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Added some experimental training techniques. Ignore for now. Still in testing.

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This commit is contained in:
Jaret Burkett
2025-05-21 02:19:54 -06:00
parent 01101be196
commit e5181d23cd
6 changed files with 240 additions and 43 deletions
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8
toolkit/config_modules.py
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@@ -325,6 +325,8 @@ class TrainConfig:
self.adapter_assist_type: Optional[str] = kwargs.get('adapter_assist_type', 't2i') # t2i, control_net
self.noise_multiplier = kwargs.get('noise_multiplier', 1.0)
self.target_noise_multiplier = kwargs.get('target_noise_multiplier', 1.0)
self.random_noise_multiplier = kwargs.get('random_noise_multiplier', 0.0)
self.random_noise_shift = kwargs.get('random_noise_shift', 0.0)
self.img_multiplier = kwargs.get('img_multiplier', 1.0)
self.noisy_latent_multiplier = kwargs.get('noisy_latent_multiplier', 1.0)
self.latent_multiplier = kwargs.get('latent_multiplier', 1.0)
@@ -333,7 +335,6 @@ class TrainConfig:
# multiplier applied to loos on regularization images
self.reg_weight = kwargs.get('reg_weight', 1.0)
self.num_train_timesteps = kwargs.get('num_train_timesteps', 1000)
self.random_noise_shift = kwargs.get('random_noise_shift', 0.0)
# automatically adapte the vae scaling based on the image norm
self.adaptive_scaling_factor = kwargs.get('adaptive_scaling_factor', False)
@@ -412,7 +413,7 @@ class TrainConfig:
self.correct_pred_norm = kwargs.get('correct_pred_norm', False)
self.correct_pred_norm_multiplier = kwargs.get('correct_pred_norm_multiplier', 1.0)
self.loss_type = kwargs.get('loss_type', 'mse') # mse, mae, wavelet, pixelspace
self.loss_type = kwargs.get('loss_type', 'mse') # mse, mae, wavelet, pixelspace, cfm
# scale the prediction by this. Increase for more detail, decrease for less
self.pred_scaler = kwargs.get('pred_scaler', 1.0)
@@ -436,7 +437,8 @@ class TrainConfig:
# adds an additional loss to the network to encourage it output a normalized standard deviation
self.target_norm_std = kwargs.get('target_norm_std', None)
self.target_norm_std_value = kwargs.get('target_norm_std_value', 1.0)
self.timestep_type = kwargs.get('timestep_type', 'sigmoid') # sigmoid, linear, lognorm_blend
self.timestep_type = kwargs.get('timestep_type', 'sigmoid') # sigmoid, linear, lognorm_blend, next_sample
self.next_sample_timesteps = kwargs.get('next_sample_timesteps', 8)
self.linear_timesteps = kwargs.get('linear_timesteps', False)
self.linear_timesteps2 = kwargs.get('linear_timesteps2', False)
self.disable_sampling = kwargs.get('disable_sampling', False)