diff --git a/invokeai/app/invocations/tiled_multi_diffusion_denoise_latents.py b/invokeai/app/invocations/tiled_multi_diffusion_denoise_latents.py
index 53b8ed7fef..6274204c14 100644
--- a/invokeai/app/invocations/tiled_multi_diffusion_denoise_latents.py
+++ b/invokeai/app/invocations/tiled_multi_diffusion_denoise_latents.py
@@ -24,7 +24,7 @@ from invokeai.app.invocations.primitives import LatentsOutput
 from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.backend.lora import LoRAModelRaw
 from invokeai.backend.model_patcher import ModelPatcher
-from invokeai.backend.stable_diffusion.diffusers_pipeline import ControlNetData
+from invokeai.backend.stable_diffusion.diffusers_pipeline import ControlNetData, PipelineIntermediateState
 from invokeai.backend.stable_diffusion.multi_diffusion_pipeline import (
     MultiDiffusionPipeline,
     MultiDiffusionRegionConditioning,
@@ -170,6 +170,12 @@ class TiledMultiDiffusionDenoiseLatents(BaseInvocation):
             min_overlap=self.tile_min_overlap,
         )
 
+        # Get the unet's config so that we can pass the base to dispatch_progress().
+        unet_config = context.models.get_config(self.unet.unet.key)
+
+        def step_callback(state: PipelineIntermediateState) -> None:
+            context.util.sd_step_callback(state, unet_config.base)
+
         # Prepare an iterator that yields the UNet's LoRA models and their weights.
         def _lora_loader() -> Iterator[Tuple[LoRAModelRaw, float]]:
             for lora in self.unet.loras:
@@ -250,8 +256,7 @@ class TiledMultiDiffusionDenoiseLatents(BaseInvocation):
                 scheduler_step_kwargs=scheduler_step_kwargs,
                 noise=noise,
                 timesteps=timesteps,
-                # TODO(ryand): Add proper callback.
-                callback=lambda x: None,
+                callback=step_callback,
             )
 
         result_latents = result_latents.to("cpu")