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small fixes

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Jan 2021-11-23 17:24:24 +01:00
commit 8df69209c7
1 changed files with 3 additions and 16 deletions
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models/Model_SAEHD/Model.py
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@ -152,6 +152,7 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
self.options['blur_out_mask'] = io.input_bool ("Blur out mask", default_blur_out_mask, help_message='Blurs nearby area outside of applied face mask of training samples. The result is the background near the face is smoothed and less noticeable on swapped face. The exact xseg mask in src and dst faceset is required.') self.options['blur_out_mask'] = io.input_bool ("Blur out mask", default_blur_out_mask, help_message='Blurs nearby area outside of applied face mask of training samples. The result is the background near the face is smoothed and less noticeable on swapped face. The exact xseg mask in src and dst faceset is required.')
default_gan_power = self.options['gan_power'] = self.load_or_def_option('gan_power', 0.0) default_gan_power = self.options['gan_power'] = self.load_or_def_option('gan_power', 0.0)
default_gan_version = self.options['gan_version'] = self.load_or_def_option('gan_version', 2)
default_gan_patch_size = self.options['gan_patch_size'] = self.load_or_def_option('gan_patch_size', self.options['resolution'] // 8) default_gan_patch_size = self.options['gan_patch_size'] = self.load_or_def_option('gan_patch_size', self.options['resolution'] // 8)
default_gan_dims = self.options['gan_dims'] = self.load_or_def_option('gan_dims', 16) default_gan_dims = self.options['gan_dims'] = self.load_or_def_option('gan_dims', 16)
default_gan_smoothing = self.options['gan_smoothing'] = self.load_or_def_option('gan_smoothing', 0.1) default_gan_smoothing = self.options['gan_smoothing'] = self.load_or_def_option('gan_smoothing', 0.1)
@ -179,6 +180,8 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
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 value is 0.1"), 0.0, 10.0 ) 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 value is 0.1"), 0.0, 10.0 )
if self.options['gan_power'] != 0.0: if self.options['gan_power'] != 0.0:
self.options['gan_version'] = np.clip (io.input_int("GAN version", default_gan_version, add_info="2 or 3", help_message="Choose GAN version (v2: 7/16/2020, v3: 1/3/2021):"), 2, 3)
if self.options['gan_version'] == 3: if self.options['gan_version'] == 3:
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 ) 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 )
self.options['gan_patch_size'] = gan_patch_size self.options['gan_patch_size'] = gan_patch_size
@ -437,22 +440,6 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
y = tf.where(tf.equal(y, 0), tf.ones_like(y), y) y = tf.where(tf.equal(y, 0), tf.ones_like(y), y)
gpu_target_dst = gpu_target_dst*gpu_target_dstm_all + (x/y)*gpu_target_dstm_anti gpu_target_dst = gpu_target_dst*gpu_target_dstm_all + (x/y)*gpu_target_dstm_anti
gpu_target_srcm_anti = 1-gpu_target_srcm
gpu_target_dstm_anti = 1-gpu_target_dstm
if blur_out_mask:
sigma = resolution / 128
x = nn.gaussian_blur(gpu_target_src*gpu_target_srcm_anti, sigma)
y = 1-nn.gaussian_blur(gpu_target_srcm, sigma)
y = tf.where(tf.equal(y, 0), tf.ones_like(y), y)
gpu_target_src = gpu_target_src*gpu_target_srcm + (x/y)*gpu_target_srcm_anti
x = nn.gaussian_blur(gpu_target_dst*gpu_target_dstm_anti, sigma)
y = 1-nn.gaussian_blur(gpu_target_dstm, sigma)
y = tf.where(tf.equal(y, 0), tf.ones_like(y), y)
gpu_target_dst = gpu_target_dst*gpu_target_dstm + (x/y)*gpu_target_dstm_anti
# process model tensors # process model tensors
if 'df' in archi_type: if 'df' in archi_type: