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