add src-src merge and use stored aligned img mat for merging

main.py

@@ -164,7 +164,8 @@ if __name__ == "__main__":
                       output_mask_path       = Path(arguments.output_mask_dir),
                       aligned_path           = Path(arguments.aligned_dir) if arguments.aligned_dir is not None else None,
                       force_gpu_idxs         = arguments.force_gpu_idxs,
-                      cpu_only               = arguments.cpu_only)
+                      cpu_only               = arguments.cpu_only,
+                      src_src                = arguments.src_src)
 
     p = subparsers.add_parser( "merge", help="Merger")
     p.add_argument('--input-dir', required=True, action=fixPathAction, dest="input_dir", help="Input directory. A directory containing the files you wish to process.")
@@ -176,6 +177,7 @@ if __name__ == "__main__":
     p.add_argument('--force-model-name', dest="force_model_name", default=None, help="Forcing to choose model name from model/ folder.")
     p.add_argument('--cpu-only', action="store_true", dest="cpu_only", default=False, help="Merge on CPU.")
     p.add_argument('--force-gpu-idxs', dest="force_gpu_idxs", default=None, help="Force to choose GPU indexes separated by comma.")
+    p.add_argument('--src-src', action="store_true", dest="src_src", default=False, help="Enables special src-src predicted output.")
     p.set_defaults(func=process_merge)
 
     videoed_parser = subparsers.add_parser( "videoed", help="Video processing.").add_subparsers()

mainscripts/Merger.py

@@ -26,7 +26,8 @@ def main (model_class_name=None,
           output_mask_path=None,
           aligned_path=None,
           force_gpu_idxs=None,
-          cpu_only=None):
+          cpu_only=None,
+          src_src=False):
     io.log_info ("Running merger.\r\n")
 
     try:
@@ -47,6 +48,7 @@ def main (model_class_name=None,
         # Initialize model
         import models
         model = models.import_model(model_class_name)(is_training=False,
+                                                      src_src=src_src,
                                                       saved_models_path=saved_models_path,
                                                       force_gpu_idxs=force_gpu_idxs,
                                                       cpu_only=cpu_only)
@@ -68,7 +70,10 @@ def main (model_class_name=None,
                                                     place_model_on_cpu=True,
                                                     run_on_cpu=run_on_cpu)
 
-        is_interactive = io.input_bool ("Use interactive merger?", True) if not io.is_colab() else False
+        if src_src:
+            is_interactive = False
+        else:
+            is_interactive = io.input_bool ("Use interactive merger?", True) if not io.is_colab() else False
 
         if not is_interactive:
             cfg.ask_settings()
@@ -110,7 +115,7 @@ def main (model_class_name=None,
                     io.log_err (f"{filepath.name} is not a dfl image file")
                     continue
 
-                source_filename = dflimg.get_source_filename()
+                source_filename = filepath.name if src_src else dflimg.get_source_filename()
                 if source_filename is None:
                     continue
 
@@ -121,7 +126,7 @@ def main (model_class_name=None,
                     alignments[ source_filename_stem ] = []
 
                 alignments_ar = alignments[ source_filename_stem ]
-                alignments_ar.append ( (dflimg.get_source_landmarks(), filepath, source_filepath ) )
+                alignments_ar.append ( (dflimg.get_source_landmarks(), filepath, source_filepath, dflimg.get_image_to_face_mat(), dflimg.get_shape()[0]) )
 
                 if len(alignments_ar) > 1:
                     multiple_faces_detected = True
@@ -137,23 +142,32 @@ def main (model_class_name=None,
                     for _, filepath, source_filepath in a_ar:
                         io.log_info (f"alignment {filepath.name} refers to {source_filepath.name} ")
                     io.log_info ("")
-
-                alignments[a_key] = [ a[0] for a in a_ar]
+                alignments[a_key] = [ (a[0], a[3], a[4]) for a in a_ar]
 
             if multiple_faces_detected:
                 io.log_info ("It is strongly recommended to process the faces separatelly.")
                 io.log_info ("Use 'recover original filename' to determine the exact duplicates.")
                 io.log_info ("")
 
-            frames = [ InteractiveMergerSubprocessor.Frame( frame_info=FrameInfo(filepath=Path(p),
-                                                                     landmarks_list=alignments.get(Path(p).stem, None)
-                                                                    )
-                                              )
-                       for p in input_path_image_paths ]
+            frames = []
+            for p in input_path_image_paths:
+                alignment = alignments.get(Path(p).stem, None)
+                landmarks_list = None
+                image_to_face_mat = None
+                aligned_size = None
+                if alignment is not None:
+                    landmarks_list, image_to_face_mat, aligned_size = alignment[0]
+                    landmarks_list = [landmarks_list]
+                frame_info = FrameInfo(filepath=Path(p), landmarks_list=landmarks_list, image_to_face_mat=image_to_face_mat, aligned_size=aligned_size)
+                frame = InteractiveMergerSubprocessor.Frame(frame_info=frame_info)
+                frames.append(frame)
 
             if multiple_faces_detected:
                 io.log_info ("Warning: multiple faces detected. Motion blur will not be used.")
                 io.log_info ("")
+            elif src_src:
+                io.log_info ("SRC-SRC mode configured, skipping motion blur calculation...")
+                io.log_info ("")
             else:
                 s = 256
                 local_pts = [ (s//2-1, s//2-1), (s//2-1,0) ] #center+up
@@ -206,6 +220,7 @@ def main (model_class_name=None,
                             output_mask_path       = output_mask_path,
                             model_iter             = model.get_iter(),
                             subprocess_count       = subprocess_count,
+                            src_src                = src_src,
                         ).run()
 
         model.finalize()

mainscripts/Trainer.py

@@ -69,6 +69,7 @@ def trainerThread (s2c, c2s, e,
             start_time = time.time()
 
             save_interval_min = 15
+            tensorboard_preview_interval_min = 5
 
             if not training_data_src_path.exists():
                 training_data_src_path.mkdir(exist_ok=True, parents=True)
@@ -96,20 +97,14 @@ def trainerThread (s2c, c2s, e,
 
             is_reached_goal = model.is_reached_iter_goal()
 
-            train_summary_writer = None
             if tensorboard_dir is not None:
-                try:
-                    import tensorboardX
-                    if not os.path.exists(tensorboard_dir):
-                        os.makedirs(tensorboard_dir)
-                    summary_writer_folder = os.path.join(tensorboard_dir, model.model_name)
-                    train_summary_writer = tensorboardX.SummaryWriter(summary_writer_folder)
-                    if start_tensorboard:
-                        tb_tool = TensorBoardTool(tensorboard_dir)
-                        tb_tool.run()
-                except:
-                    print("Error importing tensorboardX, please ensure it is installed (pip install tensorboardX)")
-                    print("Continuing training without tensorboard logging...")
+                c2s.put({ 
+                    'op': 'tb', 
+                    'action': 'init', 
+                    'model_name': model.model_name,
+                    'tensorboard_dir': tensorboard_dir,
+                    'start_tensorboard': start_tensorboard
+                })
 
             shared_state = { 'after_save' : False }
             loss_string = ""
@@ -122,12 +117,29 @@ def trainerThread (s2c, c2s, e,
                     
             def model_backup():
                 if not debug and not is_reached_goal:
-                    model.create_backup()             
+                    model.create_backup()   
+
+            def log_step(step, step_time, src_loss, dst_loss):
+                c2s.put({ 
+                    'op': 'tb', 
+                    'action': 'step', 
+                    'step': step,
+                    'step_time': step_time,
+                    'src_loss': src_loss,
+                    'dst_loss': dst_loss
+                })
+            
+            def log_previews(step, previews, static_previews):
+                c2s.put({
+                    'op': 'tb',
+                    'action': 'preview',
+                    'step': step,
+                    'previews': previews,
+                    'static_previews': static_previews
+                })
 
             def send_preview():
                 if not debug:
-                    if train_summary_writer is not None:
-                        log_tensorboard_model_previews(iter, model, train_summary_writer)
                     previews = model.get_previews()
                     c2s.put ( {'op':'show', 'previews': previews, 'iter':model.get_iter(), 'loss_history': model.get_loss_history().copy() } )
                 else:
@@ -144,6 +156,7 @@ def trainerThread (s2c, c2s, e,
                 io.log_info('Starting. Press "Enter" to stop training and save model.')
 
             last_save_time = time.time()
+            last_preview_time = time.time()
 
             execute_programs = [ [x[0], x[1], time.time() ] for x in execute_programs ]
 
@@ -203,13 +216,8 @@ def trainerThread (s2c, c2s, e,
                             else:
                                 io.log_info (loss_string, end='\r')
 
-                        if train_summary_writer is not None:
-                            # report iteration time summary
-                            train_summary_writer.add_scalar('iteration time', iter_time, iter)
-                            # report loss summary
-                            src_loss, dst_loss = loss_history[-1]
-                            train_summary_writer.add_scalar('loss/src', src_loss, iter)
-                            train_summary_writer.add_scalar('loss/dst', dst_loss, iter)
+                        loss_entry = loss_history[-1]
+                        log_step(iter, iter_time, loss_entry[0], loss_entry[1] if len(loss_entry) > 1 else None)
 
                         if model.get_iter() == 1:
                             model_save()
@@ -220,6 +228,12 @@ def trainerThread (s2c, c2s, e,
                             is_reached_goal = True
                             io.log_info ('You can use preview now.')
 
+                if not is_reached_goal and (time.time() - last_preview_time) >= tensorboard_preview_interval_min*60:
+                    last_preview_time += tensorboard_preview_interval_min*60
+                    previews = model.get_previews()
+                    static_previews = model.get_static_previews()
+                    log_previews(iter, previews, static_previews)
+
                 if not is_reached_goal and (time.time() - last_save_time) >= save_interval_min*60:
                     last_save_time += save_interval_min*60
                     model_save()
@@ -262,7 +276,54 @@ def trainerThread (s2c, c2s, e,
         break
     c2s.put ( {'op':'close'} )
 
+_train_summary_writer = None
+def init_writer(model_name, tensorboard_dir, start_tensorboard):
+    import tensorboardX
+    global _train_summary_writer
 
+    if not os.path.exists(tensorboard_dir):
+        os.makedirs(tensorboard_dir)
+    summary_writer_folder = os.path.join(tensorboard_dir, model_name)
+    _train_summary_writer = tensorboardX.SummaryWriter(summary_writer_folder)
+
+    if start_tensorboard:
+        tb_tool = TensorBoardTool(tensorboard_dir)
+        tb_tool.run()
+
+    return _train_summary_writer
+
+def get_writer():
+    global _train_summary_writer
+    return _train_summary_writer
+
+def handle_tensorboard_op(input):
+    train_summary_writer = get_writer()
+    action = input['action']
+    if action == 'init':
+        model_name = input['model_name']
+        tensorboard_dir = input['tensorboard_dir']
+        start_tensorboard = input['start_tensorboard']
+        train_summary_writer = init_writer(model_name, tensorboard_dir, start_tensorboard)
+    if train_summary_writer is not None:
+        if action == 'step':
+            step = input['step']
+            step_time = input['step_time']
+            src_loss = input['src_loss']
+            dst_loss = input['dst_loss']
+            # report iteration time summary
+            train_summary_writer.add_scalar('iteration time', step_time, step)
+            # report loss summary
+            train_summary_writer.add_scalar('loss/src', src_loss, step)
+            if dst_loss is not None:
+                train_summary_writer.add_scalar('loss/dst', dst_loss, step)
+        elif action == 'preview':
+            step = input['step']
+            previews = input['previews']
+            static_previews = input['static_previews']
+            if previews is not None:
+                log_tensorboard_previews(step, previews, 'preview', train_summary_writer)
+            if static_previews is not None:
+                log_tensorboard_previews(step, static_previews, 'static_preview', train_summary_writer)
 
 def main(**kwargs):
     io.log_info ("Running trainer.\r\n")
@@ -283,7 +344,9 @@ def main(**kwargs):
             if not c2s.empty():
                 input = c2s.get()
                 op = input.get('op','')
-                if op == 'close':
+                if op == 'tb':
+                    handle_tensorboard_op(input)
+                elif op == 'close':
                     break
             try:
                 io.process_messages(0.1)
@@ -333,6 +396,8 @@ def main(**kwargs):
                                 previews.append ( (preview_name, cv2.resize(preview_rgb, (max_w, max_h))) )
                         selected_preview = selected_preview % len(previews)
                         update_preview = True
+                elif op == 'tb':
+                    handle_tensorboard_op(input)
                 elif op == 'close':
                     break
 

merger/FrameInfo.py

@@ -1,8 +1,10 @@
 from pathlib import Path
 
 class FrameInfo(object):
-    def __init__(self, filepath=None, landmarks_list=None):
+    def __init__(self, filepath=None, landmarks_list=None, image_to_face_mat=None, aligned_size=512):
         self.filepath = filepath
         self.landmarks_list = landmarks_list or []
         self.motion_deg = 0
-        self.motion_power = 0
+        self.motion_power = 0
+        self.image_to_face_mat = image_to_face_mat
+        self.aligned_size = aligned_size

