Instructions to use Disty0/sd3_vae_anime_ft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Disty0/sd3_vae_anime_ft with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Disty0/sd3_vae_anime_ft", 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
- Local Apps Settings
- Draw Things
- DiffusionBee
Create README.md
Browse filesThis VAE was finetuned on PNG only anime illustrations for 512 steps.
Used fp32 weights + fp16 mixed precision with learning rate 4e-6 and effective batch size of 16.
This training was to test my VAE decoder training code and 512 step model turned out to be better than i expected and fixes the color shifting issues of the original SD3 VAE pretty well.
I stopped messing with SD3 after a while but i decided to release this VAE finetune instead of deleting it.
Original Image:

Original SD3 VAE:

Anime VAE Finetune:
