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
How was TAESD trained?
#4
by thomaseding - opened
If it's not too much trouble to answer, I'm very curious how your TAESD was trained. For example was it entirely from scratch (and with what dataset) or did you somehow slurp trained information from the original SD VAE?
I added some basic information on GitHub here https://github.com/madebyollin/taesd/issues/11
(Decoder is trained from-scratch on a mix of laion and danbooru data; encoder is distilled from the original VAE encoder)
Thank you for the reply. Much appreciated
thomaseding changed discussion status to closed