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[SD3] TAESD3 docs (#8607)

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Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
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Álvaro Somoza
2024-06-18 10:56:38 -04:00
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docs/source/en/api/pipelines/stable_diffusion/stable_diffusion_3.md
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@@ -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
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.