diff --git a/invokeai/app/invocations/spandrel_image_to_image.py b/invokeai/app/invocations/spandrel_image_to_image.py
index ae4f48ef77..74b29fbc8c 100644
--- a/invokeai/app/invocations/spandrel_image_to_image.py
+++ b/invokeai/app/invocations/spandrel_image_to_image.py
@@ -61,6 +61,7 @@ class SpandrelImageToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
         tile_size: int,
         spandrel_model: SpandrelImageToImageModel,
         is_canceled: Callable[[], bool],
+        step_callback: Callable[[int, int], None],
     ) -> Image.Image:
         # Compute the image tiles.
         if tile_size > 0:
@@ -103,7 +104,12 @@ class SpandrelImageToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
         image_tensor = image_tensor.to(device=spandrel_model.device, dtype=spandrel_model.dtype)
 
         # Run the model on each tile.
-        for tile, scaled_tile in tqdm(list(zip(tiles, scaled_tiles, strict=True)), desc="Upscaling Tiles"):
+        pbar = tqdm(list(zip(tiles, scaled_tiles, strict=True)), desc="Upscaling Tiles")
+
+        # Update progress, starting with 0.
+        step_callback(0, pbar.total)
+
+        for tile, scaled_tile in pbar:
             # Exit early if the invocation has been canceled.
             if is_canceled():
                 raise CanceledException
@@ -136,12 +142,27 @@ class SpandrelImageToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
                 :,
             ] = output_tile[top_overlap:, left_overlap:, :]
 
+            step_callback(pbar.n + 1, pbar.total)
+
         # Convert the output tensor to a PIL image.
         np_image = output_tensor.detach().numpy().astype(np.uint8)
         pil_image = Image.fromarray(np_image)
 
         return pil_image
 
+    def _get_step_callback(self, context: InvocationContext) -> Callable[[int, int], None]:
+        invocation_type = self.get_type()
+
+        def step_callback(step: int, total_steps: int) -> None:
+            context.util.signal_progress(
+                name=invocation_type,
+                step=step,
+                total_steps=total_steps,
+                message="Processing image",
+            )
+
+        return step_callback
+
     @torch.inference_mode()
     def invoke(self, context: InvocationContext) -> ImageOutput:
         # Images are converted to RGB, because most models don't support an alpha channel. In the future, we may want to
@@ -156,7 +177,9 @@ class SpandrelImageToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
             assert isinstance(spandrel_model, SpandrelImageToImageModel)
 
             # Upscale the image
-            pil_image = self.upscale_image(image, self.tile_size, spandrel_model, context.util.is_canceled)
+            pil_image = self.upscale_image(
+                image, self.tile_size, spandrel_model, context.util.is_canceled, self._get_step_callback(context)
+            )
 
         image_dto = context.images.save(image=pil_image)
         return ImageOutput.build(image_dto)
@@ -202,7 +225,9 @@ class SpandrelImageToImageAutoscaleInvocation(SpandrelImageToImageInvocation):
             assert isinstance(spandrel_model, SpandrelImageToImageModel)
 
             # First pass of upscaling. Note: `pil_image` will be mutated.
-            pil_image = self.upscale_image(image, self.tile_size, spandrel_model, context.util.is_canceled)
+            pil_image = self.upscale_image(
+                image, self.tile_size, spandrel_model, context.util.is_canceled, self._get_step_callback(context)
+            )
 
             # Some models don't upscale the image, but we have no way to know this in advance. We'll check if the model
             # upscaled the image and run the loop below if it did. We'll require the model to upscale both dimensions
@@ -213,7 +238,13 @@ class SpandrelImageToImageAutoscaleInvocation(SpandrelImageToImageInvocation):
                 # This is an upscale model, so we should keep upscaling until we reach the target size.
                 iterations = 1
                 while pil_image.width < target_width or pil_image.height < target_height:
-                    pil_image = self.upscale_image(pil_image, self.tile_size, spandrel_model, context.util.is_canceled)
+                    pil_image = self.upscale_image(
+                        pil_image,
+                        self.tile_size,
+                        spandrel_model,
+                        context.util.is_canceled,
+                        self._get_step_callback(context),
+                    )
                     iterations += 1
 
                     # Sanity check to prevent excessive or infinite loops. All known upscaling models are at least 2x.