Make lora code a bit cleaner.

comfy/lora.py

@@ -43,7 +43,7 @@ def load_lora(lora, to_load):
             if mid_name is not None and mid_name in lora.keys():
                 mid = lora[mid_name]
                 loaded_keys.add(mid_name)
-            patch_dict[to_load[x]] = (lora[A_name], lora[B_name], alpha, mid)
+            patch_dict[to_load[x]] = ("lora", (lora[A_name], lora[B_name], alpha, mid))
             loaded_keys.add(A_name)
             loaded_keys.add(B_name)
 
@@ -64,7 +64,7 @@ def load_lora(lora, to_load):
                 loaded_keys.add(hada_t1_name)
                 loaded_keys.add(hada_t2_name)
 
-            patch_dict[to_load[x]] = (lora[hada_w1_a_name], lora[hada_w1_b_name], alpha, lora[hada_w2_a_name], lora[hada_w2_b_name], hada_t1, hada_t2)
+            patch_dict[to_load[x]] = ("loha", (lora[hada_w1_a_name], lora[hada_w1_b_name], alpha, lora[hada_w2_a_name], lora[hada_w2_b_name], hada_t1, hada_t2))
             loaded_keys.add(hada_w1_a_name)
             loaded_keys.add(hada_w1_b_name)
             loaded_keys.add(hada_w2_a_name)
@@ -116,7 +116,7 @@ def load_lora(lora, to_load):
             loaded_keys.add(lokr_t2_name)
 
         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_w1, lokr_w2, alpha, lokr_w1_a, lokr_w1_b, lokr_w2_a, lokr_w2_b, lokr_t2)
+            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))
 
 
         w_norm_name = "{}.w_norm".format(x)
@@ -126,21 +126,21 @@ def load_lora(lora, to_load):
 
         if w_norm is not None:
             loaded_keys.add(w_norm_name)
-            patch_dict[to_load[x]] = (w_norm,)
+            patch_dict[to_load[x]] = ("diff", (w_norm,))
             if b_norm is not None:
                 loaded_keys.add(b_norm_name)
-                patch_dict["{}.bias".format(to_load[x][:-len(".weight")])] = (b_norm,)
+                patch_dict["{}.bias".format(to_load[x][:-len(".weight")])] = ("diff", (b_norm,))
 
         diff_name = "{}.diff".format(x)
         diff_weight = lora.get(diff_name, None)
         if diff_weight is not None:
-            patch_dict[to_load[x]] = (diff_weight,)
+            patch_dict[to_load[x]] = ("diff", (diff_weight,))
             loaded_keys.add(diff_name)
 
         diff_bias_name = "{}.diff_b".format(x)
         diff_bias = lora.get(diff_bias_name, None)
         if diff_bias is not None:
-            patch_dict["{}.bias".format(to_load[x][:-len(".weight")])] = (diff_bias,)
+            patch_dict["{}.bias".format(to_load[x][:-len(".weight")])] = ("diff", (diff_bias,))
             loaded_keys.add(diff_bias_name)
 
     for x in lora.keys():

comfy/model_patcher.py

@@ -217,13 +217,19 @@ class ModelPatcher:
                 v = (self.calculate_weight(v[1:], v[0].clone(), key), )
 
             if len(v) == 1:
+                patch_type = "diff"
+            elif len(v) == 2:
+                patch_type = v[0]
+                v = v[1]
+
+            if patch_type == "diff":
                 w1 = v[0]
                 if alpha != 0.0:
                     if w1.shape != weight.shape:
                         print("WARNING SHAPE MISMATCH {} WEIGHT NOT MERGED {} != {}".format(key, w1.shape, weight.shape))
                     else:
                         weight += alpha * comfy.model_management.cast_to_device(w1, weight.device, weight.dtype)
-            elif len(v) == 4: #lora/locon
+            elif patch_type == "lora": #lora/locon
                 mat1 = comfy.model_management.cast_to_device(v[0], weight.device, torch.float32)
                 mat2 = comfy.model_management.cast_to_device(v[1], weight.device, torch.float32)
                 if v[2] is not None:
@@ -237,7 +243,7 @@ class ModelPatcher:
                     weight += (alpha * torch.mm(mat1.flatten(start_dim=1), mat2.flatten(start_dim=1))).reshape(weight.shape).type(weight.dtype)
                 except Exception as e:
                     print("ERROR", key, e)
-            elif len(v) == 8: #lokr
+            elif patch_type == "lokr":
                 w1 = v[0]
                 w2 = v[1]
                 w1_a = v[3]
@@ -276,7 +282,7 @@ class ModelPatcher:
                     weight += alpha * torch.kron(w1, w2).reshape(weight.shape).type(weight.dtype)
                 except Exception as e:
                     print("ERROR", key, e)
-            else: #loha
+            elif patch_type == "loha":
                 w1a = v[0]
                 w1b = v[1]
                 if v[2] is not None:
@@ -305,6 +311,8 @@ class ModelPatcher:
                     weight += (alpha * m1 * m2).reshape(weight.shape).type(weight.dtype)
                 except Exception as e:
                     print("ERROR", key, e)
+            else:
+                print("patch type not recognized", patch_type, key)
 
         return weight