Merge new sd-scripts updates

README.md

@@ -213,7 +213,15 @@ This will store your a backup file with your current locally installed pip packa
 
 ## Change History
 
-* 2023/03/26 (v21.3.6)
+* 2023/03/28 (v21.3.6)
+    - Fix issues when `--persistent_data_loader_workers` is specified.
+        - The batch members of the bucket are not shuffled.
+        - `--caption_dropout_every_n_epochs` does not work.
+        - These issues occurred because the epoch transition was not recognized correctly. Thanks to u-haru for reporting the issue.
+    - Fix an issue that images are loaded twice in Windows environment.
+    - Add Min-SNR Weighting strategy. Details are in [#308](https://github.com/kohya-ss/sd-scripts/pull/308). Thank you to AI-Casanova for this great work!
+        - Add `--min_snr_gamma` option to training scripts, 5 is recommended by paper.
+        - The Min SNR gamma fiels can be found unser the advanced training tab in all trainers.
     - Fixed the error while images are ended with capital image extensions. Thanks to @kvzn. https://github.com/bmaltais/kohya_ss/pull/454
 * 2023/03/26 (v21.3.5)
     - Fix for https://github.com/bmaltais/kohya_ss/issues/230

dreambooth_gui.py

@@ -108,6 +108,7 @@ def save_configuration(
     sample_prompts,
     additional_parameters,
     vae_batch_size,
+    min_snr_gamma,
 ):
     # Get list of function parameters and values
     parameters = list(locals().items())
@@ -216,6 +217,7 @@ def open_configuration(
     sample_prompts,
     additional_parameters,
     vae_batch_size,
+    min_snr_gamma,
 ):
     # Get list of function parameters and values
     parameters = list(locals().items())
@@ -306,6 +308,7 @@ def train_model(
     sample_prompts,
     additional_parameters,
     vae_batch_size,
+    min_snr_gamma,
 ):
     if pretrained_model_name_or_path == '':
         msgbox('Source model information is missing')
@@ -335,12 +338,17 @@ def train_model(
     subfolders = [
         f
         for f in os.listdir(train_data_dir)
-        if os.path.isdir(os.path.join(train_data_dir, f)) and not f.startswith('.')
+        if os.path.isdir(os.path.join(train_data_dir, f))
+        and not f.startswith('.')
     ]
 
     # Check if subfolders are present. If not let the user know and return
     if not subfolders:
-        print('\033[33mNo subfolders were found in', train_data_dir, ' can\'t train\...033[0m')
+        print(
+            '\033[33mNo subfolders were found in',
+            train_data_dir,
+            " can't train\...033[0m",
+        )
         return
 
     total_steps = 0
@@ -351,7 +359,11 @@ def train_model(
         try:
             repeats = int(folder.split('_')[0])
         except ValueError:
-            print('\033[33mSubfolder', folder, 'does not have a proper repeat value, please correct the name or remove it... can\'t train...\033[0m')
+            print(
+                '\033[33mSubfolder',
+                folder,
+                "does not have a proper repeat value, please correct the name or remove it... can't train...\033[0m",
+            )
             continue
 
         # Count the number of images in the folder
@@ -359,12 +371,15 @@ def train_model(
             [
                 f
                 for f, lower_f in (
-                    (file, file.lower()) for file in os.listdir(os.path.join(train_data_dir, folder))
+                    (file, file.lower())
+                    for file in os.listdir(
+                        os.path.join(train_data_dir, folder)
+                    )
                 )
                 if lower_f.endswith(('.jpg', '.jpeg', '.png', '.webp'))
             ]
         )
-        
+
         if num_images == 0:
             print(f'{folder} folder contain no images, skipping...')
         else:
@@ -376,7 +391,11 @@ def train_model(
             print('\033[33mFolder', folder, ':', steps, 'steps\033[0m')
 
     if total_steps == 0:
-        print('\033[33mNo images were found in folder', train_data_dir, '... please rectify!\033[0m')
+        print(
+            '\033[33mNo images were found in folder',
+            train_data_dir,
+            '... please rectify!\033[0m',
+        )
         return
 
     # Print the result
@@ -385,7 +404,9 @@ def train_model(
     if reg_data_dir == '':
         reg_factor = 1
     else:
-        print('\033[94mRegularisation images are used... Will double the number of steps required...\033[0m')
+        print(
+            '\033[94mRegularisation images are used... Will double the number of steps required...\033[0m'
+        )
         reg_factor = 2
 
     # calculate max_train_steps
@@ -497,6 +518,7 @@ def train_model(
         noise_offset=noise_offset,
         additional_parameters=additional_parameters,
         vae_batch_size=vae_batch_size,
+        min_snr_gamma=min_snr_gamma,
     )
 
     run_cmd += run_cmd_sample(
@@ -704,6 +726,7 @@ def dreambooth_tab(
                 noise_offset,
                 additional_parameters,
                 vae_batch_size,
+                min_snr_gamma,
             ) = gradio_advanced_training()
             color_aug.change(
                 color_aug_changed,
@@ -805,6 +828,7 @@ def dreambooth_tab(
         sample_prompts,
         additional_parameters,
         vae_batch_size,
+        min_snr_gamma,
     ]
 
     button_open_config.click(

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
@@ -19,10 +20,8 @@ from library.config_util import (
     ConfigSanitizer,
     BlueprintGenerator,
 )
-
-
-def collate_fn(examples):
-    return examples[0]
+import library.custom_train_functions as custom_train_functions
+from library.custom_train_functions import apply_snr_weight
 
 
 def train(args):
@@ -64,6 +63,11 @@ def train(args):
     blueprint = blueprint_generator.generate(user_config, args, tokenizer=tokenizer)
     train_dataset_group = config_util.generate_dataset_group_by_blueprint(blueprint.dataset_group)
 
+    current_epoch = Value("i", 0)
+    current_step = Value("i", 0)
+    ds_for_collater = train_dataset_group if args.max_data_loader_n_workers == 0 else None
+    collater = train_util.collater_class(current_epoch, current_step, ds_for_collater)
+
     if args.debug_dataset:
         train_util.debug_dataset(train_dataset_group)
         return
@@ -187,16 +191,21 @@ 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,
     )
 
     # 学習ステップ数を計算する
     if args.max_train_epochs is not None:
-        args.max_train_steps = args.max_train_epochs * math.ceil(len(train_dataloader) / accelerator.num_processes / args.gradient_accumulation_steps)
+        args.max_train_steps = args.max_train_epochs * math.ceil(
+            len(train_dataloader) / accelerator.num_processes / args.gradient_accumulation_steps
+        )
         print(f"override steps. steps for {args.max_train_epochs} epochs is / 指定エポックまでのステップ数: {args.max_train_steps}")
 
+    # データセット側にも学習ステップを送信
+    train_dataset_group.set_max_train_steps(args.max_train_steps)
+
     # lr schedulerを用意する
     lr_scheduler = train_util.get_scheduler_fix(args, optimizer, accelerator.num_processes)
 
@@ -255,13 +264,14 @@ 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
 
         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:
@@ -302,7 +312,14 @@ def train(args):
                 else:
                     target = noise
 
-                loss = torch.nn.functional.mse_loss(noise_pred.float(), target.float(), reduction="mean")
+                if args.min_snr_gamma:
+                    # do not mean over batch dimension for snr weight
+                    loss = torch.nn.functional.mse_loss(noise_pred.float(), target.float(), reduction="none")
+                    loss = loss.mean([1, 2, 3])
+                    loss = apply_snr_weight(loss, timesteps, noise_scheduler, args.min_snr_gamma)
+                    loss = loss.mean()  # mean over batch dimension
+                else:
+                    loss = torch.nn.functional.mse_loss(noise_pred.float(), target.float(), reduction="mean")
 
                 accelerator.backward(loss)
                 if accelerator.sync_gradients and args.max_grad_norm != 0.0:
@@ -396,6 +413,7 @@ def setup_parser() -> argparse.ArgumentParser:
     train_util.add_sd_saving_arguments(parser)
     train_util.add_optimizer_arguments(parser)
     config_util.add_config_arguments(parser)
+    custom_train_functions.add_custom_train_arguments(parser)
 
     parser.add_argument("--diffusers_xformers", action="store_true", help="use xformers by diffusers / Diffusersでxformersを使用する")
     parser.add_argument("--train_text_encoder", action="store_true", help="train text encoder / text encoderも学習する")

finetune_gui.py

@@ -104,7 +104,9 @@ def save_configuration(
     sample_every_n_epochs,
     sample_sampler,
     sample_prompts,
-    additional_parameters,vae_batch_size,
+    additional_parameters,
+    vae_batch_size,
+    min_snr_gamma,
 ):
     # Get list of function parameters and values
     parameters = list(locals().items())
@@ -217,7 +219,9 @@ def open_configuration(
     sample_every_n_epochs,
     sample_sampler,
     sample_prompts,
-    additional_parameters,vae_batch_size,
+    additional_parameters,
+    vae_batch_size,
+    min_snr_gamma,
 ):
     # Get list of function parameters and values
     parameters = list(locals().items())
@@ -312,7 +316,9 @@ def train_model(
     sample_every_n_epochs,
     sample_sampler,
     sample_prompts,
-    additional_parameters,vae_batch_size,
+    additional_parameters,
+    vae_batch_size,
+    min_snr_gamma,
 ):
     if check_if_model_exist(output_name, output_dir, save_model_as):
         return
@@ -473,6 +479,7 @@ def train_model(
         noise_offset=noise_offset,
         additional_parameters=additional_parameters,
         vae_batch_size=vae_batch_size,
+        min_snr_gamma=min_snr_gamma,
     )
 
     run_cmd += run_cmd_sample(
@@ -690,6 +697,7 @@ def finetune_tab():
                 noise_offset,
                 additional_parameters,
                 vae_batch_size,
+                min_snr_gamma,
             ) = gradio_advanced_training()
             color_aug.change(
                 color_aug_changed,
@@ -785,6 +793,7 @@ def finetune_tab():
         sample_prompts,
         additional_parameters,
         vae_batch_size,
+        min_snr_gamma,
     ]
 
     button_run.click(train_model, inputs=settings_list)

library/common_gui.py

@@ -840,6 +840,7 @@ def gradio_advanced_training():
         xformers = gr.Checkbox(label='Use xformers', value=True)
         color_aug = gr.Checkbox(label='Color augmentation', value=False)
         flip_aug = gr.Checkbox(label='Flip augmentation', value=False)
+        min_snr_gamma = gr.Slider(label='Min SNR gamma', value = 0, minimum=0, maximum=20, step=1)
     with gr.Row():
         bucket_no_upscale = gr.Checkbox(
             label="Don't upscale bucket resolution", value=True
@@ -914,6 +915,7 @@ def gradio_advanced_training():
         noise_offset,
         additional_parameters,
         vae_batch_size,
+        min_snr_gamma,
     )
 
 
@@ -949,13 +951,15 @@ def run_cmd_advanced_training(**kwargs):
         f' --bucket_reso_steps={int(kwargs.get("bucket_reso_steps", 1))}'
         if int(kwargs.get('bucket_reso_steps', 64)) >= 1
         else '',
+        f' --min_snr_gamma={int(kwargs.get("min_snr_gamma", 0))}'
+        if int(kwargs.get('min_snr_gamma', 0)) >= 1
+        else '',
         ' --save_state' if kwargs.get('save_state') else '',
         ' --mem_eff_attn' if kwargs.get('mem_eff_attn') else '',
         ' --color_aug' if kwargs.get('color_aug') else '',
         ' --flip_aug' if kwargs.get('flip_aug') else '',
         ' --shuffle_caption' if kwargs.get('shuffle_caption') else '',
-        ' --gradient_checkpointing'
-        if kwargs.get('gradient_checkpointing')
+        ' --gradient_checkpointing' if kwargs.get('gradient_checkpointing')
         else '',
         ' --full_fp16' if kwargs.get('full_fp16') else '',
         ' --xformers' if kwargs.get('xformers') else '',

library/config_util.py

@@ -4,6 +4,7 @@ from dataclasses import (
   dataclass,
 )
 import functools
+import random
 from textwrap import dedent, indent
 import json
 from pathlib import Path
@@ -56,6 +57,8 @@ class BaseSubsetParams:
   caption_dropout_rate: float = 0.0
   caption_dropout_every_n_epochs: int = 0
   caption_tag_dropout_rate: float = 0.0
+  token_warmup_min: int = 1
+  token_warmup_step: float = 0
 
 @dataclass
 class DreamBoothSubsetParams(BaseSubsetParams):
@@ -137,6 +140,8 @@ class ConfigSanitizer:
     "random_crop": bool,
     "shuffle_caption": bool,
     "keep_tokens": int,
+    "token_warmup_min": int,
+    "token_warmup_step": Any(float,int),
   }
   # DO means DropOut
   DO_SUBSET_ASCENDABLE_SCHEMA = {
@@ -406,6 +411,8 @@ def generate_dataset_group_by_blueprint(dataset_group_blueprint: DatasetGroupBlu
           flip_aug: {subset.flip_aug}
           face_crop_aug_range: {subset.face_crop_aug_range}
           random_crop: {subset.random_crop}
+          token_warmup_min: {subset.token_warmup_min},
+          token_warmup_step: {subset.token_warmup_step},
       """), "  ")
 
       if is_dreambooth:
@@ -422,9 +429,12 @@ def generate_dataset_group_by_blueprint(dataset_group_blueprint: DatasetGroupBlu
   print(info)
 
   # make buckets first because it determines the length of dataset
+  # and set the same seed for all datasets
+  seed = random.randint(0, 2**31) # actual seed is seed + epoch_no
   for i, dataset in enumerate(datasets):
     print(f"[Dataset {i}]")
     dataset.make_buckets()
+    dataset.set_seed(seed)
 
   return DatasetGroup(datasets)
 
@@ -491,7 +501,6 @@ def load_user_config(file: str) -> dict:
 
   return config
 
-
 # for config test
 if __name__ == "__main__":
   parser = argparse.ArgumentParser()

library/custom_train_functions.py

@@ -1,14 +1,18 @@
 import torch
+import argparse
 
-def apply_snr_weight(loss, noisy_latents, latents, gamma):
-    gamma = gamma
-    if gamma:
-        sigma = torch.sub(noisy_latents, latents)
-        zeros = torch.zeros_like(sigma) 
-        alpha_mean_sq = torch.nn.functional.mse_loss(latents.float(), zeros.float(), reduction="none").mean([1, 2, 3])
-        sigma_mean_sq = torch.nn.functional.mse_loss(sigma.float(), zeros.float(), reduction="none").mean([1, 2, 3])
-        snr = torch.div(alpha_mean_sq, sigma_mean_sq)
-        gamma_over_snr = torch.div(torch.ones_like(snr) * gamma, snr)
-        snr_weight = torch.minimum(gamma_over_snr, torch.ones_like(gamma_over_snr)).float()
-        loss = loss * snr_weight
-    return loss
+def apply_snr_weight(loss, timesteps, noise_scheduler, gamma): 
+  alphas_cumprod = noise_scheduler.alphas_cumprod
+  sqrt_alphas_cumprod = torch.sqrt(alphas_cumprod)
+  sqrt_one_minus_alphas_cumprod = torch.sqrt(1.0 - alphas_cumprod)
+  alpha = sqrt_alphas_cumprod
+  sigma = sqrt_one_minus_alphas_cumprod
+  all_snr = (alpha / sigma) ** 2
+  snr = torch.stack([all_snr[t] for t in timesteps])
+  gamma_over_snr = torch.div(torch.ones_like(snr)*gamma,snr)
+  snr_weight = torch.minimum(gamma_over_snr,torch.ones_like(gamma_over_snr)).float() #from paper
+  loss = loss * snr_weight
+  return loss
+
+def add_custom_train_arguments(parser: argparse.ArgumentParser):
+  parser.add_argument("--min_snr_gamma", type=float, default=None, help="gamma for reducing the weight of high loss timesteps. Lower numbers have stronger effect. 5 is recommended by paper. / 低いタイムステップでの高いlossに対して重みを減らすためのgamma値、低いほど効果が強く、論文では5が推奨")

library/model_util.py

@@ -1046,10 +1046,14 @@ def save_stable_diffusion_checkpoint(v2, output_file, text_encoder, unet, ckpt_p
   key_count = len(state_dict.keys())
   new_ckpt = {'state_dict': state_dict}
 
-  if 'epoch' in checkpoint:
-    epochs += checkpoint['epoch']
-  if 'global_step' in checkpoint:
-    steps += checkpoint['global_step']
+  # epoch and global_step are sometimes not int
+  try:
+    if 'epoch' in checkpoint:
+      epochs += checkpoint['epoch']
+    if 'global_step' in checkpoint:
+      steps += checkpoint['global_step']
+  except:
+    pass
 
   new_ckpt['epoch'] = epochs
   new_ckpt['global_step'] = steps

library/train_util.py

@@ -276,6 +276,8 @@ class BaseSubset:
         caption_dropout_rate: float,
         caption_dropout_every_n_epochs: int,
         caption_tag_dropout_rate: float,
+        token_warmup_min: int,
+        token_warmup_step: Union[float, int],
     ) -> None:
         self.image_dir = image_dir
         self.num_repeats = num_repeats
@@ -289,6 +291,9 @@ class BaseSubset:
         self.caption_dropout_every_n_epochs = caption_dropout_every_n_epochs
         self.caption_tag_dropout_rate = caption_tag_dropout_rate
 
+        self.token_warmup_min = token_warmup_min  # step=0におけるタグの数
+        self.token_warmup_step = token_warmup_step  # N（N<1ならN*max_train_steps）ステップ目でタグの数が最大になる
+
         self.img_count = 0
 
 
@@ -309,6 +314,8 @@ class DreamBoothSubset(BaseSubset):
         caption_dropout_rate,
         caption_dropout_every_n_epochs,
         caption_tag_dropout_rate,
+        token_warmup_min,
+        token_warmup_step,
     ) -> None:
         assert image_dir is not None, "image_dir must be specified / image_dirは指定が必須です"
 
@@ -324,6 +331,8 @@ class DreamBoothSubset(BaseSubset):
             caption_dropout_rate,
             caption_dropout_every_n_epochs,
             caption_tag_dropout_rate,
+            token_warmup_min,
+            token_warmup_step,
         )
 
         self.is_reg = is_reg
@@ -351,6 +360,8 @@ class FineTuningSubset(BaseSubset):
         caption_dropout_rate,
         caption_dropout_every_n_epochs,
         caption_tag_dropout_rate,
+        token_warmup_min,
+        token_warmup_step,
     ) -> None:
         assert metadata_file is not None, "metadata_file must be specified / metadata_fileは指定が必須です"
 
@@ -366,6 +377,8 @@ class FineTuningSubset(BaseSubset):
             caption_dropout_rate,
             caption_dropout_every_n_epochs,
             caption_tag_dropout_rate,
+            token_warmup_min,
+            token_warmup_step,
         )
 
         self.metadata_file = metadata_file
@@ -404,6 +417,10 @@ class BaseDataset(torch.utils.data.Dataset):
 
         self.current_epoch: int = 0  # インスタンスがepochごとに新しく作られるようなので外側から渡さないとダメ
 
+        self.current_step: int = 0
+        self.max_train_steps: int = 0
+        self.seed: int = 0
+
         # augmentation
         self.aug_helper = AugHelper()
 
@@ -419,9 +436,19 @@ class BaseDataset(torch.utils.data.Dataset):
 
         self.replacements = {}
 
+    def set_seed(self, seed):
+        self.seed = seed
+
     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
-        self.shuffle_buckets()
+
+    def set_current_step(self, step):
+        self.current_step = step
+
+    def set_max_train_steps(self, max_train_steps):
+        self.max_train_steps = max_train_steps
 
     def set_tag_frequency(self, dir_name, captions):
         frequency_for_dir = self.tag_frequency.get(dir_name, {})
@@ -452,7 +479,16 @@ class BaseDataset(torch.utils.data.Dataset):
         if is_drop_out:
             caption = ""
         else:
-            if subset.shuffle_caption or subset.caption_tag_dropout_rate > 0:
+            if subset.shuffle_caption or subset.token_warmup_step > 0 or subset.caption_tag_dropout_rate > 0:
+                tokens = [t.strip() for t in caption.strip().split(",")]
+                if subset.token_warmup_step < 1: # 初回に上書きする
+                    subset.token_warmup_step = math.floor(subset.token_warmup_step * self.max_train_steps)
+                if subset.token_warmup_step and self.current_step < subset.token_warmup_step:
+                    tokens_len = (
+                        math.floor((self.current_step) * ((len(tokens) - subset.token_warmup_min) / (subset.token_warmup_step)))
+                        + subset.token_warmup_min
+                    )
+                    tokens = tokens[:tokens_len]
 
                 def dropout_tags(tokens):
                     if subset.caption_tag_dropout_rate <= 0:
@@ -464,10 +500,10 @@ class BaseDataset(torch.utils.data.Dataset):
                     return l
 
                 fixed_tokens = []
-                flex_tokens = [t.strip() for t in caption.strip().split(",")]
+                flex_tokens = tokens[:]
                 if subset.keep_tokens > 0:
                     fixed_tokens = flex_tokens[: subset.keep_tokens]
-                    flex_tokens = flex_tokens[subset.keep_tokens :]
+                    flex_tokens = tokens[subset.keep_tokens :]
 
                 if subset.shuffle_caption:
                     random.shuffle(flex_tokens)
@@ -637,6 +673,9 @@ class BaseDataset(torch.utils.data.Dataset):
         self._length = len(self.buckets_indices)
 
     def shuffle_buckets(self):
+        # set random seed for this epoch
+        random.seed(self.seed + self.current_epoch)
+
         random.shuffle(self.buckets_indices)
         self.bucket_manager.shuffle()
 
@@ -1043,7 +1082,7 @@ class DreamBoothDataset(BaseDataset):
                         self.register_image(info, subset)
                         n += info.num_repeats
                     else:
-                        info.num_repeats += 1
+                        info.num_repeats += 1  # rewrite registered info
                         n += 1
                     if n >= num_train_images:
                         break
@@ -1104,6 +1143,8 @@ class FineTuningDataset(BaseDataset):
                 # path情報を作る
                 if os.path.exists(image_key):
                     abs_path = image_key
+                elif os.path.exists(os.path.splitext(image_key)[0] + ".npz"):
+                    abs_path = os.path.splitext(image_key)[0] + ".npz"
                 else:
                     npz_path = os.path.join(subset.image_dir, image_key + ".npz")
                     if os.path.exists(npz_path):
@@ -1285,6 +1326,14 @@ class DatasetGroup(torch.utils.data.ConcatDataset):
         for dataset in self.datasets:
             dataset.set_current_epoch(epoch)
 
+    def set_current_step(self, step):
+        for dataset in self.datasets:
+            dataset.set_current_step(step)
+
+    def set_max_train_steps(self, max_train_steps):
+        for dataset in self.datasets:
+            dataset.set_max_train_steps(max_train_steps)
+
     def disable_token_padding(self):
         for dataset in self.datasets:
             dataset.disable_token_padding()
@@ -1292,37 +1341,55 @@ class DatasetGroup(torch.utils.data.ConcatDataset):
 
 def debug_dataset(train_dataset, show_input_ids=False):
     print(f"Total dataset length (steps) / データセットの長さ（ステップ数）: {len(train_dataset)}")
-    print("Escape for exit. / Escキーで中断、終了します")
+    print("`S` for next step, `E` for next epoch no. , Escape for exit. / Sキーで次のステップ、Eキーで次のエポック、Escキーで中断、終了します")
 
-    train_dataset.set_current_epoch(1)
-    k = 0
-    indices = list(range(len(train_dataset)))
-    random.shuffle(indices)
-    for i, idx in enumerate(indices):
-        example = train_dataset[idx]
-        if example["latents"] is not None:
-            print(f"sample has latents from npz file: {example['latents'].size()}")
-        for j, (ik, cap, lw, iid) in enumerate(
-            zip(example["image_keys"], example["captions"], example["loss_weights"], example["input_ids"])
-        ):
-            print(f'{ik}, size: {train_dataset.image_data[ik].image_size}, loss weight: {lw}, caption: "{cap}"')
-            if show_input_ids:
-                print(f"input ids: {iid}")
-            if example["images"] is not None:
-                im = example["images"][j]
-                print(f"image size: {im.size()}")
-                im = ((im.numpy() + 1.0) * 127.5).astype(np.uint8)
-                im = np.transpose(im, (1, 2, 0))  # c,H,W -> H,W,c
-                im = im[:, :, ::-1]  # RGB -> BGR (OpenCV)
-                if os.name == "nt":  # only windows
-                    cv2.imshow("img", im)
-                k = cv2.waitKey()
-                cv2.destroyAllWindows()
-                if k == 27:
-                    break
-        if k == 27 or (example["images"] is None and i >= 8):
+    epoch = 1
+    while True:
+        print(f"epoch: {epoch}")
+
+        steps = (epoch - 1) * len(train_dataset) + 1
+        indices = list(range(len(train_dataset)))
+        random.shuffle(indices)
+
+        k = 0
+        for i, idx in enumerate(indices):
+            train_dataset.set_current_epoch(epoch)
+            train_dataset.set_current_step(steps)
+            print(f"steps: {steps} ({i + 1}/{len(train_dataset)})")
+
+            example = train_dataset[idx]
+            if example["latents"] is not None:
+                print(f"sample has latents from npz file: {example['latents'].size()}")
+            for j, (ik, cap, lw, iid) in enumerate(
+                zip(example["image_keys"], example["captions"], example["loss_weights"], example["input_ids"])
+            ):
+                print(f'{ik}, size: {train_dataset.image_data[ik].image_size}, loss weight: {lw}, caption: "{cap}"')
+                if show_input_ids:
+                    print(f"input ids: {iid}")
+                if example["images"] is not None:
+                    im = example["images"][j]
+                    print(f"image size: {im.size()}")
+                    im = ((im.numpy() + 1.0) * 127.5).astype(np.uint8)
+                    im = np.transpose(im, (1, 2, 0))  # c,H,W -> H,W,c
+                    im = im[:, :, ::-1]  # RGB -> BGR (OpenCV)
+                    if os.name == "nt":  # only windows
+                        cv2.imshow("img", im)
+                    k = cv2.waitKey()
+                    cv2.destroyAllWindows()
+                    if k == 27 or k == ord("s") or k == ord("e"):
+                        break
+            steps += 1
+
+            if k == ord("e"):
+                break
+            if k == 27 or (example["images"] is None and i >= 8):
+                k = 27
+                break
+        if k == 27:
             break
 
+        epoch += 1
+
 
 def glob_images(directory, base="*"):
     img_paths = []
@@ -1331,8 +1398,8 @@ def glob_images(directory, base="*"):
             img_paths.extend(glob.glob(os.path.join(glob.escape(directory), base + ext)))
         else:
             img_paths.extend(glob.glob(glob.escape(os.path.join(directory, base + ext))))
-    # img_paths = list(set(img_paths))                    # 重複を排除
-    # img_paths.sort()
+    img_paths = list(set(img_paths))  # 重複を排除
+    img_paths.sort()
     return img_paths
 
 
@@ -1344,8 +1411,8 @@ def glob_images_pathlib(dir_path, recursive):
     else:
         for ext in IMAGE_EXTENSIONS:
             image_paths += list(dir_path.glob("*" + ext))
-    # image_paths = list(set(image_paths))        # 重複を排除
-    # image_paths.sort()
+    image_paths = list(set(image_paths))  # 重複を排除
+    image_paths.sort()
     return image_paths
 
 
@@ -1963,9 +2030,6 @@ def add_training_arguments(parser: argparse.ArgumentParser, support_dreambooth:
         parser.add_argument(
             "--prior_loss_weight", type=float, default=1.0, help="loss weight for regularization images / 正則化画像のlossの重み"
         )
-    
-    parser.add_argument("--min_snr_gamma", type=float, default=5, help="gamma for reducing the weight of high loss timesteps. Lower numbers have stronger effect. 5 is recommended by paper.")
-
 
 
 def verify_training_args(args: argparse.Namespace):
@@ -2041,6 +2105,20 @@ def add_dataset_arguments(
         "--bucket_no_upscale", action="store_true", help="make bucket for each image without upscaling / 画像を拡大せずbucketを作成します"
     )
 
+    parser.add_argument(
+        "--token_warmup_min",
+        type=int,
+        default=1,
+        help="start learning at N tags (token means comma separated strinfloatgs) / タグ数をN個から増やしながら学習する",
+    )
+
+    parser.add_argument(
+        "--token_warmup_step",
+        type=float,
+        default=0,
+        help="tag length reaches maximum on N steps (or N*max_train_steps if N<1) / N（N<1ならN*max_train_steps）ステップでタグ長が最大になる。デフォルトは0（最初から最大）",
+    )
+
     if support_caption_dropout:
         # Textual Inversion はcaptionのdropoutをsupportしない
         # いわゆるtensorのDropoutと紛らわしいのでprefixにcaptionを付けておく　every_n_epochsは他と平仄を合わせてdefault Noneに
@@ -2975,3 +3053,24 @@ class ImageLoadingDataset(torch.utils.data.Dataset):
 
 
 # endregion
+
+
+# collate_fn用 epoch,stepはmultiprocessing.Value
+class collater_class:
+    def __init__(self, epoch, step, dataset):
+        self.current_epoch = epoch
+        self.current_step = step
+        self.dataset = dataset # not used if worker_info is not None, in case of multiprocessing
+
+    def __call__(self, examples):
+        worker_info = torch.utils.data.get_worker_info()
+        # worker_info is None in the main process
+        if worker_info is not None:
+            dataset = worker_info.dataset
+        else:
+            dataset = self.dataset
+
+        # set epoch and step
+        dataset.set_current_epoch(self.current_epoch.value)
+        dataset.set_current_step(self.current_step.value)
+        return examples[0]

lora_gui.py

@@ -123,7 +123,9 @@ def save_configuration(
     sample_every_n_epochs,
     sample_sampler,
     sample_prompts,
-    additional_parameters,vae_batch_size,
+    additional_parameters,
+    vae_batch_size,
+    min_snr_gamma,
 ):
     # Get list of function parameters and values
     parameters = list(locals().items())
@@ -240,7 +242,9 @@ def open_configuration(
     sample_every_n_epochs,
     sample_sampler,
     sample_prompts,
-    additional_parameters,vae_batch_size,
+    additional_parameters,
+    vae_batch_size,
+    min_snr_gamma,
 ):
     # Get list of function parameters and values
     parameters = list(locals().items())
@@ -257,7 +261,7 @@ def open_configuration(
         with open(file_path, 'r') as f:
             my_data = json.load(f)
             print('Loading config...')
-            
+
             # Update values to fix deprecated use_8bit_adam checkbox, set appropriate optimizer if it is set to True, etc.
             my_data = update_my_data(my_data)
     else:
@@ -348,7 +352,9 @@ def train_model(
     sample_every_n_epochs,
     sample_sampler,
     sample_prompts,
-    additional_parameters,vae_batch_size,
+    additional_parameters,
+    vae_batch_size,
+    min_snr_gamma,
 ):
     print_only_bool = True if print_only.get('label') == 'True' else False
 
@@ -420,13 +426,15 @@ def train_model(
             [
                 f
                 for f, lower_f in (
-                    (file, file.lower()) for file in os.listdir(os.path.join(train_data_dir, folder))
+                    (file, file.lower())
+                    for file in os.listdir(
+                        os.path.join(train_data_dir, folder)
+                    )
                 )
                 if lower_f.endswith(('.jpg', '.jpeg', '.png', '.webp'))
             ]
         )
 
-
         print(f'Folder {folder}: {num_images} images found')
 
         # Calculate the total number of steps for this folder
@@ -591,6 +599,7 @@ def train_model(
         noise_offset=noise_offset,
         additional_parameters=additional_parameters,
         vae_batch_size=vae_batch_size,
+        min_snr_gamma=min_snr_gamma,
     )
 
     run_cmd += run_cmd_sample(
@@ -649,10 +658,12 @@ def lora_tab(
         v_parameterization,
         save_model_as,
         model_list,
-    ) = gradio_source_model(save_model_as_choices = [
-                    'ckpt',
-                    'safetensors',
-                ])
+    ) = gradio_source_model(
+        save_model_as_choices=[
+            'ckpt',
+            'safetensors',
+        ]
+    )
 
     with gr.Tab('Folders'):
         with gr.Row():
@@ -897,6 +908,7 @@ def lora_tab(
                 noise_offset,
                 additional_parameters,
                 vae_batch_size,
+                min_snr_gamma,
             ) = gradio_advanced_training()
             color_aug.change(
                 color_aug_changed,
@@ -1015,6 +1027,7 @@ def lora_tab(
         sample_prompts,
         additional_parameters,
         vae_batch_size,
+        min_snr_gamma,
     ]
 
     button_open_config.click(
@@ -1104,7 +1117,7 @@ def UI(**kwargs):
     if kwargs.get('inbrowser', False):
         launch_kwargs['inbrowser'] = kwargs.get('inbrowser', False)
     if kwargs.get('listen', True):
-        launch_kwargs['server_name'] = "0.0.0.0"
+        launch_kwargs['server_name'] = '0.0.0.0'
     print(launch_kwargs)
     interface.launch(**launch_kwargs)
 
@@ -1128,7 +1141,9 @@ if __name__ == '__main__':
         '--inbrowser', action='store_true', help='Open in browser'
     )
     parser.add_argument(
-        '--listen', action='store_true', help='Launch gradio with server name 0.0.0.0, allowing LAN access'
+        '--listen',
+        action='store_true',
+        help='Launch gradio with server name 0.0.0.0, allowing LAN access',
     )
 
     args = parser.parse_args()

networks/resize_lora.py

@@ -11,6 +11,8 @@ import numpy as np
 
 MIN_SV = 1e-6
 
+# Model save and load functions
+
 def load_state_dict(file_name, dtype):
   if model_util.is_safetensors(file_name):
     sd = load_file(file_name)
@@ -39,12 +41,13 @@ def save_to_file(file_name, model, state_dict, dtype, metadata):
     torch.save(model, file_name)
 
 
+# Indexing functions
+
 def index_sv_cumulative(S, target):
   original_sum = float(torch.sum(S))
   cumulative_sums = torch.cumsum(S, dim=0)/original_sum
   index = int(torch.searchsorted(cumulative_sums, target)) + 1
-  if index >= len(S):
-    index = len(S) - 1
+  index = max(1, min(index, len(S)-1))
 
   return index
 
@@ -54,8 +57,16 @@ def index_sv_fro(S, target):
   s_fro_sq = float(torch.sum(S_squared))
   sum_S_squared = torch.cumsum(S_squared, dim=0)/s_fro_sq
   index = int(torch.searchsorted(sum_S_squared, target**2)) + 1
-  if index >= len(S):
-    index = len(S) - 1
+  index = max(1, min(index, len(S)-1))
+
+  return index
+
+
+def index_sv_ratio(S, target):
+  max_sv = S[0]
+  min_sv = max_sv/target
+  index = int(torch.sum(S > min_sv).item())
+  index = max(1, min(index, len(S)-1))
 
   return index
 
@@ -125,26 +136,24 @@ def merge_linear(lora_down, lora_up, device):
     return weight
   
 
+# Calculate new rank
+
 def rank_resize(S, rank, dynamic_method, dynamic_param, scale=1):
     param_dict = {}
 
     if dynamic_method=="sv_ratio":
         # Calculate new dim and alpha based off ratio
-        max_sv = S[0]
-        min_sv = max_sv/dynamic_param
-        new_rank = max(torch.sum(S > min_sv).item(),1)
+        new_rank = index_sv_ratio(S, dynamic_param) + 1
         new_alpha = float(scale*new_rank)
 
     elif dynamic_method=="sv_cumulative":
         # Calculate new dim and alpha based off cumulative sum
-        new_rank = index_sv_cumulative(S, dynamic_param)
-        new_rank = max(new_rank, 1)
+        new_rank = index_sv_cumulative(S, dynamic_param) + 1
         new_alpha = float(scale*new_rank)
 
     elif dynamic_method=="sv_fro":
         # Calculate new dim and alpha based off sqrt sum of squares
-        new_rank = index_sv_fro(S, dynamic_param)
-        new_rank = min(max(new_rank, 1), len(S)-1)
+        new_rank = index_sv_fro(S, dynamic_param) + 1
         new_alpha = float(scale*new_rank)
     else:
         new_rank = rank
@@ -172,7 +181,7 @@ def rank_resize(S, rank, dynamic_method, dynamic_param, scale=1):
     param_dict["new_alpha"] = new_alpha
     param_dict["sum_retained"] = (s_rank)/s_sum
     param_dict["fro_retained"] = fro_percent
-    param_dict["max_ratio"] = S[0]/S[new_rank]
+    param_dict["max_ratio"] = S[0]/S[new_rank - 1]
 
     return param_dict
 

textual_inversion_gui.py

@@ -112,7 +112,9 @@ def save_configuration(
     sample_every_n_epochs,
     sample_sampler,
     sample_prompts,
-    additional_parameters,vae_batch_size,
+    additional_parameters,
+    vae_batch_size,
+    min_snr_gamma,
 ):
     # Get list of function parameters and values
     parameters = list(locals().items())
@@ -225,7 +227,9 @@ def open_configuration(
     sample_every_n_epochs,
     sample_sampler,
     sample_prompts,
-    additional_parameters,vae_batch_size,
+    additional_parameters,
+    vae_batch_size,
+    min_snr_gamma,
 ):
     # Get list of function parameters and values
     parameters = list(locals().items())
@@ -320,7 +324,9 @@ def train_model(
     sample_every_n_epochs,
     sample_sampler,
     sample_prompts,
-    additional_parameters,vae_batch_size,
+    additional_parameters,
+    vae_batch_size,
+    min_snr_gamma,
 ):
     if pretrained_model_name_or_path == '':
         msgbox('Source model information is missing')
@@ -376,7 +382,10 @@ def train_model(
             [
                 f
                 for f, lower_f in (
-                    (file, file.lower()) for file in os.listdir(os.path.join(train_data_dir, folder))
+                    (file, file.lower())
+                    for file in os.listdir(
+                        os.path.join(train_data_dir, folder)
+                    )
                 )
                 if lower_f.endswith(('.jpg', '.jpeg', '.png', '.webp'))
             ]
@@ -511,6 +520,7 @@ def train_model(
         noise_offset=noise_offset,
         additional_parameters=additional_parameters,
         vae_batch_size=vae_batch_size,
+        min_snr_gamma=min_snr_gamma,
     )
     run_cmd += f' --token_string="{token_string}"'
     run_cmd += f' --init_word="{init_word}"'
@@ -569,10 +579,12 @@ def ti_tab(
         v_parameterization,
         save_model_as,
         model_list,
-    ) = gradio_source_model(save_model_as_choices = [
-                    'ckpt',
-                    'safetensors',
-                ])
+    ) = gradio_source_model(
+        save_model_as_choices=[
+            'ckpt',
+            'safetensors',
+        ]
+    )
 
     with gr.Tab('Folders'):
         with gr.Row():
@@ -774,6 +786,7 @@ def ti_tab(
                 noise_offset,
                 additional_parameters,
                 vae_batch_size,
+                min_snr_gamma,
             ) = gradio_advanced_training()
             color_aug.change(
                 color_aug_changed,
@@ -881,6 +894,7 @@ def ti_tab(
         sample_prompts,
         additional_parameters,
         vae_batch_size,
+        min_snr_gamma,
     ]
 
     button_open_config.click(

train_db - Copy.py

@@ -0,0 +1,426 @@
+# DreamBooth training
+# XXX dropped option: fine_tune
+
+import gc
+import time
+import argparse
+import itertools
+import math
+import os
+import toml
+from multiprocessing import Value
+
+from tqdm import tqdm
+import torch
+from accelerate.utils import set_seed
+import diffusers
+from diffusers import DDPMScheduler
+
+import library.train_util as train_util
+import library.config_util as config_util
+from library.config_util import (
+    ConfigSanitizer,
+    BlueprintGenerator,
+)
+import library.custom_train_functions as custom_train_functions
+from library.custom_train_functions import apply_snr_weight 
+
+def train(args):
+    train_util.verify_training_args(args)
+    train_util.prepare_dataset_args(args, False)
+
+    cache_latents = args.cache_latents
+
+    if args.seed is not None:
+        set_seed(args.seed)  # 乱数系列を初期化する
+
+    tokenizer = train_util.load_tokenizer(args)
+
+    blueprint_generator = BlueprintGenerator(ConfigSanitizer(True, False, True))
+    if args.dataset_config is not None:
+        print(f"Load dataset config from {args.dataset_config}")
+        user_config = config_util.load_user_config(args.dataset_config)
+        ignored = ["train_data_dir", "reg_data_dir"]
+        if any(getattr(args, attr) is not None for attr in ignored):
+            print(
+                "ignore following options because config file is found: {0} / 設定ファイルが利用されるため以下のオプションは無視されます: {0}".format(
+                    ", ".join(ignored)
+                )
+            )
+    else:
+        user_config = {
+            "datasets": [
+                {"subsets": config_util.generate_dreambooth_subsets_config_by_subdirs(args.train_data_dir, args.reg_data_dir)}
+            ]
+        }
+
+    blueprint = blueprint_generator.generate(user_config, args, tokenizer=tokenizer)
+    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()
+
+    if args.debug_dataset:
+        train_util.debug_dataset(train_dataset_group)
+        return
+
+    if cache_latents:
+        assert (
+            train_dataset_group.is_latent_cacheable()
+        ), "when caching latents, either color_aug or random_crop cannot be used / latentをキャッシュするときはcolor_augとrandom_cropは使えません"
+
+    # acceleratorを準備する
+    print("prepare accelerator")
+
+    if args.gradient_accumulation_steps > 1:
+        print(
+            f"gradient_accumulation_steps is {args.gradient_accumulation_steps}. accelerate does not support gradient_accumulation_steps when training multiple models (U-Net and Text Encoder), so something might be wrong"
+        )
+        print(
+            f"gradient_accumulation_stepsが{args.gradient_accumulation_steps}に設定されています。accelerateは複数モデル（U-NetおよびText Encoder）の学習時にgradient_accumulation_stepsをサポートしていないため結果は未知数です"
+        )
+
+    accelerator, unwrap_model = train_util.prepare_accelerator(args)
+
+    # mixed precisionに対応した型を用意しておき適宜castする
+    weight_dtype, save_dtype = train_util.prepare_dtype(args)
+
+    # モデルを読み込む
+    text_encoder, vae, unet, load_stable_diffusion_format = train_util.load_target_model(args, weight_dtype)
+
+    # verify load/save model formats
+    if load_stable_diffusion_format:
+        src_stable_diffusion_ckpt = args.pretrained_model_name_or_path
+        src_diffusers_model_path = None
+    else:
+        src_stable_diffusion_ckpt = None
+        src_diffusers_model_path = args.pretrained_model_name_or_path
+
+    if args.save_model_as is None:
+        save_stable_diffusion_format = load_stable_diffusion_format
+        use_safetensors = args.use_safetensors
+    else:
+        save_stable_diffusion_format = args.save_model_as.lower() == "ckpt" or args.save_model_as.lower() == "safetensors"
+        use_safetensors = args.use_safetensors or ("safetensors" in args.save_model_as.lower())
+
+    # モデルに xformers とか memory efficient attention を組み込む
+    train_util.replace_unet_modules(unet, args.mem_eff_attn, args.xformers)
+
+    # 学習を準備する
+    if cache_latents:
+        vae.to(accelerator.device, dtype=weight_dtype)
+        vae.requires_grad_(False)
+        vae.eval()
+        with torch.no_grad():
+            train_dataset_group.cache_latents(vae, args.vae_batch_size)
+        vae.to("cpu")
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
+        gc.collect()
+
+    # 学習を準備する：モデルを適切な状態にする
+    train_text_encoder = args.stop_text_encoder_training is None or args.stop_text_encoder_training >= 0
+    unet.requires_grad_(True)  # 念のため追加
+    text_encoder.requires_grad_(train_text_encoder)
+    if not train_text_encoder:
+        print("Text Encoder is not trained.")
+
+    if args.gradient_checkpointing:
+        unet.enable_gradient_checkpointing()
+        text_encoder.gradient_checkpointing_enable()
+
+    if not cache_latents:
+        vae.requires_grad_(False)
+        vae.eval()
+        vae.to(accelerator.device, dtype=weight_dtype)
+
+    # 学習に必要なクラスを準備する
+    print("prepare optimizer, data loader etc.")
+    if train_text_encoder:
+        trainable_params = itertools.chain(unet.parameters(), text_encoder.parameters())
+    else:
+        trainable_params = unet.parameters()
+
+    _, _, optimizer = train_util.get_optimizer(args, trainable_params)
+
+    # dataloaderを準備する
+    # DataLoaderのプロセス数：0はメインプロセスになる
+    n_workers = min(args.max_data_loader_n_workers, os.cpu_count() - 1)  # cpu_count-1 ただし最大で指定された数まで
+    train_dataloader = torch.utils.data.DataLoader(
+        train_dataset_group,
+        batch_size=1,
+        shuffle=True,
+        collate_fn=collater,
+        num_workers=n_workers,
+        persistent_workers=args.persistent_data_loader_workers,
+    )
+
+    # 学習ステップ数を計算する
+    if args.max_train_epochs is not None:
+        args.max_train_steps = args.max_train_epochs * math.ceil(len(train_dataloader) / accelerator.num_processes / args.gradient_accumulation_steps)
+        print(f"override steps. steps for {args.max_train_epochs} epochs is / 指定エポックまでのステップ数: {args.max_train_steps}")
+
+    # データセット側にも学習ステップを送信
+    train_dataset_group.set_max_train_steps(args.max_train_steps)
+
+    if args.stop_text_encoder_training is None:
+        args.stop_text_encoder_training = args.max_train_steps + 1  # do not stop until end
+
+    # lr schedulerを用意する TODO gradient_accumulation_stepsの扱いが何かおかしいかもしれない。後で確認する
+    lr_scheduler = train_util.get_scheduler_fix(args, optimizer, accelerator.num_processes)
+
+    # 実験的機能：勾配も含めたfp16学習を行う　モデル全体をfp16にする
+    if args.full_fp16:
+        assert (
+            args.mixed_precision == "fp16"
+        ), "full_fp16 requires mixed precision='fp16' / full_fp16を使う場合はmixed_precision='fp16'を指定してください。"
+        print("enable full fp16 training.")
+        unet.to(weight_dtype)
+        text_encoder.to(weight_dtype)
+
+    # acceleratorがなんかよろしくやってくれるらしい
+    if train_text_encoder:
+        unet, text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
+            unet, text_encoder, optimizer, train_dataloader, lr_scheduler
+        )
+    else:
+        unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(unet, optimizer, train_dataloader, lr_scheduler)
+
+    if not train_text_encoder:
+        text_encoder.to(accelerator.device, dtype=weight_dtype)  # to avoid 'cpu' vs 'cuda' error
+
+    # 実験的機能：勾配も含めたfp16学習を行う　PyTorchにパッチを当ててfp16でのgrad scaleを有効にする
+    if args.full_fp16:
+        train_util.patch_accelerator_for_fp16_training(accelerator)
+
+    # resumeする
+    if args.resume is not None:
+        print(f"resume training from state: {args.resume}")
+        accelerator.load_state(args.resume)
+
+    # epoch数を計算する
+    num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
+    num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
+    if (args.save_n_epoch_ratio is not None) and (args.save_n_epoch_ratio > 0):
+        args.save_every_n_epochs = math.floor(num_train_epochs / args.save_n_epoch_ratio) or 1
+
+    # 学習する
+    total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
+    print("running training / 学習開始")
+    print(f"  num train images * repeats / 学習画像の数×繰り返し回数: {train_dataset_group.num_train_images}")
+    print(f"  num reg images / 正則化画像の数: {train_dataset_group.num_reg_images}")
+    print(f"  num batches per epoch / 1epochのバッチ数: {len(train_dataloader)}")
+    print(f"  num epochs / epoch数: {num_train_epochs}")
+    print(f"  batch size per device / バッチサイズ: {args.train_batch_size}")
+    print(f"  total train batch size (with parallel & distributed & accumulation) / 総バッチサイズ（並列学習、勾配合計含む）: {total_batch_size}")
+    print(f"  gradient ccumulation steps / 勾配を合計するステップ数 = {args.gradient_accumulation_steps}")
+    print(f"  total optimization steps / 学習ステップ数: {args.max_train_steps}")
+
+    progress_bar = tqdm(range(args.max_train_steps), smoothing=0, disable=not accelerator.is_local_main_process, desc="steps")
+    global_step = 0
+
+    noise_scheduler = DDPMScheduler(
+        beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", num_train_timesteps=1000, clip_sample=False
+    )
+
+    if accelerator.is_main_process:
+        accelerator.init_trackers("dreambooth")
+
+    loss_list = []
+    loss_total = 0.0
+    for epoch in range(num_train_epochs):
+        print(f"epoch {epoch+1}/{num_train_epochs}")
+        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()
+        # train==True is required to enable gradient_checkpointing
+        if args.gradient_checkpointing or global_step < args.stop_text_encoder_training:
+            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}")
+                if not args.gradient_checkpointing:
+                    text_encoder.train(False)
+                text_encoder.requires_grad_(False)
+
+            with accelerator.accumulate(unet):
+                with torch.no_grad():
+                    # latentに変換
+                    if cache_latents:
+                        latents = batch["latents"].to(accelerator.device)
+                    else:
+                        latents = vae.encode(batch["images"].to(dtype=weight_dtype)).latent_dist.sample()
+                    latents = latents * 0.18215
+                b_size = latents.shape[0]
+
+                # Sample noise that we'll add to the latents
+                noise = torch.randn_like(latents, device=latents.device)
+                if args.noise_offset:
+                    # https://www.crosslabs.org//blog/diffusion-with-offset-noise
+                    noise += args.noise_offset * torch.randn((latents.shape[0], latents.shape[1], 1, 1), device=latents.device)
+
+                # Get the text embedding for conditioning
+                with torch.set_grad_enabled(global_step < args.stop_text_encoder_training):
+                    input_ids = batch["input_ids"].to(accelerator.device)
+                    encoder_hidden_states = train_util.get_hidden_states(
+                        args, input_ids, tokenizer, text_encoder, None if not args.full_fp16 else weight_dtype
+                    )
+
+                # Sample a random timestep for each image
+                timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (b_size,), device=latents.device)
+                timesteps = timesteps.long()
+
+                # Add noise to the latents according to the noise magnitude at each timestep
+                # (this is the forward diffusion process)
+                noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
+
+                # Predict the noise residual
+                noise_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample
+
+                if args.v_parameterization:
+                    # v-parameterization training
+                    target = noise_scheduler.get_velocity(latents, noise, timesteps)
+                else:
+                    target = noise
+
+                loss = torch.nn.functional.mse_loss(noise_pred.float(), target.float(), reduction="none")
+                loss = loss.mean([1, 2, 3])
+
+                loss_weights = batch["loss_weights"]  # 各sampleごとのweight
+                loss = loss * loss_weights
+
+                if args.min_snr_gamma:
+                  loss = apply_snr_weight(loss, timesteps, noise_scheduler, args.min_snr_gamma)
+
+
+                loss = loss.mean()  # 平均なのでbatch_sizeで割る必要なし
+
+                accelerator.backward(loss)
+                if accelerator.sync_gradients and args.max_grad_norm != 0.0:
+                    if train_text_encoder:
+                        params_to_clip = itertools.chain(unet.parameters(), text_encoder.parameters())
+                    else:
+                        params_to_clip = unet.parameters()
+                    accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)
+
+                optimizer.step()
+                lr_scheduler.step()
+                optimizer.zero_grad(set_to_none=True)
+
+            # Checks if the accelerator has performed an optimization step behind the scenes
+            if accelerator.sync_gradients:
+                progress_bar.update(1)
+                global_step += 1
+
+                train_util.sample_images(
+                    accelerator, args, None, global_step, accelerator.device, vae, tokenizer, text_encoder, unet
+                )
+
+            current_loss = loss.detach().item()
+            if args.logging_dir is not None:
+                logs = {"loss": current_loss, "lr": float(lr_scheduler.get_last_lr()[0])}
+                if args.optimizer_type.lower() == "DAdaptation".lower():  # tracking d*lr value
+                    logs["lr/d*lr"] = (
+                        lr_scheduler.optimizers[0].param_groups[0]["d"] * lr_scheduler.optimizers[0].param_groups[0]["lr"]
+                    )
+                accelerator.log(logs, step=global_step)
+
+            if epoch == 0:
+                loss_list.append(current_loss)
+            else:
+                loss_total -= loss_list[step]
+                loss_list[step] = current_loss
+            loss_total += current_loss
+            avr_loss = loss_total / len(loss_list)
+            logs = {"loss": avr_loss}  # , "lr": lr_scheduler.get_last_lr()[0]}
+            progress_bar.set_postfix(**logs)
+
+            if global_step >= args.max_train_steps:
+                break
+
+        if args.logging_dir is not None:
+            logs = {"loss/epoch": loss_total / len(loss_list)}
+            accelerator.log(logs, step=epoch + 1)
+
+        accelerator.wait_for_everyone()
+
+        if args.save_every_n_epochs is not None:
+            src_path = src_stable_diffusion_ckpt if save_stable_diffusion_format else src_diffusers_model_path
+            train_util.save_sd_model_on_epoch_end(
+                args,
+                accelerator,
+                src_path,
+                save_stable_diffusion_format,
+                use_safetensors,
+                save_dtype,
+                epoch,
+                num_train_epochs,
+                global_step,
+                unwrap_model(text_encoder),
+                unwrap_model(unet),
+                vae,
+            )
+
+        train_util.sample_images(accelerator, args, epoch + 1, global_step, accelerator.device, vae, tokenizer, text_encoder, unet)
+
+    is_main_process = accelerator.is_main_process
+    if is_main_process:
+        unet = unwrap_model(unet)
+        text_encoder = unwrap_model(text_encoder)
+
+    accelerator.end_training()
+
+    if args.save_state:
+        train_util.save_state_on_train_end(args, accelerator)
+
+    del accelerator  # この後メモリを使うのでこれは消す
+
+    if is_main_process:
+        src_path = src_stable_diffusion_ckpt if save_stable_diffusion_format else src_diffusers_model_path
+        train_util.save_sd_model_on_train_end(
+            args, src_path, save_stable_diffusion_format, use_safetensors, save_dtype, epoch, global_step, text_encoder, unet, vae
+        )
+        print("model saved.")
+
+
+def setup_parser() -> argparse.ArgumentParser:
+    parser = argparse.ArgumentParser()
+
+    train_util.add_sd_models_arguments(parser)
+    train_util.add_dataset_arguments(parser, True, False, True)
+    train_util.add_training_arguments(parser, True)
+    train_util.add_sd_saving_arguments(parser)
+    train_util.add_optimizer_arguments(parser)
+    config_util.add_config_arguments(parser)
+    custom_train_functions.add_custom_train_arguments(parser)
+
+    parser.add_argument(
+        "--no_token_padding",
+        action="store_true",
+        help="disable token padding (same as Diffuser's DreamBooth) / トークンのpaddingを無効にする（Diffusers版DreamBoothと同じ動作）",
+    )
+    parser.add_argument(
+        "--stop_text_encoder_training",
+        type=int,
+        default=None,
+        help="steps to stop text encoder training, -1 for no training / Text Encoderの学習を止めるステップ数、-1で最初から学習しない",
+    )
+
+    return parser
+
+
+if __name__ == "__main__":
+    parser = setup_parser()
+
+    args = parser.parse_args()
+    args = train_util.read_config_from_file(args, parser)
+
+    train(args)

train_db.py

@@ -8,9 +8,9 @@ import itertools
 import math
 import os
 import toml
+from multiprocessing import Value
 
 from tqdm import tqdm
-from library.custom_train_functions import apply_snr_weight
 import torch
 from accelerate.utils import set_seed
 import diffusers
@@ -22,10 +22,8 @@ from library.config_util import (
     ConfigSanitizer,
     BlueprintGenerator,
 )
-
-
-def collate_fn(examples):
-    return examples[0]
+import library.custom_train_functions as custom_train_functions
+from library.custom_train_functions import apply_snr_weight
 
 
 def train(args):
@@ -60,6 +58,11 @@ def train(args):
     blueprint = blueprint_generator.generate(user_config, args, tokenizer=tokenizer)
     train_dataset_group = config_util.generate_dataset_group_by_blueprint(blueprint.dataset_group)
 
+    current_epoch = Value("i", 0)
+    current_step = Value("i", 0)
+    ds_for_collater = train_dataset_group if args.max_data_loader_n_workers == 0 else None
+    collater = train_util.collater_class(current_epoch, current_step, ds_for_collater)
+
     if args.no_token_padding:
         train_dataset_group.disable_token_padding()
 
@@ -153,16 +156,21 @@ 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,
     )
 
     # 学習ステップ数を計算する
     if args.max_train_epochs is not None:
-        args.max_train_steps = args.max_train_epochs * math.ceil(len(train_dataloader) / accelerator.num_processes / args.gradient_accumulation_steps)
+        args.max_train_steps = args.max_train_epochs * math.ceil(
+            len(train_dataloader) / accelerator.num_processes / args.gradient_accumulation_steps
+        )
         print(f"override steps. steps for {args.max_train_epochs} epochs is / 指定エポックまでのステップ数: {args.max_train_steps}")
 
+    # データセット側にも学習ステップを送信
+    train_dataset_group.set_max_train_steps(args.max_train_steps)
+
     if args.stop_text_encoder_training is None:
         args.stop_text_encoder_training = args.max_train_steps + 1  # do not stop until end
 
@@ -230,7 +238,7 @@ 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
 
         # 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()
@@ -239,6 +247,7 @@ 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}")
@@ -291,8 +300,9 @@ def train(args):
 
                 loss_weights = batch["loss_weights"]  # 各sampleごとのweight
                 loss = loss * loss_weights
-                
-                loss = apply_snr_weight(loss, noisy_latents, latents, args.min_snr_gamma)
+
+                if args.min_snr_gamma:
+                    loss = apply_snr_weight(loss, timesteps, noise_scheduler, args.min_snr_gamma)
 
                 loss = loss.mean()  # 平均なのでbatch_sizeで割る必要なし
 
@@ -393,6 +403,7 @@ def setup_parser() -> argparse.ArgumentParser:
     train_util.add_sd_saving_arguments(parser)
     train_util.add_optimizer_arguments(parser)
     config_util.add_config_arguments(parser)
+    custom_train_functions.add_custom_train_arguments(parser)
 
     parser.add_argument(
         "--no_token_padding",

train_network - Copy.py

@@ -0,0 +1,710 @@
+from torch.nn.parallel import DistributedDataParallel as DDP
+import importlib
+import argparse
+import gc
+import math
+import os
+import random
+import time
+import json
+import toml
+from multiprocessing import Value
+
+from tqdm import tqdm
+import torch
+from accelerate.utils import set_seed
+from diffusers import DDPMScheduler
+
+import library.train_util as train_util
+from library.train_util import (
+    DreamBoothDataset,
+)
+import library.config_util as config_util
+from library.config_util import (
+    ConfigSanitizer,
+    BlueprintGenerator,
+)
+import library.custom_train_functions as custom_train_functions
+from library.custom_train_functions import apply_snr_weight 
+
+
+# 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}
+
+    if args.network_train_unet_only:
+        logs["lr/unet"] = float(lr_scheduler.get_last_lr()[0])
+    elif args.network_train_text_encoder_only:
+        logs["lr/textencoder"] = float(lr_scheduler.get_last_lr()[0])
+    else:
+        logs["lr/textencoder"] = float(lr_scheduler.get_last_lr()[0])
+        logs["lr/unet"] = float(lr_scheduler.get_last_lr()[-1])  # may be same to textencoder
+
+    if args.optimizer_type.lower() == "DAdaptation".lower():  # tracking d*lr value of unet.
+        logs["lr/d*lr"] = lr_scheduler.optimizers[-1].param_groups[0]["d"] * lr_scheduler.optimizers[-1].param_groups[0]["lr"]
+
+    return logs
+
+
+def train(args):
+    session_id = random.randint(0, 2**32)
+    training_started_at = time.time()
+    train_util.verify_training_args(args)
+    train_util.prepare_dataset_args(args, True)
+
+    cache_latents = args.cache_latents
+    use_dreambooth_method = args.in_json is None
+    use_user_config = args.dataset_config is not None
+
+    if args.seed is not None:
+        set_seed(args.seed)
+
+    tokenizer = train_util.load_tokenizer(args)
+
+    # データセットを準備する
+    blueprint_generator = BlueprintGenerator(ConfigSanitizer(True, True, True))
+    if use_user_config:
+        print(f"Load dataset config from {args.dataset_config}")
+        user_config = config_util.load_user_config(args.dataset_config)
+        ignored = ["train_data_dir", "reg_data_dir", "in_json"]
+        if any(getattr(args, attr) is not None for attr in ignored):
+            print(
+                "ignore following options because config file is found: {0} / 設定ファイルが利用されるため以下のオプションは無視されます: {0}".format(
+                    ", ".join(ignored)
+                )
+            )
+    else:
+        if use_dreambooth_method:
+            print("Use DreamBooth method.")
+            user_config = {
+                "datasets": [
+                    {"subsets": config_util.generate_dreambooth_subsets_config_by_subdirs(args.train_data_dir, args.reg_data_dir)}
+                ]
+            }
+        else:
+            print("Train with captions.")
+            user_config = {
+                "datasets": [
+                    {
+                        "subsets": [
+                            {
+                                "image_dir": args.train_data_dir,
+                                "metadata_file": args.in_json,
+                            }
+                        ]
+                    }
+                ]
+            }
+
+    blueprint = blueprint_generator.generate(user_config, args, tokenizer=tokenizer)
+    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
+    if len(train_dataset_group) == 0:
+        print(
+            "No data found. Please verify arguments (train_data_dir must be the parent of folders with images) / 画像がありません。引数指定を確認してください（train_data_dirには画像があるフォルダではなく、画像があるフォルダの親フォルダを指定する必要があります）"
+        )
+        return
+
+    if cache_latents:
+        assert (
+            train_dataset_group.is_latent_cacheable()
+        ), "when caching latents, either color_aug or random_crop cannot be used / latentをキャッシュするときはcolor_augとrandom_cropは使えません"
+
+    # acceleratorを準備する
+    print("prepare accelerator")
+    accelerator, unwrap_model = train_util.prepare_accelerator(args)
+    is_main_process = accelerator.is_main_process
+
+    # mixed precisionに対応した型を用意しておき適宜castする
+    weight_dtype, save_dtype = train_util.prepare_dtype(args)
+
+    # モデルを読み込む
+    text_encoder, vae, unet, _ = train_util.load_target_model(args, weight_dtype)
+
+    # work on low-ram device
+    if args.lowram:
+        text_encoder.to("cuda")
+        unet.to("cuda")
+
+    # モデルに xformers とか memory efficient attention を組み込む
+    train_util.replace_unet_modules(unet, args.mem_eff_attn, args.xformers)
+
+    # 学習を準備する
+    if cache_latents:
+        vae.to(accelerator.device, dtype=weight_dtype)
+        vae.requires_grad_(False)
+        vae.eval()
+        with torch.no_grad():
+            train_dataset_group.cache_latents(vae, args.vae_batch_size)
+        vae.to("cpu")
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
+        gc.collect()
+
+    # prepare network
+    import sys
+
+    sys.path.append(os.path.dirname(__file__))
+    print("import network module:", args.network_module)
+    network_module = importlib.import_module(args.network_module)
+
+    net_kwargs = {}
+    if args.network_args is not None:
+        for net_arg in args.network_args:
+            key, value = net_arg.split("=")
+            net_kwargs[key] = value
+
+    # if a new network is added in future, add if ~ then blocks for each network (;'∀')
+    network = network_module.create_network(1.0, args.network_dim, args.network_alpha, vae, text_encoder, unet, **net_kwargs)
+    if network is None:
+        return
+
+    if args.network_weights is not None:
+        print("load network weights from:", args.network_weights)
+        network.load_weights(args.network_weights)
+
+    train_unet = not args.network_train_text_encoder_only
+    train_text_encoder = not args.network_train_unet_only
+    network.apply_to(text_encoder, unet, train_text_encoder, train_unet)
+
+    if args.gradient_checkpointing:
+        unet.enable_gradient_checkpointing()
+        text_encoder.gradient_checkpointing_enable()
+        network.enable_gradient_checkpointing()  # may have no effect
+
+    # 学習に必要なクラスを準備する
+    print("prepare optimizer, data loader etc.")
+
+    trainable_params = network.prepare_optimizer_params(args.text_encoder_lr, args.unet_lr)
+    optimizer_name, optimizer_args, optimizer = train_util.get_optimizer(args, trainable_params)
+
+    # dataloaderを準備する
+    # DataLoaderのプロセス数：0はメインプロセスになる
+    n_workers = min(args.max_data_loader_n_workers, os.cpu_count() - 1)  # cpu_count-1 ただし最大で指定された数まで
+    
+    train_dataloader = torch.utils.data.DataLoader(
+        train_dataset_group,
+        batch_size=1,
+        shuffle=True,
+        collate_fn=collater,
+        num_workers=n_workers,
+        persistent_workers=args.persistent_data_loader_workers,
+    )
+
+    # 学習ステップ数を計算する
+    if args.max_train_epochs is not None:
+        args.max_train_steps = args.max_train_epochs * math.ceil(len(train_dataloader) / accelerator.num_processes / args.gradient_accumulation_steps)
+        if is_main_process:
+            print(f"override steps. steps for {args.max_train_epochs} epochs is / 指定エポックまでのステップ数: {args.max_train_steps}")
+
+    # データセット側にも学習ステップを送信
+    train_dataset_group.set_max_train_steps(args.max_train_steps)
+
+    # lr schedulerを用意する
+    lr_scheduler = train_util.get_scheduler_fix(args, optimizer, accelerator.num_processes)
+
+    # 実験的機能：勾配も含めたfp16学習を行う　モデル全体をfp16にする
+    if args.full_fp16:
+        assert (
+            args.mixed_precision == "fp16"
+        ), "full_fp16 requires mixed precision='fp16' / full_fp16を使う場合はmixed_precision='fp16'を指定してください。"
+        print("enable full fp16 training.")
+        network.to(weight_dtype)
+
+    # acceleratorがなんかよろしくやってくれるらしい
+    if train_unet and train_text_encoder:
+        unet, text_encoder, network, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
+            unet, text_encoder, network, optimizer, train_dataloader, lr_scheduler
+        )
+    elif train_unet:
+        unet, network, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
+            unet, network, optimizer, train_dataloader, lr_scheduler
+        )
+    elif train_text_encoder:
+        text_encoder, network, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
+            text_encoder, network, optimizer, train_dataloader, lr_scheduler
+        )
+    else:
+        network, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(network, optimizer, train_dataloader, lr_scheduler)
+
+    unet.requires_grad_(False)
+    unet.to(accelerator.device, dtype=weight_dtype)
+    text_encoder.requires_grad_(False)
+    text_encoder.to(accelerator.device)
+    if args.gradient_checkpointing:  # according to TI example in Diffusers, train is required
+        unet.train()
+        text_encoder.train()
+
+        # set top parameter requires_grad = True for gradient checkpointing works
+        if type(text_encoder) == DDP:
+            text_encoder.module.text_model.embeddings.requires_grad_(True)
+        else:
+            text_encoder.text_model.embeddings.requires_grad_(True)
+    else:
+        unet.eval()
+        text_encoder.eval()
+
+    # support DistributedDataParallel
+    if type(text_encoder) == DDP:
+        text_encoder = text_encoder.module
+        unet = unet.module
+        network = network.module
+
+    network.prepare_grad_etc(text_encoder, unet)
+
+    if not cache_latents:
+        vae.requires_grad_(False)
+        vae.eval()
+        vae.to(accelerator.device, dtype=weight_dtype)
+
+    # 実験的機能：勾配も含めたfp16学習を行う　PyTorchにパッチを当ててfp16でのgrad scaleを有効にする
+    if args.full_fp16:
+        train_util.patch_accelerator_for_fp16_training(accelerator)
+
+    # resumeする
+    if args.resume is not None:
+        print(f"resume training from state: {args.resume}")
+        accelerator.load_state(args.resume)
+
+    # epoch数を計算する
+    num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
+    num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
+    if (args.save_n_epoch_ratio is not None) and (args.save_n_epoch_ratio > 0):
+        args.save_every_n_epochs = math.floor(num_train_epochs / args.save_n_epoch_ratio) or 1
+
+    # 学習する
+    # TODO: find a way to handle total batch size when there are multiple datasets
+    total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
+
+    if is_main_process:
+        print("running training / 学習開始")
+        print(f"  num train images * repeats / 学習画像の数×繰り返し回数: {train_dataset_group.num_train_images}")
+        print(f"  num reg images / 正則化画像の数: {train_dataset_group.num_reg_images}")
+        print(f"  num batches per epoch / 1epochのバッチ数: {len(train_dataloader)}")
+        print(f"  num epochs / epoch数: {num_train_epochs}")
+        print(f"  batch size per device / バッチサイズ: {', '.join([str(d.batch_size) for d in train_dataset_group.datasets])}")
+        # print(f"  total train batch size (with parallel & distributed & accumulation) / 総バッチサイズ（並列学習、勾配合計含む）: {total_batch_size}")
+        print(f"  gradient accumulation steps / 勾配を合計するステップ数 = {args.gradient_accumulation_steps}")
+        print(f"  total optimization steps / 学習ステップ数: {args.max_train_steps}")
+
+    # TODO refactor metadata creation and move to util
+    metadata = {
+        "ss_session_id": session_id,  # random integer indicating which group of epochs the model came from
+        "ss_training_started_at": training_started_at,  # unix timestamp
+        "ss_output_name": args.output_name,
+        "ss_learning_rate": args.learning_rate,
+        "ss_text_encoder_lr": args.text_encoder_lr,
+        "ss_unet_lr": args.unet_lr,
+        "ss_num_train_images": train_dataset_group.num_train_images,
+        "ss_num_reg_images": train_dataset_group.num_reg_images,
+        "ss_num_batches_per_epoch": len(train_dataloader),
+        "ss_num_epochs": num_train_epochs,
+        "ss_gradient_checkpointing": args.gradient_checkpointing,
+        "ss_gradient_accumulation_steps": args.gradient_accumulation_steps,
+        "ss_max_train_steps": args.max_train_steps,
+        "ss_lr_warmup_steps": args.lr_warmup_steps,
+        "ss_lr_scheduler": args.lr_scheduler,
+        "ss_network_module": args.network_module,
+        "ss_network_dim": args.network_dim,  # None means default because another network than LoRA may have another default dim
+        "ss_network_alpha": args.network_alpha,  # some networks may not use this value
+        "ss_mixed_precision": args.mixed_precision,
+        "ss_full_fp16": bool(args.full_fp16),
+        "ss_v2": bool(args.v2),
+        "ss_clip_skip": args.clip_skip,
+        "ss_max_token_length": args.max_token_length,
+        "ss_cache_latents": bool(args.cache_latents),
+        "ss_seed": args.seed,
+        "ss_lowram": args.lowram,
+        "ss_noise_offset": args.noise_offset,
+        "ss_training_comment": args.training_comment,  # will not be updated after training
+        "ss_sd_scripts_commit_hash": train_util.get_git_revision_hash(),
+        "ss_optimizer": optimizer_name + (f"({optimizer_args})" if len(optimizer_args) > 0 else ""),
+        "ss_max_grad_norm": args.max_grad_norm,
+        "ss_caption_dropout_rate": args.caption_dropout_rate,
+        "ss_caption_dropout_every_n_epochs": args.caption_dropout_every_n_epochs,
+        "ss_caption_tag_dropout_rate": args.caption_tag_dropout_rate,
+        "ss_face_crop_aug_range": args.face_crop_aug_range,
+        "ss_prior_loss_weight": args.prior_loss_weight,
+    }
+
+    if use_user_config:
+        # save metadata of multiple datasets
+        # NOTE: pack "ss_datasets" value as json one time
+        #   or should also pack nested collections as json?
+        datasets_metadata = []
+        tag_frequency = {}  # merge tag frequency for metadata editor
+        dataset_dirs_info = {}  # merge subset dirs for metadata editor
+
+        for dataset in train_dataset_group.datasets:
+            is_dreambooth_dataset = isinstance(dataset, DreamBoothDataset)
+            dataset_metadata = {
+                "is_dreambooth": is_dreambooth_dataset,
+                "batch_size_per_device": dataset.batch_size,
+                "num_train_images": dataset.num_train_images,  # includes repeating
+                "num_reg_images": dataset.num_reg_images,
+                "resolution": (dataset.width, dataset.height),
+                "enable_bucket": bool(dataset.enable_bucket),
+                "min_bucket_reso": dataset.min_bucket_reso,
+                "max_bucket_reso": dataset.max_bucket_reso,
+                "tag_frequency": dataset.tag_frequency,
+                "bucket_info": dataset.bucket_info,
+            }
+
+            subsets_metadata = []
+            for subset in dataset.subsets:
+                subset_metadata = {
+                    "img_count": subset.img_count,
+                    "num_repeats": subset.num_repeats,
+                    "color_aug": bool(subset.color_aug),
+                    "flip_aug": bool(subset.flip_aug),
+                    "random_crop": bool(subset.random_crop),
