SAE : WARNING, RETRAIN IS REQUIRED !

fixed model sizes from previous update.
avoided bug in ML framework(keras) that forces to train the model on random noise.

Converter: added blur on the same keys as sharpness

Added new model 'TrueFace'. This is a GAN model ported from https://github.com/NVlabs/FUNIT
Model produces near zero morphing and high detail face.
Model has higher failure rate than other models.
Keep src and dst faceset in same lighting conditions.

models/Model_TrueFace/Model.py

@@ -0,0 +1,180 @@
+import numpy as np
+
+from facelib import FaceType
+from interact import interact as io
+from models import ModelBase
+from nnlib import nnlib, FUNIT
+from samplelib import *
+
+class TrueFaceModel(ModelBase):
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs,
+                            ask_sort_by_yaw=False,
+                            ask_random_flip=False,
+                            ask_src_scale_mod=False)
+
+    #override
+    def onInitializeOptions(self, is_first_run, ask_override):
+        default_resolution = 128
+        default_face_type = 'f'
+
+        if is_first_run:
+            resolution = self.options['resolution'] = io.input_int(f"Resolution ( 64-256 ?:help skip:{default_resolution}) : ", default_resolution, help_message="More resolution requires more VRAM and time to train. Value will be adjusted to multiple of 16.")
+            resolution = np.clip (resolution, 64, 256)
+            while np.modf(resolution / 16)[0] != 0.0:
+                resolution -= 1
+        else:
+            self.options['resolution'] = self.options.get('resolution', default_resolution)
+
+        if is_first_run:
+            self.options['face_type'] = io.input_str ("Half or Full face? (h/f, ?:help skip:f) : ", default_face_type, ['h','f'], help_message="").lower()
+        else:
+            self.options['face_type'] = self.options.get('face_type', default_face_type)
+
+    #override
+    def onInitialize(self, batch_size=-1, **in_options):
+        exec(nnlib.code_import_all, locals(), globals())
+        self.set_vram_batch_requirements({2:1,3:1,4:4,5:8,6:16})
+
+        resolution = self.options['resolution']
+        face_type = FaceType.FULL if self.options['face_type'] == 'f' else FaceType.HALF
+
+        self.model = FUNIT( face_type_str=FaceType.toString(face_type),
+                            batch_size=self.batch_size,
+                            encoder_nf=64,
+                            encoder_downs=2,
+                            encoder_res_blk=2,
+                            class_downs=4,
+                            class_nf=64,
+                            class_latent=64,
+                            mlp_nf=256,
+                            mlp_blks=2,
+                            dis_nf=64,
+                            dis_res_blks=10,
+                            num_classes=2,
+                            subpixel_decoder=True,
+                            initialize_weights=self.is_first_run(),
+                            is_training=self.is_training_mode
+                           )
+
+        if not self.is_first_run():
+            self.load_weights_safe(self.model.get_model_filename_list())
+
+        t = SampleProcessor.Types
+        face_type = t.FACE_TYPE_FULL if self.options['face_type'] == 'f' else t.FACE_TYPE_HALF
+        if self.is_training_mode:
+
+            output_sample_types=[ {'types': (t.IMG_TRANSFORMED, face_type, t.MODE_BGR), 'resolution':resolution, 'normalize_tanh':True},
+                                 ]
+
+            self.set_training_data_generators ([
+                    SampleGeneratorFace(self.training_data_src_path, debug=self.is_debug(), batch_size=self.batch_size,
+                        sample_process_options=SampleProcessor.Options(random_flip=True),
+                        output_sample_types=output_sample_types ),
+
+                    SampleGeneratorFace(self.training_data_dst_path, debug=self.is_debug(), batch_size=self.batch_size,
+                        sample_process_options=SampleProcessor.Options(random_flip=True),
