Add extra norm module into built-in lora ext

refer to LyCORIS 1.9.0.dev6
add new option and module for training norm layer
(Which is reported to be good for style)

extensions-builtin/Lora/networks.py

@@ -7,6 +7,7 @@ import network_hada
 import network_ia3
 import network_lokr
 import network_full
+import network_norm
 
 import torch
 from typing import Union
@@ -19,6 +20,7 @@ module_types = [
     network_ia3.ModuleTypeIa3(),
     network_lokr.ModuleTypeLokr(),
     network_full.ModuleTypeFull(),
+    network_norm.ModuleTypeNorm(),
 ]
 
 
@@ -31,6 +33,8 @@ suffix_conversion = {
     "resnets": {
         "conv1": "in_layers_2",
         "conv2": "out_layers_3",
+        "norm1": "in_layers_0",
+        "norm2": "out_layers_0",
         "time_emb_proj": "emb_layers_1",
         "conv_shortcut": "skip_connection",
     }
@@ -258,20 +262,25 @@ def load_networks(names, te_multipliers=None, unet_multipliers=None, dyn_dims=No
     purge_networks_from_memory()
 
 
-def network_restore_weights_from_backup(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.MultiheadAttention]):
+def network_restore_weights_from_backup(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.GroupNorm, torch.nn.LayerNorm, torch.nn.MultiheadAttention]):
     weights_backup = getattr(self, "network_weights_backup", None)
+    bias_backup = getattr(self, "network_bias_backup", None)
 
-    if weights_backup is None:
+    if weights_backup is None and bias_backup is None:
         return
 
-    if isinstance(self, torch.nn.MultiheadAttention):
-        self.in_proj_weight.copy_(weights_backup[0])
-        self.out_proj.weight.copy_(weights_backup[1])
-    else:
-        self.weight.copy_(weights_backup)
+    if weights_backup is not None:
+        if isinstance(self, torch.nn.MultiheadAttention):
+            self.in_proj_weight.copy_(weights_backup[0])
+            self.out_proj.weight.copy_(weights_backup[1])
+        else:
+            self.weight.copy_(weights_backup)
+
+    if bias_backup is not None:
+        self.bias.copy_(bias_backup)
 
 
-def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.MultiheadAttention]):
+def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.GroupNorm, torch.nn.LayerNorm, torch.nn.MultiheadAttention]):
     """
     Applies the currently selected set of networks to the weights of torch layer self.
     If weights already have this particular set of networks applied, does nothing.
@@ -294,6 +303,11 @@ def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn
 
         self.network_weights_backup = weights_backup
 
+    bias_backup = getattr(self, "network_bias_backup", None)
+    if bias_backup is None and getattr(self, 'bias', None) is not None:
+        bias_backup = self.bias.to(devices.cpu, copy=True)
+        self.network_bias_backup = bias_backup
+
     if current_names != wanted_names:
         network_restore_weights_from_backup(self)
 
@@ -301,13 +315,15 @@ def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn
             module = net.modules.get(network_layer_name, None)
             if module is not None and hasattr(self, 'weight'):
                 with torch.no_grad():
-                    updown = module.calc_updown(self.weight)
+                    updown, ex_bias = module.calc_updown(self.weight)
 
                     if len(self.weight.shape) == 4 and self.weight.shape[1] == 9:
                         # inpainting model. zero pad updown to make channel[1]  4 to 9
                         updown = torch.nn.functional.pad(updown, (0, 0, 0, 0, 0, 5))
 
                     self.weight += updown
+                    if getattr(self, 'bias', None) is not None:
+                        self.bias += ex_bias
                     continue
 
             module_q = net.modules.get(network_layer_name + "_q_proj", None)
@@ -397,6 +413,36 @@ def network_Conv2d_load_state_dict(self, *args, **kwargs):
     return torch.nn.Conv2d_load_state_dict_before_network(self, *args, **kwargs)
 
 
+def network_GroupNorm_forward(self, input):
+    if shared.opts.lora_functional:
+        return network_forward(self, input, torch.nn.GroupNorm_forward_before_network)
+
+    network_apply_weights(self)
+
+    return torch.nn.GroupNorm_forward_before_network(self, input)
+
+
+def network_GroupNorm_load_state_dict(self, *args, **kwargs):
+    network_reset_cached_weight(self)
+
+    return torch.nn.GroupNorm_load_state_dict_before_network(self, *args, **kwargs)
+
+
+def network_LayerNorm_forward(self, input):
+    if shared.opts.lora_functional:
+        return network_forward(self, input, torch.nn.LayerNorm_forward_before_network)
+
+    network_apply_weights(self)
+
+    return torch.nn.LayerNorm_forward_before_network(self, input)
+
+
+def network_LayerNorm_load_state_dict(self, *args, **kwargs):
+    network_reset_cached_weight(self)
+
+    return torch.nn.LayerNorm_load_state_dict_before_network(self, *args, **kwargs)
+
+
 def network_MultiheadAttention_forward(self, *args, **kwargs):
     network_apply_weights(self)