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7 changed files with 97 additions and 67 deletions
22
README.md
22
README.md
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@ -41,6 +41,11 @@ DeepFaceLab is used by such popular youtube channels as
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| [Futuring Machine](https://www.youtube.com/channel/UCC5BbFxqLQgfnWPhprmQLVg)| [RepresentUS](https://www.youtube.com/channel/UCRzgK52MmetD9aG8pDOID3g)|
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|---|---|
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| [DeepFakeCreator](https://www.youtube.com/channel/UCkNFhcYNLQ5hr6A6lZ56mKA)| [DeepFaker](https://www.youtube.com/channel/UCkHecfDTcSazNZSKPEhtPVQ)|
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</td></tr>
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<tr><td colspan=2 align="center">
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@ -140,12 +145,14 @@ DeepFaceLab is used by such popular youtube channels as
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<tr><td colspan=2 align="center">
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## Manipulate politicians speech
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(requires a skill in video editors such as *Adobe After Effects* or *Davinci Resolve*)
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## Manipulate politicians lips
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(voice replacement is not included!)
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(also requires a skill in video editors such as *Adobe After Effects* or *Davinci Resolve*)
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<img src="doc/political_speech1.jpg" align="center">
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 https://www.youtube.com/watch?v=2Z1oA3GYPaY
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 https://www.youtube.com/watch?v=rYKkQ3BOo_E
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<img src="doc/political_speech2.jpg" align="center">
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@ -192,7 +199,7 @@ Unfortunately, there is no "make everything ok" button in DeepFaceLab. You shoul
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</td></tr>
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<tr><td align="right">
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<a href="https://tinyurl.com/yb6gw8hu">Windows (magnet link)</a>
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<a href="https://tinyurl.com/y8lntghz">Windows (magnet link)</a>
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</td><td align="center">Last release. Use torrent client to download.</td></tr>
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<tr><td align="right">
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@ -267,6 +274,11 @@ Unfortunately, there is no "make everything ok" button in DeepFaceLab. You shoul
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</td></tr>
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<tr><td align="right">
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<a href="https://discord.gg/S2h7kPySQp">Discord channel (English / Русский)</a>
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</td><td align="center"></td></tr>
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<tr><td align="right">
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<a href="https://www.reddit.com/r/RUdeepfakes/new/">reddit r/RUdeepfakes/</a>
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</td><td align="center">Постим русские дипфейки сюда !</td></tr>
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@ -288,7 +300,7 @@ Unfortunately, there is no "make everything ok" button in DeepFaceLab. You shoul
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</td><td align="center">the biggest NSFW English community</td></tr>
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<tr><td align="right">
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<a href="https://www.reddit.com/r/SFWdeepfakes/new/">reddit r/SFWdeepfakes/</a>
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<a href="https://www.reddit.com/r/DeepFakesSFW/new/">reddit r/DeepFakesSFW/</a>
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</td><td align="center">Post your deepfakes there !</td></tr>
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<tr><td align="right">
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@ -94,7 +94,7 @@ class UNetPatchDiscriminator(nn.ModelBase):
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ts *= s
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return rf
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def find_archi(self, target_patch_size, max_layers=6):
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def find_archi(self, target_patch_size, max_layers=9):
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"""
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Find the best configuration of layers using only 3x3 convs for target patch size
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"""
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@ -106,12 +106,12 @@ class UNetPatchDiscriminator(nn.ModelBase):
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layers = []
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sum_st = 0
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layers.append ( [3, 2])
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sum_st += 2
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for i in range(layers_count-1):
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st = 1 + (1 if val & (1 << i) !=0 else 0 )
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layers.append ( [3, st ])
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sum_st += st
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layers.append ( [3, 2])
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sum_st += 2
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sum_st += st
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rf = self.calc_receptive_field_size(layers)
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@ -130,7 +130,8 @@ class UNetPatchDiscriminator(nn.ModelBase):
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q=x[np.abs(np.array(x)-target_patch_size).argmin()]
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return s[q][2]
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def on_build(self, patch_size, in_ch):
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def on_build(self, patch_size, in_ch, base_ch = 16):
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class ResidualBlock(nn.ModelBase):
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def on_build(self, ch, kernel_size=3 ):
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self.conv1 = nn.Conv2D( ch, ch, kernel_size=kernel_size, padding='SAME')
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@ -145,12 +146,13 @@ class UNetPatchDiscriminator(nn.ModelBase):
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prev_ch = in_ch
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self.convs = []
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self.res = []
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self.res1 = []
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self.res2 = []
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self.upconvs = []
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self.upres = []
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self.upres1 = []
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self.upres2 = []
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layers = self.find_archi(patch_size)
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base_ch = 16
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level_chs = { i-1:v for i,v in enumerate([ min( base_ch * (2**i), 512 ) for i in range(len(layers)+1)]) }
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self.in_conv = nn.Conv2D( in_ch, level_chs[-1], kernel_size=1, padding='VALID')
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@ -158,12 +160,14 @@ class UNetPatchDiscriminator(nn.ModelBase):
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for i, (kernel_size, strides) in enumerate(layers):
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self.convs.append ( nn.Conv2D( level_chs[i-1], level_chs[i], kernel_size=kernel_size, strides=strides, padding='SAME') )
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self.res.append ( ResidualBlock(level_chs[i]) )
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self.res1.append ( ResidualBlock(level_chs[i]) )
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self.res2.append ( ResidualBlock(level_chs[i]) )
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self.upconvs.insert (0, nn.Conv2DTranspose( level_chs[i]*(2 if i != len(layers)-1 else 1), level_chs[i-1], kernel_size=kernel_size, strides=strides, padding='SAME') )
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self.upres.insert (0, ResidualBlock(level_chs[i-1]*2) )
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self.upres1.insert (0, ResidualBlock(level_chs[i-1]*2) )
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self.upres2.insert (0, ResidualBlock(level_chs[i-1]*2) )
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self.out_conv = nn.Conv2D( level_chs[-1]*2, 1, kernel_size=1, padding='VALID')
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self.center_out = nn.Conv2D( level_chs[len(layers)-1], 1, kernel_size=1, padding='VALID')
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@ -171,20 +175,22 @@ class UNetPatchDiscriminator(nn.ModelBase):
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def forward(self, x):
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x = tf.nn.leaky_relu( self.in_conv(x), 0.1 )
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x = tf.nn.leaky_relu( self.in_conv(x), 0.2 )
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encs = []
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for conv, res in zip(self.convs, self.res):
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for conv, res1,res2 in zip(self.convs, self.res1, self.res2):
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encs.insert(0, x)
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x = tf.nn.leaky_relu( conv(x), 0.1 )
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x = res(x)
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x = tf.nn.leaky_relu( conv(x), 0.2 )
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x = res1(x)
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x = res2(x)
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center_out, x = self.center_out(x), tf.nn.leaky_relu( self.center_conv(x), 0.2 )
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center_out, x = self.center_out(x), self.center_conv(x)
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for i, (upconv, enc, upres) in enumerate(zip(self.upconvs, encs, self.upres)):
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x = tf.nn.leaky_relu( upconv(x), 0.1 )
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for i, (upconv, enc, upres1, upres2 ) in enumerate(zip(self.upconvs, encs, self.upres1, self.upres2)):
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x = tf.nn.leaky_relu( upconv(x), 0.2 )
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x = tf.concat( [enc, x], axis=nn.conv2d_ch_axis)
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x = upres(x)
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x = upres1(x)
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x = upres2(x)
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return center_out, self.out_conv(x)
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@ -76,15 +76,26 @@ class nn():
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if first_run:
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io.log_info("Caching GPU kernels...")
