Update giga_App.py

giga_App.py

@@ -1,11 +1,11 @@
 import gradio as gr
+from gradio_imageslider import ImageSlider
 from PIL import Image
 import numpy as np
 from aura_sr import AuraSR
 import torch
 import os
 import time
-from pathlib import Path
 import platform
 import argparse
 
@@ -16,6 +16,17 @@ def open_folder():
     elif platform.system() == "Linux":
         os.system(f'xdg-open "{open_folder_path}"')
 
+def get_placeholder_image():
+    """
+    Creates a placeholder image (if not already present) and returns its file path.
+    This placeholder is a blank (white) image that will be used for progress updates.
+    """
+    placeholder_path = "placeholder.png"
+    if not os.path.exists(placeholder_path):
+        placeholder = Image.new("RGB", (256, 256), (255, 255, 255))
+        placeholder.save(placeholder_path)
+    return placeholder_path
+
 # Force CPU usage
 torch.set_default_tensor_type(torch.FloatTensor)
 
@@ -34,7 +45,9 @@ def process_single_image(input_image_path, reduce_seams):
         raise gr.Error("Please provide an image to upscale.")
 
     # Send an initial progress update.
-    yield [[], "Starting upscaling..."]
+    # Instead of (None, None), we use the placeholder image file paths.
+    placeholder = get_placeholder_image()
+    yield [(placeholder, placeholder), "Starting upscaling..."]
 
     # Load the image.
     pil_image = Image.open(input_image_path)
@@ -42,11 +55,9 @@ def process_single_image(input_image_path, reduce_seams):
     # Upscale using the chosen method.
     start_time = time.time()
     if reduce_seams:
-        # Using upscale_4x_overlapped to reduce seam artifacts.
         print("using reduce seams")
         upscaled_image = aura_sr.upscale_4x_overlapped(pil_image)
     else:
-        # Default upscaling method.
         upscaled_image = aura_sr.upscale_4x(pil_image)
     processing_time = time.time() - start_time
     print(f"Processing time: {processing_time:.2f} seconds")
@@ -66,8 +77,8 @@ def process_single_image(input_image_path, reduce_seams):
     
     upscaled_image.save(output_path)
 
-    # Send the final progress update along with the before/after gallery.
-    yield [[input_image_path, output_path],
+    # Send the final progress update along with the before/after slider images.
+    yield [(input_image_path, output_path),
            f"Upscaling complete in {processing_time:.2f} seconds"]
 
 def process_batch(input_folder, output_folder=None, reduce_seams=False):
@@ -84,7 +95,6 @@ def process_batch(input_folder, output_folder=None, reduce_seams=False):
     processed_files = 0
     results = []
 
-    # Initial progress update.
     yield [results, "Starting batch processing..."]
 
     for filename in input_files:
@@ -107,16 +117,13 @@ def process_batch(input_folder, output_folder=None, reduce_seams=False):
             counter += 1
 
         upscaled_image.save(output_path)
-
         processed_files += 1
         results.append(output_path)
-        # Yield progress update after processing each image.
         yield [results, f"Processed {processed_files}/{total_files}: {filename} in {processing_time:.2f} seconds"]
 
-    # Final update.
     yield [results, f"Batch processing complete. {processed_files} images processed."]
 
-title = """<h1 align="center">AuraSR Giga Upscaler V2 by SECourses - Upscales to 4x</h1>
+title = """<h1 align="center">AuraSR Giga Upscaler V3 by SECourses - Upscales to 4x</h1>
 <p><center>AuraSR: new open source super-resolution upscaler based on GigaGAN. Works perfect on some images and fails on some images so give it a try</center></p>
 <p><center>Works very fast and very VRAM friendly</center></p>
 <h2 align="center">Latest version on : <a href="https://www.patreon.com/posts/110060645">https://www.patreon.com/posts/110060645</a></h2>
@@ -132,43 +139,44 @@ def create_demo():
                     input_image = gr.Image(label="Input Image", type="filepath")
                     reduce_seams = gr.Checkbox(
                         label="Reduce Seam Artifacts",
-                        value=False,
+                        value=True,
                         info="upscale_4x upscales the image in tiles that do not overlap. This can result in seams. Use upscale_4x_overlapped to reduce seams. This will double the time upscaling by taking an additional pass and averaging the results."
                     )
                     process_btn = gr.Button(value="Upscale Image", variant="primary")
                 with gr.Column(scale=1):
-                    output_gallery = gr.Gallery(label="Before / After", columns=2)
+                    # Use the ImageSlider component for comparing before & after images.
+                    # "filepath" type means the component expects image file paths.
+                    output_slider = ImageSlider(label="Before / After", type="filepath", slider_color="blue")
                     progress_text = gr.Markdown("Progress messages will appear here.")
                     btn_open_outputs = gr.Button("Open Outputs Folder", variant="primary")
                     btn_open_outputs.click(fn=open_folder)
-
-            # The function now yields two outputs: a gallery and a progress message.
+            
             process_btn.click(
                 fn=process_single_image,
                 inputs=[input_image, reduce_seams],
-                outputs=[output_gallery, progress_text]
+                outputs=[output_slider, progress_text]
             )
-
+        
         with gr.Tab("Batch Processing"):
             with gr.Row():
                 input_folder = gr.Textbox(label="Input Folder Path")
                 output_folder = gr.Textbox(label="Output Folder Path (Optional)")
                 reduce_seams_batch = gr.Checkbox(
                     label="Reduce Seam Artifacts",
-                    value=False,
+                    value=True,
                     info="upscale_4x upscales the image in tiles that do not overlap. This can result in seams. Use upscale_4x_overlapped to reduce seams. This will double the time upscaling by taking an additional pass and averaging the results."
                 )
             batch_process_btn = gr.Button(value="Process Batch", variant="primary")
             with gr.Column():
                 output_gallery_batch = gr.Gallery(label="Processed Images")
                 progress_text_batch = gr.Markdown("Progress messages will appear here.")
-
+            
             batch_process_btn.click(
                 fn=process_batch,
                 inputs=[input_folder, output_folder, reduce_seams_batch],
                 outputs=[output_gallery_batch, progress_text_batch]
             )
-
+    
     return demo
 
 if __name__ == "__main__":