diff --git a/comfy/samplers.py b/comfy/samplers.py
index 6a386511..cd25bc35 100644
--- a/comfy/samplers.py
+++ b/comfy/samplers.py
@@ -467,6 +467,13 @@ def linear_quadratic_schedule(model_sampling, steps, threshold_noise=0.025, line
         sigma_schedule = [1.0 - x for x in sigma_schedule]
     return torch.FloatTensor(sigma_schedule) * model_sampling.sigma_max.cpu()
 
+# Referenced from https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15608
+def kl_optimal_scheduler(n: int, sigma_min: float, sigma_max: float) -> torch.Tensor:
+    adj_idxs = torch.arange(n, dtype=torch.float).div_(n - 1)
+    sigmas = adj_idxs.new_zeros(n + 1)
+    sigmas[:-1] = (adj_idxs * math.atan(sigma_min) + (1 - adj_idxs) * math.atan(sigma_max)).tan_()
+    return sigmas
+
 def get_mask_aabb(masks):
     if masks.numel() == 0:
         return torch.zeros((0, 4), device=masks.device, dtype=torch.int)
@@ -913,7 +920,7 @@ def sample(model, noise, positive, negative, cfg, device, sampler, sigmas, model
     return cfg_guider.sample(noise, latent_image, sampler, sigmas, denoise_mask, callback, disable_pbar, seed)
 
 
-SCHEDULER_NAMES = ["normal", "karras", "exponential", "sgm_uniform", "simple", "ddim_uniform", "beta", "linear_quadratic"]
+SCHEDULER_NAMES = ["normal", "karras", "exponential", "sgm_uniform", "simple", "ddim_uniform", "beta", "linear_quadratic", "kl_optimal"]
 SAMPLER_NAMES = KSAMPLER_NAMES + ["ddim", "uni_pc", "uni_pc_bh2"]
 
 def calculate_sigmas(model_sampling, scheduler_name, steps):
@@ -933,6 +940,8 @@ def calculate_sigmas(model_sampling, scheduler_name, steps):
         sigmas = beta_scheduler(model_sampling, steps)
     elif scheduler_name == "linear_quadratic":
         sigmas = linear_quadratic_schedule(model_sampling, steps)
+    elif scheduler_name == "kl_optimal":
+        sigmas = kl_optimal_scheduler(n=steps, sigma_min=float(model_sampling.sigma_min), sigma_max=float(model_sampling.sigma_max))
     else:
         logging.error("error invalid scheduler {}".format(scheduler_name))
     return sigmas