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") - LTX.io
How to use Lightricks/LTX-2.3 with LTX.io:
# Install the LTX-2 pipelines git clone https://github.com/Lightricks/LTX-2.git cd LTX-2 uv sync --frozen
# Download the weights from this repo, plus the Gemma text encoder hf download Lightricks/LTX-2.3 --local-dir models/LTX-2.3 hf download google/gemma-3-12b-it-qat-q4_0-unquantized --local-dir models/gemma-3-12b
# Fast pipeline (distilled model, no distilled LoRA needed) uv run python -m ltx_pipelines.distilled \ --distilled-checkpoint-path models/LTX-2.3/<distilled-checkpoint>.safetensors \ --spatial-upsampler-path models/LTX-2.3/<spatial-upsampler>.safetensors \ --gemma-root models/gemma-3-12b \ --prompt "A beautiful sunset over the ocean" \ --output-path output.mp4 # For image-to-video, add: --image path/to/image.jpg 0 0.8# HQ pipeline (two-stage, higher quality) uv run python -m ltx_pipelines.ti2vid_two_stages_hq \ --checkpoint-path models/LTX-2.3/<checkpoint>.safetensors \ --distilled-lora models/LTX-2.3/<distilled-lora>.safetensors 0.8 \ --spatial-upsampler-path models/LTX-2.3/<spatial-upsampler>.safetensors \ --gemma-root models/gemma-3-12b \ --prompt "A beautiful sunset over the ocean" \ --output-path output.mp4 # For image-to-video, add: --image path/to/image.jpg 0 0.8 - Notebooks
- Google Colab
- Kaggle
How to deploy LTX2.3 as a service in ubuntu system?
I use SGLang to deploy LTX2.3, but do I need the configuration files of LTX2.3 when starting up, such as model_index.json, etc.?
How much memory is needed? I have 64G of memory and 64GB of swap memory. However, the error -9 is still reported during startup. Does this mean there is insufficient memory? Is there an official document? I'm not using comfyui to deploy, I want to deploy it as a service
We don't have official documentation for SGLang support. If there's enough demand we can look at adding that.
Error -9 is an OOM so yes, that's not enough memory. Which version of the model checkpoint are you using? If it's not Lightricks/LTX-2.3-fp8, give that a try.
It's Lightricks/LTX-2.3-fp8. Even if SG-Lang is not applicable, the normal startup error is -9 Not enough memory. I use 64G memory + 100G swap space, 24G video memory, and still get error -9. It cannot be started without SG-Lang. Does LTX2.3 need to use the model_index.json of LTX2.0 and some other basic things, such as audio_vae, connectors, text_encoder, tokenizer, etc. I really can’t find a way to use pyhton3 to run LTX2.3 directly on the Internet. Can you give me a tutorial?
This is what I did. I downloaded the basic version of LTX2.0, excluding the weight file, and then used the weight file of Lightricks/LTX-2.3-fp8 to put it in the root directory. Is this right?