Instructions to use Lightricks/LTX-Video with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Lightricks/LTX-Video with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image, export_to_video # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Lightricks/LTX-Video", dtype=torch.bfloat16, device_map="cuda") pipe.to("cuda") prompt = "A man with short gray hair plays a red electric guitar." image = load_image( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/guitar-man.png" ) output = pipe(image=image, prompt=prompt).frames[0] export_to_video(output, "output.mp4") - Notebooks
- Google Colab
- Kaggle
Explicit `text-to-video` pipeline tag
Browse filesThis PR explicitly sets `text-to-video` as pipeline_tag instead of getting it inferred by the server. It will help with discoverability + enable the text-to-video inference widget on the model page.
Note that the model is still tagged as both `text-to-video` and `image-to-video` so nothing will break in term of usage.
cc @sbrandeis
README.md
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tags:
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- ltx-video
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- text-to-video
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- image-to-video
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pinned: true
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language:
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- en
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license: other
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# LTX-Video Model Card
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tags:
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- ltx-video
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- image-to-video
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pinned: true
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language:
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- en
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license: other
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pipeline_tag: text-to-video
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---
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# LTX-Video Model Card
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