Update app.py

app.py

@@ -116,14 +116,8 @@ def generate_image(
         transformer=transformer
     )
     
-    # 关键修复：手动设置 pipeline 使用的设备
-    # 这确保 _execution_device 返回正确的设备
-    pipe._execution_device = device
-    
-    # 同时确保 processor 也在正确设备上处理
-    # 修改 pipe 的 device 属性（如果存在）
-    if hasattr(pipe, 'device'):
-        pipe.device = device
+    # 注意：不需要手动设置 _execution_device
+    # 修复后的 pipeline_qwenimage_edit.py 会直接从 text_encoder 获取设备
     
     print(f"✅ Model loaded!")
     print(f"   text_encoder device: {next(text_encoder.parameters()).device}")
@@ -178,7 +172,7 @@ def load_transformer(device, dtype):
                 return QwenImageTransformer2DModel.from_pretrained(
                     TRANSFORMER_PATH,
                     torch_dtype=dtype,
-                    use_safetensors=False
+                    use_safetensors=False  # 使用 .bin 文件
                 ).to(device).eval()
             else:
                 return QwenImageTransformer2DModel.from_pretrained(
@@ -192,18 +186,21 @@ def load_transformer(device, dtype):
         # HuggingFace repo path
         path_parts = TRANSFORMER_PATH.split('/')
         if len(path_parts) >= 3:
-            repo_id = '/'.join(path_parts[:2])
-            subfolder = path_parts[2]
+            # 路径格式: "Skywork/Unipic3-DMD/ema_transformer"
+            repo_id = '/'.join(path_parts[:2])  # "Skywork/Unipic3-DMD"
+            subfolder = '/'.join(path_parts[2:])  # "ema_transformer"
             return QwenImageTransformer2DModel.from_pretrained(
                 repo_id,
                 subfolder=subfolder,
                 torch_dtype=dtype,
+                use_safetensors=False  # 使用 .bin 文件
             ).to(device).eval()
         else:
             return QwenImageTransformer2DModel.from_pretrained(
                 TRANSFORMER_PATH,
                 subfolder='transformer',
                 torch_dtype=dtype,
+                use_safetensors=False
             ).to(device).eval()