make Scheduler a fl.Module + Change name Scheduler -> Solver

README.md

@@ -19,7 +19,7 @@ ______________________________________________________________________
 
 ## Latest News 🔥
 
-- Added [Euler's method](https://arxiv.org/abs/2206.00364) to schedulers (contributed by [@israfelsr](https://github.com/israfelsr))
+- Added [Euler's method](https://arxiv.org/abs/2206.00364) to solvers (contributed by [@israfelsr](https://github.com/israfelsr))
 - Added [DINOv2](https://github.com/facebookresearch/dinov2) for high-performance visual features (contributed by [@Laurent2916](https://github.com/Laurent2916))
 - Added [FreeU](https://github.com/ChenyangSi/FreeU) for improved quality at no cost (contributed by [@isamu-isozaki](https://github.com/isamu-isozaki))
 - Added [Restart Sampling](https://github.com/Newbeeer/diffusion_restart_sampling) for improved image generation ([example](https://github.com/Newbeeer/diffusion_restart_sampling/issues/4))

scripts/conversion/convert_diffusers_controlnet.py

@@ -37,8 +37,8 @@ def convert(args: Args) -> dict[str, torch.Tensor]:
     clip_text_embedding = torch.rand(1, 77, 768)
     unet.set_clip_text_embedding(clip_text_embedding=clip_text_embedding)
 
-    scheduler = DPMSolver(num_inference_steps=10)
-    timestep = scheduler.timesteps[0].unsqueeze(dim=0)
+    solver = DPMSolver(num_inference_steps=10)
+    timestep = solver.timesteps[0].unsqueeze(dim=0)
     unet.set_timestep(timestep=timestep.unsqueeze(dim=0))
 
     x = torch.randn(1, 4, 64, 64)

src/refiners/foundationals/latent_diffusion/__init__.py

@@ -5,7 +5,7 @@ from refiners.foundationals.latent_diffusion.auto_encoder import (
     LatentDiffusionAutoencoder,
 )
 from refiners.foundationals.latent_diffusion.freeu import SDFreeUAdapter
-from refiners.foundationals.latent_diffusion.schedulers import DPMSolver, Scheduler
+from refiners.foundationals.latent_diffusion.solvers import DPMSolver, Solver
 from refiners.foundationals.latent_diffusion.stable_diffusion_1 import (
     SD1ControlnetAdapter,
     SD1IPAdapter,
@@ -33,7 +33,7 @@ __all__ = [
     "SDXLIPAdapter",
     "SDXLT2IAdapter",
     "DPMSolver",
-    "Scheduler",
+    "Solver",
     "CLIPTextEncoderL",
     "LatentDiffusionAutoencoder",
     "SDFreeUAdapter",

src/refiners/foundationals/latent_diffusion/model.py

@@ -7,7 +7,7 @@ from torch import Tensor, device as Device, dtype as DType
 
 import refiners.fluxion.layers as fl
 from refiners.foundationals.latent_diffusion.auto_encoder import LatentDiffusionAutoencoder
-from refiners.foundationals.latent_diffusion.schedulers.scheduler import Scheduler
+from refiners.foundationals.latent_diffusion.solvers.solver import Solver
 
 T = TypeVar("T", bound="fl.Module")
 
@@ -20,7 +20,7 @@ class LatentDiffusionModel(fl.Module, ABC):
         unet: fl.Module,
         lda: LatentDiffusionAutoencoder,
         clip_text_encoder: fl.Module,
-        scheduler: Scheduler,
+        solver: Solver,
         device: Device | str = "cpu",
         dtype: DType = torch.float32,
     ) -> None:
@@ -30,10 +30,10 @@ class LatentDiffusionModel(fl.Module, ABC):
         self.unet = unet.to(device=self.device, dtype=self.dtype)
         self.lda = lda.to(device=self.device, dtype=self.dtype)
         self.clip_text_encoder = clip_text_encoder.to(device=self.device, dtype=self.dtype)
-        self.scheduler = scheduler.to(device=self.device, dtype=self.dtype)
+        self.solver = solver.to(device=self.device, dtype=self.dtype)
 
     def set_inference_steps(self, num_steps: int, first_step: int = 0) -> None:
-        self.scheduler = self.scheduler.rebuild(num_inference_steps=num_steps, first_inference_step=first_step)
+        self.solver = self.solver.rebuild(num_inference_steps=num_steps, first_inference_step=first_step)
 
     def init_latents(
         self,
@@ -51,15 +51,15 @@ class LatentDiffusionModel(fl.Module, ABC):
         if init_image is None:
             return noise
         encoded_image = self.lda.encode_image(image=init_image.resize(size=(width, height)))
-        return self.scheduler.add_noise(
+        return self.solver.add_noise(
             x=encoded_image,
             noise=noise,
-            step=self.scheduler.first_inference_step,
+            step=self.solver.first_inference_step,
         )
 
     @property
     def steps(self) -> list[int]:
-        return self.scheduler.inference_steps
+        return self.solver.inference_steps
 
     @abstractmethod
     def set_unet_context(self, *, timestep: Tensor, clip_text_embedding: Tensor, **_: Tensor) -> None:
@@ -82,12 +82,12 @@ class LatentDiffusionModel(fl.Module, ABC):
     def forward(
         self, x: Tensor, step: int, *, clip_text_embedding: Tensor, condition_scale: float = 7.5, **kwargs: Tensor
     ) -> Tensor:
-        timestep = self.scheduler.timesteps[step].unsqueeze(dim=0)
+        timestep = self.solver.timesteps[step].unsqueeze(dim=0)
         self.set_unet_context(timestep=timestep, clip_text_embedding=clip_text_embedding, **kwargs)
 
         latents = torch.cat(tensors=(x, x))  # for classifier-free guidance
-        # scale latents for schedulers that need it
-        latents = self.scheduler.scale_model_input(latents, step=step)
+        # scale latents for solvers that need it
+        latents = self.solver.scale_model_input(latents, step=step)
         unconditional_prediction, conditional_prediction = self.unet(latents).chunk(2)
 
         # classifier-free guidance
@@ -101,14 +101,14 @@ class LatentDiffusionModel(fl.Module, ABC):
                 x=x, noise=unconditional_prediction, step=step, clip_text_embedding=clip_text_embedding, **kwargs
             )
 
-        return self.scheduler(x, predicted_noise=predicted_noise, step=step)
+        return self.solver(x, predicted_noise=predicted_noise, step=step)
 
     def structural_copy(self: TLatentDiffusionModel) -> TLatentDiffusionModel:
         return self.__class__(
             unet=self.unet.structural_copy(),
             lda=self.lda.structural_copy(),
             clip_text_encoder=self.clip_text_encoder.structural_copy(),
-            scheduler=self.scheduler,
+            solver=self.solver,
             device=self.device,
             dtype=self.dtype,
         )

src/refiners/foundationals/latent_diffusion/multi_diffusion.py

@@ -51,7 +51,7 @@ class MultiDiffusion(Generic[T, D], ABC):
             match step:
                 case step if step == target.start_step and target.init_latents is not None:
                     noise_view = target.crop(noise)
-                    view = self.ldm.scheduler.add_noise(
+                    view = self.ldm.solver.add_noise(
                         x=target.init_latents,
                         noise=noise_view,
                         step=step,

src/refiners/foundationals/latent_diffusion/restart.py

@@ -5,22 +5,22 @@ from typing import Generic, TypeVar
 import torch
 
 from refiners.foundationals.latent_diffusion.model import LatentDiffusionModel
-from refiners.foundationals.latent_diffusion.schedulers.ddim import DDIM
-from refiners.foundationals.latent_diffusion.schedulers.scheduler import Scheduler
+from refiners.foundationals.latent_diffusion.solvers.ddim import DDIM
+from refiners.foundationals.latent_diffusion.solvers.solver import Solver
 
 T = TypeVar("T", bound=LatentDiffusionModel)
 
 
 def add_noise_interval(
-    scheduler: Scheduler,
+    solver: Solver,
     /,
     x: torch.Tensor,
     noise: torch.Tensor,
     initial_timestep: torch.Tensor,
     target_timestep: torch.Tensor,
 ) -> torch.Tensor:
-    initial_cumulative_scale_factors = scheduler.cumulative_scale_factors[initial_timestep]
-    target_cumulative_scale_factors = scheduler.cumulative_scale_factors[target_timestep]
+    initial_cumulative_scale_factors = solver.cumulative_scale_factors[initial_timestep]
+    target_cumulative_scale_factors = solver.cumulative_scale_factors[target_timestep]
 
     factor = target_cumulative_scale_factors / initial_cumulative_scale_factors
     noised_x = factor * x + torch.sqrt(1 - factor**2) * noise
@@ -33,7 +33,7 @@ class Restart(Generic[T]):
     Implements the restart sampling strategy from the paper "Restart Sampling for Improving Generative Processes"
     (https://arxiv.org/pdf/2306.14878.pdf)
 
-    Works only with the DDIM scheduler for now.
+    Works only with the DDIM solver for now.
     """
 
     ldm: T
@@ -43,7 +43,7 @@ class Restart(Generic[T]):
     end_time: float = 2
 
     def __post_init__(self) -> None:
-        assert isinstance(self.ldm.scheduler, DDIM), "Restart sampling only works with DDIM scheduler"
+        assert isinstance(self.ldm.solver, DDIM), "Restart sampling only works with DDIM solver"
 
     def __call__(
         self,
@@ -53,15 +53,15 @@ class Restart(Generic[T]):
         condition_scale: float = 7.5,
         **kwargs: torch.Tensor,
     ) -> torch.Tensor:
-        original_scheduler = self.ldm.scheduler
-        new_scheduler = DDIM(self.ldm.scheduler.num_inference_steps, device=self.device, dtype=self.dtype)
-        new_scheduler.timesteps = self.timesteps
-        self.ldm.scheduler = new_scheduler
+        original_solver = self.ldm.solver
+        new_solver = DDIM(self.ldm.solver.num_inference_steps, device=self.device, dtype=self.dtype)
+        new_solver.timesteps = self.timesteps
+        self.ldm.solver = new_solver
 
         for _ in range(self.num_iterations):
             noise = torch.randn_like(input=x, device=self.device, dtype=self.dtype)
             x = add_noise_interval(
-                new_scheduler,
+                new_solver,
                 x=x,
                 noise=noise,
                 initial_timestep=self.timesteps[-1],
@@ -73,18 +73,18 @@ class Restart(Generic[T]):
                     x, step=step, clip_text_embedding=clip_text_embedding, condition_scale=condition_scale, **kwargs
                 )
 
-        self.ldm.scheduler = original_scheduler
+        self.ldm.solver = original_solver
 
         return x
 
     @cached_property
     def start_step(self) -> int:
-        sigmas = self.ldm.scheduler.noise_std / self.ldm.scheduler.cumulative_scale_factors
-        return int(torch.argmin(input=torch.abs(input=sigmas[self.ldm.scheduler.timesteps] - self.start_time)))
+        sigmas = self.ldm.solver.noise_std / self.ldm.solver.cumulative_scale_factors
+        return int(torch.argmin(input=torch.abs(input=sigmas[self.ldm.solver.timesteps] - self.start_time)))
 
