use skip_first_batches to skip, close pillow image

library/train_util.py

@@ -649,8 +649,15 @@ class BaseDataset(torch.utils.data.Dataset):
 
     def set_current_epoch(self, epoch):
         if not self.current_epoch == epoch:  # epochが切り替わったらバケツをシャッフルする
-            self.shuffle_buckets()
-        self.current_epoch = epoch
+            if epoch > self.current_epoch:
+                logger.info("epoch is incremented. current_epoch: {}, epoch: {}".format(self.current_epoch, epoch))
+                num_epochs = epoch - self.current_epoch
+                for _ in range(num_epochs):
+                    self.current_epoch += 1
+                    self.shuffle_buckets()
+            else:
+                logger.warning("epoch is not incremented. current_epoch: {}, epoch: {}".format(self.current_epoch, epoch))
+                self.current_epoch = epoch
 
     def set_current_step(self, step):
         self.current_step = step
@@ -941,7 +948,7 @@ class BaseDataset(torch.utils.data.Dataset):
         self._length = len(self.buckets_indices)
 
     def shuffle_buckets(self):
-        # set random seed for this epoch
+        # set random seed for this epoch: current_epoch is not incremented
         random.seed(self.seed + self.current_epoch)
 
         random.shuffle(self.buckets_indices)
@@ -2346,10 +2353,10 @@ def load_arbitrary_dataset(args, tokenizer) -> MinimalDataset:
 
 
 def load_image(image_path):
-    image = Image.open(image_path)
-    if not image.mode == "RGB":
-        image = image.convert("RGB")
-    img = np.array(image, np.uint8)
+    with Image.open(image_path) as image:
+        if not image.mode == "RGB":
+            image = image.convert("RGB")
+        img = np.array(image, np.uint8)
     return img
 
 

train_network.py

@@ -793,7 +793,6 @@ class NetworkTrainer:
                         f"initial_step is specified but not resuming. lr scheduler will be started from the beginning / initial_stepが指定されていますがresumeしていないため、lr schedulerは最初から始まります"
                     )
                 logger.info(f"skipping {initial_step} steps / {initial_step}ステップをスキップします")
-                initial_step *= accelerator.num_processes * args.gradient_accumulation_steps
             else:
                 # if not, only epoch no is skipped for informative purpose
                 epoch_to_start = initial_step // math.ceil(
@@ -865,23 +864,26 @@ class NetworkTrainer:
             accelerator.print(f"\nepoch {epoch+1}/{num_train_epochs}")
             current_epoch.value = epoch + 1
 
-            if initial_step > len(train_dataloader):
+            steps_per_epoch = math.ceil(len(train_dataloader) / accelerator.num_processes / args.gradient_accumulation_steps)
+            if initial_step > steps_per_epoch:
                 logger.info(f"skipping epoch {epoch+1} because initial_step (multiplied) is {initial_step}")
-                initial_step -= len(train_dataloader)
+                initial_step -= steps_per_epoch
                 continue
 
             metadata["ss_epoch"] = str(epoch + 1)
 
             accelerator.unwrap_model(network).on_epoch_start(text_encoder, unet)
 
-            for step, batch in enumerate(train_dataloader):
-                current_step.value = global_step
+            active_dataloader = train_dataloader
+            if initial_step > 0:
+                logger.info(f"skipping {initial_step} batches in epoch {epoch+1}")
+                active_dataloader = accelerator.skip_first_batches(
+                    train_dataloader, initial_step * args.gradient_accumulation_steps
+                )
+                initial_step = 0
 
-                if initial_step > 0:
-                    # logger.info(f"skipping step {step+1} because initial_step (multiplied) is {initial_step}")
-                    loss_recorder.add(epoch=epoch, step=step, loss=0)  # add dummy loss
-                    initial_step -= 1
-                    continue
+            for step, batch in enumerate(active_dataloader):
+                current_step.value = global_step
 
                 with accelerator.accumulate(training_model):
                     on_step_start(text_encoder, unet)