MOVA - SGLang Documentation

1. Model Introduction

MOVA generates video and audio together with an asymmetric dual-tower model connected by bidirectional cross-attention. Its strongest use cases are speaking subjects, visible sound-producing events, and scenes where ambient audio must track the picture rather than be synthesized by a later cascade. The public 360p and 720p checkpoints both generate up to 8 seconds. Choose 360p for the lighter deployment and 720p for output resolution; MOVA is less suitable when the task needs long-form continuity or the richer image/video/audio reference conditioning provided by H3.

Checkpoint	Best fit	Output limit
`OpenMOSS-Team/MOVA-360p`	Faster, lower-memory joint audiovisual generation	Up to 8 seconds at 360p
`OpenMOSS-Team/MOVA-720p`	Higher-resolution lip-sync and environment audio	Up to 8 seconds at 720p

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.

3.2 Configuration Tips

See Performance Optimization for acceleration features and their runtime requirements.

--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)

Command

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

Command

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

Command

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

Advanced Usage

DBCache Parameters: DBCache controls block-level caching behavior:

Parameter	Env Variable	Default	Description
Fn	`SGLANG_CACHE_DIT_FN`	1	Number of first blocks to always compute
Bn	`SGLANG_CACHE_DIT_BN`	0	Number of last blocks to always compute
W	`SGLANG_CACHE_DIT_WARMUP`	4	Warmup steps before caching starts
R	`SGLANG_CACHE_DIT_RDT`	0.24	Residual difference threshold
MC	`SGLANG_CACHE_DIT_MC`	3	Maximum continuous cached steps

TaylorSeer Configuration: TaylorSeer improves caching accuracy using Taylor expansion:

Parameter	Env Variable	Default	Description
Enable	`SGLANG_CACHE_DIT_TAYLORSEER`	false	Enable TaylorSeer calibrator
Order	`SGLANG_CACHE_DIT_TS_ORDER`	1	Taylor expansion order (1 or 2)

Combined Configuration Example:

Command

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:

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:

Command

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

Result:

Output

================= 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:

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:

Command

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

​1. Model Introduction

​2. SGLang-diffusion Installation

​3. Model Deployment

​3.1 Basic Configuration

​3.2 Configuration Tips

​4. API Usage

​4.1 CLI Generation (sglang generate)

​4.2 Generate a Video

​4.3 Advanced Usage

​4.3.1 Cache-DiT Acceleration

​4.3.2 CPU Offload

​5. Benchmark

​5.1 Speedup Benchmark

​5.1.1 Generate a video

​5.1.2 Generate videos with high concurrency

1. Model Introduction

2. SGLang-diffusion Installation

3. Model Deployment

3.1 Basic Configuration

3.2 Configuration Tips

4. API Usage

4.1 CLI Generation (sglang generate)

4.2 Generate a Video

4.3 Advanced Usage

4.3.1 Cache-DiT Acceleration

4.3.2 CPU Offload

5. Benchmark

5.1 Speedup Benchmark

5.1.1 Generate a video

5.1.2 Generate videos with high concurrency