fix alpha is ignored

networks/lora_flux.py

@@ -307,7 +307,9 @@ def create_network_from_weights(multiplier, file, ae, text_encoders, flux, weigh
 
     module_class = LoRAInfModule if for_inference else LoRAModule
 
-    network = LoRANetwork(text_encoders, flux, multiplier=multiplier, module_class=module_class)
+    network = LoRANetwork(
+        text_encoders, flux, multiplier=multiplier, modules_dim=modules_dim, modules_alpha=modules_alpha, module_class=module_class
+    )
     return network, weights_sd
 
 
@@ -331,6 +333,8 @@ class LoRANetwork(torch.nn.Module):
         conv_lora_dim: Optional[int] = None,
         conv_alpha: Optional[float] = None,
         module_class: Type[object] = LoRAModule,
+        modules_dim: Optional[Dict[str, int]] = None,
+        modules_alpha: Optional[Dict[str, int]] = None,
         varbose: Optional[bool] = False,
     ) -> None:
         super().__init__()
@@ -348,6 +352,9 @@ class LoRANetwork(torch.nn.Module):
         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")
+        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}"
@@ -381,6 +388,12 @@ class LoRANetwork(torch.nn.Module):
                             dim = None
                             alpha = None
 
+                            if modules_dim is not None:
+                                # モジュール指定あり
+                                if lora_name in modules_dim:
+                                    dim = modules_dim[lora_name]
+                                    alpha = modules_alpha[lora_name]
+                            else:
                                 # 通常、すべて対象とする
                                 if is_linear or is_conv2d_1x1:
                                     dim = self.lora_dim