| license: mit | |
| # 🍰 Tiny AutoEncoder for Stable Diffusion | |
| [TAESD](https://github.com/madebyollin/taesd) is very tiny autoencoder which uses the same "latent API" as Stable Diffusion's VAE. | |
| TAESD is useful for [real-time previewing](https://twitter.com/madebyollin/status/1679356448655163394) of the SD generation process. | |
| This repo contains `.safetensors` versions of the TAESD weights. | |
| For SDXL, use [TAESDXL](https://huggingface.co/madebyollin/taesdxl/) instead (the SD and SDXL VAEs are [incompatible](https://huggingface.co/madebyollin/sdxl-vae-fp16-fix/discussions/6#64b8a9c13707b7d603c6ac16)). | |
| ## Using in 🧨 diffusers | |
| ```python | |
| import torch | |
| from diffusers import DiffusionPipeline, AutoencoderTiny | |
| pipe = DiffusionPipeline.from_pretrained( | |
| "stabilityai/stable-diffusion-2-1-base", torch_dtype=torch.float16 | |
| ) | |
| pipe.vae = AutoencoderTiny.from_pretrained("madebyollin/taesd", torch_dtype=torch.float16) | |
| pipe = pipe.to("cuda") | |
| prompt = "slice of delicious New York-style cheesecake topped with berries, mint, chocolate crumble" | |
| image = pipe(prompt, num_inference_steps=50, generator=torch.Generator("cpu").manual_seed(0x7A35D)).images[0] | |
| image.save("cheesecake.png") | |
| ``` | |
|  |