[schedulers] create `self.sigmas` during __init__ (#6006)

* fix dpm
* all scheulers

src/diffusers/schedulers/scheduling_deis_multistep.py

@@ -162,6 +162,7 @@ class DEISMultistepScheduler(SchedulerMixin, ConfigMixin):
         self.alpha_t = torch.sqrt(self.alphas_cumprod)
         self.sigma_t = torch.sqrt(1 - self.alphas_cumprod)
         self.lambda_t = torch.log(self.alpha_t) - torch.log(self.sigma_t)
+        self.sigmas = ((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5
 
         # standard deviation of the initial noise distribution
         self.init_noise_sigma = 1.0

src/diffusers/schedulers/scheduling_dpmsolver_multistep.py

@@ -189,6 +189,7 @@ class DPMSolverMultistepScheduler(SchedulerMixin, ConfigMixin):
         self.alpha_t = torch.sqrt(self.alphas_cumprod)
         self.sigma_t = torch.sqrt(1 - self.alphas_cumprod)
         self.lambda_t = torch.log(self.alpha_t) - torch.log(self.sigma_t)
+        self.sigmas = ((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5
 
         # standard deviation of the initial noise distribution
         self.init_noise_sigma = 1.0

src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py

@@ -184,6 +184,7 @@ class DPMSolverMultistepInverseScheduler(SchedulerMixin, ConfigMixin):
         self.alpha_t = torch.sqrt(self.alphas_cumprod)
         self.sigma_t = torch.sqrt(1 - self.alphas_cumprod)
         self.lambda_t = torch.log(self.alpha_t) - torch.log(self.sigma_t)
+        self.sigmas = ((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5
 
         # standard deviation of the initial noise distribution
         self.init_noise_sigma = 1.0

src/diffusers/schedulers/scheduling_dpmsolver_singlestep.py

@@ -172,6 +172,7 @@ class DPMSolverSinglestepScheduler(SchedulerMixin, ConfigMixin):
         self.alpha_t = torch.sqrt(self.alphas_cumprod)
         self.sigma_t = torch.sqrt(1 - self.alphas_cumprod)
         self.lambda_t = torch.log(self.alpha_t) - torch.log(self.sigma_t)
+        self.sigmas = ((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5
 
         # standard deviation of the initial noise distribution
         self.init_noise_sigma = 1.0

src/diffusers/schedulers/scheduling_unipc_multistep.py

@@ -175,6 +175,7 @@ class UniPCMultistepScheduler(SchedulerMixin, ConfigMixin):
         self.alpha_t = torch.sqrt(self.alphas_cumprod)
         self.sigma_t = torch.sqrt(1 - self.alphas_cumprod)
         self.lambda_t = torch.log(self.alpha_t) - torch.log(self.sigma_t)
+        self.sigmas = ((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5
 
         # standard deviation of the initial noise distribution
         self.init_noise_sigma = 1.0