diff --git a/invokeai/backend/stable_diffusion/diffusers_pipeline.py b/invokeai/backend/stable_diffusion/diffusers_pipeline.py
index e5427f6de6..b65aaffb8c 100644
--- a/invokeai/backend/stable_diffusion/diffusers_pipeline.py
+++ b/invokeai/backend/stable_diffusion/diffusers_pipeline.py
@@ -289,7 +289,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         seed: int,
         timesteps: torch.Tensor,
         init_timestep: torch.Tensor,
-        callback: Callable[[PipelineIntermediateState], None] = None,
+        callback: Callable[[PipelineIntermediateState], None],
         control_data: List[ControlNetData] = None,
         ip_adapter_data: Optional[list[IPAdapterData]] = None,
         t2i_adapter_data: Optional[list[T2IAdapterData]] = None,
@@ -363,11 +363,11 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         timesteps,
         conditioning_data: TextConditioningData,
         scheduler_step_kwargs: dict[str, Any],
+        callback: Callable[[PipelineIntermediateState], None],
         mask_guidance: AddsMaskGuidance | None = None,
         control_data: List[ControlNetData] = None,
         ip_adapter_data: Optional[list[IPAdapterData]] = None,
         t2i_adapter_data: Optional[list[T2IAdapterData]] = None,
-        callback: Callable[[PipelineIntermediateState], None] = None,
     ) -> torch.Tensor:
         self._adjust_memory_efficient_attention(latents)
 
@@ -394,16 +394,15 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
             attn_ctx = unet_attention_patcher.apply_ip_adapter_attention(self.invokeai_diffuser.model)
 
         with attn_ctx:
-            if callback is not None:
-                callback(
-                    PipelineIntermediateState(
-                        step=-1,
-                        order=self.scheduler.order,
-                        total_steps=len(timesteps),
-                        timestep=self.scheduler.config.num_train_timesteps,
-                        latents=latents,
-                    )
+            callback(
+                PipelineIntermediateState(
+                    step=-1,
+                    order=self.scheduler.order,
+                    total_steps=len(timesteps),
+                    timestep=self.scheduler.config.num_train_timesteps,
+                    latents=latents,
                 )
+            )
 
             for i, t in enumerate(self.progress_bar(timesteps)):
                 batched_t = t.expand(batch_size)
@@ -422,17 +421,16 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
                 latents = step_output.prev_sample
                 predicted_original = getattr(step_output, "pred_original_sample", None)
 
-                if callback is not None:
-                    callback(
-                        PipelineIntermediateState(
-                            step=i,
-                            order=self.scheduler.order,
-                            total_steps=len(timesteps),
-                            timestep=int(t),
-                            latents=latents,
-                            predicted_original=predicted_original,
-                        )
+                callback(
+                    PipelineIntermediateState(
+                        step=i,
+                        order=self.scheduler.order,
+                        total_steps=len(timesteps),
+                        timestep=int(t),
+                        latents=latents,
+                        predicted_original=predicted_original,
                     )
+                )
 
             return latents