improve FrankenSolver

It now takes a Scheduler factory instead of a Scheduler.
This lets the user potentially recreate the Scheduler on `rebuild`.

It also properly sets the device and dtype on rebuild,
and it has better typing.

src/refiners/foundationals/latent_diffusion/solvers/franken.py

@@ -1,10 +1,42 @@
 import dataclasses
-from typing import Any, cast
+from typing import Any, Callable, Protocol, TypeVar
 
-from torch import Generator, Tensor
+from torch import Generator, Tensor, device as Device, dtype as DType, float32
 
 from refiners.foundationals.latent_diffusion.solvers.solver import Solver, TimestepSpacing
 
+# Should be Tensor, but some Diffusers schedulers
+# are improperly typed as only accepting `int`.
+SchedulerTimestepT = Any
+
+
+class SchedulerOutputLike(Protocol):
+    @property
+    def prev_sample(self) -> Tensor: ...
+
+
+class SchedulerLike(Protocol):
+    timesteps: Tensor
+
+    @property
+    def init_noise_sigma(self) -> Tensor | float: ...
+
+    def set_timesteps(self, num_inference_steps: int, *args: Any, **kwargs: Any) -> None: ...
+
+    def scale_model_input(self, sample: Tensor, timestep: SchedulerTimestepT) -> Tensor: ...
+
+    def step(
+        self,
+        model_output: Tensor,
+        timestep: SchedulerTimestepT,
+        sample: Tensor,
+        *args: Any,
+        **kwargs: Any,
+    ) -> SchedulerOutputLike | tuple[Any]: ...
+
+
+TFrankenSolver = TypeVar("TFrankenSolver", bound="FrankenSolver")
+
 
 class FrankenSolver(Solver):
     """Lets you use Diffusers Schedulers as Refiners Solvers.
@@ -14,7 +46,7 @@ class FrankenSolver(Solver):
         from refiners.foundationals.latent_diffusion.solvers import FrankenSolver
 
         scheduler = EulerDiscreteScheduler(...)
-        solver = FrankenSolver(scheduler, num_inference_steps=steps)
+        solver = FrankenSolver(lambda: scheduler, num_inference_steps=steps)
     """
 
     default_params = dataclasses.replace(
@@ -24,27 +56,40 @@ class FrankenSolver(Solver):
 
     def __init__(
         self,
-        diffusers_scheduler: Any,
+        get_diffusers_scheduler: Callable[[], SchedulerLike],
         num_inference_steps: int,
         first_inference_step: int = 0,
-        **kwargs: Any,
+        device: Device | str = "cpu",
+        dtype: DType = float32,
+        **kwargs: Any,  # for typing, ignored
     ) -> None:
-        self.diffusers_scheduler = diffusers_scheduler
-        diffusers_scheduler.set_timesteps(num_inference_steps)
-        super().__init__(num_inference_steps=num_inference_steps, first_inference_step=first_inference_step)
+        self.get_diffusers_scheduler = get_diffusers_scheduler
+        self.diffusers_scheduler = self.get_diffusers_scheduler()
+        self.diffusers_scheduler.set_timesteps(num_inference_steps)
+        super().__init__(
+            num_inference_steps=num_inference_steps,
+            first_inference_step=first_inference_step,
+            device=device,
+            dtype=dtype,
+        )
 
     def _generate_timesteps(self) -> Tensor:
         return self.diffusers_scheduler.timesteps
 
+    def to(self: TFrankenSolver, device: Device | str | None = None, dtype: DType | None = None) -> TFrankenSolver:
+        return super().to(device=device, dtype=dtype)  # type: ignore
+
     def rebuild(
         self,
         num_inference_steps: int | None,
         first_inference_step: int | None = None,
     ) -> "FrankenSolver":
         return self.__class__(
-            diffusers_scheduler=self.diffusers_scheduler,
+            get_diffusers_scheduler=self.get_diffusers_scheduler,
             num_inference_steps=self.num_inference_steps if num_inference_steps is None else num_inference_steps,
             first_inference_step=self.first_inference_step if first_inference_step is None else first_inference_step,
+            device=self.device,
+            dtype=self.dtype,
         )
 
     def scale_model_input(self, x: Tensor, step: int) -> Tensor:
@@ -54,4 +99,6 @@ def scale_model_input(self, x: Tensor, step: int) -> Tensor:
 
     def __call__(self, x: Tensor, predicted_noise: Tensor, step: int, generator: Generator | None = None) -> Tensor:
         timestep = self.timesteps[step]
-        return cast(Tensor, self.diffusers_scheduler.step(predicted_noise, timestep, x).prev_sample)
+        r = self.diffusers_scheduler.step(predicted_noise, timestep, x)
+        assert not isinstance(r, tuple), "scheduler returned a tuple"
+        return r.prev_sample

tests/foundationals/latent_diffusion/test_solvers.py

@@ -138,7 +138,7 @@ def test_franken_diffusers():
     diffusers_scheduler.set_timesteps(30)
 
     diffusers_scheduler_2 = EulerDiscreteScheduler(**params)  # type: ignore
-    solver = FrankenSolver(diffusers_scheduler_2, num_inference_steps=30)
+    solver = FrankenSolver(lambda: diffusers_scheduler_2, num_inference_steps=30)
     assert equal(solver.timesteps, diffusers_scheduler.timesteps)
 
     sample = randn(1, 4, 32, 32)