chat_anything/sad_talker/facerender/animate.py

import os
import cv2
import yaml
import numpy as np
import warnings
from skimage import img_as_ubyte
import safetensors
import safetensors.torch 
warnings.filterwarnings('ignore')


import imageio
import torch
import torchvision


from chat_anything.sad_talker.facerender.modules.keypoint_detector import HEEstimator, KPDetector
from chat_anything.sad_talker.facerender.modules.mapping import MappingNet
from chat_anything.sad_talker.facerender.modules.generator import OcclusionAwareGenerator, OcclusionAwareSPADEGenerator
from chat_anything.sad_talker.facerender.modules.make_animation import make_animation 

from pydub import AudioSegment 
from chat_anything.sad_talker.utils.face_enhancer import enhancer_generator_with_len, enhancer_list
from chat_anything.sad_talker.utils.paste_pic import paste_pic
from chat_anything.sad_talker.utils.videoio import save_video_with_watermark

try:
    import webui  # in webui
    in_webui = True
except:
    in_webui = False

class AnimateFromCoeff():

    def __init__(self, sadtalker_path, device):

        with open(sadtalker_path['facerender_yaml']) as f:
            config = yaml.safe_load(f)

        generator = OcclusionAwareSPADEGenerator(**config['model_params']['generator_params'],
                                                    **config['model_params']['common_params'])
        kp_extractor = KPDetector(**config['model_params']['kp_detector_params'],
                                    **config['model_params']['common_params'])
        he_estimator = HEEstimator(**config['model_params']['he_estimator_params'],
                               **config['model_params']['common_params'])
        mapping = MappingNet(**config['model_params']['mapping_params'])

        generator.to(device)
        kp_extractor.to(device)
        he_estimator.to(device)
        mapping.to(device)
        for param in generator.parameters():
            param.requires_grad = False
        for param in kp_extractor.parameters():
            param.requires_grad = False 
        for param in he_estimator.parameters():
            param.requires_grad = False
        for param in mapping.parameters():
            param.requires_grad = False

        if sadtalker_path is not None:
            if 'checkpoint' in sadtalker_path: # use safe tensor
                self.load_cpk_facevid2vid_safetensor(sadtalker_path['checkpoint'], kp_detector=kp_extractor, generator=generator, he_estimator=None)
            else:
                self.load_cpk_facevid2vid(sadtalker_path['free_view_checkpoint'], kp_detector=kp_extractor, generator=generator, he_estimator=he_estimator)
        else:
            raise AttributeError("Checkpoint should be specified for video head pose estimator.")

        if  sadtalker_path['mappingnet_checkpoint'] is not None:
            self.load_cpk_mapping(sadtalker_path['mappingnet_checkpoint'], mapping=mapping)
        else:
            raise AttributeError("Checkpoint should be specified for video head pose estimator.") 

        devices = list(range(torch.cuda.device_count()))
        device = 0 
        generator = torch.nn.DataParallel(generator, device_ids=devices, output_device=device)
        kp_extractor = torch.nn.DataParallel(kp_extractor, device_ids=devices, output_device=device)
        he_estimator = torch.nn.DataParallel(he_estimator, device_ids=devices, output_device=device)
        mapping = torch.nn.DataParallel(mapping, device_ids=devices, output_device=device)
        
        self.kp_extractor = kp_extractor
        self.generator = generator
        self.he_estimator = he_estimator
        self.mapping = mapping

        self.kp_extractor.eval()
        self.generator.eval()
        self.he_estimator.eval()
        self.mapping.eval()
         
        self.device = device
    
    def load_cpk_facevid2vid_safetensor(self, checkpoint_path, generator=None, 
                        kp_detector=None, he_estimator=None,  
                        device="cpu"):

        checkpoint = safetensors.torch.load_file(checkpoint_path)

