diff --git a/invokeai/app/invocations/tiled_stable_diffusion_refine.py b/invokeai/app/invocations/tiled_stable_diffusion_refine.py
index 592a00f71c..7c4cf6fe92 100644
--- a/invokeai/app/invocations/tiled_stable_diffusion_refine.py
+++ b/invokeai/app/invocations/tiled_stable_diffusion_refine.py
@@ -55,6 +55,10 @@ class TiledStableDiffusionRefineInvocation(BaseInvocation):
     negative_conditioning: ConditioningField = InputField(
         description=FieldDescriptions.negative_cond, input=Input.Connection
     )
+    # TODO(ryand): Add multiple-of validation.
+    tile_height: int = InputField(default=512, gt=0, description="Height of the tiles.")
+    tile_width: int = InputField(default=512, gt=0, description="Width of the tiles.")
+    tile_min_overlap: int = InputField(default=16, gt=0, description="Minimum overlap between tiles.")
     steps: int = InputField(default=10, gt=0, description=FieldDescriptions.steps)
     cfg_scale: float | list[float] = InputField(default=7.5, description=FieldDescriptions.cfg_scale, title="CFG Scale")
     denoising_start: float = InputField(
@@ -93,6 +97,7 @@ class TiledStableDiffusionRefineInvocation(BaseInvocation):
     control_model: ModelIdentifierField = InputField(
         description=FieldDescriptions.controlnet_model, ui_type=UIType.ControlNetModel
     )
+    control_weight: float = InputField(default=0.6)
 
     @field_validator("cfg_scale")
     def ge_one(cls, v: list[float] | float) -> list[float] | float:
@@ -173,9 +178,9 @@ class TiledStableDiffusionRefineInvocation(BaseInvocation):
         tiles = calc_tiles_min_overlap(
             image_height=input_image.height,
             image_width=input_image.width,
-            tile_height=512,
-            tile_width=512,
-            min_overlap=128,
+            tile_height=self.tile_height,
+            tile_width=self.tile_width,
+            min_overlap=self.tile_min_overlap,
         )
 
         # Convert the input image to a torch.Tensor.
@@ -299,7 +304,7 @@ class TiledStableDiffusionRefineInvocation(BaseInvocation):
                 controlnet_data = self.run_controlnet(
                     image=image_tile_pil,
                     controlnet_model=controlnet_model,
-                    weight=1.0,
+                    weight=self.control_weight,
                     do_classifier_free_guidance=True,
                     width=width,
                     height=height,