Fix divide by zero RuntimeWarning

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]