diff --git a/k_diffusion/sampling.py b/k_diffusion/sampling.py
index 9b301c5b..a9c3333c 100644
--- a/k_diffusion/sampling.py
+++ b/k_diffusion/sampling.py
@@ -351,8 +351,6 @@ def dpm_solver_fast(self, x, t_start, t_end, nfe, eta=0., s_noise=1.):
     def dpm_solver_adaptive(self, x, t_start, t_end, order=3, rtol=0.05, atol=0.0078, h_init=0.05, pcoeff=0., icoeff=1., dcoeff=0., accept_safety=0.81):
         if order not in {2, 3}:
             raise ValueError('order should be 2 or 3')
-        if t_start == 0 or t_end == 0:
-            raise ValueError('t_start and t_end should not be 0')
         forward = t_end > t_start
         if forward and h_init <= 0:
             raise ValueError('For forward ODE integration, h_init must be positive')
@@ -400,6 +398,8 @@ def dpm_solver_adaptive(self, x, t_start, t_end, order=3, rtol=0.05, atol=0.0078
 @torch.no_grad()
 def sample_dpm_fast(model, x, sigma_min, sigma_max, n, extra_args=None, callback=None, disable=None, eta=0., s_noise=1.):
     """DPM-Solver-Fast (fixed step size). See https://arxiv.org/abs/2206.00927."""
+    if sigma_min <= 0 or sigma_max <= 0:
+        raise ValueError('sigma_min and sigma_max must not be 0')
     with tqdm(total=n, disable=disable) as pbar:
         dpm_solver = DPMSolver(model, extra_args, eps_callback=pbar.update)
         if callback is not None:
@@ -410,6 +410,8 @@ def sample_dpm_fast(model, x, sigma_min, sigma_max, n, extra_args=None, callback
 @torch.no_grad()
 def sample_dpm_adaptive(model, x, sigma_min, sigma_max, extra_args=None, callback=None, disable=None, order=3, rtol=0.05, atol=0.0078, h_init=0.05, pcoeff=0., icoeff=1., dcoeff=0., accept_safety=0.81, return_info=False):
     """DPM-Solver-12 and 23 (adaptive step size). See https://arxiv.org/abs/2206.00927."""
+    if sigma_min <= 0 or sigma_max <= 0:
+        raise ValueError('sigma_min and sigma_max must not be 0')
     with tqdm(disable=disable) as pbar:
         dpm_solver = DPMSolver(model, extra_args, eps_callback=pbar.update)
         if callback is not None: