update loraplus on dylora/lofa_fa

networks/dylora.py

@@ -18,10 +18,13 @@
 import torch
 from torch import nn
 from library.utils import setup_logging
+
 setup_logging()
 import logging
+
 logger = logging.getLogger(__name__)
 
+
 class DyLoRAModule(torch.nn.Module):
     """
     replaces forward method of the original Linear, instead of replacing the original Linear module.
@@ -195,7 +198,7 @@ def create_network(
             conv_alpha = 1.0
         else:
             conv_alpha = float(conv_alpha)
-            
+
     if unit is not None:
         unit = int(unit)
     else:
@@ -211,6 +214,16 @@ def create_network(
         unit=unit,
         varbose=True,
     )
+
+    loraplus_lr_ratio = kwargs.get("loraplus_lr_ratio", None)
+    loraplus_unet_lr_ratio = kwargs.get("loraplus_unet_lr_ratio", None)
+    loraplus_text_encoder_lr_ratio = kwargs.get("loraplus_text_encoder_lr_ratio", None)
+    loraplus_lr_ratio = float(loraplus_lr_ratio) if loraplus_lr_ratio is not None else None
+    loraplus_unet_lr_ratio = float(loraplus_unet_lr_ratio) if loraplus_unet_lr_ratio is not None else None
+    loraplus_text_encoder_lr_ratio = float(loraplus_text_encoder_lr_ratio) if loraplus_text_encoder_lr_ratio is not None else None
+    if loraplus_lr_ratio is not None or loraplus_unet_lr_ratio is not None or loraplus_text_encoder_lr_ratio is not None:
+        network.set_loraplus_lr_ratio(loraplus_lr_ratio, loraplus_unet_lr_ratio, loraplus_text_encoder_lr_ratio)
+
     return network
 
 
@@ -280,6 +293,10 @@ def __init__(
         self.alpha = alpha
         self.apply_to_conv = apply_to_conv
 
+        self.loraplus_lr_ratio = None
+        self.loraplus_unet_lr_ratio = None
+        self.loraplus_text_encoder_lr_ratio = None
+
         if modules_dim is not None:
             logger.info("create LoRA network from weights")
         else:
@@ -320,9 +337,9 @@ def create_modules(is_unet, root_module: torch.nn.Module, target_replace_modules
                             lora = module_class(lora_name, child_module, self.multiplier, dim, alpha, unit)
                             loras.append(lora)
             return loras
-        
+
         text_encoders = text_encoder if type(text_encoder) == list else [text_encoder]
-        
+
         self.text_encoder_loras = []
         for i, text_encoder in enumerate(text_encoders):
             if len(text_encoders) > 1:
@@ -331,7 +348,7 @@ def create_modules(is_unet, root_module: torch.nn.Module, target_replace_modules
             else:
                 index = None
                 logger.info("create LoRA for Text Encoder")
-            
+
             text_encoder_loras = create_modules(False, text_encoder, DyLoRANetwork.TEXT_ENCODER_TARGET_REPLACE_MODULE)
             self.text_encoder_loras.extend(text_encoder_loras)
 
@@ -346,6 +363,11 @@ def create_modules(is_unet, root_module: torch.nn.Module, target_replace_modules
         self.unet_loras = create_modules(True, unet, target_modules)
         logger.info(f"create LoRA for U-Net: {len(self.unet_loras)} modules.")
 
+    def set_loraplus_lr_ratio(self, loraplus_lr_ratio, loraplus_unet_lr_ratio, loraplus_text_encoder_lr_ratio):
+        self.loraplus_lr_ratio = loraplus_lr_ratio
+        self.loraplus_unet_lr_ratio = loraplus_unet_lr_ratio
+        self.loraplus_text_encoder_lr_ratio = loraplus_text_encoder_lr_ratio
+
     def set_multiplier(self, multiplier):
         self.multiplier = multiplier
         for lora in self.text_encoder_loras + self.unet_loras:
@@ -407,15 +429,7 @@ def merge_to(self, text_encoder, unet, weights_sd, dtype, device):
     """
 
     # 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
-    ):
+    def prepare_optimizer_params(self, text_encoder_lr, unet_lr, default_lr):
         self.requires_grad_(True)
         all_params = []
 
@@ -452,15 +466,13 @@ def assemble_params(loras, lr, ratio):
             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
+                self.loraplus_text_encoder_lr_ratio or self.loraplus_ratio,
             )
             all_params.extend(params)
 
         if self.unet_loras:
             params = assemble_params(
-                self.unet_loras,
-                default_lr if unet_lr is None else unet_lr,
-                unet_loraplus_ratio or loraplus_ratio
+                self.unet_loras, default_lr if unet_lr is None else unet_lr, self.loraplus_unet_lr_ratio or self.loraplus_ratio
             )
             all_params.extend(params)
 

networks/lora.py

@@ -499,7 +499,8 @@ def create_network(
     loraplus_lr_ratio = float(loraplus_lr_ratio) if loraplus_lr_ratio is not None else None
     loraplus_unet_lr_ratio = float(loraplus_unet_lr_ratio) if loraplus_unet_lr_ratio is not None else None
     loraplus_text_encoder_lr_ratio = float(loraplus_text_encoder_lr_ratio) if loraplus_text_encoder_lr_ratio is not None else None
-    network.set_loraplus_lr_ratio(loraplus_lr_ratio, loraplus_unet_lr_ratio, loraplus_text_encoder_lr_ratio)
+    if loraplus_lr_ratio is not None or loraplus_unet_lr_ratio is not None or loraplus_text_encoder_lr_ratio is not None:
+        network.set_loraplus_lr_ratio(loraplus_lr_ratio, loraplus_unet_lr_ratio, loraplus_text_encoder_lr_ratio)
 
     if block_lr_weight is not None:
         network.set_block_lr_weight(block_lr_weight)
@@ -855,6 +856,10 @@ def __init__(
         self.rank_dropout = rank_dropout
         self.module_dropout = module_dropout
 
+        self.loraplus_lr_ratio = None
+        self.loraplus_unet_lr_ratio = None
+        self.loraplus_text_encoder_lr_ratio = None
+
         if modules_dim is not None:
             logger.info(f"create LoRA network from weights")
         elif block_dims is not None:

networks/lora_fa.py

@@ -15,8 +15,10 @@
 import torch
 import re
 from library.utils import setup_logging
+
 setup_logging()
 import logging
+
 logger = logging.getLogger(__name__)
 
 RE_UPDOWN = re.compile(r"(up|down)_blocks_(\d+)_(resnets|upsamplers|downsamplers|attentions)_(\d+)_")
@@ -504,6 +506,15 @@ def create_network(
     if up_lr_weight is not None or mid_lr_weight is not None or down_lr_weight is not None:
         network.set_block_lr_weight(up_lr_weight, mid_lr_weight, down_lr_weight)
 
+    loraplus_lr_ratio = kwargs.get("loraplus_lr_ratio", None)
+    loraplus_unet_lr_ratio = kwargs.get("loraplus_unet_lr_ratio", None)
+    loraplus_text_encoder_lr_ratio = kwargs.get("loraplus_text_encoder_lr_ratio", None)
+    loraplus_lr_ratio = float(loraplus_lr_ratio) if loraplus_lr_ratio is not None else None
+    loraplus_unet_lr_ratio = float(loraplus_unet_lr_ratio) if loraplus_unet_lr_ratio is not None else None
+    loraplus_text_encoder_lr_ratio = float(loraplus_text_encoder_lr_ratio) if loraplus_text_encoder_lr_ratio is not None else None
+    if loraplus_lr_ratio is not None or loraplus_unet_lr_ratio is not None or loraplus_text_encoder_lr_ratio is not None:
+        network.set_loraplus_lr_ratio(loraplus_lr_ratio, loraplus_unet_lr_ratio, loraplus_text_encoder_lr_ratio)
+
     return network
 
