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MOVA

1. Model Introduction​

MOVA (MOSS Video and Audio) is a foundation model developed by the SII-OpenMOSS Team, designed to break the "silent era" of open-source video generation. Unlike cascaded pipelines that generate sound as an afterthought, MOVA synthesizes video and audio simultaneously in a single inference pass for perfect alignment. It adopts an Asymmetric Dual-Tower Architecture, fusing pre-trained video and audio towers through a bidirectional cross-attention mechanism to maintain tight synchronization between video and audio during generation.

MOVA-360p is suitable for fast inference and resource-constrained environments. MOVA-720p provides higher resolution video generation. Both versions support generating up to 8 seconds of video-audio content.

Key Features:

  • Native Bimodal Generation: Generates high-fidelity video and synchronized audio in a single inference pass, eliminating error accumulation from cascaded pipelines
  • Precise Lip-Sync: Achieves state-of-the-art performance in multilingual lip-synchronization (LSE-D: 7.094, LSE-C: 7.452 with Dual CFG on Verse-Bench Set3)
  • Environment-Aware Sound Effects: Generates corresponding environmental sound effects including physical interaction sounds, ambient sounds, and spatial/textural sound feedback
  • Fully Open-Source: Model weights, inference code, training pipelines, and LoRA fine-tuning scripts are all open-sourced

For more details, please refer to the MOVA-360p HuggingFace page, the MOVA-720p HuggingFace page, the GitHub repository, and the technical report (arXiv).

2. SGLang-diffusion Installation​

SGLang-diffusion offers multiple installation methods. You can choose the most suitable installation method based on your hardware platform and requirements.

Please refer to the official SGLang-diffusion installation guide for installation instructions.

3. Model Deployment​

This section provides deployment configurations optimized for different hardware platforms and use cases.

3.1 Basic Configuration​

MOVA supports both online serving and CLI generation modes. The recommended launch configurations vary by hardware and resolution.

Interactive Command Generator: Use the configuration selector below to automatically generate the appropriate deployment command for your hardware platform.

Hardware Platform
Resolution
Generated Command
export SG_OUTPUT_DIR=/root/output_mova
mkdir -p "$SG_OUTPUT_DIR"

sglang serve \
  --model-path OpenMOSS-Team/MOVA-360p \
  --host 0.0.0.0 \
  --port 30002 \
  --adjust-frames false \
  --num-gpus 8 \
  --ring-degree 2 \
  --ulysses-degree 4 \
  --tp 1 \
  --enable-torch-compile \
  --save-output \
  --output-dir "$SG_OUTPUT_DIR"

3.2 Configuration Tips​

Current supported optimization all listed here.

  • --num-gpus: Number of GPUs to use
  • --tp: Tensor parallelism size (should not be larger than 1 if text encoder offload is enabled, as layer-wise offload plus prefetch is faster)
  • --ring-degree: The degree of ring attention-style SP in USP
  • --ulysses-degree: The degree of DeepSpeed-Ulysses-style SP in USP
  • --adjust-frames: Whether to adjust frames automatically (set to false for MOVA)
  • --enable-torch-compile: Enable torch.compile for faster inference

4. API Usage​

For complete API documentation, please refer to the official API usage guide.

4.1 CLI Generation (sglang generate)​

sglang generate \
  --model-path OpenMOSS-Team/MOVA-720p \
  --prompt "A man in a blue blazer and glasses speaks in a formal indoor setting, \
  framed by wooden furniture and a filled bookshelf. \
  Quiet room acoustics underscore his measured tone as he delivers his remarks. \
  At one point, he says, \"I would also believe that this advance in AI recently wasn't unexpected.\"" \
  --image-path "<YOUR-IMAGE-PATH>" \
  --adjust-frames false \
  --num-gpus 8 \
  --ring-degree 2 \
  --ulysses-degree 4 \
  --num-frames 193 \
  --fps 24 \
  --seed 67 \
  --num-inference-steps 25 \
  --enable-torch-compile \
  --save-output

4.2 Generate a Video​

curl -X POST "http://0.0.0.0:30002/v1/videos" \
  -F "prompt=A man in a blue blazer and glasses speaks in a formal indoor setting, framed by wooden furniture and a filled bookshelf. Quiet room acoustics underscore his measured tone as he delivers his remarks. At one point, he says, \"I would also believe that this advance in AI recently wasn't unexpected.\"" \
  -F "input_reference=@<YOUR-IMAGE-PATH>" \
  -F "size=640x352" \
  -F "num_frames=193" \
  -F "fps=24" \
  -F "seed=67" \
  -F "guidance_scale=5.0" \
  -F "num_inference_steps=25" \
  -o create_video.json

4.3 Advanced Usage​

4.3.1 Cache-DiT Acceleration​

SGLang integrates Cache-DiT, a caching acceleration engine for Diffusion Transformers (DiT), to achieve up to 7.4x inference speedup with minimal quality loss. You can set SGLANG_CACHE_DIT_ENABLED=True to enable it. For more details, please refer to the SGLang Cache-DiT documentation.

Basic Usage

SGLANG_CACHE_DIT_ENABLED=true sglang serve --model-path OpenMOSS-Team/MOVA-720p

Advanced Usage

  • DBCache Parameters: DBCache controls block-level caching behavior:

    ParameterEnv VariableDefaultDescription
    FnSGLANG_CACHE_DIT_FN1Number of first blocks to always compute
    BnSGLANG_CACHE_DIT_BN0Number of last blocks to always compute
    WSGLANG_CACHE_DIT_WARMUP4Warmup steps before caching starts
    RSGLANG_CACHE_DIT_RDT0.24Residual difference threshold
    MCSGLANG_CACHE_DIT_MC3Maximum continuous cached steps

  • TaylorSeer Configuration: TaylorSeer improves caching accuracy using Taylor expansion:

    ParameterEnv VariableDefaultDescription
    EnableSGLANG_CACHE_DIT_TAYLORSEERfalseEnable TaylorSeer calibrator
    OrderSGLANG_CACHE_DIT_TS_ORDER1Taylor expansion order (1 or 2)

    Combined Configuration Example:

SGLANG_CACHE_DIT_ENABLED=true \
SGLANG_CACHE_DIT_FN=2 \
SGLANG_CACHE_DIT_BN=1 \
SGLANG_CACHE_DIT_WARMUP=4 \
SGLANG_CACHE_DIT_RDT=0.4 \
SGLANG_CACHE_DIT_MC=4 \
SGLANG_CACHE_DIT_TAYLORSEER=true \
SGLANG_CACHE_DIT_TS_ORDER=2 \
sglang serve --model-path OpenMOSS-Team/MOVA-720p

4.3.2 CPU Offload​

  • --dit-cpu-offload: Use CPU offload for DiT inference. Enable if run out of memory.
  • --text-encoder-cpu-offload: Use CPU offload for text encoder inference.
  • --vae-cpu-offload: Use CPU offload for VAE.
  • --pin-cpu-memory: Pin memory for CPU offload. Only added as a temp workaround if it throws "CUDA error: invalid argument".

5. Benchmark​

5.1 Speedup Benchmark​

5.1.1 Generate a video​

Test Environment:

  • Hardware: NVIDIA H200 x 8
  • git revision: 443b1a8
  • Model: OpenMOSS-Team/MOVA-720p

Server Command:

sglang serve --model-path OpenMOSS-Team/MOVA-720p --port 30002 \
  --adjust-frames false --num-gpus 8 --ring-degree 2 --ulysses-degree 4 \
  --tp 1 --enable-torch-compile

Benchmark Command:

python3 -m sglang.multimodal_gen.benchmarks.bench_serving \
    --task image-to-video --dataset vbench --num-prompts 1 --max-concurrency 1 \
    --port 30002

Result:

================= Serving Benchmark Result =================
Task:                                    image-to-video
Model:                                   OpenMOSS-Team/MOVA-720p
Dataset:                                 vbench
--------------------------------------------------
Benchmark duration (s):                  590.76
Request rate:                            inf
Max request concurrency:                 1
Successful requests:                     1/1
--------------------------------------------------
Request throughput (req/s):              0.00
Latency Mean (s):                        590.7549
Latency Median (s):                      590.7549
Latency P99 (s):                         590.7549
--------------------------------------------------
Peak Memory Max (MB):                    74996.00
Peak Memory Mean (MB):                   74996.00
Peak Memory Median (MB):                 74996.00
============================================================

5.1.2 Generate videos with high concurrency​

Server Command:

sglang serve --model-path OpenMOSS-Team/MOVA-720p --port 30002 \
  --adjust-frames false --num-gpus 8 --ring-degree 2 --ulysses-degree 4 \
  --tp 1 --enable-torch-compile

Benchmark Command:

python3 -m sglang.multimodal_gen.benchmarks.bench_serving \
    --task image-to-video --dataset vbench --num-prompts 20 --max-concurrency 20 \
    --port 30002