Reduce memory usage when applying DORA: #3557

comfy/model_patcher.py

@@ -9,7 +9,7 @@ import comfy.model_management
 from comfy.types import UnetWrapperFunction
 
 
-def apply_weight_decompose(dora_scale, weight):
+def weight_decompose_scale(dora_scale, weight):
     weight_norm = (
         weight.transpose(0, 1)
         .reshape(weight.shape[1], -1)
@@ -18,7 +18,7 @@ def apply_weight_decompose(dora_scale, weight):
         .transpose(0, 1)
     )
 
-    return weight * (dora_scale / weight_norm).type(weight.dtype)
+    return (dora_scale / weight_norm).type(weight.dtype)
 
 def set_model_options_patch_replace(model_options, patch, name, block_name, number, transformer_index=None):
     to = model_options["transformer_options"].copy()
@@ -365,7 +365,7 @@ class ModelPatcher:
                 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)
+                        weight *= weight_decompose_scale(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":
@@ -407,7 +407,7 @@ 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)
+                        weight *= weight_decompose_scale(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":
@@ -439,7 +439,7 @@ 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)
+                        weight *= weight_decompose_scale(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":
@@ -456,7 +456,7 @@ 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)
+                        weight *= weight_decompose_scale(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: