should suffice for decoding fullres preview videos