Update src/facerender/animate.py

src/facerender/animate.py

@@ -10,13 +10,10 @@ import safetensors.torch
 
 warnings.filterwarnings('ignore')
 
-
-
 import imageio
 import torch
 import torchvision
 
-
 from src.facerender.modules.keypoint_detector import HEEstimator, KPDetector
 from src.facerender.modules.mapping import MappingNet
 from src.facerender.modules.generator import OcclusionAwareGenerator, OcclusionAwareSPADEGenerator
@@ -33,46 +30,47 @@ try:
 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'])
+                                                 **config['model_params']['common_params'])
         kp_extractor = KPDetector(**config['model_params']['kp_detector_params'],
-                                    **config['model_params']['common_params'])
+                                  **config['model_params']['common_params'])
         he_estimator = HEEstimator(**config['model_params']['he_estimator_params'],
-                               **config['model_params']['common_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 
+            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
+            if 'checkpoint' in sadtalker_path:
                 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:
+        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.") 
+            raise AttributeError("Checkpoint should be specified for video head pose estimator.")
 
         self.kp_extractor = kp_extractor
         self.generator = generator
@@ -83,201 +81,76 @@ class AnimateFromCoeff():
         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"):
+                                        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
+            x_generator = {k.replace('generator.', ''): v for k, v in checkpoint.items() if 'generator' in k}
             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
+            x_generator = {k.replace('kp_extractor.', ''): v for k, v in checkpoint.items() if 'kp_extractor' in k}
             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
+            x_generator = {k.replace('he_estimator.', ''): v for k, v in checkpoint.items() if 'he_estimator' in k}
             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"):
+                              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:
+        if discriminator is not None and 'discriminator' in checkpoint:
+            discriminator.load_state_dict(checkpoint['discriminator'])
+        if optimizer_generator is not None and 'optimizer_generator' in checkpoint:
             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:
+        if optimizer_discriminator is not None and 'optimizer_discriminator' in checkpoint:
+            optimizer_discriminator.load_state_dict(checkpoint['optimizer_discriminator'])
+        if optimizer_kp_detector is not None and 'optimizer_kp_detector' in checkpoint:
             optimizer_kp_detector.load_state_dict(checkpoint['optimizer_kp_detector'])
-        if optimizer_he_estimator is not None:
+        if optimizer_he_estimator is not None and 'optimizer_he_estimator' in checkpoint:
             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'):
-    
-    # Eğer .tar dosyasıysa içeriğini kontrol et
-    if checkpoint_path.endswith(".tar"):
-        try:
-            with tarfile.open(checkpoint_path, "r") as tar:
-                members = tar.getnames()
-                if not any(name.startswith("storages") for name in members):
-                    print("⚠️ 'storages' klasörü .tar dosyasında bulunamadı. Devam ediliyor...")
-                else:
-                    print("✔️ 'storages' bulundu.")
-        except Exception as e:
-            print(f"Tar kontrol hatası: {e}")
-
-    # Checkpoint yükle
-    checkpoint = torch.load(checkpoint_path, map_location=torch.device(device))
-
-    # Mapping yükleme
-    if mapping is not None and 'mapping' in checkpoint:
-        mapping.load_state_dict(checkpoint['mapping'])
-
-    # Diğer parametreler varsa
-    if discriminator is not None and 'discriminator' in checkpoint:
-        discriminator.load_state_dict(checkpoint['discriminator'])
+        return checkpoint.get('epoch', 0)
 
-    if optimizer_mapping is not None and 'optimizer_mapping' in checkpoint:
-        optimizer_mapping.load_state_dict(checkpoint['optimizer_mapping'])
-
-    if optimizer_discriminator is not None and 'optimizer_discriminator' in checkpoint:
-        optimizer_discriminator.load_state_dict(checkpoint['optimizer_discriminator'])
-
-    return checkpoint.get('epoch', 0)
-
-
-    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))
-
-        av_path = os.path.join(video_save_dir, video_name)
-        return_path = av_path 
-        
-        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")
-
-        save_video_with_watermark(path, new_audio_path, av_path, watermark= False)
-        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)
-            return_path = full_video_path
-            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'The generated video is named {video_save_dir}/{video_name_full}') 
-        else:
-            full_video_path = av_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
+    def load_cpk_mapping(self, checkpoint_path, mapping=None, discriminator=None,
+                         optimizer_mapping=None, optimizer_discriminator=None, device='cpu'):
 
+        if checkpoint_path.endswith(".tar"):
             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)
+                with tarfile.open(checkpoint_path, "r") as tar:
+                    members = tar.getnames()
+                    if not any(name.startswith("storages") for name in members):
+                        print("⚠️ 'storages' klasörü .tar dosyasında bulunamadı. Devam ediliyor...")
+                    else:
+                        print("✔️ 'storages' bulundu.")
+            except Exception as e:
+                print(f"Tar kontrol hatası: {e}")
 
-        os.remove(path)
-        os.remove(new_audio_path)
+        checkpoint = torch.load(checkpoint_path, map_location=torch.device(device))
 
-        return return_path
+        if mapping is not None and 'mapping' in checkpoint:
+            mapping.load_state_dict(checkpoint['mapping'])
+        if discriminator is not None and 'discriminator' in checkpoint:
+            discriminator.load_state_dict(checkpoint['discriminator'])
+        if optimizer_mapping is not None and 'optimizer_mapping' in checkpoint:
+            optimizer_mapping.load_state_dict(checkpoint['optimizer_mapping'])
+        if optimizer_discriminator is not None and 'optimizer_discriminator' in checkpoint:
+            optimizer_discriminator.load_state_dict(checkpoint['optimizer_discriminator'])
 
+        return checkpoint.get('epoch', 0)