Replace print with logger if they are logs (#905)

* Add get_my_logger()

* Use logger instead of print

* Fix log level

* Removed line-breaks for readability

* Use setup_logging()

* Add rich to requirements.txt

* Make simple

* Use logger instead of print

---------

Co-authored-by: Kohya S <52813779+kohya-ss@users.noreply.github.com>

sdxl_train.py

@@ -20,6 +20,10 @@ from diffusers import DDPMScheduler
 from library import sdxl_model_util
 
 import library.train_util as train_util
+from library.utils import setup_logging
+setup_logging()
+import logging
+logger = logging.getLogger(__name__)
 import library.config_util as config_util
 import library.sdxl_train_util as sdxl_train_util
 from library.config_util import (
@@ -117,18 +121,18 @@ def train(args):
     if args.dataset_class is None:
         blueprint_generator = BlueprintGenerator(ConfigSanitizer(True, True, False, True))
         if args.dataset_config is not None:
-            print(f"Load dataset config from {args.dataset_config}")
+            logger.info(f"Load dataset config from {args.dataset_config}")
             user_config = config_util.load_user_config(args.dataset_config)
             ignored = ["train_data_dir", "in_json"]
             if any(getattr(args, attr) is not None for attr in ignored):
-                print(
+                logger.warning(
                     "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("Using DreamBooth method.")
+                logger.info("Using DreamBooth method.")
                 user_config = {
                     "datasets": [
                         {
@@ -139,7 +143,7 @@ def train(args):
                     ]
                 }
             else:
-                print("Training with captions.")
+                logger.info("Training with captions.")
                 user_config = {
                     "datasets": [
                         {
@@ -169,7 +173,7 @@ def train(args):
         train_util.debug_dataset(train_dataset_group, True)
         return
     if len(train_dataset_group) == 0:
-        print(
+        logger.error(
             "No data found. Please verify the metadata file and train_data_dir option. / 画像がありません。メタデータおよびtrain_data_dirオプションを確認してください。"
         )
         return
@@ -185,7 +189,7 @@ def train(args):
         ), "when caching text encoder output, either caption_dropout_rate, shuffle_caption, token_warmup_step or caption_tag_dropout_rate cannot be used / text encoderの出力をキャッシュするときはcaption_dropout_rate, shuffle_caption, token_warmup_step, caption_tag_dropout_rateは使えません"
 
     # acceleratorを準備する
-    print("prepare accelerator")
+    logger.info("prepare accelerator")
     accelerator = 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
@@ -537,7 +541,7 @@ def train(args):
                     # assert ((encoder_hidden_states1.to("cpu") - ehs1.to(dtype=weight_dtype)).abs().max() > 1e-2).sum() <= b_size * 2
                     # assert ((encoder_hidden_states2.to("cpu") - ehs2.to(dtype=weight_dtype)).abs().max() > 1e-2).sum() <= b_size * 2
                     # assert ((pool2.to("cpu") - p2.to(dtype=weight_dtype)).abs().max() > 1e-2).sum() <= b_size * 2
-                    # print("text encoder outputs verified")
+                    # logger.info("text encoder outputs verified")
 
                 # get size embeddings
                 orig_size = batch["original_sizes_hw"]
@@ -724,7 +728,7 @@ def train(args):
             logit_scale,
             ckpt_info,
         )
-        print("model saved.")
+        logger.info("model saved.")
 
 
 def setup_parser() -> argparse.ArgumentParser: