Instructions to use Lightricks/LTX-Video-0.9.7-dev with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Lightricks/LTX-Video-0.9.7-dev with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Lightricks/LTX-Video-0.9.7-dev", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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@@ -212,7 +212,7 @@ def round_to_nearest_resolution_acceptable_by_vae(height, width):
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return height, width
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image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/penguin.png")
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video = load_video(export_to_video([image]))
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condition1 = LTXVideoCondition(video=video, frame_index=0)
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prompt = "A cute little penguin takes out a book and starts reading it"
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return height, width
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image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/penguin.png")
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video = load_video(export_to_video([image])) # compress the image using video compression as the model was trained on videos
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condition1 = LTXVideoCondition(video=video, frame_index=0)
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prompt = "A cute little penguin takes out a book and starts reading it"
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