diff --git a/src/diffusers/schedulers/scheduling_deis_multistep.py b/src/diffusers/schedulers/scheduling_deis_multistep.py
index a6afe744bd88..39763191bce1 100644
--- a/src/diffusers/schedulers/scheduling_deis_multistep.py
+++ b/src/diffusers/schedulers/scheduling_deis_multistep.py
@@ -293,7 +293,7 @@ def _threshold_sample(self, sample: torch.FloatTensor) -> torch.FloatTensor:
     # Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._sigma_to_t
     def _sigma_to_t(self, sigma, log_sigmas):
         # get log sigma
-        log_sigma = np.log(sigma)
+        log_sigma = np.log(np.maximum(sigma, 1e-10))
 
         # get distribution
         dists = log_sigma - log_sigmas[:, np.newaxis]
diff --git a/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py b/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py
index 6b1a43630fa6..f841eeccbeac 100644
--- a/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py
+++ b/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py
@@ -313,7 +313,7 @@ def _threshold_sample(self, sample: torch.FloatTensor) -> torch.FloatTensor:
     # Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._sigma_to_t
     def _sigma_to_t(self, sigma, log_sigmas):
         # get log sigma
-        log_sigma = np.log(sigma)
+        log_sigma = np.log(np.maximum(sigma, 1e-10))
 
         # get distribution
         dists = log_sigma - log_sigmas[:, np.newaxis]
diff --git a/src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py b/src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py
index fa8f362bd3b5..d9b414ff7adb 100644
--- a/src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py
+++ b/src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py
@@ -323,7 +323,7 @@ def _threshold_sample(self, sample: torch.FloatTensor) -> torch.FloatTensor:
     # Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._sigma_to_t
     def _sigma_to_t(self, sigma, log_sigmas):
         # get log sigma
-        log_sigma = np.log(sigma)
+        log_sigma = np.log(np.maximum(sigma, 1e-10))
 
         # get distribution
         dists = log_sigma - log_sigmas[:, np.newaxis]
diff --git a/src/diffusers/schedulers/scheduling_dpmsolver_sde.py b/src/diffusers/schedulers/scheduling_dpmsolver_sde.py
index d39efbe724fb..60c6341a945b 100644
--- a/src/diffusers/schedulers/scheduling_dpmsolver_sde.py
+++ b/src/diffusers/schedulers/scheduling_dpmsolver_sde.py
@@ -373,7 +373,7 @@ def t_fn(_sigma):
     # copied from diffusers.schedulers.scheduling_euler_discrete._sigma_to_t
     def _sigma_to_t(self, sigma, log_sigmas):
         # get log sigma
-        log_sigma = np.log(sigma)
+        log_sigma = np.log(np.maximum(sigma, 1e-10))
 
         # get distribution
         dists = log_sigma - log_sigmas[:, np.newaxis]
diff --git a/src/diffusers/schedulers/scheduling_dpmsolver_singlestep.py b/src/diffusers/schedulers/scheduling_dpmsolver_singlestep.py
index bb7dc21e6fdb..befc79c2f21c 100644
--- a/src/diffusers/schedulers/scheduling_dpmsolver_singlestep.py
+++ b/src/diffusers/schedulers/scheduling_dpmsolver_singlestep.py
@@ -327,7 +327,7 @@ def _threshold_sample(self, sample: torch.FloatTensor) -> torch.FloatTensor:
     # Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._sigma_to_t
     def _sigma_to_t(self, sigma, log_sigmas):
         # get log sigma
-        log_sigma = np.log(sigma)
+        log_sigma = np.log(np.maximum(sigma, 1e-10))
 
         # get distribution
         dists = log_sigma - log_sigmas[:, np.newaxis]
diff --git a/src/diffusers/schedulers/scheduling_euler_discrete.py b/src/diffusers/schedulers/scheduling_euler_discrete.py
index 0875e1af3325..bc703a8f072c 100644
--- a/src/diffusers/schedulers/scheduling_euler_discrete.py
+++ b/src/diffusers/schedulers/scheduling_euler_discrete.py
@@ -278,7 +278,7 @@ def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.devic
 
     def _sigma_to_t(self, sigma, log_sigmas):
         # get log sigma
-        log_sigma = np.log(sigma)
+        log_sigma = np.log(np.maximum(sigma, 1e-10))
 
         # get distribution
         dists = log_sigma - log_sigmas[:, np.newaxis]
diff --git a/src/diffusers/schedulers/scheduling_heun_discrete.py b/src/diffusers/schedulers/scheduling_heun_discrete.py
index a5827bbc8610..db5797f7d238 100644
--- a/src/diffusers/schedulers/scheduling_heun_discrete.py
+++ b/src/diffusers/schedulers/scheduling_heun_discrete.py
@@ -280,7 +280,7 @@ def set_timesteps(
     # Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._sigma_to_t
     def _sigma_to_t(self, sigma, log_sigmas):
         # get log sigma
-        log_sigma = np.log(sigma)
+        log_sigma = np.log(np.maximum(sigma, 1e-10))
 
         # get distribution
         dists = log_sigma - log_sigmas[:, np.newaxis]
diff --git a/src/diffusers/schedulers/scheduling_k_dpm_2_ancestral_discrete.py b/src/diffusers/schedulers/scheduling_k_dpm_2_ancestral_discrete.py
index a0137b83fda1..115436c8e360 100644
--- a/src/diffusers/schedulers/scheduling_k_dpm_2_ancestral_discrete.py
+++ b/src/diffusers/schedulers/scheduling_k_dpm_2_ancestral_discrete.py
@@ -301,7 +301,7 @@ def set_timesteps(
     # Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._sigma_to_t
     def _sigma_to_t(self, sigma, log_sigmas):
         # get log sigma
-        log_sigma = np.log(sigma)
+        log_sigma = np.log(np.maximum(sigma, 1e-10))
 
         # get distribution
         dists = log_sigma - log_sigmas[:, np.newaxis]
diff --git a/src/diffusers/schedulers/scheduling_k_dpm_2_discrete.py b/src/diffusers/schedulers/scheduling_k_dpm_2_discrete.py
index ddea57e8c167..1c25738af274 100644
--- a/src/diffusers/schedulers/scheduling_k_dpm_2_discrete.py
+++ b/src/diffusers/schedulers/scheduling_k_dpm_2_discrete.py
@@ -312,7 +312,7 @@ def _init_step_index(self, timestep):
     # Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._sigma_to_t
     def _sigma_to_t(self, sigma, log_sigmas):
         # get log sigma
-        log_sigma = np.log(sigma)
+        log_sigma = np.log(np.maximum(sigma, 1e-10))
 
         # get distribution
         dists = log_sigma - log_sigmas[:, np.newaxis]
diff --git a/src/diffusers/schedulers/scheduling_lms_discrete.py b/src/diffusers/schedulers/scheduling_lms_discrete.py
index 9bee37d59ee1..05126377763e 100644
--- a/src/diffusers/schedulers/scheduling_lms_discrete.py
+++ b/src/diffusers/schedulers/scheduling_lms_discrete.py
@@ -305,7 +305,7 @@ def _init_step_index(self, timestep):
     # copied from diffusers.schedulers.scheduling_euler_discrete._sigma_to_t
     def _sigma_to_t(self, sigma, log_sigmas):
         # get log sigma
-        log_sigma = np.log(sigma)
+        log_sigma = np.log(np.maximum(sigma, 1e-10))
 
         # get distribution
         dists = log_sigma - log_sigmas[:, np.newaxis]
diff --git a/src/diffusers/schedulers/scheduling_unipc_multistep.py b/src/diffusers/schedulers/scheduling_unipc_multistep.py
index 741b03b6d3a2..3bd7d2931764 100644
--- a/src/diffusers/schedulers/scheduling_unipc_multistep.py
+++ b/src/diffusers/schedulers/scheduling_unipc_multistep.py
@@ -307,7 +307,7 @@ def _threshold_sample(self, sample: torch.FloatTensor) -> torch.FloatTensor:
     # Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._sigma_to_t
     def _sigma_to_t(self, sigma, log_sigmas):
         # get log sigma
-        log_sigma = np.log(sigma)
+        log_sigma = np.log(np.maximum(sigma, 1e-10))
 
         # get distribution
         dists = log_sigma - log_sigmas[:, np.newaxis]