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[Docs] fix: minor formatting in the Würstchen docs (#4965)

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fix: minor formatting in the docs
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Sayak Paul
2023-09-11 12:42:53 +05:30
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parent 4191ddee11
commit 88735249da
1 changed files with 4 additions and 2 deletions
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6
docs/source/en/api/pipelines/wuerstchen.md
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@@ -18,6 +18,7 @@ After the initial paper release, we have improved numerous things in the archite
- Better quality
We are releasing 3 checkpoints for the text-conditional image generation model (Stage C). Those are:
- v2-base
- v2-aesthetic
- v2-interpolated (50% interpolation between v2-base and v2-aesthetic)
@@ -58,7 +59,7 @@ output = pipeline(
).images
```
For explanation purposes, we can also initialize the two main pipelines of Würstchen individually. Würstchen consists of 3 stages: Stage C, Stage B, Stage A. They all have different jobs and work only together. When generating text-conditional images, Stage C will first generate the latents in a very compressed latent space. This is what happens in the `prior_pipeline`. Afterwards, the generated latents will be passed to Stage B, which decompresses the latents into a bigger latent space of a VQGAN. These latents can then be decoded by Stage A, which is a VQGAN, into the pixel-space. Stage B & Stage A are both encapsulated in the `decoder_pipeline`. For more details, take a look the [paper](https://huggingface.co/papers/2306.00637).
For explanation purposes, we can also initialize the two main pipelines of Würstchen individually. Würstchen consists of 3 stages: Stage C, Stage B, Stage A. They all have different jobs and work only together. When generating text-conditional images, Stage C will first generate the latents in a very compressed latent space. This is what happens in the `prior_pipeline`. Afterwards, the generated latents will be passed to Stage B, which decompresses the latents into a bigger latent space of a VQGAN. These latents can then be decoded by Stage A, which is a VQGAN, into the pixel-space. Stage B & Stage A are both encapsulated in the `decoder_pipeline`. For more details, take a look at the [paper](https://huggingface.co/papers/2306.00637).
```python
import torch
@@ -97,7 +98,7 @@ decoder_output = decoder_pipeline(
```
## Speed-Up Inference
You can make use of ``torch.compile`` function and gain a speed-up of about 2-3x:
You can make use of `torch.compile` function and gain a speed-up of about 2-3x:
```python
pipeline.prior = torch.compile(pipeline.prior, mode="reduce-overhead", fullgraph=True)
@@ -105,6 +106,7 @@ pipeline.decoder = torch.compile(pipeline.decoder, mode="reduce-overhead", fullg
```
## Limitations
- Due to the high compression employed by Würstchen, generations can lack a good amount
of detail. To our human eye, this is especially noticeable in faces, hands etc.
- **Images can only be generated in 128-pixel steps**, e.g. the next higher resolution