 
@@ -529,7 +540,9 @@ def parse_floats(s):
             len(block_dims) == num_total_blocks
         ), f"block_dims must have {num_total_blocks} elements / block_dimsは{num_total_blocks}個指定してください"
     else:
-        logger.warning(f"block_dims is not specified. all dims are set to {network_dim} / block_dimsが指定されていません。すべてのdimは{network_dim}になります")
+        logger.warning(
+            f"block_dims is not specified. all dims are set to {network_dim} / block_dimsが指定されていません。すべてのdimは{network_dim}になります"
+        )
         block_dims = [network_dim] * num_total_blocks
 
     if block_alphas is not None:
@@ -803,21 +816,31 @@ def __init__(
         self.rank_dropout = rank_dropout
         self.module_dropout = module_dropout
 
+        self.loraplus_lr_ratio = None
+        self.loraplus_unet_lr_ratio = None
+        self.loraplus_text_encoder_lr_ratio = None
+
         if modules_dim is not None:
             logger.info(f"create LoRA network from weights")
         elif block_dims is not None:
             logger.info(f"create LoRA network from block_dims")
-            logger.info(f"neuron dropout: p={self.dropout}, rank dropout: p={self.rank_dropout}, module dropout: p={self.module_dropout}")
+            logger.info(
+                f"neuron dropout: p={self.dropout}, rank dropout: p={self.rank_dropout}, module dropout: p={self.module_dropout}"
+            )
             logger.info(f"block_dims: {block_dims}")
             logger.info(f"block_alphas: {block_alphas}")
             if conv_block_dims is not None:
                 logger.info(f"conv_block_dims: {conv_block_dims}")
                 logger.info(f"conv_block_alphas: {conv_block_alphas}")
         else:
             logger.info(f"create LoRA network. base dim (rank): {lora_dim}, alpha: {alpha}")
-            logger.info(f"neuron dropout: p={self.dropout}, rank dropout: p={self.rank_dropout}, module dropout: p={self.module_dropout}")
+            logger.info(
+                f"neuron dropout: p={self.dropout}, rank dropout: p={self.rank_dropout}, module dropout: p={self.module_dropout}"
+            )
             if self.conv_lora_dim is not None:
-                logger.info(f"apply LoRA to Conv2d with kernel size (3,3). dim (rank): {self.conv_lora_dim}, alpha: {self.conv_alpha}")
+                logger.info(
+                    f"apply LoRA to Conv2d with kernel size (3,3). dim (rank): {self.conv_lora_dim}, alpha: {self.conv_alpha}"
+                )
 
         # create module instances
         def create_modules(
@@ -939,6 +962,11 @@ def create_modules(
             assert lora.lora_name not in names, f"duplicated lora name: {lora.lora_name}"
             names.add(lora.lora_name)
 
+    def set_loraplus_lr_ratio(self, loraplus_lr_ratio, loraplus_unet_lr_ratio, loraplus_text_encoder_lr_ratio):
+        self.loraplus_lr_ratio = loraplus_lr_ratio
+        self.loraplus_unet_lr_ratio = loraplus_unet_lr_ratio
+        self.loraplus_text_encoder_lr_ratio = loraplus_text_encoder_lr_ratio
+
     def set_multiplier(self, multiplier):
         self.multiplier = multiplier
         for lora in self.text_encoder_loras + self.unet_loras:
@@ -1033,15 +1061,7 @@ def get_lr_weight(self, lora: LoRAModule) -> float:
         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,
-        text_encoder_loraplus_ratio=None,
-        unet_loraplus_ratio=None,
-        loraplus_ratio=None
-    ):
+    def prepare_optimizer_params(self, text_encoder_lr, unet_lr, default_lr):
         self.requires_grad_(True)
         all_params = []
 
@@ -1078,7 +1098,7 @@ def assemble_params(loras, lr, ratio):
             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
+                self.loraplus_text_encoder_lr_ratio or self.loraplus_ratio,
             )
             all_params.extend(params)
 
@@ -1097,15 +1117,15 @@ def assemble_params(loras, lr, ratio):
                     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
+                        self.loraplus_unet_lr_ratio or self.loraplus_ratio,
                     )
                     all_params.extend(params)
 
             else:
                 params = assemble_params(
                     self.unet_loras,
                     unet_lr if unet_lr is not None else default_lr,
-                    unet_loraplus_ratio or loraplus_ratio
+                    self.loraplus_unet_lr_ratio or self.loraplus_ratio,
                 )
                 all_params.extend(params)