Fix issue with lora in some cases when combined with model merging.

comfy/sd.py

@@ -202,6 +202,14 @@ def model_lora_keys_unet(model, key_map={}):
             key_map["lora_unet_{}".format(key_lora)] = "diffusion_model.{}".format(diffusers_keys[k])
     return key_map
 
+def set_attr(obj, attr, value):
+    attrs = attr.split(".")
+    for name in attrs[:-1]:
+        obj = getattr(obj, name)
+    prev = getattr(obj, attrs[-1])
+    setattr(obj, attrs[-1], torch.nn.Parameter(value))
+    del prev
+
 class ModelPatcher:
     def __init__(self, model, load_device, offload_device, size=0):
         self.size = size
@@ -340,10 +348,11 @@ class ModelPatcher:
             weight = model_sd[key]
 
             if key not in self.backup:
-                self.backup[key] = weight.to(self.offload_device, copy=True)
+                self.backup[key] = weight.to(self.offload_device)
 
             temp_weight = weight.to(torch.float32, copy=True)
-            weight[:] = self.calculate_weight(self.patches[key], temp_weight, key).to(weight.dtype)
+            out_weight = self.calculate_weight(self.patches[key], temp_weight, key).to(weight.dtype)
+            set_attr(self.model, key, out_weight)
             del temp_weight
         return self.model
 
@@ -439,13 +448,6 @@ class ModelPatcher:
 
     def unpatch_model(self):
         keys = list(self.backup.keys())
-        def set_attr(obj, attr, value):
-            attrs = attr.split(".")
-            for name in attrs[:-1]:
-                obj = getattr(obj, name)
-            prev = getattr(obj, attrs[-1])
-            setattr(obj, attrs[-1], torch.nn.Parameter(value))
-            del prev
 
         for k in keys:
             set_attr(self.model, k, self.backup[k])

execution.py

@@ -6,7 +6,6 @@ import threading
 import heapq
 import traceback
 import gc
-import time
 
 import torch
 import nodes