        if generator is not None:
            x_generator = {}
            for k,v in checkpoint.items():
                if 'generator' in k:
                    x_generator[k.replace('generator.', '')] = v
            generator.load_state_dict(x_generator)
        if kp_detector is not None:
            x_generator = {}
            for k,v in checkpoint.items():
                if 'kp_extractor' in k:
                    x_generator[k.replace('kp_extractor.', '')] = v
            kp_detector.load_state_dict(x_generator)
        if he_estimator is not None:
            x_generator = {}
            for k,v in checkpoint.items():
                if 'he_estimator' in k:
                    x_generator[k.replace('he_estimator.', '')] = v
            he_estimator.load_state_dict(x_generator)
        
        return None

    def load_cpk_facevid2vid(self, checkpoint_path, generator=None, discriminator=None, 
                        kp_detector=None, he_estimator=None, optimizer_generator=None, 
                        optimizer_discriminator=None, optimizer_kp_detector=None, 
                        optimizer_he_estimator=None, device="cpu"):
        checkpoint = torch.load(checkpoint_path, map_location=torch.device(device))
        if generator is not None:
            generator.load_state_dict(checkpoint['generator'])
        if kp_detector is not None:
            kp_detector.load_state_dict(checkpoint['kp_detector'])
        if he_estimator is not None:
            he_estimator.load_state_dict(checkpoint['he_estimator'])
        if discriminator is not None:
            try:
               discriminator.load_state_dict(checkpoint['discriminator'])
            except:
               print ('No discriminator in the state-dict. Dicriminator will be randomly initialized')
        if optimizer_generator is not None:
            optimizer_generator.load_state_dict(checkpoint['optimizer_generator'])
        if optimizer_discriminator is not None:
            try:
                optimizer_discriminator.load_state_dict(checkpoint['optimizer_discriminator'])
            except RuntimeError as e:
                print ('No discriminator optimizer in the state-dict. Optimizer will be not initialized')
        if optimizer_kp_detector is not None:
            optimizer_kp_detector.load_state_dict(checkpoint['optimizer_kp_detector'])
        if optimizer_he_estimator is not None:
            optimizer_he_estimator.load_state_dict(checkpoint['optimizer_he_estimator'])

        return checkpoint['epoch']
    
    def load_cpk_mapping(self, checkpoint_path, mapping=None, discriminator=None,
                 optimizer_mapping=None, optimizer_discriminator=None, device='cpu'):
        checkpoint = torch.load(checkpoint_path,  map_location=torch.device(device))
        if mapping is not None:
            mapping.load_state_dict(checkpoint['mapping'])
        if discriminator is not None:
            discriminator.load_state_dict(checkpoint['discriminator'])
        if optimizer_mapping is not None:
            optimizer_mapping.load_state_dict(checkpoint['optimizer_mapping'])
        if optimizer_discriminator is not None:
            optimizer_discriminator.load_state_dict(checkpoint['optimizer_discriminator'])

        return checkpoint['epoch']

    def generate(self, x, video_save_dir, pic_path, crop_info, enhancer=None, background_enhancer=None, preprocess='crop', img_size=256):

        source_image=x['source_image'].type(torch.FloatTensor)
        source_semantics=x['source_semantics'].type(torch.FloatTensor)
        target_semantics=x['target_semantics_list'].type(torch.FloatTensor) 
        source_image=source_image.to(self.device)
        source_semantics=source_semantics.to(self.device)
        target_semantics=target_semantics.to(self.device)
        if 'yaw_c_seq' in x:
            yaw_c_seq = x['yaw_c_seq'].type(torch.FloatTensor)
            yaw_c_seq = x['yaw_c_seq'].to(self.device)
        else:
            yaw_c_seq = None
        if 'pitch_c_seq' in x:
            pitch_c_seq = x['pitch_c_seq'].type(torch.FloatTensor)
            pitch_c_seq = x['pitch_c_seq'].to(self.device)
        else:
            pitch_c_seq = None
        if 'roll_c_seq' in x:
            roll_c_seq = x['roll_c_seq'].type(torch.FloatTensor) 
            roll_c_seq = x['roll_c_seq'].to(self.device)
        else:
            roll_c_seq = None

        frame_num = x['frame_num']

        predictions_video = make_animation(source_image, source_semantics, target_semantics,
                                        self.generator, self.kp_extractor, self.he_estimator, self.mapping, 
                                        yaw_c_seq, pitch_c_seq, roll_c_seq, use_exp = True)

        predictions_video = predictions_video.reshape((-1,)+predictions_video.shape[2:])
        predictions_video = predictions_video[:frame_num]

        video = []
        for idx in range(predictions_video.shape[0]):
            image = predictions_video[idx]
            image = np.transpose(image.data.cpu().numpy(), [1, 2, 0]).astype(np.float32)
            video.append(image)
        result = img_as_ubyte(video)
       
