persistent_workersを有効にした際にキャプションが変化しなくなるバグ修正

fine_tune.py

@@ -62,6 +62,7 @@ def train(args):
         }
 
     blueprint = blueprint_generator.generate(user_config, args, tokenizer=tokenizer)
+    config_util.blueprint_args_conflict(args,blueprint)
     train_dataset_group = config_util.generate_dataset_group_by_blueprint(blueprint.dataset_group)
 
     if args.debug_dataset:
@@ -259,13 +260,13 @@ def train(args):
     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)
 
         for m in training_models:
             m.train()
 
         loss_total = 0
         for step, batch in enumerate(train_dataloader):
-            train_dataset_group.set_current_step(global_step)
             with accelerator.accumulate(training_models[0]):  # 複数モデルに対応していない模様だがとりあえずこうしておく
                 with torch.no_grad():
                     if "latents" in batch and batch["latents"] is not None:

library/config_util.py

@@ -497,6 +497,14 @@ def load_user_config(file: str) -> dict:
 
   return config
 
+def blueprint_args_conflict(args,blueprint:Blueprint):
+  # train_dataset_group.set_current_epoch()とtrain_dataset_group.set_current_step()がWorkerを生成するタイミングで適用される影響で、persistent_workers有効時はずっと一定になってしまうため無効にする
+  for b in blueprint.dataset_group.datasets:
+      for t in b.subsets:
+          if args.persistent_data_loader_workers and (t.params.caption_dropout_every_n_epochs > 0 or t.params.token_warmup_step>0):
+              print("Warning: %s: caption_dropout_every_n_epochs and token_warmup_step is ignored because --persistent_data_loader_workers option is used / --persistent_data_loader_workersオプションが使われているため、caption_dropout_every_n_epochs及びtoken_warmup_stepは無視されます。"%(t.params.image_dir))
+              t.params.caption_dropout_every_n_epochs = 0
+              t.params.token_warmup_step = 0
 
 # for config test
 if __name__ == "__main__":

train_db.py

@@ -57,6 +57,7 @@ def train(args):
         }
 
     blueprint = blueprint_generator.generate(user_config, args, tokenizer=tokenizer)
+    config_util.blueprint_args_conflict(args,blueprint)
     train_dataset_group = config_util.generate_dataset_group_by_blueprint(blueprint.dataset_group)
 
     if args.no_token_padding:
@@ -233,6 +234,7 @@ def train(args):
     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)
 
         # 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()
@@ -241,7 +243,6 @@ def train(args):
             text_encoder.train()
 
         for step, batch in enumerate(train_dataloader):
-            train_dataset_group.set_current_step(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}")

train_network.py

@@ -98,6 +98,7 @@ def train(args):
             }
 
     blueprint = blueprint_generator.generate(user_config, args, tokenizer=tokenizer)
+    config_util.blueprint_args_conflict(args,blueprint)
     train_dataset_group = config_util.generate_dataset_group_by_blueprint(blueprint.dataset_group)
 
     if args.debug_dataset:
@@ -501,13 +502,13 @@ def train(args):
         if is_main_process:
             print(f"epoch {epoch+1}/{num_train_epochs}")
         train_dataset_group.set_current_epoch(epoch + 1)
+        train_dataset_group.set_current_step(global_step)
 
         metadata["ss_epoch"] = str(epoch + 1)
 
         network.on_epoch_start(text_encoder, unet)
 
         for step, batch in enumerate(train_dataloader):
-            train_dataset_group.set_current_step(global_step)
             with accelerator.accumulate(network):
                 with torch.no_grad():
                     if "latents" in batch and batch["latents"] is not None:

train_textual_inversion.py

@@ -183,6 +183,7 @@ def train(args):
             }
 
     blueprint = blueprint_generator.generate(user_config, args, tokenizer=tokenizer)
+    config_util.blueprint_args_conflict(args,blueprint)
     train_dataset_group = config_util.generate_dataset_group_by_blueprint(blueprint.dataset_group)
 
     # make captions: tokenstring tokenstring1 tokenstring2 ...tokenstringn という文字列に書き換える超乱暴な実装
@@ -335,12 +336,12 @@ def train(args):
     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)
 
         text_encoder.train()
 
         loss_total = 0
         for step, batch in enumerate(train_dataloader):
-            train_dataset_group.set_current_step(global_step)
             with accelerator.accumulate(text_encoder):
                 with torch.no_grad():
                     if "latents" in batch and batch["latents"] is not None: