Update giga_App.py
Browse files- giga_App.py +26 -2
giga_App.py
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@@ -9,6 +9,9 @@ import time
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import platform
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import argparse
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def open_folder():
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open_folder_path = os.path.abspath("outputs")
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if platform.system() == "Windows":
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@@ -82,6 +85,10 @@ def process_single_image(input_image_path, reduce_seams):
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f"Upscaling complete in {processing_time:.2f} seconds"]
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def process_batch(input_folder, output_folder=None, reduce_seams=False):
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if not input_folder:
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raise gr.Error("Please provide an input folder path.")
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@@ -98,6 +105,11 @@ def process_batch(input_folder, output_folder=None, reduce_seams=False):
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yield [results, "Starting batch processing..."]
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for filename in input_files:
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input_path = os.path.join(input_folder, filename)
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pil_image = Image.open(input_path)
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@@ -123,7 +135,12 @@ def process_batch(input_folder, output_folder=None, reduce_seams=False):
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yield [results, f"Batch processing complete. {processed_files} images processed."]
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-
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<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>
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<p><center>Works very fast and very VRAM friendly</center></p>
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<h2 align="center">Latest version on : <a href="https://www.patreon.com/posts/121441873">https://www.patreon.com/posts/121441873</a></h2>
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@@ -166,7 +183,9 @@ def create_demo():
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value=True,
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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."
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)
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with gr.Column():
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output_gallery_batch = gr.Gallery(label="Processed Images")
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progress_text_batch = gr.Markdown("Progress messages will appear here.")
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@@ -176,6 +195,11 @@ def create_demo():
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inputs=[input_folder, output_folder, reduce_seams_batch],
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outputs=[output_gallery_batch, progress_text_batch]
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)
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return demo
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import platform
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import argparse
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# Global variable to control batch processing cancellation.
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stop_batch_flag = False
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def open_folder():
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open_folder_path = os.path.abspath("outputs")
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if platform.system() == "Windows":
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f"Upscaling complete in {processing_time:.2f} seconds"]
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def process_batch(input_folder, output_folder=None, reduce_seams=False):
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global stop_batch_flag
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# Reset the stop flag for each new batch process.
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stop_batch_flag = False
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if not input_folder:
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raise gr.Error("Please provide an input folder path.")
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yield [results, "Starting batch processing..."]
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for filename in input_files:
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# Check if the stop flag has been set.
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if stop_batch_flag:
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yield [results, "Batch processing cancelled by user."]
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return
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input_path = os.path.join(input_folder, filename)
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pil_image = Image.open(input_path)
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yield [results, f"Batch processing complete. {processed_files} images processed."]
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def stop_batch_process():
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global stop_batch_flag
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stop_batch_flag = True
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return "Stop button clicked. Cancelling batch processing..."
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title = """<h1 align="center">AuraSR Giga Upscaler V4 by SECourses - Upscales to 4x</h1>
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<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>
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<p><center>Works very fast and very VRAM friendly</center></p>
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<h2 align="center">Latest version on : <a href="https://www.patreon.com/posts/121441873">https://www.patreon.com/posts/121441873</a></h2>
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value=True,
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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."
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)
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with gr.Row():
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batch_process_btn = gr.Button(value="Process Batch", variant="primary")
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stop_batch_btn = gr.Button(value="Stop Batch Processing", variant="secondary")
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with gr.Column():
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output_gallery_batch = gr.Gallery(label="Processed Images")
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progress_text_batch = gr.Markdown("Progress messages will appear here.")
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inputs=[input_folder, output_folder, reduce_seams_batch],
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outputs=[output_gallery_batch, progress_text_batch]
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)
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stop_batch_btn.click(
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fn=stop_batch_process,
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inputs=[],
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outputs=[progress_text_batch]
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)
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return demo
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