diff --git a/comfy/conds.py b/comfy/conds.py
index f2564e7ef..5af3e93ea 100644
--- a/comfy/conds.py
+++ b/comfy/conds.py
@@ -1,6 +1,7 @@
 import torch
 import math
 import comfy.utils
+import logging
 
 
 class CONDRegular:
@@ -16,6 +17,9 @@ class CONDRegular:
     def can_concat(self, other):
         if self.cond.shape != other.cond.shape:
             return False
+        if self.cond.device != other.cond.device:
+            logging.warning("WARNING: conds not on same device, skipping concat.")
+            return False
         return True
 
     def concat(self, others):
@@ -51,6 +55,9 @@ class CONDCrossAttn(CONDRegular):
             diff = mult_min // min(s1[1], s2[1])
             if diff > 4: #arbitrary limit on the padding because it's probably going to impact performance negatively if it's too much
                 return False
+        if self.cond.device != other.cond.device:
+            logging.warning("WARNING: conds not on same device: skipping concat.")
+            return False
         return True
 
     def concat(self, others):
diff --git a/comfy/model_base.py b/comfy/model_base.py
index 3a9c031ea..a06686436 100644
--- a/comfy/model_base.py
+++ b/comfy/model_base.py
@@ -162,7 +162,7 @@ class BaseModel(torch.nn.Module):
         xc = self.model_sampling.calculate_input(sigma, x)
 
         if c_concat is not None:
-            xc = torch.cat([xc] + [c_concat], dim=1)
+            xc = torch.cat([xc] + [comfy.model_management.cast_to_device(c_concat, xc.device, xc.dtype)], dim=1)
 
         context = c_crossattn
         dtype = self.get_dtype()
@@ -401,7 +401,7 @@ class SD21UNCLIP(BaseModel):
         unclip_conditioning = kwargs.get("unclip_conditioning", None)
         device = kwargs["device"]
         if unclip_conditioning is None:
-            return torch.zeros((1, self.adm_channels))
+            return torch.zeros((1, self.adm_channels), device=device)
         else:
             return unclip_adm(unclip_conditioning, device, self.noise_augmentor, kwargs.get("unclip_noise_augment_merge", 0.05), kwargs.get("seed", 0) - 10)
 
@@ -615,9 +615,11 @@ class IP2P:
 
         if image is None:
             image = torch.zeros_like(noise)
+        else:
+            image = image.to(device=device)
 
         if image.shape[1:] != noise.shape[1:]:
-            image = utils.common_upscale(image.to(device), noise.shape[-1], noise.shape[-2], "bilinear", "center")
+            image = utils.common_upscale(image, noise.shape[-1], noise.shape[-2], "bilinear", "center")
 
         image = utils.resize_to_batch_size(image, noise.shape[0])
         return self.process_ip2p_image_in(image)
@@ -696,7 +698,7 @@ class StableCascade_B(BaseModel):
         #size of prior doesn't really matter if zeros because it gets resized but I still want it to get batched
         prior = kwargs.get("stable_cascade_prior", torch.zeros((1, 16, (noise.shape[2] * 4) // 42, (noise.shape[3] * 4) // 42), dtype=noise.dtype, layout=noise.layout, device=noise.device))
 
-        out["effnet"] = comfy.conds.CONDRegular(prior)
+        out["effnet"] = comfy.conds.CONDRegular(prior.to(device=noise.device))
         out["sca"] = comfy.conds.CONDRegular(torch.zeros((1,)))
         return out
 
@@ -1161,10 +1163,10 @@ class WAN21_Vace(WAN21):
 
         vace_frames_out = []
         for j in range(len(vace_frames)):
-            vf = vace_frames[j].clone()
+            vf = vace_frames[j].to(device=noise.device, dtype=noise.dtype, copy=True)
             for i in range(0, vf.shape[1], 16):
                 vf[:, i:i + 16] = self.process_latent_in(vf[:, i:i + 16])
-            vf = torch.cat([vf, mask[j]], dim=1)
+            vf = torch.cat([vf, mask[j].to(device=noise.device, dtype=noise.dtype)], dim=1)
             vace_frames_out.append(vf)
 
         vace_frames = torch.stack(vace_frames_out, dim=1)