make scheduler an actual abstract base class

src/refiners/foundationals/latent_diffusion/schedulers/scheduler.py

@@ -1,11 +1,11 @@
-from abc import abstractmethod
+from abc import ABC, abstractmethod
 from torch import Tensor, device as Device, dtype as DType, linspace, float32, sqrt, log
 from typing import TypeVar
 
 T = TypeVar("T", bound="Scheduler")
 
 
-class Scheduler:
+class Scheduler(ABC):
     """
     A base class for creating a diffusion model scheduler.
 

tests/foundationals/latent_diffusion/test_schedulers.py

@@ -5,7 +5,14 @@ from torch import linspace, float32, randn, Tensor, allclose
 
 
 def test_scheduler_utils():
-    scheduler = Scheduler(10, 20, 0.1, 0.2, "cpu")
+    class DummyScheduler(Scheduler):
+        def __call__(self, x: Tensor, noise: Tensor, step: int) -> Tensor:
+            return Tensor()
+
+        def _generate_timesteps(self) -> Tensor:
+            return Tensor()
+
+    scheduler = DummyScheduler(10, 20, 0.1, 0.2, "cpu")
     scale_factors = (
         1.0
         - linspace(