+                    "shuffle_caption": bool(subset.shuffle_caption),
+                    "keep_tokens": subset.keep_tokens,
+                }
+
+                image_dir_or_metadata_file = None
+                if subset.image_dir:
+                    image_dir = os.path.basename(subset.image_dir)
+                    subset_metadata["image_dir"] = image_dir
+                    image_dir_or_metadata_file = image_dir
+
+                if is_dreambooth_dataset:
+                    subset_metadata["class_tokens"] = subset.class_tokens
+                    subset_metadata["is_reg"] = subset.is_reg
+                    if subset.is_reg:
+                        image_dir_or_metadata_file = None  # not merging reg dataset
+                else:
+                    metadata_file = os.path.basename(subset.metadata_file)
+                    subset_metadata["metadata_file"] = metadata_file
+                    image_dir_or_metadata_file = metadata_file  # may overwrite
+
+                subsets_metadata.append(subset_metadata)
+
+                # merge dataset dir: not reg subset only
+                # TODO update additional-network extension to show detailed dataset config from metadata
+                if image_dir_or_metadata_file is not None:
+                    # datasets may have a certain dir multiple times
+                    v = image_dir_or_metadata_file
+                    i = 2
+                    while v in dataset_dirs_info:
+                        v = image_dir_or_metadata_file + f" ({i})"
+                        i += 1
+                    image_dir_or_metadata_file = v
+
+                    dataset_dirs_info[image_dir_or_metadata_file] = {"n_repeats": subset.num_repeats, "img_count": subset.img_count}
+
+            dataset_metadata["subsets"] = subsets_metadata
+            datasets_metadata.append(dataset_metadata)
+
+            # merge tag frequency:
+            for ds_dir_name, ds_freq_for_dir in dataset.tag_frequency.items():
+                # あるディレクトリが複数のdatasetで使用されている場合、一度だけ数える
+                # もともと繰り返し回数を指定しているので、キャプション内でのタグの出現回数と、それが学習で何度使われるかは一致しない
+                # なので、ここで複数datasetの回数を合算してもあまり意味はない
+                if ds_dir_name in tag_frequency:
+                    continue
+                tag_frequency[ds_dir_name] = ds_freq_for_dir
+
+        metadata["ss_datasets"] = json.dumps(datasets_metadata)
+        metadata["ss_tag_frequency"] = json.dumps(tag_frequency)
+        metadata["ss_dataset_dirs"] = json.dumps(dataset_dirs_info)
+    else:
+        # conserving backward compatibility when using train_dataset_dir and reg_dataset_dir
+        assert (
+            len(train_dataset_group.datasets) == 1
+        ), f"There should be a single dataset but {len(train_dataset_group.datasets)} found. This seems to be a bug. / データセットは1個だけ存在するはずですが、実際には{len(train_dataset_group.datasets)}個でした。プログラムのバグかもしれません。"
+
+        dataset = train_dataset_group.datasets[0]
+
+        dataset_dirs_info = {}
+        reg_dataset_dirs_info = {}
+        if use_dreambooth_method:
+            for subset in dataset.subsets:
+                info = reg_dataset_dirs_info if subset.is_reg else dataset_dirs_info
+                info[os.path.basename(subset.image_dir)] = {"n_repeats": subset.num_repeats, "img_count": subset.img_count}
+        else:
+            for subset in dataset.subsets:
+                dataset_dirs_info[os.path.basename(subset.metadata_file)] = {
+                    "n_repeats": subset.num_repeats,
+                    "img_count": subset.img_count,
+                }
+
+        metadata.update(
+            {
+                "ss_batch_size_per_device": args.train_batch_size,
+                "ss_total_batch_size": total_batch_size,
+                "ss_resolution": args.resolution,
+                "ss_color_aug": bool(args.color_aug),
+                "ss_flip_aug": bool(args.flip_aug),
+                "ss_random_crop": bool(args.random_crop),
+                "ss_shuffle_caption": bool(args.shuffle_caption),
+                "ss_enable_bucket": bool(dataset.enable_bucket),
+                "ss_bucket_no_upscale": bool(dataset.bucket_no_upscale),
+                "ss_min_bucket_reso": dataset.min_bucket_reso,
+                "ss_max_bucket_reso": dataset.max_bucket_reso,
+                "ss_keep_tokens": args.keep_tokens,
+                "ss_dataset_dirs": json.dumps(dataset_dirs_info),
+                "ss_reg_dataset_dirs": json.dumps(reg_dataset_dirs_info),
+                "ss_tag_frequency": json.dumps(dataset.tag_frequency),
+                "ss_bucket_info": json.dumps(dataset.bucket_info),
+            }
+        )
+
+    # add extra args
+    if args.network_args:
+        metadata["ss_network_args"] = json.dumps(net_kwargs)
+        # for key, value in net_kwargs.items():
+        #   metadata["ss_arg_" + key] = value
+
+    # model name and hash
+    if args.pretrained_model_name_or_path is not None:
+        sd_model_name = args.pretrained_model_name_or_path
+        if os.path.exists(sd_model_name):
+            metadata["ss_sd_model_hash"] = train_util.model_hash(sd_model_name)
+            metadata["ss_new_sd_model_hash"] = train_util.calculate_sha256(sd_model_name)
+            sd_model_name = os.path.basename(sd_model_name)
+        metadata["ss_sd_model_name"] = sd_model_name
+
+    if args.vae is not None:
+        vae_name = args.vae
+        if os.path.exists(vae_name):
+            metadata["ss_vae_hash"] = train_util.model_hash(vae_name)
+            metadata["ss_new_vae_hash"] = train_util.calculate_sha256(vae_name)
+            vae_name = os.path.basename(vae_name)
+        metadata["ss_vae_name"] = vae_name
+
+    metadata = {k: str(v) for k, v in metadata.items()}
+
+    # make minimum metadata for filtering
+    minimum_keys = ["ss_network_module", "ss_network_dim", "ss_network_alpha", "ss_network_args"]
+    minimum_metadata = {}
+    for key in minimum_keys:
+        if key in metadata:
+            minimum_metadata[key] = metadata[key]
+
+    progress_bar = tqdm(range(args.max_train_steps), smoothing=0, disable=not accelerator.is_local_main_process, desc="steps")
+    global_step = 0
+
+    noise_scheduler = DDPMScheduler(
+        beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", num_train_timesteps=1000, clip_sample=False
+    )
+    if accelerator.is_main_process:
+        accelerator.init_trackers("network_train")
+
+    loss_list = []
+    loss_total = 0.0
+    del train_dataset_group
+    for epoch in range(num_train_epochs):
+        if is_main_process:
+            print(f"epoch {epoch+1}/{num_train_epochs}")
+        current_epoch.value = epoch+1
+
+        metadata["ss_epoch"] = str(epoch + 1)
+
+        network.on_epoch_start(text_encoder, unet)
+
+        for step, batch in enumerate(train_dataloader):
+            current_step.value = global_step
+            with accelerator.accumulate(network):
+                with torch.no_grad():
+                    if "latents" in batch and batch["latents"] is not None:
+                        latents = batch["latents"].to(accelerator.device)
+                    else:
+                        # latentに変換
+                        latents = vae.encode(batch["images"].to(dtype=weight_dtype)).latent_dist.sample()
+                    latents = latents * 0.18215
+                b_size = latents.shape[0]
+
+                with torch.set_grad_enabled(train_text_encoder):
+                    # Get the text embedding for conditioning
+                    input_ids = batch["input_ids"].to(accelerator.device)
+                    encoder_hidden_states = train_util.get_hidden_states(args, input_ids, tokenizer, text_encoder, weight_dtype)
+
+                # Sample noise that we'll add to the latents
+                noise = torch.randn_like(latents, device=latents.device)
+                if args.noise_offset:
+                    # https://www.crosslabs.org//blog/diffusion-with-offset-noise
+                    noise += args.noise_offset * torch.randn((latents.shape[0], latents.shape[1], 1, 1), device=latents.device)
+
+                # Sample a random timestep for each image
+                timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (b_size,), device=latents.device)
+                timesteps = timesteps.long()
+                # Add noise to the latents according to the noise magnitude at each timestep
+                # (this is the forward diffusion process)
+                noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
+
+                # Predict the noise residual
+                with accelerator.autocast():
+                    noise_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample
+
+                if args.v_parameterization:
+                    # v-parameterization training
+                    target = noise_scheduler.get_velocity(latents, noise, timesteps)
+                else:
+                    target = noise
+
+                loss = torch.nn.functional.mse_loss(noise_pred.float(), target.float(), reduction="none")
+                loss = loss.mean([1, 2, 3])
+
+                loss_weights = batch["loss_weights"]  # 各sampleごとのweight
+                loss = loss * loss_weights
+                 
+                if args.min_snr_gamma:
+                  loss = apply_snr_weight(loss, timesteps, noise_scheduler, args.min_snr_gamma)
+
+                loss = loss.mean()  # 平均なのでbatch_sizeで割る必要なし
+
+                accelerator.backward(loss)
+                if accelerator.sync_gradients and args.max_grad_norm != 0.0:
+                    params_to_clip = network.get_trainable_params()
+                    accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)
+
+                optimizer.step()
+                lr_scheduler.step()
+                optimizer.zero_grad(set_to_none=True)
+
+            # Checks if the accelerator has performed an optimization step behind the scenes
+            if accelerator.sync_gradients:
+                progress_bar.update(1)
+                global_step += 1
+
+                train_util.sample_images(
+                    accelerator, args, None, global_step, accelerator.device, vae, tokenizer, text_encoder, unet
+                )
+
+            current_loss = loss.detach().item()
+            if epoch == 0:
+                loss_list.append(current_loss)
+            else:
+                loss_total -= loss_list[step]
+                loss_list[step] = current_loss
+            loss_total += current_loss
+            avr_loss = loss_total / len(loss_list)
+            logs = {"loss": avr_loss}  # , "lr": lr_scheduler.get_last_lr()[0]}
+            progress_bar.set_postfix(**logs)
+
+            if args.logging_dir is not None:
+                logs = generate_step_logs(args, current_loss, avr_loss, lr_scheduler)
+                accelerator.log(logs, step=global_step)
+
+            if global_step >= args.max_train_steps:
+                break
+
+        if args.logging_dir is not None:
+            logs = {"loss/epoch": loss_total / len(loss_list)}
+            accelerator.log(logs, step=epoch + 1)
+
+        accelerator.wait_for_everyone()
+
+        if args.save_every_n_epochs is not None:
+            model_name = train_util.DEFAULT_EPOCH_NAME if args.output_name is None else args.output_name
+
+            def save_func():
+                ckpt_name = train_util.EPOCH_FILE_NAME.format(model_name, epoch + 1) + "." + args.save_model_as
+                ckpt_file = os.path.join(args.output_dir, ckpt_name)
+                metadata["ss_training_finished_at"] = str(time.time())
+                print(f"saving checkpoint: {ckpt_file}")
+                unwrap_model(network).save_weights(ckpt_file, save_dtype, minimum_metadata if args.no_metadata else metadata)
+
+            def remove_old_func(old_epoch_no):
+                old_ckpt_name = train_util.EPOCH_FILE_NAME.format(model_name, old_epoch_no) + "." + args.save_model_as
+                old_ckpt_file = os.path.join(args.output_dir, old_ckpt_name)
+                if os.path.exists(old_ckpt_file):
+                    print(f"removing old checkpoint: {old_ckpt_file}")
+                    os.remove(old_ckpt_file)
+
+            if is_main_process:
+                saving = train_util.save_on_epoch_end(args, save_func, remove_old_func, epoch + 1, num_train_epochs)
+                if saving and args.save_state:
+                    train_util.save_state_on_epoch_end(args, accelerator, model_name, epoch + 1)
+
+        train_util.sample_images(accelerator, args, epoch + 1, global_step, accelerator.device, vae, tokenizer, text_encoder, unet)
+
+        # end of epoch
+
+    metadata["ss_epoch"] = str(num_train_epochs)
+    metadata["ss_training_finished_at"] = str(time.time())
+
+    if is_main_process:
+        network = unwrap_model(network)
+
+    accelerator.end_training()
+
+    if args.save_state:
+        train_util.save_state_on_train_end(args, accelerator)
+
+    del accelerator  # この後メモリを使うのでこれは消す
+
+    if is_main_process:
+        os.makedirs(args.output_dir, exist_ok=True)
+
+        model_name = train_util.DEFAULT_LAST_OUTPUT_NAME if args.output_name is None else args.output_name
+        ckpt_name = model_name + "." + args.save_model_as
+        ckpt_file = os.path.join(args.output_dir, ckpt_name)
+
+        print(f"save trained model to {ckpt_file}")
+        network.save_weights(ckpt_file, save_dtype, minimum_metadata if args.no_metadata else metadata)
+        print("model saved.")
+
+
+def setup_parser() -> argparse.ArgumentParser:
+    parser = argparse.ArgumentParser()
+
+    train_util.add_sd_models_arguments(parser)
+    train_util.add_dataset_arguments(parser, True, True, True)
+    train_util.add_training_arguments(parser, True)
+    train_util.add_optimizer_arguments(parser)
+    config_util.add_config_arguments(parser)
+    custom_train_functions.add_custom_train_arguments(parser)
+
+    parser.add_argument("--no_metadata", action="store_true", help="do not save metadata in output model / メタデータを出力先モデルに保存しない")
+    parser.add_argument(
+        "--save_model_as",
+        type=str,
+        default="safetensors",
+        choices=[None, "ckpt", "pt", "safetensors"],
+        help="format to save the model (default is .safetensors) / モデル保存時の形式（デフォルトはsafetensors）",
+    )
+
+    parser.add_argument("--unet_lr", type=float, default=None, help="learning rate for U-Net / U-Netの学習率")
+    parser.add_argument("--text_encoder_lr", type=float, default=None, help="learning rate for Text Encoder / Text Encoderの学習率")
+
+    parser.add_argument("--network_weights", type=str, default=None, help="pretrained weights for network / 学習するネットワークの初期重み")
+    parser.add_argument("--network_module", type=str, default=None, help="network module to train / 学習対象のネットワークのモジュール")
+    parser.add_argument(
+        "--network_dim", type=int, default=None, help="network dimensions (depends on each network) / モジュールの次元数（ネットワークにより定義は異なります）"
+    )
+    parser.add_argument(
+        "--network_alpha",
+        type=float,
+        default=1,
+        help="alpha for LoRA weight scaling, default 1 (same as network_dim for same behavior as old version) / LoRaの重み調整のalpha値、デフォルト1（旧バージョンと同じ動作をするにはnetwork_dimと同じ値を指定）",
+    )
+    parser.add_argument(
+        "--network_args", type=str, default=None, nargs="*", help="additional argmuments for network (key=value) / ネットワークへの追加の引数"
+    )
+    parser.add_argument("--network_train_unet_only", action="store_true", help="only training U-Net part / U-Net関連部分のみ学習する")
+    parser.add_argument(
+        "--network_train_text_encoder_only", action="store_true", help="only training Text Encoder part / Text Encoder関連部分のみ学習する"
+    )
+    parser.add_argument(
+        "--training_comment", type=str, default=None, help="arbitrary comment string stored in metadata / メタデータに記録する任意のコメント文字列"
+    )
+
+    return parser
+
+
+if __name__ == "__main__":
+    parser = setup_parser()
+
+    args = parser.parse_args()
+    args = train_util.read_config_from_file(args, parser)
+
+    train(args)

