Update pipeline.py

pipeline.py

@@ -1,605 +1,3 @@
-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