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)