diff --git a/modules/sd_schedulers.py b/modules/sd_schedulers.py
index 4ddb778501a..0c09af8d0b5 100644
--- a/modules/sd_schedulers.py
+++ b/modules/sd_schedulers.py
@@ -61,6 +61,14 @@ def loglinear_interp(t_steps, num_steps):
 
     return torch.FloatTensor(sigmas).to(device)
 
+def kl_optimal(n, sigma_min, sigma_max, device):
+    alpha_min = torch.arctan(torch.tensor(sigma_min, device=device))
+    alpha_max = torch.arctan(torch.tensor(sigma_max, device=device))
+    step_indices = torch.arange(n + 1, device=device)
+    sigmas = torch.tan(step_indices / n * alpha_min + (1.0 - step_indices / n) * alpha_max)
+    return sigmas
+
+
 schedulers = [
     Scheduler('automatic', 'Automatic', None),
     Scheduler('uniform', 'Uniform', uniform, need_inner_model=True),
@@ -68,6 +76,7 @@ def loglinear_interp(t_steps, num_steps):
     Scheduler('exponential', 'Exponential', k_diffusion.sampling.get_sigmas_exponential),
     Scheduler('polyexponential', 'Polyexponential', k_diffusion.sampling.get_sigmas_polyexponential, default_rho=1.0),
     Scheduler('sgm_uniform', 'SGM Uniform', sgm_uniform, need_inner_model=True, aliases=["SGMUniform"]),
+    Scheduler('kl_optimal', 'KL Optimal', kl_optimal),
     Scheduler('align_your_steps', 'Align Your Steps', get_align_your_steps_sigmas),
 ]