mirror of
https://github.com/iperov/DeepFaceLab.git
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5ac7e5d7f1
Pixel loss may help to enhance fine details and stabilize face color. Use it only if quality does not improve over time. SAE: previous SAE model will not work with this update. Greatly decreased chance of model collapse. Increased model accuracy. Residual blocks now default and this option has been removed. Improved 'learn mask'. Added masked preview (switch by space key) Converter: fixed rct/lct in seamless mode added mask mode (6) learned*FAN-prd*FAN-dst added mask editor, its created for refining dataset for FANSeg model, and not for production, but you can spend your time and test it in regular fakes with face obstructions
201 lines
8.7 KiB
Python
201 lines
8.7 KiB
Python
import numpy as np
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from nnlib import nnlib
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from models import ModelBase
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from facelib import FaceType
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from samplelib import *
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from interact import interact as io
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class Model(ModelBase):
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#override
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def onInitializeOptions(self, is_first_run, ask_override):
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if is_first_run:
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self.options['lighter_ae'] = io.input_bool ("Use lightweight autoencoder? (y/n, ?:help skip:n) : ", False, help_message="Lightweight autoencoder is faster, requires less VRAM, sacrificing overall quality. If your GPU VRAM <= 4, you should to choose this option.")
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else:
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default_lighter_ae = self.options.get('created_vram_gb', 99) <= 4 #temporally support old models, deprecate in future
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if 'created_vram_gb' in self.options.keys():
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self.options.pop ('created_vram_gb')
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self.options['lighter_ae'] = self.options.get('lighter_ae', default_lighter_ae)
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if is_first_run or ask_override:
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def_pixel_loss = self.options.get('pixel_loss', False)
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self.options['pixel_loss'] = io.input_bool ("Use pixel loss? (y/n, ?:help skip: n/default ) : ", def_pixel_loss, help_message="Pixel loss may help to enhance fine details and stabilize face color. Use it only if quality does not improve over time.")
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else:
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self.options['pixel_loss'] = self.options.get('pixel_loss', False)
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#override
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def onInitialize(self):
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exec(nnlib.import_all(), locals(), globals())
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self.set_vram_batch_requirements( {2.5:4} )
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bgr_shape, mask_shape, self.encoder, self.decoder_src, self.decoder_dst = self.Build( self.options['lighter_ae'] )
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if not self.is_first_run():
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weights_to_load = [ [self.encoder , 'encoder.h5'],
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[self.decoder_src, 'decoder_src.h5'],
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[self.decoder_dst, 'decoder_dst.h5']
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]
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self.load_weights_safe(weights_to_load)
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input_src_bgr = Input(bgr_shape)
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input_src_mask = Input(mask_shape)
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input_dst_bgr = Input(bgr_shape)
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input_dst_mask = Input(mask_shape)
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rec_src_bgr, rec_src_mask = self.decoder_src( self.encoder(input_src_bgr) )
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rec_dst_bgr, rec_dst_mask = self.decoder_dst( self.encoder(input_dst_bgr) )
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self.ae = Model([input_src_bgr,input_src_mask,input_dst_bgr,input_dst_mask], [rec_src_bgr, rec_src_mask, rec_dst_bgr, rec_dst_mask] )
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self.ae.compile(optimizer=Adam(lr=5e-5, beta_1=0.5, beta_2=0.999),
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loss=[ DSSIMMSEMaskLoss(input_src_mask, is_mse=self.options['pixel_loss']), 'mae', DSSIMMSEMaskLoss(input_dst_mask, is_mse=self.options['pixel_loss']), 'mae' ] )
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self.src_view = K.function([input_src_bgr],[rec_src_bgr, rec_src_mask])
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self.dst_view = K.function([input_dst_bgr],[rec_dst_bgr, rec_dst_mask])
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if self.is_training_mode:
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f = SampleProcessor.TypeFlags
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self.set_training_data_generators ([
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SampleGeneratorFace(self.training_data_src_path, sort_by_yaw_target_samples_path=self.training_data_dst_path if self.sort_by_yaw else None,
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debug=self.is_debug(), batch_size=self.batch_size,
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sample_process_options=SampleProcessor.Options(random_flip=self.random_flip, scale_range=np.array([-0.05, 0.05])+self.src_scale_mod / 100.0 ),
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output_sample_types=[ [f.WARPED_TRANSFORMED | f.FACE_ALIGN_HALF | f.MODE_BGR, 128],
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[f.TRANSFORMED | f.FACE_ALIGN_HALF | f.MODE_BGR, 128],
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[f.TRANSFORMED | f.FACE_ALIGN_HALF | f.MODE_M | f.FACE_MASK_FULL, 128] ] ),
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SampleGeneratorFace(self.training_data_dst_path, debug=self.is_debug(), batch_size=self.batch_size,
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sample_process_options=SampleProcessor.Options(random_flip=self.random_flip),
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output_sample_types=[ [f.WARPED_TRANSFORMED | f.FACE_ALIGN_HALF | f.MODE_BGR, 128],
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[f.TRANSFORMED | f.FACE_ALIGN_HALF | f.MODE_BGR, 128],
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[f.TRANSFORMED | f.FACE_ALIGN_HALF | f.MODE_M | f.FACE_MASK_FULL, 128] ] )
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])
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#override
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def onSave(self):
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self.save_weights_safe( [[self.encoder, 'encoder.h5'],
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[self.decoder_src, 'decoder_src.h5'],
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[self.decoder_dst, 'decoder_dst.h5']] )
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#override
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def onTrainOneIter(self, sample, generators_list):
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warped_src, target_src, target_src_mask = sample[0]
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warped_dst, target_dst, target_dst_mask = sample[1]
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total, loss_src_bgr, loss_src_mask, loss_dst_bgr, loss_dst_mask = self.ae.train_on_batch( [warped_src, target_src_mask, warped_dst, target_dst_mask], [target_src, target_src_mask, target_dst, target_dst_mask] )
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return ( ('loss_src', loss_src_bgr), ('loss_dst', loss_dst_bgr) )
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#override
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def onGetPreview(self, sample):
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test_A = sample[0][1][0:4] #first 4 samples
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test_A_m = sample[0][2][0:4] #first 4 samples
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test_B = sample[1][1][0:4]
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test_B_m = sample[1][2][0:4]
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AA, mAA = self.src_view([test_A])
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AB, mAB = self.src_view([test_B])
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BB, mBB = self.dst_view([test_B])
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mAA = np.repeat ( mAA, (3,), -1)
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mAB = np.repeat ( mAB, (3,), -1)
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mBB = np.repeat ( mBB, (3,), -1)
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st = []
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for i in range(0, len(test_A)):
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st.append ( np.concatenate ( (
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test_A[i,:,:,0:3],
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AA[i],
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#mAA[i],
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test_B[i,:,:,0:3],
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BB[i],
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#mBB[i],
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AB[i],
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#mAB[i]
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), axis=1) )
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return [ ('H128', np.concatenate ( st, axis=0 ) ) ]
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def predictor_func (self, face):
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x, mx = self.src_view ( [ face[np.newaxis,...] ] )
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return x[0], mx[0][...,0]
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#override
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def get_converter(self):
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from converters import ConverterMasked
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return ConverterMasked(self.predictor_func,
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predictor_input_size=128,
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face_type=FaceType.HALF,
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base_erode_mask_modifier=100,
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base_blur_mask_modifier=100)
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def Build(self, lighter_ae):
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exec(nnlib.code_import_all, locals(), globals())
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bgr_shape = (128, 128, 3)
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mask_shape = (128, 128, 1)
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def downscale (dim):
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def func(x):
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return LeakyReLU(0.1)(Conv2D(dim, 5, strides=2, padding='same')(x))
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return func
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def upscale (dim):
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def func(x):
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return PixelShuffler()(LeakyReLU(0.1)(Conv2D(dim * 4, 3, strides=1, padding='same')(x)))
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return func
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def Encoder(input_shape):
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input_layer = Input(input_shape)
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x = input_layer
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if not lighter_ae:
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x = downscale(128)(x)
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x = downscale(256)(x)
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x = downscale(512)(x)
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x = downscale(1024)(x)
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x = Dense(512)(Flatten()(x))
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x = Dense(8 * 8 * 512)(x)
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x = Reshape((8, 8, 512))(x)
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x = upscale(512)(x)
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else:
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x = downscale(128)(x)
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x = downscale(256)(x)
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x = downscale(512)(x)
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x = downscale(1024)(x)
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x = Dense(256)(Flatten()(x))
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x = Dense(8 * 8 * 256)(x)
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x = Reshape((8, 8, 256))(x)
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x = upscale(256)(x)
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return Model(input_layer, x)
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def Decoder():
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if not lighter_ae:
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input_ = Input(shape=(16, 16, 512))
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x = input_
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x = upscale(512)(x)
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x = upscale(256)(x)
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x = upscale(128)(x)
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y = input_ #mask decoder
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y = upscale(512)(y)
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y = upscale(256)(y)
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y = upscale(128)(y)
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else:
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input_ = Input(shape=(16, 16, 256))
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x = input_
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x = upscale(256)(x)
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x = upscale(128)(x)
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x = upscale(64)(x)
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y = input_ #mask decoder
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y = upscale(256)(y)
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y = upscale(128)(y)
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y = upscale(64)(y)
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x = Conv2D(3, kernel_size=5, padding='same', activation='sigmoid')(x)
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y = Conv2D(1, kernel_size=5, padding='same', activation='sigmoid')(y)
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return Model(input_, [x,y])
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return bgr_shape, mask_shape, Encoder(bgr_shape), Decoder(), Decoder()
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