Wan2.1 - SGLang Documentation

1. Model Introduction

Wan2.1 is a broad open video family covering text-to-video and image-to-video across 1.3B and 14B checkpoints. Its practical strengths are motion-rich generation, temporal consistency, and readable Chinese/English text, with 480p and 720p variants for different quality and memory targets. Choose the 1.3B T2V model for consumer-GPU experiments and the 14B models when quality matters more than footprint. Wan2.1 is a dense DiT family; for timestep-specialized MoE capacity or the unified 5B TI2V path, use Wan2.2 instead.

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

The Wan2.1 series offers models in multiple sizes and resolutions. SGLang supports Wan2.1 deployment on NVIDIA B200, B300, H200, H100, and AMD MI300X, MI325X, MI355X GPUs and Ascend A2/A3 Series NPUs. The recommended launch configurations vary by hardware, model size, and memory headroom. Interactive Command Generator: Use the configuration selector below to automatically generate an appropriate deployment command for your model variant and options.

3.2 Configuration Tips

Current supported optimization options are listed in the SGLang diffusion support matrix.

--vae-path: Path to a custom VAE model or HuggingFace model ID. If not specified, the VAE will be loaded from the main model path.
--num-gpus {NUM_GPUS}: Number of GPUs to use.
--tp-size {TP_SIZE}: Tensor parallelism size (for the encoder/DiT; keep (\leq 1) if relying heavily on CPU offload).
--sp-degree {SP_SIZE}: Sequence parallelism degree.
--ulysses-degree {ULYSSES_DEGREE}: Degree of DeepSpeed-Ulysses-style SP in USP.
--ring-degree {RING_DEGREE}: Degree of ring attention-style SP in USP.
--text-encoder-cpu-offload, --dit-cpu-offload, --vae-cpu-offload: Use CPU offload to reduce peak GPU memory when needed.

4. Model Invocation

4.1 Basic Usage

For more API usage and request examples, please refer to: SGLang Diffusion OpenAI API

4.1.1 Launch a server and then send requests

Command

sglang serve --model-path Wan-AI/Wan2.1-T2V-14B-Diffusers --port 30000

curl http://127.0.0.1:30000/v1/images/generations \
  -o >(jq -r '.data[0].b64_json' | base64 --decode > example.png) \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -d '{
    "model": "Wan-AI/Wan2.1-T2V-14B-Diffusers",
    "prompt": "A cute baby sea otter",
    "n": 1,
    "size": "1024x1024",
    "response_format": "b64_json"
  }'

4.1.2 Generate a video without launching a server

Command

SERVER_ARGS=(
  --model-path Wan-AI/Wan2.1-T2V-14B-Diffusers
  --text-encoder-cpu-offload
  --pin-cpu-memory
  --num-gpus 4
  --ulysses-degree=2
  --enable-cfg-parallel
)

SAMPLING_ARGS=(
  --prompt "A curious raccoon"
  --save-output
  --output-path outputs
  --output-file-name "A curious raccoon.mp4"
)

sglang generate "${SERVER_ARGS[@]}" "${SAMPLING_ARGS[@]}"

4.2 Advanced Usage

4.2.1 Cache-DiT Acceleration

SGLang integrates Cache-DiT, a caching acceleration engine for Diffusion Transformers (DiT), to achieve significant inference speedups 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 Wan-AI/Wan2.1-T2V-14B-Diffusers

Advanced Usage 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 Wan-AI/Wan2.1-T2V-14B-Diffusers

4.2.2 GPU Optimization

--dit-cpu-offload: Use CPU offload for DiT inference. Enable if you run out of memory with FSDP.
--text-encoder-cpu-offload: Use CPU offload for text encoder inference.
--image-encoder-cpu-offload: Use CPU offload for image encoder inference.
--vae-cpu-offload: Use CPU offload for VAE.
--pin-cpu-memory: Pin memory for CPU offload. Use as a workaround if you see “CUDA error: invalid argument”.

4.2.3 Supported LoRA Registry

SGLang supports applying Wan2.1 LoRA adapters on top of base models:

origin model	supported LoRA
Wan-AI/Wan2.1-T2V-14B	NIVEDAN/wan2.1-lora
Wan-AI/Wan2.1-I2V-14B-720P	valiantcat/Wan2.1-Fight-LoRA

Example:

Command

sglang serve --model-path Wan-AI/Wan2.1-T2V-14B-Diffusers --port 30000 \
    --lora-path NIVEDAN/wan2.1-lora

5. Reference Benchmark

The following benchmark is a point-in-time reference for one model, hardware platform, SGLang image, and parameter set. It is not a complete hardware support matrix. Test Environment:

Hardware: AMD MI300X GPU (1x)
Model: Wan-AI/Wan2.1-T2V-14B-Diffusers
SGLang Docker Image Version: 0.5.9

5.1 How to Run Benchmarks with SGLang

You can use the built-in SGLang diffusion benchmark script to evaluate Wan2.1 performance on your hardware.

