Implement GLora.

comfy/lora.py

@@ -118,6 +118,17 @@ def load_lora(lora, to_load):
         if (lokr_w1 is not None) or (lokr_w2 is not None) or (lokr_w1_a is not None) or (lokr_w2_a is not None):
             patch_dict[to_load[x]] = ("lokr", (lokr_w1, lokr_w2, alpha, lokr_w1_a, lokr_w1_b, lokr_w2_a, lokr_w2_b, lokr_t2))
 
+        #glora
+        a1_name = "{}.a1.weight".format(x)
+        a2_name = "{}.a2.weight".format(x)
+        b1_name = "{}.b1.weight".format(x)
+        b2_name = "{}.b2.weight".format(x)
+        if a1_name in lora:
+            patch_dict[to_load[x]] = ("glora", (lora[a1_name], lora[a2_name], lora[b1_name], lora[b2_name], alpha))
+            loaded_keys.add(a1_name)
+            loaded_keys.add(a2_name)
+            loaded_keys.add(b1_name)
+            loaded_keys.add(b2_name)
 
         w_norm_name = "{}.w_norm".format(x)
         b_norm_name = "{}.b_norm".format(x)

comfy/model_patcher.py

@@ -311,6 +311,16 @@ class ModelPatcher:
                     weight += (alpha * m1 * m2).reshape(weight.shape).type(weight.dtype)
                 except Exception as e:
                     print("ERROR", key, e)
+            elif patch_type == "glora":
+                if v[4] is not None:
+                    alpha *= v[4] / v[0].shape[0]
+
+                a1 = comfy.model_management.cast_to_device(v[0].flatten(start_dim=1), weight.device, torch.float32)
+                a2 = comfy.model_management.cast_to_device(v[1].flatten(start_dim=1), weight.device, torch.float32)
+                b1 = comfy.model_management.cast_to_device(v[2].flatten(start_dim=1), weight.device, torch.float32)
+                b2 = comfy.model_management.cast_to_device(v[3].flatten(start_dim=1), weight.device, torch.float32)
+
+                weight += ((torch.mm(b2, b1) + torch.mm(torch.mm(weight.flatten(start_dim=1), a2), a1)) * alpha).reshape(weight.shape).type(weight.dtype)
             else:
                 print("patch type not recognized", patch_type, key)