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#import tensorflow as tf
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import tensorflow.compat.v1 as tf
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import tensorflow
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tf_version = getattr(tensorflow,'VERSION', None)
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if tf_version is None:
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tf_version = tensorflow.version.GIT_VERSION
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if tf_version[0] == 'v':
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tf_version = tf_version[1:]
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if tf_version[0] == '2':
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tf = tensorflow.compat.v1
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else:
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tf = tensorflow
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import logging
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# Disable tensorflow warnings
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tf_logger = logging.getLogger('tensorflow')
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tf_logger.setLevel(logging.ERROR)
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tf.disable_v2_behavior()
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if tf_version[0] == '2':
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tf.disable_v2_behavior()
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nn.tf = tf
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# Initialize framework
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@ -77,7 +77,7 @@ class SAEHDModel(ModelBase):
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resolution = np.clip ( (resolution // 16) * 16, min_res, max_res)
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self.options['resolution'] = resolution
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self.options['face_type'] = io.input_str ("Face type", default_face_type, ['h','mf','f','wf','head', 'custom'], help_message="Half / mid face / full face / whole face / head / custom. Half face has better resolution, but covers less area of cheeks. Mid face is 30% wider than half face. 'Whole face' covers full area of face include forehead. 'head' covers full head, but requires XSeg for src and dst faceset.").lower()
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while True:
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archi = io.input_str ("AE architecture", default_archi, help_message=\
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"""
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@ -138,7 +138,11 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
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self.options['mouth_prio'] = io.input_bool ("Mouth priority", default_mouth_prio, help_message='Helps to fix mouth problems during training by forcing the neural network to train mouth with higher priority similar to eyes ')
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self.options['uniform_yaw'] = io.input_bool ("Uniform yaw distribution of samples", default_uniform_yaw, help_message='Helps to fix blurry side faces due to small amount of them in the faceset.')
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default_gan_power = self.options['gan_power'] = self.load_or_def_option('gan_power', 0.0)
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default_gan_patch_size = self.options['gan_patch_size'] = self.load_or_def_option('gan_patch_size', self.options['resolution'] // 8)
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default_gan_dims = self.options['gan_dims'] = self.load_or_def_option('gan_dims', 16)
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if self.is_first_run() or ask_override:
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self.options['models_opt_on_gpu'] = io.input_bool ("Place models and optimizer on GPU", default_models_opt_on_gpu, help_message="When you train on one GPU, by default model and optimizer weights are placed on GPU to accelerate the process. You can place they on CPU to free up extra VRAM, thus set bigger dimensions.")
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@ -148,11 +152,15 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
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self.options['random_warp'] = io.input_bool ("Enable random warp of samples", default_random_warp, help_message="Random warp is required to generalize facial expressions of both faces. When the face is trained enough, you can disable it to get extra sharpness and reduce subpixel shake for less amount of iterations.")
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self.options['gan_power'] = np.clip ( io.input_number ("GAN power", default_gan_power, add_info="0.0 .. 10.0", help_message="Train the network in Generative Adversarial manner. Forces the neural network to learn small details of the face. Enable it only when the face is trained enough and don't disable. Typical fine value is 0.05"), 0.0, 10.0 )
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if (self.options['gan_power'] != 0):
