import argparse
import gradio as gr
from gradio_i18n import Translate, gettext as _
from modules.live_portrait.live_portrait_inferencer import LivePortraitInferencer
from modules.utils.paths import *
from modules.utils.helper import str2bool
from modules.utils.constants import *
class App:
def __init__(self,
args=None):
self.args = args
self.app = gr.Blocks(css=GRADIO_CSS)
self.i18n = Translate(I18N_YAML_PATH)
self.inferencer = LivePortraitInferencer(
model_dir=args.model_dir if args else MODELS_DIR,
output_dir=args.output_dir if args else OUTPUTS_DIR
)
@staticmethod
def create_expression_parameters():
return [
gr.Dropdown(label=_("Model Type"), visible=False, interactive=False,
choices=[item.value for item in ModelType], value=ModelType.HUMAN.value),
gr.Slider(label=_("Rotate Pitch"), minimum=-20, maximum=20, step=0.5, value=0),
gr.Slider(label=_("Rotate Yaw"), minimum=-20, maximum=20, step=0.5, value=0),
gr.Slider(label=_("Rotate Roll"), minimum=-20, maximum=20, step=0.5, value=0),
gr.Slider(label=_("Blink"), info=_("Value above 5 may appear distorted"), elem_id="blink_slider",
minimum=-20, maximum=20, step=0.5, value=0),
gr.Slider(label=_("Eyebrow"), minimum=-40, maximum=20, step=0.5, value=0),
gr.Slider(label=_("Wink"), minimum=0, maximum=25, step=0.5, value=0),
gr.Slider(label=_("Pupil X"), minimum=-20, maximum=20, step=0.5, value=0),
gr.Slider(label=_("Pupil Y"), minimum=-20, maximum=20, step=0.5, value=0),
gr.Slider(label=_("AAA"), minimum=-30, maximum=120, step=1, value=0),
gr.Slider(label=_("EEE"), minimum=-20, maximum=20, step=0.2, value=0),
gr.Slider(label=_("WOO"), minimum=-20, maximum=20, step=0.2, value=0),
gr.Slider(label=_("Smile"), minimum=-2.0, maximum=2.0, step=0.01, value=0),
gr.Slider(label=_("Source Ratio"), minimum=0, maximum=1, step=0.01, value=1),
gr.Slider(label=_("Sample Ratio"), minimum=-0.2, maximum=1.2, step=0.01, value=1, visible=False),
gr.Dropdown(label=_("Sample Parts"), visible=False,
choices=[part.value for part in SamplePart], value=SamplePart.ALL.value),
gr.Slider(label=_("Face Crop Factor"), minimum=1.5, maximum=2.5, step=0.1, value=2),
gr.Checkbox(label=_("Enable Image Restoration"),
info=_("This enables image restoration with RealESRGAN but slows down the speed"), value=False)
]
@staticmethod
def create_video_parameters():
return [
gr.Dropdown(label=_("Model Type"), visible=False, interactive=False,
choices=[item.value for item in ModelType],
value=ModelType.HUMAN.value),
gr.Slider(label=_("First frame eyes alignment factor"), minimum=0, maximum=1, step=0.01, value=1),
gr.Slider(label=_("First frame mouth alignment factor"), minimum=0, maximum=1, step=0.01, value=1),
gr.Slider(label=_("Face Crop Factor"), minimum=1.5, maximum=2.5, step=0.1, value=2),
gr.Checkbox(label=_("Enable Image Restoration"),
info=_("This enables image restoration with RealESRGAN but slows down the speed"), value=False)
]
def launch(self):
with self.app:
with self.i18n:
gr.Markdown(REPO_MARKDOWN, elem_id="md_project")
with gr.Tabs():
with gr.TabItem(_("Expression Editor")):
with gr.Row():
with gr.Column():
img_ref = gr.Image(label=_("Reference Image"))
with gr.Row():
btn_gen = gr.Button("GENERATE", visible=False)
with gr.Row(equal_height=True):
with gr.Column(scale=9):
img_out = gr.Image(label=_("Output Image"))
with gr.Column(scale=1):
expression_parameters = self.create_expression_parameters()
btn_openfolder = gr.Button('📂')
with gr.Accordion("Opt in features", visible=False):
img_sample = gr.Image()
params = expression_parameters + [img_ref]
opt_in_features_params = [img_sample]
gr.on(
triggers=[param.change for param in params],
fn=self.inferencer.edit_expression,
inputs=params + opt_in_features_params,
outputs=img_out,
queue=True
)
btn_openfolder.click(
fn=lambda: self.open_folder(self.args.output_dir), inputs=None, outputs=None
)
btn_gen.click(self.inferencer.edit_expression,
inputs=params + opt_in_features_params,
outputs=img_out)
with gr.TabItem(_("Video Driven")):
with gr.Row():
img_ref = gr.Image(label=_("Reference Image"))
vid_driven = gr.Video(label=_("Expression Video"), max_length=2)
with gr.Column():
vid_params = self.create_video_parameters()
with gr.Row():
btn_gen = gr.Button(_("GENERATE"), variant="primary")
with gr.Row(equal_height=True):
with gr.Column(scale=9):
vid_out = gr.Video(label=_("Output Video"), scale=9)
with gr.Column(scale=1):
btn_openfolder = gr.Button('📂')
params = vid_params + [img_ref, vid_driven]
btn_gen.click(
fn=self.inferencer.create_video,
inputs=params,
outputs=vid_out
)
btn_openfolder.click(
fn=lambda: self.open_folder(os.path.join(self.args.output_dir, "videos")),
inputs=None, outputs=None
)
gradio_launch_args = {
"inbrowser": self.args.inbrowser,
"share": self.args.share,
"server_name": self.args.server_name,
"server_port": self.args.server_port,
"root_path": self.args.root_path,
"auth": (self.args.username, self.args.password) if self.args.username and self.args.password else None,
}
self.app.queue(
default_concurrency_limit=1
).launch(**gradio_launch_args)
@staticmethod
def open_folder(folder_path: str):
if not os.path.exists(folder_path):
os.makedirs(folder_path, exist_ok=True)
print(f"The directory path {folder_path} has newly created.")
os.system(f"start {folder_path}")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--share', type=str2bool, default=False, nargs='?', const=True, help='Gradio share value')
parser.add_argument('--inbrowser', type=str2bool, default=True, nargs='?', const=True,
help='Whether to automatically starts on the browser or not')
parser.add_argument('--server_name', type=str, default=None, help='Gradio server host')
parser.add_argument('--server_port', type=int, default=None, help='Gradio server port')
parser.add_argument('--root_path', type=str, default=None, help='Gradio root path')
parser.add_argument('--username', type=str, default=None, help='Gradio authentication username')
parser.add_argument('--password', type=str, default=None, help='Gradio authentication password')
parser.add_argument('--model_dir', type=str, default=MODELS_DIR,
help='Directory path of the LivePortrait models')
parser.add_argument('--output_dir', type=str, default=OUTPUTS_DIR,
help='Directory path of the outputs')
_args = parser.parse_args()
app = App(args=_args)
app.launch()