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 nsagar05/LTX-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use nsagar05/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("nsagar05/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") - Notebooks
- Google Colab
- Kaggle
Download tokenizer/processor_config.json from nsagar05/LTX-2: direct link, hf CLI and curl.
- Browser
- Download file 70 Bytes
-
https://huggingface.co/nsagar05/LTX-2/resolve/main/tokenizer/processor_config.json
- Command line
-
hf download hf://nsagar05/LTX-2/tokenizer/processor_config.json
-
curl -L -o processor_config.json https://huggingface.co/nsagar05/LTX-2/resolve/main/tokenizer/processor_config.json
70 Bytes
| { | |
| "image_seq_length": 256, | |
| "processor_class": "Gemma3Processor" | |
| } | |