k-diffusion-euler (#1019)

* k-diffusion-euler

* make style make quality

* make fix-copies

* fix tests for euler a

* Update src/diffusers/schedulers/scheduling_euler_ancestral_discrete.py

Co-authored-by: Anton Lozhkov <aglozhkov@gmail.com>

* Update src/diffusers/schedulers/scheduling_euler_ancestral_discrete.py

Co-authored-by: Anton Lozhkov <aglozhkov@gmail.com>

* Update src/diffusers/schedulers/scheduling_euler_discrete.py

Co-authored-by: Anton Lozhkov <aglozhkov@gmail.com>

* Update src/diffusers/schedulers/scheduling_euler_discrete.py

Co-authored-by: Anton Lozhkov <aglozhkov@gmail.com>

* remove unused arg and method

* update doc

* quality

* make flake happy

* use logger instead of warn

* raise error instead of deprication

* don't require scipy

* pass generator in step

* fix tests

* Apply suggestions from code review

Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

* Update tests/test_scheduler.py

Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* remove unused generator

* pass generator as extra_step_kwargs

* update tests

* pass generator as kwarg

* pass generator as kwarg

* quality

* fix test for lms

* fix tests

Co-authored-by: patil-suraj <surajp815@gmail.com>
Co-authored-by: Anton Lozhkov <aglozhkov@gmail.com>
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

src/diffusers/__init__.py

@@ -41,6 +41,8 @@ if is_torch_available():
     from .schedulers import (
         DDIMScheduler,
         DDPMScheduler,
+        EulerAncestralDiscreteScheduler,
+        EulerDiscreteScheduler,
         IPNDMScheduler,
         KarrasVeScheduler,
         PNDMScheduler,

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py

@@ -9,7 +9,13 @@ from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
 from ...configuration_utils import FrozenDict
 from ...models import AutoencoderKL, UNet2DConditionModel
 from ...pipeline_utils import DiffusionPipeline
-from ...schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
+from ...schedulers import (
+    DDIMScheduler,
+    EulerAncestralDiscreteScheduler,
+    EulerDiscreteScheduler,
+    LMSDiscreteScheduler,
+    PNDMScheduler,
+)
 from ...utils import deprecate, logging
 from . import StableDiffusionPipelineOutput
 from .safety_checker import StableDiffusionSafetyChecker
@@ -52,7 +58,9 @@ class StableDiffusionPipeline(DiffusionPipeline):
         text_encoder: CLIPTextModel,
         tokenizer: CLIPTokenizer,
         unet: UNet2DConditionModel,
-        scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
+        scheduler: Union[
+            DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler, EulerDiscreteScheduler, EulerAncestralDiscreteScheduler
+        ],
         safety_checker: StableDiffusionSafetyChecker,
         feature_extractor: CLIPFeatureExtractor,
     ):
@@ -334,6 +342,11 @@ class StableDiffusionPipeline(DiffusionPipeline):
         if accepts_eta:
             extra_step_kwargs["eta"] = eta
 
+        # check if the scheduler accepts generator
+        accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
+        if accepts_generator:
+            extra_step_kwargs["generator"] = generator
+
         for i, t in enumerate(self.progress_bar(timesteps_tensor)):
             # expand the latents if we are doing classifier free guidance
             latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py

@@ -10,7 +10,13 @@ from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
 from ...configuration_utils import FrozenDict
 from ...models import AutoencoderKL, UNet2DConditionModel
 from ...pipeline_utils import DiffusionPipeline
-from ...schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
+from ...schedulers import (
+    DDIMScheduler,
+    EulerAncestralDiscreteScheduler,
+    EulerDiscreteScheduler,
+    LMSDiscreteScheduler,
+    PNDMScheduler,
+)
 from ...utils import deprecate, logging
 from . import StableDiffusionPipelineOutput
 from .safety_checker import StableDiffusionSafetyChecker
@@ -63,7 +69,9 @@ class StableDiffusionImg2ImgPipeline(DiffusionPipeline):
         text_encoder: CLIPTextModel,
         tokenizer: CLIPTokenizer,
         unet: UNet2DConditionModel,
-        scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
+        scheduler: Union[
+            DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler, EulerDiscreteScheduler, EulerAncestralDiscreteScheduler
+        ],
         safety_checker: StableDiffusionSafetyChecker,
         feature_extractor: CLIPFeatureExtractor,
     ):
@@ -335,6 +343,11 @@ class StableDiffusionImg2ImgPipeline(DiffusionPipeline):
         if accepts_eta:
             extra_step_kwargs["eta"] = eta
 
+        # check if the scheduler accepts generator
+        accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
+        if accepts_generator:
+            extra_step_kwargs["generator"] = generator
+
         latents = init_latents
 
         t_start = max(num_inference_steps - init_timestep + offset, 0)

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py

@@ -379,6 +379,11 @@ class StableDiffusionInpaintPipeline(DiffusionPipeline):
         if accepts_eta:
             extra_step_kwargs["eta"] = eta
 
+        # check if the scheduler accepts generator
+        accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
+        if accepts_generator:
+            extra_step_kwargs["generator"] = generator
+
         for i, t in enumerate(self.progress_bar(timesteps_tensor)):
             # expand the latents if we are doing classifier free guidance
             latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint_legacy.py

@@ -352,6 +352,11 @@ class StableDiffusionInpaintPipelineLegacy(DiffusionPipeline):
         if accepts_eta:
             extra_step_kwargs["eta"] = eta
 
+        # check if the scheduler accepts generator
+        accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
+        if accepts_generator:
+            extra_step_kwargs["generator"] = generator
+
         latents = init_latents
 
         t_start = max(num_inference_steps - init_timestep + offset, 0)

src/diffusers/schedulers/__init__.py

@@ -19,6 +19,8 @@ from ..utils import is_flax_available, is_scipy_available, is_torch_available
 if is_torch_available():
     from .scheduling_ddim import DDIMScheduler
     from .scheduling_ddpm import DDPMScheduler
+    from .scheduling_euler_ancestral_discrete import EulerAncestralDiscreteScheduler
+    from .scheduling_euler_discrete import EulerDiscreteScheduler
     from .scheduling_ipndm import IPNDMScheduler
     from .scheduling_karras_ve import KarrasVeScheduler
     from .scheduling_pndm import PNDMScheduler

src/diffusers/schedulers/scheduling_euler_ancestral_discrete.py

@@ -0,0 +1,261 @@
+# Copyright 2022 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, deprecate, logging
+from .scheduling_utils import SchedulerMixin
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+@dataclass
+# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
+class EulerAncestralDiscreteSchedulerOutput(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 EulerAncestralDiscreteScheduler(SchedulerMixin, ConfigMixin):
+    """
+    Ancestral sampling with Euler method steps. Based on the original k-diffusion implementation by Katherine Crowson:
+    https://github.com/crowsonkb/k-diffusion/blob/481677d114f6ea445aa009cf5bd7a9cdee909e47/k_diffusion/sampling.py#L72
+
+    [`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__`
+    function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`.
+    [`~ConfigMixin`] also provides general loading and saving functionality via the [`~ConfigMixin.save_config`] and
+    [`~ConfigMixin.from_config`] functions.
+
+    Args:
+        num_train_timesteps (`int`): number of diffusion steps used to train the model.
+        beta_start (`float`): the starting `beta` value of inference.
+        beta_end (`float`): the final `beta` value.
+        beta_schedule (`str`):
+            the beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from
+            `linear` or `scaled_linear`.
+        trained_betas (`np.ndarray`, optional):
+            option to pass an array of betas directly to the constructor to bypass `beta_start`, `beta_end` etc.
+
+    """
+
+    @register_to_config
+    def __init__(
+        self,
+        num_train_timesteps: int = 1000,
+        beta_start: float = 0.0001,
+        beta_end: float = 0.02,
+        beta_schedule: str = "linear",
+        trained_betas: Optional[np.ndarray] = None,
+    ):
+        if trained_betas is not None:
+            self.betas = torch.from_numpy(trained_betas)
+        elif beta_schedule == "linear":
+            self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32)
+        elif beta_schedule == "scaled_linear":
+            # this schedule is very specific to the latent diffusion model.
+            self.betas = (
+                torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float32) ** 2
+            )
+        else:
+            raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}")
+
+        self.alphas = 1.0 - self.betas
+        self.alphas_cumprod = torch.cumprod(self.alphas, dim=0)
+
+        sigmas = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
+        sigmas = np.concatenate([sigmas[::-1], [0.0]]).astype(np.float32)
+        self.sigmas = torch.from_numpy(sigmas)
+
+        # standard deviation of the initial noise distribution
+        self.init_noise_sigma = self.sigmas.max()
+
+        # setable values
+        self.num_inference_steps = None
+        timesteps = np.linspace(0, num_train_timesteps - 1, num_train_timesteps, dtype=float)[::-1].copy()
+        self.timesteps = torch.from_numpy(timesteps)
+        self.is_scale_input_called = False
+
+    def scale_model_input(
+        self, sample: torch.FloatTensor, timestep: Union[float, torch.FloatTensor]
+    ) -> torch.FloatTensor:
+        """
+        Scales the denoising model input by `(sigma**2 + 1) ** 0.5` to match the Euler algorithm.
+
+        Args:
+            sample (`torch.FloatTensor`): input sample
+            timestep (`float` or `torch.FloatTensor`): the current timestep in the diffusion chain
+
+        Returns:
+            `torch.FloatTensor`: scaled input sample
+        """
+        if isinstance(timestep, torch.Tensor):
+            timestep = timestep.to(self.timesteps.device)
+        step_index = (self.timesteps == timestep).nonzero().item()
+        sigma = self.sigmas[step_index]
+        sample = sample / ((sigma**2 + 1) ** 0.5)
+        self.is_scale_input_called = True
+        return sample
+
+    def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.device] = None):
+        """
+        Sets the timesteps used for the diffusion chain. Supporting function 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
+
+        timesteps = np.linspace(0, self.config.num_train_timesteps - 1, num_inference_steps, dtype=float)[::-1].copy()
+        sigmas = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
+        sigmas = np.interp(timesteps, np.arange(0, len(sigmas)), sigmas)
+        sigmas = np.concatenate([sigmas, [0.0]]).astype(np.float32)
+        self.sigmas = torch.from_numpy(sigmas).to(device=device)
+        self.timesteps = torch.from_numpy(timesteps).to(device=device)
+
+    def step(
+        self,
+        model_output: torch.FloatTensor,
+        timestep: Union[float, torch.FloatTensor],
+        sample: torch.FloatTensor,
+        generator: Optional[torch.Generator] = None,
+        return_dict: bool = True,
+    ) -> Union[EulerAncestralDiscreteSchedulerOutput, Tuple]:
+        """
+        Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion
+        process from the learned model outputs (most often the predicted noise).
+
+        Args:
+            model_output (`torch.FloatTensor`): direct output from learned diffusion model.
+            timestep (`float`): current timestep in the diffusion chain.
+            sample (`torch.FloatTensor`):
+                current instance of sample being created by diffusion process.
+            generator (`torch.Generator`, optional): Random number generator.
+            return_dict (`bool`): option for returning tuple rather than EulerAncestralDiscreteSchedulerOutput class
+
+        Returns:
+            [`~schedulers.scheduling_utils.EulerAncestralDiscreteSchedulerOutput`] or `tuple`:
+            [`~schedulers.scheduling_utils.EulerAncestralDiscreteSchedulerOutput`] if `return_dict` is True, otherwise
+            a `tuple`. When returning a tuple, 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"
+                " `EulerDiscreteScheduler.step()` is not supported. Make sure to pass"
+                " one of the `scheduler.timesteps` as a timestep.",
+            )
+
+        if not self.is_scale_input_called:
+            logger.warn(
+                "The `scale_model_input` function should be called before `step` to ensure correct denoising. "
+                "See `StableDiffusionPipeline` for a usage example."
+            )
+
+        if isinstance(timestep, torch.Tensor):
+            timestep = timestep.to(self.timesteps.device)
+
+        step_index = (self.timesteps == timestep).nonzero().item()
+        sigma = self.sigmas[step_index]
+
+        # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
+        pred_original_sample = sample - sigma * model_output
+        sigma_from = self.sigmas[step_index]
+        sigma_to = self.sigmas[step_index + 1]
+        sigma_up = (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
+        sigma_down = (sigma_to**2 - sigma_up**2) ** 0.5
+
+        # 2. Convert to an ODE derivative
+        derivative = (sample - pred_original_sample) / sigma
+
+        dt = sigma_down - sigma
+
+        prev_sample = sample + derivative * dt
+
+        device = model_output.device if torch.is_tensor(model_output) else "cpu"
+        noise = torch.randn(model_output.shape, dtype=model_output.dtype, generator=generator).to(device)
+        prev_sample = prev_sample + noise * sigma_up
+
+        if not return_dict:
+            return (prev_sample,)
+
+        return EulerAncestralDiscreteSchedulerOutput(
+            prev_sample=prev_sample, pred_original_sample=pred_original_sample
+        )
+
+    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
+        self.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
+            self.timesteps = self.timesteps.to(original_samples.device, dtype=torch.float32)
+            timesteps = timesteps.to(original_samples.device, dtype=torch.float32)
+        else:
+            self.timesteps = self.timesteps.to(original_samples.device)
+            timesteps = timesteps.to(original_samples.device)
+
+        schedule_timesteps = self.timesteps
+
+        if isinstance(timesteps, torch.IntTensor) or isinstance(timesteps, torch.LongTensor):
+            deprecate(
+                "timesteps as indices",
+                "0.8.0",
+                "Passing integer indices  (e.g. from `enumerate(timesteps)`) as timesteps to"
+                " `EulerAncestralDiscreteScheduler.add_noise()` will not be supported in future versions. Make sure to"
+                " pass values from `scheduler.timesteps` as timesteps.",
+                standard_warn=False,
+            )
+            step_indices = timesteps
+        else:
+            step_indices = [(schedule_timesteps == t).nonzero().item() for t in timesteps]
+
+        sigma = self.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_euler_discrete.py

@@ -0,0 +1,270 @@
+# Copyright 2022 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, deprecate, logging
+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 EulerDiscreteSchedulerOutput(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 EulerDiscreteScheduler(SchedulerMixin, ConfigMixin):
+    """
+    Euler scheduler (Algorithm 2) from Karras et al. (2022) https://arxiv.org/abs/2206.00364. . Based on the original
+    k-diffusion implementation by Katherine Crowson:
+    https://github.com/crowsonkb/k-diffusion/blob/481677d114f6ea445aa009cf5bd7a9cdee909e47/k_diffusion/sampling.py#L51
+
+    [`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__`
+    function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`.
+    [`~ConfigMixin`] also provides general loading and saving functionality via the [`~ConfigMixin.save_config`] and
+    [`~ConfigMixin.from_config`] functions.
+
+    Args:
+        num_train_timesteps (`int`): number of diffusion steps used to train the model.
+        beta_start (`float`): the starting `beta` value of inference.
+        beta_end (`float`): the final `beta` value.
+        beta_schedule (`str`):
+            the beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from
+            `linear` or `scaled_linear`.
+        trained_betas (`np.ndarray`, optional):
+            option to pass an array of betas directly to the constructor to bypass `beta_start`, `beta_end` etc.
+
+    """
+
+    @register_to_config
+    def __init__(
+        self,
+        num_train_timesteps: int = 1000,
+        beta_start: float = 0.0001,
+        beta_end: float = 0.02,
+        beta_schedule: str = "linear",
+        trained_betas: Optional[np.ndarray] = None,
+    ):
+        if trained_betas is not None:
+            self.betas = torch.from_numpy(trained_betas)
+        elif beta_schedule == "linear":
+            self.betas = torch.linspace(beta_start, beta_end, num_train_timesteps, dtype=torch.float32)
+        elif beta_schedule == "scaled_linear":
+            # this schedule is very specific to the latent diffusion model.
+            self.betas = (
+                torch.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=torch.float32) ** 2
+            )
+        else:
+            raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}")
+
+        self.alphas = 1.0 - self.betas
+        self.alphas_cumprod = torch.cumprod(self.alphas, dim=0)
+
+        sigmas = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
+        sigmas = np.concatenate([sigmas[::-1], [0.0]]).astype(np.float32)
+        self.sigmas = torch.from_numpy(sigmas)
+
+        # standard deviation of the initial noise distribution
+        self.init_noise_sigma = self.sigmas.max()
+
+        # setable values
+        self.num_inference_steps = None
+        timesteps = np.linspace(0, num_train_timesteps - 1, num_train_timesteps, dtype=float)[::-1].copy()
+        self.timesteps = torch.from_numpy(timesteps)
+        self.is_scale_input_called = False
+
+    def scale_model_input(
+        self, sample: torch.FloatTensor, timestep: Union[float, torch.FloatTensor]
+    ) -> torch.FloatTensor:
+        """
+        Scales the denoising model input by `(sigma**2 + 1) ** 0.5` to match the Euler algorithm.
+
+        Args:
+            sample (`torch.FloatTensor`): input sample
+            timestep (`float` or `torch.FloatTensor`): the current timestep in the diffusion chain
+
+        Returns:
+            `torch.FloatTensor`: scaled input sample
+        """
+        if isinstance(timestep, torch.Tensor):
+            timestep = timestep.to(self.timesteps.device)
+        step_index = (self.timesteps == timestep).nonzero().item()
+        sigma = self.sigmas[step_index]
+        sample = sample / ((sigma**2 + 1) ** 0.5)
+        self.is_scale_input_called = True
+        return sample
+
+    def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.device] = None):
+        """
+        Sets the timesteps used for the diffusion chain. Supporting function 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
+
+        timesteps = np.linspace(0, self.config.num_train_timesteps - 1, num_inference_steps, dtype=float)[::-1].copy()
+        sigmas = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
+        sigmas = np.interp(timesteps, np.arange(0, len(sigmas)), sigmas)
+        sigmas = np.concatenate([sigmas, [0.0]]).astype(np.float32)
+        self.sigmas = torch.from_numpy(sigmas).to(device=device)
+        self.timesteps = torch.from_numpy(timesteps).to(device=device)
+
+    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[EulerDiscreteSchedulerOutput, Tuple]:
+        """
+        Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion
+        process from the learned model outputs (most often the predicted noise).
+
+        Args:
+            model_output (`torch.FloatTensor`): direct output from learned diffusion model.
+            timestep (`float`): current timestep in the diffusion chain.
+            sample (`torch.FloatTensor`):
+                current instance of sample being created by diffusion process.
+            s_churn (`float`)
+            s_tmin  (`float`)
+            s_tmax  (`float`)
+            s_noise (`float`)
+            generator (`torch.Generator`, optional): Random number generator.
+            return_dict (`bool`): option for returning tuple rather than EulerDiscreteSchedulerOutput class
+
+        Returns:
+            [`~schedulers.scheduling_utils.EulerDiscreteSchedulerOutput`] or `tuple`:
+            [`~schedulers.scheduling_utils.EulerDiscreteSchedulerOutput`] if `return_dict` is True, otherwise a
+            `tuple`. When returning a tuple, 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"
+                " `EulerDiscreteScheduler.step()` is not supported. Make sure to pass"
+                " one of the `scheduler.timesteps` as a timestep.",
+            )
+
+        if not self.is_scale_input_called:
+            logger.warn(
+                "The `scale_model_input` function should be called before `step` to ensure correct denoising. "
+                "See `StableDiffusionPipeline` for a usage example."
+            )
+
+        if isinstance(timestep, torch.Tensor):
+            timestep = timestep.to(self.timesteps.device)
+
+        step_index = (self.timesteps == timestep).nonzero().item()
+        sigma = self.sigmas[step_index]
+
+        gamma = min(s_churn / (len(self.sigmas) - 1), 2**0.5 - 1) if s_tmin <= sigma <= s_tmax else 0.0
+
+        device = model_output.device if torch.is_tensor(model_output) else "cpu"
+        noise = torch.randn(model_output.shape, dtype=model_output.dtype, generator=generator).to(device)
+        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 = sample - sigma_hat * model_output
+
+        # 2. Convert to an ODE derivative
+        derivative = (sample - pred_original_sample) / sigma_hat
+
+        dt = self.sigmas[step_index + 1] - sigma_hat
+
+        prev_sample = sample + derivative * dt
+
+        if not return_dict:
+            return (prev_sample,)
+
+        return EulerDiscreteSchedulerOutput(prev_sample=prev_sample, pred_original_sample=pred_original_sample)
+
+    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
+        self.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
+            self.timesteps = self.timesteps.to(original_samples.device, dtype=torch.float32)
+            timesteps = timesteps.to(original_samples.device, dtype=torch.float32)
+        else:
+            self.timesteps = self.timesteps.to(original_samples.device)
+            timesteps = timesteps.to(original_samples.device)
+
+        schedule_timesteps = self.timesteps
+
+        if isinstance(timesteps, torch.IntTensor) or isinstance(timesteps, torch.LongTensor):
+            deprecate(
+                "timesteps as indices",
+                "0.8.0",
+                "Passing integer indices  (e.g. from `enumerate(timesteps)`) as timesteps to"
+                " `EulerDiscreteScheduler.add_noise()` will not be supported in future versions. Make sure to"
+                " pass values from `scheduler.timesteps` as timesteps.",
+                standard_warn=False,
+            )
+            step_indices = timesteps
+        else:
+            step_indices = [(schedule_timesteps == t).nonzero().item() for t in timesteps]
+
+        sigma = self.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/utils/dummy_pt_objects.py

@@ -272,6 +272,36 @@ class DDPMScheduler(metaclass=DummyObject):
         requires_backends(cls, ["torch"])
 
 
+class EulerAncestralDiscreteScheduler(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 EulerDiscreteScheduler(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 IPNDMScheduler(metaclass=DummyObject):
     _backends = ["torch"]
 

tests/pipelines/stable_diffusion/test_stable_diffusion.py

@@ -24,6 +24,8 @@ import torch
 from diffusers import (
     AutoencoderKL,
     DDIMScheduler,
+    EulerAncestralDiscreteScheduler,
+    EulerDiscreteScheduler,
     LMSDiscreteScheduler,
     PNDMScheduler,
     StableDiffusionPipeline,
@@ -361,6 +363,96 @@ class StableDiffusionPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
         assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
         assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
 
+    def test_stable_diffusion_k_euler_ancestral(self):
+        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
+        unet = self.dummy_cond_unet
+        scheduler = EulerAncestralDiscreteScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear")
+        vae = self.dummy_vae
+        bert = self.dummy_text_encoder
+        tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
+
+        # make sure here that pndm scheduler skips prk
+        sd_pipe = StableDiffusionPipeline(
+            unet=unet,
+            scheduler=scheduler,
+            vae=vae,
+            text_encoder=bert,
+            tokenizer=tokenizer,
+            safety_checker=None,
+            feature_extractor=self.dummy_extractor,
+        )
+        sd_pipe = sd_pipe.to(device)
+        sd_pipe.set_progress_bar_config(disable=None)
+
+        prompt = "A painting of a squirrel eating a burger"
+        generator = torch.Generator(device=device).manual_seed(0)
+        output = sd_pipe([prompt], generator=generator, guidance_scale=6.0, num_inference_steps=2, output_type="np")
+
+        image = output.images
+
+        generator = torch.Generator(device=device).manual_seed(0)
+        image_from_tuple = sd_pipe(
+            [prompt],
+            generator=generator,
+            guidance_scale=6.0,
+            num_inference_steps=2,
+            output_type="np",
+            return_dict=False,
+        )[0]
+
+        image_slice = image[0, -3:, -3:, -1]
+        image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]
+
+        assert image.shape == (1, 128, 128, 3)
+        expected_slice = np.array([0.5067, 0.4689, 0.4614, 0.5233, 0.4903, 0.5112, 0.524, 0.5069, 0.4785])
+        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
+        assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
+
+    def test_stable_diffusion_k_euler(self):
+        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
+        unet = self.dummy_cond_unet
+        scheduler = EulerDiscreteScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear")
+        vae = self.dummy_vae
+        bert = self.dummy_text_encoder
+        tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
+
+        # make sure here that pndm scheduler skips prk
+        sd_pipe = StableDiffusionPipeline(
+            unet=unet,
+            scheduler=scheduler,
+            vae=vae,
+            text_encoder=bert,
+            tokenizer=tokenizer,
+            safety_checker=None,
+            feature_extractor=self.dummy_extractor,
+        )
+        sd_pipe = sd_pipe.to(device)
+        sd_pipe.set_progress_bar_config(disable=None)
+
+        prompt = "A painting of a squirrel eating a burger"
+        generator = torch.Generator(device=device).manual_seed(0)
+        output = sd_pipe([prompt], generator=generator, guidance_scale=6.0, num_inference_steps=2, output_type="np")
+
+        image = output.images
+
+        generator = torch.Generator(device=device).manual_seed(0)
+        image_from_tuple = sd_pipe(
+            [prompt],
+            generator=generator,
+            guidance_scale=6.0,
+            num_inference_steps=2,
+            output_type="np",
+            return_dict=False,
+        )[0]
+
+        image_slice = image[0, -3:, -3:, -1]
+        image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]
+
+        assert image.shape == (1, 128, 128, 3)
+        expected_slice = np.array([0.5067, 0.4689, 0.4614, 0.5233, 0.4903, 0.5112, 0.524, 0.5069, 0.4785])
+        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
+        assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
+
     def test_stable_diffusion_attention_chunk(self):
         device = "cpu"  # ensure determinism for the device-dependent torch.Generator
         unet = self.dummy_cond_unet

tests/test_scheduler.py

@@ -12,6 +12,7 @@
 # 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.
+import inspect
 import tempfile
 import unittest
 from typing import Dict, List, Tuple
@@ -22,6 +23,8 @@ import torch
 from diffusers import (
     DDIMScheduler,
     DDPMScheduler,
+    EulerAncestralDiscreteScheduler,
+    EulerDiscreteScheduler,
     IPNDMScheduler,
     LMSDiscreteScheduler,
     PNDMScheduler,
@@ -77,7 +80,11 @@ class SchedulerCommonTest(unittest.TestCase):
 
         num_inference_steps = kwargs.pop("num_inference_steps", None)
 
+        # TODO(Suraj) - delete the following two lines once DDPM, DDIM, and PNDM have timesteps casted to float by default
         for scheduler_class in self.scheduler_classes:
+            if scheduler_class in (EulerAncestralDiscreteScheduler, EulerDiscreteScheduler, LMSDiscreteScheduler):
+                time_step = float(time_step)
+
             sample = self.dummy_sample
             residual = 0.1 * sample
 
@@ -94,7 +101,13 @@ class SchedulerCommonTest(unittest.TestCase):
             elif num_inference_steps is not None and not hasattr(scheduler, "set_timesteps"):
                 kwargs["num_inference_steps"] = num_inference_steps
 
+            # Set the seed before step() as some schedulers are stochastic like EulerAncestralDiscreteScheduler, EulerDiscreteScheduler
+            if "generator" in set(inspect.signature(scheduler.step).parameters.keys()):
+                kwargs["generator"] = torch.Generator().manual_seed(0)
             output = scheduler.step(residual, time_step, sample, **kwargs).prev_sample
+
+            if "generator" in set(inspect.signature(scheduler.step).parameters.keys()):
+                kwargs["generator"] = torch.Generator().manual_seed(0)
             new_output = new_scheduler.step(residual, time_step, sample, **kwargs).prev_sample
 
             assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"
@@ -106,6 +119,9 @@ class SchedulerCommonTest(unittest.TestCase):
         num_inference_steps = kwargs.pop("num_inference_steps", None)
 
         for scheduler_class in self.scheduler_classes:
+            if scheduler_class in (EulerAncestralDiscreteScheduler, EulerDiscreteScheduler, LMSDiscreteScheduler):
+                time_step = float(time_step)
+
             sample = self.dummy_sample
             residual = 0.1 * sample
 
@@ -122,9 +138,12 @@ class SchedulerCommonTest(unittest.TestCase):
             elif num_inference_steps is not None and not hasattr(scheduler, "set_timesteps"):
                 kwargs["num_inference_steps"] = num_inference_steps
 
-            torch.manual_seed(0)
+            if "generator" in set(inspect.signature(scheduler.step).parameters.keys()):
+                kwargs["generator"] = torch.Generator().manual_seed(0)
             output = scheduler.step(residual, time_step, sample, **kwargs).prev_sample
-            torch.manual_seed(0)
+
+            if "generator" in set(inspect.signature(scheduler.step).parameters.keys()):
+                kwargs["generator"] = torch.Generator().manual_seed(0)
             new_output = new_scheduler.step(residual, time_step, sample, **kwargs).prev_sample
 
             assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"
@@ -141,6 +160,10 @@ class SchedulerCommonTest(unittest.TestCase):
             scheduler_config = self.get_scheduler_config()
             scheduler = scheduler_class(**scheduler_config)
 
+            timestep = 1
+            if scheduler_class in (EulerAncestralDiscreteScheduler, EulerDiscreteScheduler, LMSDiscreteScheduler):
+                timestep = float(timestep)
+
             with tempfile.TemporaryDirectory() as tmpdirname:
                 scheduler.save_config(tmpdirname)
                 new_scheduler = scheduler_class.from_config(tmpdirname)
@@ -151,10 +174,13 @@ class SchedulerCommonTest(unittest.TestCase):
             elif num_inference_steps is not None and not hasattr(scheduler, "set_timesteps"):
                 kwargs["num_inference_steps"] = num_inference_steps
 
-            torch.manual_seed(0)
-            output = scheduler.step(residual, 1, sample, **kwargs).prev_sample
-            torch.manual_seed(0)
-            new_output = new_scheduler.step(residual, 1, sample, **kwargs).prev_sample
+            if "generator" in set(inspect.signature(scheduler.step).parameters.keys()):
+                kwargs["generator"] = torch.Generator().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.Generator().manual_seed(0)
+            new_output = new_scheduler.step(residual, timestep, sample, **kwargs).prev_sample
 
             assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"
 
@@ -163,7 +189,14 @@ class SchedulerCommonTest(unittest.TestCase):
 
         num_inference_steps = kwargs.pop("num_inference_steps", None)
 
+        timestep_0 = 0
+        timestep_1 = 1
+
         for scheduler_class in self.scheduler_classes:
+            if scheduler_class in (EulerAncestralDiscreteScheduler, EulerDiscreteScheduler, LMSDiscreteScheduler):
+                timestep_0 = float(timestep_0)
+                timestep_1 = float(timestep_1)
+
             scheduler_config = self.get_scheduler_config()
             scheduler = scheduler_class(**scheduler_config)
 
@@ -175,8 +208,8 @@ class SchedulerCommonTest(unittest.TestCase):
             elif num_inference_steps is not None and not hasattr(scheduler, "set_timesteps"):
                 kwargs["num_inference_steps"] = num_inference_steps
 
-            output_0 = scheduler.step(residual, 0, sample, **kwargs).prev_sample
-            output_1 = scheduler.step(residual, 1, sample, **kwargs).prev_sample
+            output_0 = scheduler.step(residual, timestep_0, sample, **kwargs).prev_sample
+            output_1 = scheduler.step(residual, timestep_1, sample, **kwargs).prev_sample
 
             self.assertEqual(output_0.shape, sample.shape)
             self.assertEqual(output_0.shape, output_1.shape)
@@ -216,6 +249,9 @@ class SchedulerCommonTest(unittest.TestCase):
             timestep = 1
 
         for scheduler_class in self.scheduler_classes:
+            if scheduler_class in (EulerAncestralDiscreteScheduler, EulerDiscreteScheduler, LMSDiscreteScheduler):
+                timestep = float(timestep)
+
             scheduler_config = self.get_scheduler_config()
             scheduler = scheduler_class(**scheduler_config)
 
@@ -227,6 +263,9 @@ class SchedulerCommonTest(unittest.TestCase):
             elif num_inference_steps is not None and not hasattr(scheduler, "set_timesteps"):
                 kwargs["num_inference_steps"] = num_inference_steps
 
+            # 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.Generator().manual_seed(0)
             outputs_dict = scheduler.step(residual, timestep, sample, **kwargs)
 
             if num_inference_steps is not None and hasattr(scheduler, "set_timesteps"):
@@ -234,6 +273,9 @@ class SchedulerCommonTest(unittest.TestCase):
             elif num_inference_steps is not None and not hasattr(scheduler, "set_timesteps"):
                 kwargs["num_inference_steps"] = num_inference_steps
 
+            # 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.Generator().manual_seed(0)
             outputs_tuple = scheduler.step(residual, timestep, sample, return_dict=False, **kwargs)
 
             recursive_check(outputs_tuple, outputs_dict)
@@ -933,6 +975,117 @@ class LMSDiscreteSchedulerTest(SchedulerCommonTest):
         assert abs(result_mean.item() - 1.31) < 1e-3
 
 
+class EulerDiscreteSchedulerTest(SchedulerCommonTest):
+    scheduler_classes = (EulerDiscreteScheduler,)
+    num_inference_steps = 10
+
+    def get_scheduler_config(self, **kwargs):
+        config = {
+            "num_train_timesteps": 1100,
+            "beta_start": 0.0001,
+            "beta_end": 0.02,
+            "beta_schedule": "linear",
+            "trained_betas": None,
+        }
+
+        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_betas(self):
+        for beta_start, beta_end in zip([0.00001, 0.0001, 0.001], [0.0002, 0.002, 0.02]):
+            self.check_over_configs(beta_start=beta_start, beta_end=beta_end)
+
+    def test_schedules(self):
+        for schedule in ["linear", "scaled_linear"]:
+            self.check_over_configs(beta_schedule=schedule)
+
+    def test_full_loop_no_noise(self):
+        scheduler_class = self.scheduler_classes[0]
+        scheduler_config = self.get_scheduler_config()
+        scheduler = scheduler_class(**scheduler_config)
+
+        scheduler.set_timesteps(self.num_inference_steps)
+
+        generator = torch.Generator().manual_seed(0)
+
+        model = self.dummy_model()
+        sample = self.dummy_sample_deter * scheduler.init_noise_sigma
+
+        for i, t in enumerate(scheduler.timesteps):
+            sample = scheduler.scale_model_input(sample, t)
+
+            model_output = model(sample, t)
+
+            output = scheduler.step(model_output, t, sample, generator=generator)
+            sample = output.prev_sample
+
+        result_sum = torch.sum(torch.abs(sample))
+        result_mean = torch.mean(torch.abs(sample))
+        print(result_sum, result_mean)
+
+        assert abs(result_sum.item() - 10.0807) < 1e-2
+        assert abs(result_mean.item() - 0.0131) < 1e-3
+
+
+class EulerAncestralDiscreteSchedulerTest(SchedulerCommonTest):
+    scheduler_classes = (EulerAncestralDiscreteScheduler,)
+    num_inference_steps = 10
+
+    def get_scheduler_config(self, **kwargs):
+        config = {
+            "num_train_timesteps": 1100,
+            "beta_start": 0.0001,
+            "beta_end": 0.02,
+            "beta_schedule": "linear",
+            "trained_betas": None,
+        }
+
+        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_betas(self):
+        for beta_start, beta_end in zip([0.00001, 0.0001, 0.001], [0.0002, 0.002, 0.02]):
+            self.check_over_configs(beta_start=beta_start, beta_end=beta_end)
+
+    def test_schedules(self):
+        for schedule in ["linear", "scaled_linear"]:
+            self.check_over_configs(beta_schedule=schedule)
+
+    def test_full_loop_no_noise(self):
+        scheduler_class = self.scheduler_classes[0]
+        scheduler_config = self.get_scheduler_config()
+        scheduler = scheduler_class(**scheduler_config)
+
+        scheduler.set_timesteps(self.num_inference_steps)
+
+        generator = torch.Generator().manual_seed(0)
+
+        model = self.dummy_model()
+        sample = self.dummy_sample_deter * scheduler.init_noise_sigma
+
+        for i, t in enumerate(scheduler.timesteps):
+            sample = scheduler.scale_model_input(sample, t)
+
+            model_output = model(sample, t)
+
+            output = scheduler.step(model_output, t, sample, generator=generator)
+            sample = output.prev_sample
+
+        result_sum = torch.sum(torch.abs(sample))
+        result_mean = torch.mean(torch.abs(sample))
+        print(result_sum, result_mean)
+        assert abs(result_sum.item() - 152.3192) < 1e-2
+        assert abs(result_mean.item() - 0.1983) < 1e-3
+
+
 class IPNDMSchedulerTest(SchedulerCommonTest):
     scheduler_classes = (IPNDMScheduler,)
     forward_default_kwargs = (("num_inference_steps", 50),)