diff --git a/invokeai/backend/stable_diffusion/diffusion/shared_invokeai_diffusion.py b/invokeai/backend/stable_diffusion/diffusion/shared_invokeai_diffusion.py
index f418133e49f..a1e1cd3d882 100644
--- a/invokeai/backend/stable_diffusion/diffusion/shared_invokeai_diffusion.py
+++ b/invokeai/backend/stable_diffusion/diffusion/shared_invokeai_diffusion.py
@@ -337,9 +337,13 @@ def _apply_standard_conditioning(
             )
             cross_attention_kwargs["percent_through"] = step_index / total_step_count
 
+        uncond_embeds = conditioning_data.uncond_text.embeds.to(x.device)
+        cond_embeds = conditioning_data.cond_text.embeds.to(x.device)
         both_conditionings, encoder_attention_mask = self._concat_conditionings_for_batch(
-            conditioning_data.uncond_text.embeds, conditioning_data.cond_text.embeds
+            uncond_embeds, cond_embeds
         )
+        if added_cond_kwargs is not None:
+            added_cond_kwargs = {k: v.to(x.device) for k, v in added_cond_kwargs.items()}
         both_results = self.model_forward_callback(
             x_twice,
             sigma_twice,
@@ -428,11 +432,15 @@ def _apply_standard_conditioning_sequentially(
             )
             cross_attention_kwargs["percent_through"] = step_index / total_step_count
 
+        uncond_embeds = conditioning_data.uncond_text.embeds.to(x.device)
+        if added_cond_kwargs is not None:
+            added_cond_kwargs = {k: v.to(x.device) for k, v in added_cond_kwargs.items()}
+
         # Run unconditioned UNet denoising (i.e. negative prompt).
         unconditioned_next_x = self.model_forward_callback(
             x,
             sigma,
-            conditioning_data.uncond_text.embeds,
+            uncond_embeds,
             cross_attention_kwargs=cross_attention_kwargs,
             down_block_additional_residuals=uncond_down_block,
             mid_block_additional_residual=uncond_mid_block,
@@ -465,8 +473,8 @@ def _apply_standard_conditioning_sequentially(
         added_cond_kwargs = None
         if conditioning_data.is_sdxl():
             added_cond_kwargs = {
-                "text_embeds": conditioning_data.cond_text.pooled_embeds,
-                "time_ids": conditioning_data.cond_text.add_time_ids,
+                "text_embeds": conditioning_data.cond_text.pooled_embeds.to(x.device),
+                "time_ids": conditioning_data.cond_text.add_time_ids.to(x.device),
             }
 
         # Prepare prompt regions for the conditioned pass.
@@ -476,11 +484,12 @@ def _apply_standard_conditioning_sequentially(
             )
             cross_attention_kwargs["percent_through"] = step_index / total_step_count
 
+        cond_embeds = conditioning_data.cond_text.embeds.to(x.device)
         # Run conditioned UNet denoising (i.e. positive prompt).
         conditioned_next_x = self.model_forward_callback(
             x,
             sigma,
-            conditioning_data.cond_text.embeds,
+            cond_embeds,
             cross_attention_kwargs=cross_attention_kwargs,
             down_block_additional_residuals=cond_down_block,
             mid_block_additional_residual=cond_mid_block,