sde_ve_pipeline.py

from typing import List, Optional, Tuple, Union

import torch
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.utils import BaseOutput
from diffusers.models.embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from diffusers.models.modeling_utils import ModelMixin
from diffusers.models.unets.unet_2d_blocks import UNetMidBlock2D, get_down_block, get_up_block


@dataclass
class UNet2DOutput(BaseOutput):
    """
    The output of [`UNet2DModel`].
    Args:
        sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            The hidden states output from the last layer of the model.
    """

    sample: torch.FloatTensor


class UNet2DModel(ModelMixin, ConfigMixin):
    r"""
    A 2D UNet model that takes a noisy sample and a timestep and returns a sample shaped output.
    This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented
    for all models (such as downloading or saving).
    Parameters:
        sample_size (`int` or `Tuple[int, int]`, *optional*, defaults to `None`):
            Height and width of input/output sample. Dimensions must be a multiple of `2 ** (len(block_out_channels) -
            1)`.
        in_channels (`int`, *optional*, defaults to 3): Number of channels in the input sample.
        out_channels (`int`, *optional*, defaults to 3): Number of channels in the output.
        center_input_sample (`bool`, *optional*, defaults to `False`): Whether to center the input sample.
        time_embedding_type (`str`, *optional*, defaults to `"positional"`): Type of time embedding to use.
        freq_shift (`int`, *optional*, defaults to 0): Frequency shift for Fourier time embedding.
        flip_sin_to_cos (`bool`, *optional*, defaults to `True`):
            Whether to flip sin to cos for Fourier time embedding.
        down_block_types (`Tuple[str]`, *optional*, defaults to `("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D")`):
            Tuple of downsample block types.
        mid_block_type (`str`, *optional*, defaults to `"UNetMidBlock2D"`):
            Block type for middle of UNet, it can be either `UNetMidBlock2D` or `UnCLIPUNetMidBlock2D`.
        up_block_types (`Tuple[str]`, *optional*, defaults to `("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D")`):
            Tuple of upsample block types.
        block_out_channels (`Tuple[int]`, *optional*, defaults to `(224, 448, 672, 896)`):
            Tuple of block output channels.
        layers_per_block (`int`, *optional*, defaults to `2`): The number of layers per block.
        mid_block_scale_factor (`float`, *optional*, defaults to `1`): The scale factor for the mid block.
        downsample_padding (`int`, *optional*, defaults to `1`): The padding for the downsample convolution.
        downsample_type (`str`, *optional*, defaults to `conv`):
            The downsample type for downsampling layers. Choose between "conv" and "resnet"
        upsample_type (`str`, *optional*, defaults to `conv`):
            The upsample type for upsampling layers. Choose between "conv" and "resnet"
        dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
        act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use.
        attention_head_dim (`int`, *optional*, defaults to `8`): The attention head dimension.
        norm_num_groups (`int`, *optional*, defaults to `32`): The number of groups for normalization.
        attn_norm_num_groups (`int`, *optional*, defaults to `None`):
            If set to an integer, a group norm layer will be created in the mid block's [`Attention`] layer with the
            given number of groups. If left as `None`, the group norm layer will only be created if
            `resnet_time_scale_shift` is set to `default`, and if created will have `norm_num_groups` groups.
        norm_eps (`float`, *optional*, defaults to `1e-5`): The epsilon for normalization.
        resnet_time_scale_shift (`str`, *optional*, defaults to `"default"`): Time scale shift config
            for ResNet blocks (see [`~models.resnet.ResnetBlock2D`]). Choose from `default` or `scale_shift`.
        class_embed_type (`str`, *optional*, defaults to `None`):
            The type of class embedding to use which is ultimately summed with the time embeddings. Choose from `None`,
            `"timestep"`, or `"identity"`.
        num_class_embeds (`int`, *optional*, defaults to `None`):
            Input dimension of the learnable embedding matrix to be projected to `time_embed_dim` when performing class
            conditioning with `class_embed_type` equal to `None`.
    """

    @register_to_config
    def __init__(
        self,
        sample_size: Optional[Union[int, Tuple[int, int]]] = None,
        in_channels: int = 3,
        out_channels: int = 3,
        center_input_sample: bool = False,
        time_embedding_type: str = "positional",
        freq_shift: int = 0,
        flip_sin_to_cos: bool = True,
        down_block_types: Tuple[str, ...] = ("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D"),
        up_block_types: Tuple[str, ...] = ("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D"),
        block_out_channels: Tuple[int, ...] = (224, 448, 672, 896),
        layers_per_block: int = 2,
        mid_block_scale_factor: float = 1,
        downsample_padding: int = 1,
        downsample_type: str = "conv",
        upsample_type: str = "conv",
        dropout: float = 0.0,
        act_fn: str = "silu",
        attention_head_dim: Optional[int] = 8,
        norm_num_groups: int = 32,
        attn_norm_num_groups: Optional[int] = None,
        norm_eps: float = 1e-5,
        resnet_time_scale_shift: str = "default",
        add_attention: bool = True,
        class_embed_type: Optional[str] = None,
        num_class_embeds: Optional[int] = None,
        num_train_timesteps: Optional[int] = None,
        set_W_to_weight: Optional[bool] = True,
    ):
        super().__init__()

        self.sample_size = sample_size
        time_embed_dim = block_out_channels[0] * 4

        # Check inputs
        if len(down_block_types) != len(up_block_types):
            raise ValueError(
                f"Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}."
            )

        if len(block_out_channels) != len(down_block_types):
            raise ValueError(
                f"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}."
            )

        # input
        self.conv_in = nn.Conv2d(in_channels, block_out_channels[0], kernel_size=3, padding=(1, 1))

        # time
        if time_embedding_type == "fourier":
            self.time_proj = GaussianFourierProjection(embedding_size=block_out_channels[0], scale=16, set_W_to_weight=set_W_to_weight)
            timestep_input_dim = 2 * block_out_channels[0]
        elif time_embedding_type == "positional":
            self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift)
            timestep_input_dim = block_out_channels[0]
        elif time_embedding_type == "learned":
            self.time_proj = nn.Embedding(num_train_timesteps, block_out_channels[0])
            timestep_input_dim = block_out_channels[0]

        self.time_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim)

        # class embedding
        if class_embed_type is None and num_class_embeds is not None:
            self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim)
        elif class_embed_type == "timestep":
            self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim)
        elif class_embed_type == "identity":
            self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim)
        else:
            self.class_embedding = None

        self.down_blocks = nn.ModuleList([])
        self.mid_block = None
        self.up_blocks = nn.ModuleList([])

        # down
        output_channel = block_out_channels[0]
        for i, down_block_type in enumerate(down_block_types):
            input_channel = output_channel
            output_channel = block_out_channels[i]
            is_final_block = i == len(block_out_channels) - 1

            down_block = get_down_block(
                down_block_type,
                num_layers=layers_per_block,
                in_channels=input_channel,
                out_channels=output_channel,
                temb_channels=time_embed_dim,
                add_downsample=not is_final_block,
                resnet_eps=norm_eps,
                resnet_act_fn=act_fn,
                resnet_groups=norm_num_groups,
                attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel,
                downsample_padding=downsample_padding,
                resnet_time_scale_shift=resnet_time_scale_shift,
                downsample_type=downsample_type,
                dropout=dropout,
            )
            self.down_blocks.append(down_block)

        # mid
        self.mid_block = UNetMidBlock2D(
            in_channels=block_out_channels[-1],
            temb_channels=time_embed_dim,
            dropout=dropout,
            resnet_eps=norm_eps,
            resnet_act_fn=act_fn,
            output_scale_factor=mid_block_scale_factor,
            resnet_time_scale_shift=resnet_time_scale_shift,
            attention_head_dim=attention_head_dim if attention_head_dim is not None else block_out_channels[-1],
            resnet_groups=norm_num_groups,
            attn_groups=attn_norm_num_groups,
            add_attention=add_attention,
        )

        # up
        reversed_block_out_channels = list(reversed(block_out_channels))
        output_channel = reversed_block_out_channels[0]
        for i, up_block_type in enumerate(up_block_types):
            prev_output_channel = output_channel
            output_channel = reversed_block_out_channels[i]
            input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)]

            is_final_block = i == len(block_out_channels) - 1

            up_block = get_up_block(
                up_block_type,
                num_layers=layers_per_block + 1,
                in_channels=input_channel,
                out_channels=output_channel,
                prev_output_channel=prev_output_channel,
                temb_channels=time_embed_dim,
                add_upsample=not is_final_block,
                resnet_eps=norm_eps,
                resnet_act_fn=act_fn,
                resnet_groups=norm_num_groups,
                attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel,
                resnet_time_scale_shift=resnet_time_scale_shift,
                upsample_type=upsample_type,
                dropout=dropout,
            )
            self.up_blocks.append(up_block)
            prev_output_channel = output_channel

