diff --git a/invokeai/backend/stable_diffusion/diffusion/custom_atttention.py b/invokeai/backend/stable_diffusion/diffusion/custom_atttention.py
index 3386c72556..ac53bf911d 100644
--- a/invokeai/backend/stable_diffusion/diffusion/custom_atttention.py
+++ b/invokeai/backend/stable_diffusion/diffusion/custom_atttention.py
@@ -1,5 +1,6 @@
+from dataclasses import dataclass
 from itertools import cycle, islice
-from typing import List, Optional, TypedDict, cast
+from typing import List, Optional, cast
 
 import torch
 import torch.nn.functional as F
@@ -10,7 +11,8 @@ from invokeai.backend.stable_diffusion.diffusion.regional_ip_data import Regiona
 from invokeai.backend.stable_diffusion.diffusion.regional_prompt_data import RegionalPromptData
 
 
-class IPAdapterAttentionWeights(TypedDict):
+@dataclass
+class IPAdapterAttentionWeights:
     ip_adapter_weights: List[IPAttentionProcessorWeights]
     skip: bool
 
@@ -63,7 +65,6 @@ class CustomAttnProcessor2_0(AttnProcessor2_0):
         is_cross_attention = encoder_hidden_states is not None
 
         # Start unmodified block from AttnProcessor2_0.
-        # vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv
         residual = hidden_states
         if attn.spatial_norm is not None:
             hidden_states = attn.spatial_norm(hidden_states, temb)
@@ -77,7 +78,6 @@ class CustomAttnProcessor2_0(AttnProcessor2_0):
         batch_size, sequence_length, _ = (
             hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
         )
-        # ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
         # End unmodified block from AttnProcessor2_0.
 
         _, query_seq_len, _ = hidden_states.shape
@@ -140,22 +140,22 @@ class CustomAttnProcessor2_0(AttnProcessor2_0):
                 ip_masks = regional_ip_data.get_masks(query_seq_len=query_seq_len)
 
                 # Pad weight tensor list to match size of regional embeds
-                self._ip_adapter_attention_weights["ip_adapter_weights"] = list(
+                self._ip_adapter_attention_weights.ip_adapter_weights = list(
                     islice(
-                        cycle(self._ip_adapter_attention_weights["ip_adapter_weights"]),
+                        cycle(self._ip_adapter_attention_weights.ip_adapter_weights),
                         len(regional_ip_data.image_prompt_embeds),
                     )
                 )
 
                 assert (
                     len(regional_ip_data.image_prompt_embeds)
-                    == len(self._ip_adapter_attention_weights["ip_adapter_weights"])
+                    == len(self._ip_adapter_attention_weights.ip_adapter_weights)
                     == len(regional_ip_data.scales)
                     == ip_masks.shape[1]
                 )
 
                 for ipa_index, ipa_embed in enumerate(regional_ip_data.image_prompt_embeds):
-                    ipa_weights = self._ip_adapter_attention_weights["ip_adapter_weights"][ipa_index]
+                    ipa_weights = self._ip_adapter_attention_weights.ip_adapter_weights[ipa_index]
                     ipa_scale = regional_ip_data.scales[ipa_index]
                     ip_mask = ip_masks[0, ipa_index, ...]
 
@@ -168,7 +168,7 @@ class CustomAttnProcessor2_0(AttnProcessor2_0):
 
                     # Expected ip_hidden_state shape: (batch_size, num_ip_images, ip_seq_len, ip_image_embedding)
 
-                    if not self._ip_adapter_attention_weights["skip"]:
+                    if not self._ip_adapter_attention_weights.skip:
                         ip_key = ipa_weights.to_k_ip(ip_hidden_states)
                         ip_value = ipa_weights.to_v_ip(ip_hidden_states)
 
@@ -215,5 +215,7 @@ class CustomAttnProcessor2_0(AttnProcessor2_0):
             hidden_states = hidden_states + residual
 
         hidden_states = hidden_states / attn.rescale_output_factor
+        # End of unmodified block from AttnProcessor2_0
 
+        # casting torch.Tensor to torch.FloatTensor to avoid type issues
         return cast(torch.FloatTensor, hidden_states)
diff --git a/invokeai/backend/stable_diffusion/diffusion/unet_attention_patcher.py b/invokeai/backend/stable_diffusion/diffusion/unet_attention_patcher.py
index 52cfc2c573..df9b8d6386 100644
--- a/invokeai/backend/stable_diffusion/diffusion/unet_attention_patcher.py
+++ b/invokeai/backend/stable_diffusion/diffusion/unet_attention_patcher.py
@@ -33,7 +33,7 @@ class UNetAttentionPatcher:
                 # "attn1" processors do not use IP-Adapters.
                 attn_procs[name] = CustomAttnProcessor2_0()
             else:
-                ip_adapter_attention_weights: IPAdapterAttentionWeights = {"ip_adapter_weights": [], "skip": False}
+                ip_adapter_attention_weights = IPAdapterAttentionWeights(ip_adapter_weights=[], skip=False)
                 for ip_adapter in self._ip_adapters:
                     ip_adapter_weight = ip_adapter["ip_adapter"].attn_weights.get_attention_processor_weights(idx)
                     skip = True
@@ -41,8 +41,8 @@ class UNetAttentionPatcher:
                         if block in name:
                             skip = False
                             break
-
-                    ip_adapter_attention_weights.update({"ip_adapter_weights": [ip_adapter_weight], "skip": skip})
+                    ip_adapter_attention_weights.ip_adapter_weights = [ip_adapter_weight]
+                    ip_adapter_attention_weights.skip = skip
 
                 # Collect the weights from each IP Adapter for the idx'th attention processor.