diff --git a/invokeai/backend/ip_adapter/attention_processor.py b/invokeai/backend/ip_adapter/attention_processor.py
index 96ab5f876a..c06d7d113c 100644
--- a/invokeai/backend/ip_adapter/attention_processor.py
+++ b/invokeai/backend/ip_adapter/attention_processor.py
@@ -138,29 +138,29 @@ class IPAttnProcessor2_0(torch.nn.Module):
 
                 ip_hidden_states = ipa_embed
 
-                # ip_hidden_state.shape = (batch_size, num_ip_images, ip_seq_len, ip_image_embedding)
+                # Expected ip_hidden_state shape: (batch_size, num_ip_images, ip_seq_len, ip_image_embedding)
 
                 ip_key = ipa_weights.to_k_ip(ip_hidden_states)
                 ip_value = ipa_weights.to_v_ip(ip_hidden_states)
 
-                # ip_key.shape, 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)
 
-                # ip_key.shape, 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(
                     query, ip_key, ip_value, attn_mask=None, dropout_p=0.0, is_causal=False
                 )
 
-                # ip_hidden_states.shape: (batch_size, num_heads, query_seq_len, head_dim)
+                # Expected ip_hidden_states shape: (batch_size, num_heads, query_seq_len, head_dim)
 
                 ip_hidden_states = ip_hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
                 ip_hidden_states = ip_hidden_states.to(query.dtype)
 
-                # ip_hidden_states.shape: (batch_size, query_seq_len, num_heads * head_dim)
+                # Expected ip_hidden_states shape: (batch_size, query_seq_len, num_heads * head_dim)
 
                 hidden_states = hidden_states + scale * ip_hidden_states