Dynamic interface + time

app.py

@@ -91,7 +91,7 @@ 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 = (127, 127, 127, 0))
+    black_mask = Image.new(mode = input_image.mode, size = (original_width - 20, original_height - 20), color = (0, 0, 0, 0))
     mask_image.paste(black_mask, (enlarge_left + 10, enlarge_top + 10))
     mask_image = mask_image.filter(ImageFilter.BoxBlur(10))
 
@@ -126,26 +126,85 @@ def predict(source_img, enlarge_top, enlarge_right, enlarge_bottom, enlarge_left
         mask_image
     ]
 
-title = "Uncrop"
-description = "<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/><br/>🚀 Powered by <i>SDXL 1.0</i> artificial intellingence<br/><ul><li>If you need to change the <b>view angle</b> of your image, I recommend you to use <i>Zero123</i>,</li><li>If you need to <b>upscale</b> your image, I recommend you to use <i>Ilaria Upscaler</i>,</li><li>If you need to <b>slightly change</b> your image, I recommend you to use <i>Image-to-Image SDXL</i>,</li><li>If you need to change <b>one detail</b> on your image, I recommend you to use <i>Inpaint SDXL</i>.</li></ul><br/>🐌 Slow process... ~20 min with 20 inference steps, ~6 hours with 25 inference steps.<br>You can duplicate this space on a free account, it works on CPU.<br/><a href='https://huggingface.co/spaces/Fabrice-TIERCELIN/Uncrop?duplicate=true'><img src='https://img.shields.io/badge/-Duplicate%20Space-blue?labelColor=white&style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAP5JREFUOE+lk7FqAkEURY+ltunEgFXS2sZGIbXfEPdLlnxJyDdYB62sbbUKpLbVNhyYFzbrrA74YJlh9r079973psed0cvUD4A+4HoCjsA85X0Dfn/RBLBgBDxnQPfAEJgBY+A9gALA4tcbamSzS4xq4FOQAJgCDwV2CPKV8tZAJcAjMMkUe1vX+U+SMhfAJEHasQIWmXNN3abzDwHUrgcRGmYcgKe0bxrblHEB4E/pndMazNpSZGcsZdBlYJcEL9Afo75molJyM2FxmPgmgPqlWNLGfwZGG6UiyEvLzHYDmoPkDDiNm9JR9uboiONcBXrpY1qmgs21x1QwyZcpvxt9NS09PlsPAAAAAElFTkSuQmCC&logoWidth=14'></a><br/><br/>⚖️ You can use, modify and share the generated images but not for commercial uses."
-gr.Interface(fn = predict, inputs = [
-    gr.Image(label = "Your image", source = "upload", type = "numpy"),
-    gr.Number(minimum = 0, value = 64, precision = 0, label = "Enlarge on top", info = "in pixels"),
-    gr.Number(minimum = 0, value = 64, precision = 0, label = "Enlarge on right", info = "in pixels"),
-    gr.Number(minimum = 0, value = 64, precision = 0, label = "Enlarge on bottom", info = "in pixels"),
-    gr.Number(minimum = 0, value = 64, precision = 0, label = "Enlarge on left", info = "in pixels"),
-    gr.Textbox(label = 'Prompt', info = "Describe the subject, the background and the style of image; 77 token limit", placeholder = 'Describe what you want to see in the entire image'),
-    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'),
-    gr.Slider(minimum = 0, maximum = 1000, value = 1000, step = 1, label = "Denoising", info = "lower=irrelevant result, higher=relevant result"),
-    gr.Slider(minimum = 10, maximum = 25, value = 10, step = 1, label = "Number of inference steps", info = "lower=faster, higher=image quality"),
-    gr.Slider(minimum = 1, maximum = 13, value = 7, step = 0.1, label = "Classifier-Free Guidance Scale", info = "lower=image quality, higher=follow the prompt"),
-    gr.Checkbox(label = "Randomize seed (not working, always checked)", value = True, info = "If checked, result is always different"),
-    gr.Slider(minimum = 0, maximum = max_64_bit_int, step = 1, randomize = True, label = "Seed (if not randomized)"),
-    gr.Checkbox(label = "Debug mode", value = True, info = "Show intermediate results")
-], outputs = [
-    gr.Image(label = "Uncropped image"),
-    gr.Label(label = "Information"),
-    gr.Image(label = "Original image"),
-    gr.Image(label = "Enlarged image"),
-    gr.Image(label = "Mask image")
-], title = title, description = description).launch(max_threads = True)
+with gr.Blocks() as interface:
+    gr.Markdown(
+        """
+        <h1 style="text-align: center;">Uncrop</h1>
+        <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/>
+        <br/>
+        🚀 Powered by <i>SDXL 1.0</i> artificial intellingence
+        <br/>
+        <ul>
+        <li>If you need to change the <b>view angle</b> of your image, I recommend you to use <i>Zero123</i>,</li>
+        <li>If you need to <b>upscale</b> your image, I recommend you to use <i>Ilaria Upscaler</i>,</li>
+        <li>If you need to <b>slightly change</b> your image, I recommend you to use <i>Image-to-Image SDXL</i>,</li>
+        <li>If you need to change <b>one detail</b> on your image, I recommend you to use <i>Inpaint SDXL</i>.</li>
+        </ul>
+        <br/>🐌 Slow process... ~20 min with 20 inference steps, ~6 hours with 25 inference steps.<br>You can duplicate this space on a free account, it works on CPU.<br/>
+        <a href='https://huggingface.co/spaces/Fabrice-TIERCELIN/Uncrop?duplicate=true'><img src='https://img.shields.io/badge/-Duplicate%20Space-blue?labelColor=white&style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAP5JREFUOE+lk7FqAkEURY+ltunEgFXS2sZGIbXfEPdLlnxJyDdYB62sbbUKpLbVNhyYFzbrrA74YJlh9r079973psed0cvUD4A+4HoCjsA85X0Dfn/RBLBgBDxnQPfAEJgBY+A9gALA4tcbamSzS4xq4FOQAJgCDwV2CPKV8tZAJcAjMMkUe1vX+U+SMhfAJEHasQIWmXNN3abzDwHUrgcRGmYcgKe0bxrblHEB4E/pndMazNpSZGcsZdBlYJcEL9Afo75molJyM2FxmPgmgPqlWNLGfwZGG6UiyEvLzHYDmoPkDDiNm9JR9uboiONcBXrpY1qmgs21x1QwyZcpvxt9NS09PlsPAAAAAElFTkSuQmCC&logoWidth=14'></a>
+        <br/>
+        <br/>
+        ⚖️ You can use, modify and share the generated images but not for commercial uses.
+
+        """
+    )
+    with gr.Row():
+        with gr.Column():
+        with gr.Column():
+            enlarge_top = gr.Number(minimum = 0, value = 64, precision = 0, label = "Enlarge on top", info = "in pixels")
+        with gr.Column():
+    with gr.Row():
+        with gr.Column():
+            enlarge_left = gr.Number(minimum = 0, value = 64, precision = 0, label = "Enlarge on left", info = "in pixels")
+        with gr.Column():
+            source_img = gr.Image(label = "Your image", source = "upload", type = "numpy")
+        with gr.Column():
+            enlarge_right = gr.Number(minimum = 0, value = 64, precision = 0, label = "Enlarge on right", info = "in pixels")
+    with gr.Row():
+        with gr.Column():
+        with gr.Column():
+            enlarge_bottom = gr.Number(minimum = 0, value = 64, precision = 0, label = "Enlarge on bottom", info = "in pixels")
+        with gr.Column():
+    with gr.Row():
+        with gr.Row():
+            prompt = gr.Textbox(label = 'Prompt', info = "Describe the subject, the background and the style of image; 77 token limit", placeholder = 'Describe what you want to see in the entire image')
+        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")
+            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")
+        with gr.Row():
+            submit = gr.Button("Uncrop", variant = "primary")
+    with gr.Row():
+        uncropped_image = gr.Image(label = "Uncropped image")
+        information = gr.Label(label = "Information")
+        original_image = gr.Image(label = "Original image")
+        enlarged_image = gr.Image(label = "Enlarged image")
+        mask_image = gr.Image(label = "Mask image")
+    submit.click(predict, inputs = [
+        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
+    ], outputs = [
+        uncropped_image,
+        information,
+        original_image,
+        enlarged_image,
+        mask_image
+    ], scroll_to_output = True)
+
+    interface.queue().launch()