diff --git a/api/onnx_web/chain/correct_gfpgan.py b/api/onnx_web/chain/correct_gfpgan.py
index b43489bb..b27d4299 100644
--- a/api/onnx_web/chain/correct_gfpgan.py
+++ b/api/onnx_web/chain/correct_gfpgan.py
@@ -4,7 +4,6 @@ from os import path
 import numpy as np
 from PIL import Image
 
-
 from ..params import DeviceParams, ImageParams, StageParams, UpscaleParams
 from ..server.device_pool import JobContext
 from ..utils import ServerContext, run_gc
diff --git a/api/onnx_web/chain/upscale_resrgan.py b/api/onnx_web/chain/upscale_resrgan.py
index 395d8706..f311945e 100644
--- a/api/onnx_web/chain/upscale_resrgan.py
+++ b/api/onnx_web/chain/upscale_resrgan.py
@@ -76,9 +76,7 @@ def load_resrgan(
     logger.debug("loading Real ESRGAN upscale model from %s", model_path)
 
     # TODO: shouldn't need the PTH file
-    model_path_pth = path.join(
-        server.cache_path, ("%s.pth" % params.upscale_model)
-    )
+    model_path_pth = path.join(server.cache_path, ("%s.pth" % params.upscale_model))
     upsampler = RealESRGANer(
         scale=params.scale,
         model_path=model_path_pth,
diff --git a/api/onnx_web/diffusion/load.py b/api/onnx_web/diffusion/load.py
index 919694d3..d0658dc4 100644
--- a/api/onnx_web/diffusion/load.py
+++ b/api/onnx_web/diffusion/load.py
@@ -20,7 +20,7 @@ from diffusers import (
 
 try:
     from diffusers import DEISMultistepScheduler
-except:
+except ImportError:
     from .stub_scheduler import StubScheduler as DEISMultistepScheduler
 
 from ..params import DeviceParams, Size
diff --git a/api/onnx_web/diffusion/stub_scheduler.py b/api/onnx_web/diffusion/stub_scheduler.py
index 8de415a0..8377236a 100644
--- a/api/onnx_web/diffusion/stub_scheduler.py
+++ b/api/onnx_web/diffusion/stub_scheduler.py
@@ -1,6 +1,8 @@
 from typing import Any
 
 
-class StubScheduler():
-  def step(self, model_output: Any, timestep: int, sample: Any, return_dict: bool = True) -> None:
-    raise NotImplemented("scheduler not available, try updating diffusers")
+class StubScheduler:
+    def step(
+        self, model_output: Any, timestep: int, sample: Any, return_dict: bool = True
+    ) -> None:
+        raise NotImplementedError("scheduler not available, try updating diffusers")