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
Delete vae/vae_config.json
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vae/vae_config.json
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{"_class_name": "CausalVideoAutoencoder", "dims": 3, "in_channels": 3, "out_channels": 3, "latent_channels": 128, "blocks": [["res_x", 4], ["compress_all", 1], ["res_x_y", 1], ["res_x", 3], ["compress_all", 1], ["res_x_y", 1], ["res_x", 3], ["compress_all", 1], ["res_x", 3], ["res_x", 4]], "scaling_factor": 1.0, "norm_layer": "pixel_norm", "patch_size": 4, "latent_log_var": "uniform", "use_quant_conv": false, "causal_decoder": false}
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