Commented out debugging lines

sd_scripts/library/custom_train_functions.py

@@ -490,14 +490,14 @@ def apply_multichannel_masked_loss(loss, batch, weight1, weight2, weight3):
     mask1 = batch["conditioning_images"].to(dtype=loss.dtype)[:, 0].unsqueeze(1)
     mask2 = batch["conditioning_images"].to(dtype=loss.dtype)[:, 1].unsqueeze(1)
 
-    # Logging for debugging if needed
-    threshold = 0.5
-    red_pixels = (mask1 > threshold).sum().item()
-    green_pixels = (mask2 > threshold).sum().item()
-
-    total_pixels = mask1.numel()
-
-    logger.info(f"Red pixels: {red_pixels}, green pixels: {green_pixels}, total pixels: {total_pixels}")
+    # # Logging for debugging if needed
+    # threshold = 0.5
+    # red_pixels = (mask1 > threshold).sum().item()
+    # green_pixels = (mask2 > threshold).sum().item()
+    #
+    # total_pixels = mask1.numel()
+    #
+    # logger.info(f"Red pixels: {red_pixels}, green pixels: {green_pixels}, total pixels: {total_pixels}")
 
     # resize to the same size as the loss
     mask1 = torch.nn.functional.interpolate(mask1, size=loss.shape[2:], mode="area")
@@ -522,11 +522,11 @@ def apply_multichannel_masked_loss(loss, batch, weight1, weight2, weight3):
     detail_loss = (detail_weight * (torch.log(torch.tensor(loss.numel(), device=loss.device).float()) - torch.log(bowtie_pixels.float())) * (loss * mask2))
 
     detail_loss = torch.maximum(torch.tensor(0.0, dtype=loss.dtype, device=loss.device), -1 + detail_loss)
-
-    logger.info(f"Previous loss: {loss.sum()}")
-    logger.info(f"Total background loss: {background_loss.sum()}")
-    logger.info(f"Total character loss: {character_loss.sum()}")
-    logger.info(f"Total detail loss: {detail_loss.sum()}")
+    #
+    # logger.info(f"Previous loss: {loss.sum()}")
+    # logger.info(f"Total background loss: {background_loss.sum()}")
+    # logger.info(f"Total character loss: {character_loss.sum()}")
+    # logger.info(f"Total detail loss: {detail_loss.sum()}")
 
     final_loss = background_loss + character_loss + detail_loss