diff --git a/server.py b/server.py
index 45cc34f..eda9cb1 100644
--- a/server.py
+++ b/server.py
@@ -229,7 +229,8 @@ def get_tasks(tasks:list=[]):
             tasks = requests.get("http://localhost:3000/api/drawing?status=waiting").json()
             if len(tasks) == 0: time.sleep(2)
         except:
-            print("get tasks error")
+            # 打印当前时间
+            print("get tasks error", time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()))
             time.sleep(2)
     return tasks
 
@@ -241,10 +242,11 @@ def main_dev(opt):
     while True:
         for task in get_tasks():                   # 遍历 tasks 返回 dict
             print('task:', task)                   # 打印任务
-            update_task_status(task, "running", 0) # 更新任务状态为运行中
 
             # 如果模型不同，重新加载模型(注意释放内存)
             if task['ckpt'] != model_name:
+                # 修改状态为加载模型
+                update_task_status(task, "loading", 0)
                 # 获取环境配置
                 model_name = task['ckpt']
                 opt.config = f'/data/{model_name}.yaml'
@@ -257,6 +259,9 @@ def main_dev(opt):
                 print(f"加载模型到显存: {model_name}..")
                 model = load_model_from_config(config, f"{opt.ckpt}", device)
                 print(f"加载到显存完成: {model_name}")
+            
+            # 更新任务状态为运行中
+            update_task_status(task, "running", 0)
 
             # 使用指定的模型和配置文件进行推理一组参数
             if opt.plms:
@@ -300,13 +305,16 @@ def main_dev(opt):
             start_code = None
             if opt.fixed_code:
                 start_code = torch.randn([task['number'], opt.C, opt.H // opt.f, opt.W // opt.f], device=device)
+
+            # 更新进度
+            update_task_status(task, "running", 0.1)
+
             # 生成图片
             precision_scope = autocast if opt.precision == "autocast" or opt.bf16 else nullcontext
             with torch.no_grad(), precision_scope(opt.device), model.ema_scope():
                 images = []
                 # 执行指定的任务批次 (row)(task['number'])
                 for n in trange(1, desc="Sampling"):
-                    print("Sampling:", data)
                     for prompts in tqdm(data, desc="data"):
                         uc = None
                         if opt.scale != 1.0:
@@ -327,7 +335,6 @@ def main_dev(opt):
                         x_samples = model.decode_first_stage(samples)
                         x_samples = torch.clamp((x_samples + 1.0) / 2.0, min=0.0, max=1.0)
                         for x_sample in x_samples:
-                            print("Sample count:", sample_count)
                             imge_path = os.path.join(sample_path, f"{base_count:05}.png")
                             imge_path = os.path.abspath(imge_path) # 转换为绝对路径
                             x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
@@ -337,7 +344,6 @@ def main_dev(opt):
                             base_count += 1
                             sample_count += 1
                             images.append(imge_path)
-                        print("Sample count:", sample_count)
                 update_task_status(task=task, status='done', progress=1, data=images) # 修改任务状态为完成
         print("批次任务结束..")
         #break
diff --git a/server/api/img/[id].ts b/server/api/img/[id].ts
index 450c2f6..15fd9a3 100644
--- a/server/api/img/[id].ts
+++ b/server/api/img/[id].ts
@@ -8,7 +8,7 @@ export default defineEventHandler(async event => {
         if (!fs.existsSync('outputs')) {
             // 打印当前执行目录
             console.log('cwd:', process.cwd())
-            path = `../../outputs/txt2img-samples/samples/${event.context.params.id}`
+            path = `../outputs/txt2img-samples/samples/${event.context.params.id}`
         }
         console.log('path:', path)
         return fs.readFileSync(path)