import os import platform import uuid import shutil from pydub import AudioSegment import spaces import torch import gradio as gr from huggingface_hub import snapshot_download from examples.get_examples import get_examples from src.facerender.pirender_animate import AnimateFromCoeff_PIRender from src.utils.preprocess import CropAndExtract from src.test_audio2coeff import Audio2Coeff from src.facerender.animate import AnimateFromCoeff from src.generate_batch import get_data from src.generate_facerender_batch import get_facerender_data from src.utils.init_path import init_path checkpoint_path = 'checkpoints' config_path = 'src/config' device = "cuda" if torch.cuda.is_available( ) else "mps" if platform.system() == 'Darwin' else "cpu" os.environ['TORCH_HOME'] = checkpoint_path snapshot_download(repo_id='vinthony/SadTalker-V002rc', local_dir=checkpoint_path, local_dir_use_symlinks=True) def mp3_to_wav(mp3_filename, wav_filename, frame_rate): AudioSegment.from_file(file=mp3_filename).set_frame_rate( frame_rate).export(wav_filename, format="wav") @spaces.GPU(duration=120) def generate_video(source_image, driven_audio, preprocess='crop', still_mode=False, use_enhancer=False, batch_size=1, size=256, pose_style=0, facerender='facevid2vid', exp_scale=1.0, use_ref_video=False, ref_video=None, ref_info=None, use_idle_mode=False, length_of_audio=0, use_blink=True, result_dir='./results/'): # Initialize models and paths sadtalker_paths = init_path( checkpoint_path, config_path, size, False, preprocess) audio_to_coeff = Audio2Coeff(sadtalker_paths, device) preprocess_model = CropAndExtract(sadtalker_paths, device) animate_from_coeff = AnimateFromCoeff(sadtalker_paths, device) if facerender == 'facevid2vid' and device != 'mps' \ else AnimateFromCoeff_PIRender(sadtalker_paths, device) # Create directories for saving results time_tag = str(uuid.uuid4()) save_dir = os.path.join(result_dir, time_tag) os.makedirs(save_dir, exist_ok=True) input_dir = os.path.join(save_dir, 'input') os.makedirs(input_dir, exist_ok=True) # Process source image pic_path = os.path.join(input_dir, os.path.basename(source_image)) shutil.move(source_image, input_dir) # Process driven audio if driven_audio and os.path.isfile(driven_audio): audio_path = os.path.join(input_dir, os.path.basename(driven_audio)) if '.mp3' in audio_path: mp3_to_wav(driven_audio, audio_path.replace('.mp3', '.wav'), 16000) audio_path = audio_path.replace('.mp3', '.wav') else: shutil.move(driven_audio, input_dir) elif use_idle_mode: audio_path = os.path.join( input_dir, 'idlemode_'+str(length_of_audio)+'.wav') AudioSegment.silent( duration=1000*length_of_audio).export(audio_path, format="wav") else: assert use_ref_video and ref_info == 'all' # Process reference video if use_ref_video and ref_info == 'all': ref_video_videoname = os.path.splitext(os.path.split(ref_video)[-1])[0] audio_path = os.path.join(save_dir, ref_video_videoname+'.wav') os.system( f"ffmpeg -y -hide_banner -loglevel error -i {ref_video} {audio_path}") ref_video_frame_dir = os.path.join(save_dir, ref_video_videoname) os.makedirs(ref_video_frame_dir, exist_ok=True) ref_video_coeff_path, _, _ = preprocess_model.generate( ref_video, ref_video_frame_dir, preprocess, source_image_flag=False) else: ref_video_coeff_path = None # Preprocess source image first_frame_dir = os.path.join(save_dir, 'first_frame_dir') os.makedirs(first_frame_dir, exist_ok=True) first_coeff_path, crop_pic_path, crop_info = preprocess_model.generate( pic_path, first_frame_dir, preprocess, True, size) if first_coeff_path is None: raise AttributeError("No face is detected") # Determine reference coefficients ref_pose_coeff_path, ref_eyeblink_coeff_path = None, None if use_ref_video: if ref_info == 'pose': ref_pose_coeff_path = ref_video_coeff_path elif ref_info == 'blink': ref_eyeblink_coeff_path = ref_video_coeff_path elif ref_info == 'pose+blink': ref_pose_coeff_path = ref_eyeblink_coeff_path = ref_video_coeff_path else: ref_pose_coeff_path = ref_eyeblink_coeff_path = None # Generate coefficients from audio or reference video if use_ref_video and ref_info == 'all': coeff_path = ref_video_coeff_path else: batch = get_data(first_coeff_path, audio_path, device, ref_eyeblink_coeff_path=ref_eyeblink_coeff_path, still=still_mode, idlemode=use_idle_mode, length_of_audio=length_of_audio, use_blink=use_blink) coeff_path = audio_to_coeff.generate( batch, save_dir, pose_style, ref_pose_coeff_path) # Generate video from coefficients data = get_facerender_data(coeff_path, crop_pic_path, first_coeff_path, audio_path, batch_size, still_mode=still_mode, preprocess=preprocess, size=size, expression_scale=exp_scale, facemodel=facerender) return_path = animate_from_coeff.generate(data, save_dir, pic_path, crop_info, enhancer='gfpgan' if use_enhancer else None, preprocess=preprocess, img_size=size) video_name = data['video_name'] print(f'The generated video is named {video_name} in {save_dir}') return return_path # Gradio UI with gr.Blocks(analytics_enabled=False) as demo: with gr.Row(): with gr.Column(variant='panel'): with gr.Tabs(elem_id="sadtalker_source_image"): with gr.TabItem('Source image'): with gr.Row(): source_image = gr.Image( label="Source image", sources="upload", type="filepath", elem_id="img2img_image") with gr.Tabs(elem_id="sadtalker_driven_audio"): with gr.TabItem('Driving Methods'): gr.Markdown( "Possible driving combinations:
1. Audio only 2. Audio/IDLE Mode + Ref Video(pose, blink, pose+blink) 3. IDLE Mode only 4. Ref Video only (all) ") with gr.Row(): driven_audio = gr.Audio( label="Input audio", sources="upload", type="filepath") driven_audio_no = gr.Audio( label="Use IDLE mode, no audio is required", sources="upload", type="filepath", visible=False) with gr.Column(): use_idle_mode = gr.Checkbox( label="Use Idle Animation") length_of_audio = gr.Number( value=5, label="The length(seconds) of the generated video.") use_idle_mode.change(lambda choice: (gr.update(visible=not choice), gr.update(visible=choice)), inputs=use_idle_mode, outputs=[driven_audio, driven_audio_no]) with gr.Row(): ref_video = gr.Video( label="Reference Video", sources="upload", elem_id="vidref") with gr.Column(): use_ref_video = gr.Checkbox( label="Use Reference Video") ref_info = gr.Radio(['pose', 'blink', 'pose+blink', 'all'], value='pose', label='Reference Video', info="How to borrow from reference Video?((fully transfer, aka, video driving mode))") ref_video.change(lambda path: gr.update( value=path is not None), inputs=ref_video, outputs=use_ref_video) with gr.Column(variant='panel'): with gr.Tabs(elem_id="sadtalker_checkbox"): with gr.TabItem('Settings'): with gr.Column(variant='panel'): with gr.Row(): pose_style = gr.Slider( minimum=0, maximum=45, step=1, label="Pose style", value=0) exp_weight = gr.Slider( minimum=0, maximum=3, step=0.1, label="expression scale", value=1) blink_every = gr.Checkbox( label="use eye blink", value=True) with gr.Row(): size_of_image = gr.Radio( [256, 512], value=256, label='face model resolution', info="use 256/512 model?") preprocess_type = gr.Radio( ['crop', 'resize', 'full', 'extcrop', 'extfull'], value='crop', label='preprocess', info="How to handle input image?") with gr.Row(): is_still_mode = gr.Checkbox( label="Still Mode (fewer head motion, works with preprocess `full`)") facerender = gr.Radio( ['facevid2vid', 'pirender'], value='facevid2vid', label='facerender', info="which face render?") with gr.Row(): batch_size = gr.Slider( label="batch size in generation", step=1, maximum=10, value=1) enhancer = gr.Checkbox( label="GFPGAN as Face enhancer", value=True) submit = gr.Button( 'Generate', elem_id="sadtalker_generate", variant='primary') with gr.Tabs(elem_id="sadtalker_generated"): gen_video = gr.Video(label="Generated video") submit.click( fn=generate_video, inputs=[source_image, driven_audio, preprocess_type, is_still_mode, enhancer, batch_size, size_of_image, pose_style, facerender, exp_weight, use_ref_video, ref_video, ref_info, use_idle_mode, length_of_audio, blink_every], outputs=[gen_video], ) with gr.Row(): gr.Examples(examples=get_examples(), inputs=[source_image, driven_audio, preprocess_type, is_still_mode, enhancer], outputs=[gen_video], fn=generate_video) demo.launch(debug=True)