diff --git a/invokeai/app/invocations/latent.py b/invokeai/app/invocations/latent.py
index 59897f5333..44cfd67d0c 100644
--- a/invokeai/app/invocations/latent.py
+++ b/invokeai/app/invocations/latent.py
@@ -50,6 +50,7 @@ from invokeai.app.invocations.primitives import DenoiseMaskOutput, ImageOutput,
 from invokeai.app.invocations.t2i_adapter import T2IAdapterField
 from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.app.util.controlnet_utils import prepare_control_image
+from invokeai.backend.ip_adapter.ip_adapter import IPAdapter
 from invokeai.backend.lora import LoRAModelRaw
 from invokeai.backend.model_manager import BaseModelType, LoadedModel
 from invokeai.backend.model_manager.config import MainConfigBase, ModelVariantType
@@ -674,22 +675,13 @@ class DenoiseLatentsInvocation(BaseInvocation):
     def prep_ip_adapter_image_prompts(
         self,
         context: InvocationContext,
-        ip_adapter: Optional[Union[IPAdapterField, list[IPAdapterField]]],
+        ip_adapters: List[IPAdapterField],
     ) -> List[Tuple[torch.Tensor, torch.Tensor]]:
         """Run the IPAdapter CLIPVisionModel, returning image prompt embeddings."""
-        # ip_adapter could be a list or a single IPAdapterField. Normalize to a list here.
-        if ip_adapter is None:
-            return []
-
-        if not isinstance(ip_adapter, list):
-            ip_adapter = [ip_adapter]
-
-        if len(ip_adapter) == 0:
-            return []
-
         image_prompts = []
-        for single_ip_adapter in ip_adapter:
+        for single_ip_adapter in ip_adapters:
             with context.models.load(single_ip_adapter.ip_adapter_model) as ip_adapter_model:
+                assert isinstance(ip_adapter_model, IPAdapter)
                 image_encoder_model_info = context.models.load(single_ip_adapter.image_encoder_model)
                 # `single_ip_adapter.image` could be a list or a single ImageField. Normalize to a list here.
                 single_ipa_image_fields = single_ip_adapter.image
@@ -710,36 +702,23 @@ class DenoiseLatentsInvocation(BaseInvocation):
     def prep_ip_adapter_data(
         self,
         context: InvocationContext,
-        ip_adapter: Optional[Union[IPAdapterField, list[IPAdapterField]]],
+        ip_adapters: List[IPAdapterField],
+        image_prompts: List[Tuple[torch.Tensor, torch.Tensor]],
         exit_stack: ExitStack,
         latent_height: int,
         latent_width: int,
         dtype: torch.dtype,
-        image_prompts: List[Tuple[torch.Tensor, torch.Tensor]],
-    ) -> Optional[list[IPAdapterData]]:
-        """If IP-Adapter is enabled, then this function loads the requisite models, and adds the image prompt embeddings
-        to the `conditioning_data` (in-place).
-        """
-        if ip_adapter is None:
-            return None
-
-        # ip_adapter could be a list or a single IPAdapterField. Normalize to a list here.
-        if not isinstance(ip_adapter, list):
-            ip_adapter = [ip_adapter]
-
-        if len(ip_adapter) == 0:
-            return None
-
+    ) -> Optional[List[IPAdapterData]]:
+        """If IP-Adapter is enabled, then this function loads the requisite models and adds the image prompt conditioning data."""
         ip_adapter_data_list = []
-        assert len(ip_adapter) == len(image_prompts)
-        for single_ip_adapter in ip_adapter:
+        assert len(ip_adapters) == len(image_prompts)
+        for single_ip_adapter in ip_adapters:
             ip_adapter_model = exit_stack.enter_context(context.models.load(single_ip_adapter.ip_adapter_model))
 
             image_prompt_embeds, uncond_image_prompt_embeds = image_prompts.pop(0)
 
-            mask = single_ip_adapter.mask
-            if mask is not None:
-                mask = context.tensors.load(mask.tensor_name)
+            mask_field = single_ip_adapter.mask
+            mask = context.tensors.load(mask_field.tensor_name) if mask_field is not None else None
             mask = self._preprocess_regional_prompt_mask(mask, latent_height, latent_width, dtype=dtype)
 
             ip_adapter_data_list.append(
@@ -754,7 +733,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
                 )
             )
 
-        return ip_adapter_data_list
+        return ip_adapter_data_list if len(ip_adapter_data_list) > 0 else None
 
     def run_t2i_adapters(
         self,
@@ -932,7 +911,15 @@ class DenoiseLatentsInvocation(BaseInvocation):
                 do_classifier_free_guidance=True,
             )
 
-            image_prompts = self.prep_ip_adapter_image_prompts(context=context, ip_adapter=self.ip_adapter)
+            ip_adapters: List[IPAdapterField] = []
+            if self.ip_adapter is not None:
+                # ip_adapter could be a list or a single IPAdapterField. Normalize to a list here.
+                if isinstance(self.ip_adapter, list):
+                    ip_adapters = self.ip_adapter
+                else:
+                    ip_adapters = [self.ip_adapter]
+
+            image_prompts = self.prep_ip_adapter_image_prompts(context=context, ip_adapters=ip_adapters)
 
             # get the unet's config so that we can pass the base to dispatch_progress()
             unet_config = context.models.get_config(self.unet.unet.key)
@@ -992,12 +979,12 @@ class DenoiseLatentsInvocation(BaseInvocation):
 
                 ip_adapter_data = self.prep_ip_adapter_data(
                     context=context,
-                    ip_adapter=self.ip_adapter,
+                    ip_adapters=ip_adapters,
+                    image_prompts=image_prompts,
                     exit_stack=exit_stack,
                     latent_height=latent_height,
                     latent_width=latent_width,
                     dtype=unet.dtype,
-                    image_prompts=image_prompts,
                 )
 
                 num_inference_steps, timesteps, init_timestep, scheduler_step_kwargs = self.init_scheduler(