diff --git a/modules/sd_schedulers.py b/modules/sd_schedulers.py
index 118beea5df1..84b0abb6aca 100644
--- a/modules/sd_schedulers.py
+++ b/modules/sd_schedulers.py
@@ -76,6 +76,16 @@ def kl_optimal(n, sigma_min, sigma_max, device):
     sigmas = torch.tan(step_indices / n * alpha_min + (1.0 - step_indices / n) * alpha_max)
     return sigmas
 
+
+def simple_scheduler(n, sigma_min, sigma_max, inner_model, device):
+    sigs = []
+    ss = len(inner_model.sigmas) / n
+    for x in range(n):
+        sigs += [float(inner_model.sigmas[-(1 + int(x * ss))])]
+    sigs += [0.0]
+    return torch.FloatTensor(sigs).to(device)
+
+
 def normal_scheduler(n, sigma_min, sigma_max, inner_model, device, sgm=False, floor=False):
     start = inner_model.sigma_to_t(torch.tensor(sigma_max))
     end = inner_model.sigma_to_t(torch.tensor(sigma_min))
@@ -92,6 +102,7 @@ def normal_scheduler(n, sigma_min, sigma_max, inner_model, device, sgm=False, fl
     sigs += [0.0]
     return torch.FloatTensor(sigs).to(device)
 
+
 def ddim_scheduler(n, sigma_min, sigma_max, inner_model, device):
     sigs = []
     ss = max(len(inner_model.sigmas) // n, 1)
@@ -113,6 +124,7 @@ def ddim_scheduler(n, sigma_min, sigma_max, inner_model, device):
     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),
+    Scheduler('simple', 'Simple', simple_scheduler, need_inner_model=True),
     Scheduler('normal', 'Normal', normal_scheduler, need_inner_model=True),
     Scheduler('ddim', 'DDIM', ddim_scheduler, need_inner_model=True),
 ]