Upload app.py

app.py

@@ -62,6 +62,7 @@ pipe = pipe.to(device)
 apply_hidiffusion(pipe)
 
 if not IS_SPACES_ZERO:
+    # apply_hidiffusion(pipe)
     # pipe.enable_xformers_memory_efficient_attention()
     pipe.enable_model_cpu_offload()
 pipe.enable_vae_tiling()
@@ -105,8 +106,8 @@ def predict(
     intensity_threshold=3,
     progress=gr.Progress(track_tqdm=True),
 ):
-    #if IS_SPACES_ZERO:
-    #    apply_hidiffusion(pipe)
+    # if IS_SPACES_ZERO:
+    #     apply_hidiffusion(pipe)
     if input_image is None:
         raise gr.Error("Please upload an image.")
     padded_image = pad_image(input_image).resize((1024, 1024)).convert("RGB")
@@ -141,7 +142,7 @@ def predict(
     output_image = images.images[0]
     print(f"Time taken: {time.time() - last_time}")
     return output_image, padded_image, anyline_image
-    #return (padded_image, output_image), padded_image, anyline_image # for ImageSlider
+    # return (padded_image, output_image), padded_image, anyline_image # for ImageSlider
 
 
 css = """
@@ -251,7 +252,7 @@ SDXL Controlnet [TheMistoAI/MistoLine](https://huggingface.co/TheMistoAI/MistoLi
             btn = gr.Button()
         with gr.Column(scale=2):
             with gr.Group():
-                #image_slider = ImageSlider(position=0.5, type="pil") # broken...
+                # image_slider = ImageSlider(position=0.5, type="pil") # broken...
                 image_slider = gr.Image(type="pil", label="Output Image")
             with gr.Row():
                 padded_image = gr.Image(type="pil", label="Padded Image")