Merge pull request #1233 from rockerBOO/lora-plus

Add LoRA+ support

library/train_util.py

@@ -2920,6 +2920,9 @@ def add_optimizer_arguments(parser: argparse.ArgumentParser):
         default=1,
         help="Polynomial power for polynomial scheduler / polynomialスケジューラでのpolynomial power",
     )
+    parser.add_argument("--loraplus_lr_ratio", default=None, type=float, help="LoRA+ learning rate ratio")
+    parser.add_argument("--loraplus_unet_lr_ratio", default=None, type=float, help="LoRA+ UNet learning rate ratio")
+    parser.add_argument("--loraplus_text_encoder_lr_ratio", default=None, type=float, help="LoRA+ text encoder learning rate ratio")
 
 
 def add_training_arguments(parser: argparse.ArgumentParser, support_dreambooth: bool):

networks/dylora.py

@@ -406,27 +406,63 @@ class DyLoRANetwork(torch.nn.Module):
         logger.info(f"weights are merged")
     """
 
-    def prepare_optimizer_params(self, text_encoder_lr, unet_lr, default_lr):
+    # 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,
+        text_encoder_loraplus_ratio=None,
+        unet_loraplus_ratio=None,
+        loraplus_ratio=None
+    ):
         self.requires_grad_(True)
         all_params = []
 
-        def enumerate_params(loras):
-            params = []
+        def assemble_params(loras, lr, ratio):
+            param_groups = {"lora": {}, "plus": {}}
             for lora in loras:
-                params.extend(lora.parameters())
+                for name, param in lora.named_parameters():
+                    if ratio is not None and "lora_B" in name:
+                        param_groups["plus"][f"{lora.lora_name}.{name}"] = param
+                    else:
+                        param_groups["lora"][f"{lora.lora_name}.{name}"] = param
+
+            params = []
+            for key in param_groups.keys():
+                param_data = {"params": param_groups[key].values()}
+
+                if len(param_data["params"]) == 0:
+                    continue
+
+                if lr is not None:
+                    if key == "plus":
+                        param_data["lr"] = lr * ratio
+                    else:
+                        param_data["lr"] = lr
+
+                if param_data.get("lr", None) == 0 or param_data.get("lr", None) is None:
+                    continue
+
+                params.append(param_data)
+
             return params
 
         if self.text_encoder_loras:
-            param_data = {"params": enumerate_params(self.text_encoder_loras)}
-            if text_encoder_lr is not None:
-                param_data["lr"] = text_encoder_lr
-            all_params.append(param_data)
+            params = assemble_params(
+                self.text_encoder_loras,
+                text_encoder_lr if text_encoder_lr is not None else default_lr,
+                text_encoder_loraplus_ratio or loraplus_ratio
+            )
+            all_params.extend(params)
 
         if self.unet_loras:
-            param_data = {"params": enumerate_params(self.unet_loras)}
-            if unet_lr is not None:
-                param_data["lr"] = unet_lr
-            all_params.append(param_data)
+            params = assemble_params(
+                self.unet_loras,
+                default_lr if unet_lr is None else unet_lr,
+                unet_loraplus_ratio or loraplus_ratio
+            )
+            all_params.extend(params)
 
         return all_params
 

networks/lora.py

@@ -1034,21 +1034,55 @@ class LoRANetwork(torch.nn.Module):
         return lr_weight
 
     # 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):
+    def prepare_optimizer_params(
+        self,
+        text_encoder_lr,
+        unet_lr,
+        default_lr,
+        text_encoder_loraplus_ratio=None,
+        unet_loraplus_ratio=None,
+        loraplus_ratio=None
+    ):
         self.requires_grad_(True)
         all_params = []
 
