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| #!/usr/bin/env python | |
| from __future__ import annotations | |
| import pathlib | |
| import gradio as gr | |
| from dualstylegan import Model | |
| DESCRIPTION = '''# Portrait Style Transfer with [DualStyleGAN](https://github.com/williamyang1991/DualStyleGAN) | |
| <img id="overview" alt="overview" src="https://raw.githubusercontent.com/williamyang1991/DualStyleGAN/main/doc_images/overview.jpg" /> | |
| ''' | |
| def get_style_image_url(style_name: str) -> str: | |
| base_url = 'https://raw.githubusercontent.com/williamyang1991/DualStyleGAN/main/doc_images' | |
| filenames = { | |
| 'cartoon': 'cartoon_overview.jpg', | |
| 'caricature': 'caricature_overview.jpg', | |
| 'anime': 'anime_overview.jpg', | |
| 'arcane': 'Reconstruction_arcane_overview.jpg', | |
| 'comic': 'Reconstruction_comic_overview.jpg', | |
| 'pixar': 'Reconstruction_pixar_overview.jpg', | |
| 'slamdunk': 'Reconstruction_slamdunk_overview.jpg', | |
| } | |
| return f'{base_url}/{filenames[style_name]}' | |
| def get_style_image_markdown_text(style_name: str) -> str: | |
| url = get_style_image_url(style_name) | |
| return f'<img id="style-image" src="{url}" alt="style image">' | |
| def update_slider(choice: str) -> dict: | |
| max_vals = { | |
| 'cartoon': 316, | |
| 'caricature': 198, | |
| 'anime': 173, | |
| 'arcane': 99, | |
| 'comic': 100, | |
| 'pixar': 121, | |
| 'slamdunk': 119, | |
| } | |
| return gr.update(maximum=max_vals[choice]) | |
| def update_style_image(style_name: str) -> dict: | |
| text = get_style_image_markdown_text(style_name) | |
| return gr.update(value=text) | |
| model = Model() | |
| with gr.Blocks(css='style.css') as demo: | |
| gr.Markdown(DESCRIPTION) | |
| with gr.Box(): | |
| gr.Markdown('''## Step 1 (Preprocess Input Image) | |
| - Drop an image containing a near-frontal face to the **Input Image**. | |
| - If there are multiple faces in the image, hit the Edit button in the upper right corner and crop the input image beforehand. | |
| - Hit the **Preprocess** button. | |
| - Choose the encoder version. Default is Z+ encoder which has better stylization performance. W+ encoder better reconstructs the input image to preserve more details. | |
| - The final result will be based on this **Reconstructed Face**. So, if the reconstructed image is not satisfactory, you may want to change the input image. | |
| ''') | |
| with gr.Row(): | |
| encoder_type = gr.Radio(label='Encoder Type', | |
| choices=[ | |
| 'Z+ encoder (better stylization)', | |
| 'W+ encoder (better reconstruction)' | |
| ], | |
| value='Z+ encoder (better stylization)') | |
| with gr.Row(): | |
| with gr.Column(): | |
| with gr.Row(): | |
| input_image = gr.Image(label='Input Image', | |
| type='filepath') | |
| with gr.Row(): | |
| preprocess_button = gr.Button('Preprocess') | |
| with gr.Column(): | |
| with gr.Row(): | |
| aligned_face = gr.Image(label='Aligned Face', | |
| type='numpy', | |
| interactive=False) | |
| with gr.Column(): | |
| reconstructed_face = gr.Image(label='Reconstructed Face', | |
| type='numpy') | |
| instyle = gr.State() | |
| with gr.Row(): | |
| paths = sorted(pathlib.Path('images').glob('*.jpg')) | |
| gr.Examples(examples=[[path.as_posix()] for path in paths], | |
| inputs=input_image) | |
| with gr.Box(): | |
| gr.Markdown('''## Step 2 (Select Style Image) | |
| - Select **Style Type**. | |
| - Select **Style Image Index** from the image table below. | |
| ''') | |
| with gr.Row(): | |
| with gr.Column(): | |
| style_type = gr.Radio(label='Style Type', | |
| choices=model.style_types, | |
| value=model.style_types[0]) | |
| text = get_style_image_markdown_text('cartoon') | |
| style_image = gr.Markdown(value=text) | |
| style_index = gr.Slider(label='Style Image Index', | |
| minimum=0, | |
| maximum=316, | |
| step=1, | |
| value=26) | |
| with gr.Row(): | |
| gr.Examples( | |
| examples=[ | |
| ['cartoon', 26], | |
| ['caricature', 65], | |
| ['arcane', 63], | |
| ['pixar', 80], | |
| ], | |
| inputs=[style_type, style_index], | |
| ) | |
| with gr.Box(): | |
| gr.Markdown('''## Step 3 (Generate Style Transferred Image) | |
| - Adjust **Structure Weight** and **Color Weight**. | |
| - These are weights for the style image, so the larger the value, the closer the resulting image will be to the style image. | |
| - Tips: For W+ encoder, better way of (Structure Only) is to uncheck (Structure Only) and set Color weight to 0. | |
| - Hit the **Generate** button. | |
| ''') | |
| with gr.Row(): | |
| with gr.Column(): | |
| with gr.Row(): | |
| structure_weight = gr.Slider(label='Structure Weight', | |
| minimum=0, | |
| maximum=1, | |
| step=0.1, | |
| value=0.6) | |
| with gr.Row(): | |
| color_weight = gr.Slider(label='Color Weight', | |
| minimum=0, | |
| maximum=1, | |
| step=0.1, | |
| value=1) | |
| with gr.Row(): | |
| structure_only = gr.Checkbox(label='Structure Only', | |
| value=False) | |
| with gr.Row(): | |
| generate_button = gr.Button('Generate') | |
| with gr.Column(): | |
| result = gr.Image(label='Result') | |
| with gr.Row(): | |
| gr.Examples( | |
| examples=[ | |
| [0.6, 1.0], | |
| [0.3, 1.0], | |
| [0.0, 1.0], | |
| [1.0, 0.0], | |
| ], | |
| inputs=[structure_weight, color_weight], | |
| ) | |
| preprocess_button.click( | |
| fn=model.detect_and_align_face, | |
| inputs=[input_image], | |
| outputs=aligned_face, | |
| ) | |
| aligned_face.change( | |
| fn=model.reconstruct_face, | |
| inputs=[aligned_face, encoder_type], | |
| outputs=[ | |
| reconstructed_face, | |
| instyle, | |
| ], | |
| ) | |
| style_type.change( | |
| fn=update_slider, | |
| inputs=style_type, | |
| outputs=style_index, | |
| ) | |
| style_type.change( | |
| fn=update_style_image, | |
| inputs=style_type, | |
| outputs=style_image, | |
| ) | |
| generate_button.click( | |
| fn=model.generate, | |
| inputs=[ | |
| style_type, | |
| style_index, | |
| structure_weight, | |
| color_weight, | |
| structure_only, | |
| instyle, | |
| ], | |
| outputs=result, | |
| ) | |
| demo.queue(max_size=20).launch() | |