Wan2.2 - SGLang Documentation

1. Model Introduction

Wan2.2 extends the Wan family with timestep-specialized Mixture-of-Experts models and a unified 5B TI2V checkpoint. It targets cinematic composition, lighting, and complex motion while supporting text-to-video, image-to-video, and hybrid text/image conditioning. Choose the A14B MoE checkpoints for maximum T2V or I2V capacity and the 5B TI2V model for a smaller unified 720p-at-24-fps path. MoE reduces active compute relative to total capacity but does not remove the memory cost of loading expert weights, so hardware selection still matters.

The Wan2.2 TI2V 5B checkpoint currently has known quality issues when it is used for image-to-video generation. Use Wan-AI/Wan2.2-I2V-A14B-Diffusers when I2V quality is the priority.

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 Wan2.2 series offers models in various sizes, architectures and input types, optimized for different hardware platforms. The recommended launch configurations vary by hardware and model size. Interactive Command Generator: Use the configuration selector below to automatically generate the appropriate deployment command for your hardware platform, model size. SGLang supports serving Wan2.2 on NVIDIA B200, H200, AMD MI300X, MI325X, MI355X GPUs and Ascend A2/A3 Series NPUs.

3.2 Configuration Tips

See Performance Optimization for acceleration features and their runtime requirements.

--vae-path: Path to a custom VAE model or HuggingFace model ID (e.g., fal/FLUX.2-Tiny-AutoEncoder). 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 (only for the encoder; should not be larger than 1 if text encoder offload is enabled, as layer-wise offload plus prefetch is faster)
--sp-degree {SP_SIZE}: Sequence parallelism size (typically should match the number of GPUs)
--ulysses-degree {ULYSSES_DEGREE}: The degree of DeepSpeed-Ulysses-style SP in USP
--ring-degree {RING_DEGREE}: The degree of ring attention-style SP in USP

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/Wan2.2-T2V-A14B-Diffusers --port 3000

curl http://127.0.0.1:3000/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/Wan2.2-T2V-A14B-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/Wan2.2-T2V-A14B-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 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 Wan-AI/Wan2.2-T2V-A14B-Diffusers

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 Wan-AI/Wan2.2-T2V-A14B-Diffusers

4.2.2 GPU Optimization

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

4.2.3 Supported LoRA Registry

origin model	supported LoRA
Wan-AI/Wan2.2-I2V-A14B-Diffusers	lightx2v/Wan2.2-Distill-Loras
Wan-AI/Wan2.2-T2V-A14B-Diffusers	Cseti/wan2.2-14B-Arcane_Jinx-lora-v1

Example:

Command

sglang serve --model-path Wan-AI/Wan2.2-T2V-A14B-Diffusers --port 3000 \
    --lora-path Cseti/wan2.2-14B-Arcane_Jinx-lora-v1

5. Benchmark

Test Environment:

Hardware: NVIDIA B200 GPU (1x)
Model: Wan-AI/Wan2.2-T2V-A14B-Diffusers
sglang diffusion version: 0.5.6.post2

5.1 Speedup Benchmark

5.1.1 Generate a video

NVIDIA B200
Ascend A3 Series

Server Command:

Command

sglang serve --model-path Wan-AI/Wan2.2-T2V-A14B-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 =================
Model:                                   Wan-AI/Wan2.2-T2V-A14B-Diffusers
Dataset:                                 vbench
Task:                                    text-to-video
--------------------------------------------------
Benchmark duration (s):                  630.43
Request rate:                            inf
Max request concurrency:                 1
Successful requests:                     1/1
--------------------------------------------------
Request throughput (req/s):              0.00
Latency Mean (s):                        630.4277
Latency Median (s):                      630.4277
Latency P99 (s):                         630.4277
--------------------------------------------------
Peak Memory Max (MB):                    62627.41
Peak Memory Mean (MB):                   62627.41
Peak Memory Median (MB):                 62627.41

============================================================

Server Command:

Command

#One A3 Series card has 2 npu chips. Using four A3 Series cards in benchmarking
sglang serve \
  --model-path /models/Wan-AI/Wan2.2-T2V-A14B-Diffusers/ \
  --tp-size 2 \
  --sp-degree 4 \
  --num-gpus 8 \
  --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/Wan2.2-T2V-A14B-Diffusers/
Dataset:                                      vbench
--------------------------------------------------
Benchmark duration (s):                       214.50
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):                             214.50
Latency Median (s):                           214.50
Latency P90 (s):                              214.50
Latency P95 (s):                              214.50
Latency P99 (s):                              214.50
--------------------------------------------------
Peak Memory Max (MB):                         46692.00
Peak Memory Mean (MB):                        46692.00
Peak Memory Median (MB):                      46692.00
------------------------------------------------------------

5.1.2 Generate videos with high concurrency

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/Wan2.2-T2V-A14B-Diffusers

Benchmark Command:

Command

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

Result:

Output

================= Serving Benchmark Result =================
Model:                                   Wan-AI/Wan2.2-T2V-A14B-Diffusers
Dataset:                                 vbench
Task:                                    text-to-video
--------------------------------------------------
Benchmark duration (s):                  5163.21
Request rate:                            inf
Max request concurrency:                 20
Successful requests:                     20/20
--------------------------------------------------
Request throughput (req/s):              0.00
Latency Mean (s):                        2739.7695
Latency Median (s):                      2742.0673
Latency P99 (s):                         5121.6331
--------------------------------------------------
Peak Memory Max (MB):                    72523.56
Peak Memory Mean (MB):                   70253.34
Peak Memory Median (MB):                 70824.46

============================================================

Server Command:

Command

#One A3 Series card has 2 npu chips. Using four A3 Series cards in benchmarking
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/Wan2.2-T2V-A14B-Diffusers/ \
  --tp-size 2 \
  --sp-degree 4 \
  --num-gpus 8 \
  --attention-backend laser_attn

Benchmark Command:

Command

python -m sglang.multimodal_gen.benchmarks.bench_serving \
  --dataset vbench \
  --task text-to-video \
  --num-prompts 20 \
  --max-concurrency 20

Result:

Output

================= Serving Benchmark Result =================
Task:                                         text-to-video
Model:                                        Wan-AI/Wan2.2-T2V-A14B-Diffusers/
Dataset:                                      vbench
--------------------------------------------------
Benchmark duration (s):                       4384.65
Request rate:                                 inf
Max request concurrency:                      20
Successful requests:                          20/20
Completed outputs:                            20
Outputs per prompt:                           1
--------------------------------------------------
Request throughput (req/s):                   0.00
Output throughput (outputs/s):                0.00
Latency Mean (s):                             2304.17
Latency Median (s):                           2297.69
Latency P90 (s):                              3972.32
Latency P95 (s):                              4178.99
Latency P99 (s):                              4343.52
--------------------------------------------------
Peak Memory Max (MB):                         46692.00
Peak Memory Mean (MB):                        46691.90
Peak Memory Median (MB):                      46692.00
------------------------------------------------------------

​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. 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. 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. Benchmark

5.1 Speedup Benchmark

5.1.1 Generate a video

5.1.2 Generate videos with high concurrency