diff --git a/extensions-builtin/sd_forge_controlllite/lib_controllllite/lib_controllllite.py b/extensions-builtin/sd_forge_controlllite/lib_controllllite/lib_controllllite.py
index 3ef4f72c..9e917c4f 100644
--- a/extensions-builtin/sd_forge_controlllite/lib_controllllite/lib_controllllite.py
+++ b/extensions-builtin/sd_forge_controlllite/lib_controllllite/lib_controllllite.py
@@ -25,7 +25,7 @@ def extra_options_to_module_prefix(extra_options):
     return module_pfx
 
 
-def load_control_net_lllite_patch(ctrl_sd, cond_image, multiplier, num_steps, start_percent, end_percent):
+def load_control_net_lllite_patch(ctrl_sd, cond_image, multiplier, num_steps, start_percent, end_percent, model_dtype):
     # calculate start and end step
     start_step = math.floor(num_steps * start_percent) if start_percent > 0 else 0
     end_step = math.floor(num_steps * end_percent) if end_percent > 0 else num_steps
@@ -63,6 +63,7 @@ def load_control_net_lllite_patch(ctrl_sd, cond_image, multiplier, num_steps, st
             num_steps=num_steps,
             start_step=start_step,
             end_step=end_step,
+            dtype=model_dtype
         )
         info = module.load_state_dict(weights)
         modules[module_name] = module
@@ -125,6 +126,7 @@ class LLLiteModule(torch.nn.Module):
         num_steps: int,
         start_step: int,
         end_step: int,
+        dtype: torch.dtype = torch.float32
     ):
         super().__init__()
         self.name = name
@@ -134,6 +136,7 @@ class LLLiteModule(torch.nn.Module):
         self.start_step = start_step
         self.end_step = end_step
         self.is_first = False
+        self.dtype = dtype
 
         modules = []
         modules.append(torch.nn.Conv2d(3, cond_emb_dim // 2, kernel_size=4, stride=4, padding=0))  # to latent (from VAE) size*2
@@ -181,6 +184,7 @@ class LLLiteModule(torch.nn.Module):
         self.cond_image = None
         self.cond_emb = None
         self.current_step = 0
+        self.to(dtype=dtype)
 
     # @torch.inference_mode()
     def set_cond_image(self, cond_image):
@@ -210,6 +214,8 @@ class LLLiteModule(torch.nn.Module):
 
         if self.cond_emb is None:
             # print(f"cond_emb is None, {self.name}")
+            # test bad idea
+            #self.cond_image = self.cond_image.view(dtype=x.dtype)
             cx = self.conditioning1(self.cond_image.to(x.device, dtype=x.dtype))
             if not self.is_conv2d:
                 # reshape / b,c,h,w -> b,h*w,c
@@ -260,7 +266,8 @@ class LLLiteLoader:
         # cond_image is b,h,w,3, 0-1
 
         model_lllite = model.clone()
-        patch = load_control_net_lllite_patch(state_dict, cond_image, strength, steps, start_percent, end_percent)
+        patch = load_control_net_lllite_patch(state_dict, cond_image, strength, steps, start_percent, end_percent,
+                                              model_lllite.model.storage_dtype)
         if patch is not None:
             model_lllite.set_model_attn1_patch(patch)
             model_lllite.set_model_attn2_patch(patch)