diff --git a/invokeai/app/invocations/ip_adapter.py b/invokeai/app/invocations/ip_adapter.py
index ff025011cb..d9fb7a78e5 100644
--- a/invokeai/app/invocations/ip_adapter.py
+++ b/invokeai/app/invocations/ip_adapter.py
@@ -64,8 +64,9 @@ class IPAdapterInvocation(BaseInvocation):
     )
 
     # weight: float = InputField(default=1.0, description="The weight of the IP-Adapter.", ui_type=UIType.Float)
-    weight: Union[float, List[float]] = InputField(default=1, ge=0, description="The weight given to the IP-Adapter",
-                                                   ui_type=UIType.Float, title="Weight")
+    weight: Union[float, List[float]] = InputField(
+        default=1, ge=0, description="The weight given to the IP-Adapter", ui_type=UIType.Float, title="Weight"
+    )
 
     begin_step_percent: float = InputField(
         default=0, ge=-1, le=2, description="When the IP-Adapter is first applied (% of total steps)"
diff --git a/invokeai/backend/stable_diffusion/diffusers_pipeline.py b/invokeai/backend/stable_diffusion/diffusers_pipeline.py
index f57a152315..c45094b9b0 100644
--- a/invokeai/backend/stable_diffusion/diffusers_pipeline.py
+++ b/invokeai/backend/stable_diffusion/diffusers_pipeline.py
@@ -423,7 +423,8 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
             # As it is now, the IP-Adapter will silently be skipped.
             weight = ip_adapter_data.weight[0] if isinstance(ip_adapter_data.weight, List) else ip_adapter_data.weight
             attn_ctx = ip_adapter_data.ip_adapter_model.apply_ip_adapter_attention(
-                unet=self.invokeai_diffuser.model, scale=weight,
+                unet=self.invokeai_diffuser.model,
+                scale=weight,
             )
             self.use_ip_adapter = True
         else:
@@ -510,10 +511,14 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         latent_model_input = self.scheduler.scale_model_input(latents, timestep)
 
         # handle IP-Adapter
-        if self.use_ip_adapter and ip_adapter_data is not None: # somewhat redundant but logic is clearer
+        if self.use_ip_adapter and ip_adapter_data is not None:  # somewhat redundant but logic is clearer
             first_adapter_step = math.floor(ip_adapter_data.begin_step_percent * total_step_count)
             last_adapter_step = math.ceil(ip_adapter_data.end_step_percent * total_step_count)
-            weight = ip_adapter_data.weight[step_index] if isinstance(ip_adapter_data.weight, List) else ip_adapter_data.weight
+            weight = (
+                ip_adapter_data.weight[step_index]
+                if isinstance(ip_adapter_data.weight, List)
+                else ip_adapter_data.weight
+            )
             if step_index >= first_adapter_step and step_index <= last_adapter_step:
                 # only apply IP-Adapter if current step is within the IP-Adapter's begin/end step range
                 # ip_adapter_data.ip_adapter_model.set_scale(ip_adapter_data.weight)