        # out
        num_groups_out = norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4, 32)
        self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[0], num_groups=num_groups_out, eps=norm_eps)
        self.conv_act = nn.SiLU()
        self.conv_out = nn.Conv2d(block_out_channels[0], out_channels, kernel_size=3, padding=1)

    def forward(
        self,
        sample: torch.FloatTensor,
        timestep: Union[torch.Tensor, float, int],
        class_labels: Optional[torch.Tensor] = None,
        return_dict: bool = True,
    ) -> Union[UNet2DOutput, Tuple]:
        r"""
        The [`UNet2DModel`] forward method.
        Args:
            sample (`torch.FloatTensor`):
                The noisy input tensor with the following shape `(batch, channel, height, width)`.
            timestep (`torch.FloatTensor` or `float` or `int`): The number of timesteps to denoise an input.
            class_labels (`torch.FloatTensor`, *optional*, defaults to `None`):
                Optional class labels for conditioning. Their embeddings will be summed with the timestep embeddings.
            return_dict (`bool`, *optional*, defaults to `True`):
                Whether or not to return a [`~models.unet_2d.UNet2DOutput`] instead of a plain tuple.
        Returns:
            [`~models.unet_2d.UNet2DOutput`] or `tuple`:
                If `return_dict` is True, an [`~models.unet_2d.UNet2DOutput`] is returned, otherwise a `tuple` is
                returned where the first element is the sample tensor.
        """
        # 0. center input if necessary
        if self.config.center_input_sample:
            sample = 2 * sample - 1.0

        # 1. time
        timesteps = timestep
        if not torch.is_tensor(timesteps):
            timesteps = torch.tensor([timesteps], dtype=torch.long, device=sample.device)
        elif torch.is_tensor(timesteps) and len(timesteps.shape) == 0:
            timesteps = timesteps[None].to(sample.device)

        # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
        timesteps = timesteps * torch.ones(sample.shape[0], dtype=timesteps.dtype, device=timesteps.device)

        t_emb = self.time_proj(timesteps)

        # timesteps does not contain any weights and will always return f32 tensors
        # but time_embedding might actually be running in fp16. so we need to cast here.
        # there might be better ways to encapsulate this.
        t_emb = t_emb.to(dtype=self.dtype)
        emb = self.time_embedding(t_emb)

        if self.class_embedding is not None:
            if class_labels is None:
                raise ValueError("class_labels should be provided when doing class conditioning")

            if self.config.class_embed_type == "timestep":
                class_labels = self.time_proj(class_labels)

            class_emb = self.class_embedding(class_labels).to(dtype=self.dtype)
            emb = emb + class_emb
        elif self.class_embedding is None and class_labels is not None:
            raise ValueError("class_embedding needs to be initialized in order to use class conditioning")

        # 2. pre-process
        skip_sample = sample
        sample = self.conv_in(sample)

        # 3. down
        down_block_res_samples = (sample,)
        for downsample_block in self.down_blocks:
            if hasattr(downsample_block, "skip_conv"):
                sample, res_samples, skip_sample = downsample_block(
                    hidden_states=sample, temb=emb, skip_sample=skip_sample
                )
            else:
                sample, res_samples = downsample_block(hidden_states=sample, temb=emb)

            down_block_res_samples += res_samples

        # 4. mid
        sample = self.mid_block(sample, emb)

        # 5. up
        skip_sample = None
        for upsample_block in self.up_blocks:
            res_samples = down_block_res_samples[-len(upsample_block.resnets) :]
            down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)]

            if hasattr(upsample_block, "skip_conv"):
                sample, skip_sample = upsample_block(sample, res_samples, emb, skip_sample)
            else:
                sample = upsample_block(sample, res_samples, emb)

        # 6. post-process
        sample = self.conv_norm_out(sample)
        sample = self.conv_act(sample)
        sample = self.conv_out(sample)

        if skip_sample is not None:
            sample += skip_sample

        if self.config.time_embedding_type == "fourier":
            timesteps = timesteps.reshape((sample.shape[0], *([1] * len(sample.shape[1:]))))
            sample = sample / timesteps

        if not return_dict:
            return (sample,)

        return UNet2DOutput(sample=sample)

import math

from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.utils import BaseOutput
from diffusers.utils.torch_utils import randn_tensor
from diffusers.schedulers.scheduling_utils import SchedulerMixin, SchedulerOutput

@dataclass
class SdeVeOutput(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.
        prev_sample_mean (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
            Mean averaged `prev_sample` over previous timesteps.
    """

    prev_sample: torch.FloatTensor
    prev_sample_mean: torch.FloatTensor


class ScoreSdeVeScheduler(SchedulerMixin, ConfigMixin):
    """
    `ScoreSdeVeScheduler` is a variance exploding stochastic differential equation (SDE) scheduler.
    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:
        num_train_timesteps (`int`, defaults to 1000):
            The number of diffusion steps to train the model.
        snr (`float`, defaults to 0.15):
            A coefficient weighting the step from the `model_output` sample (from the network) to the random noise.
        sigma_min (`float`, defaults to 0.01):
            The initial noise scale for the sigma sequence in the sampling procedure. The minimum sigma should mirror
            the distribution of the data.
        sigma_max (`float`, defaults to 1348.0):
            The maximum value used for the range of continuous timesteps passed into the model.
        sampling_eps (`float`, defaults to 1e-5):
            The end value of sampling where timesteps decrease progressively from 1 to epsilon.
        correct_steps (`int`, defaults to 1):
            The number of correction steps performed on a produced sample.
    """

    order = 1

    @register_to_config
    def __init__(
        self,
        num_train_timesteps: int = 2000,
        snr: float = 0.15,
        sigma_min: float = 0.01,
        sigma_max: float = 1348.0,
        sampling_eps: float = 1e-5,
        correct_steps: int = 1,
    ):
        # standard deviation of the initial noise distribution
        self.init_noise_sigma = sigma_max

        # setable values
        self.timesteps = None

        self.set_sigmas(num_train_timesteps, sigma_min, sigma_max, sampling_eps)

    def scale_model_input(self, sample: torch.FloatTensor, timestep: Optional[int] = None) -> torch.FloatTensor:
        """
        Ensures interchangeability with schedulers that need to scale the denoising model input depending on the
        current timestep.
        Args:
            sample (`torch.FloatTensor`):
                The input sample.
            timestep (`int`, *optional*):
                The current timestep in the diffusion chain.
        Returns:
            `torch.FloatTensor`:
                A scaled input sample.
        """
        return sample

    def set_timesteps(
        self, num_inference_steps: int, sampling_eps: float = None, device: Union[str, torch.device] = None
    ):
        """
        Sets the continuous 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.
            sampling_eps (`float`, *optional*):
                The final timestep value (overrides value given during scheduler instantiation).
            device (`str` or `torch.device`, *optional*):
                The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
        """
        sampling_eps = sampling_eps if sampling_eps is not None else self.config.sampling_eps

        self.timesteps = torch.linspace(1, sampling_eps, num_inference_steps, device=device)

    def set_sigmas(
        self, num_inference_steps: int, sigma_min: float = None, sigma_max: float = None, sampling_eps: float = None
    ):
        """
        Sets the noise scales used for the diffusion chain (to be run before inference). The sigmas control the weight
        of the `drift` and `diffusion` components of the sample update.
        Args:
            num_inference_steps (`int`):
                The number of diffusion steps used when generating samples with a pre-trained model.
            sigma_min (`float`, optional):
                The initial noise scale value (overrides value given during scheduler instantiation).
            sigma_max (`float`, optional):
                The final noise scale value (overrides value given during scheduler instantiation).
            sampling_eps (`float`, optional):
                The final timestep value (overrides value given during scheduler instantiation).
        """
        sigma_min = sigma_min if sigma_min is not None else self.config.sigma_min
        sigma_max = sigma_max if sigma_max is not None else self.config.sigma_max
        sampling_eps = sampling_eps if sampling_eps is not None else self.config.sampling_eps
        if self.timesteps is None:
            self.set_timesteps(num_inference_steps, sampling_eps)

        self.sigmas = sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
        self.discrete_sigmas = torch.exp(torch.linspace(math.log(sigma_min), math.log(sigma_max), num_inference_steps))
        self.sigmas = torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps])

