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from dataclasses import dataclass |
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from typing import Optional, Tuple, Union |
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import torch |
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import torch.nn as nn |
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from ...configuration_utils import ConfigMixin, register_to_config |
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from ...utils import BaseOutput |
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from ..embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps |
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from ..modeling_utils import ModelMixin |
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from .unet_2d_blocks import UNetMidBlock2D, get_down_block, get_up_block |
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@dataclass |
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class UNet2DOutput(BaseOutput): |
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""" |
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The output of [`UNet2DModel`]. |
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Args: |
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sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)`): |
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The hidden states output from the last layer of the model. |
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""" |
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sample: torch.Tensor |
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class UNet2DModel(ModelMixin, ConfigMixin): |
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r""" |
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A 2D UNet model that takes a noisy sample and a timestep and returns a sample shaped output. |
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This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented |
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for all models (such as downloading or saving). |
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Parameters: |
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sample_size (`int` or `Tuple[int, int]`, *optional*, defaults to `None`): |
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Height and width of input/output sample. Dimensions must be a multiple of `2 ** (len(block_out_channels) - |
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1)`. |
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in_channels (`int`, *optional*, defaults to 3): Number of channels in the input sample. |
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out_channels (`int`, *optional*, defaults to 3): Number of channels in the output. |
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center_input_sample (`bool`, *optional*, defaults to `False`): Whether to center the input sample. |
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time_embedding_type (`str`, *optional*, defaults to `"positional"`): Type of time embedding to use. |
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freq_shift (`int`, *optional*, defaults to 0): Frequency shift for Fourier time embedding. |
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flip_sin_to_cos (`bool`, *optional*, defaults to `True`): |
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Whether to flip sin to cos for Fourier time embedding. |
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down_block_types (`Tuple[str]`, *optional*, defaults to `("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D")`): |
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Tuple of downsample block types. |
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mid_block_type (`str`, *optional*, defaults to `"UNetMidBlock2D"`): |
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Block type for middle of UNet, it can be either `UNetMidBlock2D` or `UnCLIPUNetMidBlock2D`. |
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up_block_types (`Tuple[str]`, *optional*, defaults to `("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D")`): |
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Tuple of upsample block types. |
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block_out_channels (`Tuple[int]`, *optional*, defaults to `(224, 448, 672, 896)`): |
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Tuple of block output channels. |
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layers_per_block (`int`, *optional*, defaults to `2`): The number of layers per block. |
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mid_block_scale_factor (`float`, *optional*, defaults to `1`): The scale factor for the mid block. |
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downsample_padding (`int`, *optional*, defaults to `1`): The padding for the downsample convolution. |
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downsample_type (`str`, *optional*, defaults to `conv`): |
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The downsample type for downsampling layers. Choose between "conv" and "resnet" |
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upsample_type (`str`, *optional*, defaults to `conv`): |
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The upsample type for upsampling layers. Choose between "conv" and "resnet" |
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dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use. |
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act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use. |
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attention_head_dim (`int`, *optional*, defaults to `8`): The attention head dimension. |
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norm_num_groups (`int`, *optional*, defaults to `32`): The number of groups for normalization. |
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attn_norm_num_groups (`int`, *optional*, defaults to `None`): |
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If set to an integer, a group norm layer will be created in the mid block's [`Attention`] layer with the |
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given number of groups. If left as `None`, the group norm layer will only be created if |
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`resnet_time_scale_shift` is set to `default`, and if created will have `norm_num_groups` groups. |
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norm_eps (`float`, *optional*, defaults to `1e-5`): The epsilon for normalization. |
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resnet_time_scale_shift (`str`, *optional*, defaults to `"default"`): Time scale shift config |
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for ResNet blocks (see [`~models.resnet.ResnetBlock2D`]). Choose from `default` or `scale_shift`. |
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class_embed_type (`str`, *optional*, defaults to `None`): |
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The type of class embedding to use which is ultimately summed with the time embeddings. Choose from `None`, |
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`"timestep"`, or `"identity"`. |
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num_class_embeds (`int`, *optional*, defaults to `None`): |
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Input dimension of the learnable embedding matrix to be projected to `time_embed_dim` when performing class |
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conditioning with `class_embed_type` equal to `None`. |
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""" |
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@register_to_config |
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def __init__( |
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self, |
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sample_size: Optional[Union[int, Tuple[int, int]]] = None, |
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in_channels: int = 3, |
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out_channels: int = 3, |
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center_input_sample: bool = False, |
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time_embedding_type: str = "positional", |
