dora_scale support for lora file.

comfy/lora.py

@@ -21,6 +21,12 @@ def load_lora(lora, to_load):
             alpha = lora[alpha_name].item()
             loaded_keys.add(alpha_name)
 
+        dora_scale_name = "{}.dora_scale".format(x)
+        dora_scale = None
+        if dora_scale_name in lora.keys():
+            dora_scale = lora[dora_scale_name]
+            loaded_keys.add(dora_scale_name)
+
         regular_lora = "{}.lora_up.weight".format(x)
         diffusers_lora = "{}_lora.up.weight".format(x)
         transformers_lora = "{}.lora_linear_layer.up.weight".format(x)
@@ -44,7 +50,7 @@ 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", (lora[A_name], lora[B_name], alpha, mid))
+            patch_dict[to_load[x]] = ("lora", (lora[A_name], lora[B_name], alpha, mid, dora_scale))
             loaded_keys.add(A_name)
             loaded_keys.add(B_name)
 
@@ -65,7 +71,7 @@ def load_lora(lora, to_load):
                 loaded_keys.add(hada_t1_name)
                 loaded_keys.add(hada_t2_name)
 
-            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))
+            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, dora_scale))
             loaded_keys.add(hada_w1_a_name)
             loaded_keys.add(hada_w1_b_name)
             loaded_keys.add(hada_w2_a_name)
@@ -117,7 +123,7 @@ 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", (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, dora_scale))
 
         #glora
         a1_name = "{}.a1.weight".format(x)
@@ -125,7 +131,7 @@ def load_lora(lora, to_load):
         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))
+            patch_dict[to_load[x]] = ("glora", (lora[a1_name], lora[a2_name], lora[b1_name], lora[b2_name], alpha, dora_scale))
             loaded_keys.add(a1_name)
             loaded_keys.add(a2_name)
             loaded_keys.add(b1_name)

comfy/model_patcher.py

@@ -7,6 +7,18 @@ import uuid
 import comfy.utils
 import comfy.model_management
 
+def apply_weight_decompose(dora_scale, weight):
+    weight_norm = (
+        weight.transpose(0, 1)
+        .reshape(weight.shape[1], -1)
+        .norm(dim=1, keepdim=True)
+        .reshape(weight.shape[1], *[1] * (weight.dim() - 1))
+        .transpose(0, 1)
+    )
+
+    return weight * (dora_scale / weight_norm)
+
+
 class ModelPatcher:
     def __init__(self, model, load_device, offload_device, size=0, current_device=None, weight_inplace_update=False):
         self.size = size
@@ -309,6 +321,7 @@ class ModelPatcher:
             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)
+                dora_scale = v[4]
                 if v[2] is not None:
                     alpha *= v[2] / mat2.shape[0]
                 if v[3] is not None:
@@ -318,6 +331,8 @@ class ModelPatcher:
                     mat2 = torch.mm(mat2.transpose(0, 1).flatten(start_dim=1), mat3.transpose(0, 1).flatten(start_dim=1)).reshape(final_shape).transpose(0, 1)
                 try:
                     weight += (alpha * torch.mm(mat1.flatten(start_dim=1), mat2.flatten(start_dim=1))).reshape(weight.shape).type(weight.dtype)
+                    if dora_scale is not None:
+                        weight = apply_weight_decompose(comfy.model_management.cast_to_device(dora_scale, weight.device, torch.float32), weight)
                 except Exception as e:
                     logging.error("ERROR {} {} {}".format(patch_type, key, e))
             elif patch_type == "lokr":
@@ -328,6 +343,7 @@ class ModelPatcher:
                 w2_a = v[5]
                 w2_b = v[6]
                 t2 = v[7]
+                dora_scale = v[8]
                 dim = None
 
                 if w1 is None:
@@ -357,6 +373,8 @@ class ModelPatcher:
 
                 try:
                     weight += alpha * torch.kron(w1, w2).reshape(weight.shape).type(weight.dtype)
+                    if dora_scale is not None:
+                        weight = apply_weight_decompose(comfy.model_management.cast_to_device(dora_scale, weight.device, torch.float32), weight)
                 except Exception as e:
                     logging.error("ERROR {} {} {}".format(patch_type, key, e))
             elif patch_type == "loha":
@@ -366,6 +384,7 @@ class ModelPatcher:
                     alpha *= v[2] / w1b.shape[0]
                 w2a = v[3]
                 w2b = v[4]
+                dora_scale = v[7]
                 if v[5] is not None: #cp decomposition
                     t1 = v[5]
                     t2 = v[6]
@@ -386,12 +405,16 @@ class ModelPatcher:
 
                 try:
                     weight += (alpha * m1 * m2).reshape(weight.shape).type(weight.dtype)
+                    if dora_scale is not None:
+                        weight = apply_weight_decompose(comfy.model_management.cast_to_device(dora_scale, weight.device, torch.float32), weight)
                 except Exception as e:
                     logging.error("ERROR {} {} {}".format(patch_type, key, e))
             elif patch_type == "glora":
                 if v[4] is not None:
                     alpha *= v[4] / v[0].shape[0]
 
+                dora_scale = v[5]
+
                 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)
@@ -399,6 +422,8 @@ class ModelPatcher:
 
                 try:
                     weight += ((torch.mm(b2, b1) + torch.mm(torch.mm(weight.flatten(start_dim=1), a2), a1)) * alpha).reshape(weight.shape).type(weight.dtype)
+                    if dora_scale is not None:
+                        weight = apply_weight_decompose(comfy.model_management.cast_to_device(dora_scale, weight.device, torch.float32), weight)
                 except Exception as e:
                     logging.error("ERROR {} {} {}".format(patch_type, key, e))
             else: