4 files changed, 63 insertions, 63 deletions
diff --git a/extensions-builtin/Lora/lora_patches.py b/extensions-builtin/Lora/lora_patches.py
new file mode 100644
index 00000000..b394d8e9
--- /dev/null
+++ b/extensions-builtin/Lora/lora_patches.py
@@ -0,0 +1,31 @@
+import torch
+
+import networks
+from modules import patches
+
+
+class LoraPatches:
+    def __init__(self):
+        self.Linear_forward = patches.patch(__name__, torch.nn.Linear, 'forward', networks.network_Linear_forward)
+        self.Linear_load_state_dict = patches.patch(__name__, torch.nn.Linear, '_load_from_state_dict', networks.network_Linear_load_state_dict)
+        self.Conv2d_forward = patches.patch(__name__, torch.nn.Conv2d, 'forward', networks.network_Conv2d_forward)
+        self.Conv2d_load_state_dict = patches.patch(__name__, torch.nn.Conv2d, '_load_from_state_dict', networks.network_Conv2d_load_state_dict)
+        self.GroupNorm_forward = patches.patch(__name__, torch.nn.GroupNorm, 'forward', networks.network_GroupNorm_forward)
+        self.GroupNorm_load_state_dict = patches.patch(__name__, torch.nn.GroupNorm, '_load_from_state_dict', networks.network_GroupNorm_load_state_dict)
+        self.LayerNorm_forward = patches.patch(__name__, torch.nn.LayerNorm, 'forward', networks.network_LayerNorm_forward)
+        self.LayerNorm_load_state_dict = patches.patch(__name__, torch.nn.LayerNorm, '_load_from_state_dict', networks.network_LayerNorm_load_state_dict)
+        self.MultiheadAttention_forward = patches.patch(__name__, torch.nn.MultiheadAttention, 'forward', networks.network_MultiheadAttention_forward)
+        self.MultiheadAttention_load_state_dict = patches.patch(__name__, torch.nn.MultiheadAttention, '_load_from_state_dict', networks.network_MultiheadAttention_load_state_dict)
+
+    def undo(self):
+        self.Linear_forward = patches.undo(__name__, torch.nn.Linear, 'forward')
+        self.Linear_load_state_dict = patches.undo(__name__, torch.nn.Linear, '_load_from_state_dict')
+        self.Conv2d_forward = patches.undo(__name__, torch.nn.Conv2d, 'forward')
+        self.Conv2d_load_state_dict = patches.undo(__name__, torch.nn.Conv2d, '_load_from_state_dict')
+        self.GroupNorm_forward = patches.undo(__name__, torch.nn.GroupNorm, 'forward')
+        self.GroupNorm_load_state_dict = patches.undo(__name__, torch.nn.GroupNorm, '_load_from_state_dict')
+        self.LayerNorm_forward = patches.undo(__name__, torch.nn.LayerNorm, 'forward')
+        self.LayerNorm_load_state_dict = patches.undo(__name__, torch.nn.LayerNorm, '_load_from_state_dict')
+        self.MultiheadAttention_forward = patches.undo(__name__, torch.nn.MultiheadAttention, 'forward')
+        self.MultiheadAttention_load_state_dict = patches.undo(__name__, torch.nn.MultiheadAttention, '_load_from_state_dict')
+
diff --git a/extensions-builtin/Lora/network_full.py b/extensions-builtin/Lora/network_full.py
index 109b4c2c..bf6930e9 100644
--- a/extensions-builtin/Lora/network_full.py
+++ b/extensions-builtin/Lora/network_full.py
@@ -14,9 +14,14 @@ class NetworkModuleFull(network.NetworkModule):
         super().__init__(net, weights)
 
         self.weight = weights.w.get("diff")
+        self.ex_bias = weights.w.get("diff_b")
 
     def calc_updown(self, orig_weight):
         output_shape = self.weight.shape
         updown = self.weight.to(orig_weight.device, dtype=orig_weight.dtype)
+        if self.ex_bias is not None:
+            ex_bias = self.ex_bias.to(orig_weight.device, dtype=orig_weight.dtype)
+        else:
+            ex_bias = None
 
