diff --git a/comfy/k_diffusion/sampling.py b/comfy/k_diffusion/sampling.py
index eb088d92b..6c1a25e7e 100644
--- a/comfy/k_diffusion/sampling.py
+++ b/comfy/k_diffusion/sampling.py
@@ -688,6 +688,66 @@ def sample_dpmpp_3m_sde(model, x, sigmas, extra_args=None, callback=None, disabl
         h_1, h_2 = h, h_1
     return x
 
+@torch.no_grad()
+def sample_restart(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1., restart_list = None):
+    """Implements restart sampling in Restart Sampling for Improving Generative Processes (2023)"""
+    '''Restart_list format: {min_sigma: [ restart_steps, restart_times, max_sigma]}'''
+    '''If restart_list is None: will choose restart_list automatically, otherwise will use the given restart_list'''
+    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, get_sigmas_karras
+    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 or not second_order:
+            # Euler method
+            x = x + d * dt
+        else:
+            # Heun's method
+            x_2 = x + d * dt
+            denoised_2 = model(x_2, new_sigma * s_in, **extra_args)
+            d_2 = to_d(x_2, new_sigma, denoised_2)
+            d_prime = (d + d_2) / 2
+            x = x + d_prime * dt
+        step_id += 1
+        return x
+    steps = sigmas.shape[0] - 1
+    if restart_list is None:
+        if steps >= 20:
+            restart_steps = 9
+            restart_times = 1
+            if steps >= 36:
+                restart_steps = steps // 4
+                restart_times = 2
+            sigmas = get_sigmas_karras(steps - restart_steps * restart_times, sigmas[-2].item(), sigmas[0].item(), 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:
+            restart_steps, restart_times, restart_max = restart_list[i + 1]
+            min_idx = i + 1
+            max_idx = int(torch.argmin(abs(sigmas - restart_max), dim=0))
+            if max_idx < min_idx:
+                sigma_restart = get_sigmas_karras(restart_steps, sigmas[min_idx].item(), sigmas[max_idx].item(), device=sigmas.device)[:-1] # remove the zero at the end
+                while restart_times > 0:
+                    restart_times -= 1
+                    x = x + torch.randn_like(x) * s_noise * (sigmas[max_idx] ** 2 - sigmas[min_idx] ** 2) ** 0.5
+                    for (old_sigma, new_sigma) in zip(sigma_restart[:-1], sigma_restart[1:]):
+                        x = heun_step(x, old_sigma, new_sigma)
+    return x
+
 @torch.no_grad()
 def sample_dpmpp_3m_sde_gpu(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
     sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()