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https://github.com/iperov/DeepFaceLab.git
synced 2025-07-06 21:12:07 -07:00
changed help message for pixel loss:
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
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iperov
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22 changed files with 715 additions and 387 deletions
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@ -63,13 +63,11 @@ class SAEModel(ModelBase):
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self.options['e_ch_dims'] = np.clip ( io.input_int("Encoder dims per channel (21-85 ?:help skip:%d) : " % (default_e_ch_dims) , default_e_ch_dims, help_message="More encoder dims help to recognize more facial features, but require more VRAM. You can fine-tune model size to fit your GPU." ), 21, 85 )
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default_d_ch_dims = self.options['e_ch_dims'] // 2
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self.options['d_ch_dims'] = np.clip ( io.input_int("Decoder dims per channel (10-85 ?:help skip:%d) : " % (default_d_ch_dims) , default_d_ch_dims, help_message="More decoder dims help to get better details, but require more VRAM. You can fine-tune model size to fit your GPU." ), 10, 85 )
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self.options['d_residual_blocks'] = io.input_bool ("Add residual blocks to decoder? (y/n, ?:help skip:n) : ", False, help_message="These blocks help to get better details, but require more computing time.")
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self.options['remove_gray_border'] = io.input_bool ("Remove gray border? (y/n, ?:help skip:n) : ", False, help_message="Removes gray border of predicted face, but requires more computing resources.")
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else:
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self.options['ae_dims'] = self.options.get('ae_dims', default_ae_dims)
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self.options['e_ch_dims'] = self.options.get('e_ch_dims', default_e_ch_dims)
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self.options['d_ch_dims'] = self.options.get('d_ch_dims', default_d_ch_dims)
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self.options['d_residual_blocks'] = self.options.get('d_residual_blocks', False)
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self.options['remove_gray_border'] = self.options.get('remove_gray_border', False)
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if is_first_run:
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@ -81,7 +79,7 @@ class SAEModel(ModelBase):
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default_bg_style_power = 0.0
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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: %s ) : " % (yn_str[def_pixel_loss]), def_pixel_loss, help_message="Default DSSIM loss good for initial understanding structure of faces. Use pixel loss after 60k iters to enhance fine details. Warning: this option may cause collapse the model, make a backup of Model folder before apply it.")
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self.options['pixel_loss'] = io.input_bool ("Use pixel loss? (y/n, ?:help skip: %s ) : " % (yn_str[def_pixel_loss]), 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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default_face_style_power = default_face_style_power if is_first_run else self.options.get('face_style_power', default_face_style_power)
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self.options['face_style_power'] = np.clip ( io.input_number("Face style power ( 0.0 .. 100.0 ?:help skip:%.2f) : " % (default_face_style_power), default_face_style_power,
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@ -105,7 +103,7 @@ class SAEModel(ModelBase):
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ae_dims = self.options['ae_dims']
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e_ch_dims = self.options['e_ch_dims']
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d_ch_dims = self.options['d_ch_dims']
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d_residual_blocks = self.options['d_residual_blocks']
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d_residual_blocks = True
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bgr_shape = (resolution, resolution, 3)
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mask_shape = (resolution, resolution, 1)
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@ -127,7 +125,9 @@ class SAEModel(ModelBase):
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target_dstm_ar = [ Input ( ( mask_shape[0] // (2**i) ,)*2 + (mask_shape[-1],) ) for i in range(ms_count-1, -1, -1)]
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padding = 'reflect' if self.options['remove_gray_border'] else 'zero'
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common_flow_kwargs = { 'padding': padding }
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common_flow_kwargs = { 'padding': padding,
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'norm': 'bn',
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'act':'prelu' }
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weights_to_load = []
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if self.options['archi'] == 'liae':
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@ -302,7 +302,7 @@ class SAEModel(ModelBase):
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self.src_dst_mask_train = K.function (feed,[src_mask_loss, dst_mask_loss], self.src_dst_mask_opt.get_updates(src_mask_loss+dst_mask_loss, src_dst_mask_loss_train_weights) )
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if self.options['learn_mask']:
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self.AE_view = K.function ([warped_src, warped_dst], [pred_src_src[-1], pred_dst_dst[-1], pred_src_dst[-1], pred_src_dstm[-1]])
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self.AE_view = K.function ([warped_src, warped_dst], [pred_src_src[-1], pred_dst_dst[-1], pred_dst_dstm[-1], pred_src_dst[-1], pred_src_dstm[-1]])
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else:
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self.AE_view = K.function ([warped_src, warped_dst], [pred_src_src[-1], pred_dst_dst[-1], pred_src_dst[-1] ] )
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@ -310,7 +310,7 @@ class SAEModel(ModelBase):
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else:
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self.load_weights_safe(weights_to_load)
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if self.options['learn_mask']:
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self.AE_convert = K.function ([warped_dst],[ pred_src_dst[-1], pred_src_dstm[-1] ])
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self.AE_convert = K.function ([warped_dst],[ pred_src_dst[-1], pred_dst_dstm[-1], pred_src_dstm[-1] ])
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else:
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self.AE_convert = K.function ([warped_dst],[ pred_src_dst[-1] ])
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@ -391,24 +391,34 @@ class SAEModel(ModelBase):
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test_B_m = sample[1][2][0:4]
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if self.options['learn_mask']:
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S, D, SS, DD, SD, SDM = [ np.clip(x, 0.0, 1.0) for x in ([test_A,test_B] + self.AE_view ([test_A, test_B]) ) ]
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SDM, = [ np.repeat (x, (3,), -1) for x in [SDM] ]
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S, D, SS, DD, DDM, SD, SDM = [ np.clip(x, 0.0, 1.0) for x in ([test_A,test_B] + self.AE_view ([test_A, test_B]) ) ]
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DDM, SDM, = [ np.repeat (x, (3,), -1) for x in [DDM, SDM] ]
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else:
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S, D, SS, DD, SD, = [ np.clip(x, 0.0, 1.0) for x in ([test_A,test_B] + self.AE_view ([test_A, test_B]) ) ]
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result = []
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st = []
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for i in range(0, len(test_A)):
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ar = S[i], SS[i], D[i], DD[i], SD[i]
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#if self.options['learn_mask']:
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# ar += (SDM[i],)
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st.append ( np.concatenate ( ar, axis=1) )
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return [ ('SAE', np.concatenate (st, axis=0 )), ]
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result += [ ('SAE', np.concatenate (st, axis=0 )), ]
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if self.options['learn_mask']:
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st_m = []
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for i in range(0, len(test_A)):
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ar = S[i], SS[i], D[i], DD[i]*DDM[i], SD[i]*(DDM[i]*SDM[i])
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st_m.append ( np.concatenate ( ar, axis=1) )
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result += [ ('SAE masked', np.concatenate (st_m, axis=0 )), ]
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return result
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def predictor_func (self, face):
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if self.options['learn_mask']:
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bgr, mask = self.AE_convert ([face[np.newaxis,...]])
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return bgr[0], mask[0][...,0]
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bgr, mask_dst_dstm, mask_src_dstm = self.AE_convert ([face[np.newaxis,...]])
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mask = mask_dst_dstm[0] * mask_src_dstm[0]
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return bgr[0], mask[...,0]
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else:
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bgr, = self.AE_convert ([face[np.newaxis,...]])
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return bgr[0]
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@ -440,23 +450,39 @@ class SAEModel(ModelBase):
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def initialize_nn_functions():
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exec (nnlib.import_all(), locals(), globals())
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def BatchNorm():
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return BatchNormalization(axis=-1)
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def NormPass(x):
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return x
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def Norm(norm=''):
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if norm == 'bn':
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return BatchNormalization(axis=-1)
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else:
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return NormPass
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def Act(act='', lrelu_alpha=0.1):
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if act == 'prelu':
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return PReLU()
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else:
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return LeakyReLU(alpha=lrelu_alpha)
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class ResidualBlock(object):
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def __init__(self, filters, kernel_size=3, padding='zero', use_reflection_padding=False):
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def __init__(self, filters, kernel_size=3, padding='zero', use_reflection_padding=False, norm='', act='', **kwargs):
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self.filters = filters
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self.kernel_size = kernel_size
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self.padding = padding
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self.norm = norm
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self.act = act
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def __call__(self, inp):
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var_x = inp
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var_x = Conv2D(self.filters, kernel_size=self.kernel_size, padding=self.padding)(var_x)
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var_x = LeakyReLU(alpha=0.2)(var_x)
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var_x = Conv2D(self.filters, kernel_size=self.kernel_size, padding=self.padding)(var_x)
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var_x = Add()([var_x, inp])
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var_x = LeakyReLU(alpha=0.2)(var_x)
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return var_x
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x = inp
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x = Conv2D(self.filters, kernel_size=self.kernel_size, padding=self.padding)(x)
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x = Act(self.act, lrelu_alpha=0.2)(x)
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x = Norm(self.norm)(x)
