Enhance docstrings and type hints in PNDMScheduler class

- Updated parameter descriptions to include default values and specific types using Literal for better clarity.
- Improved docstring formatting and consistency across methods, including detailed explanations for the `_get_prev_sample` method.
- Added type hints for method return types to enhance code readability and maintainability.

Author: delmalih

src/diffusers/schedulers/scheduling_pndm.py

@@ -79,15 +79,14 @@ class PNDMScheduler(SchedulerMixin, ConfigMixin):
     methods the library implements for all schedulers such as loading and saving.
 
     Args:
-        num_train_timesteps (`int`, defaults to 1000):
+        num_train_timesteps (`int`, defaults to `1000`):
             The number of diffusion steps to train the model.
-        beta_start (`float`, defaults to 0.0001):
+        beta_start (`float`, defaults to `0.0001`):
             The starting `beta` value of inference.
-        beta_end (`float`, defaults to 0.02):
+        beta_end (`float`, defaults to `0.02`):
             The final `beta` value.
-        beta_schedule (`str`, defaults to `"linear"`):
-            The beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from
-            `linear`, `scaled_linear`, or `squaredcos_cap_v2`.
+        beta_schedule (`"linear"`, `"scaled_linear"`, or `"squaredcos_cap_v2"`, defaults to `"linear"`):
+            The beta schedule, a mapping from a beta range to a sequence of betas for stepping the model.
         trained_betas (`np.ndarray`, *optional*):
             Pass an array of betas directly to the constructor to bypass `beta_start` and `beta_end`.
         skip_prk_steps (`bool`, defaults to `False`):
@@ -97,14 +96,13 @@ class PNDMScheduler(SchedulerMixin, ConfigMixin):
             Each diffusion step uses the alphas product value at that step and at the previous one. For the final step
             there is no previous alpha. When this option is `True` the previous alpha product is fixed to `1`,
             otherwise it uses the alpha value at step 0.
-        prediction_type (`str`, defaults to `epsilon`, *optional*):
+        prediction_type (`"epsilon"` or `"v_prediction"`, defaults to `"epsilon"`):
             Prediction type of the scheduler function; can be `epsilon` (predicts the noise of the diffusion process)
-            or `v_prediction` (see section 2.4 of [Imagen Video](https://imagen.research.google/video/paper.pdf)
-            paper).
-        timestep_spacing (`str`, defaults to `"leading"`):
+            or `v_prediction` (see section 2.4 of [Imagen Video](https://huggingface.co/papers/2210.02303) paper).
+        timestep_spacing (`"linspace"`, `"leading"`, or `"trailing"`, defaults to `"leading"`):
             The way the timesteps should be scaled. Refer to Table 2 of the [Common Diffusion Noise Schedules and
             Sample Steps are Flawed](https://huggingface.co/papers/2305.08891) for more information.
-        steps_offset (`int`, defaults to 0):
+        steps_offset (`int`, defaults to `0`):
             An offset added to the inference steps, as required by some model families.
     """
 
@@ -117,12 +115,12 @@ def __init__(
         num_train_timesteps: int = 1000,
         beta_start: float = 0.0001,
         beta_end: float = 0.02,
-        beta_schedule: str = "linear",
+        beta_schedule: Literal["linear", "scaled_linear", "squaredcos_cap_v2"] = "linear",
         trained_betas: Optional[Union[np.ndarray, List[float]]] = None,
         skip_prk_steps: bool = False,
         set_alpha_to_one: bool = False,
-        prediction_type: str = "epsilon",
-        timestep_spacing: str = "leading",
+        prediction_type: Literal["epsilon", "v_prediction"] = "epsilon",
+        timestep_spacing: Literal["linspace", "leading", "trailing"] = "leading",
         steps_offset: int = 0,
     ):
         if trained_betas is not None:
@@ -164,7 +162,7 @@ def __init__(
         self.plms_timesteps = None
         self.timesteps = None
 
-    def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.device] = None):
+    def set_timesteps(self, num_inference_steps: int, device: Optional[Union[str, torch.device]] = None) -> None:
         """
         Sets the discrete timesteps used for the diffusion chain (to be run before inference).
 
@@ -243,7 +241,7 @@ def step(
                 The current discrete timestep in the diffusion chain.
             sample (`torch.Tensor`):
                 A current instance of a sample created by the diffusion process.
-            return_dict (`bool`):
+            return_dict (`bool`, defaults to `True`):
                 Whether or not to return a [`~schedulers.scheduling_utils.SchedulerOutput`] or `tuple`.
 
         Returns:
@@ -276,14 +274,13 @@ def step_prk(
                 The current discrete timestep in the diffusion chain.
             sample (`torch.Tensor`):
                 A current instance of a sample created by the diffusion process.
-            return_dict (`bool`):
+            return_dict (`bool`, defaults to `True`):
                 Whether or not to return a [`~schedulers.scheduling_utils.SchedulerOutput`] or tuple.
 
         Returns:
             [`~schedulers.scheduling_utils.SchedulerOutput`] or `tuple`:
                 If return_dict is `True`, [`~schedulers.scheduling_utils.SchedulerOutput`] is returned, otherwise a
                 tuple is returned where the first element is the sample tensor.
-
         """
         if self.num_inference_steps is None:
             raise ValueError(
@@ -335,14 +332,13 @@ def step_plms(
                 The current discrete timestep in the diffusion chain.
             sample (`torch.Tensor`):
                 A current instance of a sample created by the diffusion process.
-            return_dict (`bool`):
+            return_dict (`bool`, defaults to `True`):
                 Whether or not to return a [`~schedulers.scheduling_utils.SchedulerOutput`] or tuple.
 
         Returns:
             [`~schedulers.scheduling_utils.SchedulerOutput`] or `tuple`:
                 If return_dict is `True`, [`~schedulers.scheduling_utils.SchedulerOutput`] is returned, otherwise a
                 tuple is returned where the first element is the sample tensor.
-
         """
         if self.num_inference_steps is None:
             raise ValueError(
@@ -403,19 +399,37 @@ def scale_model_input(self, sample: torch.Tensor, *args, **kwargs) -> torch.Tens
         """
         return sample
 
-    def _get_prev_sample(self, sample, timestep, prev_timestep, model_output):
-        # See formula (9) of PNDM paper https://huggingface.co/papers/2202.09778
-        # this function computes x_(t−δ) using the formula of (9)
-        # Note that x_t needs to be added to both sides of the equation
-
-        # Notation (<variable name> -> <name in paper>
-        # alpha_prod_t -> α_t
-        # alpha_prod_t_prev -> α_(t−δ)
-        # beta_prod_t -> (1 - α_t)
-        # beta_prod_t_prev -> (1 - α_(t−δ))
-        # sample -> x_t
-        # model_output -> e_θ(x_t, t)
-        # prev_sample -> x_(t−δ)
+    def _get_prev_sample(
+        self, sample: torch.Tensor, timestep: int, prev_timestep: int, model_output: torch.Tensor
+    ) -> torch.Tensor:
+        """
+        Compute the previous sample x_(t-δ) from the current sample x_t using formula (9) from the PNDM paper.
+
+        See formula (9) of [PNDM paper](https://huggingface.co/papers/2202.09778)
+
+        Notation (<variable name> -> <name in paper>):
+        - alpha_prod_t -> α_t
+        - alpha_prod_t_prev -> α_(t−δ)
+        - beta_prod_t -> (1 - α_t)
+        - beta_prod_t_prev -> (1 - α_(t−δ))
+        - sample -> x_t
+        - model_output -> e_θ(x_t, t)
+        - prev_sample -> x_(t−δ)
+
+        Args:
+            sample (`torch.Tensor`):
+                The current sample x_t.
+            timestep (`int`):
+                The current timestep t.
+            prev_timestep (`int`):
+                The previous timestep (t-δ).
+            model_output (`torch.Tensor`):
+                The model output e_θ(x_t, t).
+
+        Returns:
+            `torch.Tensor`:
+                The previous sample x_(t-δ).
+        """
         alpha_prod_t = self.alphas_cumprod[timestep]
         alpha_prod_t_prev = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
         beta_prod_t = 1 - alpha_prod_t
@@ -489,5 +503,5 @@ def add_noise(
         noisy_samples = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
         return noisy_samples
 
-    def __len__(self):
+    def __len__(self) -> int:
         return self.config.num_train_timesteps