Instructions to use madebyollin/taesd with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use madebyollin/taesd 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/taesd", 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
add diffusers compatible parameters
#1
by sayakpaul HF Staff - opened
sayakpaul changed pull request status to open
Thanks Sayak! Confirmed it works & is safe to merge.
One question regarding the sample code - do you know of an elegant way to enable TAESD live previewing (showing TAESD decode of x0 at each step) in diffusers?
I'd prefer to have sample code demonstrating TAESD previewing + SD-VAE final decode (instead of no previewing + TAESD final decode)... but I also don't want to make the sample code super messy 🥲
madebyollin changed pull request status to merged
