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#!/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 = """
<div align=center>
<h1 style="font-weight: 900; margin-bottom: 7px;">
Portrait Style Transfer with <a href="https://github.com/williamyang1991/VToonify">VToonify</a>
</h1>
<p>For faster inference without waiting in queue, you may duplicate the space and use the GPU setting.
<br/>
<a href="https://huggingface.co/spaces/PKUWilliamYang/VToonify?duplicate=true">
<img style="margin-top: 0em; margin-bottom: 0em" src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>
<p/>
<video id="video" width=50% controls="" preload="none" poster="https://repository-images.githubusercontent.com/534480768/53715b0f-a2df-4daa-969c-0e74c102d339">
<source id="mp4" src="https://user-images.githubusercontent.com/18130694/189483939-0fc4a358-fb34-43cc-811a-b22adb820d57.mp4
" type="video/mp4">
</videos>
</div>
"""
FOOTER = '<div align=center><img id="visitor-badge" alt="visitor badge" src="https://visitor-badge.laobi.icu/badge?page_id=williamyang1991/VToonify" /></div>'
ARTICLE = r"""
If VToonify is helpful, please help to ⭐ the <a href='https://github.com/williamyang1991/VToonify' target='_blank'>Github Repo</a>. Thanks!
[](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 <a rel="license" href="https://github.com/williamyang1991/VToonify/blob/main/LICENSE.md">S-Lab License 1.0</a>.
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 <b>williamyang@pku.edu.cn</b>.
"""
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",
)
model = Model(device="cuda")
with gr.Blocks(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
"""
)
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.
- **<font color=red>Solution to [Error: no face detected!]</font>**: 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.
"""
)
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(
"""
"""
)
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, style_type],
outputs=[result_face, output_info],
)
vtoonify_button.click(
fn=model.video_tooniy,
inputs=[aligned_video, instyle, exstyle, style_degree, style_type],
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,
# )
demo.queue(concurrency_count=1, max_size=4)
demo.launch(server_port=8266)
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