lora以外も対応

fine_tune.py

@@ -6,6 +6,7 @@ import gc
 import math
 import os
 import toml
+from multiprocessing import Value
 
 from tqdm import tqdm
 import torch
@@ -21,10 +22,6 @@ 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, True)
@@ -65,6 +62,10 @@ 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.debug_dataset:
         train_util.debug_dataset(train_dataset_group)
         return
@@ -188,7 +189,7 @@ 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,
     )
@@ -259,14 +260,14 @@ 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)
+        current_epoch.value = 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):
+            current_step.value = 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/train_util.py

@@ -2987,3 +2987,14 @@ class ImageLoadingDataset(torch.utils.data.Dataset):
 
 
 # endregion
+
+# colalte_fn用 epoch,stepはmultiprocessing.Value
+class collater_class:
+    def __init__(self,epoch,step):
+        self.current_epoch=epoch
+        self.current_step=step
+    def __call__(self, examples):
+        dataset = torch.utils.data.get_worker_info().dataset
+        dataset.set_current_epoch(self.current_epoch.value)
+        dataset.set_current_step(self.current_step.value)
+        return examples[0]

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)
+        current_epoch.value = 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()
@@ -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}")

train_network.py

@@ -25,17 +25,6 @@ from library.config_util import (
     BlueprintGenerator,
 )
 
-class collater_class:
-    def __init__(self,epoch,step):
-        self.current_epoch=epoch
-        self.current_step=step
-    def __call__(self, examples):
-        dataset = torch.utils.data.get_worker_info().dataset
-        dataset.set_current_epoch(self.current_epoch.value)
-        dataset.set_current_step(self.current_step.value)
-        return examples[0]
-
-
 # TODO 他のスクリプトと共通化する
 def generate_step_logs(args: argparse.Namespace, current_loss, avr_loss, lr_scheduler):
     logs = {"loss/current": current_loss, "loss/average": avr_loss}
@@ -110,7 +99,7 @@ def train(args):
 
     current_epoch = Value('i',0)
     current_step = Value('i',0)
-    collater = collater_class(current_epoch,current_step)
+    collater = train_util.collater_class(current_epoch,current_step)
 
     if args.debug_dataset:
         train_util.debug_dataset(train_dataset_group)

train_textual_inversion.py

@@ -4,6 +4,7 @@ import gc
 import math
 import os
 import toml
+from multiprocessing import Value
 
 from tqdm import tqdm
 import torch
@@ -71,10 +72,6 @@ imagenet_style_templates_small = [
 ]
 
 
-def collate_fn(examples):
-    return examples[0]
-
-
 def train(args):
     if args.output_name is None:
         args.output_name = args.token_string
@@ -186,6 +183,10 @@ 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)
+
     # make captions: tokenstring tokenstring1 tokenstring2 ...tokenstringn という文字列に書き換える超乱暴な実装
     if use_template:
         print("use template for training captions. is object: {args.use_object_template}")
@@ -251,7 +252,7 @@ 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,
     )
@@ -335,13 +336,14 @@ 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)
+        current_epoch.value = epoch+1
-        train_dataset_group.set_current_step(global_step)
 
         text_encoder.train()
 
         loss_total = 0
+
         for step, batch in enumerate(train_dataloader):
+            current_step.value = global_step
             with accelerator.accumulate(text_encoder):
                 with torch.no_grad():
                     if "latents" in batch and batch["latents"] is not None: