diff --git a/invokeai/app/invocations/latent.py b/invokeai/app/invocations/latent.py
index b764b3b336..78f8f624f2 100644
--- a/invokeai/app/invocations/latent.py
+++ b/invokeai/app/invocations/latent.py
@@ -6,6 +6,7 @@ from typing import List, Literal, Optional, Union
 import einops
 import numpy as np
 import torch
+from torch import mps
 import torchvision.transforms as T
 from diffusers.image_processor import VaeImageProcessor
 from diffusers.models.attention_processor import (
@@ -541,6 +542,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
             # https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
             result_latents = result_latents.to("cpu")
             torch.cuda.empty_cache()
+            mps.empty_cache()
 
             name = f"{context.graph_execution_state_id}__{self.id}"
             context.services.latents.save(name, result_latents)
@@ -612,6 +614,7 @@ class LatentsToImageInvocation(BaseInvocation):
 
             # clear memory as vae decode can request a lot
             torch.cuda.empty_cache()
+            mps.empty_cache()
 
             with torch.inference_mode():
                 # copied from diffusers pipeline
@@ -624,6 +627,7 @@ class LatentsToImageInvocation(BaseInvocation):
                 image = VaeImageProcessor.numpy_to_pil(np_image)[0]
 
         torch.cuda.empty_cache()
+        mps.empty_cache()
 
         image_dto = context.services.images.create(
             image=image,
@@ -683,6 +687,7 @@ class ResizeLatentsInvocation(BaseInvocation):
         # https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
         resized_latents = resized_latents.to("cpu")
         torch.cuda.empty_cache()
+        mps.empty_cache()
 
         name = f"{context.graph_execution_state_id}__{self.id}"
         # context.services.latents.set(name, resized_latents)
@@ -719,6 +724,7 @@ class ScaleLatentsInvocation(BaseInvocation):
         # https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
         resized_latents = resized_latents.to("cpu")
         torch.cuda.empty_cache()
+        mps.empty_cache()
 
         name = f"{context.graph_execution_state_id}__{self.id}"
         # context.services.latents.set(name, resized_latents)
@@ -875,6 +881,7 @@ class BlendLatentsInvocation(BaseInvocation):
         # https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
         blended_latents = blended_latents.to("cpu")
         torch.cuda.empty_cache()
+        mps.empty_cache()
 
         name = f"{context.graph_execution_state_id}__{self.id}"
         # context.services.latents.set(name, resized_latents)
diff --git a/invokeai/backend/model_management/model_cache.py b/invokeai/backend/model_management/model_cache.py
index 6d0f36ad8c..2db46e9f64 100644
--- a/invokeai/backend/model_management/model_cache.py
+++ b/invokeai/backend/model_management/model_cache.py
@@ -26,6 +26,7 @@ from pathlib import Path
 from typing import Any, Dict, Optional, Type, Union, types
 
 import torch
+from torch import mps
 
 import invokeai.backend.util.logging as logger
 
@@ -406,6 +407,7 @@ class ModelCache(object):
 
         gc.collect()
         torch.cuda.empty_cache()
+        mps.empty_cache()
 
         self.logger.debug(f"After unloading: cached_models={len(self._cached_models)}")
 
@@ -426,6 +428,7 @@ class ModelCache(object):
 
         gc.collect()
         torch.cuda.empty_cache()
+        mps.empty_cache()
 
     def _local_model_hash(self, model_path: Union[str, Path]) -> str:
         sha = hashlib.sha256()