show warning message for sample images in XTI

train_textual_inversion_XTI.py

@@ -83,6 +83,11 @@ def train(args):
     train_util.verify_training_args(args)
     train_util.prepare_dataset_args(args, True)
 
+    if args.sample_every_n_steps is not None or args.sample_every_n_epochs is not None:
+        print(
+            "sample_every_n_steps and sample_every_n_epochs are not supported in this script currently / sample_every_n_stepsとsample_every_n_epochsは現在このスクリプトではサポートされていません"
+        )
+
     cache_latents = args.cache_latents
 
     if args.seed is not None:
@@ -123,7 +128,24 @@ def train(args):
     assert len(tokenizer) - 1 == token_ids[-1], f"token ids is not end of tokenize: {len(tokenizer)}"
 
     token_strings_XTI = []
-    XTI_layers = ['IN01', 'IN02', 'IN04', 'IN05', 'IN07', 'IN08', 'MID', 'OUT03', 'OUT04', 'OUT05', 'OUT06', 'OUT07', 'OUT08', 'OUT09', 'OUT10', 'OUT11']
+    XTI_layers = [
+        "IN01",
+        "IN02",
+        "IN04",
+        "IN05",
+        "IN07",
+        "IN08",
+        "MID",
+        "OUT03",
+        "OUT04",
+        "OUT05",
+        "OUT06",
+        "OUT07",
+        "OUT08",
+        "OUT09",
+        "OUT10",
+        "OUT11",
+    ]
     for layer_name in XTI_layers:
         token_strings_XTI += [f"{t}_{layer_name}" for t in token_strings]
 
@@ -193,8 +215,8 @@ 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)
     train_dataset_group.enable_XTI(XTI_layers, token_strings=token_strings)
-    current_epoch = Value('i',0)
+    current_epoch = Value("i", 0)
-    current_step = 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)
 
@@ -273,7 +295,9 @@ def train(args):
 
     # 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
@@ -371,7 +395,12 @@ def train(args):
                 # 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 = torch.stack([train_util.get_hidden_states(args, s, tokenizer, text_encoder, weight_dtype) for s in torch.split(input_ids, 1, dim=1)])
+                encoder_hidden_states = torch.stack(
+                    [
+                        train_util.get_hidden_states(args, s, tokenizer, text_encoder, weight_dtype)
+                        for s in torch.split(input_ids, 1, dim=1)
+                    ]
+                )
 
                 # Sample noise that we'll add to the latents
                 noise = torch.randn_like(latents, device=latents.device)
@@ -511,7 +540,24 @@ def train(args):
 def save_weights(file, updated_embs, save_dtype):
     updated_embs = updated_embs.reshape(16, -1, updated_embs.shape[-1])
     updated_embs = updated_embs.chunk(16)
-    XTI_layers = ['IN01', 'IN02', 'IN04', 'IN05', 'IN07', 'IN08', 'MID', 'OUT03', 'OUT04', 'OUT05', 'OUT06', 'OUT07', 'OUT08', 'OUT09', 'OUT10', 'OUT11']
+    XTI_layers = [
+        "IN01",
+        "IN02",
+        "IN04",
+        "IN05",
+        "IN07",
+        "IN08",
+        "MID",
+        "OUT03",
+        "OUT04",
+        "OUT05",
+        "OUT06",
+        "OUT07",
+        "OUT08",
+        "OUT09",
+        "OUT10",
+        "OUT11",
+    ]
     state_dict = {}
     for i, layer_name in enumerate(XTI_layers):
         state_dict[layer_name] = updated_embs[i].squeeze(0).detach().clone().to("cpu").to(save_dtype)