+                        output_sample_types=output_sample_types )
+                   ])
+        else:
+            generator = SampleGeneratorFace(self.training_data_src_path, batch_size=1,
+                                sample_process_options=SampleProcessor.Options(),
+                                output_sample_types=[ {'types': (t.IMG_SOURCE, face_type, t.MODE_BGR), 'resolution':resolution, 'normalize_tanh':True} ] )
+
+            io.log_info("Calculating average src face style...")
+            codes = []
+            for i in io.progress_bar_generator(range(generator.get_total_sample_count())):
+                codes += self.model.get_average_class_code( generator.generate_next() )
+
+            self.average_class_code = np.mean ( np.array(codes), axis=0 )[None,...]
+
+
+    #override
+    def get_model_filename_list(self):
+        return self.model.get_model_filename_list()
+
+    #override
+    def onSave(self):
+        self.save_weights_safe(self.model.get_model_filename_list())
+
+    #override
+    def onTrainOneIter(self, generators_samples, generators_list):
+        bs = self.batch_size
+        lbs = bs // 2
+        hbs = bs - lbs
+
+        src, = generators_samples[0]
+        dst, = generators_samples[1]
+
+        xa  = np.concatenate ( [src[0:lbs], dst[0:lbs]], axis=0 )
+
+        la = np.concatenate ( [ np.array ([0]*lbs, np.int32),
+                                np.array ([1]*lbs, np.int32) ] )
+
+        xb = np.concatenate ( [src[lbs:], dst[lbs:]], axis=0 )
+
+        lb = np.concatenate ( [ np.array ([0]*hbs, np.int32),
+                                np.array ([1]*hbs, np.int32) ] )
+
+        rnd_list = np.arange(lbs*2)
+        np.random.shuffle(rnd_list)
+        xa  = xa[rnd_list,...]
+        la = la[rnd_list,...]
+        la = la[...,None]
+
+        rnd_list = np.arange(hbs*2)
+        np.random.shuffle(rnd_list)
+        xb = xb[rnd_list,...]
+        lb = lb[rnd_list,...]
+        lb = lb[...,None]
+
+        G_loss, D_loss = self.model.train(xa,la,xb,lb)
+
+        return ( ('G_loss', G_loss), ('D_loss', D_loss), )
+
+    #override
+    def onGetPreview(self, generators_samples):
+        xa  = generators_samples[0][0]
+        xb  = generators_samples[1][0]
+
+        view_samples = min(4, xa.shape[0])
+
+
+        s_xa_mean = self.model.get_average_class_code([xa])[0][None,...]
+        s_xb_mean = self.model.get_average_class_code([xb])[0][None,...]
+
+        s_xab_mean = self.model.get_average_class_code([ np.concatenate( [xa,xb], axis=0) ])[0][None,...]
+
+        lines = []
+
+        for i in range(view_samples):
+            xaxa, = self.model.convert  ([ xa[i:i+1], s_xa_mean  ] )
+            xbxb, = self.model.convert  ([ xb[i:i+1], s_xb_mean  ] )
+            xbxa, = self.model.convert  ([ xb[i:i+1], s_xa_mean  ] )
+
+            xa_i,xb_i,xaxa,xbxb,xbxa = [ np.clip(x/2+0.5, 0, 1) for x in [xa[i], xb[i], xaxa[0],xbxb[0],xbxa[0]] ]
+
+            lines += [ np.concatenate( (xa_i, xaxa, xb_i, xbxb, xbxa), axis=1) ]
+
+        r = np.concatenate ( lines, axis=0 )
+        return [ ('TrueFace', r ) ]
+
+    def predictor_func (self, face=None, dummy_predict=False):
+        if dummy_predict:
+            self.model.convert ([ np.zeros ( (1, self.options['resolution'], self.options['resolution'], 3), dtype=np.float32 ), self.average_class_code ])
+        else:
+            bgr, = self.model.convert ([  face[np.newaxis,...]*2-1, self.average_class_code  ])
+            return bgr[0] / 2 + 0.5
+
+    #override
+    def get_ConverterConfig(self):
+        face_type = FaceType.FULL
+
+        import converters
+        return self.predictor_func, (self.options['resolution'], self.options['resolution'], 3), converters.ConverterConfigMasked(face_type=face_type,
+                                     default_mode = 1,
+                                     clip_hborder_mask_per=0.0625 if (face_type == FaceType.FULL) else 0,
+                                    )
+
+Model = TrueFaceModel