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[docs] minor stuff to ltx video docs. #10229

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merged 2 commits into from
Dec 16, 2024
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[docs] minor stuff to ltx video docs. #10229

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ad383ca
minor stuff to ltx video docs.
sayakpaul Dec 16, 2024
71d53eb
Merge branch 'main' into ltx-docs
sayakpaul Dec 16, 2024
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24 changes: 18 additions & 6 deletions docs/source/en/api/pipelines/ltx_video.md
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Original file line number Diff line number Diff line change
Expand Up @@ -31,26 +31,38 @@ import torch
from diffusers import AutoencoderKLLTXVideo, LTXImageToVideoPipeline, LTXVideoTransformer3DModel

single_file_url = "https://huggingface.co/Lightricks/LTX-Video/ltx-video-2b-v0.9.safetensors"
transformer = LTXVideoTransformer3DModel.from_single_file(single_file_url, torch_dtype=torch.bfloat16)
transformer = LTXVideoTransformer3DModel.from_single_file(
single_file_url, torch_dtype=torch.bfloat16
)
vae = AutoencoderKLLTXVideo.from_single_file(single_file_url, torch_dtype=torch.bfloat16)
pipe = LTXImageToVideoPipeline.from_pretrained("Lightricks/LTX-Video", transformer=transformer, vae=vae, torch_dtype=torch.bfloat16)
pipe = LTXImageToVideoPipeline.from_pretrained(
"Lightricks/LTX-Video", transformer=transformer, vae=vae, torch_dtype=torch.bfloat16
)

# ... inference code ...
```

Alternatively, the pipeline can be used to load the weights with [~FromSingleFileMixin.from_single_file`].
Alternatively, the pipeline can be used to load the weights with [`~FromSingleFileMixin.from_single_file`].

```python
import torch
from diffusers import LTXImageToVideoPipeline
from transformers import T5EncoderModel, T5Tokenizer

single_file_url = "https://huggingface.co/Lightricks/LTX-Video/ltx-video-2b-v0.9.safetensors"
text_encoder = T5EncoderModel.from_pretrained("Lightricks/LTX-Video", subfolder="text_encoder", torch_dtype=torch.bfloat16)
tokenizer = T5Tokenizer.from_pretrained("Lightricks/LTX-Video", subfolder="tokenizer", torch_dtype=torch.bfloat16)
pipe = LTXImageToVideoPipeline.from_single_file(single_file_url, text_encoder=text_encoder, tokenizer=tokenizer, torch_dtype=torch.bfloat16)
text_encoder = T5EncoderModel.from_pretrained(
"Lightricks/LTX-Video", subfolder="text_encoder", torch_dtype=torch.bfloat16
)
tokenizer = T5Tokenizer.from_pretrained(
"Lightricks/LTX-Video", subfolder="tokenizer", torch_dtype=torch.bfloat16
)
pipe = LTXImageToVideoPipeline.from_single_file(
single_file_url, text_encoder=text_encoder, tokenizer=tokenizer, torch_dtype=torch.bfloat16
)
```

Refer to [this section](https://huggingface.co/docs/diffusers/main/en/api/pipelines/cogvideox#memory-optimization) to learn more about optimizing memory consumption.

## LTXPipeline

[[autodoc]] LTXPipeline
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