-        def enumerate_params(loras):
-            params = []
+        def assemble_params(loras, lr, ratio):
+            param_groups = {"lora": {}, "plus": {}}
             for lora in loras:
-                params.extend(lora.parameters())
+                for name, param in lora.named_parameters():
+                    if ratio is not None and "lora_up" in name:
+                        param_groups["plus"][f"{lora.lora_name}.{name}"] = param
+                    else:
+                        param_groups["lora"][f"{lora.lora_name}.{name}"] = param
+
+            params = []
+            for key in param_groups.keys():
+                param_data = {"params": param_groups[key].values()}
+
+                if len(param_data["params"]) == 0:
+                    continue
+
+                if lr is not None:
+                    if key == "plus":
+                        param_data["lr"] = lr * ratio
+                    else:
+                        param_data["lr"] = lr
+
+                if param_data.get("lr", None) == 0 or param_data.get("lr", None) is None:
+                    print("NO LR skipping!")
+                    continue
+
+                params.append(param_data)
+
             return params
 
         if self.text_encoder_loras:
-            param_data = {"params": enumerate_params(self.text_encoder_loras)}
-            if text_encoder_lr is not None:
-                param_data["lr"] = text_encoder_lr
-            all_params.append(param_data)
+            params = assemble_params(
+                self.text_encoder_loras,
+                text_encoder_lr if text_encoder_lr is not None else default_lr,
+                text_encoder_loraplus_ratio or loraplus_ratio
+            )
+            all_params.extend(params)
 
         if self.unet_loras:
             if self.block_lr:
@@ -1062,21 +1096,20 @@ class LoRANetwork(torch.nn.Module):
 
                 # blockごとにパラメータを設定する
                 for idx, block_loras in block_idx_to_lora.items():
-                    param_data = {"params": enumerate_params(block_loras)}
-
-                    if unet_lr is not None:
-                        param_data["lr"] = unet_lr * self.get_lr_weight(block_loras[0])
-                    elif default_lr is not None:
-                        param_data["lr"] = default_lr * self.get_lr_weight(block_loras[0])
-                    if ("lr" in param_data) and (param_data["lr"] == 0):
-                        continue
-                    all_params.append(param_data)
+                    params = assemble_params(
+                        block_loras,
+                        (unet_lr if unet_lr is not None else default_lr) * self.get_lr_weight(block_loras[0]),
+                        unet_loraplus_ratio or loraplus_ratio
+                    )
+                    all_params.extend(params)
 
             else:
-                param_data = {"params": enumerate_params(self.unet_loras)}
-                if unet_lr is not None:
-                    param_data["lr"] = unet_lr
-                all_params.append(param_data)
+                params = assemble_params(
+                    self.unet_loras,
+                    unet_lr if unet_lr is not None else default_lr,
+                    unet_loraplus_ratio or loraplus_ratio
+                )
+                all_params.extend(params)
 
         return all_params
 

networks/lora_fa.py

@@ -1033,22 +1033,54 @@ class LoRANetwork(torch.nn.Module):
         return lr_weight
 
     # 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):
+    def prepare_optimizer_params(
+        self,
+        text_encoder_lr,
+        unet_lr,
+        default_lr,
+        text_encoder_loraplus_ratio=None,
+        unet_loraplus_ratio=None,
+        loraplus_ratio=None
+    ):
         self.requires_grad_(True)
         all_params = []
 
-        def enumerate_params(loras: List[LoRAModule]):
-            params = []
+        def assemble_params(loras, lr, ratio):
+            param_groups = {"lora": {}, "plus": {}}
             for lora in loras:
-                # params.extend(lora.parameters())
-                params.extend(lora.get_trainable_params())
+                for name, param in lora.named_parameters():
+                    if ratio is not None and "lora_up" in name:
+                        param_groups["plus"][f"{lora.lora_name}.{name}"] = param
+                    else:
+                        param_groups["lora"][f"{lora.lora_name}.{name}"] = param
+
+            params = []
+            for key in param_groups.keys():
+                param_data = {"params": param_groups[key].values()}
+
+                if len(param_data["params"]) == 0:
+                    continue
+
+                if lr is not None:
+                    if key == "plus":
+                        param_data["lr"] = lr * ratio
+                    else:
+                        param_data["lr"] = lr
+
+                if param_data.get("lr", None) == 0 or param_data.get("lr", None) is None:
+                    continue
+
+                params.append(param_data)
+
             return params
 
         if self.text_encoder_loras:
-            param_data = {"params": enumerate_params(self.text_encoder_loras)}
-            if text_encoder_lr is not None:
-                param_data["lr"] = text_encoder_lr
-            all_params.append(param_data)
+            params = assemble_params(
+                self.text_encoder_loras,
+                text_encoder_lr if text_encoder_lr is not None else default_lr,
+                text_encoder_loraplus_ratio or loraplus_ratio
+            )
+            all_params.extend(params)
 
         if self.unet_loras:
             if self.block_lr:
@@ -1062,21 +1094,20 @@ class LoRANetwork(torch.nn.Module):
 
                 # blockごとにパラメータを設定する
                 for idx, block_loras in block_idx_to_lora.items():
-                    param_data = {"params": enumerate_params(block_loras)}
-
-                    if unet_lr is not None:
-                        param_data["lr"] = unet_lr * self.get_lr_weight(block_loras[0])
-                    elif default_lr is not None:
-                        param_data["lr"] = default_lr * self.get_lr_weight(block_loras[0])
-                    if ("lr" in param_data) and (param_data["lr"] == 0):
-                        continue
-                    all_params.append(param_data)
+                    params = assemble_params(
+                        block_loras,
+                        (unet_lr if unet_lr is not None else default_lr) * self.get_lr_weight(block_loras[0]),
+                        unet_loraplus_ratio or loraplus_ratio
+                    )
+                    all_params.extend(params)
 
             else:
-                param_data = {"params": enumerate_params(self.unet_loras)}
-                if unet_lr is not None:
-                    param_data["lr"] = unet_lr
-                all_params.append(param_data)
+                params = assemble_params(
+                    self.unet_loras,
+                    unet_lr if unet_lr is not None else default_lr,
+                    unet_loraplus_ratio or loraplus_ratio
+                )
+                all_params.extend(params)
 
         return all_params
 
@@ -1093,6 +1124,9 @@ class LoRANetwork(torch.nn.Module):
     def get_trainable_params(self):
         return self.parameters()
 
+    def get_trainable_named_params(self):
+        return self.named_parameters()
+
     def save_weights(self, file, dtype, metadata):
         if metadata is not None and len(metadata) == 0:
             metadata = None

train_network.py

@@ -64,34 +64,69 @@ class NetworkTrainer:
 
         lrs = lr_scheduler.get_last_lr()
 
-        if args.network_train_text_encoder_only or len(lrs) <= 2:  # not block lr (or single block)
-            if args.network_train_unet_only:
-                logs["lr/unet"] = float(lrs[0])
-            elif args.network_train_text_encoder_only:
-                logs["lr/textencoder"] = float(lrs[0])
-            else:
-                logs["lr/textencoder"] = float(lrs[0])
-                logs["lr/unet"] = float(lrs[-1])  # may be same to textencoder
-
-            if (
-                args.optimizer_type.lower().startswith("DAdapt".lower()) or args.optimizer_type.lower() == "Prodigy".lower()
-            ):  # tracking d*lr value of unet.
-                logs["lr/d*lr"] = (
-                    lr_scheduler.optimizers[-1].param_groups[0]["d"] * lr_scheduler.optimizers[-1].param_groups[0]["lr"]
-                )
-        else:
+        if len(lrs) > 4:
             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])
+                lora_plus = ""
+                group_id = i
+
+                if args.loraplus_lr_ratio is not None or args.loraplus_unet_lr_ratio is not None:
+                    lora_plus = '_lora+' if i % 2 == 1 else '' 
+                    group_id = int((i / 2) + (i % 2 + 0.5))
+
+                logs[f"lr/group{group_id}{lora_plus}"] = 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}"] = (
+                    logs[f"lr/d*lr/group{group_id}{lora_plus}"] = (
                         lr_scheduler.optimizers[-1].param_groups[i]["d"] * lr_scheduler.optimizers[-1].param_groups[i]["lr"]
                     )
 
+        else:
+            if args.network_train_text_encoder_only:
+                if args.loraplus_lr_ratio is not None or  args.loraplus_text_encoder_lr_ratio is not None:
+                    logs["lr/textencoder"] = float(lrs[0])
+                    logs["lr/textencoder_lora+"] = float(lrs[1])
+                else:
+                    logs["lr/textencoder"] = float(lrs[0])
+                
+            elif args.network_train_unet_only:
+                if args.loraplus_lr_ratio is not None or args.loraplus_unet_lr_ratio is not None:
+                    logs["lr/unet"] = float(lrs[0])
+                    logs["lr/unet_lora+"] = float(lrs[1])
+                else:
+                    logs["lr/unet"] = float(lrs[0])
+            else:
+                if len(lrs) == 2:
+                    if args.loraplus_text_encoder_lr_ratio is not None and args.loraplus_unet_lr_ratio is None:
+                        logs["lr/textencoder"] = float(lrs[0])
+                        logs["lr/textencoder_lora+"] = float(lrs[1])
+                    elif args.loraplus_unet_lr_ratio is not None and args.loraplus_text_encoder_lr_ratio is None:
+                        logs["lr/unet"] = float(lrs[0])
+                        logs["lr/unet_lora+"] = float(lrs[1])
+                    elif args.loraplus_unet_lr_ratio is None and args.loraplus_text_encoder_lr_ratio is None and args.loraplus_lr_ratio is not None:
+                        logs["lr/all"] = float(lrs[0])
+                        logs["lr/all_lora+"] = float(lrs[1])
+                    else:
+                        logs["lr/textencoder"] = float(lrs[0])
+                        logs["lr/unet"] = float(lrs[-1])
+                elif len(lrs) == 4:
+                    logs["lr/textencoder"] = float(lrs[0])
+                    logs["lr/textencoder_lora+"] = float(lrs[1])
+                    logs["lr/unet"] = float(lrs[2])
+                    logs["lr/unet_lora+"] = float(lrs[3])
+                else:
+                    logs["lr/all"] = float(lrs[0])
+
+            if (
+                args.optimizer_type.lower().startswith("DAdapt".lower()) or args.optimizer_type.lower() == "Prodigy".lower()
+            ):  # tracking d*lr value of unet.
+                logs["lr/d*lr"] = (
+                    lr_scheduler.optimizers[-1].param_groups[0]["d"] * lr_scheduler.optimizers[-1].param_groups[0]["lr"]
+                )
+
         return logs
 
     def assert_extra_args(self, args, train_dataset_group):
@@ -338,7 +373,7 @@ class NetworkTrainer:
 
         # 後方互換性を確保するよ
         try:
-            trainable_params = network.prepare_optimizer_params(args.text_encoder_lr, args.unet_lr, args.learning_rate)
+            trainable_params = network.prepare_optimizer_params(args.text_encoder_lr, args.unet_lr, args.learning_rate, args.loraplus_text_encoder_lr_ratio, args.loraplus_unet_lr_ratio, args.loraplus_lr_ratio)
         except TypeError:
             accelerator.print(
                 "Deprecated: use prepare_optimizer_params(text_encoder_lr, unet_lr, learning_rate) instead of prepare_optimizer_params(text_encoder_lr, unet_lr)"
@@ -347,6 +382,11 @@ class NetworkTrainer:
 
         optimizer_name, optimizer_args, optimizer = train_util.get_optimizer(args, trainable_params)
 
+        if args.loraplus_lr_ratio is not None or args.loraplus_text_encoder_lr_ratio is not None or args.loraplus_unet_lr_ratio is not None:
+            assert (
+                (optimizer_name != "Prodigy" and "DAdapt" not in optimizer_name)
+            ), "LoRA+ and Prodigy/DAdaptation is not supported"
+
         # dataloaderを準備する
         # DataLoaderのプロセス数：0 は persistent_workers が使えないので注意
         n_workers = min(args.max_data_loader_n_workers, os.cpu_count())  # cpu_count or max_data_loader_n_workers