处理完成后修改任务状态

server.py

@@ -213,6 +213,12 @@ def put_watermark(img, wm_encoder=None):
 import time
 import requests
 
+# 对任务状态的修改
+def update_task_status(task:dict, status:str, progress:int):
+    task["status"] = status
+    task["progress"] = progress
+    requests.put(f"http://localhost:3000/api/drawing/{task['id']}", data=task)
+
 def main_dev(opt):
     model_name = ''   # 默认模型
     model      = None # 默认模型
@@ -303,7 +309,7 @@ def main_dev(opt):
             start_code = None
             if opt.fixed_code:
                 start_code = torch.randn([opt.n_samples, opt.C, opt.H // opt.f, opt.W // opt.f], device=device)
-            # x
+            # 切换模型
             if opt.torchscript or opt.ipex:
                 transformer = model.cond_stage_model.model
                 unet = model.model.diffusion_model
@@ -410,8 +416,9 @@ def main_dev(opt):
                     grid = put_watermark(grid, wm_encoder)
                     grid.save(os.path.join(outpath, f'grid-{grid_count:04}.png'))
                     grid_count += 1
-            print(f"Your samples are ready and waiting for you here: \n{outpath} \n"
-                  f" \nEnjoy.")
+            print(f"Your samples are ready and waiting for you here: \n{outpath} \n", f" \nEnjoy.")
+            # 修改任务状态为完成
+            update_task_status(task=item, status='done', progress=1)
         # 任务结束, 等待20s后退出
         print("任务结束, 等待20s后退出..")
         time.sleep(20)

server/api/drawing.ts

@@ -38,7 +38,6 @@ export default defineEventHandler(async event => {
         const body = await readBody(event)                     // 读取body数据到
         body.tid = Math.random().toString(36).substring(2, 34) // 生成随机任务ID
         body.uid = 1234567890                                  // 用户ID
-        body.status = 'waiting'                                // 任务状态
         body.createdAt = new Date().getTime()                  // 设定创建时间
         // 验证需要输入的参数
         body.model =  'SD2'                                    // 模型
@@ -52,6 +51,9 @@ export default defineEventHandler(async event => {
         body.sampler = 'pndm'                                  // 扩散采样器
         body.prompt_guidance = 0.5                             // 提示词权重
         body.quality_details = 25                              // 质量和细节(步数)
+        // 输出参数
+        body.status = 'waiting'                                // 任务状态
+        body.progress = 0.00                                   // 任务进度
         body.image = ''                                        // 图片数据
         body.data = null                                       // 生成结果
         console.log('task:', body)