        ### the generated video is 256x256, so we keep the aspect ratio, 
        original_size = crop_info[0]
        if original_size:
            result = [ cv2.resize(result_i,(img_size, int(img_size * original_size[1]/original_size[0]) )) for result_i in result ]
        
        video_name = x['video_name']  + '.mp4'
        path = os.path.join(video_save_dir, 'temp_'+video_name)
        imageio.mimsave(path, result,  fps=float(25))
        # try:
        #     enhanced_images_gen_with_len = enhancer_generator_with_len(path, method='gfpgan', bg_upsampler='realesrgan')
        #     imageio.mimsave(path, enhanced_images_gen_with_len, fps=float(25))
        # except:
        #     enhanced_images_gen_with_len = enhancer_list(path, method='gfpgan', bg_upsampler='realesrgan')
        #     imageio.mimsave(path, enhanced_images_gen_with_len, fps=float(25))
        
        av_path = os.path.join(video_save_dir, video_name)
        audio_path =  x['audio_path'] 
        audio_name = os.path.splitext(os.path.split(audio_path)[-1])[0]
        new_audio_path = os.path.join(video_save_dir, audio_name+'.wav')
        start_time = 0
        # cog will not keep the .mp3 filename
        sound = AudioSegment.from_file(audio_path)
        frames = frame_num 
        end_time = start_time + frames*1/25*1000
        word1=sound.set_frame_rate(16000)
        word = word1[start_time:end_time]
        word.export(new_audio_path, format="wav")
        print("============================")
        print("saved moving images:", path)
        # print(f'The generated video is named {video_save_dir}/{video_name}') 
        if 'full' in preprocess.lower():
            # only add watermark to the full image.
            video_name_full = x['video_name']  + '_full.mp4'
            full_video_path = os.path.join(video_save_dir, video_name_full)
            paste_pic(path, pic_path, crop_info, new_audio_path, full_video_path, extended_crop= True if 'ext' in preprocess.lower() else False)
            print(f"full video:{full_video_path}")
            return_path = full_video_path
        else:
            save_video_with_watermark(path, new_audio_path, av_path, watermark= False)
            return_path = av_path
            print(f"crop video:{return_path}")
        print("the given temp file:", return_path)
        return return_path
        # #### paste back then enhancers
        # if enhancer:
        #     video_name_enhancer = x['video_name']  + '_enhanced.mp4'
        #     enhanced_path = os.path.join(video_save_dir, 'temp_'+video_name_enhancer)
        #     av_path_enhancer = os.path.join(video_save_dir, video_name_enhancer) 
        #     return_path = av_path_enhancer

        #     try:
        #         enhanced_images_gen_with_len = enhancer_generator_with_len(full_video_path, method=enhancer, bg_upsampler=background_enhancer)
        #         imageio.mimsave(enhanced_path, enhanced_images_gen_with_len, fps=float(25))
        #     except:
        #         enhanced_images_gen_with_len = enhancer_list(full_video_path, method=enhancer, bg_upsampler=background_enhancer)
        #         imageio.mimsave(enhanced_path, enhanced_images_gen_with_len, fps=float(25))
            
        #     save_video_with_watermark(enhanced_path, new_audio_path, av_path_enhancer, watermark= False)
        #     print(f'The generated video is named {video_save_dir}/{video_name_enhancer}')
        #     # os.remove(enhanced_path)

        # # os.remove(path)
        # os.remove(new_audio_path)

        # return return_path