Hongsheng Liuanddeepseek-v4-pro
c0e132d973
Signed-off-by: hsliu <liuhongsheng4@huawei.com> Co-authored-by: deepseek-v4-pro <noreply@anthropic.com>
122 lines
3.6 KiB
Markdown
122 lines
3.6 KiB
Markdown
# LTX-2
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> Text-to-video and image-to-video serving
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## Summary
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- Vendor: Lightricks
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- Model: `Lightricks/LTX-2`
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- Task: Text-to-video generation and image-to-video generation
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- Mode: Online serving with the OpenAI-compatible video API
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- Maintainer: Community
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## When to use this recipe
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Use this recipe when you want to deploy the base `Lightricks/LTX-2` model
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for both text-to-video and image-to-video generation. The same model ID is used for
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both tasks; switch the pipeline class with `--model-class-name` depending on
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whether you want T2V or I2V.
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## References
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- Model: <https://huggingface.co/Lightricks/LTX-2>
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- User guide:
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[`docs/user_guide/examples/online_serving/text_to_video.md`](../../docs/user_guide/examples/online_serving/text_to_video.md)
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- Example guide:
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[`examples/online_serving/text_to_video/README.md`](../../examples/online_serving/text_to_video/README.md)
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[`examples/online_serving/image_to_video/README.md`](../../examples/online_serving/image_to_video/README.md)
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## Hardware Support
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## GPU
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### 1x H200 141GB (Text-to-Video)
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#### Environment
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- OS: Ubuntu 22.04.5 LTS
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- Python: 3.12+
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- Driver / runtime: NVIDIA CUDA environment with H200 141GB GPU
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- vLLM version: Match the repository requirements from your current checkout
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- vLLM-Omni version or commit: Use the commit you are deploying from
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#### Command
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```bash
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vllm serve Lightricks/LTX-2 \
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--omni \
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--model-class-name LTX2Pipeline
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```
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#### Verification
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```bash
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curl http://localhost:8000/health
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curl -X POST http://localhost:8000/v1/videos \
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-F "prompt=A cinematic close-up of ocean waves at golden hour." \
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-F "negative_prompt=worst quality, inconsistent motion, blurry, jittery, distorted" \
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-F "width=512" \
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-F "height=768" \
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-F "num_frames=121" \
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-F "fps=24" \
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-F "num_inference_steps=40" \
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-F "guidance_scale=4.0" \
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-o ltx2_t2v_h200.mp4
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```
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#### Notes
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- Memory usage: Model loads at ~73.5GB, peaks at ~73.5 GiB during inference.
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- Key flags:
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- `--model-class-name LTX2Pipeline` selects the text-to-video pipeline explicitly.
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- Known limitations:
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- This recipe is tested on 1x H200 validation; add measured latency and VRAM after running it on your target machine.
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### 1x H200 141GB (Image-to-Video)
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#### Environment
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- OS: Ubuntu 22.04.5 LTS
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- Python: 3.12+
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- Driver / runtime: NVIDIA CUDA environment with H200 141GB GPU
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- vLLM version: Match the repository requirements from your current checkout
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- vLLM-Omni version or commit: Use the commit you are deploying from
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#### Command
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```bash
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vllm serve Lightricks/LTX-2 \
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--omni \
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--model-class-name LTX2ImageToVideoPipeline
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```
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#### Verification
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```bash
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curl http://localhost:8000/health
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curl -X POST http://localhost:8000/v1/videos/sync \
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-F "prompt=A plush toy astronaut gently waving while the camera slowly pushes in." \
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-F "input_reference=@/absolute/path/to/reference.png" \
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-F "width=512" \
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-F "height=768" \
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-F "num_frames=121" \
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-F "fps=24" \
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-F "num_inference_steps=40" \
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-F "guidance_scale=4.0" \
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-o ltx2_i2v_h200.mp4
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```
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#### Notes
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- Memory usage: Model loads at ~73.5GB, peaks at ~73.5 GiB during inference.
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- Key flags:
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- `--model-class-name LTX2ImageToVideoPipeline` switches the same `Lightricks/LTX-2` weights into image-to-video mode.
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- `input_reference=@...` uploads the reference image directly
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- Known limitations:
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- Use `image_reference` when you want to pass a URL or JSON-safe image reference
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instead of uploading a file.
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- Do not send `input_reference` and `image_reference` in the same request.
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- This recipe is tested on 1x H200 validation; add measured latency and VRAM after running it on your target machine.
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