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
Release for Diffusers?
Will there be no official and dedicated release for Diffusers?
Yeah, we need that for fine tuning.
Actually, it's looking very much like 2.0 trained LoRAs are working much, much better on 2.3, to the degree that something is actually usable on 2.3 when it's impossible to train further on 2.0.
Actually, it's looking very much like 2.0 trained LoRAs are working much, much better on 2.3, to the degree that something is actually usable on 2.3 when it's impossible to train further on 2.0.
I just tried and you're right. I actually had to turn down the strength a lot on inference. Would love to see a comparison though.
But how to inference using diffuser
But how to inference using diffuser
How do you mean? Just use the Diffusers lib.
I don't think the diffuser yet support ltx 2.3 or does it I check last week
I don't think the diffuser yet support ltx 2.3 or does it I check last week
Yep, it's taking "forever": https://github.com/huggingface/diffusers/pull/13217
I don't think the diffuser yet support ltx 2.3 or does it I check last week
Yep, it's taking "forever": https://github.com/huggingface/diffusers/pull/13217
Yeah that pr is not yet merged