222 lines
5.5 KiB
Markdown
222 lines
5.5 KiB
Markdown
# Wan22 Video Generation Service
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Wan2.2-TI2V-5B 视频生成服务，基于 ahserver + longtasks 提供 OpenAI 兼容的异步视频生成 API。
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## Architecture
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```
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HTTP Request → ahserver (port 8079) → submit.dspy → longtasks.submit_task()
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↓ (Redis Queue)
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Wan22Tasks.process_task()
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↓
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Wan22.generate() [GPU]
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↓
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save to /data/ymq/wan22-outputs/
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↓
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task.dspy ← longtasks.get_status()
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```
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- **串行推理**: GPU 全局锁 `_GLOBAL_INFER_LOCK`，一次只跑一个任务
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- **模型常驻**: 首次任务加载 Wan2.2 模型，后续任务复用，无需重复加载
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- **异步队列**: longtasks 通过 Redis 管理任务队列，支持失败重试
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## 模型下载（离线部署）
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Real-ESRGAN x2 超分辨率模型，64MB。
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### 方法1: wget（推荐）
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```bash
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# 创建模型目录
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mkdir -p /data/ymq/models
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# 下载模型
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wget -O /data/ymq/models/RealESRGAN_x2plus.pth \
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https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth
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```
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**下载大小**: 64MB
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**下载时间**: 约10-30秒
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### 方法2: curl
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```bash
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curl -L -o /data/ymq/models/RealESRGAN_x2plus.pth \
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https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth
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```
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### 方法3: 自动下载
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服务首次运行时会自动下载模型到 `/data/ymq/models/RealESRGAN_x2plus.pth`，但需要网络能访问 GitHub。
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### 验证下载
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```bash
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ls -lh /data/ymq/models/RealESRGAN_x2plus.pth
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# 应该看到 64M 大小
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```
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### 模型来源
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- **GitHub**: https://github.com/xinntao/Real-ESRGAN
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- **Release**: v0.2.1
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- **License**: BSD 3-Clause
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- **Paper**: Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data
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## API 接口
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### 1. 提交视频生成任务
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```
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POST /api/submit
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Content-Type: application/json
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{
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"prompt": "A cinematic scene of...", // 必填，视频描述
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"size": "1280*720", // 可选，默认 1280*720
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"frame_num": 81, // 可选，帧数 (4n+1, 17~129)
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"sample_steps": 50, // 可选，采样步数
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"sample_guide_scale": 5.0, // 可选，引导比例
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"base_seed": 42, // 可选，随机种子
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"task_id": "my_custom_id" // 可选，自定义任务ID
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}
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```
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**响应**:
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```json
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{
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"task_id": "a1b2c3d4e5f6", // 用于查询状态
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"status": "queued",
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"prompt": "A cinematic scene...",
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"size": "1280*720",
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"frame_num": 81,
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"message": "task submitted",
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"check_url": "/api/task?task_id=a1b2c3d4e5f6"
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}
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```
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### 2. 查询任务状态
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```
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GET /api/task?task_id=a1b2c3d4e5f6
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```
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**响应** (PENDING):
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```json
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{
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"status": "PENDING",
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"created_at": 1712345678.0,
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"started_at": null,
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"finished_at": null
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}
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```
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**响应** (SUCCEEDED):
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```json
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{
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"status": "SUCCEEDED",
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"task_id": "a1b2c3d4e5f6",
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"video_url": "/idfile?path=a1b2c3d4e5f6.mp4",
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"video_path": "/data/ymq/wan22-outputs/a1b2c3d4e5f6.mp4",
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"size": "1280*720",
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"frame_num": 81,
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"file_size": 12345678,
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"prompt": "A cinematic scene...",
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"seed": 42,
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"created_at": 1712345678.0,
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"started_at": 1712345680.0,
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"finished_at": 1712345900.0
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}
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```
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**响应** (FAILED):
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```json
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{
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"status": "FAILED",
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"task_id": "a1b2c3d4e5f6",
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"error": "CUDA out of memory",
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"created_at": 1712345678.0
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}
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```
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### 3. 服务状态
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```
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GET /api/status
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```
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```json
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{
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"service": "wan22-video-generation",
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"model": "Wan2.2-TI2V-5B",
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"gpu_id": 2,
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"gpus": [
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{"id": 0, "util": 23, "mem_used": 5120, "mem_total": 24564},
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{"id": 1, "util": 0, "mem_used": 4, "mem_total": 24564},
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{"id": 2, "util": 45, "mem_used": 8192, "mem_total": 24564}
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]
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}
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```
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## 视频下载
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生成完成后，通过 `video_url` 下载视频：
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```
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GET /idfile?path=a1b2c3d4e5f6.mp4
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```
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或在浏览器中拼接 URL：
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```
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http://<server>:8079/idfile?path=a1b2c3d4e5f6.mp4
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```
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## 部署
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```bash
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# 启动
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cd ~/wan22-service
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WAN22_GPU_ID=2 ./start.sh
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# 停止
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./stop.sh
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# 查看日志
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tail -f wan22-service.log
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```
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环境变量:
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- `WAN22_GPU_ID`: GPU 设备号 (默认 2)
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## 文件结构
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```
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wan22-service/
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├── ah.py # 主入口: ahserver + longtasks 初始化
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├── app/
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│ └── api/
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│ ├── submit/index.dspy # POST /api/submit - 提交任务
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│ ├── task/index.dspy # GET /api/task - 查询状态
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│ └── status/index.dspy # GET /api/status - 服务状态
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├── conf/
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│ └── config.json # ahserver 配置 (端口 8079)
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├── workers/
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│ ├── generate.py # 任务执行逻辑 (惰性加载 Wan22)
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│ └── wan22_wrapper.py # Wan22 类 (OpenAI 风格封装)
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├── repo/ # Wan2.2 推理代码
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├── py3/ # Python venv
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├── start.sh / stop.sh
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├── skill/ # Hermes skill 文档
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├── README.md
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└── wan22-service.log
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```
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## Dependencies
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- ahserver (Web framework)
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- longtasks (Async task queue via Redis)
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- sqlor (Optional, for database operations)
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- torch + torchvision (GPU inference)
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- wan (Wan2.2 repo, local at `repo/wan/`)
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