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()