prior denoising scale model input

Author: williamberman

src/diffusers/schedulers/scheduling_ddim.py

@@ -46,6 +46,7 @@ class DDIMSchedulerOutput(BaseOutput):
     pred_original_sample: Optional[torch.FloatTensor] = None
 
 
+# Copied from diffusers.schedulers.scheduling_ddpm.betas_for_alpha_bar
 def betas_for_alpha_bar(num_diffusion_timesteps, max_beta=0.999) -> torch.Tensor:
     """
     Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of
@@ -72,7 +73,7 @@ def alpha_bar(time_step):
         t1 = i / num_diffusion_timesteps
         t2 = (i + 1) / num_diffusion_timesteps
         betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta))
-    return torch.tensor(betas)
+    return torch.tensor(betas, dtype=torch.float32)
 
 
 class DDIMScheduler(SchedulerMixin, ConfigMixin):

src/diffusers/schedulers/scheduling_deis_multistep.py

@@ -25,6 +25,7 @@
 from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
 
 
+# Copied from diffusers.schedulers.scheduling_ddpm.betas_for_alpha_bar
 def betas_for_alpha_bar(num_diffusion_timesteps, max_beta=0.999):
     """
     Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of

src/diffusers/schedulers/scheduling_dpmsolver_multistep.py

@@ -24,6 +24,7 @@
 from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
 
 
+# Copied from diffusers.schedulers.scheduling_ddpm.betas_for_alpha_bar
 def betas_for_alpha_bar(num_diffusion_timesteps, max_beta=0.999):
     """
     Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of

src/diffusers/schedulers/scheduling_dpmsolver_singlestep.py

@@ -24,6 +24,7 @@
 from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
 
 
+# Copied from diffusers.schedulers.scheduling_ddpm.betas_for_alpha_bar
 def betas_for_alpha_bar(num_diffusion_timesteps, max_beta=0.999):
     """
     Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of

src/diffusers/schedulers/scheduling_pndm.py

@@ -24,6 +24,7 @@
 from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
 
 
+# Copied from diffusers.schedulers.scheduling_ddpm.betas_for_alpha_bar
 def betas_for_alpha_bar(num_diffusion_timesteps, max_beta=0.999):
     """
     Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of

src/diffusers/schedulers/scheduling_repaint.py

@@ -42,6 +42,7 @@ class RePaintSchedulerOutput(BaseOutput):
     pred_original_sample: torch.FloatTensor
 
 
+# Copied from diffusers.schedulers.scheduling_ddpm.betas_for_alpha_bar
 def betas_for_alpha_bar(num_diffusion_timesteps, max_beta=0.999):
     """
     Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of

src/diffusers/schedulers/scheduling_unclip.py

@@ -43,6 +43,7 @@ class UnCLIPSchedulerOutput(BaseOutput):
     pred_original_sample: Optional[torch.FloatTensor] = None
 
 
+# Copied from diffusers.schedulers.scheduling_ddpm.betas_for_alpha_bar
 def betas_for_alpha_bar(num_diffusion_timesteps, max_beta=0.999):
     """
     Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of