Smooth border

app.py

@@ -19,30 +19,39 @@ pipe = pipe.to(device)
 def noise_color(color, noise):
     return color + random.randint(- noise, noise)
 
-def predict(source_img, enlarge_top, enlarge_right, enlarge_bottom, enlarge_left, prompt, negative_prompt, denoising_steps, num_inference_steps, guidance_scale, randomize_seed, seed, debug_mode, progress=gr.Progress()):
+def predict(
+    source_img,
+    enlarge_top,
+    enlarge_right,
+    enlarge_bottom,
+    enlarge_left,
+    prompt,
+    negative_prompt,
+    smooth_border,
+    denoising_steps,
+    num_inference_steps,
+    guidance_scale,
+    randomize_seed,
+    seed,
+    debug_mode,
+    progress = gr.Progress()):
     start = time.time()
     progress(0, desc = "Preparing data...")
 
     if source_img is None:
         raise gr.Error("Please provide an image.")
 
-    if prompt is None or prompt == "":
-        raise gr.Error("Please provide a prompt input.")
-
-    if negative_prompt is None or negative_prompt == "":
-        raise gr.Error("Please provide a negative prompt input.")
-
     if enlarge_top is None or enlarge_top == "":
-        raise gr.Error("Please provide a top input.")
+        enlarge_top = 0
 
     if enlarge_right is None or enlarge_right == "":
-        raise gr.Error("Please provide a right input.")
+        enlarge_right = 0
 
     if enlarge_bottom is None or enlarge_bottom == "":
-        raise gr.Error("Please provide a bottom input.")
+        enlarge_bottom = 0
 
     if enlarge_left is None or enlarge_left == "":
-        raise gr.Error("Please provide a left input.")
+        enlarge_left = 0
 
     if enlarge_top < 0 or enlarge_right < 0 or enlarge_bottom < 0 or enlarge_left < 0:
         raise gr.Error("Please only provide positive margins.")
@@ -50,6 +59,15 @@ def predict(source_img, enlarge_top, enlarge_right, enlarge_bottom, enlarge_left
     if enlarge_top == 0 and enlarge_right == 0 and enlarge_bottom == 0 and enlarge_left == 0:
         raise gr.Error("At least one border must be enlarged.")
 
+    if prompt is None or prompt == "":
+        raise gr.Error("Please provide a prompt input.")
+
+    if negative_prompt is None:
+        negative_prompt = ""
+
+    if smooth_border is None:
+        smooth_border = 20
+
     if randomize_seed:
         seed = random.randint(0, max_64_bit_int)
 
@@ -110,9 +128,9 @@ 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 = (127, 127, 127, 0))
-    mask_image.paste(black_mask, (enlarge_left + 10, enlarge_top + 10))
-    mask_image = mask_image.filter(ImageFilter.BoxBlur(10))
+    black_mask = Image.new(mode = input_image.mode, size = (original_width - smooth_border, original_height - smooth_border), color = (127, 127, 127, 0))
+    mask_image.paste(black_mask, (enlarge_left + (smooth_border // 2), enlarge_top + (smooth_border // 2)))
+    mask_image = mask_image.filter(ImageFilter.BoxBlur((smooth_border // 2)))
 
     limitation = "";
 
@@ -200,6 +218,7 @@ with gr.Blocks() as interface:
     with gr.Row():
          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')
+            smooth_border = gr.Slider(minimum = 0, maximum = 1024, value = 20, step = 2, label = "Smooth border", info = "lower=preserve original, higher=seamless")
             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 = 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")
@@ -228,6 +247,7 @@ with gr.Blocks() as interface:
         enlarge_left,
         prompt,
         negative_prompt,
+        smooth_border,
         denoising_steps,
         num_inference_steps,
         guidance_scale,