     @cached_property
     def end_timestep(self) -> int:
-        sigmas = self.ldm.scheduler.noise_std / self.ldm.scheduler.cumulative_scale_factors
+        sigmas = self.ldm.solver.noise_std / self.ldm.solver.cumulative_scale_factors
         return int(torch.argmin(input=torch.abs(input=sigmas - self.end_time)))
 
     @cached_property
@@ -92,7 +92,7 @@ class Restart(Generic[T]):
         return (
             torch.round(
                 torch.linspace(
-                    start=int(self.ldm.scheduler.timesteps[self.start_step]),
+                    start=int(self.ldm.solver.timesteps[self.start_step]),
                     end=self.end_timestep,
                     steps=self.num_steps,
                 )

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

@@ -1,7 +0,0 @@
-from refiners.foundationals.latent_diffusion.schedulers.ddim import DDIM
-from refiners.foundationals.latent_diffusion.schedulers.ddpm import DDPM
-from refiners.foundationals.latent_diffusion.schedulers.dpm_solver import DPMSolver
-from refiners.foundationals.latent_diffusion.schedulers.euler import EulerScheduler
-from refiners.foundationals.latent_diffusion.schedulers.scheduler import Scheduler
-
-__all__ = ["Scheduler", "DPMSolver", "DDPM", "DDIM", "EulerScheduler"]

src/refiners/foundationals/latent_diffusion/self_attention_guidance.py

@@ -9,7 +9,7 @@ import refiners.fluxion.layers as fl
 from refiners.fluxion.adapters.adapter import Adapter
 from refiners.fluxion.context import Contexts
 from refiners.fluxion.utils import gaussian_blur, interpolate
-from refiners.foundationals.latent_diffusion.schedulers.scheduler import Scheduler
+from refiners.foundationals.latent_diffusion.solvers.solver import Solver
 
 if TYPE_CHECKING:
     from refiners.foundationals.latent_diffusion.stable_diffusion_1.unet import SD1UNet
@@ -89,13 +89,13 @@ class SAGAdapter(Generic[T], fl.Chain, Adapter[T]):
         return interpolate(attn_mask, Size((h, w)))
 
     def compute_degraded_latents(
-        self, scheduler: Scheduler, latents: Tensor, noise: Tensor, step: int, classifier_free_guidance: bool = True
+        self, solver: Solver, latents: Tensor, noise: Tensor, step: int, classifier_free_guidance: bool = True
     ) -> Tensor:
         sag_mask = self.compute_sag_mask(latents=latents, classifier_free_guidance=classifier_free_guidance)
-        original_latents = scheduler.remove_noise(x=latents, noise=noise, step=step)
+        original_latents = solver.remove_noise(x=latents, noise=noise, step=step)
         degraded_latents = gaussian_blur(original_latents, kernel_size=self.kernel_size, sigma=self.sigma)
         degraded_latents = degraded_latents * sag_mask + original_latents * (1 - sag_mask)
-        return scheduler.add_noise(degraded_latents, noise=noise, step=step)
+        return solver.add_noise(degraded_latents, noise=noise, step=step)
 
     def init_context(self) -> Contexts:
         return {"self_attention_map": {"middle_block_attn_map": None, "middle_block_attn_shape": []}}

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

@@ -0,0 +1,7 @@
+from refiners.foundationals.latent_diffusion.solvers.ddim import DDIM
+from refiners.foundationals.latent_diffusion.solvers.ddpm import DDPM
+from refiners.foundationals.latent_diffusion.solvers.dpm import DPMSolver
+from refiners.foundationals.latent_diffusion.solvers.euler import Euler
+from refiners.foundationals.latent_diffusion.solvers.solver import Solver
+
+__all__ = ["Solver", "DPMSolver", "DDPM", "DDIM", "Euler"]

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

@@ -1,9 +1,9 @@
 from torch import Generator, Tensor, arange, device as Device, dtype as Dtype, float32, sqrt, tensor
 
-from refiners.foundationals.latent_diffusion.schedulers.scheduler import NoiseSchedule, Scheduler
+from refiners.foundationals.latent_diffusion.solvers.solver import NoiseSchedule, Solver
 
 
-class DDIM(Scheduler):
+class DDIM(Solver):
     def __init__(
         self,
         num_inference_steps: int,
@@ -25,15 +25,14 @@ class DDIM(Scheduler):
             device=device,
             dtype=dtype,
         )
-        self.timesteps = self._generate_timesteps()
 
     def _generate_timesteps(self) -> Tensor:
         """
         Generates decreasing timesteps with 'leading' spacing and offset of 1
-        similar to diffusers settings for the DDIM scheduler in Stable Diffusion 1.5
+        similar to diffusers settings for the DDIM solver in Stable Diffusion 1.5
         """
         step_ratio = self.num_train_timesteps // self.num_inference_steps
-        timesteps = arange(start=0, end=self.num_inference_steps, step=1, device=self.device) * step_ratio + 1
+        timesteps = arange(start=0, end=self.num_inference_steps, step=1) * step_ratio + 1
         return timesteps.flip(0)
 
     def __call__(self, x: Tensor, predicted_noise: Tensor, step: int, generator: Generator | None = None) -> Tensor:

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

@@ -1,9 +1,9 @@
 from torch import Generator, Tensor, arange, device as Device, dtype as DType
 
-from refiners.foundationals.latent_diffusion.schedulers.scheduler import NoiseSchedule, Scheduler
+from refiners.foundationals.latent_diffusion.solvers.solver import NoiseSchedule, Solver
 
 
-class DDPM(Scheduler):
+class DDPM(Solver):
     """
     Denoising Diffusion Probabilistic Model
 

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

@@ -3,10 +3,10 @@ from collections import deque
 import numpy as np
 from torch import Generator, Tensor, device as Device, dtype as Dtype, exp, float32, tensor
 
-from refiners.foundationals.latent_diffusion.schedulers.scheduler import NoiseSchedule, Scheduler
+from refiners.foundationals.latent_diffusion.solvers.solver import NoiseSchedule, Solver
 
 
-class DPMSolver(Scheduler):
+class DPMSolver(Solver):
     """
     Implements DPM-Solver++ from https://arxiv.org/abs/2211.01095
 
@@ -48,7 +48,6 @@ class DPMSolver(Scheduler):
         # ...and we want the same result as the original codebase.
         return tensor(
             np.linspace(0, self.num_train_timesteps - 1, self.num_inference_steps + 1).round().astype(int)[1:],
-            device=self.device,
         ).flip(0)
 
     def rebuild(

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

@@ -2,10 +2,10 @@ import numpy as np
 import torch
 from torch import Generator, Tensor, device as Device, dtype as Dtype, float32, tensor
 
-from refiners.foundationals.latent_diffusion.schedulers.scheduler import NoiseSchedule, Scheduler
+from refiners.foundationals.latent_diffusion.solvers.solver import NoiseSchedule, Solver
 
 
-class EulerScheduler(Scheduler):
+class Euler(Solver):
     def __init__(
         self,
         num_inference_steps: int,
@@ -40,9 +40,7 @@ class EulerScheduler(Scheduler):
         # numpy.linspace(0,999,31)[15] is 499.49999999999994
         # torch.linspace(0,999,31)[15] is 499.5
         # ...and we want the same result as the original codebase.
-        timesteps = torch.tensor(
-            np.linspace(0, self.num_train_timesteps - 1, self.num_inference_steps), dtype=self.dtype, device=self.device
-        ).flip(0)
+        timesteps = torch.tensor(np.linspace(0, self.num_train_timesteps - 1, self.num_inference_steps)).flip(0)
         return timesteps
 
     def _generate_sigmas(self) -> Tensor:

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

@@ -4,7 +4,9 @@ from typing import TypeVar
 
 from torch import Generator, Tensor, device as Device, dtype as DType, float32, linspace, log, sqrt
 
-T = TypeVar("T", bound="Scheduler")
+from refiners.fluxion import layers as fl
+
+T = TypeVar("T", bound="Solver")
 
 
 class NoiseSchedule(str, Enum):
@@ -13,11 +15,11 @@ class NoiseSchedule(str, Enum):
     KARRAS = "karras"
 
 
-class Scheduler(ABC):
+class Solver(fl.Module, ABC):
     """
-    A base class for creating a diffusion model scheduler.
+    A base class for creating a diffusion model solver.
 
-    The Scheduler creates a sequence of noise and scaling factors used in the diffusion process,
+    Solver creates a sequence of noise and scaling factors used in the diffusion process,
     which gradually transforms the original data distribution into a Gaussian one.
 
     This process is described using several parameters such as initial and final diffusion rates,
@@ -36,9 +38,8 @@ class Scheduler(ABC):
         first_inference_step: int = 0,
         device: Device | str = "cpu",
         dtype: DType = float32,
-    ):
-        self.device: Device = Device(device)
-        self.dtype: DType = dtype
+    ) -> None:
+        super().__init__()
         self.num_inference_steps = num_inference_steps
         self.num_train_timesteps = num_train_timesteps
         self.initial_diffusion_rate = initial_diffusion_rate
@@ -50,6 +51,7 @@ class Scheduler(ABC):
         self.noise_std = sqrt(1.0 - self.scale_factors.cumprod(dim=0))
         self.signal_to_noise_ratios = log(self.cumulative_scale_factors) - log(self.noise_std)
         self.timesteps = self._generate_timesteps()
+        self.to(device=device, dtype=dtype)
 
     @abstractmethod
     def __call__(self, x: Tensor, predicted_noise: Tensor, step: int, generator: Generator | None = None) -> Tensor:
@@ -69,57 +71,6 @@ class Scheduler(ABC):
         """
         ...
 
