Cleanup unused code and formatting

train_network.py

@@ -318,8 +318,27 @@ class NetworkTrainer:
 
     # endregion
 
-    def process_batch(self, batch, tokenizers, text_encoders, unet, network, vae: AutoencoderKL, noise_scheduler, vae_dtype, weight_dtype, accelerator, args, text_encoding_strategy: strategy_sd.SdTextEncodingStrategy, tokenize_strategy: strategy_sd.SdTokenizeStrategy, is_train=True, train_text_encoder=True, train_unet=True) -> torch.Tensor:
+    def process_batch(
-
+        self, 
+        batch, 
+        text_encoders, 
+        unet, 
+        network, 
+        vae, 
+        noise_scheduler, 
+        vae_dtype, 
+        weight_dtype, 
+        accelerator, 
+        args, 
+        text_encoding_strategy: strategy_sd.SdTextEncodingStrategy, 
+        tokenize_strategy: strategy_sd.SdTokenizeStrategy, 
+        is_train=True, 
+        train_text_encoder=True, 
+        train_unet=True
+    ) -> torch.Tensor:
+        """
+        Process a batch for the network
+        """
         with torch.no_grad():
             if "latents" in batch and batch["latents"] is not None:
                 latents = typing.cast(torch.FloatTensor, batch["latents"].to(accelerator.device))
@@ -334,7 +353,6 @@ class NetworkTrainer:
 
             latents = self.shift_scale_latents(args, latents)
 
-
         text_encoder_conds = []
         text_encoder_outputs_list = batch.get("text_encoder_outputs_list", None)
         if text_encoder_outputs_list is not None:
@@ -371,13 +389,6 @@ class NetworkTrainer:
                     if encoded_text_encoder_conds[i] is not None:
                         text_encoder_conds[i] = encoded_text_encoder_conds[i]
 
-        batch_size = latents.shape[0]
-
-
-        # Predict the noise residual
-        # and add noise to the latents
-        # with noise offset and/or multires noise if specified
-
         # sample noise, call unet, get target
         noise_pred, target, timesteps, weighting = self.get_noise_pred_and_target(
             args,
@@ -1288,7 +1299,23 @@ class NetworkTrainer:
                     # temporary, for batch processing
                     self.on_step_start(args, accelerator, network, text_encoders, unet, batch, weight_dtype)
 
-                    loss = self.process_batch(batch, tokenizers, text_encoders, unet, network, vae, noise_scheduler, vae_dtype, weight_dtype, accelerator, args, text_encoding_strategy, tokenize_strategy, is_train=True, train_text_encoder=train_text_encoder, train_unet=train_unet)
+                    loss = self.process_batch(batch, 
+                        text_encoders, 
+                        unet, 
+                        network, 
+                        vae, 
+                        noise_scheduler, 
+                        vae_dtype, 
+                        weight_dtype, 
+                        accelerator, 
+                        args, 
+                        text_encoding_strategy, 
+                        tokenize_strategy, 
+                        is_train=True, 
+                        train_text_encoder=train_text_encoder, 
+                        train_unet=train_unet
+                    )
+
                     accelerator.backward(loss)
                     if accelerator.sync_gradients:
                         self.all_reduce_network(accelerator, network)  # sync DDP grad manually
@@ -1366,12 +1393,26 @@ class NetworkTrainer:
                         if val_step >= validation_steps:
                             break
 
-                        loss = self.process_batch(batch, tokenizers, text_encoders, unet, network, vae, noise_scheduler, vae_dtype, weight_dtype, accelerator, args, text_encoding_strategy, tokenize_strategy, is_train=False)
+                        loss = self.process_batch(
-                        
+                            batch, 
+                            text_encoders, 
+                            unet, 
+                            network, 
+                            vae, 
+                            noise_scheduler, 
+                            vae_dtype, 
+                            weight_dtype, 
+                            accelerator, 
+                            args, 
+                            text_encoding_strategy, 
+                            tokenize_strategy, 
+                            is_train=False
+                        )
+
                         val_loss_recorder.add(epoch=epoch, step=val_step, loss=loss.detach().item())
                         val_progress_bar.update(1)
                         val_progress_bar.set_postfix({ "val_avg_loss": val_loss_recorder.moving_average })
-    
+
                         if is_tracking:
                             logs = {"loss/current_val_loss": loss.detach().item()}
                             # accelerator.log(logs, step=(len(val_dataloader) * epoch) + 1 + val_step)
@@ -1397,7 +1438,21 @@ class NetworkTrainer:
                     if val_step >= validation_steps:
                         break
 
-                    loss = self.process_batch(batch, tokenizers, text_encoders, unet, network, vae, noise_scheduler, vae_dtype, weight_dtype, accelerator, args, text_encoding_strategy, tokenize_strategy, is_train=False)
+                    loss = self.process_batch(
+                        batch, 
+                        text_encoders, 
+                        unet, 
+                        network, 
+                        vae, 
+                        noise_scheduler, 
+                        vae_dtype, 
+                        weight_dtype, 
+                        accelerator, 
+                        args, 
+                        text_encoding_strategy, 
+                        tokenize_strategy, 
+                        is_train=False
+                    )
 
                     current_loss = loss.detach().item()
                     val_loss_recorder.add(epoch=epoch, step=val_step, loss=current_loss)