Instructions to use Lightricks/LTX-2.3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Lightricks/LTX-2.3 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-2.3", 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
LTX-2.3 is Great — But the Docs & ComfyUI Examples Need Work
Just want to vent a little — LTX-2/LTX-2.3 is a great model, but the Docs + ComfyUI examples are somewhat... lacking. The Example Workflows should include sample inputs and prompts, and the ComfyUI examples should highlight specific features with use-case demonstrations. Right now, some features you know exist, but it's unclear what they actually do or how to use them. Also, the LoRA examples really should come with visual reference images.
Also, what direction will LTX-2.5 take — will it go the route of simply scaling up the parameter size like 2.3 did, or will it take a new approach like Wan2.2 (just wanted to bring it up) which uses a MoE (Mixture of Experts) architecture split into 2 experts: one handling high-noise stages controlling motion, objects, and anatomy, and another handling low-noise stages for refinement and fine detail — (essentially the high/low noise experts are Wan2.1 models each handled by a separate model) — or will it go in a completely different direction altogether? (Curious what others think about this)
It is a free and open source model released two days ago. Feel free to extend documentation and add more examples where you feel it's lacking. Great way to get into open source. Thats what community is about.