diff --git a/library/train_util.py b/library/train_util.py
index b65f06b9..672aa597 100644
--- a/library/train_util.py
+++ b/library/train_util.py
@@ -6253,10 +6253,14 @@ def append_lr_to_logs_with_names(logs, lr_scheduler, optimizer_type, names):
         name = names[lr_index]
         logs["lr/" + name] = float(lrs[lr_index])
 
-        if optimizer_type.lower().startswith("DAdapt".lower()) or optimizer_type.lower() == "Prodigy".lower():
+        if optimizer_type.lower().startswith("DAdapt".lower()) or optimizer_type.lower().startswith("Prodigy".lower()):
             logs["lr/d*lr/" + name] = (
                 lr_scheduler.optimizers[-1].param_groups[lr_index]["d"] * lr_scheduler.optimizers[-1].param_groups[lr_index]["lr"]
             )
+            if "effective_lr" in lr_scheduler.optimizers[-1].param_groups[lr_index]:
+                logs["lr/d*eff_lr/" + name] = (
+                    lr_scheduler.optimizers[-1].param_groups[lr_index]["d"] * lr_scheduler.optimizers[-1].param_groups[lr_index]["effective_lr"]
+                )
 
 
 # scheduler:
diff --git a/train_network.py b/train_network.py
index 2ee671e9..6b8ed9bd 100644
--- a/train_network.py
+++ b/train_network.py
@@ -90,40 +90,23 @@ class NetworkTrainer:
             if lr_descriptions is not None:
                 lr_desc = lr_descriptions[i]
             else:
-                idx = i - (0 if args.network_train_unet_only else -1)
+                idx = i - (0 if args.network_train_unet_only else 1)
                 if idx == -1:
                     lr_desc = "textencoder"
                 else:
                     if len(lrs) > 2:
-                        lr_desc = f"group{idx}"
+                        lr_desc = f"group{i}"
                     else:
                         lr_desc = "unet"
 
             logs[f"lr/{lr_desc}"] = lr
 
-            if args.optimizer_type.lower().startswith("DAdapt".lower()) or args.optimizer_type.lower() == "Prodigy".lower():
-                # tracking d*lr value
-                logs[f"lr/d*lr/{lr_desc}"] = (
-                    lr_scheduler.optimizers[-1].param_groups[i]["d"] * lr_scheduler.optimizers[-1].param_groups[i]["lr"]
-                )
-            if (
-                args.optimizer_type.lower().endswith("ProdigyPlusScheduleFree".lower()) and optimizer is not None
-            ):  # tracking d*lr value of unet.
-                logs["lr/d*lr"] = optimizer.param_groups[0]["d"] * optimizer.param_groups[0]["lr"]
-        else:
-            idx = 0
-            if not args.network_train_unet_only:
-                logs["lr/textencoder"] = float(lrs[0])
-                idx = 1
-
-            for i in range(idx, len(lrs)):
-                logs[f"lr/group{i}"] = float(lrs[i])
-                if args.optimizer_type.lower().startswith("DAdapt".lower()) or args.optimizer_type.lower() == "Prodigy".lower():
-                    logs[f"lr/d*lr/group{i}"] = (
-                        lr_scheduler.optimizers[-1].param_groups[i]["d"] * lr_scheduler.optimizers[-1].param_groups[i]["lr"]
-                    )
-                if args.optimizer_type.lower().endswith("ProdigyPlusScheduleFree".lower()) and optimizer is not None:
-                    logs[f"lr/d*lr/group{i}"] = optimizer.param_groups[i]["d"] * optimizer.param_groups[i]["lr"]
+            if args.optimizer_type.lower().startswith("DAdapt".lower()) or args.optimizer_type.lower().startswith("Prodigy".lower()):
+                opt = lr_scheduler.optimizers[-1] if hasattr(lr_scheduler, "optimizers") else optimizer
+                if opt is not None:
+                    logs[f"lr/d*lr/{lr_desc}"] = opt.param_groups[i]["d"] * opt.param_groups[i]["lr"]
+                    if "effective_lr" in opt.param_groups[i]:
+                        logs[f"lr/d*eff_lr/{lr_desc}"] = opt.param_groups[i]["d"] * opt.param_groups[i]["effective_lr"]
 
         return logs