Image-to-Video
Diffusers
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-2-3
ltx-video
ltxv
lightricks
Eval Results
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
How to deploy LTX2.3 as a service in ubuntu system?
#55
by danielHang - opened
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.
rluxemburg changed discussion status to closed