change method name, repo is private in default etc

fine_tune.py

@@ -231,7 +231,7 @@ def train(args):
         train_util.patch_accelerator_for_fp16_training(accelerator)
 
     # resumeする
-    train_util.resume(accelerator, args)
+    train_util.resume_from_local_or_hf_if_specified(accelerator, args)
 
     # epoch数を計算する
     num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)

library/huggingface_util.py

@@ -30,7 +30,7 @@ def upload(
     repo_type = args.huggingface_repo_type
     token = args.huggingface_token
     path_in_repo = args.huggingface_path_in_repo + dest_suffix
-    private = args.huggingface_repo_visibility == "private"
+    private = args.huggingface_repo_visibility is None or args.huggingface_repo_visibility != "public"
     api = HfApi(token=token)
     if not exists_repo(repo_id=repo_id, repo_type=repo_type, token=token):
         api.create_repo(repo_id=repo_id, repo_type=repo_type, private=private)

library/train_util.py

@@ -490,7 +490,7 @@ class BaseDataset(torch.utils.data.Dataset):
         else:
             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
+                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 = (
@@ -1898,12 +1898,28 @@ def add_optimizer_arguments(parser: argparse.ArgumentParser):
 def add_training_arguments(parser: argparse.ArgumentParser, support_dreambooth: bool):
     parser.add_argument("--output_dir", type=str, default=None, help="directory to output trained model / 学習後のモデル出力先ディレクトリ")
     parser.add_argument("--output_name", type=str, default=None, help="base name of trained model file / 学習後のモデルの拡張子を除くファイル名")
-    parser.add_argument("--huggingface_repo_id", type=str, default=None, help="huggingface repo name to upload / huggingfaceにアップロードするリポジトリ名")
+    parser.add_argument(
-    parser.add_argument("--huggingface_repo_type", type=str, default=None, help="huggingface repo type to upload / huggingfaceにアップロードするリポジトリの種類")
+        "--huggingface_repo_id", type=str, default=None, help="huggingface repo name to upload / huggingfaceにアップロードするリポジトリ名"
-    parser.add_argument("--huggingface_path_in_repo", type=str, default=None, help="huggingface model path to upload files / huggingfaceにアップロードするファイルのパス")
+    )
+    parser.add_argument(
+        "--huggingface_repo_type", type=str, default=None, help="huggingface repo type to upload / huggingfaceにアップロードするリポジトリの種類"
+    )
+    parser.add_argument(
+        "--huggingface_path_in_repo",
+        type=str,
+        default=None,
+        help="huggingface model path to upload files / huggingfaceにアップロードするファイルのパス",
+    )
     parser.add_argument("--huggingface_token", type=str, default=None, help="huggingface token / huggingfaceのトークン")
-    parser.add_argument("--huggingface_repo_visibility", type=str, default=None, help="huggingface repository visibility / huggingfaceにアップロードするリポジトリの公開設定")
+    parser.add_argument(
-    parser.add_argument("--save_state_to_huggingface", action="store_true", help="save state to huggingface / huggingfaceにstateを保存する")
+        "--huggingface_repo_visibility",
+        type=str,
+        default=None,
+        help="huggingface repository visibility ('public' for public, 'private' or None for private) / huggingfaceにアップロードするリポジトリの公開設定（'public'で公開、'private'またはNoneで非公開）",
+    )
+    parser.add_argument(
+        "--save_state_to_huggingface", action="store_true", help="save state to huggingface / huggingfaceにstateを保存する"
+    )
     parser.add_argument(
         "--resume_from_huggingface",
         action="store_true",
@@ -2278,55 +2294,56 @@ def read_config_from_file(args: argparse.Namespace, parser: argparse.ArgumentPar
 
 # region utils
 
-def resume(accelerator, args):
-    if args.resume:
-        print(f"resume training from state: {args.resume}")
-        if args.resume_from_huggingface:
-            repo_id = args.resume.split("/")[0] + "/" + args.resume.split("/")[1]
-            path_in_repo = "/".join(args.resume.split("/")[2:])
-            revision = None
-            repo_type = None
-            if ":" in path_in_repo:
-                divided = path_in_repo.split(":")
-                if len(divided) == 2:
-                    path_in_repo, revision = divided
-                    repo_type = "model"
-                else:
-                    path_in_repo, revision, repo_type = divided
-            print(
-                f"Downloading state from huggingface: {repo_id}/{path_in_repo}@{revision}"
-            )
 
-            list_files = huggingface_util.list_dir(
+def resume_from_local_or_hf_if_specified(accelerator, args):
-                repo_id=repo_id,
+    if not args.resume:
-                subfolder=path_in_repo,
+        return
-                revision=revision,
-                token=args.huggingface_token,
-                repo_type=repo_type,
-            )
 
-            async def download(filename) -> str:
+    if not args.resume_from_huggingface:
-                def task():
+        print(f"resume training from local state: {args.resume}")
-                    return hf_hub_download(
+        accelerator.load_state(args.resume)
-                        repo_id=repo_id,
+        return
-                        filename=filename,
-                        revision=revision,
-                        repo_type=repo_type,
-                        token=args.huggingface_token,
-                    )
 
-                return await asyncio.get_event_loop().run_in_executor(None, task)
+    print(f"resume training from huggingface state: {args.resume}")
-
+    repo_id = args.resume.split("/")[0] + "/" + args.resume.split("/")[1]
-            loop = asyncio.get_event_loop()
+    path_in_repo = "/".join(args.resume.split("/")[2:])
-            results = loop.run_until_complete(
+    revision = None
-                asyncio.gather(
+    repo_type = None
-                    *[download(filename=filename.rfilename) for filename in list_files]
+    if ":" in path_in_repo:
-                )
+        divided = path_in_repo.split(":")
-            )
+        if len(divided) == 2:
-            dirname = os.path.dirname(results[0])
+            path_in_repo, revision = divided
-            accelerator.load_state(dirname)
+            repo_type = "model"
         else:
-            accelerator.load_state(args.resume)
+            path_in_repo, revision, repo_type = divided
+    print(f"Downloading state from huggingface: {repo_id}/{path_in_repo}@{revision}")
+
+    list_files = huggingface_util.list_dir(
+        repo_id=repo_id,
+        subfolder=path_in_repo,
+        revision=revision,
+        token=args.huggingface_token,
+        repo_type=repo_type,
+    )
+
+    async def download(filename) -> str:
+        def task():
+            return hf_hub_download(
+                repo_id=repo_id,
+                filename=filename,
+                revision=revision,
+                repo_type=repo_type,
+                token=args.huggingface_token,
+            )
+
+        return await asyncio.get_event_loop().run_in_executor(None, task)
+
+    loop = asyncio.get_event_loop()
+    results = loop.run_until_complete(asyncio.gather(*[download(filename=filename.rfilename) for filename in list_files]))
+    if len(results) == 0:
+        raise ValueError("No files found in the specified repo id/path/revision / 指定されたリポジトリID/パス/リビジョンにファイルが見つかりませんでした")
+    dirname = os.path.dirname(results[0])
+    accelerator.load_state(dirname)
 
 
 def get_optimizer(args, trainable_params):
@@ -2713,7 +2730,7 @@ def prepare_dtype(args: argparse.Namespace):
     return weight_dtype, save_dtype
 
 
-def load_target_model(args: argparse.Namespace, weight_dtype, device='cpu'):
+def load_target_model(args: argparse.Namespace, weight_dtype, device="cpu"):
     name_or_path = args.pretrained_model_name_or_path
     name_or_path = os.readlink(name_or_path) if os.path.islink(name_or_path) else name_or_path
     load_stable_diffusion_format = os.path.isfile(name_or_path)  # determine SD or Diffusers
@@ -2883,6 +2900,7 @@ def save_state_on_epoch_end(args: argparse.Namespace, accelerator, model_name, e
     state_dir = os.path.join(args.output_dir, EPOCH_STATE_NAME.format(model_name, epoch_no))
     accelerator.save_state(state_dir)
     if args.save_state_to_huggingface:
+        print("uploading state to huggingface.")
         huggingface_util.upload(args, state_dir, "/" + EPOCH_STATE_NAME.format(model_name, epoch_no))
 
     last_n_epochs = args.save_last_n_epochs_state if args.save_last_n_epochs_state else args.save_last_n_epochs
@@ -2894,6 +2912,17 @@ def save_state_on_epoch_end(args: argparse.Namespace, accelerator, model_name, e
             shutil.rmtree(state_dir_old)
 
 
+def save_state_on_train_end(args: argparse.Namespace, accelerator):
+    print("saving last state.")
+    os.makedirs(args.output_dir, exist_ok=True)
+    model_name = DEFAULT_LAST_OUTPUT_NAME if args.output_name is None else args.output_name
+    state_dir = os.path.join(args.output_dir, LAST_STATE_NAME.format(model_name))
+    accelerator.save_state(state_dir)
+    if args.save_state_to_huggingface:
+        print("uploading last state to huggingface.")
+        huggingface_util.upload(args, state_dir, "/" + LAST_STATE_NAME.format(model_name))
+
+
 def save_sd_model_on_train_end(
     args: argparse.Namespace,
     src_path: str,
@@ -2932,13 +2961,6 @@ def save_sd_model_on_train_end(
             huggingface_util.upload(args, out_dir, "/" + model_name, force_sync_upload=True)
 
 
-def save_state_on_train_end(args: argparse.Namespace, accelerator):
-    print("saving last state.")
-    os.makedirs(args.output_dir, exist_ok=True)
-    model_name = DEFAULT_LAST_OUTPUT_NAME if args.output_name is None else args.output_name
-    accelerator.save_state(os.path.join(args.output_dir, LAST_STATE_NAME.format(model_name)))
-
-
 # scheduler:
 SCHEDULER_LINEAR_START = 0.00085
 SCHEDULER_LINEAR_END = 0.0120
@@ -3168,7 +3190,7 @@ 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
+        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()

train_db.py

@@ -202,7 +202,7 @@ def train(args):
         train_util.patch_accelerator_for_fp16_training(accelerator)
 
     # resumeする
-    train_util.resume(accelerator, args)
+    train_util.resume_from_local_or_hf_if_specified(accelerator, args)
 
     # epoch数を計算する
     num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)

train_network.py

@@ -310,7 +310,7 @@ def train(args):
         train_util.patch_accelerator_for_fp16_training(accelerator)
 
     # resumeする
-    train_util.resume(accelerator, args)
+    train_util.resume_from_local_or_hf_if_specified(accelerator, args)
 
     # epoch数を計算する
     num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)

train_textual_inversion.py

@@ -305,7 +305,7 @@ def train(args):
         text_encoder.to(weight_dtype)
 
     # resumeする
-    train_util.resume(accelerator, args)
+    train_util.resume_from_local_or_hf_if_specified(accelerator, args)
 
     # epoch数を計算する
     num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)

train_textual_inversion_XTI.py

@@ -341,9 +341,7 @@ def train(args):
         text_encoder.to(weight_dtype)
 
     # resumeする
-    if args.resume is not None:
+    train_util.resume_from_local_or_hf_if_specified(accelerator, args)
-        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)