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") - Inference
- Notebooks
- Google Colab
- Kaggle
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README.md
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license: other
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library_name: diffusers
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---
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# LTX-Video Model Card
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# Part 4. Downscale the video to the expected resolution
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video = [frame.resize((expected_width, expected_height)) for frame in video]
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export_to_video(video, "output.mp4", fps=
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```
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To learn more, check out the [official documentation](https://huggingface.co/docs/diffusers/main/en/api/pipelines/ltx_video).
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license: other
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library_name: diffusers
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base_model:
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- Qwen/Qwen-Image-Edit
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---
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# LTX-Video Model Card
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# Part 4. Downscale the video to the expected resolution
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video = [frame.resize((expected_width, expected_height)) for frame in video]
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export_to_video(video, "output.mp4", fps=48)
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```
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To learn more, check out the [official documentation](https://huggingface.co/docs/diffusers/main/en/api/pipelines/ltx_video).
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