docs: improve docstring scheduling_flow_match_euler_discrete.py

src/diffusers/schedulers/scheduling_flow_match_euler_discrete.py

@@ -14,7 +14,7 @@
 
 import math
 from dataclasses import dataclass
-from typing import List, Optional, Tuple, Union
+from typing import List, Literal, Optional, Tuple, Union
 
 import numpy as np
 import torch
@@ -102,12 +102,21 @@ def __init__(
         use_karras_sigmas: Optional[bool] = False,
         use_exponential_sigmas: Optional[bool] = False,
         use_beta_sigmas: Optional[bool] = False,
-        time_shift_type: str = "exponential",
+        time_shift_type: Literal["exponential", "linear"] = "exponential",
         stochastic_sampling: bool = False,
     ):
         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:
+        if (
+            sum(
+                [
+                    self.config.use_beta_sigmas,
+                    self.config.use_exponential_sigmas,
+                    self.config.use_karras_sigmas,
+                ]
+            )
+            > 1
+        ):
             raise ValueError(
                 "Only one of `config.use_beta_sigmas`, `config.use_exponential_sigmas`, `config.use_karras_sigmas` can be used."
             )
@@ -166,6 +175,13 @@ def set_begin_index(self, begin_index: int = 0):
         self._begin_index = begin_index
 
     def set_shift(self, shift: float):
+        """
+        Sets the shift value for the scheduler.
+
+        Args:
+            shift (`float`):
+                The shift value to be set.
+        """
         self._shift = shift
 
     def scale_noise(
@@ -218,10 +234,25 @@ def scale_noise(
 
         return sample
 
-    def _sigma_to_t(self, sigma):
+    def _sigma_to_t(self, sigma) -> float:
         return sigma * self.config.num_train_timesteps
 
-    def time_shift(self, mu: float, sigma: float, t: torch.Tensor):
+    def time_shift(self, mu: float, sigma: float, t: torch.Tensor) -> torch.Tensor:
+        """
+        Apply time shifting to the sigmas.
+
+        Args:
+            mu (`float`):
+                The mu parameter for the time shift.
+            sigma (`float`):
+                The sigma parameter for the time shift.
+            t (`torch.Tensor`):
+                The input timesteps.
+
+        Returns:
+            `torch.Tensor`:
+                The time-shifted timesteps.
+        """
         if self.config.time_shift_type == "exponential":
             return self._time_shift_exponential(mu, sigma, t)
         elif self.config.time_shift_type == "linear":
@@ -302,7 +333,9 @@ def set_timesteps(
         if sigmas is None:
             if timesteps is None:
                 timesteps = np.linspace(
-                    self._sigma_to_t(self.sigma_max), self._sigma_to_t(self.sigma_min), num_inference_steps
+                    self._sigma_to_t(self.sigma_max),
+                    self._sigma_to_t(self.sigma_min),
+                    num_inference_steps,
                 )
             sigmas = timesteps / self.config.num_train_timesteps
         else:
@@ -350,7 +383,24 @@ def set_timesteps(
         self._step_index = None
         self._begin_index = None
 
-    def index_for_timestep(self, timestep, schedule_timesteps=None):
+    def index_for_timestep(
+        self,
+        timestep: Union[float, torch.FloatTensor],
+        schedule_timesteps: Optional[torch.FloatTensor] = None,
+    ) -> int:
+        """
+        Get the index for the given timestep.
+
+        Args:
+            timestep (`float` or `torch.FloatTensor`):
+                The timestep to find the index for.
+            schedule_timesteps (`torch.FloatTensor`, *optional*):
+                The schedule timesteps to validate against. If `None`, the scheduler's timesteps are used.
+
+        Returns:
+            `int`:
+                The index of the timestep.
+        """
         if schedule_timesteps is None:
             schedule_timesteps = self.timesteps
 
@@ -364,7 +414,7 @@ def index_for_timestep(self, timestep, schedule_timesteps=None):
 
         return indices[pos].item()
 
-    def _init_step_index(self, timestep):
+    def _init_step_index(self, timestep: Union[float, torch.FloatTensor]) -> None:
         if self.begin_index is None:
             if isinstance(timestep, torch.Tensor):
                 timestep = timestep.to(self.timesteps.device)
@@ -405,7 +455,7 @@ def step(
                 A random number generator.
             per_token_timesteps (`torch.Tensor`, *optional*):
                 The timesteps for each token in the sample.
-            return_dict (`bool`):
+            return_dict (`bool`, defaults to `True`):
                 Whether or not to return a
                 [`~schedulers.scheduling_flow_match_euler_discrete.FlowMatchEulerDiscreteSchedulerOutput`] or tuple.
 
@@ -474,7 +524,7 @@ def step(
         return FlowMatchEulerDiscreteSchedulerOutput(prev_sample=prev_sample)
 
     # 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).
@@ -595,11 +645,11 @@ def _convert_to_beta(
         )
         return sigmas
 
-    def _time_shift_exponential(self, mu, sigma, t):
+    def _time_shift_exponential(self, mu: float, sigma: float, t: torch.Tensor) -> torch.Tensor:
         return math.exp(mu) / (math.exp(mu) + (1 / t - 1) ** sigma)
 
-    def _time_shift_linear(self, mu, sigma, t):
+    def _time_shift_linear(self, mu: float, sigma: float, t: torch.Tensor) -> torch.Tensor:
         return mu / (mu + (1 / t - 1) ** sigma)
 
-    def __len__(self):
+    def __len__(self) -> int:
         return self.config.num_train_timesteps

src/diffusers/schedulers/scheduling_unipc_multistep.py

@@ -482,6 +482,21 @@ def set_timesteps(
 
     # Copied from diffusers.schedulers.scheduling_flow_match_euler_discrete.FlowMatchEulerDiscreteScheduler.time_shift
     def time_shift(self, mu: float, sigma: float, t: torch.Tensor):
+        """
+        Apply time shifting to the sigmas.
+
+        Args:
+            mu (`float`):
+                The mu parameter for the time shift.
+            sigma (`float`):
+                The sigma parameter for the time shift.
+            t (`torch.Tensor`):
+                The input timesteps.
+
+        Returns:
+            `torch.Tensor`:
+                The time-shifted timesteps.
+        """
         if self.config.time_shift_type == "exponential":
             return self._time_shift_exponential(mu, sigma, t)
         elif self.config.time_shift_type == "linear":