lint

modules/sd_schedulers.py

@@ -4,16 +4,16 @@ import torch
 
 import k_diffusion
 
+import numpy as np
+
+from modules import shared
+
 def to_d(x, sigma, denoised):
     """Converts a denoiser output to a Karras ODE derivative."""
     return (x - denoised) / sigma
 
 k_diffusion.sampling.to_d = to_d
 
-import numpy as np
-
-from modules import shared
-
 @dataclasses.dataclass
 class Scheduler:
     name: str