Apply suggestions from code review

Author: patrickvonplaten

src/diffusers/schedulers/scheduling_consistency_decoder.py

@@ -26,8 +26,7 @@ def betas_for_alpha_bar(
 
     Args:
         num_diffusion_timesteps (`int`): the number of betas to produce.
-        max_beta (`float`): the maximum beta to use
-        use values lower than 1 to
+        max_beta (`float`): the maximum beta to use. Use values lower than 1 to
                      prevent singularities.
         alpha_transform_type (`str`, *optional*, default to `cosine`): the type of noise schedule for alpha_bar.
                      Choose from `cosine` or `exp`

src/diffusers/schedulers/scheduling_ddim.py

@@ -63,8 +63,7 @@ def betas_for_alpha_bar(
 
     Args:
         num_diffusion_timesteps (`int`): the number of betas to produce.
-        max_beta (`float`): the maximum beta to use
-        use values lower than 1 to
+        max_beta (`float`): the maximum beta to use. Use values lower than 1 to
                      prevent singularities.
         alpha_transform_type (`str`, *optional*, default to `cosine`): the type of noise schedule for alpha_bar.
                      Choose from `cosine` or `exp`
@@ -162,8 +161,7 @@ class DDIMScheduler(SchedulerMixin, ConfigMixin):
             `set_alpha_to_one=False` to make the last step use step 0 for the previous alpha product like in Stable
             Diffusion.
         prediction_type (`str`, defaults to `epsilon`, *optional*):
-            Prediction type of the scheduler function
-            can be `epsilon` (predicts the noise of the diffusion process),
+            Prediction type of the scheduler function. Can be `epsilon` (predicts the noise of the diffusion process),
             `sample` (directly predicts the noisy sample`) or `v_prediction` (see section 2.4 of [Imagen
             Video](https://imagen.research.google/video/paper.pdf) paper).
         thresholding (`bool`, defaults to `False`):

src/diffusers/schedulers/scheduling_ddim_inverse.py

@@ -61,8 +61,7 @@ def betas_for_alpha_bar(
 
     Args:
         num_diffusion_timesteps (`int`): the number of betas to produce.
-        max_beta (`float`): the maximum beta to use
-        use values lower than 1 to
+        max_beta (`float`): the maximum beta to use. Use values lower than 1 to
                      prevent singularities.
         alpha_transform_type (`str`, *optional*, default to `cosine`): the type of noise schedule for alpha_bar.
                      Choose from `cosine` or `exp`
@@ -160,8 +159,7 @@ class DDIMInverseScheduler(SchedulerMixin, ConfigMixin):
             `set_alpha_to_one=False` to make the last step use `num_train_timesteps - 1` for the previous alpha
             product.
         prediction_type (`str`, defaults to `epsilon`, *optional*):
-            Prediction type of the scheduler function
-            can be `epsilon` (predicts the noise of the diffusion process),
+            Prediction type of the scheduler function. Can be `epsilon` (predicts the noise of the diffusion process),
             `sample` (directly predicts the noisy sample`) or `v_prediction` (see section 2.4 of [Imagen
             Video](https://imagen.research.google/video/paper.pdf) paper).
         timestep_spacing (`str`, defaults to `"leading"`):

src/diffusers/schedulers/scheduling_ddim_parallel.py

@@ -63,8 +63,7 @@ def betas_for_alpha_bar(
 
     Args:
         num_diffusion_timesteps (`int`): the number of betas to produce.
-        max_beta (`float`): the maximum beta to use
-        use values lower than 1 to
+        max_beta (`float`): the maximum beta to use. Use values lower than 1 to
                      prevent singularities.
         alpha_transform_type (`str`, *optional*, default to `cosine`): the type of noise schedule for alpha_bar.
                      Choose from `cosine` or `exp`

src/diffusers/schedulers/scheduling_ddpm.py

@@ -60,8 +60,7 @@ def betas_for_alpha_bar(
 
     Args:
         num_diffusion_timesteps (`int`): the number of betas to produce.
-        max_beta (`float`): the maximum beta to use
-        use values lower than 1 to
+        max_beta (`float`): the maximum beta to use; Use values lower than 1 to
                      prevent singularities.
         alpha_transform_type (`str`, *optional*, default to `cosine`): the type of noise schedule for alpha_bar.
                      Choose from `cosine` or `exp`
@@ -115,8 +114,7 @@ class DDPMScheduler(SchedulerMixin, ConfigMixin):
         clip_sample_range (`float`, defaults to 1.0):
             The maximum magnitude for sample clipping. Valid only when `clip_sample=True`.
         prediction_type (`str`, defaults to `epsilon`, *optional*):
-            Prediction type of the scheduler function
-            can be `epsilon` (predicts the noise of the diffusion process),
+            Prediction type of the scheduler function. Can be `epsilon` (predicts the noise of the diffusion process),
             `sample` (directly predicts the noisy sample`) or `v_prediction` (see section 2.4 of [Imagen
             Video](https://imagen.research.google/video/paper.pdf) paper).
         thresholding (`bool`, defaults to `False`):

src/diffusers/schedulers/scheduling_ddpm_parallel.py

@@ -62,8 +62,7 @@ def betas_for_alpha_bar(
 
     Args:
         num_diffusion_timesteps (`int`): the number of betas to produce.
-        max_beta (`float`): the maximum beta to use
-        use values lower than 1 to
+        max_beta (`float`): the maximum beta to use. Use values lower than 1 to
                      prevent singularities.
         alpha_transform_type (`str`, *optional*, default to `cosine`): the type of noise schedule for alpha_bar.
                      Choose from `cosine` or `exp`

src/diffusers/schedulers/scheduling_ddpm_wuerstchen.py

@@ -56,8 +56,7 @@ def betas_for_alpha_bar(
 
     Args:
         num_diffusion_timesteps (`int`): the number of betas to produce.
-        max_beta (`float`): the maximum beta to use
-        use values lower than 1 to
+        max_beta (`float`): the maximum beta to use. Use values lower than 1 to
                      prevent singularities.
         alpha_transform_type (`str`, *optional*, default to `cosine`): the type of noise schedule for alpha_bar.
                      Choose from `cosine` or `exp`

src/diffusers/schedulers/scheduling_deis_multistep.py

@@ -42,8 +42,7 @@ def betas_for_alpha_bar(
 
     Args:
         num_diffusion_timesteps (`int`): the number of betas to produce.
-        max_beta (`float`): the maximum beta to use
-        use values lower than 1 to
+        max_beta (`float`): the maximum beta to use. Use values lower than 1 to
                      prevent singularities.
         alpha_transform_type (`str`, *optional*, default to `cosine`): the type of noise schedule for alpha_bar.
                      Choose from `cosine` or `exp`