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giga_App.py

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+import gradio as gr
+from PIL import Image
+import numpy as np
+from aura_sr import AuraSR
+import torch
+import os
+import time
+from pathlib import Path
+import argparse
+
+# Force CPU usage
+torch.set_default_tensor_type(torch.FloatTensor)
+
+# Override torch.load to always use CPU
+original_load = torch.load
+torch.load = lambda *args, **kwargs: original_load(*args, **kwargs, map_location=torch.device('cpu'))
+
+# Initialize the AuraSR model
+aura_sr = AuraSR.from_pretrained("fal/AuraSR-v2")
+
+# Restore original torch.load
+torch.load = original_load
+
+def process_single_image(input_image_path):
+    if input_image_path is None:
+        raise gr.Error("Please provide an image to upscale.")
+
+    # Load the image
+    pil_image = Image.open(input_image_path)
+
+    # Upscale the image using AuraSR
+    start_time = time.time()
+    upscaled_image = aura_sr.upscale_4x(pil_image)
+    processing_time = time.time() - start_time
+
+    print(f"Processing time: {processing_time:.2f} seconds")
+
+    # Save the upscaled image
+    output_folder = "outputs"
+    os.makedirs(output_folder, exist_ok=True)
+    
+    input_filename = os.path.basename(input_image_path)
+    output_filename = os.path.splitext(input_filename)[0]
+    output_path = os.path.join(output_folder, output_filename + ".png")
+    
+    counter = 1
+    while os.path.exists(output_path):
+        output_path = os.path.join(output_folder, f"{output_filename}_{counter:04d}.png")
+        counter += 1
+    
+    upscaled_image.save(output_path)
+
+    return [input_image_path, output_path]
+
+def process_batch(input_folder, output_folder=None):
+    if not input_folder:
+        raise gr.Error("Please provide an input folder path.")
+
+    if not output_folder:
+        output_folder = "outputs"
+
+    os.makedirs(output_folder, exist_ok=True)
+
+    input_files = [f for f in os.listdir(input_folder) if f.lower().endswith(('.png', '.jpg', '.jpeg', '.bmp', '.tiff'))]
+    total_files = len(input_files)
+    processed_files = 0
+    results = []
+
+    for filename in input_files:
+        input_path = os.path.join(input_folder, filename)
+        pil_image = Image.open(input_path)
+
+        start_time = time.time()
+        upscaled_image = aura_sr.upscale_4x(pil_image)
+        processing_time = time.time() - start_time
+
+        output_filename = os.path.splitext(filename)[0] + ".png"
+        output_path = os.path.join(output_folder, output_filename)
+        
+        counter = 1
+        while os.path.exists(output_path):
+            output_path = os.path.join(output_folder, f"{os.path.splitext(filename)[0]}_{counter:04d}.png")
+            counter += 1
+
+        upscaled_image.save(output_path)
+
+        processed_files += 1
+        print(f"Processed {processed_files}/{total_files}: {filename} in {processing_time:.2f} seconds")
+        
+        results.append(output_path)
+
+    print(f"Batch processing complete. {processed_files} images processed.")
+    return results
+
+title = """<h1 align="center">AuraSR Giga Upscaler V1 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></h1>
+"""
+
+def create_demo():
+    with gr.Blocks() as demo:
+        gr.HTML(title)
+        
+        with gr.Tab("Single Image"):
+            with gr.Row():
+                with gr.Column(scale=1):
+                    input_image = gr.Image(label="Input Image", type="filepath")
+                    process_btn = gr.Button(value="Upscale Image", variant="primary")
+                with gr.Column(scale=1):
+                    output_gallery = gr.Gallery(label="Before / After", columns=2)
+
+            process_btn.click(
+                fn=process_single_image,
+                inputs=[input_image],
+                outputs=output_gallery
+            )
+
+        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)")
+            batch_process_btn = gr.Button(value="Process Batch", variant="primary")
+            output_gallery = gr.Gallery(label="Processed Images")
+
+            batch_process_btn.click(
+                fn=process_batch,
+                inputs=[input_folder, output_folder],
+                outputs=output_gallery
+            )
+
+    return demo
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="AuraSR Image Upscaling")
+    parser.add_argument("--share", action="store_true", help="Create a publicly shareable link")
+    args = parser.parse_args()
+
+    demo = create_demo()
+    demo.launch(debug=True, inbrowser=True, share=args.share)

giga_requirements.txt

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+--extra-index-url https://download.pytorch.org/whl/cu118
+aura-sr
+gradio-imageslider
+gradio
+torch==2.2.0
+torchvision==0.17.0
+numpy==1.26.4