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
File size: 616 Bytes
7bdc1de | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 | {
"_class_name": "LTX2Pipeline",
"_diffusers_version": "0.37.0.dev0",
"audio_vae": [
"diffusers",
"AutoencoderKLLTX2Audio"
],
"connectors": [
"ltx2",
"LTX2TextConnectors"
],
"scheduler": [
"diffusers",
"FlowMatchEulerDiscreteScheduler"
],
"text_encoder": [
"transformers",
"Gemma3ForConditionalGeneration"
],
"tokenizer": [
"transformers",
"GemmaTokenizerFast"
],
"transformer": [
"diffusers",
"LTX2VideoTransformer3DModel"
],
"vae": [
"diffusers",
"AutoencoderKLLTX2Video"
],
"vocoder": [
"ltx2",
"LTX2Vocoder"
]
}
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