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x = Conv2D(self.filters, kernel_size=self.kernel_size, padding=self.padding)(x)
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x = Add()([x, inp])
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x = Act(self.act, lrelu_alpha=0.2)(x)
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x = Norm(self.norm)(x)
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return x
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SAEModel.ResidualBlock = ResidualBlock
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def ResidualBlock_pre (**base_kwargs):
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@ -466,9 +492,9 @@ class SAEModel(ModelBase):
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return func
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SAEModel.ResidualBlock_pre = ResidualBlock_pre
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def downscale (dim, padding='zero'):
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def downscale (dim, padding='zero', norm='', act='', **kwargs):
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def func(x):
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return LeakyReLU(0.1)(Conv2D(dim, kernel_size=5, strides=2, padding=padding)(x))
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return Norm(norm)( Act(act) (Conv2D(dim, kernel_size=5, strides=2, padding=padding)(x)) )
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return func
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SAEModel.downscale = downscale
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@ -479,9 +505,9 @@ class SAEModel(ModelBase):
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return func
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SAEModel.downscale_pre = downscale_pre
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def upscale (dim, padding='zero'):
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def upscale (dim, padding='zero', norm='', act='', **kwargs):
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def func(x):
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return SubpixelUpscaler()(LeakyReLU(0.1)(Conv2D(dim * 4, kernel_size=3, strides=1, padding=padding)(x)))
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return SubpixelUpscaler()(Norm(norm)(Act(act)(Conv2D(dim * 4, kernel_size=3, strides=1, padding=padding)(x))))
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return func
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SAEModel.upscale = upscale
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@ -492,7 +518,7 @@ class SAEModel(ModelBase):
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return func
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SAEModel.upscale_pre = upscale_pre
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def to_bgr (output_nc, padding='zero'):
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def to_bgr (output_nc, padding='zero', **kwargs):
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def func(x):
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return Conv2D(output_nc, kernel_size=5, padding=padding, activation='sigmoid')(x)
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return func
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@ -506,10 +532,10 @@ class SAEModel(ModelBase):
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SAEModel.to_bgr_pre = to_bgr_pre
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@staticmethod
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def LIAEEncFlow(resolution, ch_dims, padding='zero', **kwargs):
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def LIAEEncFlow(resolution, ch_dims, **kwargs):
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exec (nnlib.import_all(), locals(), globals())
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upscale = SAEModel.upscale_pre(padding=padding)
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downscale = SAEModel.downscale_pre(padding=padding)
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upscale = SAEModel.upscale_pre(**kwargs)
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downscale = SAEModel.downscale_pre(**kwargs)
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def func(input):
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dims = K.int_shape(input)[-1]*ch_dims
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@ -525,9 +551,9 @@ class SAEModel(ModelBase):
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return func
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@staticmethod
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def LIAEInterFlow(resolution, ae_dims=256, padding='zero', **kwargs):
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def LIAEInterFlow(resolution, ae_dims=256, **kwargs):
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exec (nnlib.import_all(), locals(), globals())
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upscale = SAEModel.upscale_pre(padding=padding)
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upscale = SAEModel.upscale_pre(**kwargs)
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lowest_dense_res=resolution // 16
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def func(input):
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@ -540,12 +566,12 @@ class SAEModel(ModelBase):
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return func
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@staticmethod
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def LIAEDecFlow(output_nc,ch_dims, multiscale_count=1, add_residual_blocks=False, padding='zero', **kwargs):
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def LIAEDecFlow(output_nc,ch_dims, multiscale_count=1, add_residual_blocks=False, padding='zero', norm='', **kwargs):
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exec (nnlib.import_all(), locals(), globals())
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upscale = SAEModel.upscale_pre(padding=padding)
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to_bgr = SAEModel.to_bgr_pre(padding=padding)
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upscale = SAEModel.upscale_pre(**kwargs)
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to_bgr = SAEModel.to_bgr_pre(**kwargs)
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dims = output_nc * ch_dims
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ResidualBlock = SAEModel.ResidualBlock_pre(padding=padding)
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ResidualBlock = SAEModel.ResidualBlock_pre(**kwargs)
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def func(input):
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x = input[0]
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