5.1.1 Generate a single video

NVIDIA B200
Ascend A3 Series

Server Command:

Command

sglang serve --model-path Wan-AI/Wan2.1-T2V-14B-Diffusers

Benchmark Command:

Command

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

Result:

Output

================= Serving Benchmark Result =================
Task:                                    text-to-video
Model:                                   Wan-AI/Wan2.1-T2V-14B-Diffusers
Dataset:                                 vbench
--------------------------------------------------
Benchmark duration (s):                  1958.41
Request rate:                            inf
Max request concurrency:                 1
Successful requests:                     1/1
--------------------------------------------------
Request throughput (req/s):              0.00
Latency Mean (s):                        1958.4059
Latency Median (s):                      1958.4059
Latency P99 (s):                         1958.4059
--------------------------------------------------
Peak Memory Max (MB):                    59662.00
Peak Memory Mean (MB):                   59662.00
Peak Memory Median (MB):                 59662.00
============================================================

Server Command:

Command

#One A3 Series card has 2 npu chips. Benchmark was done with two A3 Series cards
sglang serve \
  --model-path /models/Wan-AI/Wan2.1-T2V-14B-Diffusers/ \
  --tp-size 2 \
  --sp-degree 2 \
  --num-gpus 4 \
  --attention-backend laser_attn

Benchmark Command:

Command

python -m sglang.multimodal_gen.benchmarks.bench_serving \
  --dataset vbench \
  --task text-to-video \
  --num-prompts 1 \
  --max-concurrency 1

Result:

Output

================= Serving Benchmark Result =================
Task:                                         text-to-video
Model:                                        Wan-AI/Wan2.1-T2V-14B-Diffusers/
Dataset:                                      vbench
--------------------------------------------------
Benchmark duration (s):                       1282.90
Request rate:                                 inf
Max request concurrency:                      1
Successful requests:                          1/1
Completed outputs:                            1
Outputs per prompt:                           1
--------------------------------------------------
Request throughput (req/s):                   0.00
Output throughput (outputs/s):                0.00
Latency Mean (s):                             1282.90
Latency Median (s):                           1282.90
Latency P90 (s):                              1282.90
Latency P95 (s):                              1282.90
Latency P99 (s):                              1282.90
--------------------------------------------------
Peak Memory Max (MB):                         31938.00
Peak Memory Mean (MB):                        31938.00
Peak Memory Median (MB):                      31938.00
============================================================

5.1.2 Generate videos with Cache-DiT acceleration

NVIDIA B200
Ascend A3 Series

Server Command:

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 Wan-AI/Wan2.1-T2V-14B-Diffusers

Benchmark Command:

Command

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

Result:

Output

================= Serving Benchmark Result =================
Task:                                    text-to-video
Model:                                   Wan-AI/Wan2.1-T2V-14B-Diffusers
Dataset:                                 vbench
--------------------------------------------------
Benchmark duration (s):                  556.99
Request rate:                            inf
Max request concurrency:                 1
Successful requests:                     1/1
--------------------------------------------------
Request throughput (req/s):              0.00
Latency Mean (s):                        556.9885
Latency Median (s):                      556.9885
Latency P99 (s):                         556.9885
--------------------------------------------------
Peak Memory Max (MB):                    69306.00
Peak Memory Mean (MB):                   69306.00
Peak Memory Median (MB):                 69306.00
============================================================

Server Command:

Command

#One A3 Series card has 2 npu chips. Benchmark was done with two A3 Series cards
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_CACHE_DIT_ENABLED=true sglang serve \
  --model-path /models/Wan-AI/Wan2.1-T2V-14B-Diffusers/ \
  --tp-size 2 \
  --sp-degree 2 \
  --num-gpus 4 \
  --attention-backend laser_attn

Benchmark Command:

Command

python -m sglang.multimodal_gen.benchmarks.bench_serving \
  --dataset vbench \
  --task text-to-video \
  --num-prompts 1 \
  --max-concurrency 1

Result:

Output

================= Serving Benchmark Result =================
Task:                                         text-to-video
Model:                                        Wan-AI/Wan2.1-T2V-14B-Diffusers/
Dataset:                                      vbench
--------------------------------------------------
Benchmark duration (s):                       413.88
Request rate:                                 inf
Max request concurrency:                      1
Successful requests:                          1/1
Completed outputs:                            1
Outputs per prompt:                           1
--------------------------------------------------
Request throughput (req/s):                   0.00
Output throughput (outputs/s):                0.00
Latency Mean (s):                             413.88
Latency Median (s):                           413.88
Latency P90 (s):                              413.88
Latency P95 (s):                              413.88
Latency P99 (s):                              413.88
--------------------------------------------------
Peak Memory Max (MB):                         32782.00
Peak Memory Mean (MB):                        32782.00
Peak Memory Median (MB):                      32782.00
============================================================

​1. Model Introduction

​2. SGLang-diffusion Installation

​3. Model Deployment

​3.1 Basic Configuration

​3.2 Configuration Tips

​4. Model Invocation

​4.1 Basic Usage

​4.1.1 Launch a server and then send requests

​4.1.2 Generate a video without launching a server

​4.2 Advanced Usage

​4.2.1 Cache-DiT Acceleration

​4.2.2 GPU Optimization

​4.2.3 Supported LoRA Registry

​5. Reference Benchmark

​5.1 How to Run Benchmarks with SGLang

​5.1.1 Generate a single video

​5.1.2 Generate videos with Cache-DiT acceleration

1. Model Introduction

2. SGLang-diffusion Installation

3. Model Deployment

3.1 Basic Configuration

3.2 Configuration Tips

4. Model Invocation

4.1 Basic Usage

4.1.1 Launch a server and then send requests

4.1.2 Generate a video without launching a server

4.2 Advanced Usage

4.2.1 Cache-DiT Acceleration

4.2.2 GPU Optimization

4.2.3 Supported LoRA Registry

5. Reference Benchmark

5.1 How to Run Benchmarks with SGLang

5.1.1 Generate a single video

5.1.2 Generate videos with Cache-DiT acceleration