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>

networks/oft.py

@@ -8,7 +8,10 @@ from transformers import CLIPTextModel
 import numpy as np
 import torch
 import re
-
+from library.utils import setup_logging
+setup_logging()
+import logging
+logger = logging.getLogger(__name__)
 
 RE_UPDOWN = re.compile(r"(up|down)_blocks_(\d+)_(resnets|upsamplers|downsamplers|attentions)_(\d+)_")
 
@@ -237,7 +240,7 @@ class OFTNetwork(torch.nn.Module):
         self.dim = dim
         self.alpha = alpha
 
-        print(
+        logger.info(
             f"create OFT network. num blocks: {self.dim}, constraint: {self.alpha}, multiplier: {self.multiplier}, enable_conv: {enable_conv}"
         )
 
@@ -258,7 +261,7 @@ class OFTNetwork(torch.nn.Module):
                         if is_linear or is_conv2d_1x1 or (is_conv2d and enable_conv):
                             oft_name = prefix + "." + name + "." + child_name
                             oft_name = oft_name.replace(".", "_")
-                            # print(oft_name)
+                            # logger.info(oft_name)
 
                             oft = module_class(
                                 oft_name,
@@ -279,7 +282,7 @@ class OFTNetwork(torch.nn.Module):
             target_modules += OFTNetwork.UNET_TARGET_REPLACE_MODULE_CONV2D_3X3
 
         self.unet_ofts: List[OFTModule] = create_modules(unet, target_modules)
-        print(f"create OFT for U-Net: {len(self.unet_ofts)} modules.")
+        logger.info(f"create OFT for U-Net: {len(self.unet_ofts)} modules.")
 
         # assertion
         names = set()
@@ -316,7 +319,7 @@ class OFTNetwork(torch.nn.Module):
 
     # TODO refactor to common function with apply_to
     def merge_to(self, text_encoder, unet, weights_sd, dtype, device):
-        print("enable OFT for U-Net")
+        logger.info("enable OFT for U-Net")
 
         for oft in self.unet_ofts:
             sd_for_lora = {}
@@ -326,7 +329,7 @@ class OFTNetwork(torch.nn.Module):
             oft.load_state_dict(sd_for_lora, False)
             oft.merge_to()
 
-        print(f"weights are merged")
+        logger.info(f"weights are merged")
 
     # 二つのText Encoderに別々の学習率を設定できるようにするといいかも
     def prepare_optimizer_params(self, text_encoder_lr, unet_lr, default_lr):
@@ -338,11 +341,11 @@ class OFTNetwork(torch.nn.Module):
             for oft in ofts:
                 params.extend(oft.parameters())
 
-            # print num of params
+            # logger.info num of params
             num_params = 0
             for p in params:
                 num_params += p.numel()
-            print(f"OFT params: {num_params}")
+            logger.info(f"OFT params: {num_params}")
             return params
 
         param_data = {"params": enumerate_params(self.unet_ofts)}