#!/usr/bin/env python from __future__ import annotations import argparse import pathlib import torch import gradio as gr from vtoonify_model import Model def parse_args() -> argparse.Namespace: parser = argparse.ArgumentParser() parser.add_argument('--device', type=str, default='cpu') parser.add_argument('--theme', type=str) parser.add_argument('--share', action='store_true') parser.add_argument('--port', type=int) parser.add_argument('--disable-queue', dest='enable_queue', action='store_false') return parser.parse_args() DESCRIPTION = '''

Portrait Style Transfer with VToonify

''' FOOTER = '
visitor badge
' ARTICLE = r""" If VToonify is helpful, please help to ⭐ the Github Repo. Thanks! [![GitHub Stars](https://img.shields.io/github/stars/williamyang1991/VToonify?style=social)](https://github.com/williamyang1991/VToonify) --- 📝 **Citation** If our work is useful for your research, please consider citing: ```bibtex @article{yang2022Vtoonify, title={VToonify: Controllable High-Resolution Portrait Video Style Transfer}, author={Yang, Shuai and Jiang, Liming and Liu, Ziwei and Loy, Chen Change}, journal={ACM Transactions on Graphics (TOG)}, volume={41}, number={6}, articleno={203}, pages={1--15}, year={2022}, publisher={ACM New York, NY, USA}, doi={10.1145/3550454.3555437}, } ``` 📋 **License** This project is licensed under S-Lab License 1.0. Redistribution and use for non-commercial purposes should follow this license. 📧 **Contact** If you have any questions, please feel free to reach me out at williamyang@pku.edu.cn. """ def update_slider(choice: str) -> dict: if type(choice) == str and choice.endswith('-d'): return gr.Slider.update(maximum=1, minimum=0, value=0.5) else: return gr.Slider.update(maximum=0.5, minimum=0.5, value=0.5) def set_example_image(example: list) -> dict: return gr.Image.update(value=example[0]) def set_example_video(example: list) -> dict: return gr.Video.update(value=example[0]), sample_video = ['./vtoonify/data/529_2.mp4','./vtoonify/data/7154235.mp4','./vtoonify/data/651.mp4','./vtoonify/data/908.mp4'] sample_vid = gr.Video(label='Video file') #for displaying the example example_videos = gr.components.Dataset(components=[sample_vid], samples=[[path] for path in sample_video], type='values', label='Video Examples') def main(): args = parse_args() args.device = 'cuda' if torch.cuda.is_available() else 'cpu' print('*** Now using %s.'%(args.device)) model = Model(device=args.device) with gr.Blocks(theme=args.theme, css='style.css') as demo: gr.Markdown(DESCRIPTION) with gr.Box(): gr.Markdown('''## Step 1(Select Style) - Select **Style Type**. - Type with `-d` means it supports style degree adjustment. - Type without `-d` usually has better toonification quality. ''') with gr.Row(): with gr.Column(): gr.Markdown('''Select Style Type''') with gr.Row(): style_type = gr.Radio(label='Style Type', choices=['cartoon1','cartoon1-d','cartoon2-d','cartoon3-d', 'cartoon4','cartoon4-d','cartoon5-d','comic1-d', 'comic2-d','arcane1','arcane1-d','arcane2', 'arcane2-d', 'caricature1','caricature2','pixar','pixar-d', 'illustration1-d', 'illustration2-d', 'illustration3-d', 'illustration4-d', 'illustration5-d', ] ) exstyle = gr.Variable() with gr.Row(): loadmodel_button = gr.Button('Load Model') with gr.Row(): load_info = gr.Textbox(label='Process Information', interactive=False, value='No model loaded.') with gr.Column(): gr.Markdown('''Reference Styles ![example](https://raw.githubusercontent.com/williamyang1991/tmpfile/master/vtoonify/style.jpg)''') with gr.Box(): gr.Markdown('''## Step 2 (Preprocess Input Image / Video) - Drop an image/video containing a near-frontal face to the **Input Image**/**Input Video**. - Hit the **Rescale Image**/**Rescale First Frame** button. - Rescale the input to make it best fit the model. - The final image result will be based on this **Rescaled Face**. Use padding parameters to adjust the background space. - **Solution to [Error: no face detected!]