Default parameters

app.py

@@ -44,10 +44,17 @@ def predict(source_img, enlarge_top, enlarge_right, enlarge_bottom, enlarge_left
     random.seed(seed)
     #pipe = pipe.manual_seed(seed)
 
-    imageio.imwrite("data.png", source_img)
+    try:
+        imageio.imwrite("data.png", source_img)
+    except:
+        raise gr.Error("Can't read input image. You can try to first save your image in another format (.webp, .png, .jpeg, .bmp...).")
 
     # Input image
-    input_image = Image.open("data.png").convert("RGB")
+    try:
+        input_image = Image.open("data.png").convert("RGB")
+    except:
+        raise gr.Error("Can't open input image. You can try to first save your image in another format (.webp, .png, .jpeg, .bmp...).")
+
     original_height, original_width, original_channel = np.array(input_image).shape
     output_width = enlarge_left + original_width + enlarge_right
     output_height = enlarge_top + original_height + enlarge_bottom
@@ -91,7 +98,7 @@ def predict(source_img, enlarge_top, enlarge_right, enlarge_bottom, enlarge_left
 
     # Mask
     mask_image = Image.new(mode = input_image.mode, size = (output_width, output_height), color = (255, 255, 255, 0))
-    black_mask = Image.new(mode = input_image.mode, size = (original_width - 20, original_height - 20), color = (0, 0, 0, 0))
+    black_mask = Image.new(mode = input_image.mode, size = (original_width - 20, original_height - 20), color = (127, 127, 127, 0))
     mask_image.paste(black_mask, (enlarge_left + 10, enlarge_top + 10))
     mask_image = mask_image.filter(ImageFilter.BoxBlur(10))
 
@@ -129,7 +136,7 @@ def predict(source_img, enlarge_top, enlarge_right, enlarge_bottom, enlarge_left
 with gr.Blocks() as interface:
     gr.Markdown(
         """
-        <big style="text-align: center;">Uncrop</big>
+        <p style="text-align: center;"><b><big>Uncrop</big></b></p>
         <br/>
         <p style="text-align: center;">Enlarges the point of view of your image, up to 1 million pixels, freely, without account, without watermark, which can be downloaded</p>
         <br/>
@@ -177,11 +184,11 @@ with gr.Blocks() as interface:
          with gr.Accordion("Advanced options", open = False):
             negative_prompt = gr.Textbox(label = 'Negative prompt', placeholder = 'Describe what you do NOT want to see in the entire image', value = 'Border, frame, painting, scribbling, smear, noise, blur, watermark')
             denoising_steps = gr.Slider(minimum = 0, maximum = 1000, value = 1000, step = 1, label = "Denoising", info = "lower=irrelevant result, higher=relevant result")
-            num_inference_steps = gr.Slider(minimum = 10, maximum = 25, value = 10, step = 1, label = "Number of inference steps", info = "lower=faster, higher=image quality")
+            num_inference_steps = gr.Slider(minimum = 10, maximum = 25, value = 20, step = 1, label = "Number of inference steps", info = "lower=faster, higher=image quality")
             guidance_scale = gr.Slider(minimum = 1, maximum = 13, value = 7, step = 0.1, label = "Classifier-Free Guidance Scale", info = "lower=image quality, higher=follow the prompt")
             randomize_seed = gr.Checkbox(label = "Randomize seed (not working, always checked)", value = True, info = "If checked, result is always different")
             seed = gr.Slider(minimum = 0, maximum = max_64_bit_int, step = 1, randomize = True, label = "Seed (if not randomized)")
-            debug_mode = gr.Checkbox(label = "Debug mode", value = True, info = "Show intermediate results")
+            debug_mode = gr.Checkbox(label = "Debug mode", value = False, info = "Show intermediate results")
     with gr.Row():
         submit = gr.Button("Uncrop", variant = "primary")
     with gr.Row():