Update typos to the latest version and add dependabot.yml

.github/dependabot.yml

@@ -0,0 +1,7 @@
+---
+version: 2
+updates:
+  - package-ecosystem: "github-actions"
+    directory: "/"
+    schedule:
+      interval: "monthly"

.github/workflows/typos.yml

@@ -18,4 +18,4 @@ jobs:
       - uses: actions/checkout@v3
 
       - name: typos-action
-        uses: crate-ci/typos@v1.13.10
+        uses: crate-ci/typos@v1.16.15

_typos.toml

@@ -9,7 +9,25 @@ parms="parms"
 nin="nin"
 extention="extention" # Intentionally left
 nd="nd"
+shs="shs"
+sts="sts"
+scs="scs"
+cpc="cpc"
+coc="coc"
+cic="cic"
+msm="msm"
+usu="usu"
+ici="ici"
+lvl="lvl"
+dii="dii"
+muk="muk"
+ori="ori"
+hru="hru"
+rik="rik"
+koo="koo"
+yos="yos"
+wn="wn"
 
 
 [files]
-extend-exclude = ["_typos.toml"]
+extend-exclude = ["_typos.toml", "venv"]

fine_tune.py

@@ -80,8 +80,8 @@ def train(args):
 
     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)
+    ds_for_collator = train_dataset_group if args.max_data_loader_n_workers == 0 else None
+    collator = train_util.collator_class(current_epoch, current_step, ds_for_collator)
 
     if args.debug_dataset:
         train_util.debug_dataset(train_dataset_group)
@@ -208,7 +208,7 @@ def fn_recursive_set_mem_eff(module: torch.nn.Module):
         train_dataset_group,
         batch_size=1,
         shuffle=True,
-        collate_fn=collater,
+        collate_fn=collator,
         num_workers=n_workers,
         persistent_workers=args.persistent_data_loader_workers,
     )

gen_img_diffusers.py

@@ -3364,7 +3364,7 @@ def setup_parser() -> argparse.ArgumentParser:
     )
     parser.add_argument("--network_mul", type=float, default=None, nargs="*", help="additional network multiplier / 追加ネットワークの効果の倍率")
     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"
+        "--network_args", type=str, default=None, nargs="*", help="additional arguments for network (key=value) / ネットワークへの追加の引数"
     )
     parser.add_argument("--network_show_meta", action="store_true", help="show metadata of network model / ネットワークモデルのメタデータを表示する")
     parser.add_argument("--network_merge", action="store_true", help="merge network weights to original model / ネットワークの重みをマージする")
@@ -3390,7 +3390,7 @@ def setup_parser() -> argparse.ArgumentParser:
         "--max_embeddings_multiples",
         type=int,
         default=None,
-        help="max embeding multiples, max token length is 75 * multiples / トークン長をデフォルトの何倍とするか 75*この値 がトークン長となる",
+        help="max embedding multiples, max token length is 75 * multiples / トークン長をデフォルトの何倍とするか 75*この値 がトークン長となる",
     )
     parser.add_argument(
         "--clip_guidance_scale",
@@ -3449,7 +3449,7 @@ def setup_parser() -> argparse.ArgumentParser:
         "--highres_fix_upscaler_args",
         type=str,
         default=None,
-        help="additional argmuments for upscaler (key=value) / upscalerへの追加の引数",
+        help="additional arguments for upscaler (key=value) / upscalerへの追加の引数",
     )
     parser.add_argument(
         "--highres_fix_disable_control_net",

library/original_unet.py

@@ -131,7 +131,7 @@
 UP_BLOCK_TYPES = ["UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D"]
 
 
-# region memory effcient attention
+# region memory efficient attention
 
 # FlashAttentionを使うCrossAttention
 # based on https://github.com/lucidrains/memory-efficient-attention-pytorch/blob/main/memory_efficient_attention_pytorch/flash_attention.py

library/sdxl_original_unet.py

@@ -41,7 +41,7 @@
 
 USE_REENTRANT = True
 
-# region memory effcient attention
+# region memory efficient attention
 
 # FlashAttentionを使うCrossAttention
 # based on https://github.com/lucidrains/memory-efficient-attention-pytorch/blob/main/memory_efficient_attention_pytorch/flash_attention.py

library/train_util.py

@@ -4658,7 +4658,7 @@ def __getitem__(self, idx):
 
 
 # collate_fn用 epoch,stepはmultiprocessing.Value
-class collater_class:
+class collator_class:
     def __init__(self, epoch, step, dataset):
         self.current_epoch = epoch
         self.current_step = step

sdxl_gen_img.py

@@ -2612,7 +2612,7 @@ def setup_parser() -> argparse.ArgumentParser:
     )
     parser.add_argument("--network_mul", type=float, default=None, nargs="*", help="additional network multiplier / 追加ネットワークの効果の倍率")
     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"
+        "--network_args", type=str, default=None, nargs="*", help="additional arguments for network (key=value) / ネットワークへの追加の引数"
     )
     parser.add_argument("--network_show_meta", action="store_true", help="show metadata of network model / ネットワークモデルのメタデータを表示する")
     parser.add_argument("--network_merge", action="store_true", help="merge network weights to original model / ネットワークの重みをマージする")
@@ -2631,7 +2631,7 @@ def setup_parser() -> argparse.ArgumentParser:
         "--max_embeddings_multiples",
         type=int,
         default=None,
-        help="max embeding multiples, max token length is 75 * multiples / トークン長をデフォルトの何倍とするか 75*この値 がトークン長となる",
+        help="max embedding multiples, max token length is 75 * multiples / トークン長をデフォルトの何倍とするか 75*この値 がトークン長となる",
     )
     parser.add_argument(
         "--guide_image_path", type=str, default=None, nargs="*", help="image to CLIP guidance / CLIP guided SDでガイドに使う画像"
@@ -2666,7 +2666,7 @@ def setup_parser() -> argparse.ArgumentParser:
         "--highres_fix_upscaler_args",
         type=str,
         default=None,
-        help="additional argmuments for upscaler (key=value) / upscalerへの追加の引数",
+        help="additional arguments for upscaler (key=value) / upscalerへの追加の引数",
     )
     parser.add_argument(
         "--highres_fix_disable_control_net",

sdxl_minimal_inference.py

@@ -101,7 +101,7 @@ def get_timestep_embedding(x, outdim):
         type=str,
         nargs="*",
         default=[],
-        help="LoRA weights, only supports networks.lora, each arguement is a `path;multiplier` (semi-colon separated)",
+        help="LoRA weights, only supports networks.lora, each argument is a `path;multiplier` (semi-colon separated)",
     )
     parser.add_argument("--interactive", action="store_true")
     args = parser.parse_args()

sdxl_train.py

@@ -172,8 +172,8 @@ def train(args):
 
     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)
+    ds_for_collator = train_dataset_group if args.max_data_loader_n_workers == 0 else None
+    collator = train_util.collator_class(current_epoch, current_step, ds_for_collator)
 
     train_dataset_group.verify_bucket_reso_steps(32)
 
@@ -348,7 +348,7 @@ def fn_recursive_set_mem_eff(module: torch.nn.Module):
         train_dataset_group,
         batch_size=1,
         shuffle=True,
-        collate_fn=collater,
+        collate_fn=collator,
         num_workers=n_workers,
         persistent_workers=args.persistent_data_loader_workers,
     )

sdxl_train_control_net_lllite.py

@@ -106,8 +106,8 @@ def train(args):
 
     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)
+    ds_for_collator = train_dataset_group if args.max_data_loader_n_workers == 0 else None
+    collator = train_util.collator_class(current_epoch, current_step, ds_for_collator)
 
     train_dataset_group.verify_bucket_reso_steps(32)
 
@@ -245,7 +245,7 @@ def train(args):
         train_dataset_group,
         batch_size=1,
         shuffle=True,
-        collate_fn=collater,
+        collate_fn=collator,
         num_workers=n_workers,
         persistent_workers=args.persistent_data_loader_workers,
     )

sdxl_train_control_net_lllite_old.py

@@ -102,8 +102,8 @@ def train(args):
 
     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)
+    ds_for_collator = train_dataset_group if args.max_data_loader_n_workers == 0 else None
+    collator = train_util.collator_class(current_epoch, current_step, ds_for_collator)
 
     train_dataset_group.verify_bucket_reso_steps(32)
 
@@ -213,7 +213,7 @@ def train(args):
         train_dataset_group,
         batch_size=1,
         shuffle=True,
-        collate_fn=collater,
+        collate_fn=collator,
         num_workers=n_workers,
         persistent_workers=args.persistent_data_loader_workers,
     )

tools/cache_latents.py

@@ -86,8 +86,8 @@ def cache_to_disk(args: argparse.Namespace) -> None:
 
     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)
+    ds_for_collator = train_dataset_group if args.max_data_loader_n_workers == 0 else None
+    collator = train_util.collator_class(current_epoch, current_step, ds_for_collator)
 
     # acceleratorを準備する
     print("prepare accelerator")
@@ -120,7 +120,7 @@ def cache_to_disk(args: argparse.Namespace) -> None:
         train_dataset_group,
         batch_size=1,
         shuffle=True,
-        collate_fn=collater,
+        collate_fn=collator,
         num_workers=n_workers,
         persistent_workers=args.persistent_data_loader_workers,
     )

tools/cache_text_encoder_outputs.py

@@ -91,8 +91,8 @@ def cache_to_disk(args: argparse.Namespace) -> None:
 
     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)
+    ds_for_collator = train_dataset_group if args.max_data_loader_n_workers == 0 else None
+    collator = train_util.collator_class(current_epoch, current_step, ds_for_collator)
 
     # acceleratorを準備する
     print("prepare accelerator")
@@ -125,7 +125,7 @@ def cache_to_disk(args: argparse.Namespace) -> None:
         train_dataset_group,
         batch_size=1,
         shuffle=True,
-        collate_fn=collater,
+        collate_fn=collator,
         num_workers=n_workers,
         persistent_workers=args.persistent_data_loader_workers,
     )

train_controlnet.py

@@ -98,8 +98,8 @@ def train(args):
 
     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)
+    ds_for_collator = train_dataset_group if args.max_data_loader_n_workers == 0 else None
+    collator = train_util.collator_class(current_epoch, current_step, ds_for_collator)
 
     if args.debug_dataset:
         train_util.debug_dataset(train_dataset_group)
@@ -245,7 +245,7 @@ def train(args):
         train_dataset_group,
         batch_size=1,
         shuffle=True,
-        collate_fn=collater,
+        collate_fn=collator,
         num_workers=n_workers,
         persistent_workers=args.persistent_data_loader_workers,
     )

train_db.py

@@ -78,8 +78,8 @@ def train(args):
 
     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)
+    ds_for_collator = train_dataset_group if args.max_data_loader_n_workers == 0 else None
+    collator = train_util.collator_class(current_epoch, current_step, ds_for_collator)
 
     if args.no_token_padding:
         train_dataset_group.disable_token_padding()
@@ -177,7 +177,7 @@ def train(args):
         train_dataset_group,
         batch_size=1,
         shuffle=True,
-        collate_fn=collater,
+        collate_fn=collator,
         num_workers=n_workers,
         persistent_workers=args.persistent_data_loader_workers,
     )

train_network.py

@@ -192,8 +192,8 @@ def train(self, args):
 
         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)
+        ds_for_collator = train_dataset_group if args.max_data_loader_n_workers == 0 else None
+        collator = train_util.collator_class(current_epoch, current_step, ds_for_collator)
 
         if args.debug_dataset:
             train_util.debug_dataset(train_dataset_group)
@@ -342,7 +342,7 @@ def train(self, args):
             train_dataset_group,
             batch_size=1,
             shuffle=True,
-            collate_fn=collater,
+            collate_fn=collator,
             num_workers=n_workers,
             persistent_workers=args.persistent_data_loader_workers,
         )
@@ -954,7 +954,7 @@ def setup_parser() -> argparse.ArgumentParser:
         help="Drops neurons out of training every step (0 or None is default behavior (no dropout), 1 would drop all neurons) / 訓練時に毎ステップでニューロンをdropする（0またはNoneはdropoutなし、1は全ニューロンをdropout）",
     )
     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"
+        "--network_args", type=str, default=None, nargs="*", help="additional arguments 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(

train_textual_inversion.py

@@ -312,8 +312,8 @@ def train(self, args):
 
         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)
+        ds_for_collator = train_dataset_group if args.max_data_loader_n_workers == 0 else None
+        collator = train_util.collator_class(current_epoch, current_step, ds_for_collator)
 
         # 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:
@@ -389,7 +389,7 @@ def train(self, args):
             train_dataset_group,
             batch_size=1,
             shuffle=True,
-            collate_fn=collater,
+            collate_fn=collator,
             num_workers=n_workers,
             persistent_workers=args.persistent_data_loader_workers,
         )

train_textual_inversion_XTI.py

@@ -236,8 +236,8 @@ def train(args):
     train_dataset_group.enable_XTI(XTI_layers, token_strings=token_strings)
     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)
+    ds_for_collator = train_dataset_group if args.max_data_loader_n_workers == 0 else None
+    collator = train_util.collator_class(current_epoch, current_step, ds_for_collator)
 
     # 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:
@@ -309,7 +309,7 @@ def train(args):
         train_dataset_group,
         batch_size=1,
         shuffle=True,
-        collate_fn=collater,
+        collate_fn=collator,
         num_workers=n_workers,
         persistent_workers=args.persistent_data_loader_workers,
     )