[Type hint] scheduling karras ve (#359)

Author: santiviquez

src/diffusers/schedulers/scheduling_karras_ve.py

@@ -14,7 +14,7 @@
 
 
 from dataclasses import dataclass
-from typing import Tuple, Union
+from typing import Optional, Tuple, Union
 
 import numpy as np
 import torch
@@ -72,13 +72,13 @@ class KarrasVeScheduler(SchedulerMixin, ConfigMixin):
     @register_to_config
     def __init__(
         self,
-        sigma_min=0.02,
-        sigma_max=100,
-        s_noise=1.007,
-        s_churn=80,
-        s_min=0.05,
-        s_max=50,
-        tensor_format="pt",
+        sigma_min: float = 0.02,
+        sigma_max: float = 100,
+        s_noise: float = 1.007,
+        s_churn: float = 80,
+        s_min: float = 0.05,
+        s_max: float = 50,
+        tensor_format: str = "pt",
     ):
         # setable values
         self.num_inference_steps = None
@@ -88,7 +88,7 @@ def __init__(
         self.tensor_format = tensor_format
         self.set_format(tensor_format=tensor_format)
 
-    def set_timesteps(self, num_inference_steps):
+    def set_timesteps(self, num_inference_steps: int):
         self.num_inference_steps = num_inference_steps
         self.timesteps = np.arange(0, self.num_inference_steps)[::-1].copy()
         self.schedule = [
@@ -99,7 +99,9 @@ def set_timesteps(self, num_inference_steps):
 
         self.set_format(tensor_format=self.tensor_format)
 
-    def add_noise_to_input(self, sample, sigma, generator=None):
+    def add_noise_to_input(
+        self, sample: Union[torch.FloatTensor, np.ndarray], sigma: float, generator: Optional[torch.Generator] = None
+    ) -> Tuple[Union[torch.FloatTensor, np.ndarray], float]:
         """
         Explicit Langevin-like "churn" step of adding noise to the sample according to a factor gamma_i ≥ 0 to reach a
         higher noise level sigma_hat = sigma_i + gamma_i*sigma_i.