    def get_adjacent_sigma(self, timesteps, t):
        return torch.where(
            timesteps == 0,
            torch.zeros_like(t.to(timesteps.device)),
            self.discrete_sigmas[timesteps - 1].to(timesteps.device),
        )

    def step_pred(
        self,
        model_output: torch.FloatTensor,
        timestep: int,
        sample: torch.FloatTensor,
        generator: Optional[torch.Generator] = None,
        return_dict: bool = True,
    ) -> Union[SdeVeOutput, 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 (`int`):
                The current discrete timestep in the diffusion chain.
            sample (`torch.FloatTensor`):
                A current instance of a sample created by the diffusion process.
            generator (`torch.Generator`, *optional*):
                A random number generator.
            return_dict (`bool`, *optional*, defaults to `True`):
                Whether or not to return a [`~schedulers.scheduling_sde_ve.SdeVeOutput`] or `tuple`.
        Returns:
            [`~schedulers.scheduling_sde_ve.SdeVeOutput`] or `tuple`:
                If return_dict is `True`, [`~schedulers.scheduling_sde_ve.SdeVeOutput`] is returned, otherwise a tuple
                is returned where the first element is the sample tensor.
        """
        if self.timesteps is None:
            raise ValueError(
                "`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"
            )

        timestep = timestep * torch.ones(
            sample.shape[0], device=sample.device
        )  # torch.repeat_interleave(timestep, sample.shape[0])
        timesteps = (timestep * (len(self.timesteps) - 1)).long()

        # mps requires indices to be in the same device, so we use cpu as is the default with cuda
        timesteps = timesteps.to(self.discrete_sigmas.device)

        sigma = self.discrete_sigmas[timesteps].to(sample.device)
        adjacent_sigma = self.get_adjacent_sigma(timesteps, timestep).to(sample.device)
        drift = torch.zeros_like(sample)
        diffusion = (sigma**2 - adjacent_sigma**2) ** 0.5

        # equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
        # also equation 47 shows the analog from SDE models to ancestral sampling methods
        diffusion = diffusion.flatten()
        while len(diffusion.shape) < len(sample.shape):
            diffusion = diffusion.unsqueeze(-1)
        drift = drift - diffusion**2 * model_output

        #  equation 6: sample noise for the diffusion term of
        noise = randn_tensor(
            sample.shape, layout=sample.layout, generator=generator, device=sample.device, dtype=sample.dtype
        )
        prev_sample_mean = sample - drift  # subtract because `dt` is a small negative timestep
        # TODO is the variable diffusion the correct scaling term for the noise?
        prev_sample = prev_sample_mean + diffusion * noise  # add impact of diffusion field g

        if not return_dict:
            return (prev_sample, prev_sample_mean)

        return SdeVeOutput(prev_sample=prev_sample, prev_sample_mean=prev_sample_mean)

    def step_correct(
        self,
        model_output: torch.FloatTensor,
        sample: torch.FloatTensor,
        generator: Optional[torch.Generator] = None,
        return_dict: bool = True,
    ) -> Union[SchedulerOutput, Tuple]:
        """
        Correct the predicted sample based on the `model_output` of the network. This is often run repeatedly after
        making the prediction for the previous timestep.
        Args:
            model_output (`torch.FloatTensor`):
                The direct output from learned diffusion model.
            sample (`torch.FloatTensor`):
                A current instance of a sample created by the diffusion process.
            generator (`torch.Generator`, *optional*):
                A random number generator.
            return_dict (`bool`, *optional*, defaults to `True`):
                Whether or not to return a [`~schedulers.scheduling_sde_ve.SdeVeOutput`] or `tuple`.
        Returns:
            [`~schedulers.scheduling_sde_ve.SdeVeOutput`] or `tuple`:
                If return_dict is `True`, [`~schedulers.scheduling_sde_ve.SdeVeOutput`] is returned, otherwise a tuple
                is returned where the first element is the sample tensor.
        """
        if self.timesteps is None:
            raise ValueError(
                "`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"
            )

        # For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
        # sample noise for correction
        noise = randn_tensor(sample.shape, layout=sample.layout, generator=generator, device=sample.device).to(sample.device)

        # compute step size from the model_output, the noise, and the snr
        grad_norm = torch.norm(model_output.reshape(model_output.shape[0], -1), dim=-1).mean()
        noise_norm = torch.norm(noise.reshape(noise.shape[0], -1), dim=-1).mean()
        step_size = (self.config.snr * noise_norm / grad_norm) ** 2 * 2
        step_size = step_size * torch.ones(sample.shape[0]).to(sample.device)
        # self.repeat_scalar(step_size, sample.shape[0])

        # compute corrected sample: model_output term and noise term
        step_size = step_size.flatten()
        while len(step_size.shape) < len(sample.shape):
            step_size = step_size.unsqueeze(-1)
        prev_sample_mean = sample + step_size * model_output
        prev_sample = prev_sample_mean + ((step_size * 2) ** 0.5) * noise

        if not return_dict:
            return (prev_sample,)

        return SchedulerOutput(prev_sample=prev_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
        timesteps = timesteps.to(original_samples.device)
        sigmas = self.config.sigma_min * (self.config.sigma_max / self.config.sigma_min) ** timesteps
        noise = (
            noise * sigmas[:, None, None, None]
            if noise is not None
            else torch.randn_like(original_samples) * sigmas[:, None, None, None]
        )
        noisy_samples = noise + original_samples
        return noisy_samples

    def __len__(self):
        return self.config.num_train_timesteps

from diffusers.utils.torch_utils import randn_tensor
from diffusers.pipelines.pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class ScoreSdeVePipeline(DiffusionPipeline):
    r"""
    Pipeline for unconditional image generation.
    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
    implemented for all pipelines (downloading, saving, running on a particular device, etc.).
    Parameters:
        unet ([`UNet2DModel`]):
            A `UNet2DModel` to denoise the encoded image.
        scheduler ([`ScoreSdeVeScheduler`]):
            A `ScoreSdeVeScheduler` to be used in combination with `unet` to denoise the encoded image.
    """

    unet: UNet2DModel
    scheduler: ScoreSdeVeScheduler

    def __init__(self, unet, scheduler):
        super().__init__()
        self.register_modules(unet=unet, scheduler=scheduler)

    @torch.no_grad()
    def __call__(
        self,
        batch_size: int = 1,
        num_inference_steps: int = 2000,
        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
        output_type: Optional[str] = "pil",
        return_dict: bool = True,
        **kwargs,
    ) -> Union[ImagePipelineOutput, Tuple]:
        r"""
        The call function to the pipeline for generation.
        Args:
            batch_size (`int`, *optional*, defaults to 1):
                The number of images to generate.
            generator (`torch.Generator`, `optional`):
                A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
                generation deterministic.
            output_type (`str`, `optional`, defaults to `"pil"`):
                The output format of the generated image. Choose between `PIL.Image` or `np.array`.
            return_dict (`bool`, *optional*, defaults to `True`):
                Whether or not to return a [`ImagePipelineOutput`] instead of a plain tuple.
        Returns:
            [`~pipelines.ImagePipelineOutput`] or `tuple`:
                If `return_dict` is `True`, [`~pipelines.ImagePipelineOutput`] is returned, otherwise a `tuple` is
                returned where the first element is a list with the generated images.
        """
        img_size = self.unet.config.sample_size
        shape = (batch_size, 3, img_size, img_size)

        model = self.unet

        sample = randn_tensor(shape, generator=generator, device=self.device) * self.scheduler.init_noise_sigma
        sample = sample.to(self.device)

        self.scheduler.set_timesteps(num_inference_steps)
        self.scheduler.set_sigmas(num_inference_steps)

        for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)):
            sigma_t = self.scheduler.sigmas[i] * torch.ones(shape[0], device=self.device)

            # correction step
            for _ in range(self.scheduler.config.correct_steps):
                model_output = self.unet(sample, sigma_t).sample
                sample = self.scheduler.step_correct(model_output, sample, generator=generator).prev_sample

            # prediction step
            model_output = model(sample, sigma_t).sample
            output = self.scheduler.step_pred(model_output, t, sample, generator=generator)

            sample, sample_mean = output.prev_sample, output.prev_sample_mean

        sample = sample_mean.clamp(0, 1)
        sample = sample.cpu().permute(0, 2, 3, 1).numpy()
        if output_type == "pil":
            sample = self.numpy_to_pil(sample)

        if not return_dict:
            return (sample,)
        return ImagePipelineOutput(images=sample)