github/DeepFaceLab
1
0
Fork 0
mirror of https://github.com/iperov/DeepFaceLab.git synced 2025-08-24 07:06:20 -07:00
Code Issues Projects Releases Packages Wiki Activity Actions

feat: add ms_ssim_loss option to SAEHD prompts

Browse source
This commit is contained in:
jh 2021-03-12 08:08:36 -08:00
commit 7cb33128f0
1 changed files with 16 additions and 12 deletions
Download patch file Download diff file Expand all files Collapse all files

4
models/Model_SAEHD/Model.py
View file

@ -53,6 +53,8 @@ class SAEHDModel(ModelBase):
lr_dropout = {True:'y', False:'n'}.get(lr_dropout, lr_dropout) #backward comp
default_lr_dropout = self.options['lr_dropout'] = lr_dropout
default_ms_ssim_loss = self.options['ms_ssim_loss'] = self.load_or_def_option('ms_ssim_loss', False)
default_random_warp = self.options['random_warp'] = self.load_or_def_option('random_warp', True)
default_true_face_power = self.options['true_face_power'] = self.load_or_def_option('true_face_power', 0.0)
default_face_style_power = self.options['face_style_power'] = self.load_or_def_option('face_style_power', 0.0)
@ -147,6 +149,8 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
self.options['lr_dropout'] = io.input_str (f"Use learning rate dropout", default_lr_dropout, ['n','y','cpu'], help_message="When the face is trained enough, you can enable this option to get extra sharpness and reduce subpixel shake for less amount of iterations. Enabled it before `disable random warp` and before GAN. \nn - disabled.\ny - enabled\ncpu - enabled on CPU. This allows not to use extra VRAM, sacrificing 20% time of iteration.")
self.options['ms_ssim_loss'] = io.input_bool("Use multiscale loss?", default_ms_ssim_loss, help_message="Use Multiscale structural similarity for image quality assessment.")
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.")
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 )