**: VToonify uses dlib.get_frontal_face_detector but sometimes it fails to detect a face. You can try several times or use other images until a face is detected, then switch back to the original image. - For video input, further hit the **Rescale Video** button. - The final video result will be based on this **Rescaled Video**. To avoid overload, video is cut to at most **100/300** frames for CPU/GPU, respectively. ''') with gr.Row(): with gr.Box(): with gr.Column(): gr.Markdown('''Choose the padding parameters. ![example](https://raw.githubusercontent.com/williamyang1991/tmpfile/master/vtoonify/rescale.jpg)''') with gr.Row(): top = gr.Slider(128, 256, value=200, step=8, label='top') with gr.Row(): bottom = gr.Slider(128, 256, value=200, step=8, label='bottom') with gr.Row(): left = gr.Slider(128, 256, value=200, step=8, label='left') with gr.Row(): right = gr.Slider(128, 256, value=200, step=8, label='right') with gr.Box(): with gr.Column(): gr.Markdown('''Input''') with gr.Row(): input_image = gr.Image(label='Input Image', type='filepath') with gr.Row(): preprocess_image_button = gr.Button('Rescale Image') with gr.Row(): input_video = gr.Video(label='Input Video', mirror_webcam=False, type='filepath') with gr.Row(): preprocess_video0_button = gr.Button('Rescale First Frame') preprocess_video1_button = gr.Button('Rescale Video') with gr.Box(): with gr.Column(): gr.Markdown('''View''') with gr.Row(): input_info = gr.Textbox(label='Process Information', interactive=False, value='n.a.') with gr.Row(): aligned_face = gr.Image(label='Rescaled Face', type='numpy', interactive=False) instyle = gr.Variable() with gr.Row(): aligned_video = gr.Video(label='Rescaled Video', type='mp4', interactive=False) with gr.Row(): with gr.Column(): paths = ['./vtoonify/data/pexels-andrea-piacquadio-733872.jpg','./vtoonify/data/i5R8hbZFDdc.jpg','./vtoonify/data/yRpe13BHdKw.jpg','./vtoonify/data/ILip77SbmOE.jpg','./vtoonify/data/077436.jpg','./vtoonify/data/081680.jpg'] example_images = gr.Dataset(components=[input_image], samples=[[path] for path in paths], label='Image Examples') with gr.Column(): #example_videos = gr.Dataset(components=[input_video], samples=[['./vtoonify/data/529.mp4']], type='values') #to render video example on mouse hover/click example_videos.render() #to load sample video into input_video upon clicking on it def load_examples(video): #print("****** inside load_example() ******") #print("in_video is : ", video[0]) return video[0] example_videos.click(load_examples, example_videos, input_video) with gr.Box(): gr.Markdown('''## Step 3 (Generate Style Transferred Image/Video)''') with gr.Row(): with gr.Column(): gr.Markdown(''' - Adjust **Style Degree**. - Hit **Toonify!** to toonify one frame. Hit **VToonify!** to toonify full video. - Estimated time on 1600x1440 video of 300 frames: 1 hour (CPU); 2 mins (GPU) ''') style_degree = gr.Slider(0, 1, value=0.5, step=0.05, label='Style Degree') with gr.Column(): gr.Markdown('''![example](https://raw.githubusercontent.com/williamyang1991/tmpfile/master/vtoonify/degree.jpg) ''') with gr.Row(): output_info = gr.Textbox(label='Process Information', interactive=False, value='n.a.') with gr.Row(): with gr.Column(): with gr.Row(): result_face = gr.Image(label='Result Image', type='numpy', interactive=False) with gr.Row(): toonify_button = gr.Button('Toonify!') with gr.Column(): with gr.Row(): result_video = gr.Video(label='Result Video', type='mp4', interactive=False) with gr.Row(): vtoonify_button = gr.Button('VToonify!') gr.Markdown(ARTICLE) gr.Markdown(FOOTER) loadmodel_button.click(fn=model.load_model, inputs=[style_type], outputs=[exstyle, load_info]) style_type.change(fn=update_slider, inputs=style_type, outputs=style_degree) preprocess_image_button.click(fn=model.detect_and_align_image, inputs=[input_image, top, bottom, left, right], outputs=[aligned_face, instyle, input_info]) preprocess_video0_button.click(fn=model.detect_and_align_video, inputs=[input_video, top, bottom, left, right], outputs=[aligned_face, instyle, input_info]) preprocess_video1_button.click(fn=model.detect_and_align_full_video, inputs=[input_video, top, bottom, left, right], outputs=[aligned_video, instyle, input_info]) toonify_button.click(fn=model.image_toonify, inputs=[aligned_face, instyle, exstyle, style_degree], outputs=[result_face, output_info]) vtoonify_button.click(fn=model.video_tooniy, inputs=[aligned_video, instyle, exstyle, style_degree], outputs=[result_video, output_info]) example_images.click(fn=set_example_image, inputs=example_images, outputs=example_images.components) demo.launch( enable_queue=args.enable_queue, server_port=args.port, share=args.share, ) if __name__ == '__main__': main()