Added the service deployment code of FastAPI, including key authentication, task submission, task details viewing, and task canceling

2025-06-05 14:54:54 +00:00 · 2025-04-09 15:37:10 +08:00 · 2025-04-09 15:37:10 +08:00 · 0961b7b888
commit 0961b7b888
parent 679ccc6c68
4 changed files with 1260 additions and 0 deletions
--- a/I2V-FastAPI文档.md
+++ b/I2V-FastAPI文档.md
@ -0,0 +1,151 @@
+
+# 图像到视频生成服务API文档
+
+## 一、功能概述
+基于Wan2.1-I2V-14B-480P模型实现图像到视频生成，核心功能包括：
+1. **异步任务队列**：支持多任务排队和并发控制（最大2个并行任务）
+2. **智能分辨率适配**：
+   - 支持自动计算最佳分辨率（保持原图比例）
+   - 支持手动指定分辨率（480x832/832x480）
+3. **资源管理**：
+   - 显存优化（bfloat16精度）
+   - 生成文件自动清理（默认1小时）
+4. **安全认证**：基于API Key的Bearer Token验证
+5. **任务控制**：支持任务提交/状态查询/取消操作
+
+技术栈：
+- FastAPI框架
+- CUDA加速
+- 异步任务处理
+- Diffusers推理库
+
+---
+
+## 二、接口说明
+
+### 1. 提交生成任务
+**POST /video/submit**
+```json
+{
+  "model": "Wan2.1-I2V-14B-480P",
+  "prompt": "A dancing cat in the style of Van Gogh",
+  "image_url": "https://example.com/input.jpg",
+  "image_size": "auto",
+  "num_frames": 81,
+  "guidance_scale": 3.0,
+  "infer_steps": 30
+}
+```
+
+**响应示例**：
+```json
+{
+  "requestId": "a1b2c3d4e5f6g7h8i9j0k1l2m3n4o5p6"
+}
+```
+
+### 2. 查询任务状态
+**POST /video/status**
+```json
+{
+  "requestId": "a1b2c3d4e5f6g7h8i9j0k1l2m3n4o5p6"
+}
+```
+
+**响应示例**：
+```json
+{
+  "status": "Succeed",
+  "results": {
+    "videos": [{"url": "http://localhost:8088/videos/abcd1234.mp4"}],
+    "timings": {"inference": 90},
+    "seed": 123456
+  }
+}
+```
+
+### 3. 取消任务
+**POST /video/cancel**
+```json
+{
+  "requestId": "a1b2c3d4e5f6g7h8i9j0k1l2m3n4o5p6"
+}
+```
+
+**响应示例**：
+```json
+{
+  "status": "Succeed"
+}
+```
+
+---
+
+## 三、Postman使用指南
+
+### 1. 基础配置
+- 服务器地址：`http://ip地址:8088`
+- 认证方式：Bearer Token
+- Token值：需替换为有效API Key
+
+### 2. 提交任务
+1. 选择POST方法，URL填写`/video/submit`
+2. Headers添加：
+   ```text
+   Authorization: Bearer YOUR_API_KEY
+   Content-Type: application/json
+   ```
+3. Body示例（图像生成视频）：
+   ```json
+   {
+     "prompt": "Sunset scene with mountains",
+     "image_url": "https://example.com/mountain.jpg",
+     "image_size": "auto",
+     "num_frames": 50
+   }
+   ```
+
+### 3. 特殊处理
+- **图像下载失败**：返回400错误，包含具体原因（如URL无效/超时）
+- **显存不足**：返回500错误并提示降低分辨率
+
+---
+
+## 四、参数规范
+| 参数名           | 允许值范围                     | 必填 | 说明                                      |
+|------------------|-------------------------------|------|------------------------------------------|
+| image_url        | 有效HTTP/HTTPS URL            | 是   | 输入图像地址                              |
+| prompt           | 10-500字符                    | 是   | 视频内容描述                              |
+| image_size       | "480x832", "832x480", "auto"  | 是   | auto模式自动适配原图比例                  |
+| num_frames       | 24-120                        | 是   | 视频总帧数                                |
+| guidance_scale   | 1.0-20.0                      | 是   | 文本引导强度                              |
+| infer_steps      | 20-100                        | 是   | 推理步数                                  |
+| seed             | 0-2147483647                  | 否   | 随机种子                                  |
+
+---
+
+## 五、状态码说明
+| 状态码 | 含义                               |
+|--------|-----------------------------------|
+| 202    | 任务已接受                         |
+| 400    | 图像下载失败/参数错误              |
+| 401    | 认证失败                           |
+| 404    | 任务不存在                         |
+| 422    | 参数校验失败                       |
+| 500    | 服务端错误（显存不足/模型异常等）  |
+
+---
+
+## 六、特殊功能说明
+1. **智能分辨率适配**：
+   - 当`image_size="auto"`时，自动计算符合模型要求的最优分辨率
+   - 保持原始图像宽高比，最大像素面积不超过399,360（约640x624）
+
+2. **图像预处理**：
+   - 自动转换为RGB模式
+   - 根据目标分辨率进行等比缩放
+   
+
+**重要提示**：输入图像URL需保证公开可访问，私有资源需提供有效鉴权
+
+**提示** ：访问`http://服务器地址:8088/docs`可查看交互式API文档，支持在线测试所有接口
--- a/T2V-FastAPI文档.md
+++ b/T2V-FastAPI文档.md
@ -0,0 +1,133 @@
+
+# 视频生成服务API文档
+
+## 一、功能概述
+本服务基于Wan2.1-T2V-1.3B模型实现文本到视频生成，包含以下核心功能：
+1. **异步任务队列**：支持多任务排队和并发控制（最大2个并行任务）
+2. **资源管理**：
+   - 显存优化（使用bfloat16精度）
+   - 生成视频自动清理（默认1小时后删除）
+3. **安全认证**：基于API Key的Bearer Token验证
+4. **任务控制**：支持任务提交/状态查询/取消操作
+
+技术栈：
+- FastAPI框架
+- CUDA加速
+- 异步任务处理
+- Diffusers推理库
+
+---
+
+## 二、接口说明
+
+### 1. 提交生成任务
+**POST /video/submit**
+```json
+{
+  "model": "Wan2.1-T2V-1.3B",
+  "prompt": "A beautiful sunset over the mountains",
+  "image_size": "480x832",
+  "num_frames": 81,
+  "guidance_scale": 5.0,
+  "infer_steps": 50
+}
+```
+
+**响应示例**：
+```json
+{
+  "requestId": "a1b2c3d4e5f6g7h8i9j0k1l2m3n4o5p6"
+}
+```
+
+### 2. 查询任务状态
+**POST /video/status**
+```json
+{
+  "requestId": "a1b2c3d4e5f6g7h8i9j0k1l2m3n4o5p6"
+}
+```
+
+**响应示例**：
+```json
+{
+  "status": "Succeed",
+  "results": {
+    "videos": [{"url": "http://localhost:8088/videos/abcd1234.mp4"}],
+    "timings": {"inference": 120}
+  }
+}
+```
+
+### 3. 取消任务
+**POST /video/cancel**
+```json
+{
+  "requestId": "a1b2c3d4e5f6g7h8i9j0k1l2m3n4o5p6"
+}
+```
+
+**响应示例**：
+```json
+{
+  "status": "Succeed"
+}
+```
+
+---
+
+## 三、Postman使用指南
+
+### 1. 基础配置
+- 服务器地址：`http://ip地址:8088`
+- 认证方式：Bearer Token
+- Token值：需替换为有效API Key
+
+### 2. 提交任务
+1. 选择POST方法，输入URL：`/video/submit`
+2. Headers添加：
+   ```text
+   Authorization: Bearer YOUR_API_KEY
+   Content-Type: application/json
+   ```
+3. Body选择raw/JSON格式，输入请求参数
+
+### 3. 查询状态
+1. 新建请求，URL填写`/video/status`
+2. 使用相同认证头
+3. Body中携带requestId
+
+### 4. 取消任务
+1. 新建DELETE请求，URL填写`/video/cancel`
+2. Body携带需要取消的requestId
+
+### 注意事项
+1. 所有接口必须携带有效API Key
+2. 视频生成耗时约2-5分钟（根据参数配置）
+3. 生成视频默认保留1小时
+
+---
+
+## 四、参数规范
+| 参数名           | 允许值范围                     | 必填 | 说明                     |
+|------------------|-------------------------------|------|--------------------------|
+| prompt           | 10-500字符                    | 是   | 视频内容描述             |
+| image_size       | "480x832" 或 "832x480"        | 是   | 分辨率                   |
+| num_frames       | 24-120                        | 是   | 视频总帧数               |
+| guidance_scale   | 1.0-20.0                      | 是   | 文本引导强度             |
+| infer_steps      | 20-100                        | 是   | 推理步数                 |
+| seed             | 0-2147483647                  | 否   | 随机种子                 |
+
+---
+
+## 五、状态码说明
+| 状态码 | 含义                     |
+|--------|--------------------------|
+| 202    | 任务已接受               |
+| 401    | 认证失败                 |
+| 404    | 任务不存在               |
+| 422    | 参数校验失败             |
+| 500    | 服务端错误（显存不足等） |
+
+
+**提示**：建议使用Swagger文档进行接口测试，访问`http://服务器地址:8088/docs`可查看自动生成的API文档界面
--- a/i2v_api.py
+++ b/i2v_api.py
@ -0,0 +1,526 @@
+import os
+import torch
+import uuid
+import time
+import asyncio
+import numpy as np
+from threading import Lock
+from typing import Optional, Dict, List
+from fastapi import FastAPI, HTTPException, status, Depends
+from fastapi.staticfiles import StaticFiles
+from pydantic import BaseModel, Field, field_validator, ValidationError
+from diffusers.utils import export_to_video, load_image
+from diffusers import AutoencoderKLWan, WanImageToVideoPipeline
+from diffusers.schedulers.scheduling_unipc_multistep import UniPCMultistepScheduler
+from transformers import CLIPVisionModel
+from PIL import Image
+import requests
+from io import BytesIO
+from fastapi.security import HTTPBearer, HTTPAuthorizationCredentials
+from contextlib import asynccontextmanager
+from requests.exceptions import RequestException
+
+# 创建存储目录
+os.makedirs("generated_videos", exist_ok=True)
+os.makedirs("temp_images", exist_ok=True)
+
+# ======================
+# 生命周期管理
+# ======================
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    """资源管理器"""
+    try:
+        # 初始化认证系统
+        app.state.valid_api_keys = {
+            "密钥"
+        }
+
+        # 初始化模型
+        model_id = "./Wan2.1-I2V-14B-480P-Diffusers"
+      
+        # 加载图像编码器
+        image_encoder = CLIPVisionModel.from_pretrained(
+            model_id,
+            subfolder="image_encoder",
+            torch_dtype=torch.float32
+        )
+      
+        # 加载VAE
+        vae = AutoencoderKLWan.from_pretrained(
+            model_id,
+            subfolder="vae",
+            torch_dtype=torch.float32
+        )
+      
+        # 配置调度器
+        scheduler = UniPCMultistepScheduler(
+            prediction_type='flow_prediction',
+            use_flow_sigmas=True,
+            num_train_timesteps=1000,
+            flow_shift=3.0
+        )
+      
+        # 创建管道
+        app.state.pipe = WanImageToVideoPipeline.from_pretrained(
+            model_id,
+            vae=vae,
+            image_encoder=image_encoder,
+            torch_dtype=torch.bfloat16
+        ).to("cuda")
+        app.state.pipe.scheduler = scheduler
+
+        # 初始化任务系统
+        app.state.tasks: Dict[str, dict] = {}
+        app.state.pending_queue: List[str] = []
+        app.state.model_lock = Lock()
+        app.state.task_lock = Lock()
+        app.state.base_url = "ip地址+端口"
+        app.state.semaphore = asyncio.Semaphore(2)  # 并发限制
+
+        # 启动后台处理器
+        asyncio.create_task(task_processor())
+
+        print("✅ 系统初始化完成")
+        yield
+
+    finally:
+        # 资源清理
+        if hasattr(app.state, 'pipe'):
+            del app.state.pipe
+            torch.cuda.empty_cache()
+            print("♻️ 资源已释放")
+
+# ======================
+# FastAPI应用
+# ======================
+app = FastAPI(lifespan=lifespan)
+app.mount("/videos", StaticFiles(directory="generated_videos"), name="videos")
+# 认证模块
+security = HTTPBearer(auto_error=False)
+
+# ======================
+# 数据模型--查询参数模型
+# ======================
+class VideoSubmitRequest(BaseModel):
+    model: str = Field(
+        default="Wan2.1-I2V-14B-480P",
+        description="模型版本"
+    )
+    prompt: str = Field(
+        ...,
+        min_length=10,
+        max_length=500,
+        description="视频描述提示词，10-500个字符"
+    )
+    image_url: str = Field(
+        ...,
+        description="输入图像URL，需支持HTTP/HTTPS协议"
+    )
+    image_size: str = Field(
+        default="auto",
+        description="输出分辨率，格式：宽x高 或 auto（自动计算）"
+    )
+    negative_prompt: Optional[str] = Field(
+        default=None,
+        max_length=500,
+        description="排除不需要的内容"
+    )
+    seed: Optional[int] = Field(
+        default=None,
+        ge=0,
+        le=2147483647,
+        description="随机数种子，范围0-2147483647"
+    )
+    num_frames: int = Field(
+        default=81,
+        ge=24,
+        le=120,
+        description="视频帧数，24-89帧"
+    )
+    guidance_scale: float = Field(
+        default=3.0,
+        ge=1.0,
+        le=20.0,
+        description="引导系数，1.0-20.0"
+    )
+    infer_steps: int = Field(
+        default=30,
+        ge=20,
+        le=100,
+        description="推理步数，20-100步"
+    )
+
+    @field_validator('image_size')
+    def validate_image_size(cls, v):
+        allowed_sizes = {"480x832", "832x480", "auto"}
+        if v not in allowed_sizes:
+            raise ValueError(f"支持的分辨率: {', '.join(allowed_sizes)}")
+        return v
+
+class VideoStatusRequest(BaseModel):
+    requestId: str = Field(
+        ...,
+        min_length=32,
+        max_length=32,
+        description="32位任务ID"
+    )
+
+class VideoStatusResponse(BaseModel):
+    status: str = Field(..., description="任务状态: Succeed, InQueue, InProgress, Failed,Cancelled")
+    reason: Optional[str] = Field(None, description="失败原因")
+    results: Optional[dict] = Field(None, description="生成结果")
+    queue_position: Optional[int] = Field(None, description="队列位置")
+
+class VideoCancelRequest(BaseModel):
+    requestId: str = Field(
+        ...,
+        min_length=32,
+        max_length=32,
+        description="32位任务ID"
+    )
+
+# ======================
+# 核心逻辑
+# ======================
+async def verify_auth(credentials: HTTPAuthorizationCredentials = Depends(security)):
+    """统一认证验证"""
+    if not credentials:
+        raise HTTPException(
+            status_code=401,
+            detail={"status": "Failed", "reason": "缺少认证头"},
+            headers={"WWW-Authenticate": "Bearer"}
+        )
+    if credentials.scheme != "Bearer":
+        raise HTTPException(
+            status_code=401,
+            detail={"status": "Failed", "reason": "无效的认证方案"},
+            headers={"WWW-Authenticate": "Bearer"}
+        )
+    if credentials.credentials not in app.state.valid_api_keys:
+        raise HTTPException(
+            status_code=401,
+            detail={"status": "Failed", "reason": "无效的API密钥"},
+            headers={"WWW-Authenticate": "Bearer"}
+        )
+    return True
+
+async def task_processor():
+    """任务处理器"""
+    while True:
+        async with app.state.semaphore:
+            task_id = await get_next_task()
+            if task_id:
+                await process_task(task_id)
+            else:
+                await asyncio.sleep(0.5)
+
+async def get_next_task():
+    """获取下一个任务"""
+    with app.state.task_lock:
+        return app.state.pending_queue.pop(0) if app.state.pending_queue else None
+
+async def process_task(task_id: str):
+    """处理单个任务"""
+    task = app.state.tasks.get(task_id)
+    if not task:
+        return
+
+    try:
+        # 更新任务状态
+        task['status'] = 'InProgress'
+        task['started_at'] = int(time.time())
+        print(task['request'].image_url)
+        # 下载输入图像
+        image = await download_image(task['request'].image_url)
+        image_path = f"temp_images/{task_id}.jpg"
+        image.save(image_path)
+
+        # 生成视频
+        video_path = await generate_video(task['request'], task_id, image)
+      
+        # 生成下载链接
+        download_url = f"{app.state.base_url}/videos/{os.path.basename(video_path)}"
+      
+        # 更新任务状态
+        task.update({
+            'status': 'Succeed',
+            'download_url': download_url,
+            'completed_at': int(time.time())
+        })
+      
+        # 安排清理
+        asyncio.create_task(cleanup_files([image_path, video_path]))
+    except Exception as e:
+        handle_task_error(task, e)
+
+def handle_task_error(task: dict, error: Exception):
+    """错误处理（包含详细错误信息）"""
+    error_msg = str(error)
+  
+    # 1. 显存不足错误
+    if isinstance(error, torch.cuda.OutOfMemoryError):
+        error_msg = "显存不足，请降低分辨率"
+      
+    # 2. 网络请求相关错误
+    elif isinstance(error, (RequestException, HTTPException)):
+        # 从异常中提取具体信息
+        if isinstance(error, HTTPException):
+            # 如果是 HTTPException，获取其 detail 字段
+            error_detail = getattr(error, "detail", "")
+            error_msg = f"图像下载失败: {error_detail}"
+          
+        elif isinstance(error, Timeout):
+            error_msg = "图像下载超时，请检查网络"
+          
+        elif isinstance(error, ConnectionError):
+            error_msg = "无法连接到服务器，请检查 URL"
+          
+        elif isinstance(error, HTTPError):
+            # requests 的 HTTPError（例如 4xx/5xx 状态码）
+            status_code = error.response.status_code
+            error_msg = f"服务器返回错误状态码: {status_code}"
+          
+        else:
+            # 其他 RequestException 错误
+            error_msg = f"图像下载失败: {str(error)}"
+  
+    # 3. 其他未知错误
+    else:
+        error_msg = f"未知错误: {str(error)}"
+  
+    # 更新任务状态
+    task.update({
+        'status': 'Failed',
+        'reason': error_msg,
+        'completed_at': int(time.time())
+    })
+# ======================
+# 视频生成逻辑
+# ======================
+async def download_image(url: str) -> Image.Image:
+    """异步下载图像（包含详细错误信息）"""
+    loop = asyncio.get_event_loop()
+    try:
+        response = await loop.run_in_executor(
+            None, 
+            lambda: requests.get(url)  # 将 timeout 传递给 requests.get
+        )
+      
+        # 如果状态码非 200，主动抛出 HTTPException
+        if response.status_code != 200:
+            raise HTTPException(
+                status_code=response.status_code,
+                detail=f"服务器返回状态码 {response.status_code}"
+            )
+          
+        return Image.open(BytesIO(response.content)).convert("RGB")
+      
+    except RequestException as e:
+        # 将原始 requests 错误信息抛出
+        raise HTTPException(
+            status_code=500,
+            detail=f"请求失败: {str(e)}"
+        )
+async def generate_video(request: VideoSubmitRequest, task_id: str, image: Image.Image):
+    """异步生成入口"""
+    loop = asyncio.get_event_loop()
+    return await loop.run_in_executor(
+        None,
+        sync_generate_video,
+        request,
+        task_id,
+        image
+    )
+
+def sync_generate_video(request: VideoSubmitRequest, task_id: str, image: Image.Image):
+    """同步生成核心"""
+    with app.state.model_lock:
+        try:
+            # 解析分辨率
+            mod_value = 16  # 模型要求的模数
+            print(request.image_size)
+            print('--------------------------------')
+            if request.image_size == "auto":
+                # 原版自动计算逻辑
+                aspect_ratio = image.height / image.width
+                print(image.height,image.width)
+                max_area = 399360  # 模型基础分辨率
+              
+                # 计算理想尺寸
+                height = round(np.sqrt(max_area * aspect_ratio)) 
+                width = round(np.sqrt(max_area / aspect_ratio))
+              
+                # 应用模数调整
+                height = height // mod_value * mod_value
+                width = width // mod_value * mod_value
+                resized_image = image.resize((width, height))
+            else:
+                width_str, height_str = request.image_size.split('x')
+                width = int(width_str)
+                height = int(height_str)
+                mod_value = 16          
+                # 调整图像尺寸
+                resized_image = image.resize((width, height))
+            
+              
+            # 设置随机种子
+            generator = None
+            # 修改点1: 使用属性访问seed
+            if request.seed is not None:
+                generator = torch.Generator(device="cuda")
+                generator.manual_seed(request.seed)  # 修改点2
+                print(f"🔮 使用随机种子: {request.seed}")
+            print(resized_image)
+            print(height,width)
+
+            # 执行推理
+            output = app.state.pipe(
+                image=resized_image,
+                prompt=request.prompt,
+                negative_prompt=request.negative_prompt,
+                height=height,
+                width=width,
+                num_frames=request.num_frames,
+                guidance_scale=request.guidance_scale,
+                num_inference_steps=request.infer_steps,
+                generator=generator
+            ).frames[0]
+
+            # 导出视频
+            video_id = uuid.uuid4().hex
+            output_path = f"generated_videos/{video_id}.mp4"
+            export_to_video(output, output_path, fps=16)
+            return output_path
+        except Exception as e:
+            raise RuntimeError(f"视频生成失败: {str(e)}") from e
+
+# ======================
+# API端点
+# ======================
+@app.post("/video/submit",
+          response_model=dict,
+          status_code=status.HTTP_202_ACCEPTED,
+          tags=["视频生成"])
+async def submit_task(
+    request: VideoSubmitRequest,
+    auth: bool = Depends(verify_auth)
+):
+    """提交生成任务"""
+    # 参数验证
+    if request.image_url is None:
+        raise HTTPException(
+            status_code=422,
+            detail={"status": "Failed", "reason": "需要图像URL参数"}
+        )
+
+    # 创建任务记录
+    task_id = uuid.uuid4().hex
+    with app.state.task_lock:
+        app.state.tasks[task_id] = {
+            "request": request,
+            "status": "InQueue",
+            "created_at": int(time.time())
+        }
+        app.state.pending_queue.append(task_id)
+  
+    return {"requestId": task_id}
+
+@app.post("/video/status",
+          response_model=VideoStatusResponse,
+          tags=["视频生成"])
+async def get_status(
+    request: VideoStatusRequest,
+    auth: bool = Depends(verify_auth)
+):
+    """查询任务状态"""
+    task = app.state.tasks.get(request.requestId)
+    if not task:
+        raise HTTPException(
+            status_code=404,
+            detail={"status": "Failed", "reason": "无效的任务ID"}
+        )
+
+    # 计算队列位置（仅当在队列中时）
+    queue_pos = 0
+    if task['status'] == "InQueue" and request.requestId in app.state.pending_queue:
+        queue_pos = app.state.pending_queue.index(request.requestId) + 1
+
+    response = {
+        "status": task['status'],
+        "reason": task.get('reason'),
+        "queue_position": queue_pos if task['status'] == "InQueue" else None  # 非排队状态返回null
+    }
+
+    # 成功状态的特殊处理
+    if task['status'] == "Succeed":
+        response["results"] = {
+            "videos": [{"url": task['download_url']}],
+            "timings": {
+                "inference": task['completed_at'] - task['started_at']
+            },
+            "seed": task['request'].seed
+        }
+    # 取消状态的补充信息
+    elif task['status'] == "Cancelled":
+        response["reason"] = task.get('reason', "用户主动取消")  # 确保原因字段存在
+
+    return response
+
+@app.post("/video/cancel",
+         response_model=dict,
+         tags=["视频生成"])
+async def cancel_task(
+    request: VideoCancelRequest,
+    auth: bool = Depends(verify_auth)
+):
+    """取消排队中的生成任务"""
+    task_id = request.requestId
+  
+    with app.state.task_lock:
+        task = app.state.tasks.get(task_id)
+      
+        # 检查任务是否存在
+        if not task:
+            raise HTTPException(
+                status_code=404,
+                detail={"status": "Failed", "reason": "无效的任务ID"}
+            )
+          
+        current_status = task['status']
+      
+        # 仅允许取消排队中的任务
+        if current_status != "InQueue":
+            raise HTTPException(
+                status_code=400,
+                detail={"status": "Failed", "reason": f"仅允许取消排队任务，当前状态: {current_status}"}
+            )
+      
+        # 从队列移除
+        try:
+            app.state.pending_queue.remove(task_id)
+        except ValueError:
+            pass  # 可能已被处理
+      
+        # 更新任务状态
+        task.update({
+            "status": "Cancelled",
+            "reason": "用户主动取消",
+            "completed_at": int(time.time())
+        })
+      
+    return {"status": "Succeed"}
+
+async def cleanup_files(paths: List[str], delay: int = 3600):
+    """定时清理文件"""
+    await asyncio.sleep(delay)
+    for path in paths:
+        try:
+            if os.path.exists(path):
+                os.remove(path)
+        except Exception as e:
+            print(f"清理失败 {path}: {str(e)}")
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=8088)
--- a/t2v-api.py
+++ b/t2v-api.py
@ -0,0 +1,450 @@
+import os
+import torch
+import uuid
+import time
+import asyncio
+from enum import Enum
+from threading import Lock
+from typing import Optional, Dict, List
+from contextlib import asynccontextmanager
+from fastapi import FastAPI, HTTPException, status, Depends
+from fastapi.staticfiles import StaticFiles
+from pydantic import BaseModel, Field, field_validator, ValidationError
+from diffusers.utils import export_to_video
+from diffusers import AutoencoderKLWan, WanPipeline
+from diffusers.schedulers.scheduling_unipc_multistep import UniPCMultistepScheduler
+from fastapi.security import HTTPBearer, HTTPAuthorizationCredentials
+from fastapi.responses import JSONResponse
+
+# 创建视频存储目录
+os.makedirs("generated_videos", exist_ok=True)
+
+# 生命周期管理器
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    """管理应用生命周期"""
+    # 初始化模型和资源
+    try:
+        # 初始化认证密钥
+        app.state.valid_api_keys = {
+            "密钥"
+        }
+
+        # 初始化视频生成模型
+        model_id = "./Wan2.1-T2V-1.3B-Diffusers"
+        vae = AutoencoderKLWan.from_pretrained(
+            model_id,
+            subfolder="vae",
+            torch_dtype=torch.float32
+        )
+      
+        scheduler = UniPCMultistepScheduler(
+            prediction_type='flow_prediction',
+            use_flow_sigmas=True,
+            num_train_timesteps=1000,
+            flow_shift=3.0
+        )
+      
+        app.state.pipe = WanPipeline.from_pretrained(
+            model_id,
+            vae=vae,
+            torch_dtype=torch.bfloat16
+        ).to("cuda")
+        app.state.pipe.scheduler = scheduler
+
+        # 初始化任务系统
+        app.state.tasks: Dict[str, dict] = {}
+        app.state.pending_queue: List[str] = []
+        app.state.model_lock = Lock()
+        app.state.task_lock = Lock()
+        app.state.base_url = "ip地址+端口"
+        app.state.max_concurrent = 2
+        app.state.semaphore = asyncio.Semaphore(app.state.max_concurrent)
+
+        # 启动后台任务处理器
+        asyncio.create_task(task_processor())
+      
+        print("✅ 应用初始化完成")
+        yield
+      
+    finally:
+        # 清理资源
+        if hasattr(app.state, 'pipe'):
+            del app.state.pipe
+            torch.cuda.empty_cache()
+            print("♻️ 已释放模型资源")
+
+# 创建FastAPI应用
+app = FastAPI(lifespan=lifespan)
+app.mount("/videos", StaticFiles(directory="generated_videos"), name="videos")
+
+# 认证模块
+security = HTTPBearer(auto_error=False)
+
+# ======================
+# 数据模型--查询参数模型
+# ======================
+class VideoSubmitRequest(BaseModel):
+    model: str = Field(default="Wan2.1-T2V-1.3B",description="使用的模型版本")
+    prompt: str = Field(
+        ...,
+        min_length=10,
+        max_length=500,
+        description="视频描述提示词，10-500个字符"
+    )
+    image_size: str = Field(
+        ...,
+        description="视频分辨率，仅支持480x832或832x480"
+    )
+    negative_prompt: Optional[str] = Field(
+        default=None,
+        max_length=500,
+        description="排除不需要的内容"
+    )
+    seed: Optional[int] = Field(
+        default=None,
+        ge=0,
+        le=2147483647,
+        description="随机数种子，范围0-2147483647"
+    )
+    num_frames: int = Field(
+        default=81,
+        ge=24,
+        le=120,
+        description="视频帧数，24-120帧"
+    )
+    guidance_scale: float = Field(
+        default=5.0,
+        ge=1.0,
+        le=20.0,
+        description="引导系数，1.0-20.0"
+    )
+    infer_steps: int = Field(
+        default=50,
+        ge=20,
+        le=100,
+        description="推理步数，20-100步"
+    )
+
+    @field_validator('image_size', mode='before')
+    @classmethod
+    def validate_image_size(cls, value):
+        allowed_sizes = {"480x832", "832x480"}
+        if value not in allowed_sizes:
+            raise ValueError(f"仅支持以下分辨率: {', '.join(allowed_sizes)}")
+        return value
+
+class VideoStatusRequest(BaseModel):
+    requestId: str = Field(
+        ...,
+        min_length=32,
+        max_length=32,
+        description="32位任务ID"
+    )
+
+class VideoSubmitResponse(BaseModel):
+    requestId: str
+
+class VideoStatusResponse(BaseModel):
+    status: str = Field(..., description="任务状态: Succeed, InQueue, InProgress, Failed,Cancelled")
+    reason: Optional[str] = Field(None, description="失败原因")
+    results: Optional[dict] = Field(None, description="生成结果")
