lora以外も対応

train_db.py

@@ -8,6 +8,7 @@ import itertools
 import math
 import os
 import toml
+from multiprocessing import Value
 
 from tqdm import tqdm
 import torch
@@ -23,10 +24,6 @@ from library.config_util import (
 )
 
 
-def collate_fn(examples):
-    return examples[0]
-
-
 def train(args):
     train_util.verify_training_args(args)
     train_util.prepare_dataset_args(args, False)
@@ -60,6 +57,10 @@ def train(args):
     config_util.blueprint_args_conflict(args,blueprint)
     train_dataset_group = config_util.generate_dataset_group_by_blueprint(blueprint.dataset_group)
 
+    current_epoch = Value('i',0)
+    current_step = Value('i',0)
+    collater = train_util.collater_class(current_epoch,current_step)
+
     if args.no_token_padding:
         train_dataset_group.disable_token_padding()
 
@@ -153,7 +154,7 @@ def train(args):
         train_dataset_group,
         batch_size=1,
         shuffle=True,
-        collate_fn=collate_fn,
+        collate_fn=collater,
         num_workers=n_workers,
         persistent_workers=args.persistent_data_loader_workers,
     )
@@ -233,8 +234,7 @@ def train(args):
     loss_total = 0.0
     for epoch in range(num_train_epochs):
         print(f"epoch {epoch+1}/{num_train_epochs}")
-        train_dataset_group.set_current_epoch(epoch + 1)
-        train_dataset_group.set_current_step(global_step)
+        current_epoch.value = epoch+1
 
         # 指定したステップ数までText Encoderを学習する：epoch最初の状態
         unet.train()
@@ -243,6 +243,7 @@ def train(args):
             text_encoder.train()
 
         for step, batch in enumerate(train_dataloader):
+            current_step.value = global_step
             # 指定したステップ数でText Encoderの学習を止める
             if global_step == args.stop_text_encoder_training:
                 print(f"stop text encoder training at step {global_step}")