Revert "Replace most print()s with logging calls (#42)" (#65)

This reverts commit 6f6d3f8716.

sgm/modules/diffusionmodules/model.py

@@ -1,5 +1,4 @@
 # pytorch_diffusion + derived encoder decoder
-import logging
 import math
 from typing import Any, Callable, Optional
 
@@ -9,8 +8,6 @@ import torch.nn as nn
 from einops import rearrange
 from packaging import version
 
-logger = logging.getLogger(__name__)
-
 try:
     import xformers
     import xformers.ops
@@ -18,7 +15,7 @@ try:
     XFORMERS_IS_AVAILABLE = True
 except:
     XFORMERS_IS_AVAILABLE = False
-    logger.debug("no module 'xformers'. Processing without...")
+    print("no module 'xformers'. Processing without...")
 
 from ...modules.attention import LinearAttention, MemoryEfficientCrossAttention
 
@@ -291,14 +288,12 @@ def make_attn(in_channels, attn_type="vanilla", attn_kwargs=None):
             f"as it is too expensive. Please install xformers via e.g. 'pip install xformers==0.0.16'"
         )
         attn_type = "vanilla-xformers"
-    logger.debug(f"making attention of type '{attn_type}' with {in_channels} in_channels")
+    print(f"making attention of type '{attn_type}' with {in_channels} in_channels")
     if attn_type == "vanilla":
         assert attn_kwargs is None
         return AttnBlock(in_channels)
     elif attn_type == "vanilla-xformers":
-        logger.debug(
-            f"building MemoryEfficientAttnBlock with {in_channels} in_channels..."
-        )
+        print(f"building MemoryEfficientAttnBlock with {in_channels} in_channels...")
         return MemoryEfficientAttnBlock(in_channels)
     elif type == "memory-efficient-cross-attn":
         attn_kwargs["query_dim"] = in_channels
@@ -638,8 +633,10 @@ class Decoder(nn.Module):
         block_in = ch * ch_mult[self.num_resolutions - 1]
         curr_res = resolution // 2 ** (self.num_resolutions - 1)
         self.z_shape = (1, z_channels, curr_res, curr_res)
-        logger.debug(
-            f"Working with z of shape {self.z_shape} = {np.prod(self.z_shape)} dimensions."
+        print(
+            "Working with z of shape {} = {} dimensions.".format(
+                self.z_shape, np.prod(self.z_shape)
+            )
         )
 
         make_attn_cls = self._make_attn()