diff --git a/invokeai/app/invocations/denoise_latents.py b/invokeai/app/invocations/denoise_latents.py
index 9970949ef6..67c7f2abca 100644
--- a/invokeai/app/invocations/denoise_latents.py
+++ b/invokeai/app/invocations/denoise_latents.py
@@ -735,7 +735,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
         # The image prompts are then passed to prep_ip_adapter_data().
         image_prompts = self.prep_ip_adapter_image_prompts(context=context, ip_adapters=ip_adapters)
 
-        # get the unet's config so that we can pass the base to dispatch_progress()
+        # get the unet's config so that we can pass the base to sd_step_callback()
         unet_config = context.models.get_config(self.unet.unet.key)
 
         def step_callback(state: PipelineIntermediateState) -> None:
diff --git a/invokeai/app/invocations/tiled_multi_diffusion_denoise_latents.py b/invokeai/app/invocations/tiled_multi_diffusion_denoise_latents.py
index 6274204c14..717f8e1019 100644
--- a/invokeai/app/invocations/tiled_multi_diffusion_denoise_latents.py
+++ b/invokeai/app/invocations/tiled_multi_diffusion_denoise_latents.py
@@ -170,7 +170,7 @@ class TiledMultiDiffusionDenoiseLatents(BaseInvocation):
             min_overlap=self.tile_min_overlap,
         )
 
-        # Get the unet's config so that we can pass the base to dispatch_progress().
+        # Get the unet's config so that we can pass the base to sd_step_callback().
         unet_config = context.models.get_config(self.unet.unet.key)
 
         def step_callback(state: PipelineIntermediateState) -> None:
diff --git a/invokeai/backend/stable_diffusion/multi_diffusion_pipeline.py b/invokeai/backend/stable_diffusion/multi_diffusion_pipeline.py
index ca68233c27..e2934247ed 100644
--- a/invokeai/backend/stable_diffusion/multi_diffusion_pipeline.py
+++ b/invokeai/backend/stable_diffusion/multi_diffusion_pipeline.py
@@ -56,7 +56,7 @@ class MultiDiffusionPipeline(StableDiffusionGeneratorPipeline):
         self._adjust_memory_efficient_attention(latents)
 
         # Populate a weighted mask that will be used to combine the results from each region after every step.
-        # For now, we assume that each regions has the same weight (1.0).
+        # For now, we assume that each region has the same weight (1.0).
         region_weight_mask = torch.zeros(
             (1, 1, latent_height, latent_width), device=latents.device, dtype=latents.dtype
         )