50ec9702d0
Co-authored-by: Mick Qian <mickqian@users.noreply.github.com> Co-authored-by: Claude Opus 5 <noreply@anthropic.com>
390 lines
15 KiB
Plaintext
390 lines
15 KiB
Plaintext
---
|
|
title: Wan2.1
|
|
metatags:
|
|
description: "Deploy Wan2.1 video generation models with SGLang - community contribution guide for Wan Video's diffusion models."
|
|
---
|
|
|
|
import { DiffusionModelTags } from '/src/snippets/diffusion/model-tags.jsx';
|
|
import { Wan21Deployment } from '/src/snippets/diffusion/wan21-deployment.jsx';
|
|
|
|
<DiffusionModelTags tags={["video", "text-to-video", "image-to-video", "1.3B / 14B", "bilingual text"]} />
|
|
|
|
## 1. Model Introduction
|
|
|
|
[Wan2.1](https://github.com/Wan-Video/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](../../../docs/sglang-diffusion/installation) 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.
|
|
|
|
<Wan21Deployment />
|
|
|
|
### 3.2 Configuration Tips
|
|
|
|
Current supported optimization options are listed in the [SGLang diffusion support matrix](../../../docs/sglang-diffusion/attention_backends#platform-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](../../../docs/sglang-diffusion/api/openai_api)
|
|
|
|
#### 4.1.1 Launch a server and then send requests
|
|
|
|
```bash 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 $OPENAI_API_KEY" \
|
|
-d '{
|
|
"model": "Wan-AI/Wan2.1-T2V-14B-Diffusers",
|
|
"prompt": "A cute baby sea otter",
|
|
"n": 1,
|
|
"size": "1024x1024",
|
|
"response_format": "b64_json"
|
|
}'
|
|
```
|
|
|
|
#### 4.1.2 Generate a video without launching a server
|
|
|
|
```bash Command
|
|
SERVER_ARGS=(
|
|
--model-path Wan-AI/Wan2.1-T2V-14B-Diffusers
|
|
--text-encoder-cpu-offload
|
|
--pin-cpu-memory
|
|
--num-gpus 4
|
|
--ulysses-degree=2
|
|
--enable-cfg-parallel
|
|
)
|
|
|
|
SAMPLING_ARGS=(
|
|
--prompt "A curious raccoon"
|
|
--save-output
|
|
--output-path outputs
|
|
--output-file-name "A curious raccoon.mp4"
|
|
)
|
|
|
|
sglang generate "${SERVER_ARGS[@]}" "${SAMPLING_ARGS[@]}"
|
|
```
|
|
|
|
### 4.2 Advanced Usage
|
|
|
|
#### 4.2.1 Cache-DiT Acceleration
|
|
|
|
SGLang integrates [Cache-DiT](https://github.com/vipshop/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](../../../docs/sglang-diffusion/cache_dit).
|
|
|
|
**Basic Usage**
|
|
|
|
```bash Command
|
|
SGLANG_CACHE_DIT_ENABLED=true sglang serve --model-path Wan-AI/Wan2.1-T2V-14B-Diffusers
|
|
```
|
|
|
|
**Advanced Usage**
|
|
|
|
Combined Configuration Example:
|
|
```bash 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:
|
|
|
|
<table style={{width: "100%", borderCollapse: "collapse", tableLayout: "fixed"}}>
|
|
<colgroup>
|
|
<col style={{width: "50%"}} />
|
|
<col style={{width: "50%"}} />
|
|
</colgroup>
|
|
<thead>
|
|
<tr style={{borderBottom: "2px solid #d55816"}}>
|
|
<th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>origin model</th>
|
|
<th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.05)"}}>supported LoRA</th>
|
|
</tr>
|
|
</thead>
|
|
<tbody>
|
|
<tr>
|
|
<td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>[Wan-AI/Wan2.1-T2V-14B](https://huggingface.co/Wan-AI/Wan2.1-T2V-14B)</td>
|
|
<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>[NIVEDAN/wan2.1-lora](https://huggingface.co/NIVEDAN/wan2.1-lora)</td>
|
|
</tr>
|
|
<tr>
|
|
<td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>[Wan-AI/Wan2.1-I2V-14B-720P](https://huggingface.co/Wan-AI/Wan2.1-I2V-14B-720P)</td>
|
|
<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>[valiantcat/Wan2.1-Fight-LoRA](https://huggingface.co/valiantcat/Wan2.1-Fight-LoRA)</td>
|
|
</tr>
|
|
</tbody>
|
|
</table>
|
|
|
|
**Example**:
|
|
|
|
```bash 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
|
|
|
|
<Tabs>
|
|
<Tab title="NVIDIA B200">
|
|
**Server Command**:
|
|
|
|
```bash Command
|
|
sglang serve --model-path Wan-AI/Wan2.1-T2V-14B-Diffusers
|
|
```
|
|
|
|
**Benchmark Command**:
|
|
|
|
```bash Command
|
|
python3 -m sglang.multimodal_gen.benchmarks.bench_serving \
|
|
--dataset vbench --task text-to-video --num-prompts 1 --max-concurrency 1
|
|
```
|
|
|
|
**Result**:
|
|
|
|
```text 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
|
|
============================================================
|
|
```
|
|
</Tab>
|
|
|
|
<Tab title="Ascend A3 Series">
|
|
**Server Command**:
|
|
|
|
```bash 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**:
|
|
|
|
```bash Command
|
|
python -m sglang.multimodal_gen.benchmarks.bench_serving \
|
|
--dataset vbench \
|
|
--task text-to-video \
|
|
--num-prompts 1 \
|
|
--max-concurrency 1
|
|
```
|
|
|
|
**Result**:
|
|
|
|
```text 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
|
|
============================================================
|
|
```
|
|
</Tab>
|
|
</Tabs>
|
|
|
|
#### 5.1.2 Generate videos with Cache-DiT acceleration
|
|
|
|
<Tabs>
|
|
<Tab title="NVIDIA B200">
|
|
**Server Command**:
|
|
|
|
```bash 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**:
|
|
|
|
```bash Command
|
|
python3 -m sglang.multimodal_gen.benchmarks.bench_serving \
|
|
--dataset vbench --task text-to-video --num-prompts 1 --max-concurrency 1
|
|
```
|
|
|
|
**Result**:
|
|
|
|
```text 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
|
|
============================================================
|
|
```
|
|
</Tab>
|
|
|
|
<Tab title="Ascend A3 Series">
|
|
**Server Command**:
|
|
|
|
```bash 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**:
|
|
|
|
```bash Command
|
|
python -m sglang.multimodal_gen.benchmarks.bench_serving \
|
|
--dataset vbench \
|
|
--task text-to-video \
|
|
--num-prompts 1 \
|
|
--max-concurrency 1
|
|
```
|
|
|
|
**Result**:
|
|
|
|
```text 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
|
|
============================================================
|
|
```
|
|
</Tab>
|
|
</Tabs>
|
|
|
|
## 6. Run in ComfyUI
|
|
|
|
import { ComfyUISupport } from '/src/snippets/diffusion/comfyui-support.jsx';
|
|
|
|
<ComfyUISupport model="video" />
|