-        return self.finalize_updown(updown, orig_weight, output_shape)
+        return self.finalize_updown(updown, orig_weight, output_shape, ex_bias)
diff --git a/extensions-builtin/Lora/networks.py b/extensions-builtin/Lora/networks.py
index 22fdff4a..96f935b2 100644
--- a/extensions-builtin/Lora/networks.py
+++ b/extensions-builtin/Lora/networks.py
@@ -2,6 +2,7 @@ import logging
 import os
 import re
 
+import lora_patches
 import network
 import network_lora
 import network_hada
@@ -303,7 +304,10 @@ def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn
     wanted_names = tuple((x.name, x.te_multiplier, x.unet_multiplier, x.dyn_dim) for x in loaded_networks)
 
     weights_backup = getattr(self, "network_weights_backup", None)
-    if weights_backup is None:
+    if weights_backup is None and wanted_names != ():
+        if current_names != ():
+            raise RuntimeError("no backup weights found and current weights are not unchanged")
+
         if isinstance(self, torch.nn.MultiheadAttention):
             weights_backup = (self.in_proj_weight.to(devices.cpu, copy=True), self.out_proj.weight.to(devices.cpu, copy=True))
         else:
@@ -418,74 +422,74 @@ def network_reset_cached_weight(self: Union[torch.nn.Conv2d, torch.nn.Linear]):
 
 def network_Linear_forward(self, input):
     if shared.opts.lora_functional:
-        return network_forward(self, input, torch.nn.Linear_forward_before_network)
+        return network_forward(self, input, originals.Linear_forward)
 
     network_apply_weights(self)
 
-    return torch.nn.Linear_forward_before_network(self, input)
+    return originals.Linear_forward(self, input)
 
 
 def network_Linear_load_state_dict(self, *args, **kwargs):
     network_reset_cached_weight(self)
 
-    return torch.nn.Linear_load_state_dict_before_network(self, *args, **kwargs)
+    return originals.Linear_load_state_dict(self, *args, **kwargs)
 
 
 def network_Conv2d_forward(self, input):
     if shared.opts.lora_functional:
-        return network_forward(self, input, torch.nn.Conv2d_forward_before_network)
+        return network_forward(self, input, originals.Conv2d_forward)
 
     network_apply_weights(self)
 
-    return torch.nn.Conv2d_forward_before_network(self, input)
+    return originals.Conv2d_forward(self, input)
 
 
 def network_Conv2d_load_state_dict(self, *args, **kwargs):
     network_reset_cached_weight(self)
 
-    return torch.nn.Conv2d_load_state_dict_before_network(self, *args, **kwargs)
+    return originals.Conv2d_load_state_dict(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)
+        return network_forward(self, input, originals.GroupNorm_forward)
 
     network_apply_weights(self)
 
-    return torch.nn.GroupNorm_forward_before_network(self, input)
+    return originals.GroupNorm_forward(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)
+    return originals.GroupNorm_load_state_dict(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)
+        return network_forward(self, input, originals.LayerNorm_forward)
 
     network_apply_weights(self)
 
-    return torch.nn.LayerNorm_forward_before_network(self, input)
+    return originals.LayerNorm_forward(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)
+    return originals.LayerNorm_load_state_dict(self, *args, **kwargs)
 
 
 def network_MultiheadAttention_forward(self, *args, **kwargs):
     network_apply_weights(self)
 
-    return torch.nn.MultiheadAttention_forward_before_network(self, *args, **kwargs)
+    return originals.MultiheadAttention_forward(self, *args, **kwargs)
 
 
 def network_MultiheadAttention_load_state_dict(self, *args, **kwargs):
     network_reset_cached_weight(self)
 
-    return torch.nn.MultiheadAttention_load_state_dict_before_network(self, *args, **kwargs)
+    return originals.MultiheadAttention_load_state_dict(self, *args, **kwargs)
 
 
 def list_available_networks():
@@ -552,6 +556,9 @@ def infotext_pasted(infotext, params):
     if added:
         params["Prompt"] += "\n" + "".join(added)
 
+
+originals: lora_patches.LoraPatches = None
+
 extra_network_lora = None
 
 available_networks = {}
diff --git a/extensions-builtin/Lora/scripts/lora_script.py b/extensions-builtin/Lora/scripts/lora_script.py
index 4c6e774a..ef23968c 100644
--- a/extensions-builtin/Lora/scripts/lora_script.py
+++ b/extensions-builtin/Lora/scripts/lora_script.py
@@ -1,23 +1,19 @@
 import re
 
-import torch
 import gradio as gr
 from fastapi import FastAPI
 
 import network
 import networks
 import lora  # noqa:F401
+import lora_patches
 import extra_networks_lora
 import ui_extra_networks_lora
 from modules import script_callbacks, ui_extra_networks, extra_networks, shared
 
+
 def unload():
-    torch.nn.Linear.forward = torch.nn.Linear_forward_before_network
-    torch.nn.Linear._load_from_state_dict = torch.nn.Linear_load_state_dict_before_network
-    torch.nn.Conv2d.forward = torch.nn.Conv2d_forward_before_network
-    torch.nn.Conv2d._load_from_state_dict = torch.nn.Conv2d_load_state_dict_before_network
-    torch.nn.MultiheadAttention.forward = torch.nn.MultiheadAttention_forward_before_network
-    torch.nn.MultiheadAttention._load_from_state_dict = torch.nn.MultiheadAttention_load_state_dict_before_network
+    networks.originals.undo()
 
 
 def before_ui():
@@ -28,46 +24,7 @@ def before_ui():
     extra_networks.register_extra_network_alias(networks.extra_network_lora, "lyco")
 
 
-if not hasattr(torch.nn, 'Linear_forward_before_network'):
-    torch.nn.Linear_forward_before_network = torch.nn.Linear.forward
-
-if not hasattr(torch.nn, 'Linear_load_state_dict_before_network'):
-    torch.nn.Linear_load_state_dict_before_network = torch.nn.Linear._load_from_state_dict
-
-if not hasattr(torch.nn, 'Conv2d_forward_before_network'):
-    torch.nn.Conv2d_forward_before_network = torch.nn.Conv2d.forward
-
-if not hasattr(torch.nn, 'Conv2d_load_state_dict_before_network'):
-    torch.nn.Conv2d_load_state_dict_before_network = torch.nn.Conv2d._load_from_state_dict
-
-if not hasattr(torch.nn, 'GroupNorm_forward_before_network'):
-    torch.nn.GroupNorm_forward_before_network = torch.nn.GroupNorm.forward
-
-if not hasattr(torch.nn, 'GroupNorm_load_state_dict_before_network'):
-    torch.nn.GroupNorm_load_state_dict_before_network = torch.nn.GroupNorm._load_from_state_dict
-
-if not hasattr(torch.nn, 'LayerNorm_forward_before_network'):
-    torch.nn.LayerNorm_forward_before_network = torch.nn.LayerNorm.forward
-
-if not hasattr(torch.nn, 'LayerNorm_load_state_dict_before_network'):
-    torch.nn.LayerNorm_load_state_dict_before_network = torch.nn.LayerNorm._load_from_state_dict
-
-if not hasattr(torch.nn, 'MultiheadAttention_forward_before_network'):
-    torch.nn.MultiheadAttention_forward_before_network = torch.nn.MultiheadAttention.forward
-
-if not hasattr(torch.nn, 'MultiheadAttention_load_state_dict_before_network'):
-    torch.nn.MultiheadAttention_load_state_dict_before_network = torch.nn.MultiheadAttention._load_from_state_dict
-
-torch.nn.Linear.forward = networks.network_Linear_forward
-torch.nn.Linear._load_from_state_dict = networks.network_Linear_load_state_dict
-torch.nn.Conv2d.forward = networks.network_Conv2d_forward
-torch.nn.Conv2d._load_from_state_dict = networks.network_Conv2d_load_state_dict
-torch.nn.GroupNorm.forward = networks.network_GroupNorm_forward
-torch.nn.GroupNorm._load_from_state_dict = networks.network_GroupNorm_load_state_dict
-torch.nn.LayerNorm.forward = networks.network_LayerNorm_forward
-torch.nn.LayerNorm._load_from_state_dict = networks.network_LayerNorm_load_state_dict
-torch.nn.MultiheadAttention.forward = networks.network_MultiheadAttention_forward
-torch.nn.MultiheadAttention._load_from_state_dict = networks.network_MultiheadAttention_load_state_dict
+networks.originals = lora_patches.LoraPatches()
 
 script_callbacks.on_model_loaded(networks.assign_network_names_to_compvis_modules)
 script_callbacks.on_script_unloaded(unload)