diff --git a/ldm/models/diffusion/shared_invokeai_diffusion.py b/ldm/models/diffusion/shared_invokeai_diffusion.py
index d9163468d2..2e513d3f5a 100644
--- a/ldm/models/diffusion/shared_invokeai_diffusion.py
+++ b/ldm/models/diffusion/shared_invokeai_diffusion.py
@@ -29,7 +29,7 @@ class InvokeAIDiffuserComponent:
     * Hybrid conditioning (used for inpainting)
     '''
     debug_thresholding = False
-
+    sequential_conditioning = False
 
     @dataclass
     class ExtraConditioningInfo:
@@ -149,9 +149,13 @@ class InvokeAIDiffuserComponent:
                                                                                                            unconditioning,
                                                                                                            conditioning,
                                                                                                            cross_attention_control_types_to_do)
+        elif self.sequential_conditioning:
+            unconditioned_next_x, conditioned_next_x = self._apply_standard_conditioning_sequentially(
+                x, sigma, unconditioning, conditioning)
+
         else:
-            unconditioned_next_x, conditioned_next_x = self._apply_standard_conditioning(x, sigma, unconditioning,
-                                                                                         conditioning)
+            unconditioned_next_x, conditioned_next_x = self._apply_standard_conditioning(
+                x, sigma, unconditioning, conditioning)
 
         combined_next_x = self._combine(unconditioned_next_x, conditioned_next_x, unconditional_guidance_scale)
 
@@ -198,6 +202,16 @@ class InvokeAIDiffuserComponent:
         return unconditioned_next_x, conditioned_next_x
 
 
+    def _apply_standard_conditioning_sequentially(self, x: torch.Tensor, sigma, unconditioning: torch.Tensor, conditioning: torch.Tensor):
+        # low-memory sequential path
+        unconditioned_next_x = self.model_forward_callback(x, sigma, unconditioning)
+        conditioned_next_x = self.model_forward_callback(x, sigma, conditioning)
+        if conditioned_next_x.device.type == 'mps':
+            # prevent a result filled with zeros. seems to be a torch bug.
+            conditioned_next_x = conditioned_next_x.clone()
+        return unconditioned_next_x, conditioned_next_x
+
+
     def _apply_hybrid_conditioning(self, x, sigma, unconditioning, conditioning):
         assert isinstance(conditioning, dict)
         assert isinstance(unconditioning, dict)