Replace most print()s with logging calls (#42)

sgm/modules/encoders/modules.py

@@ -1,3 +1,4 @@
+import logging
 from contextlib import nullcontext
 from functools import partial
 from typing import Dict, List, Optional, Tuple, Union
@@ -32,6 +33,8 @@ from ...util import (
     instantiate_from_config,
 )
 
+logger = logging.getLogger(__name__)
+
 
 class AbstractEmbModel(nn.Module):
     def __init__(self):
@@ -96,7 +99,7 @@ class GeneralConditioner(nn.Module):
                 for param in embedder.parameters():
                     param.requires_grad = False
                 embedder.eval()
-            print(
+            logger.debug(
                 f"Initialized embedder #{n}: {embedder.__class__.__name__} "
                 f"with {count_params(embedder, False)} params. Trainable: {embedder.is_trainable}"
             )
@@ -727,7 +730,7 @@ class FrozenOpenCLIPImageEmbedder(AbstractEmbModel):
             )
             if tokens is not None:
                 tokens = rearrange(tokens, "(b n) t d -> b t (n d)", n=self.max_crops)
-                print(
+                logger.warning(
                     f"You are running very experimental token-concat in {self.__class__.__name__}. "
                     f"Check what you are doing, and then remove this message."
                 )
@@ -753,7 +756,7 @@ class FrozenCLIPT5Encoder(AbstractEmbModel):
             clip_version, device, max_length=clip_max_length
         )
         self.t5_encoder = FrozenT5Embedder(t5_version, device, max_length=t5_max_length)
-        print(
+        logger.debug(
             f"{self.clip_encoder.__class__.__name__} has {count_params(self.clip_encoder) * 1.e-6:.2f} M parameters, "
             f"{self.t5_encoder.__class__.__name__} comes with {count_params(self.t5_encoder) * 1.e-6:.2f} M params."
         )
@@ -795,7 +798,7 @@ class SpatialRescaler(nn.Module):
         self.interpolator = partial(torch.nn.functional.interpolate, mode=method)
         self.remap_output = out_channels is not None or remap_output
         if self.remap_output:
-            print(
+            logger.debug(
                 f"Spatial Rescaler mapping from {in_channels} to {out_channels} channels after resizing."
             )
             self.channel_mapper = nn.Conv2d(