diff --git a/comfy/controlnet.py b/comfy/controlnet.py
index 5279307c..4e20100c 100644
--- a/comfy/controlnet.py
+++ b/comfy/controlnet.py
@@ -257,12 +257,7 @@ class ControlLora(ControlNet):
         cm = self.control_model.state_dict()
 
         for k in sd:
-            weight = sd[k]
-            if weight.device == torch.device("meta"): #lowvram NOTE: this depends on the inner working of the accelerate library so it might break.
-                key_split = k.split('.')              # I have no idea why they don't just leave the weight there instead of using the meta device.
-                op = comfy.utils.get_attr(diffusion_model, '.'.join(key_split[:-1]))
-                weight = op._hf_hook.weights_map[key_split[-1]]
-
+            weight = comfy.model_management.resolve_lowvram_weight(sd[k], diffusion_model, k)
             try:
                 comfy.utils.set_attr(self.control_model, k, weight)
             except:
diff --git a/comfy/model_base.py b/comfy/model_base.py
index 979e2c65..d654f56f 100644
--- a/comfy/model_base.py
+++ b/comfy/model_base.py
@@ -3,6 +3,7 @@ from comfy.ldm.modules.diffusionmodules.openaimodel import UNetModel
 from comfy.ldm.modules.encoders.noise_aug_modules import CLIPEmbeddingNoiseAugmentation
 from comfy.ldm.modules.diffusionmodules.util import make_beta_schedule
 from comfy.ldm.modules.diffusionmodules.openaimodel import Timestep
+import comfy.model_management
 import numpy as np
 from enum import Enum
 from . import utils
@@ -93,7 +94,11 @@ class BaseModel(torch.nn.Module):
 
     def state_dict_for_saving(self, clip_state_dict, vae_state_dict):
         clip_state_dict = self.model_config.process_clip_state_dict_for_saving(clip_state_dict)
-        unet_state_dict = self.diffusion_model.state_dict()
+        unet_sd = self.diffusion_model.state_dict()
+        unet_state_dict = {}
+        for k in unet_sd:
+            unet_state_dict[k] = comfy.model_management.resolve_lowvram_weight(unet_sd[k], self.diffusion_model, k)
+
         unet_state_dict = self.model_config.process_unet_state_dict_for_saving(unet_state_dict)
         vae_state_dict = self.model_config.process_vae_state_dict_for_saving(vae_state_dict)
         if self.get_dtype() == torch.float16:
diff --git a/comfy/model_management.py b/comfy/model_management.py
index 01643449..e5c80bf6 100644
--- a/comfy/model_management.py
+++ b/comfy/model_management.py
@@ -1,6 +1,7 @@
 import psutil
 from enum import Enum
 from comfy.cli_args import args
+import comfy.utils
 import torch
 import sys
 
@@ -637,6 +638,13 @@ def soft_empty_cache():
             torch.cuda.empty_cache()
             torch.cuda.ipc_collect()
 
+def resolve_lowvram_weight(weight, model, key):
+    if weight.device == torch.device("meta"): #lowvram NOTE: this depends on the inner working of the accelerate library so it might break.
+        key_split = key.split('.')              # I have no idea why they don't just leave the weight there instead of using the meta device.
+        op = comfy.utils.get_attr(model, '.'.join(key_split[:-1]))
+        weight = op._hf_hook.weights_map[key_split[-1]]
+    return weight
+
 #TODO: might be cleaner to put this somewhere else
 import threading