-    @property
-    def all_steps(self) -> list[int]:
-        return list(range(self.num_inference_steps))
-
-    @property
-    def inference_steps(self) -> list[int]:
-        return self.all_steps[self.first_inference_step :]
-
-    def rebuild(self: T, num_inference_steps: int | None, first_inference_step: int | None = None) -> T:
-        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
-        return self.__class__(
-            num_inference_steps=num_inference_steps,
-            num_train_timesteps=self.num_train_timesteps,
-            initial_diffusion_rate=self.initial_diffusion_rate,
-            final_diffusion_rate=self.final_diffusion_rate,
-            noise_schedule=self.noise_schedule,
-            first_inference_step=first_inference_step,
-            device=self.device,
-            dtype=self.dtype,
-        )
-
-    def scale_model_input(self, x: Tensor, step: int) -> Tensor:
-        """
-        For compatibility with schedulers that need to scale the input according to the current timestep.
-        """
-        return x
-
-    def sample_power_distribution(self, power: float = 2, /) -> Tensor:
-        return (
-            linspace(
-                start=self.initial_diffusion_rate ** (1 / power),
-                end=self.final_diffusion_rate ** (1 / power),
-                steps=self.num_train_timesteps,
-                device=self.device,
-                dtype=self.dtype,
-            )
-            ** power
-        )
-
-    def sample_noise_schedule(self) -> Tensor:
-        match self.noise_schedule:
-            case "uniform":
-                return 1 - self.sample_power_distribution(1)
-            case "quadratic":
-                return 1 - self.sample_power_distribution(2)
-            case "karras":
-                return 1 - self.sample_power_distribution(7)
-            case _:
-                raise ValueError(f"Unknown noise schedule: {self.noise_schedule}")
-
     def add_noise(
         self,
         x: Tensor,
@@ -141,14 +92,77 @@ class Scheduler(ABC):
         denoised_x = (x - noise_stds * noise) / cumulative_scale_factors
         return denoised_x
 
-    def to(self: T, device: Device | str | None = None, dtype: DType | None = None) -> T:  # type: ignore
-        if device is not None:
-            self.device = Device(device)
-            self.timesteps = self.timesteps.to(device)
-        if dtype is not None:
-            self.dtype = dtype
-        self.scale_factors = self.scale_factors.to(device, dtype=dtype)
-        self.cumulative_scale_factors = self.cumulative_scale_factors.to(device, dtype=dtype)
-        self.noise_std = self.noise_std.to(device, dtype=dtype)
-        self.signal_to_noise_ratios = self.signal_to_noise_ratios.to(device, dtype=dtype)
+    @property
+    def all_steps(self) -> list[int]:
+        return list(range(self.num_inference_steps))
+
+    @property
+    def inference_steps(self) -> list[int]:
+        return self.all_steps[self.first_inference_step :]
+
+    @property
+    def device(self) -> Device:
+        return self.scale_factors.device
+
+    @property
+    def dtype(self) -> DType:
+        return self.scale_factors.dtype
+
+    @device.setter
+    def device(self, device: Device | str | None = None) -> None:
+        self.to(device=device)
+
+    @dtype.setter
+    def dtype(self, dtype: DType | None = None) -> None:
+        self.to(dtype=dtype)
+
+    def rebuild(self: T, num_inference_steps: int | None, first_inference_step: int | None = None) -> T:
+        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
+        return self.__class__(
+            num_inference_steps=num_inference_steps,
+            num_train_timesteps=self.num_train_timesteps,
+            initial_diffusion_rate=self.initial_diffusion_rate,
+            final_diffusion_rate=self.final_diffusion_rate,
+            noise_schedule=self.noise_schedule,
+            first_inference_step=first_inference_step,
+            device=self.device,
+            dtype=self.dtype,
+        )
+
+    def scale_model_input(self, x: Tensor, step: int) -> Tensor:
+        """
+        For compatibility with solvers that need to scale the input according to the current timestep.
+        """
+        return x
+
+    def sample_power_distribution(self, power: float = 2, /) -> Tensor:
+        return (
+            linspace(
+                start=self.initial_diffusion_rate ** (1 / power),
+                end=self.final_diffusion_rate ** (1 / power),
+                steps=self.num_train_timesteps,
+            )
+            ** power
+        )
+
+    def sample_noise_schedule(self) -> Tensor:
+        match self.noise_schedule:
+            case "uniform":
+                return 1 - self.sample_power_distribution(1)
+            case "quadratic":
+                return 1 - self.sample_power_distribution(2)
+            case "karras":
+                return 1 - self.sample_power_distribution(7)
+            case _:
+                raise ValueError(f"Unknown noise schedule: {self.noise_schedule}")
+
+    def to(self, device: Device | str | None = None, dtype: DType | None = None) -> "Solver":
+        super().to(device=device, dtype=dtype)
+        for name, attr in [(name, attr) for name, attr in self.__dict__.items() if isinstance(attr, Tensor)]:
+            match name:
+                case "timesteps":
+                    setattr(self, name, attr.to(device=device))
+                case _:
+                    setattr(self, name, attr.to(device=device, dtype=dtype))
         return self

src/refiners/foundationals/latent_diffusion/stable_diffusion_1/model.py

@@ -7,8 +7,8 @@ from refiners.fluxion.utils import image_to_tensor, interpolate
 from refiners.foundationals.clip.text_encoder import CLIPTextEncoderL
 from refiners.foundationals.latent_diffusion.auto_encoder import LatentDiffusionAutoencoder
 from refiners.foundationals.latent_diffusion.model import LatentDiffusionModel
-from refiners.foundationals.latent_diffusion.schedulers.dpm_solver import DPMSolver
-from refiners.foundationals.latent_diffusion.schedulers.scheduler import Scheduler
+from refiners.foundationals.latent_diffusion.solvers.dpm import DPMSolver
+from refiners.foundationals.latent_diffusion.solvers.solver import Solver
 from refiners.foundationals.latent_diffusion.stable_diffusion_1.self_attention_guidance import SD1SAGAdapter
 from refiners.foundationals.latent_diffusion.stable_diffusion_1.unet import SD1UNet
 
@@ -26,20 +26,20 @@ class StableDiffusion_1(LatentDiffusionModel):
         unet: SD1UNet | None = None,
         lda: SD1Autoencoder | None = None,
         clip_text_encoder: CLIPTextEncoderL | None = None,
-        scheduler: Scheduler | None = None,
+        solver: Solver | None = None,
         device: Device | str = "cpu",
         dtype: DType = torch.float32,
     ) -> None:
         unet = unet or SD1UNet(in_channels=4)
         lda = lda or SD1Autoencoder()
         clip_text_encoder = clip_text_encoder or CLIPTextEncoderL()
-        scheduler = scheduler or DPMSolver(num_inference_steps=30)
+        solver = solver or DPMSolver(num_inference_steps=30)
 
         super().__init__(
             unet=unet,
             lda=lda,
             clip_text_encoder=clip_text_encoder,
-            scheduler=scheduler,
+            solver=solver,
             device=device,
             dtype=dtype,
         )
@@ -82,14 +82,14 @@ class StableDiffusion_1(LatentDiffusionModel):
         assert sag is not None
 
         degraded_latents = sag.compute_degraded_latents(
-            scheduler=self.scheduler,
+            solver=self.solver,
             latents=x,
             noise=noise,
             step=step,
             classifier_free_guidance=True,
         )
 
-        timestep = self.scheduler.timesteps[step].unsqueeze(dim=0)
+        timestep = self.solver.timesteps[step].unsqueeze(dim=0)
         negative_embedding, _ = clip_text_embedding.chunk(2)
         self.set_unet_context(timestep=timestep, clip_text_embedding=negative_embedding, **kwargs)
         if "ip_adapter" in self.unet.provider.contexts:
@@ -111,14 +111,14 @@ class StableDiffusion_1_Inpainting(StableDiffusion_1):
         unet: SD1UNet | None = None,
         lda: SD1Autoencoder | None = None,
         clip_text_encoder: CLIPTextEncoderL | None = None,
-        scheduler: Scheduler | None = None,
+        solver: Solver | None = None,
         device: Device | str = "cpu",
         dtype: DType = torch.float32,
     ) -> None:
         self.mask_latents: Tensor | None = None
         self.target_image_latents: Tensor | None = None
         super().__init__(
-            unet=unet, lda=lda, clip_text_encoder=clip_text_encoder, scheduler=scheduler, device=device, dtype=dtype
+            unet=unet, lda=lda, clip_text_encoder=clip_text_encoder, solver=solver, device=device, dtype=dtype
         )
 
     def forward(
@@ -162,7 +162,7 @@ class StableDiffusion_1_Inpainting(StableDiffusion_1):
         assert self.target_image_latents is not None
 
         degraded_latents = sag.compute_degraded_latents(
-            scheduler=self.scheduler,
+            solver=self.solver,
             latents=x,
             noise=noise,
             step=step,
@@ -173,7 +173,7 @@ class StableDiffusion_1_Inpainting(StableDiffusion_1):
             dim=1,
         )
 
-        timestep = self.scheduler.timesteps[step].unsqueeze(dim=0)
+        timestep = self.solver.timesteps[step].unsqueeze(dim=0)
         negative_embedding, _ = clip_text_embedding.chunk(2)
         self.set_unet_context(timestep=timestep, clip_text_embedding=negative_embedding, **kwargs)
 

src/refiners/foundationals/latent_diffusion/stable_diffusion_xl/model.py

@@ -3,8 +3,8 @@ from torch import Tensor, device as Device, dtype as DType
 
 from refiners.foundationals.latent_diffusion.auto_encoder import LatentDiffusionAutoencoder
 from refiners.foundationals.latent_diffusion.model import LatentDiffusionModel
-from refiners.foundationals.latent_diffusion.schedulers.ddim import DDIM
-from refiners.foundationals.latent_diffusion.schedulers.scheduler import Scheduler
+from refiners.foundationals.latent_diffusion.solvers.ddim import DDIM
+from refiners.foundationals.latent_diffusion.solvers.solver import Solver
 from refiners.foundationals.latent_diffusion.stable_diffusion_xl.self_attention_guidance import SDXLSAGAdapter
 from refiners.foundationals.latent_diffusion.stable_diffusion_xl.text_encoder import DoubleTextEncoder
 from refiners.foundationals.latent_diffusion.stable_diffusion_xl.unet import SDXLUNet
@@ -23,20 +23,20 @@ class StableDiffusion_XL(LatentDiffusionModel):
         unet: SDXLUNet | None = None,
         lda: SDXLAutoencoder | None = None,
         clip_text_encoder: DoubleTextEncoder | None = None,
-        scheduler: Scheduler | None = None,
+        solver: Solver | None = None,
         device: Device | str = "cpu",
         dtype: DType = torch.float32,
     ) -> None:
         unet = unet or SDXLUNet(in_channels=4)
         lda = lda or SDXLAutoencoder()
         clip_text_encoder = clip_text_encoder or DoubleTextEncoder()
-        scheduler = scheduler or DDIM(num_inference_steps=30)
+        solver = solver or DDIM(num_inference_steps=30)
 
         super().__init__(
             unet=unet,
             lda=lda,
             clip_text_encoder=clip_text_encoder,
-            scheduler=scheduler,
+            solver=solver,
             device=device,
             dtype=dtype,
         )
@@ -131,7 +131,7 @@ class StableDiffusion_XL(LatentDiffusionModel):
         assert sag is not None
 
         degraded_latents = sag.compute_degraded_latents(
-            scheduler=self.scheduler,
+            solver=self.solver,
             latents=x,
             noise=noise,
             step=step,
@@ -140,7 +140,7 @@ class StableDiffusion_XL(LatentDiffusionModel):
 
