Image-to-Video
Diffusers
Safetensors
LTX2Pipeline
text-to-video
video-to-video
image-text-to-video
audio-to-video
text-to-audio
video-to-audio
audio-to-audio
text-to-audio-video
image-to-audio-video
image-text-to-audio-video
ltx-2
ltx-video
ltxv
lightricks
Instructions to use Lightricks/LTX-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Lightricks/LTX-2 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-2", 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") - Inference
- Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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# Train the model
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The base (dev) model is fully trainable.
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It's extremely easy to reproduce the LoRAs and IC-LoRAs we publish with the model by following the instructions on the [LTX-2 Trainer Readme](https://github.com/Lightricks/LTX-2/blob/main/packages/ltx-trainer/README.md).
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Training for motion, style or likeness (sound+appearance) can take less than an hour in many settings.
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# Train the model
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The base (dev) model is fully trainable.
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It's extremely easy to reproduce the LoRAs and IC-LoRAs we publish with the model by following the instructions on the [LTX-2 Trainer Readme](https://github.com/Lightricks/LTX-2/blob/main/packages/ltx-trainer/README.md).
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Training for motion, style or likeness (sound+appearance) can take less than an hour in many settings.
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