diff --git a/invokeai/backend/stable_diffusion/diffusion/custom_atttention.py b/invokeai/backend/stable_diffusion/diffusion/custom_atttention.py
index 2a15e4fbe2..1dc4a43b2f 100644
--- a/invokeai/backend/stable_diffusion/diffusion/custom_atttention.py
+++ b/invokeai/backend/stable_diffusion/diffusion/custom_atttention.py
@@ -1,4 +1,4 @@
-from typing import List, Optional, TypedDict
+from typing import List, Optional, TypedDict, cast
 
 import torch
 import torch.nn.functional as F
@@ -40,15 +40,17 @@ class CustomAttnProcessor2_0(AttnProcessor2_0):
     def __call__(
         self,
         attn: Attention,
-        hidden_states: torch.FloatTensor,
-        encoder_hidden_states: Optional[torch.FloatTensor] = None,
-        attention_mask: Optional[torch.FloatTensor] = None,
-        temb: Optional[torch.FloatTensor] = None,
-        # For regional prompting:
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        temb: Optional[torch.Tensor] = None,
+        # For Regional Prompting:
         regional_prompt_data: Optional[RegionalPromptData] = None,
-        percent_through: Optional[torch.FloatTensor] = None,
+        percent_through: Optional[torch.Tensor] = None,
         # For IP-Adapter:
         regional_ip_data: Optional[RegionalIPData] = None,
+        *args,
+        **kwargs,
     ) -> torch.FloatTensor:
         """Apply attention.
         Args:
@@ -155,16 +157,18 @@ class CustomAttnProcessor2_0(AttnProcessor2_0):
 
                     # Expected ip_hidden_state shape: (batch_size, num_ip_images, ip_seq_len, ip_image_embedding)
 
-                    if 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)
 
-                        # Expected ip_key and ip_value shape: (batch_size, num_ip_images, ip_seq_len, head_dim * num_heads)
+                        # Expected ip_key and ip_value shape:
+                        # (batch_size, num_ip_images, ip_seq_len, head_dim * num_heads)
 
                         ip_key = ip_key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
                         ip_value = ip_value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
 
-                        # Expected ip_key and ip_value shape: (batch_size, num_heads, num_ip_images * ip_seq_len, head_dim)
+                        # Expected ip_key and ip_value shape:
+                        # (batch_size, num_heads, num_ip_images * ip_seq_len, head_dim)
 
                         # TODO: add support for attn.scale when we move to Torch 2.1
                         ip_hidden_states = F.scaled_dot_product_attention(
@@ -193,6 +197,7 @@ class CustomAttnProcessor2_0(AttnProcessor2_0):
         hidden_states = attn.to_out[1](hidden_states)
 
         if input_ndim == 4:
+            batch_size, channel, height, width = hidden_states.shape
             hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
 
         if attn.residual_connection:
@@ -200,4 +205,4 @@ class CustomAttnProcessor2_0(AttnProcessor2_0):
 
         hidden_states = hidden_states / attn.rescale_output_factor
 
-        return hidden_states
+        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 05011e3d9a..52cfc2c573 100644
--- a/invokeai/backend/stable_diffusion/diffusion/unet_attention_patcher.py
+++ b/invokeai/backend/stable_diffusion/diffusion/unet_attention_patcher.py
@@ -36,10 +36,10 @@ class UNetAttentionPatcher:
                 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 = False
+                    skip = True
                     for block in ip_adapter["target_blocks"]:
                         if block in name:
-                            skip = True
+                            skip = False
                             break
 
                     ip_adapter_attention_weights.update({"ip_adapter_weights": [ip_adapter_weight], "skip": skip})