         negative_text_embedding, _ = clip_text_embedding.chunk(2)
         negative_pooled_embedding, _ = pooled_text_embedding.chunk(2)
-        timestep = self.scheduler.timesteps[step].unsqueeze(dim=0)
+        timestep = self.solver.timesteps[step].unsqueeze(dim=0)
         time_ids, _ = time_ids.chunk(2)
 
         self.set_unet_context(

src/refiners/training_utils/latent_diffusion.py

@@ -19,7 +19,8 @@ from refiners.foundationals.latent_diffusion import (
     SD1UNet,
     StableDiffusion_1,
 )
-from refiners.foundationals.latent_diffusion.schedulers import DDPM
+from refiners.foundationals.latent_diffusion.solvers import DDPM
+from refiners.foundationals.latent_diffusion.solvers.solver import Solver
 from refiners.foundationals.latent_diffusion.stable_diffusion_1.model import SD1Autoencoder
 from refiners.training_utils.callback import Callback
 from refiners.training_utils.config import BaseConfig
@@ -150,7 +151,7 @@ class LatentDiffusionTrainer(Trainer[ConfigType, TextEmbeddingLatentsBatch]):
         return TextEmbeddingLatentsDataset(trainer=self)
 
     @cached_property
-    def ddpm_scheduler(self) -> DDPM:
+    def ddpm_solver(self) -> Solver:
         return DDPM(
             num_inference_steps=1000,
             device=self.device,
@@ -159,7 +160,7 @@ class LatentDiffusionTrainer(Trainer[ConfigType, TextEmbeddingLatentsBatch]):
     def sample_timestep(self) -> Tensor:
         random_step = random.randint(a=self.config.latent_diffusion.min_step, b=self.config.latent_diffusion.max_step)
         self.current_step = random_step
-        return self.ddpm_scheduler.timesteps[random_step].unsqueeze(dim=0)
+        return self.ddpm_solver.timesteps[random_step].unsqueeze(dim=0)
 
     def sample_noise(self, size: tuple[int, ...], dtype: DType | None = None) -> Tensor:
         return sample_noise(
@@ -170,7 +171,7 @@ class LatentDiffusionTrainer(Trainer[ConfigType, TextEmbeddingLatentsBatch]):
         clip_text_embedding, latents = batch.text_embeddings, batch.latents
         timestep = self.sample_timestep()
         noise = self.sample_noise(size=latents.shape, dtype=latents.dtype)
-        noisy_latents = self.ddpm_scheduler.add_noise(x=latents, noise=noise, step=self.current_step)
+        noisy_latents = self.ddpm_solver.add_noise(x=latents, noise=noise, step=self.current_step)
         self.unet.set_timestep(timestep=timestep)
         self.unet.set_clip_text_embedding(clip_text_embedding=clip_text_embedding)
         prediction = self.unet(noisy_latents)
@@ -182,7 +183,7 @@ class LatentDiffusionTrainer(Trainer[ConfigType, TextEmbeddingLatentsBatch]):
             unet=self.unet,
             lda=self.lda,
             clip_text_encoder=self.text_encoder,
-            scheduler=DPMSolver(num_inference_steps=self.config.test_diffusion.num_inference_steps),
+            solver=DPMSolver(num_inference_steps=self.config.test_diffusion.num_inference_steps),
             device=self.device,
         )
         prompts = self.config.test_diffusion.prompts

tests/e2e/test_diffusion.py

@@ -24,8 +24,8 @@ from refiners.foundationals.latent_diffusion.lora import SDLoraManager
 from refiners.foundationals.latent_diffusion.multi_diffusion import DiffusionTarget
 from refiners.foundationals.latent_diffusion.reference_only_control import ReferenceOnlyControlAdapter
 from refiners.foundationals.latent_diffusion.restart import Restart
-from refiners.foundationals.latent_diffusion.schedulers import DDIM, EulerScheduler
-from refiners.foundationals.latent_diffusion.schedulers.scheduler import NoiseSchedule
+from refiners.foundationals.latent_diffusion.solvers import DDIM, Euler
+from refiners.foundationals.latent_diffusion.solvers.solver import NoiseSchedule
 from refiners.foundationals.latent_diffusion.stable_diffusion_1.multi_diffusion import SD1MultiDiffusion
 from refiners.foundationals.latent_diffusion.stable_diffusion_xl.model import StableDiffusion_XL
 from tests.utils import ensure_similar_images
@@ -491,8 +491,8 @@ def sd15_ddim(
         warn("not running on CPU, skipping")
         pytest.skip()
 
-    ddim_scheduler = DDIM(num_inference_steps=20)
-    sd15 = StableDiffusion_1(scheduler=ddim_scheduler, device=test_device)
+    ddim_solver = DDIM(num_inference_steps=20)
+    sd15 = StableDiffusion_1(solver=ddim_solver, device=test_device)
 
     sd15.clip_text_encoder.load_from_safetensors(text_encoder_weights)
     sd15.lda.load_from_safetensors(lda_weights)
@@ -509,8 +509,8 @@ def sd15_ddim_karras(
         warn("not running on CPU, skipping")
         pytest.skip()
 
-    ddim_scheduler = DDIM(num_inference_steps=20, noise_schedule=NoiseSchedule.KARRAS)
-    sd15 = StableDiffusion_1(scheduler=ddim_scheduler, device=test_device)
+    ddim_solver = DDIM(num_inference_steps=20, noise_schedule=NoiseSchedule.KARRAS)
+    sd15 = StableDiffusion_1(solver=ddim_solver, device=test_device)
 
     sd15.clip_text_encoder.load_from_safetensors(text_encoder_weights)
     sd15.lda.load_from_safetensors(lda_weights)
@@ -527,8 +527,8 @@ def sd15_euler(
         warn("not running on CPU, skipping")
         pytest.skip()
 
