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
| { | |
| "_class_name": "LTXVideoTransformer3DModel", | |
| "_diffusers_version": "0.32.0.dev0", | |
| "activation_fn": "gelu-approximate", | |
| "attention_bias": true, | |
| "attention_head_dim": 64, | |
| "attention_out_bias": true, | |
| "caption_channels": 4096, | |
| "cross_attention_dim": 2048, | |
| "in_channels": 128, | |
| "norm_elementwise_affine": false, | |
| "norm_eps": 1e-06, | |
| "num_attention_heads": 32, | |
| "num_layers": 28, | |
| "out_channels": 128, | |
| "patch_size": 1, | |
| "patch_size_t": 1, | |
| "qk_norm": "rms_norm_across_heads" | |
| } | |