merger/InteractiveMergerSubprocessor.py

@@ -84,15 +84,14 @@ class InteractiveMergerSubprocessor(Subprocessor):
             filepath = frame_info.filepath
 
             if len(frame_info.landmarks_list) == 0:
-                
-                if cfg.mode == 'raw-predict':        
+                self.log_info (f'no faces found for {filepath.name}, copying without faces')
+                if cfg.mode == 'raw-predict':
                     h,w,c = self.predictor_input_shape
                     img_bgr = np.zeros( (h,w,3), dtype=np.uint8)
-                    img_mask = np.zeros( (h,w,1), dtype=np.uint8)               
-                else:                
-                    self.log_info (f'no faces found for {filepath.name}, copying without faces')
+                    img_mask = np.zeros( (h,w,1), dtype=np.uint8)
+                else:
                     img_bgr = cv2_imread(filepath)
-                    imagelib.normalize_channels(img_bgr, 3)                    
+                    imagelib.normalize_channels(img_bgr, 3)
                     h,w,c = img_bgr.shape
                     img_mask = np.zeros( (h,w,1), dtype=img_bgr.dtype)
                     
@@ -140,7 +139,7 @@ class InteractiveMergerSubprocessor(Subprocessor):
 
 
     #override
-    def __init__(self, is_interactive, merger_session_filepath, predictor_func, predictor_input_shape, face_enhancer_func, xseg_256_extract_func, merger_config, frames, frames_root_path, output_path, output_mask_path, model_iter, subprocess_count=4):
+    def __init__(self, is_interactive, merger_session_filepath, predictor_func, predictor_input_shape, face_enhancer_func, xseg_256_extract_func, merger_config, frames, frames_root_path, output_path, output_mask_path, model_iter, subprocess_count=4, src_src=False):
         if len (frames) == 0:
             raise ValueError ("len (frames) == 0")
 
@@ -163,6 +162,8 @@ class InteractiveMergerSubprocessor(Subprocessor):
 
         self.prefetch_frame_count = self.process_count = subprocess_count
 
+        self.src_src = src_src
+
         session_data = None
         if self.is_interactive and self.merger_session_filepath.exists():
             io.input_skip_pending()

merger/MergeMasked.py

@@ -12,6 +12,17 @@ from facelib import FaceType, LandmarksProcessor
 is_windows = sys.platform[0:3] == 'win'
 xseg_input_size = 256
 
+def concat_matrix(first, second):
+    mul1 = np.vstack([*first, [0, 0, 1]])
+    mul2 = np.vstack([*second, [0, 0, 1]])
+    mul_r = np.matmul(mul1, mul2)
+    return np.delete(mul_r, (2), axis=0);
+
+def get_identity_affine_mat():
+    pt1 = np.float32([ [0, 0], [1, 0], [1, 1] ])
+    pt2 = np.float32([ [0, 0], [1, 0], [1, 1] ])
+    return cv2.getAffineTransform(pt1, pt2)
+
 def MergeMaskedFace (predictor_func, predictor_input_shape,
                      face_enhancer_func,
                      xseg_256_extract_func,
@@ -26,15 +37,28 @@ def MergeMaskedFace (predictor_func, predictor_input_shape,
     if cfg.super_resolution_power != 0:
         output_size *= 4
 
-    face_mat        = LandmarksProcessor.get_transform_mat (img_face_landmarks, output_size, face_type=cfg.face_type)
-    face_output_mat = LandmarksProcessor.get_transform_mat (img_face_landmarks, output_size, face_type=cfg.face_type, scale= 1.0 + 0.01*cfg.output_face_scale)
+    existing_mat = get_identity_affine_mat() if cfg.src_src else frame_info.image_to_face_mat
+    aligned_size = frame_info.aligned_size
+
+    if existing_mat is not None:
+        face_scale_mat = cv2.getRotationMatrix2D((0,0), 0, output_size/aligned_size)
+        face_mat = concat_matrix(face_scale_mat, existing_mat)
+        output_scale_mat = cv2.getRotationMatrix2D((output_size/2, output_size/2), 0, 1.0 + 0.01 * cfg.output_face_scale)
+        face_output_mat = concat_matrix(output_scale_mat, face_mat)
+    else:
+        face_mat = LandmarksProcessor.get_transform_mat (img_face_landmarks, output_size, face_type=cfg.face_type)
+        face_output_mat = LandmarksProcessor.get_transform_mat (img_face_landmarks, output_size, face_type=cfg.face_type, scale= 1.0 + 0.01*cfg.output_face_scale)
 
     if mask_subres_size == output_size:
         face_mask_output_mat = face_output_mat
     else:
-        face_mask_output_mat = LandmarksProcessor.get_transform_mat (img_face_landmarks, mask_subres_size, face_type=cfg.face_type, scale= 1.0 + 0.01*cfg.output_face_scale)
+        if existing_mat is not None:
+            mask_output_scale_mat = cv2.getRotationMatrix2D((0, 0), 0, mask_subres_size/output_size)
+            face_mask_output_mat = concat_matrix(mask_output_scale_mat, face_mat)
+        else:
+            face_mask_output_mat = LandmarksProcessor.get_transform_mat (img_face_landmarks, mask_subres_size, face_type=cfg.face_type, scale= 1.0 + 0.01*cfg.output_face_scale)
 
-    dst_face_bgr      = cv2.warpAffine( img_bgr        , face_mat, (output_size, output_size), flags=cv2.INTER_CUBIC )
+    dst_face_bgr      = cv2.warpAffine( img_bgr, face_mat, (output_size, output_size), flags=cv2.INTER_CUBIC )
     dst_face_bgr      = np.clip(dst_face_bgr, 0, 1)
 
     dst_face_mask_a_0 = cv2.warpAffine( img_face_mask_a, face_mat, (output_size, output_size), flags=cv2.INTER_CUBIC )
@@ -76,7 +100,11 @@ def MergeMaskedFace (predictor_func, predictor_input_shape,
 
         if cfg.mask_mode >= 7 and cfg.mask_mode <= 9:
             # obtain XSeg-dst
-            xseg_mat            = LandmarksProcessor.get_transform_mat (img_face_landmarks, xseg_input_size, face_type=cfg.face_type)
+            if existing_mat is not None:
+                xseg_scale_mat = cv2.getRotationMatrix2D((0,0), 0, xseg_input_size/aligned_size)
+                xseg_mat = concat_matrix(xseg_scale_mat, existing_mat)
+            else:
+                xseg_mat = LandmarksProcessor.get_transform_mat (img_face_landmarks, xseg_input_size, face_type=cfg.face_type)
             dst_face_xseg_bgr   = cv2.warpAffine(img_bgr, xseg_mat, (xseg_input_size,)*2, flags=cv2.INTER_CUBIC )
             dst_face_xseg_mask  = xseg_256_extract_func(dst_face_xseg_bgr)
             X_dst_face_mask_a_0 = cv2.resize (dst_face_xseg_mask, (output_size,output_size), interpolation=cv2.INTER_CUBIC)

merger/MergerConfig.py

@@ -113,6 +113,7 @@ class MergerConfigMasked(MergerConfig):
                        image_denoise_power = 0,
                        bicubic_degrade_power = 0,
                        color_degrade_power = 0,
+                       src_src = False,
                        **kwargs
                        ):
 
@@ -122,6 +123,8 @@ class MergerConfigMasked(MergerConfig):
         if self.face_type not in [FaceType.HALF, FaceType.MID_FULL, FaceType.FULL, FaceType.WHOLE_FACE, FaceType.HEAD ]:
             raise ValueError("MergerConfigMasked does not support this type of face.")
 
+        self.src_src = src_src
+
         self.default_mode = default_mode
 
         #default changeable params
@@ -188,13 +191,13 @@ class MergerConfigMasked(MergerConfig):
         self.bicubic_degrade_power = np.clip ( self.bicubic_degrade_power+diff, 0, 100)
 
     def ask_settings(self):
-        s = """Choose mode: \n"""
-        for key in mode_dict.keys():
-            s += f"""({key}) {mode_dict[key]}\n"""
-        io.log_info(s)
-        mode = io.input_int ("", mode_str_dict.get(self.default_mode, 1) )
-
-        self.mode = mode_dict.get (mode, self.default_mode )
+        if 'raw-predict' not in self.mode:
+            s = """Choose mode: \n"""
+            for key in mode_dict.keys():
+                s += f"""({key}) {mode_dict[key]}\n"""
+            io.log_info(s)
+            mode = io.input_int ("", mode_str_dict.get(self.default_mode, 1) )
+            self.mode = mode_dict.get (mode, self.default_mode)
 