-    euler_scheduler = EulerScheduler(num_inference_steps=30)
-    sd15 = StableDiffusion_1(scheduler=euler_scheduler, device=test_device)
+    euler_solver = Euler(num_inference_steps=30)
+    sd15 = StableDiffusion_1(solver=euler_solver, device=test_device)
 
     sd15.clip_text_encoder.load_from_safetensors(text_encoder_weights)
     sd15.lda.load_from_safetensors(lda_weights)
@@ -545,8 +545,8 @@ def sd15_ddim_lda_ft_mse(
         warn("not running on CPU, skipping")
         pytest.skip()
 
-    ddim_scheduler = DDIM(num_inference_steps=20)
-    sd15 = StableDiffusion_1(scheduler=ddim_scheduler, device=test_device)
+    ddim_solver = DDIM(num_inference_steps=20)
+    sd15 = StableDiffusion_1(solver=ddim_solver, device=test_device)
 
     sd15.clip_text_encoder.load_state_dict(load_from_safetensors(text_encoder_weights))
     sd15.lda.load_state_dict(load_from_safetensors(lda_ft_mse_weights))
@@ -599,8 +599,8 @@ def sdxl_ddim(
         warn(message="not running on CPU, skipping")
         pytest.skip()
 
-    scheduler = DDIM(num_inference_steps=30)
-    sdxl = StableDiffusion_XL(scheduler=scheduler, device=test_device)
+    solver = DDIM(num_inference_steps=30)
+    sdxl = StableDiffusion_XL(solver=solver, device=test_device)
 
     sdxl.clip_text_encoder.load_from_safetensors(tensors_path=sdxl_text_encoder_weights)
     sdxl.lda.load_from_safetensors(tensors_path=sdxl_lda_weights)
@@ -617,8 +617,8 @@ def sdxl_ddim_lda_fp16_fix(
         warn(message="not running on CPU, skipping")
         pytest.skip()
 
-    scheduler = DDIM(num_inference_steps=30)
-    sdxl = StableDiffusion_XL(scheduler=scheduler, device=test_device)
+    solver = DDIM(num_inference_steps=30)
+    sdxl = StableDiffusion_XL(solver=solver, device=test_device)
 
     sdxl.clip_text_encoder.load_from_safetensors(tensors_path=sdxl_text_encoder_weights)
     sdxl.lda.load_from_safetensors(tensors_path=sdxl_lda_fp16_fix_weights)
@@ -659,8 +659,8 @@ def test_diffusion_std_random_init_euler(
     sd15_euler: StableDiffusion_1, expected_image_std_random_init_euler: Image.Image, test_device: torch.device
 ):
     sd15 = sd15_euler
-    euler_scheduler = sd15_euler.scheduler
-    assert isinstance(euler_scheduler, EulerScheduler)
+    euler_solver = sd15_euler.solver
+    assert isinstance(euler_solver, Euler)
 
     prompt = "a cute cat, detailed high-quality professional image"
     negative_prompt = "lowres, bad anatomy, bad hands, cropped, worst quality"
@@ -670,7 +670,7 @@ def test_diffusion_std_random_init_euler(
 
     manual_seed(2)
     x = torch.randn(1, 4, 64, 64, device=test_device)
-    x = x * euler_scheduler.init_noise_sigma
+    x = x * euler_solver.init_noise_sigma
 
     for step in sd15.steps:
         x = sd15(
@@ -1202,7 +1202,7 @@ def test_diffusion_refonly(
 
     for step in sd15.steps:
         noise = torch.randn(2, 4, 64, 64, device=test_device)
-        noised_guide = sd15.scheduler.add_noise(guide, noise, step)
+        noised_guide = sd15.solver.add_noise(guide, noise, step)
         refonly_adapter.set_controlnet_condition(noised_guide)
         x = sd15(
             x,
@@ -1244,7 +1244,7 @@ def test_diffusion_inpainting_refonly(
 
     for step in sd15.steps:
         noise = torch.randn_like(guide)
-        noised_guide = sd15.scheduler.add_noise(guide, noise, step)
+        noised_guide = sd15.solver.add_noise(guide, noise, step)
         # See https://github.com/Mikubill/sd-webui-controlnet/pull/1275 ("1.1.170 reference-only begin to support
         # inpaint variation models")
         noised_guide = torch.cat([noised_guide, torch.zeros_like(noised_guide)[:, 0:1, :, :], guide], dim=1)

tests/foundationals/latent_diffusion/test_solvers.py

@@ -5,7 +5,7 @@ import pytest
 from torch import Tensor, allclose, device as Device, equal, isclose, randn
 
 from refiners.fluxion import manual_seed
-from refiners.foundationals.latent_diffusion.schedulers import DDIM, DDPM, DPMSolver, EulerScheduler
+from refiners.foundationals.latent_diffusion.solvers import DDIM, DDPM, DPMSolver, Euler
 
 
 def test_ddpm_diffusers():
@@ -83,7 +83,7 @@ def test_euler_diffusers():
         use_karras_sigmas=False,
     )
     diffusers_scheduler.set_timesteps(30)
-    refiners_scheduler = EulerScheduler(num_inference_steps=30)
+    refiners_scheduler = Euler(num_inference_steps=30)
 
     sample = randn(1, 4, 32, 32)
     predicted_noise = randn(1, 4, 32, 32)