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+# LTX-2 T2V and I2V
+
+> LTX-2 for text-to-video & image-to-video on 1x H200 141GB
+
+## 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`](../../docs/user_guide/examples/online_serving/text_to_video.md)
+- Example guide:
+  [`examples/online_serving/text_to_video/README.md`](../../examples/online_serving/text_to_video/README.md)
+  [`examples/online_serving/image_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
+
+```bash
+vllm serve Lightricks/LTX-2 \
+  --omni \
+  --model-class-name LTX2Pipeline
+```
+
+#### Verification
+
+```bash
+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
+
+```bash
+vllm serve Lightricks/LTX-2 \
+  --omni \
+  --model-class-name LTX2ImageToVideoPipeline
+```
+
+#### Verification
+
+```bash
+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.
diff --git a/recipes/README.md b/recipes/README.md
index 4e9536b92..d59dfcc91 100644
--- a/recipes/README.md
+++ b/recipes/README.md
@@ -29,6 +29,8 @@ recipes/
   Qwen-Image on `1x A100 80GB`, including optional step-wise continuous batching replay
 - [`Qwen/Qwen3-Omni.md`](./Qwen/Qwen3-Omni.md): online serving recipe for
   multimodal chat on `1x A100 80GB`
+- [`LTX/LTX-2.md`](./LTX/LTX-2.md): text-to-video and image-to-video serving
+  recipe for LTX-2 on `1x H200 141GB`
 - [`Wan-AI/Wan2.2-I2V.md`](./Wan-AI/Wan2.2-I2V.md): image-to-video serving
   recipe for Wan2.2 14B on `8x Ascend NPU (A2/A3)`
 - [`Tencent-Hunyuan/HunyuanImage-3.0-Instruct.md`](./Tencent-Hunyuan/HunyuanImage-3.0-Instruct.md):