train_network.py

@@ -8,6 +8,7 @@ import random
 import time
 import json
 import toml
+from multiprocessing import Value
 
 from tqdm import tqdm
 import torch
@@ -15,7 +16,6 @@ from accelerate.utils import set_seed
 from diffusers import DDPMScheduler
 
 import library.train_util as train_util
-from library.custom_train_functions import apply_snr_weight
 from library.train_util import (
     DreamBoothDataset,
 )
@@ -24,10 +24,8 @@ from library.config_util import (
     ConfigSanitizer,
     BlueprintGenerator,
 )
-
-
-def collate_fn(examples):
-    return examples[0]
+import library.custom_train_functions as custom_train_functions
+from library.custom_train_functions import apply_snr_weight 
 
 
 # TODO 他のスクリプトと共通化する
@@ -101,6 +99,11 @@ def train(args):
     blueprint = blueprint_generator.generate(user_config, args, tokenizer=tokenizer)
     train_dataset_group = config_util.generate_dataset_group_by_blueprint(blueprint.dataset_group)
 
+    current_epoch = Value('i',0)
+    current_step = Value('i',0)
+    ds_for_collater = train_dataset_group if args.max_data_loader_n_workers == 0 else None
+    collater = train_util.collater_class(current_epoch,current_step, ds_for_collater)
+
     if args.debug_dataset:
         train_util.debug_dataset(train_dataset_group)
         return
@@ -186,11 +189,12 @@ def train(args):
     # dataloaderを準備する
     # DataLoaderのプロセス数：0はメインプロセスになる
     n_workers = min(args.max_data_loader_n_workers, os.cpu_count() - 1)  # cpu_count-1 ただし最大で指定された数まで
+    
     train_dataloader = torch.utils.data.DataLoader(
         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,
     )
@@ -201,6 +205,9 @@ def train(args):
         if is_main_process:
             print(f"override steps. steps for {args.max_train_epochs} epochs is / 指定エポックまでのステップ数: {args.max_train_steps}")
 
+    # データセット側にも学習ステップを送信
+    train_dataset_group.set_max_train_steps(args.max_train_steps)
+
     # lr schedulerを用意する
     lr_scheduler = train_util.get_scheduler_fix(args, optimizer, accelerator.num_processes)
 
@@ -489,22 +496,23 @@ def train(args):
     noise_scheduler = DDPMScheduler(
         beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", num_train_timesteps=1000, clip_sample=False
     )
-
     if accelerator.is_main_process:
         accelerator.init_trackers("network_train")
 
     loss_list = []
     loss_total = 0.0
+    del train_dataset_group
     for epoch in range(num_train_epochs):
         if is_main_process:
             print(f"epoch {epoch+1}/{num_train_epochs}")
-        train_dataset_group.set_current_epoch(epoch + 1)
+        current_epoch.value = epoch+1
 
         metadata["ss_epoch"] = str(epoch + 1)
 
         network.on_epoch_start(text_encoder, unet)
 
         for step, batch in enumerate(train_dataloader):
+            current_step.value = global_step
             with accelerator.accumulate(network):
                 with torch.no_grad():
                     if "latents" in batch and batch["latents"] is not None:
@@ -529,7 +537,6 @@ def train(args):
                 # Sample a random timestep for each image
                 timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (b_size,), device=latents.device)
                 timesteps = timesteps.long()
-
                 # Add noise to the latents according to the noise magnitude at each timestep
                 # (this is the forward diffusion process)
                 noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
@@ -549,8 +556,9 @@ def train(args):
 
                 loss_weights = batch["loss_weights"]  # 各sampleごとのweight
                 loss = loss * loss_weights
-                
-                loss = apply_snr_weight(loss, noisy_latents, latents, args.min_snr_gamma)
+                 
+                if args.min_snr_gamma:
+                  loss = apply_snr_weight(loss, timesteps, noise_scheduler, args.min_snr_gamma)
 
                 loss = loss.mean()  # 平均なのでbatch_sizeで割る必要なし
 
@@ -655,6 +663,7 @@ def setup_parser() -> argparse.ArgumentParser:
     train_util.add_training_arguments(parser, True)
     train_util.add_optimizer_arguments(parser)
     config_util.add_config_arguments(parser)
+    custom_train_functions.add_custom_train_arguments(parser)
 
     parser.add_argument("--no_metadata", action="store_true", help="do not save metadata in output model / メタデータを出力先モデルに保存しない")
     parser.add_argument(

