feature: Euler scheduler (#138)

src/refiners/foundationals/latent_diffusion/model.py

@@ -11,7 +11,6 @@
 
 T = TypeVar("T", bound="fl.Module")
 
-
 TLatentDiffusionModel = TypeVar("TLatentDiffusionModel", bound="LatentDiffusionModel")
 
 
@@ -91,6 +90,8 @@ def forward(
         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)
         unconditional_prediction, conditional_prediction = self.unet(latents).chunk(2)
 
         # classifier-free guidance

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

@@ -2,10 +2,6 @@
 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.scheduler import Scheduler
+from refiners.foundationals.latent_diffusion.schedulers.euler import EulerScheduler
 
-__all__ = [
-    "Scheduler",
-    "DPMSolver",
-    "DDPM",
-    "DDIM",
-]
+__all__ = ["Scheduler", "DPMSolver", "DDPM", "DDIM", "EulerScheduler"]

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

@@ -1,4 +1,4 @@
-from torch import Tensor, arange, device as Device, dtype as Dtype, float32, sqrt, tensor
+from torch import Tensor, arange, device as Device, dtype as Dtype, float32, sqrt, tensor, Generator
 
 from refiners.foundationals.latent_diffusion.schedulers.scheduler import NoiseSchedule, Scheduler
 
@@ -34,7 +34,7 @@ def _generate_timesteps(self) -> Tensor:
         timesteps = arange(start=0, end=self.num_inference_steps, step=1, device=self.device) * step_ratio + 1
         return timesteps.flip(0)
 
-    def __call__(self, x: Tensor, noise: Tensor, step: int) -> Tensor:
+    def __call__(self, x: Tensor, noise: Tensor, step: int, generator: Generator | None = None) -> Tensor:
         timestep, previous_timestep = (
             self.timesteps[step],
             (
@@ -43,13 +43,10 @@ def __call__(self, x: Tensor, noise: Tensor, step: int) -> Tensor:
                 else tensor(data=[0], device=self.device, dtype=self.dtype)
             ),
         )
-        current_scale_factor, previous_scale_factor = (
-            self.cumulative_scale_factors[timestep],
-            (
-                self.cumulative_scale_factors[previous_timestep]
-                if previous_timestep > 0
-                else self.cumulative_scale_factors[0]
-            ),
+        current_scale_factor, previous_scale_factor = self.cumulative_scale_factors[timestep], (
+            self.cumulative_scale_factors[previous_timestep]
+            if previous_timestep > 0
+            else self.cumulative_scale_factors[0]
         )
         predicted_x = (x - sqrt(1 - current_scale_factor**2) * noise) / current_scale_factor
         denoised_x = previous_scale_factor * predicted_x + sqrt(1 - previous_scale_factor**2) * noise

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

@@ -1,9 +1,7 @@
-from collections import deque
-
-import numpy as np
-from torch import Tensor, device as Device, dtype as Dtype, exp, float32, tensor
-
 from refiners.foundationals.latent_diffusion.schedulers.scheduler import NoiseSchedule, Scheduler
+import numpy as np
+from torch import Tensor, device as Device, tensor, exp, float32, dtype as Dtype, Generator
+from collections import deque
 
 
 class DPMSolver(Scheduler):
@@ -90,12 +88,7 @@ def multistep_dpm_solver_second_order_update(self, x: Tensor, step: int) -> Tens
         )
         return denoised_x
 
-    def __call__(
-        self,
-        x: Tensor,
-        noise: Tensor,
-        step: int,
-    ) -> Tensor:
+    def __call__(self, x: Tensor, noise: Tensor, step: int, generator: Generator | None = None) -> Tensor:
         """
         Represents one step of the backward diffusion process that iteratively denoises the input data `x`.
 

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

@@ -0,0 +1,83 @@
+from refiners.foundationals.latent_diffusion.schedulers.scheduler import NoiseSchedule, Scheduler
+from torch import Tensor, device as Device, dtype as Dtype, float32, tensor, Generator
+import torch
+import numpy as np
+
+
+class EulerScheduler(Scheduler):
+    def __init__(
+        self,
+        num_inference_steps: int,
+        num_train_timesteps: int = 1_000,
+        initial_diffusion_rate: float = 8.5e-4,
+        final_diffusion_rate: float = 1.2e-2,
+        noise_schedule: NoiseSchedule = NoiseSchedule.QUADRATIC,
+        device: Device | str = "cpu",
+        dtype: Dtype = float32,
+    ):
+        if noise_schedule != NoiseSchedule.QUADRATIC:
+            raise NotImplementedError
+        super().__init__(
+            num_inference_steps=num_inference_steps,
+            num_train_timesteps=num_train_timesteps,
+            initial_diffusion_rate=initial_diffusion_rate,
+            final_diffusion_rate=final_diffusion_rate,
+            noise_schedule=noise_schedule,
+            device=device,
+            dtype=dtype,
+        )
+        self.sigmas = self._generate_sigmas()
+
+    @property
+    def init_noise_sigma(self) -> Tensor:
+        return self.sigmas.max()
+
+    def _generate_timesteps(self) -> Tensor:
+        # We need to use numpy here because:
+        # 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)
+        return timesteps
+
+    def _generate_sigmas(self) -> Tensor:
+        sigmas = self.noise_std / self.cumulative_scale_factors
+        sigmas = torch.tensor(np.interp(self.timesteps.cpu().numpy(), np.arange(0, len(sigmas)), sigmas.cpu().numpy()))
+        sigmas = torch.cat([sigmas, tensor([0.0])])
+        return sigmas.to(device=self.device, dtype=self.dtype)
+
+    def scale_model_input(self, x: Tensor, step: int) -> Tensor:
+        sigma = self.sigmas[step]
+        return x / ((sigma**2 + 1) ** 0.5)
+
+    def __call__(
+        self,
+        x: Tensor,
+        noise: Tensor,
+        step: int,
+        generator: Generator | None = None,
+        s_churn: float = 0.0,
+        s_tmin: float = 0.0,
+        s_tmax: float = float("inf"),
+        s_noise: float = 1.0,
+    ) -> Tensor:
+        sigma = self.sigmas[step]
+
+        gamma = min(s_churn / (len(self.sigmas) - 1), 2**0.5 - 1) if s_tmin <= sigma <= s_tmax else 0
+
+        alt_noise = torch.randn(noise.shape, generator=generator, device=noise.device, dtype=noise.dtype)
+        eps = alt_noise * s_noise
+        sigma_hat = sigma * (gamma + 1)
+        if gamma > 0:
+            x = x + eps * (sigma_hat**2 - sigma**2) ** 0.5
+
+        predicted_x = x - sigma_hat * noise
+
+        # 1st order Euler
+        derivative = (x - predicted_x) / sigma_hat
+        dt = self.sigmas[step + 1] - sigma_hat
+        denoised_x = x + derivative * dt
+
+        return denoised_x

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

@@ -1,9 +1,8 @@
 from abc import ABC, abstractmethod
 from enum import Enum
+from torch import Tensor, device as Device, dtype as DType, linspace, float32, sqrt, log, Generator
 from typing import TypeVar
 
-from torch import Tensor, device as Device, dtype as DType, float32, linspace, log, sqrt
-
 T = TypeVar("T", bound="Scheduler")
 
 
@@ -50,7 +49,7 @@ def __init__(
         self.timesteps = self._generate_timesteps()
 
     @abstractmethod
-    def __call__(self, x: Tensor, noise: Tensor, step: int) -> Tensor:
+    def __call__(self, x: Tensor, noise: Tensor, step: int, generator: Generator | None = None) -> Tensor:
         """
         Applies a step of the diffusion process to the input tensor `x` using the provided `noise` and `timestep`.
 
@@ -71,6 +70,12 @@ def _generate_timesteps(self) -> Tensor:
     def steps(self) -> list[int]:
         return list(range(self.num_inference_steps))
 
+    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(

tests/foundationals/latent_diffusion/test_schedulers.py

@@ -2,10 +2,10 @@
 from warnings import warn
 
 import pytest
-from torch import Tensor, allclose, device as Device, equal, randn
+from torch import Tensor, allclose, device as Device, equal, randn, isclose
 
 from refiners.fluxion import manual_seed
-from refiners.foundationals.latent_diffusion.schedulers import DDIM, DDPM, DPMSolver
+from refiners.foundationals.latent_diffusion.schedulers import DDIM, DDPM, DPMSolver, EulerScheduler
 
 
 def test_ddpm_diffusers():
@@ -63,6 +63,34 @@ def test_ddim_diffusers():
         assert allclose(diffusers_output, refiners_output, rtol=0.01), f"outputs differ at step {step}"
 
 
+def test_euler_diffusers():
+    from diffusers import EulerDiscreteScheduler
+
+    manual_seed(0)
+    diffusers_scheduler = EulerDiscreteScheduler(
+        beta_end=0.012,
+        beta_schedule="scaled_linear",
+        beta_start=0.00085,
+        num_train_timesteps=1000,
+        steps_offset=1,
+        timestep_spacing="linspace",
+        use_karras_sigmas=False,
+    )
+    diffusers_scheduler.set_timesteps(30)
+    refiners_scheduler = EulerScheduler(num_inference_steps=30)
+
+    sample = randn(1, 4, 32, 32)
+    noise = randn(1, 4, 32, 32)
+
+    assert isclose(diffusers_scheduler.init_noise_sigma, refiners_scheduler.init_noise_sigma), "init_noise_sigma differ"
+
+    for step, timestep in enumerate(diffusers_scheduler.timesteps):
+        diffusers_output = cast(Tensor, diffusers_scheduler.step(noise, timestep, sample).prev_sample)  # type: ignore
+        refiners_output = refiners_scheduler(x=sample, noise=noise, step=step)
+
+        assert allclose(diffusers_output, refiners_output, rtol=0.01), f"outputs differ at step {step}"
+
+
 def test_scheduler_remove_noise():
     from diffusers import DDIMScheduler  # type: ignore
 
@@ -84,7 +112,9 @@ def test_scheduler_remove_noise():
     noise = randn(1, 4, 32, 32)
 
     for step, timestep in enumerate(diffusers_scheduler.timesteps):
-        diffusers_output = cast(Tensor, diffusers_scheduler.step(noise, timestep, sample).pred_original_sample)  # type: ignore
+        diffusers_output = cast(
+            Tensor, diffusers_scheduler.step(noise, timestep, sample).pred_original_sample
+        )  # type: ignore
         refiners_output = refiners_scheduler.remove_noise(x=sample, noise=noise, step=step)
 
         assert allclose(diffusers_output, refiners_output, rtol=0.01), f"outputs differ at step {step}"