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>

fine_tune.py

@@ -18,6 +18,10 @@ init_ipex()
 from accelerate.utils import set_seed
 from diffusers import DDPMScheduler
 
+from library.utils import setup_logging
+setup_logging()
+import logging
+logger = logging.getLogger(__name__)
 import library.train_util as train_util
 import library.config_util as config_util
 from library.config_util import (
@@ -49,11 +53,11 @@ def train(args):
     if args.dataset_class is None:
         blueprint_generator = BlueprintGenerator(ConfigSanitizer(False, 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)
                     )
@@ -86,7 +90,7 @@ def train(args):
         train_util.debug_dataset(train_dataset_group)
         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
@@ -97,7 +101,7 @@ def train(args):
         ), "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")
+    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
@@ -461,7 +465,7 @@ def train(args):
         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.")
+        logger.info("model saved.")
 
 
 def setup_parser() -> argparse.ArgumentParser: