diff --git a/invokeai/app/invocations/flux_text_to_image.py b/invokeai/app/invocations/flux_text_to_image.py
index 19af5baae6..33a09da9bf 100644
--- a/invokeai/app/invocations/flux_text_to_image.py
+++ b/invokeai/app/invocations/flux_text_to_image.py
@@ -112,7 +112,7 @@ class FluxTextToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
         with transformer_info as transformer:
             assert isinstance(transformer, Flux)
 
-            def step_callback(img: torch.Tensor, state: PipelineIntermediateState) -> None:
+            def step_callback() -> None:
                 if context.util.is_canceled():
                     raise CanceledException
 
diff --git a/invokeai/backend/flux/sampling.py b/invokeai/backend/flux/sampling.py
index ab9d41797b..5001959e50 100644
--- a/invokeai/backend/flux/sampling.py
+++ b/invokeai/backend/flux/sampling.py
@@ -109,7 +109,7 @@ def denoise(
     vec: Tensor,
     # sampling parameters
     timesteps: list[float],
-    step_callback: Callable[[Tensor, PipelineIntermediateState], None],
+    step_callback: Callable[[], None],
     guidance: float = 4.0,
 ):
     dtype = model.txt_in.bias.dtype
@@ -123,7 +123,6 @@ def denoise(
 
     # this is ignored for schnell
     guidance_vec = torch.full((img.shape[0],), guidance, device=img.device, dtype=img.dtype)
-    step_count = 0
     for t_curr, t_prev in tqdm(list(zip(timesteps[:-1], timesteps[1:], strict=True))):
         t_vec = torch.full((img.shape[0],), t_curr, dtype=img.dtype, device=img.device)
         pred = model(
@@ -137,17 +136,7 @@ def denoise(
         )
 
         img = img + (t_prev - t_curr) * pred
-        step_callback(
-            img,
-            PipelineIntermediateState(
-                step=step_count,
-                order=0,
-                total_steps=len(timesteps),
-                timestep=math.floor(t_curr),
-                latents=img,
-            ),
-        )
-        step_count += 1
+        step_callback()
 
     return img