+    queue_position: Optional[int] = Field(None, description="队列位置")
+
+class VideoCancelRequest(BaseModel):
+    requestId: str = Field(
+        ...,
+        min_length=32,
+        max_length=32,
+        description="32位任务ID"
+    )
+
+# # 自定义HTTP异常处理器
+# @app.exception_handler(HTTPException)
+# async def http_exception_handler(request, exc):
+#     return JSONResponse(
+#         status_code=exc.status_code,
+#         content=exc.detail,  # 直接返回detail内容（不再包装在detail字段）
+#         headers=exc.headers
+#     )
+
+# ======================
+# 后台任务处理
+# ======================
+async def task_processor():
+    """处理任务队列"""
+    while True:
+        async with app.state.semaphore:
+            task_id = await get_next_task()
+            if task_id:
+                await process_task(task_id)
+            else:
+                await asyncio.sleep(0.5)
+
+async def get_next_task():
+    """获取下一个待处理任务"""
+    with app.state.task_lock:
+        if app.state.pending_queue:
+            return app.state.pending_queue.pop(0)
+    return None
+
+async def process_task(task_id: str):
+    """处理单个任务"""
+    task = app.state.tasks.get(task_id)
+    if not task:
+        return
+
+    try:
+        # 更新任务状态
+        task['status'] = 'InProgress'
+        task['started_at'] = int(time.time())
+
+        # 执行视频生成
+        video_path = await generate_video(task['request'], task_id)
+      
+        # 生成下载链接
+        download_url = f"{app.state.base_url}/videos/{os.path.basename(video_path)}"
+      
+        # 更新任务状态
+        task.update({
+            'status': 'Succeed',
+            'download_url': download_url,
+            'completed_at': int(time.time())
+        })
+      
+        # 安排自动清理
+        asyncio.create_task(auto_cleanup(video_path))
+    except Exception as e:
+        handle_task_error(task, e)
+
+def handle_task_error(task: dict, error: Exception):
+    """统一处理任务错误"""
+    error_msg = str(error)
+    if isinstance(error, torch.cuda.OutOfMemoryError):
+        error_msg = "显存不足，请降低分辨率或减少帧数"
+    elif isinstance(error, ValidationError):
+        error_msg = "参数校验失败: " + str(error)
+  
+    task.update({
+        'status': 'Failed',
+        'reason': error_msg,
+        'completed_at': int(time.time())
+    })
+
+# ======================
+# 视频生成核心逻辑
+# ======================
+async def generate_video(request: dict, task_id: str) -> str:
+    """异步执行视频生成"""
+    loop = asyncio.get_event_loop()
+    return await loop.run_in_executor(
+        None,
+        sync_generate_video,
+        request,
+        task_id
+    )
+
+def sync_generate_video(request: dict, task_id: str) -> str:
+    """同步生成视频"""
+    with app.state.model_lock:
+        try:
+            generator = None
+            if request.get('seed') is not None:
+                generator = torch.Generator(device="cuda")
+                generator.manual_seed(request['seed'])
+                print(f"🔮 使用随机种子: {request['seed']}")
+            
+            # 执行模型推理
+            result = app.state.pipe(
+                prompt=request['prompt'],
+                negative_prompt=request['negative_prompt'],
+                height=request['height'],
+                width=request['width'],
+                num_frames=request['num_frames'],
+                guidance_scale=request['guidance_scale'],
+                num_inference_steps=request['infer_steps'],
+                generator=generator
+            )
+          
+            # 导出视频文件
+            video_id = uuid.uuid4().hex
+            output_path = f"generated_videos/{video_id}.mp4"
+            export_to_video(result.frames[0], output_path, fps=16)
+          
+            return output_path
+        except Exception as e:
+            raise RuntimeError(f"视频生成失败: {str(e)}") from e
+
+# ======================
+# API端点
+# ======================
+async def verify_auth(credentials: HTTPAuthorizationCredentials = Depends(security)):
+    """认证验证"""
+    if not credentials:
+        raise HTTPException(
+            status_code=401,
+            detail={"status": "Failed", "reason": "缺少认证头"},
+            headers={"WWW-Authenticate": "Bearer"}
+        )
+    if credentials.scheme != "Bearer":
+        raise HTTPException(
+            status_code=401,
+            detail={"status": "Failed", "reason": "无效的认证方案"},
+            headers={"WWW-Authenticate": "Bearer"}
+        )
+    if credentials.credentials not in app.state.valid_api_keys:
+        raise HTTPException(
+            status_code=401,