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freq_shift: int = 0, |
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flip_sin_to_cos: bool = True, |
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down_block_types: Tuple[str, ...] = ("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D"), |
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up_block_types: Tuple[str, ...] = ("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D"), |
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block_out_channels: Tuple[int, ...] = (224, 448, 672, 896), |
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layers_per_block: int = 2, |
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mid_block_scale_factor: float = 1, |
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downsample_padding: int = 1, |
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downsample_type: str = "conv", |
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upsample_type: str = "conv", |
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dropout: float = 0.0, |
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act_fn: str = "silu", |
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attention_head_dim: Optional[int] = 8, |
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norm_num_groups: int = 32, |
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attn_norm_num_groups: Optional[int] = None, |
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norm_eps: float = 1e-5, |
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resnet_time_scale_shift: str = "default", |
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add_attention: bool = True, |
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class_embed_type: Optional[str] = None, |
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num_class_embeds: Optional[int] = None, |
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num_train_timesteps: Optional[int] = None, |
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): |
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super().__init__() |
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self.sample_size = sample_size |
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time_embed_dim = block_out_channels[0] * 4 |
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if len(down_block_types) != len(up_block_types): |
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raise ValueError( |
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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}." |
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) |
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if len(block_out_channels) != len(down_block_types): |
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raise ValueError( |
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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}." |
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) |
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self.conv_in = nn.Conv2d(in_channels, block_out_channels[0], kernel_size=3, padding=(1, 1)) |
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if time_embedding_type == "fourier": |
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self.time_proj = GaussianFourierProjection(embedding_size=block_out_channels[0], scale=16) |
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timestep_input_dim = 2 * block_out_channels[0] |
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elif time_embedding_type == "positional": |
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self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift) |
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timestep_input_dim = block_out_channels[0] |
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elif time_embedding_type == "learned": |
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self.time_proj = nn.Embedding(num_train_timesteps, block_out_channels[0]) |
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timestep_input_dim = block_out_channels[0] |
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self.time_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim) |
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if class_embed_type is None and num_class_embeds is not None: |
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self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim) |
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elif class_embed_type == "timestep": |
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self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim) |
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elif class_embed_type == "identity": |
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self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim) |
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else: |
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self.class_embedding = None |
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self.down_blocks = nn.ModuleList([]) |
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self.mid_block = None |
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self.up_blocks = nn.ModuleList([]) |
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output_channel = block_out_channels[0] |
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for i, down_block_type in enumerate(down_block_types): |
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input_channel = output_channel |
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output_channel = block_out_channels[i] |
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is_final_block = i == len(block_out_channels) - 1 |
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down_block = get_down_block( |
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down_block_type, |
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num_layers=layers_per_block, |
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in_channels=input_channel, |
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out_channels=output_channel, |
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temb_channels=time_embed_dim, |
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add_downsample=not is_final_block, |
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resnet_eps=norm_eps, |
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resnet_act_fn=act_fn, |
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resnet_groups=norm_num_groups, |
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attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel, |
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downsample_padding=downsample_padding, |
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resnet_time_scale_shift=resnet_time_scale_shift, |
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downsample_type=downsample_type, |
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dropout=dropout, |
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) |
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self.down_blocks.append(down_block) |
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self.mid_block = UNetMidBlock2D( |
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in_channels=block_out_channels[-1], |
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temb_channels=time_embed_dim, |
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dropout=dropout, |
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resnet_eps=norm_eps, |
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resnet_act_fn=act_fn, |
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output_scale_factor=mid_block_scale_factor, |
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resnet_time_scale_shift=resnet_time_scale_shift, |
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attention_head_dim=attention_head_dim if attention_head_dim is not None else block_out_channels[-1], |
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resnet_groups=norm_num_groups, |
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attn_groups=attn_norm_num_groups, |
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add_attention=add_attention, |
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) |
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reversed_block_out_channels = list(reversed(block_out_channels)) |
