diff --git a/invokeai/app/invocations/latent.py b/invokeai/app/invocations/latent.py
index 4ad63f4f89..e35de458b5 100644
--- a/invokeai/app/invocations/latent.py
+++ b/invokeai/app/invocations/latent.py
@@ -3,7 +3,7 @@ import inspect
 import math
 from contextlib import ExitStack
 from functools import singledispatchmethod
-from typing import Any, Iterator, List, Literal, Optional, Tuple, Union
+from typing import Any, Dict, Iterator, List, Literal, Optional, Tuple, Union
 
 import einops
 import numpy as np
@@ -521,9 +521,10 @@ class DenoiseLatentsInvocation(BaseInvocation):
             )
 
         if is_sdxl:
-            return SDXLConditioningInfo(
-                embeds=text_embedding, pooled_embeds=pooled_embedding, add_time_ids=add_time_ids
-            ), regions
+            return (
+                SDXLConditioningInfo(embeds=text_embedding, pooled_embeds=pooled_embedding, add_time_ids=add_time_ids),
+                regions,
+            )
         return BasicConditioningInfo(embeds=text_embedding), regions
 
     def get_conditioning_data(
@@ -825,7 +826,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
         denoising_start: float,
         denoising_end: float,
         seed: int,
-    ) -> Tuple[int, List[int], int]:
+    ) -> Tuple[int, List[int], int, Dict[str, Union[torch.Generator, float]]]:
         assert isinstance(scheduler, ConfigMixin)
         if scheduler.config.get("cpu_only", False):
             scheduler.set_timesteps(steps, device="cpu")
@@ -853,13 +854,16 @@ class DenoiseLatentsInvocation(BaseInvocation):
         timesteps = timesteps[t_start_idx : t_start_idx + t_end_idx]
         num_inference_steps = len(timesteps) // scheduler.order
 
-        scheduler_step_kwargs = {}
+        scheduler_step_kwargs: Dict[str, Union[torch.Generator, float]] = {}
         scheduler_step_signature = inspect.signature(scheduler.step)
+        print(scheduler_step_signature.parameters)
         if "generator" in scheduler_step_signature.parameters:
             # At some point, someone decided that schedulers that accept a generator should use the original seed with
             # all bits flipped. I don't know the original rationale for this, but now we must keep it like this for
             # reproducibility.
             scheduler_step_kwargs = {"generator": torch.Generator(device=device).manual_seed(seed ^ 0xFFFFFFFF)}
+        if "eta" in scheduler_step_signature.parameters:
+            scheduler_step_kwargs = {"eta": 1.0}
 
         return num_inference_steps, timesteps, init_timestep, scheduler_step_kwargs