diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py
index db7013f24..ed5e6c79b 100644
--- a/modules/sd_samplers_kdiffusion.py
+++ b/modules/sd_samplers_kdiffusion.py
@@ -38,20 +38,19 @@ samplers_k_diffusion = [
 def restart_sampler(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1.):
     """Implements restart sampling in Restart Sampling for Improving Generative Processes (2023)"""
     '''Restart_list format: {min_sigma: [ restart_steps, restart_times, max_sigma]}'''
-    restart_list = {0.1: [10, 2, 2]}
     from tqdm.auto import trange
     extra_args = {} if extra_args is None else extra_args
     s_in = x.new_ones([x.shape[0]])
     step_id = 0
     from k_diffusion.sampling import to_d, append_zero
-    def heun_step(x, old_sigma, new_sigma):
+    def heun_step(x, old_sigma, new_sigma, second_order = True):
         nonlocal step_id
         denoised = model(x, old_sigma * s_in, **extra_args)
         d = to_d(x, old_sigma, denoised)
         if callback is not None:
             callback({'x': x, 'i': step_id, 'sigma': new_sigma, 'sigma_hat': old_sigma, 'denoised': denoised})
         dt = new_sigma - old_sigma
-        if new_sigma == 0:
+        if new_sigma == 0 or not second_order:
             # Euler method
             x = x + d * dt
         else:
@@ -63,11 +62,6 @@ def restart_sampler(model, x, sigmas, extra_args=None, callback=None, disable=No
             x = x + d_prime * dt
         step_id += 1
         return x
-    # print(sigmas)
-    temp_list = dict()
-    for key, value in restart_list.items():
-        temp_list[int(torch.argmin(abs(sigmas - key), dim=0))] = value
-    restart_list = temp_list
     def get_sigmas_karras(n, sigma_min, sigma_max, rho=7., device='cpu'):
         ramp = torch.linspace(0, 1, n).to(device)
         min_inv_rho = (sigma_min ** (1 / rho))
@@ -78,6 +72,18 @@ def restart_sampler(model, x, sigmas, extra_args=None, callback=None, disable=No
             max_inv_rho = max_inv_rho.to(device)
         sigmas = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
         return append_zero(sigmas).to(device)
+    steps = sigmas.shape[0] - 1
+    if steps >= 20:
+        restart_steps = 9
+        restart_times = 2 if steps >= 36 else 1
+        sigmas = get_sigmas_karras(steps - restart_steps * restart_times, sigmas[-2], sigmas[0], device=sigmas.device)
+        restart_list = {0.1: [restart_steps + 1, restart_times, 2]}
+    else:
+        restart_list = dict()
+    temp_list = dict()
+    for key, value in restart_list.items():
+        temp_list[int(torch.argmin(abs(sigmas - key), dim=0))] = value
+    restart_list = temp_list
     for i in trange(len(sigmas) - 1, disable=disable):
         x = heun_step(x, sigmas[i], sigmas[i+1])
         if i + 1 in restart_list: