Update app.py

app.py

@@ -333,22 +333,22 @@ def run_demo_server(pipe_depth, pipe_normal, pipe_dis):
 
                 filenames = ['normal_1.jpg','normal_10.jpg']
                 # filenames.extend(["normal_%d.jpg" %(i+1) for i in range(10)])
-                # example_folder = "images"
-                print('line 337', __file__)
-                example_folder = os.path.join(os.path.dirname(__file__), "images")
-                # print(example_folder)
+                example_folder = "images"
+                # print('line 337', __file__)
+                # example_folder = os.path.join(os.path.dirname(__file__), "images")
                 
                 Examples(
                     fn=process_pipe_normal,
                     examples=[
-                        os.path.join("images", name)
+                        os.path.join(example_folder, name)
                         for name in filenames
                     ],
                     inputs=[normal_image_input],
                     outputs=[normal_image_output, normal_image_output_files],
-                    # cache_examples=True,
+                    cache_examples=True,
                     # directory_name="examples_normal",
-                    cache_examples=False,
+                    directory_name="images_cache",
+                    # cache_examples=False,
                 )
             
 
@@ -404,9 +404,9 @@ def run_demo_server(pipe_depth, pipe_normal, pipe_dis):
                     ],
                     inputs=[dis_image_input],
                     outputs=[dis_image_output, dis_image_output_files],
-                    # cache_examples=True,
-                    # directory_name="examples_dis",
-                    cache_examples=False,
+                    cache_examples=True,
+                    directory_name="images_cache",
+                    # cache_examples=False,
                 )
 
 
@@ -528,7 +528,7 @@ def main():
     dtype = torch.float16
     
     vae = AutoencoderKL.from_pretrained("guangkaixu/GenPercept", subfolder='vae').to(dtype)
-    unet_depth_v1 = UNet2DConditionModel.from_pretrained('guangkaixu/GenPercept-depth', subfolder="unet").to(dtype)
+    unet_depth_v1 = UNet2DConditionModel.from_pretrained('guangkaixu/genpercept-depth', subfolder="unet").to(dtype)
     unet_normal_v1 = UNet2DConditionModel.from_pretrained('guangkaixu/GenPercept', subfolder="unet_normal_v1").to(dtype)
     unet_dis_v1 = UNet2DConditionModel.from_pretrained('guangkaixu/GenPercept', subfolder="unet_dis_v1").to(dtype)