Unverified Commit f0b2f6ce authored by TimothyAlexisVass's avatar TimothyAlexisVass Committed by GitHub
Browse files

Fix divide by zero RuntimeWarning (#5543)

parent 32fea1cc
......@@ -293,7 +293,7 @@ class DEISMultistepScheduler(SchedulerMixin, ConfigMixin):
# 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]
......
......@@ -329,7 +329,7 @@ class DPMSolverMultistepScheduler(SchedulerMixin, ConfigMixin):
# 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]
......
......@@ -328,7 +328,7 @@ class DPMSolverMultistepInverseScheduler(SchedulerMixin, ConfigMixin):
# 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]
......
......@@ -373,7 +373,7 @@ class DPMSolverSDEScheduler(SchedulerMixin, ConfigMixin):
# 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]
......
......@@ -327,7 +327,7 @@ class DPMSolverSinglestepScheduler(SchedulerMixin, ConfigMixin):
# 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]
......
......@@ -278,7 +278,7 @@ class EulerDiscreteScheduler(SchedulerMixin, ConfigMixin):
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]
......
......@@ -280,7 +280,7 @@ class HeunDiscreteScheduler(SchedulerMixin, ConfigMixin):
# 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]
......
......@@ -301,7 +301,7 @@ class KDPM2AncestralDiscreteScheduler(SchedulerMixin, ConfigMixin):
# 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]
......
......@@ -312,7 +312,7 @@ class KDPM2DiscreteScheduler(SchedulerMixin, ConfigMixin):
# 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]
......
......@@ -305,7 +305,7 @@ class LMSDiscreteScheduler(SchedulerMixin, ConfigMixin):
# 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]
......
......@@ -307,7 +307,7 @@ class UniPCMultistepScheduler(SchedulerMixin, ConfigMixin):
# 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]
......
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