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 config.json
Browse files- vae/config.json +32 -0
vae/config.json
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{
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"_class_name": "AutoencoderKLLTX",
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"_diffusers_version": "0.32.0.dev0",
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"block_out_channels": [
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128,
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256,
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512,
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512
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],
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"decoder_causal": false,
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"encoder_causal": true,
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"in_channels": 3,
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"latent_channels": 128,
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"layers_per_block": [
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4,
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3,
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3,
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3,
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4
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],
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"out_channels": 3,
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"patch_size": 4,
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"patch_size_t": 1,
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"resnet_norm_eps": 1e-06,
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"scaling_factor": 1.0,
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"spatio_temporal_scaling": [
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true,
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true,
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true,
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false
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]
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}
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