diff --git a/brushnet/brushnet.py b/brushnet/brushnet.py
index 41ace1d..93a32bd 100644
--- a/brushnet/brushnet.py
+++ b/brushnet/brushnet.py
@@ -751,8 +751,8 @@ class BrushNetModel(ModelMixin, ConfigMixin):
         if timestep is None and time_emb is None:
             raise ValueError(f"`timestep` and `emb` are both None")
         
-        print("BN: sample.device", sample.device)
-        print("BN: TE.device", self.time_embedding.linear_1.weight.device)
+        #print("BN: sample.device", sample.device)
+        #print("BN: TE.device", self.time_embedding.linear_1.weight.device)
 
         if timestep is not None:
             # 1. time
@@ -779,7 +779,7 @@ class BrushNetModel(ModelMixin, ConfigMixin):
             # there might be better ways to encapsulate this.
             t_emb = t_emb.to(dtype=sample.dtype)
 
-            print("t_emb.device =",t_emb.device)
+            #print("t_emb.device =",t_emb.device)
 
             emb = self.time_embedding(t_emb, timestep_cond)
             aug_emb = None
diff --git a/brushnet_nodes.py b/brushnet_nodes.py
index d8702cc..d111b3b 100644
--- a/brushnet_nodes.py
+++ b/brushnet_nodes.py
@@ -265,7 +265,7 @@ class BrushNet:
             print('BrushNet: negative conditioning has not pooled_output')
             if is_SDXL:
                 print('BrushNet will not produce correct results')
-            negative_pooled_prompt_embeds = torch.empty([1, 1280], device=brushnet['brushnet'].device)
+            negative_pooled_prompt_embeds = torch.empty([1, add_text_embeds.shape[1]], device=brushnet['brushnet'].device)
 
         add_time_ids = torch.FloatTensor([[height, width, 0., 0., height, width]]).to(brushnet['brushnet'].device)
         negative_add_time_ids = add_time_ids