diff --git a/comfy/k_diffusion/sa_solver.py b/comfy/k_diffusion/sa_solver.py
index 4f226c382da..a475bc90387 100644
--- a/comfy/k_diffusion/sa_solver.py
+++ b/comfy/k_diffusion/sa_solver.py
@@ -150,7 +150,7 @@ def adams_moulton_update_few_steps(order, x, tau, model_prev_list, sigma_prev_li
     sigma_list = sigma_prev_list + [sigma]
     lambda_list = [t_fn(sigma_list[-(i + 1)]) for i in range(order)]
     lambda_t = lambda_list[0]
-    lambda_prev = lambda_list[1]
+    lambda_prev = lambda_list[1] if order >= 2 else t_fn(sigma_prev)
     h = lambda_t - lambda_prev
     gradient_coefficients = get_coefficients_fn(order, lambda_prev, lambda_t, lambda_list, tau)
     
diff --git a/comfy/k_diffusion/sampling.py b/comfy/k_diffusion/sampling.py
index 9f28b708286..3896f7bd9af 100644
--- a/comfy/k_diffusion/sampling.py
+++ b/comfy/k_diffusion/sampling.py
@@ -1093,8 +1093,8 @@ def sample_sa_solver(model, x, sigmas, extra_args=None, callback=None, disable=F
 
             # Predictor step
             x_p = sa_solver.adams_bashforth_update_few_steps(order=predictor_order_used, x=x, tau=tau_val,
-                                                            model_prev_list=model_prev_list, sigma_prev_list=sigma_prev_list,
-                                                            noise=noise, sigma=sigma)
+                                                             model_prev_list=model_prev_list, sigma_prev_list=sigma_prev_list,
+                                                             noise=noise, sigma=sigma)
 
             # Evaluation step
             denoised = model(x_p, sigma * s_in, **extra_args)
@@ -1105,8 +1105,8 @@ def sample_sa_solver(model, x, sigmas, extra_args=None, callback=None, disable=F
             # 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)
+                                                             model_prev_list=model_prev_list, sigma_prev_list=sigma_prev_list,
+                                                             noise=noise, sigma=sigma)
                                                    
             else:
                 x = x_p
@@ -1129,8 +1129,8 @@ def sample_sa_solver(model, x, sigmas, extra_args=None, callback=None, disable=F
 
     # 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])
+                                                   model_prev_list=model_prev_list, sigma_prev_list=sigma_prev_list,
+                                                   noise=0, sigma=sigmas[-1])
     return x
 
 @torch.no_grad()