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self.options['gan_old'] = io.input_bool ("Use old GAN version", default_gan_old, help_message="Use older version of GAN." )
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self.options['gan_power'] = np.clip ( io.input_number ("GAN power", default_gan_power, add_info="0.0 .. 1.0", help_message="Forces the neural network to learn small details of the face. Enable it only when the face is trained enough with lr_dropout(on) and random_warp(off), and don't disable. The higher the value, the higher the chances of artifacts. Typical fine value is 0.1"), 0.0, 1.0 )
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if self.options['gan_power'] != 0.0:
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gan_patch_size = np.clip ( io.input_int("GAN patch size", default_gan_patch_size, add_info="3-640", help_message="The higher patch size, the higher the quality, the more VRAM is required. You can get sharper edges even at the lowest setting. Typical fine value is resolution / 8." ), 3, 640 )
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self.options['gan_patch_size'] = gan_patch_size
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gan_dims = np.clip ( io.input_int("GAN dimensions", default_gan_dims, add_info="4-64", help_message="The dimensions of the GAN network. The higher dimensions, the more VRAM is required. You can get sharper edges even at the lowest setting. Typical fine value is 16." ), 4, 64 )
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self.options['gan_dims'] = gan_dims
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if 'df' in self.options['archi']:
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self.options['true_face_power'] = np.clip ( io.input_number ("'True face' power.", default_true_face_power, add_info="0.0000 .. 1.0", help_message="Experimental option. Discriminates result face to be more like src face. Higher value - stronger discrimination. Typical value is 0.01 . Comparison - https://i.imgur.com/czScS9q.png"), 0.0, 1.0 )
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else:
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@ -168,6 +176,8 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
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if self.options['pretrain'] and self.get_pretraining_data_path() is None:
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raise Exception("pretraining_data_path is not defined")
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self.gan_model_changed = (default_gan_patch_size != self.options['gan_patch_size']) or (default_gan_dims != self.options['gan_dims'])
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self.pretrain_just_disabled = (default_pretrain == True and self.options['pretrain'] == False)
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@ -289,14 +299,8 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
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if self.is_training:
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if gan_power != 0:
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if gan_old:
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self.D_src = nn.PatchDiscriminator(patch_size=resolution//16, in_ch=input_ch, name="D_src")
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self.D_src_x2 = nn.PatchDiscriminator(patch_size=resolution//32, in_ch=input_ch, name="D_src_x2")
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self.model_filename_list += [ [self.D_src, 'D_src.npy'] ]
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self.model_filename_list += [ [self.D_src_x2, 'D_src_x2.npy'] ]
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else:
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self.D_src = nn.UNetPatchDiscriminator(patch_size=resolution//16, in_ch=input_ch, name="D_src")
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self.model_filename_list += [ [self.D_src, 'D_src_v2.npy'] ]
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self.D_src = nn.UNetPatchDiscriminator(patch_size=self.options['gan_patch_size'], in_ch=input_ch, base_ch=self.options['gan_dims'], name="D_src")
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self.model_filename_list += [ [self.D_src, 'GAN.npy'] ]
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# Initialize optimizers
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lr=5e-5
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@ -321,14 +325,9 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
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self.model_filename_list += [ (self.D_code_opt, 'D_code_opt.npy') ]
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if gan_power != 0:
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self.D_src_dst_opt = OptimizerClass(lr=lr, lr_dropout=lr_dropout, clipnorm=clipnorm, name='D_src_dst_opt')
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if gan_old:
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self.D_src_dst_opt.initialize_variables ( self.D_src.get_weights()+self.D_src_x2.get_weights(), vars_on_cpu=optimizer_vars_on_cpu, lr_dropout_on_cpu=self.options['lr_dropout']=='cpu')
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self.model_filename_list += [ (self.D_src_dst_opt, 'D_src_dst_opt.npy') ]
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else:
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self.D_src_dst_opt.initialize_variables ( self.D_src.get_weights(), vars_on_cpu=optimizer_vars_on_cpu, lr_dropout_on_cpu=self.options['lr_dropout']=='cpu')#+self.D_src_x2.get_weights()
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self.model_filename_list += [ (self.D_src_dst_opt, 'D_src_v2_opt.npy') ]
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self.D_src_dst_opt = OptimizerClass(lr=lr, lr_dropout=lr_dropout, clipnorm=clipnorm, name='GAN_opt')
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self.D_src_dst_opt.initialize_variables ( self.D_src.get_weights(), vars_on_cpu=optimizer_vars_on_cpu, lr_dropout_on_cpu=self.options['lr_dropout']=='cpu')#+self.D_src_x2.get_weights()
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self.model_filename_list += [ (self.D_src_dst_opt, 'GAN_opt.npy') ]
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if self.is_training:
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# Adjust batch size for multiple GPU
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@ -412,14 +411,16 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
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gpu_target_dstm_style_blur = gpu_target_dstm_blur #default style mask is 0.5 on boundary
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gpu_target_dstm_blur = tf.clip_by_value(gpu_target_dstm_blur, 0, 0.5) * 2
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gpu_target_dst_masked = gpu_target_dst*gpu_target_dstm_blur
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gpu_target_dst_masked = gpu_target_dst*gpu_target_dstm_blur
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gpu_target_dst_style_masked = gpu_target_dst*gpu_target_dstm_style_blur
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gpu_target_dst_style_anti_masked = gpu_target_dst*(1.0 - gpu_target_dstm_style_blur)
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gpu_target_src_anti_masked = gpu_target_src*(1.0-gpu_target_srcm_blur)
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gpu_target_src_masked_opt = gpu_target_src*gpu_target_srcm_blur if masked_training else gpu_target_src
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gpu_target_dst_masked_opt = gpu_target_dst_masked if masked_training else gpu_target_dst
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gpu_pred_src_src_masked_opt = gpu_pred_src_src*gpu_target_srcm_blur if masked_training else gpu_pred_src_src
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gpu_pred_src_src_anti_masked = gpu_pred_src_src*(1.0-gpu_target_srcm_blur)
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gpu_pred_dst_dst_masked_opt = gpu_pred_dst_dst*gpu_target_dstm_blur if masked_training else gpu_pred_dst_dst
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gpu_psd_target_dst_style_masked = gpu_pred_src_dst*gpu_target_dstm_style_blur
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@ -533,19 +534,16 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
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gpu_target_src_d, \
|
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gpu_target_src_d2 = self.D_src(gpu_target_src_masked_opt)
|
||||
|
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gpu_target_src_d_ones = tf.ones_like(gpu_target_src_d)
|
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gpu_target_src_d2_ones = tf.ones_like(gpu_target_src_d2)
|
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gpu_D_src_dst_loss = (DLoss(gpu_target_src_d_ones , gpu_target_src_d) + \
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DLoss(gpu_pred_src_src_d_zeros , gpu_pred_src_src_d) ) * 0.5 + \
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(DLoss(gpu_target_src_d2_ones , gpu_target_src_d2) + \
|
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DLoss(gpu_pred_src_src_d2_zeros , gpu_pred_src_src_d2) ) * 0.5
|
||||
|
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gpu_D_src_dst_loss_gvs += [ nn.gradients (gpu_D_src_dst_loss, self.D_src.get_weights() ) ]#+self.D_src_x2.get_weights()
|
||||
|
||||
gpu_G_loss += gan_power*(DLoss(gpu_pred_src_src_d_ones, gpu_pred_src_src_d) + \
|
||||
DLoss(gpu_pred_src_src_d2_ones, gpu_pred_src_src_d2))
|
||||
|
||||
gpu_G_loss += gan_power*(DLoss(gpu_pred_src_src_d_ones, gpu_pred_src_src_d) + \
|
||||
DLoss(gpu_pred_src_src_d2_ones, gpu_pred_src_src_d2))
|
||||
|
||||
|
||||
|
||||
if masked_training:
|
||||
# Minimal src-src-bg rec with total_variation_mse to suppress random bright dots from gan
|
||||
gpu_G_loss += 0.000001*nn.total_variation_mse(gpu_pred_src_src)
|
||||
gpu_G_loss += 0.02*tf.reduce_mean(tf.square(gpu_pred_src_src_anti_masked-gpu_target_src_anti_masked),axis=[1,2,3] )
|
||||
|
||||
gpu_G_loss_gvs += [ nn.gradients ( gpu_G_loss, self.src_dst_trainable_weights ) ]
|
||||
|
||||
|
||||
|
|
@ -646,6 +644,9 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
|
|||
do_init = True
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||||
else:
|
||||
do_init = self.is_first_run()
|
||||
if self.is_training and gan_power != 0 and model == self.D_src:
|
||||
if self.gan_model_changed:
|
||||
do_init = True
|
||||
|
||||
if not do_init:
|
||||
do_init = not model.load_weights( self.get_strpath_storage_for_file(filename) )
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue