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Merged
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Jun 18, 2024
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[SD3] TAESD3 docs #8607

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tased3 docs
asomoza Jun 17, 2024
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asomoza Jun 18, 2024
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Merge branch 'main' into taesd-sd3-doc
sayakpaul Jun 18, 2024
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22 changes: 22 additions & 0 deletions docs/source/en/api/pipelines/stable_diffusion/stable_diffusion_3.md
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Expand Up @@ -197,6 +197,28 @@ image.save("sd3_hello_world.png")

Check out the full script [here](https://gist.github.com/sayakpaul/508d89d7aad4f454900813da5d42ca97).

## Tiny AutoEncoder for Stable Diffusion 3

Tiny AutoEncoder for Stable Diffusion (TAESD3) is a tiny distilled version of Stable Diffusion 3's VAE by [Ollin Boer Bohan](https://github.com/madebyollin/taesd) that can decode [`StableDiffusion3Pipeline`] latents almost instantly.

To use with Stable Diffusion 3:

```python
import torch
from diffusers import StableDiffusion3Pipeline, AutoencoderTiny

pipe = StableDiffusion3Pipeline.from_pretrained(
"stabilityai/stable-diffusion-3-medium-diffusers", torch_dtype=torch.float16
)
pipe.vae = AutoencoderTiny.from_pretrained("madebyollin/taesd3", torch_dtype=torch.float16)
pipe.vae.config.shift_factor = 0.0
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For this line, we might want to fix the underlying incompatibility (AutoencoderTiny doesn't have a shift_factor which SD3 pipeline is surprised by) rather than baking the fix into the docs :)

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That sounds good to me.

How about merging this PR and then I can take care of the fix in another PR?

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@madebyollin madebyollin Jun 18, 2024 •
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Makes sense to me! Just need to update the docs in the fix PR and then we're good to go

pipe = pipe.to("cuda")

prompt = "slice of delicious New York-style berry cheesecake"
image = pipe(prompt, num_inference_steps=25).images[0]
image.save("cheesecake.png")
```

## Loading the original checkpoints via `from_single_file`

The `SD3Transformer2DModel` and `StableDiffusion3Pipeline` classes support loading the original checkpoints via the `from_single_file` method. This method allows you to load the original checkpoint files that were used to train the models.
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