diff --git a/comfy/k_diffusion/sampling.py b/comfy/k_diffusion/sampling.py
index 605b1092..06dcc0b1 100644
--- a/comfy/k_diffusion/sampling.py
+++ b/comfy/k_diffusion/sampling.py
@@ -1095,10 +1095,6 @@ def sample_sa_solver(model, x, sigmas, extra_args=None, callback=None, disable=F
     if len(sigmas) <= 1:
         return x
     
-    if sigmas[-1] == 0:
-        sigmas = sigmas.clone()
-        sigmas[-1] = 0.001
-
     extra_args = {} if extra_args is None else extra_args
     if tau_func is None:
         model_sampling = model.inner_model.model_patcher.get_model_object('model_sampling')
@@ -1115,7 +1111,7 @@ def sample_sa_solver(model, x, sigmas, extra_args=None, callback=None, disable=F
     for i in trange(len(sigmas) - 1, disable=disable):
         sigma = sigmas[i]
         if i == 0:
-            # Init the initial values.
+            # Init the initial values
             denoised = model(x, sigma * s_in, **extra_args)
             model_prev_list.append(denoised)
             sigma_prev_list.append(sigma)
@@ -1134,22 +1130,19 @@ def sample_sa_solver(model, x, sigmas, extra_args=None, callback=None, disable=F
 
             # Evaluation step
             denoised = model(x_p, sigma * s_in, **extra_args)
-
-            # Update model_list
             model_prev_list.append(denoised) 
 
             # Corrector step
             if corrector_order_used > 0:
                 x = sa_solver.adams_moulton_update_few_steps(order=corrector_order_used, x=x, tau=tau_val,
                                                              model_prev_list=model_prev_list, sigma_prev_list=sigma_prev_list,
-                                                             noise=noise, sigma=sigma)
-                                                   
+                                                             noise=noise, sigma=sigma)                                     
             else:
                 x = x_p
 
             del noise, x_p
 
-            # Evaluation step if mode = pece and step != steps
+            # Evaluation step for PECE
             if corrector_order_used > 0 and pc_mode == 'PECE':
                 del model_prev_list[-1]
                 denoised = model(x, sigma * s_in, **extra_args)
@@ -1163,10 +1156,13 @@ def sample_sa_solver(model, x, sigmas, extra_args=None, callback=None, disable=F
         if callback is not None:
             callback({'x': x, 'i': i, 'denoised': model_prev_list[-1]})
 
-    # Extra final step
-    x = sa_solver.adams_bashforth_update_few_steps(order=1, x=x, tau=0,
-                                                   model_prev_list=model_prev_list, sigma_prev_list=sigma_prev_list,
-                                                   noise=0, sigma=sigmas[-1])
+    if sigmas[-1] == 0:
+        # Denoising step
+        x = model_prev_list[-1]
+    else:
+        x = sa_solver.adams_bashforth_update_few_steps(order=1, x=x, tau=0,
+                                                       model_prev_list=model_prev_list, sigma_prev_list=sigma_prev_list,
+                                                       noise=0, sigma=sigmas[-1])
     return x
 
 @torch.no_grad()