vllm-omni/recipes/LTX/LTX-2.3.md

LTX-2.3

22B parameter text-to-video + audio generation model served via vLLM-Omni

Summary

• Vendor: Lightricks
• Model: dg845/LTX-2.3-Diffusers
• Task: Text-to-video with synchronized audio generation
• Mode: Online serving (pure diffusion)
• Maintainer: @oglok

When to use this recipe

Use this recipe when you want to serve LTX-2.3 for text-to-video generation with audio. The model generates videos up to 20+ seconds at 768x512 resolution with 48kHz audio, all from a single text prompt. Requires a GPU with at least 96GB VRAM due to the 22B parameter transformer (~44GB weights) plus text encoder, VAE, and vocoder components.

References

• Model: https://huggingface.co/dg845/LTX-2.3-Diffusers
• Requires diffusers >= 0.38.0 (install from git: pip install git+https://github.com/huggingface/diffusers.git)

Serving

Command

vllm serve dg845/LTX-2.3-Diffusers \
  --omni \
  --model-class-name LTX23Pipeline \
  --stage-init-timeout 600

Verification

# Health check
curl http://localhost:8000/health

# Generate a 3-second video (81 frames at 24fps)
curl -X POST http://localhost:8000/v1/videos \
  -F "prompt=A majestic bald eagle soaring over a misty mountain valley at dawn, golden sunlight breaking through clouds" \
  -F "negative_prompt=blurry, low quality, distorted, watermark" \
  -F "model=dg845/LTX-2.3-Diffusers" \
  -F "num_frames=81" \
  -F "fps=24" \
  -F "size=768x512" \
  -F "num_inference_steps=30" \
  -F "guidance_scale=4.0" \
  -F "seed=42"

# Generate a 10-second video (241 frames)
curl -X POST http://localhost:8000/v1/videos \
  -F "prompt=A cozy Japanese ramen shop at night in the rain, steam rising from bowls, neon signs reflecting on wet cobblestone streets" \
  -F "model=dg845/LTX-2.3-Diffusers" \
  -F "num_frames=241" \
  -F "fps=24" \
  -F "size=768x512" \
  -F "num_inference_steps=30" \
  -F "guidance_scale=4.0"

# Generate a 20-second video (481 frames)
curl -X POST http://localhost:8000/v1/videos \
  -F "prompt=An underwater coral reef teeming with tropical fish, sea turtles gliding gracefully, National Geographic documentary style" \
  -F "model=dg845/LTX-2.3-Diffusers" \
  -F "num_frames=481" \
  -F "fps=24" \
  -F "size=768x512" \
  -F "num_inference_steps=30" \
  -F "guidance_scale=4.0"

Notes

• Memory usage: Model loads at ~36 GiB, peaks at ~62 GiB during inference
• Key flags:
  • --stage-init-timeout 600: Required for the initial torch.compile warmup (~90-140 seconds on first request)
  • --model-class-name LTX23Pipeline: Selects the LTX-2.3 pipeline (not LTX-2)
• Audio: 48kHz AAC via BWE vocoder, automatically synced with video
• CPU offloading: Text encoder (Gemma-3-12B), connectors, VAE, audio VAE, and vocoder stay on CPU and are moved to GPU only when needed
• Supported resolutions: 768x512, 512x384 (must be divisible by 32)
• Frame rate: 24 fps
• Duration: Controlled by num_frames (frames = duration_seconds * 24 + 1)
• Known limitations:
  • No image-to-video support yet (LTX23ImageToVideoPipeline is a placeholder)
  • No CFG-parallel support (single-GPU only)
  • Requires diffusers >= 0.38.0 (not yet on PyPI, install from git)

Hardware Support

GPU

1x NVIDIA RTX PRO 6000 Blackwell (96GB)

Environment

• OS: Ubuntu 22.04
• Python: 3.10+
• Driver / runtime: CUDA 13.0, Driver 580.126.09
• vLLM version: 0.19.x
• vLLM-Omni version: 0.19.x

Validated configurations

Duration	Frames	Resolution	Steps	Guidance	Inference Time	Peak VRAM
3s	81	768x512	30	4.0	~110s	~62 GB
10s	241	768x512	30	4.0	~130s	~62 GB
20s	481	768x512	30	4.0	~420s	~62 GB