diff --git a/invokeai/backend/stable_diffusion/diffusion/custom_atttention.py b/invokeai/backend/stable_diffusion/diffusion/custom_atttention.py
index 2f7523dd46..667fcd9a64 100644
--- a/invokeai/backend/stable_diffusion/diffusion/custom_atttention.py
+++ b/invokeai/backend/stable_diffusion/diffusion/custom_atttention.py
@@ -3,7 +3,6 @@ from typing import Optional
 import torch
 import torch.nn.functional as F
 from diffusers.models.attention_processor import Attention, AttnProcessor2_0
-from diffusers.utils import USE_PEFT_BACKEND
 
 from invokeai.backend.ip_adapter.ip_attention_weights import IPAttentionProcessorWeights
 from invokeai.backend.stable_diffusion.diffusion.regional_prompt_data import RegionalPromptData
@@ -51,7 +50,6 @@ class CustomAttnProcessor2_0(AttnProcessor2_0):
         encoder_hidden_states: Optional[torch.FloatTensor] = None,
         attention_mask: Optional[torch.FloatTensor] = None,
         temb: Optional[torch.FloatTensor] = None,
-        scale: float = 1.0,
         # For regional prompting:
         regional_prompt_data: Optional[RegionalPromptData] = None,
         percent_through: Optional[torch.FloatTensor] = None,
@@ -111,16 +109,15 @@ class CustomAttnProcessor2_0(AttnProcessor2_0):
         if attn.group_norm is not None:
             hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
 
-        args = () if USE_PEFT_BACKEND else (scale,)
-        query = attn.to_q(hidden_states, *args)
+        query = attn.to_q(hidden_states)
 
         if encoder_hidden_states is None:
             encoder_hidden_states = hidden_states
         elif attn.norm_cross:
             encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
 
-        key = attn.to_k(encoder_hidden_states, *args)
-        value = attn.to_v(encoder_hidden_states, *args)
+        key = attn.to_k(encoder_hidden_states)
+        value = attn.to_v(encoder_hidden_states)
 
         inner_dim = key.shape[-1]
         head_dim = inner_dim // attn.heads
@@ -187,7 +184,7 @@ class CustomAttnProcessor2_0(AttnProcessor2_0):
         # Start unmodified block from AttnProcessor2_0.
         # vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv
         # linear proj
-        hidden_states = attn.to_out[0](hidden_states, *args)
+        hidden_states = attn.to_out[0](hidden_states)
         # dropout
         hidden_states = attn.to_out[1](hidden_states)