         if 'raw' not in self.mode:
             if self.mode == 'hist-match':

models/ModelBase.py

@@ -22,6 +22,7 @@ from samplelib import SampleGeneratorBase
 
 class ModelBase(object):
     def __init__(self, is_training=False,
+                       src_src=False,
                        saved_models_path=None,
                        training_data_src_path=None,
                        training_data_dst_path=None,
@@ -36,6 +37,7 @@ class ModelBase(object):
                        silent_start=False,
                        **kwargs):
         self.is_training = is_training
+        self.src_src = src_src
         self.saved_models_path = saved_models_path
         self.training_data_src_path = training_data_src_path
         self.training_data_dst_path = training_data_dst_path

models/Model_SAEHD/Model.py

@@ -601,20 +601,29 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
                     _, gpu_pred_dst_dstm = self.decoder_dst(gpu_dst_code)
 
                 elif 'liae' in archi_type:
+                    gpu_src_code = self.encoder (self.warped_src)
+                    gpu_src_inter_AB_code = self.inter_AB (gpu_src_code)
+                    gpu_src_code = tf.concat([gpu_src_inter_AB_code,gpu_src_inter_AB_code], nn.conv2d_ch_axis )
+                    gpu_pred_src_src, gpu_pred_src_srcm = self.decoder(gpu_src_code)
+
                     gpu_dst_code = self.encoder (self.warped_dst)
                     gpu_dst_inter_B_code = self.inter_B (gpu_dst_code)
                     gpu_dst_inter_AB_code = self.inter_AB (gpu_dst_code)
                     gpu_dst_code = tf.concat([gpu_dst_inter_B_code,gpu_dst_inter_AB_code], nn.conv2d_ch_axis)
                     gpu_src_dst_code = tf.concat([gpu_dst_inter_AB_code,gpu_dst_inter_AB_code], nn.conv2d_ch_axis)
 
-                    gpu_pred_src_dst, gpu_pred_src_dstm = self.decoder(gpu_src_dst_code)
+                    gpu_pred_src_dst, gpu_pred_src_dst_dstm = self.decoder(gpu_src_dst_code)
                     _, gpu_pred_dst_dstm = self.decoder(gpu_dst_code)
 
 
-            def AE_merge( warped_dst):
-                return nn.tf_sess.run ( [gpu_pred_src_dst, gpu_pred_dst_dstm, gpu_pred_src_dstm], feed_dict={self.warped_dst:warped_dst})
+            def AE_merge(warped_dst):
+                return nn.tf_sess.run ( [gpu_pred_src_dst, gpu_pred_dst_dstm, gpu_pred_src_dst_dstm], feed_dict={self.warped_dst:warped_dst})
+            
+            def AE_src(warped_src):
+                return nn.tf_sess.run( [gpu_pred_src_src, gpu_pred_src_srcm], feed_dict={self.warped_src:warped_src})
 
             self.AE_merge = AE_merge
+            self.AE_src = AE_src
 
         # Loading/initializing all models/optimizers weights
         for model, filename in io.progress_bar_generator(self.model_filename_list, "Initializing models"):
@@ -813,9 +822,21 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
 
         return bgr[0], mask_src_dstm[0][...,0], mask_dst_dstm[0][...,0]
 
+    def src_predictor_func (self, face=None):
+        face = nn.to_data_format(face[None,...], self.model_data_format, "NHWC")
+        bgr, mask_src_dstm = [ nn.to_data_format(x,"NHWC", self.model_data_format).astype(np.float32) for x in self.AE_src (face) ]
+        return bgr[0], mask_src_dstm[0][...,0], mask_src_dstm[0][...,0]
+
     #override
     def get_MergerConfig(self):
         import merger
-        return self.predictor_func, (self.options['resolution'], self.options['resolution'], 3), merger.MergerConfigMasked(face_type=self.face_type, default_mode = 'overlay')
+        if self.src_src:
+            merger_config = merger.MergerConfigMasked(face_type=self.face_type,
+                                                default_mode='raw-predict',
+                                                mode='raw-predict',
+                                                src_src=True)
+            return self.src_predictor_func, (self.options['resolution'], self.options['resolution'], 3), merger_config
+        else:
+            return self.predictor_func, (self.options['resolution'], self.options['resolution'], 3), merger.MergerConfigMasked(face_type=self.face_type, default_mode = 'overlay')
 
 Model = SAEHDModel