train_textual_inversion - Copy.py

@@ -0,0 +1,589 @@
+import importlib
+import argparse
+import gc
+import math
+import os
+import toml
+from multiprocessing import Value
+
+from tqdm import tqdm
+import torch
+from accelerate.utils import set_seed
+import diffusers
+from diffusers import DDPMScheduler
+
+import library.train_util as train_util
+import library.config_util as config_util
+from library.config_util import (
+    ConfigSanitizer,
+    BlueprintGenerator,
+)
+import library.custom_train_functions as custom_train_functions
+from library.custom_train_functions import apply_snr_weight
+
+imagenet_templates_small = [
+    "a photo of a {}",
+    "a rendering of a {}",
+    "a cropped photo of the {}",
+    "the photo of a {}",
+    "a photo of a clean {}",
+    "a photo of a dirty {}",
+    "a dark photo of the {}",
+    "a photo of my {}",
+    "a photo of the cool {}",
+    "a close-up photo of a {}",
+    "a bright photo of the {}",
+    "a cropped photo of a {}",
+    "a photo of the {}",
+    "a good photo of the {}",
+    "a photo of one {}",
+    "a close-up photo of the {}",
+    "a rendition of the {}",
+    "a photo of the clean {}",
+    "a rendition of a {}",
+    "a photo of a nice {}",
+    "a good photo of a {}",
+    "a photo of the nice {}",
+    "a photo of the small {}",
+    "a photo of the weird {}",
+    "a photo of the large {}",
+    "a photo of a cool {}",
+    "a photo of a small {}",
+]
+
+imagenet_style_templates_small = [
+    "a painting in the style of {}",
+    "a rendering in the style of {}",
+    "a cropped painting in the style of {}",
+    "the painting in the style of {}",
+    "a clean painting in the style of {}",
+    "a dirty painting in the style of {}",
+    "a dark painting in the style of {}",
+    "a picture in the style of {}",
+    "a cool painting in the style of {}",
+    "a close-up painting in the style of {}",
+    "a bright painting in the style of {}",
+    "a cropped painting in the style of {}",
+    "a good painting in the style of {}",
+    "a close-up painting in the style of {}",
+    "a rendition in the style of {}",
+    "a nice painting in the style of {}",
+    "a small painting in the style of {}",
+    "a weird painting in the style of {}",
+    "a large painting in the style of {}",
+]
+
+
+def train(args):
+    if args.output_name is None:
+        args.output_name = args.token_string
+    use_template = args.use_object_template or args.use_style_template
+
+    train_util.verify_training_args(args)
+    train_util.prepare_dataset_args(args, True)
+
+    cache_latents = args.cache_latents
+
+    if args.seed is not None:
+        set_seed(args.seed)
+
+    tokenizer = train_util.load_tokenizer(args)
+
+    # acceleratorを準備する
+    print("prepare accelerator")
+    accelerator, unwrap_model = train_util.prepare_accelerator(args)
+
+    # mixed precisionに対応した型を用意しておき適宜castする
+    weight_dtype, save_dtype = train_util.prepare_dtype(args)
+
+    # モデルを読み込む
+    text_encoder, vae, unet, _ = train_util.load_target_model(args, weight_dtype)
+
+    # Convert the init_word to token_id
+    if args.init_word is not None:
+        init_token_ids = tokenizer.encode(args.init_word, add_special_tokens=False)
+        if len(init_token_ids) > 1 and len(init_token_ids) != args.num_vectors_per_token:
+            print(
+                f"token length for init words is not same to num_vectors_per_token, init words is repeated or truncated / 初期化単語のトークン長がnum_vectors_per_tokenと合わないため、繰り返しまたは切り捨てが発生します: length {len(init_token_ids)}"
+            )
+    else:
+        init_token_ids = None
+
+    # add new word to tokenizer, count is num_vectors_per_token
+    token_strings = [args.token_string] + [f"{args.token_string}{i+1}" for i in range(args.num_vectors_per_token - 1)]
+    num_added_tokens = tokenizer.add_tokens(token_strings)
+    assert (
+        num_added_tokens == args.num_vectors_per_token
+    ), f"tokenizer has same word to token string. please use another one / 指定したargs.token_stringは既に存在します。別の単語を使ってください: {args.token_string}"
+
+    token_ids = tokenizer.convert_tokens_to_ids(token_strings)
+    print(f"tokens are added: {token_ids}")
+    assert min(token_ids) == token_ids[0] and token_ids[-1] == token_ids[0] + len(token_ids) - 1, f"token ids is not ordered"
+    assert len(tokenizer) - 1 == token_ids[-1], f"token ids is not end of tokenize: {len(tokenizer)}"
+
+    # Resize the token embeddings as we are adding new special tokens to the tokenizer
+    text_encoder.resize_token_embeddings(len(tokenizer))
+
+    # Initialise the newly added placeholder token with the embeddings of the initializer token
+    token_embeds = text_encoder.get_input_embeddings().weight.data
+    if init_token_ids is not None:
+        for i, token_id in enumerate(token_ids):
+            token_embeds[token_id] = token_embeds[init_token_ids[i % len(init_token_ids)]]
+            # print(token_id, token_embeds[token_id].mean(), token_embeds[token_id].min())
+
+    # load weights
+    if args.weights is not None:
+        embeddings = load_weights(args.weights)
+        assert len(token_ids) == len(
+            embeddings
+        ), f"num_vectors_per_token is mismatch for weights / 指定した重みとnum_vectors_per_tokenの値が異なります: {len(embeddings)}"
+        # print(token_ids, embeddings.size())
+        for token_id, embedding in zip(token_ids, embeddings):
+            token_embeds[token_id] = embedding
+            # print(token_id, token_embeds[token_id].mean(), token_embeds[token_id].min())
+        print(f"weighs loaded")
+
+    print(f"create embeddings for {args.num_vectors_per_token} tokens, for {args.token_string}")
+
+    # データセットを準備する
+    blueprint_generator = BlueprintGenerator(ConfigSanitizer(True, True, False))
+    if args.dataset_config is not None:
+        print(f"Load dataset config from {args.dataset_config}")
+        user_config = config_util.load_user_config(args.dataset_config)
+        ignored = ["train_data_dir", "reg_data_dir", "in_json"]
+        if any(getattr(args, attr) is not None for attr in ignored):
+            print(
+                "ignore following options because config file is found: {0} / 設定ファイルが利用されるため以下のオプションは無視されます: {0}".format(
+                    ", ".join(ignored)
+                )
+            )
+    else:
+        use_dreambooth_method = args.in_json is None
+        if use_dreambooth_method:
+            print("Use DreamBooth method.")
+            user_config = {
+                "datasets": [
+                    {"subsets": config_util.generate_dreambooth_subsets_config_by_subdirs(args.train_data_dir, args.reg_data_dir)}
+                ]
+            }
+        else:
+            print("Train with captions.")
+            user_config = {
+                "datasets": [
+                    {
+                        "subsets": [
+                            {
+                                "image_dir": args.train_data_dir,
+                                "metadata_file": args.in_json,
+                            }
+                        ]
+                    }
+                ]
+            }
+
+    blueprint = blueprint_generator.generate(user_config, args, tokenizer=tokenizer)
+    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}")
+        templates = imagenet_templates_small if args.use_object_template else imagenet_style_templates_small
+        replace_to = " ".join(token_strings)
+        captions = []
+        for tmpl in templates:
+            captions.append(tmpl.format(replace_to))
+        train_dataset_group.add_replacement("", captions)
+
+        if args.num_vectors_per_token > 1:
+            prompt_replacement = (args.token_string, replace_to)
+        else:
+            prompt_replacement = None
+    else:
+        if args.num_vectors_per_token > 1:
+            replace_to = " ".join(token_strings)
+            train_dataset_group.add_replacement(args.token_string, replace_to)
+            prompt_replacement = (args.token_string, replace_to)
+        else:
+            prompt_replacement = None
+
+    if args.debug_dataset:
+        train_util.debug_dataset(train_dataset_group, show_input_ids=True)
+        return
+    if len(train_dataset_group) == 0:
+        print("No data found. Please verify arguments / 画像がありません。引数指定を確認してください")
+        return
+
+    if cache_latents:
+        assert (
+            train_dataset_group.is_latent_cacheable()
+        ), "when caching latents, either color_aug or random_crop cannot be used / latentをキャッシュするときはcolor_augとrandom_cropは使えません"
+
+    # モデルに xformers とか memory efficient attention を組み込む
+    train_util.replace_unet_modules(unet, args.mem_eff_attn, args.xformers)
+
+    # 学習を準備する
+    if cache_latents:
+        vae.to(accelerator.device, dtype=weight_dtype)
+        vae.requires_grad_(False)
+        vae.eval()
+        with torch.no_grad():
+            train_dataset_group.cache_latents(vae, args.vae_batch_size)
+        vae.to("cpu")
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
+        gc.collect()
+
+    if args.gradient_checkpointing:
+        unet.enable_gradient_checkpointing()
+        text_encoder.gradient_checkpointing_enable()
+
+    # 学習に必要なクラスを準備する
+    print("prepare optimizer, data loader etc.")
+    trainable_params = text_encoder.get_input_embeddings().parameters()
+    _, _, optimizer = train_util.get_optimizer(args, trainable_params)
+
+    # dataloaderを準備する
+    # DataLoaderのプロセス数：0はメインプロセスになる
+    n_workers = min(args.max_data_loader_n_workers, os.cpu_count() - 1)  # cpu_count-1 ただし最大で指定された数まで
+    train_dataloader = torch.utils.data.DataLoader(
+        train_dataset_group,
+        batch_size=1,
+        shuffle=True,
+        collate_fn=collater,
+        num_workers=n_workers,
+        persistent_workers=args.persistent_data_loader_workers,
+    )
+
+    # 学習ステップ数を計算する
+    if args.max_train_epochs is not None:
+        args.max_train_steps = args.max_train_epochs * math.ceil(len(train_dataloader) / accelerator.num_processes / args.gradient_accumulation_steps)
+        print(f"override steps. steps for {args.max_train_epochs} epochs is / 指定エポックまでのステップ数: {args.max_train_steps}")
+
+    # データセット側にも学習ステップを送信
+    train_dataset_group.set_max_train_steps(args.max_train_steps)
+
+    # lr schedulerを用意する
+    lr_scheduler = train_util.get_scheduler_fix(args, optimizer, accelerator.num_processes)
+
+    # acceleratorがなんかよろしくやってくれるらしい
+    text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
+        text_encoder, optimizer, train_dataloader, lr_scheduler
+    )
+
+    index_no_updates = torch.arange(len(tokenizer)) < token_ids[0]
+    # print(len(index_no_updates), torch.sum(index_no_updates))
+    orig_embeds_params = unwrap_model(text_encoder).get_input_embeddings().weight.data.detach().clone()
+
+    # Freeze all parameters except for the token embeddings in text encoder
+    text_encoder.requires_grad_(True)
+    text_encoder.text_model.encoder.requires_grad_(False)
+    text_encoder.text_model.final_layer_norm.requires_grad_(False)
+    text_encoder.text_model.embeddings.position_embedding.requires_grad_(False)
+    # text_encoder.text_model.embeddings.token_embedding.requires_grad_(True)
+
+    unet.requires_grad_(False)
+    unet.to(accelerator.device, dtype=weight_dtype)
+    if args.gradient_checkpointing:  # according to TI example in Diffusers, train is required
+        unet.train()
+    else:
+        unet.eval()
+
+    if not cache_latents:
+        vae.requires_grad_(False)
+        vae.eval()
+        vae.to(accelerator.device, dtype=weight_dtype)
+
+    # 実験的機能：勾配も含めたfp16学習を行う　PyTorchにパッチを当ててfp16でのgrad scaleを有効にする
+    if args.full_fp16:
+        train_util.patch_accelerator_for_fp16_training(accelerator)
+        text_encoder.to(weight_dtype)
+
+    # resumeする
+    if args.resume is not None:
+        print(f"resume training from state: {args.resume}")
+        accelerator.load_state(args.resume)
+
+    # epoch数を計算する
+    num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
+    num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
+    if (args.save_n_epoch_ratio is not None) and (args.save_n_epoch_ratio > 0):
+        args.save_every_n_epochs = math.floor(num_train_epochs / args.save_n_epoch_ratio) or 1
+
+    # 学習する
+    total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
+    print("running training / 学習開始")
+    print(f"  num train images * repeats / 学習画像の数×繰り返し回数: {train_dataset_group.num_train_images}")
+    print(f"  num reg images / 正則化画像の数: {train_dataset_group.num_reg_images}")
+    print(f"  num batches per epoch / 1epochのバッチ数: {len(train_dataloader)}")
+    print(f"  num epochs / epoch数: {num_train_epochs}")
+    print(f"  batch size per device / バッチサイズ: {args.train_batch_size}")
+    print(f"  total train batch size (with parallel & distributed & accumulation) / 総バッチサイズ（並列学習、勾配合計含む）: {total_batch_size}")
+    print(f"  gradient ccumulation steps / 勾配を合計するステップ数 = {args.gradient_accumulation_steps}")
+    print(f"  total optimization steps / 学習ステップ数: {args.max_train_steps}")
+
+    progress_bar = tqdm(range(args.max_train_steps), smoothing=0, disable=not accelerator.is_local_main_process, desc="steps")
+    global_step = 0
+
+    noise_scheduler = DDPMScheduler(
+        beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", num_train_timesteps=1000, clip_sample=False
+    )
+
+    if accelerator.is_main_process:
+        accelerator.init_trackers("textual_inversion")
+
+    for epoch in range(num_train_epochs):
+        print(f"epoch {epoch+1}/{num_train_epochs}")
+        current_epoch.value = epoch+1
+
+        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:
+                        latents = batch["latents"].to(accelerator.device)
+                    else:
+                        # latentに変換
+                        latents = vae.encode(batch["images"].to(dtype=weight_dtype)).latent_dist.sample()
+                    latents = latents * 0.18215
+                b_size = latents.shape[0]
+
+                # Get the text embedding for conditioning
+                input_ids = batch["input_ids"].to(accelerator.device)
+                # weight_dtype) use float instead of fp16/bf16 because text encoder is float
+                encoder_hidden_states = train_util.get_hidden_states(args, input_ids, tokenizer, text_encoder, torch.float)
+
+                # Sample noise that we'll add to the latents
+                noise = torch.randn_like(latents, device=latents.device)
+                if args.noise_offset:
+                    # https://www.crosslabs.org//blog/diffusion-with-offset-noise
+                    noise += args.noise_offset * torch.randn((latents.shape[0], latents.shape[1], 1, 1), device=latents.device)
+
+                # Sample a random timestep for each image
+                timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (b_size,), device=latents.device)
+                timesteps = timesteps.long()
+
+                # Add noise to the latents according to the noise magnitude at each timestep
+                # (this is the forward diffusion process)
+                noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
+
+                # Predict the noise residual
+                noise_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample
+
+                if args.v_parameterization:
+                    # v-parameterization training
+                    target = noise_scheduler.get_velocity(latents, noise, timesteps)
+                else:
+                    target = noise
+
+                loss = torch.nn.functional.mse_loss(noise_pred.float(), target.float(), reduction="none")
+                loss = loss.mean([1, 2, 3])
+                
+                if args.min_snr_gamma:
+                  loss = apply_snr_weight(loss, timesteps, noise_scheduler, args.min_snr_gamma)
+
+                loss_weights = batch["loss_weights"]  # 各sampleごとのweight
+                loss = loss * loss_weights
+
+                loss = loss.mean()  # 平均なのでbatch_sizeで割る必要なし
+
+                accelerator.backward(loss)
+                if accelerator.sync_gradients and args.max_grad_norm != 0.0:
+                    params_to_clip = text_encoder.get_input_embeddings().parameters()
+                    accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)
+
+                optimizer.step()
+                lr_scheduler.step()
+                optimizer.zero_grad(set_to_none=True)
+
+                # Let's make sure we don't update any embedding weights besides the newly added token
+                with torch.no_grad():
+                    unwrap_model(text_encoder).get_input_embeddings().weight[index_no_updates] = orig_embeds_params[
+                        index_no_updates
+                    ]
+
+            # Checks if the accelerator has performed an optimization step behind the scenes
+            if accelerator.sync_gradients:
+                progress_bar.update(1)
+                global_step += 1
+
+                train_util.sample_images(
+                    accelerator, args, None, global_step, accelerator.device, vae, tokenizer, text_encoder, unet, prompt_replacement
+                )
+
+            current_loss = loss.detach().item()
+            if args.logging_dir is not None:
+                logs = {"loss": current_loss, "lr": float(lr_scheduler.get_last_lr()[0])}
+                if args.optimizer_type.lower() == "DAdaptation".lower():  # tracking d*lr value
+                    logs["lr/d*lr"] = (
+                        lr_scheduler.optimizers[0].param_groups[0]["d"] * lr_scheduler.optimizers[0].param_groups[0]["lr"]
+                    )
+                accelerator.log(logs, step=global_step)
+
+            loss_total += current_loss
+            avr_loss = loss_total / (step + 1)
+            logs = {"loss": avr_loss}  # , "lr": lr_scheduler.get_last_lr()[0]}
+            progress_bar.set_postfix(**logs)
+
+            if global_step >= args.max_train_steps:
+                break
+
+        if args.logging_dir is not None:
+            logs = {"loss/epoch": loss_total / len(train_dataloader)}
+            accelerator.log(logs, step=epoch + 1)
+
+        accelerator.wait_for_everyone()
+
+        updated_embs = unwrap_model(text_encoder).get_input_embeddings().weight[token_ids].data.detach().clone()
+
+        if args.save_every_n_epochs is not None:
+            model_name = train_util.DEFAULT_EPOCH_NAME if args.output_name is None else args.output_name
+
+            def save_func():
+                ckpt_name = train_util.EPOCH_FILE_NAME.format(model_name, epoch + 1) + "." + args.save_model_as
+                ckpt_file = os.path.join(args.output_dir, ckpt_name)
+                print(f"saving checkpoint: {ckpt_file}")
+                save_weights(ckpt_file, updated_embs, save_dtype)
+
+            def remove_old_func(old_epoch_no):
+                old_ckpt_name = train_util.EPOCH_FILE_NAME.format(model_name, old_epoch_no) + "." + args.save_model_as
+                old_ckpt_file = os.path.join(args.output_dir, old_ckpt_name)
+                if os.path.exists(old_ckpt_file):
+                    print(f"removing old checkpoint: {old_ckpt_file}")
+                    os.remove(old_ckpt_file)
+
+            saving = train_util.save_on_epoch_end(args, save_func, remove_old_func, epoch + 1, num_train_epochs)
+            if saving and args.save_state:
+                train_util.save_state_on_epoch_end(args, accelerator, model_name, epoch + 1)
+
+        train_util.sample_images(
+            accelerator, args, epoch + 1, global_step, accelerator.device, vae, tokenizer, text_encoder, unet, prompt_replacement
+        )
+
+        # end of epoch
+
+    is_main_process = accelerator.is_main_process
+    if is_main_process:
+        text_encoder = unwrap_model(text_encoder)
+
+    accelerator.end_training()
+
+    if args.save_state:
+        train_util.save_state_on_train_end(args, accelerator)
+
+    updated_embs = text_encoder.get_input_embeddings().weight[token_ids].data.detach().clone()
+
+    del accelerator  # この後メモリを使うのでこれは消す
+
+    if is_main_process:
+        os.makedirs(args.output_dir, exist_ok=True)
+
+        model_name = train_util.DEFAULT_LAST_OUTPUT_NAME if args.output_name is None else args.output_name
+        ckpt_name = model_name + "." + args.save_model_as
+        ckpt_file = os.path.join(args.output_dir, ckpt_name)
+
+        print(f"save trained model to {ckpt_file}")
+        save_weights(ckpt_file, updated_embs, save_dtype)
+        print("model saved.")
+
+
+def save_weights(file, updated_embs, save_dtype):
+    state_dict = {"emb_params": updated_embs}
+
+    if save_dtype is not None:
+        for key in list(state_dict.keys()):
+            v = state_dict[key]
+            v = v.detach().clone().to("cpu").to(save_dtype)
+            state_dict[key] = v
+
+    if os.path.splitext(file)[1] == ".safetensors":
+        from safetensors.torch import save_file
+
+        save_file(state_dict, file)
+    else:
+        torch.save(state_dict, file)  # can be loaded in Web UI
+
+
+def load_weights(file):
+    if os.path.splitext(file)[1] == ".safetensors":
+        from safetensors.torch import load_file
+
+        data = load_file(file)
+    else:
+        # compatible to Web UI's file format
+        data = torch.load(file, map_location="cpu")
+        if type(data) != dict:
+            raise ValueError(f"weight file is not dict / 重みファイルがdict形式ではありません: {file}")
+
+        if "string_to_param" in data:  # textual inversion embeddings
+            data = data["string_to_param"]
+            if hasattr(data, "_parameters"):  # support old PyTorch?
+                data = getattr(data, "_parameters")
+
+    emb = next(iter(data.values()))
+    if type(emb) != torch.Tensor:
+        raise ValueError(f"weight file does not contains Tensor / 重みファイルのデータがTensorではありません: {file}")
+
+    if len(emb.size()) == 1:
+        emb = emb.unsqueeze(0)
+
+    return emb
+
+
+def setup_parser() -> argparse.ArgumentParser:
+    parser = argparse.ArgumentParser()
+
+    train_util.add_sd_models_arguments(parser)
+    train_util.add_dataset_arguments(parser, True, True, False)
+    train_util.add_training_arguments(parser, True)
+    train_util.add_optimizer_arguments(parser)
+    config_util.add_config_arguments(parser)
+    custom_train_functions.add_custom_train_arguments(parser)
+
+    parser.add_argument(
+        "--save_model_as",
+        type=str,
+        default="pt",
+        choices=[None, "ckpt", "pt", "safetensors"],
+        help="format to save the model (default is .pt) / モデル保存時の形式（デフォルトはpt）",
+    )
+
+    parser.add_argument("--weights", type=str, default=None, help="embedding weights to initialize / 学習するネットワークの初期重み")
+    parser.add_argument(
+        "--num_vectors_per_token", type=int, default=1, help="number of vectors per token / トークンに割り当てるembeddingsの要素数"
+    )
+    parser.add_argument(
+        "--token_string",
+        type=str,
+        default=None,
+        help="token string used in training, must not exist in tokenizer / 学習時に使用されるトークン文字列、tokenizerに存在しない文字であること",
+    )
+    parser.add_argument("--init_word", type=str, default=None, help="words to initialize vector / ベクトルを初期化に使用する単語、複数可")
+    parser.add_argument(
+        "--use_object_template",
+        action="store_true",
+        help="ignore caption and use default templates for object / キャプションは使わずデフォルトの物体用テンプレートで学習する",
+    )
+    parser.add_argument(
+        "--use_style_template",
+        action="store_true",
+        help="ignore caption and use default templates for stype / キャプションは使わずデフォルトのスタイル用テンプレートで学習する",
+    )
+
+    return parser
+
+
+if __name__ == "__main__":
+    parser = setup_parser()
+
+    args = parser.parse_args()
+    args = train_util.read_config_from_file(args, parser)
+
+    train(args)

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
@@ -17,6 +18,8 @@ from library.config_util import (
     ConfigSanitizer,
     BlueprintGenerator,
 )
+import library.custom_train_functions as custom_train_functions
+from library.custom_train_functions import apply_snr_weight
 
 imagenet_templates_small = [
     "a photo of a {}",
@@ -71,10 +74,6 @@ 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
@@ -185,6 +184,11 @@ def train(args):
     blueprint = blueprint_generator.generate(user_config, args, tokenizer=tokenizer)
     train_dataset_group = config_util.generate_dataset_group_by_blueprint(blueprint.dataset_group)
 
+    current_epoch = Value('i',0)
+    current_step = Value('i',0)
+    ds_for_collater = train_dataset_group if args.max_data_loader_n_workers == 0 else None
+    collater = train_util.collater_class(current_epoch,current_step, ds_for_collater)
+
     # 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}")
@@ -250,7 +254,7 @@ 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,
     )
@@ -260,6 +264,9 @@ def train(args):
         args.max_train_steps = args.max_train_epochs * math.ceil(len(train_dataloader) / accelerator.num_processes / args.gradient_accumulation_steps)
         print(f"override steps. steps for {args.max_train_epochs} epochs is / 指定エポックまでのステップ数: {args.max_train_steps}")
 
+    # データセット側にも学習ステップを送信
+    train_dataset_group.set_max_train_steps(args.max_train_steps)
+
     # lr schedulerを用意する
     lr_scheduler = train_util.get_scheduler_fix(args, optimizer, accelerator.num_processes)
 
@@ -331,12 +338,14 @@ 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
 
         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:
@@ -377,6 +386,9 @@ def train(args):
 
                 loss = torch.nn.functional.mse_loss(noise_pred.float(), target.float(), reduction="none")
                 loss = loss.mean([1, 2, 3])
+                
+                if args.min_snr_gamma:
+                  loss = apply_snr_weight(loss, timesteps, noise_scheduler, args.min_snr_gamma)
 
                 loss_weights = batch["loss_weights"]  # 各sampleごとのweight
                 loss = loss * loss_weights
@@ -534,6 +546,7 @@ def setup_parser() -> argparse.ArgumentParser:
     train_util.add_training_arguments(parser, True)
     train_util.add_optimizer_arguments(parser)
     config_util.add_config_arguments(parser)
+    custom_train_functions.add_custom_train_arguments(parser)
 
     parser.add_argument(
         "--save_model_as",