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
Create schduler_config.json
Browse files
scheduler/schduler_config.json
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{
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"_class_name": "FlowMatchEulerDiscreteScheduler",
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"_diffusers_version": "0.32.0.dev0",
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"base_image_seq_len": 1024,
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"base_shift": 0.95,
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"invert_sigmas": false,
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"max_image_seq_len": 4096,
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"max_shift": 2.05,
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"num_train_timesteps": 1000,
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"shift": 1.0,
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"shift_terminal": 0.1,
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"use_beta_sigmas": false,
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"use_dynamic_shifting": true,
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"use_exponential_sigmas": false,
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"use_karras_sigmas": false
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}
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