Actually addind the Process class.....

jobs/process/TrainVAEProcess.py

@@ -15,7 +15,7 @@ from torchvision.transforms import transforms
 from jobs.process import BaseTrainProcess
 from toolkit.kohya_model_util import load_vae, convert_diffusers_back_to_ldm
 from toolkit.data_loader import ImageDataset
-from toolkit.losses import ComparativeTotalVariation, get_gradient_penalty
+from toolkit.losses import ComparativeTotalVariation, get_gradient_penalty, PatternLoss
 from toolkit.metadata import get_meta_for_safetensors
 from toolkit.optimizer import get_optimizer
 from toolkit.style import get_style_model_and_losses
@@ -196,6 +196,7 @@ class TrainVAEProcess(BaseTrainProcess):
         self.mse_weight = self.get_conf('mse_weight', 1e0, as_type=float)
         self.tv_weight = self.get_conf('tv_weight', 1e0, as_type=float)
         self.critic_weight = self.get_conf('critic_weight', 1, as_type=float)
+        self.pattern_weight = self.get_conf('pattern_weight', 1, as_type=float)
         self.first_step = 0
 
         self.blocks_to_train = self.get_conf('blocks_to_train', ['all'])
@@ -241,6 +242,8 @@ class TrainVAEProcess(BaseTrainProcess):
         if not os.path.exists(self.save_root):
             os.makedirs(self.save_root, exist_ok=True)
 
+        self._pattern_loss = None
+
     def update_training_metadata(self):
         self.add_meta(OrderedDict({"training_info": self.get_training_info()}))
 
@@ -349,6 +352,12 @@ class TrainVAEProcess(BaseTrainProcess):
         else:
             return torch.tensor(0.0, device=self.device)
 
+    def get_pattern_loss(self, pred, target):
+        if self._pattern_loss is None:
+            self._pattern_loss = PatternLoss(pattern_size=8, dtype=self.torch_dtype).to(self.device, dtype=self.torch_dtype)
+        loss = torch.mean(self._pattern_loss(pred, target))
+        return loss
+
     def save(self, step=None):
         if not os.path.exists(self.save_root):
             os.makedirs(self.save_root, exist_ok=True)
@@ -534,6 +543,7 @@ class TrainVAEProcess(BaseTrainProcess):
             "mse": [],
             "kl": [],
             "tv": [],
+            "ptn": [],
             "crD": [],
             "crG": [],
         })
@@ -573,12 +583,13 @@ class TrainVAEProcess(BaseTrainProcess):
                 kld_loss = self.get_kld_loss(mu, logvar) * self.kld_weight
                 mse_loss = self.get_mse_loss(pred, batch) * self.mse_weight
                 tv_loss = self.get_tv_loss(pred, batch) * self.tv_weight
+                pattern_loss = self.get_pattern_loss(pred, batch) * self.pattern_weight
                 if self.use_critic:
                     critic_gen_loss = self.critic.get_critic_loss(self.vgg19_pool_4.tensor) * self.critic_weight
                 else:
                     critic_gen_loss = torch.tensor(0.0, device=self.device, dtype=self.torch_dtype)
 
-                loss = style_loss + content_loss + kld_loss + mse_loss + tv_loss + critic_gen_loss
+                loss = style_loss + content_loss + kld_loss + mse_loss + tv_loss + critic_gen_loss + pattern_loss
 
                 # Backward pass and optimization
                 optimizer.zero_grad()
@@ -600,6 +611,8 @@ class TrainVAEProcess(BaseTrainProcess):
                     loss_string += f" mse: {mse_loss.item():.2e}"
                 if self.tv_weight > 0:
                     loss_string += f" tv: {tv_loss.item():.2e}"
+                if self.pattern_weight > 0:
+                    loss_string += f" ptn: {pattern_loss.item():.2e}"
                 if self.use_critic and self.critic_weight > 0:
                     loss_string += f" crG: {critic_gen_loss.item():.2e}"
                 if self.use_critic:
@@ -628,6 +641,7 @@ class TrainVAEProcess(BaseTrainProcess):
                 epoch_losses["mse"].append(mse_loss.item())
                 epoch_losses["kl"].append(kld_loss.item())
                 epoch_losses["tv"].append(tv_loss.item())
+                epoch_losses["ptn"].append(pattern_loss.item())
                 epoch_losses["crG"].append(critic_gen_loss.item())
                 epoch_losses["crD"].append(critic_d_loss)
 
@@ -637,6 +651,7 @@ class TrainVAEProcess(BaseTrainProcess):
                 log_losses["mse"].append(mse_loss.item())
                 log_losses["kl"].append(kld_loss.item())
                 log_losses["tv"].append(tv_loss.item())
+                log_losses["ptn"].append(pattern_loss.item())
                 log_losses["crG"].append(critic_gen_loss.item())
                 log_losses["crD"].append(critic_d_loss)