Improve docstrings and type hints in scheduling_heun_discrete.py (#12726)

refactor: improve type hints for `beta_schedule`, `prediction_type`, and `timestep_spacing` parameters, and add return type hints to several methods.

Author: delmalih

src/diffusers/schedulers/scheduling_heun_discrete.py

@@ -107,12 +107,12 @@ class HeunDiscreteScheduler(SchedulerMixin, ConfigMixin):
             The starting `beta` value of inference.
         beta_end (`float`, defaults to 0.02):
             The final `beta` value.
-        beta_schedule (`str`, defaults to `"linear"`):
+        beta_schedule (`"linear"`, `"scaled_linear"`, `"squaredcos_cap_v2"`, or `"exp"`, defaults to `"linear"`):
             The beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from
-            `linear` or `scaled_linear`.
+            `linear`, `scaled_linear`, `squaredcos_cap_v2`, or `exp`.
         trained_betas (`np.ndarray`, *optional*):
             Pass an array of betas directly to the constructor to bypass `beta_start` and `beta_end`.
-        prediction_type (`str`, defaults to `epsilon`, *optional*):
+        prediction_type (`"epsilon"`, `"sample"`, or `"v_prediction"`, defaults to `"epsilon"`, *optional*):
             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://huggingface.co/papers/2210.02303) paper).
@@ -128,7 +128,7 @@ class HeunDiscreteScheduler(SchedulerMixin, ConfigMixin):
         use_beta_sigmas (`bool`, *optional*, defaults to `False`):
             Whether to use beta sigmas for step sizes in the noise schedule during the sampling process. Refer to [Beta
             Sampling is All You Need](https://huggingface.co/papers/2407.12173) for more information.
-        timestep_spacing (`str`, defaults to `"linspace"`):
+        timestep_spacing (`"linspace"`, `"leading"`, or `"trailing"`, defaults to `"linspace"`):
             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):
@@ -144,17 +144,17 @@ def __init__(
         num_train_timesteps: int = 1000,
         beta_start: float = 0.00085,  # sensible defaults
         beta_end: float = 0.012,
-        beta_schedule: str = "linear",
+        beta_schedule: Literal["linear", "scaled_linear", "squaredcos_cap_v2", "exp"] = "linear",
         trained_betas: Optional[Union[np.ndarray, List[float]]] = None,
-        prediction_type: str = "epsilon",
+        prediction_type: Literal["epsilon", "sample", "v_prediction"] = "epsilon",
         use_karras_sigmas: Optional[bool] = False,
         use_exponential_sigmas: Optional[bool] = False,
         use_beta_sigmas: Optional[bool] = False,
         clip_sample: Optional[bool] = False,
         clip_sample_range: float = 1.0,
-        timestep_spacing: str = "linspace",
+        timestep_spacing: Literal["linspace", "leading", "trailing"] = "linspace",
         steps_offset: int = 0,
-    ):
+    ) -> None:
         if self.config.use_beta_sigmas and not is_scipy_available():
             raise ImportError("Make sure to install scipy if you want to use beta sigmas.")
         if sum([self.config.use_beta_sigmas, self.config.use_exponential_sigmas, self.config.use_karras_sigmas]) > 1:
@@ -241,7 +241,7 @@ def begin_index(self):
         return self._begin_index
 
     # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.set_begin_index
-    def set_begin_index(self, begin_index: int = 0):
+    def set_begin_index(self, begin_index: int = 0) -> None:
         """
         Sets the begin index for the scheduler. This function should be run from pipeline before the inference.
 
@@ -263,7 +263,7 @@ def scale_model_input(
         Args:
             sample (`torch.Tensor`):
                 The input sample.
-            timestep (`int`, *optional*):
+            timestep (`float` or `torch.Tensor`):
                 The current timestep in the diffusion chain.
 
         Returns:
@@ -283,19 +283,19 @@ def set_timesteps(
         device: Union[str, torch.device] = None,
         num_train_timesteps: Optional[int] = None,
         timesteps: Optional[List[int]] = None,
-    ):
+    ) -> None:
         """
         Sets the discrete timesteps used for the diffusion chain (to be run before inference).
 
         Args:
-            num_inference_steps (`int`):
+            num_inference_steps (`int`, *optional*, defaults to `None`):
                 The number of diffusion steps used when generating samples with a pre-trained model.
-            device (`str` or `torch.device`, *optional*):
+            device (`str`, `torch.device`, *optional*, defaults to `None`):
                 The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
-            num_train_timesteps (`int`, *optional*):
+            num_train_timesteps (`int`, *optional*, defaults to `None`):
                 The number of diffusion steps used when training the model. If `None`, the default
                 `num_train_timesteps` attribute is used.
-            timesteps (`List[int]`, *optional*):
+            timesteps (`List[int]`, *optional*, defaults to `None`):
                 Custom timesteps used to support arbitrary spacing between timesteps. If `None`, timesteps will be
                 generated based on the `timestep_spacing` attribute. If `timesteps` is passed, `num_inference_steps`
                 must be `None`, and `timestep_spacing` attribute will be ignored.
@@ -370,7 +370,7 @@ def set_timesteps(
         self.sigmas = self.sigmas.to("cpu")  # to avoid too much CPU/GPU communication
 
     # Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._sigma_to_t
-    def _sigma_to_t(self, sigma, log_sigmas):
+    def _sigma_to_t(self, sigma: np.ndarray, log_sigmas: np.ndarray) -> np.ndarray:
         """
         Convert sigma values to corresponding timestep values through interpolation.
 
@@ -407,7 +407,7 @@ def _sigma_to_t(self, sigma, log_sigmas):
         return t
 
     # Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._convert_to_karras
-    def _convert_to_karras(self, in_sigmas: torch.Tensor, num_inference_steps) -> torch.Tensor:
+    def _convert_to_karras(self, in_sigmas: torch.Tensor, num_inference_steps: int) -> torch.Tensor:
         """
         Construct the noise schedule as proposed in [Elucidating the Design Space of Diffusion-Based Generative
         Models](https://huggingface.co/papers/2206.00364).
@@ -700,5 +700,5 @@ def add_noise(
         noisy_samples = original_samples + noise * sigma
         return noisy_samples
 
-    def __len__(self):
+    def __len__(self) -> int:
         return self.config.num_train_timesteps