Add EDMEulerScheduler (#7109)

* Add EDMEulerScheduler

* address review comments

* fix import

* fix test

* add tests

* add co-author

Co-authored-by:  @dg845 dgu8957@gmail.com

src/diffusers/__init__.py

@@ -144,6 +144,7 @@ else:
             "DPMSolverMultistepInverseScheduler",
             "DPMSolverMultistepScheduler",
             "DPMSolverSinglestepScheduler",
+            "EDMEulerScheduler",
             "EulerAncestralDiscreteScheduler",
             "EulerDiscreteScheduler",
             "HeunDiscreteScheduler",
@@ -526,6 +527,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             DPMSolverMultistepInverseScheduler,
             DPMSolverMultistepScheduler,
             DPMSolverSinglestepScheduler,
+            EDMEulerScheduler,
             EulerAncestralDiscreteScheduler,
             EulerDiscreteScheduler,
             HeunDiscreteScheduler,

src/diffusers/schedulers/__init__.py

@@ -52,6 +52,7 @@ else:
     _import_structure["scheduling_dpmsolver_multistep"] = ["DPMSolverMultistepScheduler"]
     _import_structure["scheduling_dpmsolver_multistep_inverse"] = ["DPMSolverMultistepInverseScheduler"]
     _import_structure["scheduling_dpmsolver_singlestep"] = ["DPMSolverSinglestepScheduler"]
+    _import_structure["scheduling_edm_euler"] = ["EDMEulerScheduler"]
     _import_structure["scheduling_euler_ancestral_discrete"] = ["EulerAncestralDiscreteScheduler"]
     _import_structure["scheduling_euler_discrete"] = ["EulerDiscreteScheduler"]
     _import_structure["scheduling_heun_discrete"] = ["HeunDiscreteScheduler"]
@@ -144,6 +145,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
         from .scheduling_dpmsolver_multistep import DPMSolverMultistepScheduler
         from .scheduling_dpmsolver_multistep_inverse import DPMSolverMultistepInverseScheduler
         from .scheduling_dpmsolver_singlestep import DPMSolverSinglestepScheduler
+        from .scheduling_edm_euler import EDMEulerScheduler
         from .scheduling_euler_ancestral_discrete import EulerAncestralDiscreteScheduler
         from .scheduling_euler_discrete import EulerDiscreteScheduler
         from .scheduling_heun_discrete import HeunDiscreteScheduler

src/diffusers/schedulers/scheduling_edm_euler.py

+# Copyright 2024 Katherine Crowson and The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from dataclasses import dataclass
+from typing import Optional, Tuple, Union
+
+import numpy as np
+import torch
+
+from ..configuration_utils import ConfigMixin, register_to_config
+from ..utils import BaseOutput, logging
+from ..utils.torch_utils import randn_tensor
+from .scheduling_utils import SchedulerMixin
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+@dataclass
+# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerDiscrete
+class EDMEulerSchedulerOutput(BaseOutput):
+    """
+    Output class for the scheduler's `step` function output.
+
+    Args:
+        prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
+            Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used as next model input in the
+            denoising loop.
+        pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
+            The predicted denoised sample `(x_{0})` based on the model output from the current timestep.
+            `pred_original_sample` can be used to preview progress or for guidance.
+    """
+
+    prev_sample: torch.FloatTensor
+    pred_original_sample: Optional[torch.FloatTensor] = None
+
+
+class EDMEulerScheduler(SchedulerMixin, ConfigMixin):
+    """
+    Implements the Euler scheduler in EDM formulation as presented in Karras et al. 2022 [1].
+
+    [1] Karras, Tero, et al. "Elucidating the Design Space of Diffusion-Based Generative Models."
+    https://arxiv.org/abs/2206.00364
+
+    This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic
+    methods the library implements for all schedulers such as loading and saving.
+
+    Args:
+        sigma_min (`float`, *optional*, defaults to 0.002):
+            Minimum noise magnitude in the sigma schedule. This was set to 0.002 in the EDM paper [1]; a reasonable
+            range is [0, 10].
+        sigma_max (`float`, *optional*, defaults to 80.0):
+            Maximum noise magnitude in the sigma schedule. This was set to 80.0 in the EDM paper [1]; a reasonable
+            range is [0.2, 80.0].
+        sigma_data (`float`, *optional*, defaults to 0.5):
+            The standard deviation of the data distribution. This is set to 0.5 in the EDM paper [1].
+        num_train_timesteps (`int`, defaults to 1000):
+            The number of diffusion steps to train the model.
+        prediction_type (`str`, defaults to `epsilon`, *optional*):
+            Prediction type of the scheduler function; can be `epsilon` (predicts the noise of the diffusion process),
+            `sample` (directly predicts the noisy sample`) or `v_prediction` (see section 2.4 of [Imagen
+            Video](https://imagen.research.google/video/paper.pdf) paper).
+        rho (`float`, *optional*, defaults to 7.0):
+            The rho parameter used for calculating the Karras sigma schedule, which is set to 7.0 in the EDM paper [1].
+    """
+
+    _compatibles = []
+    order = 1
+
+    @register_to_config
+    def __init__(
+        self,
+        sigma_min: float = 0.002,
+        sigma_max: float = 80.0,
+        sigma_data: float = 0.5,
+        num_train_timesteps: int = 1000,
+        prediction_type: str = "epsilon",
+        rho: float = 7.0,
+    ):
+        # setable values
+        self.num_inference_steps = None
+
+        ramp = torch.linspace(0, 1, num_train_timesteps)
+        sigmas = self._compute_sigmas(ramp)
+        self.timesteps = self.precondition_noise(sigmas)
+
+        self.sigmas = torch.cat([sigmas, torch.zeros(1, device=sigmas.device)])
+
+        self.is_scale_input_called = False
+
+        self._step_index = None
+        self._begin_index = None
+        self.sigmas = self.sigmas.to("cpu")  # to avoid too much CPU/GPU communication
+
+    @property
+    def init_noise_sigma(self):
+        # standard deviation of the initial noise distribution
+        return (self.config.sigma_max**2 + 1) ** 0.5
+
+    @property
+    def step_index(self):
+        """
+        The index counter for current timestep. It will increae 1 after each scheduler step.
+        """
+        return self._step_index
+
+    @property
+    def begin_index(self):
+        """
+        The index for the first timestep. It should be set from pipeline with `set_begin_index` method.
+        """
+        return self._begin_index
+
+    # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.set_begin_index
+    def set_begin_index(self, begin_index: int = 0):
+        """
+        Sets the begin index for the scheduler. This function should be run from pipeline before the inference.
+
+        Args:
+            begin_index (`int`):
+                The begin index for the scheduler.
+        """
+        self._begin_index = begin_index
+
+    def precondition_inputs(self, sample, sigma):
+        c_in = 1 / ((sigma**2 + self.config.sigma_data**2) ** 0.5)
+        scaled_sample = sample * c_in
+        return scaled_sample
+
+    def precondition_noise(self, sigma):
+        if not isinstance(sigma, torch.Tensor):
+            sigma = torch.tensor([sigma])
+
+        c_noise = 0.25 * torch.log(sigma)
+
+        return c_noise
+
+    def precondition_outputs(self, sample, model_output, sigma):
+        sigma_data = self.config.sigma_data
+        c_skip = sigma_data**2 / (sigma**2 + sigma_data**2)
+
+        if self.config.prediction_type == "epsilon":
+            c_out = sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
+        elif self.config.prediction_type == "v_prediction":
+            c_out = -sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
+        else:
+            raise ValueError(f"Prediction type {self.config.prediction_type} is not supported.")
+
+        denoised = c_skip * sample + c_out * model_output
+
+        return denoised
+
+    def scale_model_input(
+        self, sample: torch.FloatTensor, timestep: Union[float, torch.FloatTensor]
+    ) -> torch.FloatTensor:
+        """
+        Ensures interchangeability with schedulers that need to scale the denoising model input depending on the
+        current timestep. Scales the denoising model input by `(sigma**2 + 1) ** 0.5` to match the Euler algorithm.
+
+        Args:
+            sample (`torch.FloatTensor`):
+                The input sample.
+            timestep (`int`, *optional*):
+                The current timestep in the diffusion chain.
+
+        Returns:
+            `torch.FloatTensor`:
+                A scaled input sample.
+        """
+        if self.step_index is None:
+            self._init_step_index(timestep)
+
+        sigma = self.sigmas[self.step_index]
+        sample = self.precondition_inputs(sample, sigma)
+
+        self.is_scale_input_called = True
+        return sample
+
+    def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.device] = None):
+        """
+        Sets the discrete timesteps used for the diffusion chain (to be run before inference).
+
+        Args:
+            num_inference_steps (`int`):
+                The number of diffusion steps used when generating samples with a pre-trained model.
+            device (`str` or `torch.device`, *optional*):
+                The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
+        """
+        self.num_inference_steps = num_inference_steps
+
+        ramp = np.linspace(0, 1, self.num_inference_steps)
+        sigmas = self._compute_sigmas(ramp)
+
+        sigmas = torch.from_numpy(sigmas).to(dtype=torch.float32, device=device)
+        self.timesteps = self.precondition_noise(sigmas)
+
+        self.sigmas = torch.cat([sigmas, torch.zeros(1, device=sigmas.device)])
+        self._step_index = None
+        self._begin_index = None
+        self.sigmas = self.sigmas.to("cpu")  # to avoid too much CPU/GPU communication
+
+    # Taken from https://github.com/crowsonkb/k-diffusion/blob/686dbad0f39640ea25c8a8c6a6e56bb40eacefa2/k_diffusion/sampling.py#L17
+    def _compute_sigmas(self, ramp, sigma_min=None, sigma_max=None) -> torch.FloatTensor:
+        """Constructs the noise schedule of Karras et al. (2022)."""
+
+        sigma_min = sigma_min or self.config.sigma_min
+        sigma_max = sigma_max or self.config.sigma_max
+
+        rho = self.config.rho
+        min_inv_rho = sigma_min ** (1 / rho)
+        max_inv_rho = sigma_max ** (1 / rho)
+        sigmas = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
+        return sigmas
+
+    # Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler.index_for_timestep
+    def index_for_timestep(self, timestep, schedule_timesteps=None):
+        if schedule_timesteps is None:
+            schedule_timesteps = self.timesteps
+
+        indices = (schedule_timesteps == timestep).nonzero()
+
+        # The sigma index that is taken for the **very** first `step`
+        # is always the second index (or the last index if there is only 1)
+        # This way we can ensure we don't accidentally skip a sigma in
+        # case we start in the middle of the denoising schedule (e.g. for image-to-image)
+        pos = 1 if len(indices) > 1 else 0
+
+        return indices[pos].item()
+
+    # Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._init_step_index
+    def _init_step_index(self, timestep):
+        if self.begin_index is None:
+            if isinstance(timestep, torch.Tensor):
+                timestep = timestep.to(self.timesteps.device)
+            self._step_index = self.index_for_timestep(timestep)
+        else:
+            self._step_index = self._begin_index
+
+    def step(
+        self,
+        model_output: torch.FloatTensor,
+        timestep: Union[float, torch.FloatTensor],
+        sample: torch.FloatTensor,
+        s_churn: float = 0.0,
+        s_tmin: float = 0.0,
+        s_tmax: float = float("inf"),
+        s_noise: float = 1.0,
+        generator: Optional[torch.Generator] = None,
+        return_dict: bool = True,
+    ) -> Union[EDMEulerSchedulerOutput, Tuple]:
+        """
+        Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion
+        process from the learned model outputs (most often the predicted noise).
+
+        Args:
+            model_output (`torch.FloatTensor`):
+                The direct output from learned diffusion model.
+            timestep (`float`):
+                The current discrete timestep in the diffusion chain.
+            sample (`torch.FloatTensor`):
+                A current instance of a sample created by the diffusion process.
+            s_churn (`float`):
+            s_tmin  (`float`):
+            s_tmax  (`float`):
+            s_noise (`float`, defaults to 1.0):
+                Scaling factor for noise added to the sample.
+            generator (`torch.Generator`, *optional*):
+                A random number generator.
+            return_dict (`bool`):
+                Whether or not to return a [`~schedulers.scheduling_euler_discrete.EDMEulerSchedulerOutput`] or
+                tuple.
+
+        Returns:
+            [`~schedulers.scheduling_euler_discrete.EDMEulerSchedulerOutput`] or `tuple`:
+                If return_dict is `True`, [`~schedulers.scheduling_euler_discrete.EDMEulerSchedulerOutput`] is
+                returned, otherwise a tuple is returned where the first element is the sample tensor.
+        """
+
+        if (
+            isinstance(timestep, int)
+            or isinstance(timestep, torch.IntTensor)
+            or isinstance(timestep, torch.LongTensor)
+        ):
+            raise ValueError(
+                (
+                    "Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"
+                    " `EDMEulerScheduler.step()` is not supported. Make sure to pass"
+                    " one of the `scheduler.timesteps` as a timestep."
+                ),
+            )
+
+        if not self.is_scale_input_called:
+            logger.warning(
+                "The `scale_model_input` function should be called before `step` to ensure correct denoising. "
+                "See `StableDiffusionPipeline` for a usage example."
+            )
+
+        if self.step_index is None:
+            self._init_step_index(timestep)
+
+        # Upcast to avoid precision issues when computing prev_sample
+        sample = sample.to(torch.float32)
+
+        sigma = self.sigmas[self.step_index]
+
+        gamma = min(s_churn / (len(self.sigmas) - 1), 2**0.5 - 1) if s_tmin <= sigma <= s_tmax else 0.0
+
+        noise = randn_tensor(
+            model_output.shape, dtype=model_output.dtype, device=model_output.device, generator=generator
+        )
+
+        eps = noise * s_noise
+        sigma_hat = sigma * (gamma + 1)
+
+        if gamma > 0:
+            sample = sample + eps * (sigma_hat**2 - sigma**2) ** 0.5
+
+        # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
+        pred_original_sample = self.precondition_outputs(sample, model_output, sigma_hat)
+
+        # 2. Convert to an ODE derivative
+        derivative = (sample - pred_original_sample) / sigma_hat
+
+        dt = self.sigmas[self.step_index + 1] - sigma_hat
+
+        prev_sample = sample + derivative * dt
+
+        # Cast sample back to model compatible dtype
+        prev_sample = prev_sample.to(model_output.dtype)
+
+        # upon completion increase step index by one
+        self._step_index += 1
+
+        if not return_dict:
+            return (prev_sample,)
+
+        return EDMEulerSchedulerOutput(prev_sample=prev_sample, pred_original_sample=pred_original_sample)
+
+    # Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler.add_noise
+    def add_noise(
+        self,
+        original_samples: torch.FloatTensor,
+        noise: torch.FloatTensor,
+        timesteps: torch.FloatTensor,
+    ) -> torch.FloatTensor:
+        # Make sure sigmas and timesteps have the same device and dtype as original_samples
+        sigmas = self.sigmas.to(device=original_samples.device, dtype=original_samples.dtype)
+        if original_samples.device.type == "mps" and torch.is_floating_point(timesteps):
+            # mps does not support float64
+            schedule_timesteps = self.timesteps.to(original_samples.device, dtype=torch.float32)
+            timesteps = timesteps.to(original_samples.device, dtype=torch.float32)
+        else:
+            schedule_timesteps = self.timesteps.to(original_samples.device)
+            timesteps = timesteps.to(original_samples.device)
+
+        # self.begin_index is None when scheduler is used for training, or pipeline does not implement set_begin_index
+        if self.begin_index is None:
+            step_indices = [self.index_for_timestep(t, schedule_timesteps) for t in timesteps]
+        else:
+            step_indices = [self.begin_index] * timesteps.shape[0]
+
+        sigma = sigmas[step_indices].flatten()
+        while len(sigma.shape) < len(original_samples.shape):
+            sigma = sigma.unsqueeze(-1)
+
+        noisy_samples = original_samples + noise * sigma
+        return noisy_samples
+
+    def __len__(self):
+        return self.config.num_train_timesteps

src/diffusers/schedulers/scheduling_utils.py

@@ -45,6 +45,7 @@ class KarrasDiffusionSchedulers(Enum):
     DEISMultistepScheduler = 12
     UniPCMultistepScheduler = 13
     DPMSolverSDEScheduler = 14
+    EDMEulerScheduler = 15
 
 
 @dataclass

src/diffusers/utils/dummy_pt_objects.py

@@ -855,6 +855,21 @@ class DPMSolverSinglestepScheduler(metaclass=DummyObject):
         requires_backends(cls, ["torch"])
 
 
+class EDMEulerScheduler(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+    @classmethod
+    def from_config(cls, *args, **kwargs):
+        requires_backends(cls, ["torch"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["torch"])
+
+
 class EulerAncestralDiscreteScheduler(metaclass=DummyObject):
     _backends = ["torch"]
 

tests/pipelines/stable_diffusion_2/test_stable_diffusion_latent_upscale.py

@@ -209,6 +209,7 @@ class StableDiffusionLatentUpscalePipelineFastTests(
             "KDPM2DiscreteScheduler",
             "KDPM2AncestralDiscreteScheduler",
             "DPMSolverSDEScheduler",
+            "EDMEulerScheduler",
         ]
         components = self.get_dummy_components()
         pipe = self.pipeline_class(**components)

tests/schedulers/test_scheduler_edm_euler.py

+import inspect
+import tempfile
+import unittest
+from typing import Dict, List, Tuple
+
+import torch
+
+from diffusers import EDMEulerScheduler
+
+from .test_schedulers import SchedulerCommonTest
+
+
+class EDMEulerSchedulerTest(SchedulerCommonTest):
+    scheduler_classes = (EDMEulerScheduler,)
+    forward_default_kwargs = (("num_inference_steps", 10),)
+
+    def get_scheduler_config(self, **kwargs):
+        config = {
+            "num_train_timesteps": 256,
+            "sigma_min": 0.002,
+            "sigma_max": 80.0,
+        }
+
+        config.update(**kwargs)
+        return config
+
+    def test_timesteps(self):
+        for timesteps in [10, 50, 100, 1000]:
+            self.check_over_configs(num_train_timesteps=timesteps)
+
+    def test_prediction_type(self):
+        for prediction_type in ["epsilon", "v_prediction"]:
+            self.check_over_configs(prediction_type=prediction_type)
+
+    def test_full_loop_no_noise(self, num_inference_steps=10, seed=0):
+        scheduler_class = self.scheduler_classes[0]
+        scheduler_config = self.get_scheduler_config()
+        scheduler = scheduler_class(**scheduler_config)
+
+        scheduler.set_timesteps(num_inference_steps)
+
+        model = self.dummy_model()
+        sample = self.dummy_sample_deter * scheduler.init_noise_sigma
+
+        for i, t in enumerate(scheduler.timesteps):
+            scaled_sample = scheduler.scale_model_input(sample, t)
+
+            model_output = model(scaled_sample, t)
+
+            output = scheduler.step(model_output, t, sample)
+            sample = output.prev_sample
+
+        result_sum = torch.sum(torch.abs(sample))
+        result_mean = torch.mean(torch.abs(sample))
+
+        assert abs(result_sum.item() - 34.1855) < 1e-3
+        assert abs(result_mean.item() - 0.044) < 1e-3
+
+    def test_full_loop_device(self, num_inference_steps=10, seed=0):
+        scheduler_class = self.scheduler_classes[0]
+        scheduler_config = self.get_scheduler_config()
+        scheduler = scheduler_class(**scheduler_config)
+
+        scheduler.set_timesteps(num_inference_steps)
+
+        model = self.dummy_model()
+        sample = self.dummy_sample_deter * scheduler.init_noise_sigma
+
+        for i, t in enumerate(scheduler.timesteps):
+            scaled_sample = scheduler.scale_model_input(sample, t)
+
+            model_output = model(scaled_sample, t)
+
+            output = scheduler.step(model_output, t, sample)
+            sample = output.prev_sample
+
+        result_sum = torch.sum(torch.abs(sample))
+        result_mean = torch.mean(torch.abs(sample))
+
+        assert abs(result_sum.item() - 34.1855) < 1e-3
+        assert abs(result_mean.item() - 0.044) < 1e-3
+
+    # Override test_from_save_pretrined to use EDMEulerScheduler-specific logic
+    def test_from_save_pretrained(self):
+        kwargs = dict(self.forward_default_kwargs)
+        num_inference_steps = kwargs.pop("num_inference_steps", None)
+
+        for scheduler_class in self.scheduler_classes:
+            scheduler_config = self.get_scheduler_config()
+            scheduler = scheduler_class(**scheduler_config)
+
+            sample = self.dummy_sample
+            residual = 0.1 * sample
+
+            with tempfile.TemporaryDirectory() as tmpdirname:
+                scheduler.save_config(tmpdirname)
+                new_scheduler = scheduler_class.from_pretrained(tmpdirname)
+
+            scheduler.set_timesteps(num_inference_steps)
+            new_scheduler.set_timesteps(num_inference_steps)
+            timestep = scheduler.timesteps[0]
+
+            sample = self.dummy_sample
+
+            scaled_sample = scheduler.scale_model_input(sample, timestep)
+            residual = 0.1 * scaled_sample
+
+            new_scaled_sample = new_scheduler.scale_model_input(sample, timestep)
+            new_residual = 0.1 * new_scaled_sample
+
+            if "generator" in set(inspect.signature(scheduler.step).parameters.keys()):
+                kwargs["generator"] = torch.manual_seed(0)
+            output = scheduler.step(residual, timestep, sample, **kwargs).prev_sample
+
+            if "generator" in set(inspect.signature(scheduler.step).parameters.keys()):
+                kwargs["generator"] = torch.manual_seed(0)
+            new_output = new_scheduler.step(new_residual, timestep, sample, **kwargs).prev_sample
+
+            assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"
+
+    # Override test_from_save_pretrined to use EDMEulerScheduler-specific logic
+    def test_step_shape(self):
+        num_inference_steps = 10
+
+        scheduler_config = self.get_scheduler_config()
+        scheduler = self.scheduler_classes[0](**scheduler_config)
+
+        scheduler.set_timesteps(num_inference_steps)
+
+        timestep_0 = scheduler.timesteps[0]
+        timestep_1 = scheduler.timesteps[1]
+
+        sample = self.dummy_sample
+        scaled_sample = scheduler.scale_model_input(sample, timestep_0)
+        residual = 0.1 * scaled_sample
+
+        output_0 = scheduler.step(residual, timestep_0, sample).prev_sample
+        output_1 = scheduler.step(residual, timestep_1, sample).prev_sample
+
+        self.assertEqual(output_0.shape, sample.shape)
+        self.assertEqual(output_0.shape, output_1.shape)
+
+    # Override test_from_save_pretrined to use EDMEulerScheduler-specific logic
+    def test_scheduler_outputs_equivalence(self):
+        def set_nan_tensor_to_zero(t):
+            t[t != t] = 0
+            return t
+
+        def recursive_check(tuple_object, dict_object):
+            if isinstance(tuple_object, (List, Tuple)):
+                for tuple_iterable_value, dict_iterable_value in zip(tuple_object, dict_object.values()):
+                    recursive_check(tuple_iterable_value, dict_iterable_value)
+            elif isinstance(tuple_object, Dict):
+                for tuple_iterable_value, dict_iterable_value in zip(tuple_object.values(), dict_object.values()):
+                    recursive_check(tuple_iterable_value, dict_iterable_value)
+            elif tuple_object is None:
+                return
+            else:
+                self.assertTrue(
+                    torch.allclose(
+                        set_nan_tensor_to_zero(tuple_object), set_nan_tensor_to_zero(dict_object), atol=1e-5
+                    ),
+                    msg=(
+                        "Tuple and dict output are not equal. Difference:"
+                        f" {torch.max(torch.abs(tuple_object - dict_object))}. Tuple has `nan`:"
+                        f" {torch.isnan(tuple_object).any()} and `inf`: {torch.isinf(tuple_object)}. Dict has"
+                        f" `nan`: {torch.isnan(dict_object).any()} and `inf`: {torch.isinf(dict_object)}."
+                    ),
+                )
+
+        kwargs = dict(self.forward_default_kwargs)
+        num_inference_steps = kwargs.pop("num_inference_steps", 50)
+
+        timestep = 0
+
+        for scheduler_class in self.scheduler_classes:
+            scheduler_config = self.get_scheduler_config()
+            scheduler = scheduler_class(**scheduler_config)
+
+            scheduler.set_timesteps(num_inference_steps)
+            timestep = scheduler.timesteps[0]
+
+            sample = self.dummy_sample
+            scaled_sample = scheduler.scale_model_input(sample, timestep)
+            residual = 0.1 * scaled_sample
+
+            # Set the seed before state as some schedulers are stochastic like EulerAncestralDiscreteScheduler, EulerDiscreteScheduler
+            if "generator" in set(inspect.signature(scheduler.step).parameters.keys()):
+                kwargs["generator"] = torch.manual_seed(0)
+            outputs_dict = scheduler.step(residual, timestep, sample, **kwargs)
+
+            scheduler.set_timesteps(num_inference_steps)
+
+            scaled_sample = scheduler.scale_model_input(sample, timestep)
+            residual = 0.1 * scaled_sample
+
+            # Set the seed before state as some schedulers are stochastic like EulerAncestralDiscreteScheduler, EulerDiscreteScheduler
+            if "generator" in set(inspect.signature(scheduler.step).parameters.keys()):
+                kwargs["generator"] = torch.manual_seed(0)
+            outputs_tuple = scheduler.step(residual, timestep, sample, return_dict=False, **kwargs)
+
+            recursive_check(outputs_tuple, outputs_dict)
+
+    @unittest.skip(reason="EDMEulerScheduler does not support beta schedules.")
+    def test_trained_betas(self):
+        pass

tests/schedulers/test_schedulers.py

@@ -30,6 +30,7 @@ from diffusers import (
     DDIMScheduler,
     DEISMultistepScheduler,
     DiffusionPipeline,
+    EDMEulerScheduler,
     EulerAncestralDiscreteScheduler,
     EulerDiscreteScheduler,
     IPNDMScheduler,
@@ -385,6 +386,9 @@ class SchedulerCommonTest(unittest.TestCase):
                 scaled_sigma_max = scheduler.sigma_to_t(scheduler.config.sigma_max)
                 time_step = scaled_sigma_max
 
+            if scheduler_class == EDMEulerScheduler:
+                time_step = scheduler.timesteps[-1]
+
             if scheduler_class == VQDiffusionScheduler:
                 num_vec_classes = scheduler_config["num_vec_classes"]
                 sample = self.dummy_sample(num_vec_classes)
@@ -693,6 +697,8 @@ class SchedulerCommonTest(unittest.TestCase):
                     # Get valid timestep based on sigma_max, which should always be in timestep schedule.
                     scaled_sigma_max = scheduler.sigma_to_t(scheduler.config.sigma_max)
                     scaled_sample = scheduler.scale_model_input(sample, scaled_sigma_max)
+                elif scheduler_class == EDMEulerScheduler:
+                    scaled_sample = scheduler.scale_model_input(sample, scheduler.timesteps[-1])
                 else:
                     scaled_sample = scheduler.scale_model_input(sample, 0.0)
                 self.assertEqual(sample.shape, scaled_sample.shape)
@@ -710,6 +716,8 @@ class SchedulerCommonTest(unittest.TestCase):
                 # Get valid timestep based on sigma_max, which should always be in timestep schedule.
                 scaled_sigma_max = scheduler.sigma_to_t(scheduler.config.sigma_max)
                 scaled_sample = scheduler.scale_model_input(sample, scaled_sigma_max)
+            if scheduler_class == EDMEulerScheduler:
+                scaled_sample = scheduler.scale_model_input(sample, scheduler.timesteps[-1])
             else:
                 scaled_sample = scheduler.scale_model_input(sample, 0.0)
             self.assertEqual(sample.shape, scaled_sample.shape)