vllm-omni/recipes/LTX/LTX-2.md

LTX-2

Text-to-video and image-to-video serving

Summary

• Vendor: Lightricks
• Model: Lightricks/LTX-2
• Task: Text-to-video generation and image-to-video generation
• Mode: Online serving with the OpenAI-compatible video API
• Maintainer: Community

When to use this recipe

Use this recipe when you want to deploy the base Lightricks/LTX-2 model for both text-to-video and image-to-video generation. The same model ID is used for both tasks; switch the pipeline class with --model-class-name depending on whether you want T2V or I2V.

References

• Model: https://huggingface.co/Lightricks/LTX-2
• User guide: docs/user_guide/examples/online_serving/text_to_video.md
• Example guide: examples/online_serving/text_to_video/README.md examples/online_serving/image_to_video/README.md

Hardware Support

GPU

1x H200 141GB (Text-to-Video)

Environment

• OS: Ubuntu 22.04.5 LTS
• Python: 3.12+
• Driver / runtime: NVIDIA CUDA environment with H200 141GB GPU
• vLLM version: Match the repository requirements from your current checkout
• vLLM-Omni version or commit: Use the commit you are deploying from

Command

vllm serve Lightricks/LTX-2 \
  --omni \
  --model-class-name LTX2Pipeline

Verification

curl http://localhost:8000/health

curl -X POST http://localhost:8000/v1/videos \
  -F "prompt=A cinematic close-up of ocean waves at golden hour." \
  -F "negative_prompt=worst quality, inconsistent motion, blurry, jittery, distorted" \
  -F "width=512" \
  -F "height=768" \
  -F "num_frames=121" \
  -F "fps=24" \
  -F "num_inference_steps=40" \
  -F "guidance_scale=4.0" \
  -o ltx2_t2v_h200.mp4

Notes

• Memory usage: Model loads at ~73.5GB, peaks at ~73.5 GiB during inference.
• Key flags:
  • --model-class-name LTX2Pipeline selects the text-to-video pipeline explicitly.
• Known limitations:
  • This recipe is tested on 1x H200 validation; add measured latency and VRAM after running it on your target machine.

1x H200 141GB (Image-to-Video)

Environment

• OS: Ubuntu 22.04.5 LTS
• Python: 3.12+
• Driver / runtime: NVIDIA CUDA environment with H200 141GB GPU
• vLLM version: Match the repository requirements from your current checkout
• vLLM-Omni version or commit: Use the commit you are deploying from

Command

vllm serve Lightricks/LTX-2 \
  --omni \
  --model-class-name LTX2ImageToVideoPipeline

Verification

curl http://localhost:8000/health

curl -X POST http://localhost:8000/v1/videos/sync \
  -F "prompt=A plush toy astronaut gently waving while the camera slowly pushes in." \
  -F "input_reference=@/absolute/path/to/reference.png" \
  -F "width=512" \
  -F "height=768" \
  -F "num_frames=121" \
  -F "fps=24" \
  -F "num_inference_steps=40" \
  -F "guidance_scale=4.0" \
  -o ltx2_i2v_h200.mp4

Notes

• Memory usage: Model loads at ~73.5GB, peaks at ~73.5 GiB during inference.
• Key flags:
  • --model-class-name LTX2ImageToVideoPipeline switches the same Lightricks/LTX-2 weights into image-to-video mode.
  • input_reference=@... uploads the reference image directly
• Known limitations:
  • Use image_reference when you want to pass a URL or JSON-safe image reference instead of uploading a file.
  • Do not send input_reference and image_reference in the same request.
  • This recipe is tested on 1x H200 validation; add measured latency and VRAM after running it on your target machine.