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-2-3
ltx-video
ltxv
lightricks
Instructions to use diffusers/LTX-2.3-Distilled-Diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use diffusers/LTX-2.3-Distilled-Diffusers 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("diffusers/LTX-2.3-Distilled-Diffusers", 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
- Xet hash:
- 77b782d089ff83b901ed4d3447353e35e15cab200a15205fbf5f33254744182e
- Size of remote file:
- 1.45 GB
- SHA256:
- 425f0dfa227dee5d0ff3d9720563370810409a439c302ca74f0f944057ce55c5
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.