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
Add latent upsampler to checkpoint
Browse files
latent_upsampler/config.json
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{
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"_class_name": "LTX2LatentUpsamplerModel",
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"_diffusers_version": "0.
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"dims": 3,
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"in_channels": 128,
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"mid_channels": 1024,
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{
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"_class_name": "LTX2LatentUpsamplerModel",
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"_diffusers_version": "0.37.0.dev0",
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"dims": 3,
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"in_channels": 128,
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"mid_channels": 1024,
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