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

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minor stuff to ltx video docs.
This commit is contained in:
Sayak Paul
2024-12-16 12:24:14 +05:30
committed by GitHub
parent 3bf5400a64
commit e68092a471
1 changed files with 18 additions and 6 deletions
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24
docs/source/en/api/pipelines/ltx_video.md
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@@ -31,14 +31,18 @@ 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
@@ -46,11 +50,19 @@ 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