+            detail={"status": "Failed", "reason": "无效的API密钥"},
+            headers={"WWW-Authenticate": "Bearer"}
+        )
+    return True
+
+@app.post("/video/submit",
+          response_model=VideoSubmitResponse,
+          status_code=status.HTTP_202_ACCEPTED,
+          tags=["视频生成"],
+          summary="提交视频生成请求")
+async def submit_video_task(
+    request: VideoSubmitRequest,
+    auth: bool = Depends(verify_auth)
+):
+    """提交新的视频生成任务"""
+    try:
+        # 解析分辨率参数
+        width, height = map(int, request.image_size.split('x'))
+      
+        # 创建任务记录
+        task_id = uuid.uuid4().hex
+        task_data = {
+            'request': {
+                'prompt': request.prompt,
+                'negative_prompt': request.negative_prompt,
+                'width': width,
+                'height': height,
+                'num_frames': request.num_frames,
+                'guidance_scale': request.guidance_scale,
+                'infer_steps': request.infer_steps,
+                'seed': request.seed
+            },
+            'status': 'InQueue',
+            'created_at': int(time.time())
+        }
+      
+        # 加入任务队列
+        with app.state.task_lock:
+            app.state.tasks[task_id] = task_data
+            app.state.pending_queue.append(task_id)
+      
+        return {"requestId": task_id}
+  
+    except ValidationError as e:
+        raise HTTPException(
+            status_code=422,
+            detail={"status": "Failed", "reason": str(e)}
+        )
+
+@app.post("/video/status",
+          response_model=VideoStatusResponse,
+          tags=["视频生成"],
+          summary="查询任务状态")
+async def get_video_status(
+    request: VideoStatusRequest,
+    auth: bool = Depends(verify_auth)
+):
+    """查询任务状态"""
+    task = app.state.tasks.get(request.requestId)
+    if not task:
+        raise HTTPException(
+            status_code=404,
+            detail={"status": "Failed", "reason": "无效的任务ID"}
+        )
+
+    # 计算队列位置（仅当在队列中时）
+    queue_pos = 0
+    if task['status'] == "InQueue" and request.requestId in app.state.pending_queue:
+        queue_pos = app.state.pending_queue.index(request.requestId) + 1
+
+    response = {
+        "status": task['status'],
+        "reason": task.get('reason'),
+        "queue_position": queue_pos if task['status'] == "InQueue" else None  # 非排队状态返回null
+    }
+
+    # 成功状态的特殊处理
+    if task['status'] == "Succeed":
+        response["results"] = {
+            "videos": [{"url": task['download_url']}],
+            "timings": {
+                "inference": task['completed_at'] - task['started_at']
+            },
+            "seed": task['request']['seed']
+        }
+    # 取消状态的补充信息
+    elif task['status'] == "Cancelled":
+        response["reason"] = task.get('reason', "用户主动取消")  # 确保原因字段存在
+
+    return response
+
+
+@app.post("/video/cancel",
+         response_model=dict,
+         tags=["视频生成"])
+async def cancel_task(
+    request: VideoCancelRequest,
+    auth: bool = Depends(verify_auth)
+):
+    """取消排队中的生成任务"""
+    task_id = request.requestId
+  
+    with app.state.task_lock:
+        task = app.state.tasks.get(task_id)
+      
+        # 检查任务是否存在
+        if not task:
+            raise HTTPException(
+                status_code=404,
+                detail={"status": "Failed", "reason": "无效的任务ID"}
+            )
+          
+        current_status = task['status']
+      
+        # 仅允许取消排队中的任务
+        if current_status != "InQueue":
+            raise HTTPException(
+                status_code=400,
+                detail={"status": "Failed", "reason": f"仅允许取消排队任务，当前状态: {current_status}"}
+            )
+      
+        # 从队列移除
+        try:
+            app.state.pending_queue.remove(task_id)
+        except ValueError:
+            pass  # 可能已被处理
+      
+        # 更新任务状态
+        task.update({
+            "status": "Cancelled",
+            "reason": "用户主动取消",
+            "completed_at": int(time.time())
+        })
+      
+    return {"status": "Succeed"}
+
+
+# ======================
+# 工具函数
+# ======================
+async def auto_cleanup(file_path: str, delay: int = 3600):
+    """自动清理生成的视频文件"""
+    await asyncio.sleep(delay)
+    try:
+        if os.path.exists(file_path):
+            os.remove(file_path)
+            print(f"已清理文件: {file_path}")
+    except Exception as e:
+        print(f"文件清理失败: {str(e)}")
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=8088)