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output_channel = reversed_block_out_channels[0] |
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for i, up_block_type in enumerate(up_block_types): |
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prev_output_channel = output_channel |
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output_channel = reversed_block_out_channels[i] |
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input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)] |
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is_final_block = i == len(block_out_channels) - 1 |
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up_block = get_up_block( |
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up_block_type, |
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num_layers=layers_per_block + 1, |
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in_channels=input_channel, |
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out_channels=output_channel, |
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prev_output_channel=prev_output_channel, |
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temb_channels=time_embed_dim, |
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add_upsample=not is_final_block, |
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resnet_eps=norm_eps, |
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resnet_act_fn=act_fn, |
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resnet_groups=norm_num_groups, |
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attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel, |
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resnet_time_scale_shift=resnet_time_scale_shift, |
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upsample_type=upsample_type, |
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dropout=dropout, |
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) |
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self.up_blocks.append(up_block) |
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prev_output_channel = output_channel |
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num_groups_out = norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4, 32) |
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self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[0], num_groups=num_groups_out, eps=norm_eps) |
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self.conv_act = nn.SiLU() |
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self.conv_out = nn.Conv2d(block_out_channels[0], out_channels, kernel_size=3, padding=1) |
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def forward( |
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self, |
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sample: torch.Tensor, |
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timestep: Union[torch.Tensor, float, int], |
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class_labels: Optional[torch.Tensor] = None, |
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return_dict: bool = True, |
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) -> Union[UNet2DOutput, Tuple]: |
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r""" |
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The [`UNet2DModel`] forward method. |
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Args: |
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sample (`torch.Tensor`): |
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The noisy input tensor with the following shape `(batch, channel, height, width)`. |
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timestep (`torch.Tensor` or `float` or `int`): The number of timesteps to denoise an input. |
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class_labels (`torch.Tensor`, *optional*, defaults to `None`): |
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Optional class labels for conditioning. Their embeddings will be summed with the timestep embeddings. |
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return_dict (`bool`, *optional*, defaults to `True`): |
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Whether or not to return a [`~models.unets.unet_2d.UNet2DOutput`] instead of a plain tuple. |
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Returns: |
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[`~models.unets.unet_2d.UNet2DOutput`] or `tuple`: |
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If `return_dict` is True, an [`~models.unets.unet_2d.UNet2DOutput`] is returned, otherwise a `tuple` is |
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returned where the first element is the sample tensor. |
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""" |
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if self.config.center_input_sample: |
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sample = 2 * sample - 1.0 |
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timesteps = timestep |
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if not torch.is_tensor(timesteps): |
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timesteps = torch.tensor([timesteps], dtype=torch.long, device=sample.device) |
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elif torch.is_tensor(timesteps) and len(timesteps.shape) == 0: |
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timesteps = timesteps[None].to(sample.device) |
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timesteps = timesteps * torch.ones(sample.shape[0], dtype=timesteps.dtype, device=timesteps.device) |
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t_emb = self.time_proj(timesteps) |
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t_emb = t_emb.to(dtype=self.dtype) |
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emb = self.time_embedding(t_emb) |
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if self.class_embedding is not None: |
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if class_labels is None: |
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raise ValueError("class_labels should be provided when doing class conditioning") |
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if self.config.class_embed_type == "timestep": |
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class_labels = self.time_proj(class_labels) |
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class_emb = self.class_embedding(class_labels).to(dtype=self.dtype) |
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emb = emb + class_emb |
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elif self.class_embedding is None and class_labels is not None: |
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raise ValueError("class_embedding needs to be initialized in order to use class conditioning") |
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skip_sample = sample |
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sample = self.conv_in(sample) |
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down_block_res_samples = (sample,) |
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for downsample_block in self.down_blocks: |
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if hasattr(downsample_block, "skip_conv"): |
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sample, res_samples, skip_sample = downsample_block( |
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hidden_states=sample, temb=emb, skip_sample=skip_sample |
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) |
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else: |
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sample, res_samples = downsample_block(hidden_states=sample, temb=emb) |
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down_block_res_samples += res_samples |
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sample = self.mid_block(sample, emb) |
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skip_sample = None |
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for upsample_block in self.up_blocks: |
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res_samples = down_block_res_samples[-len(upsample_block.resnets) :] |
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down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)] |
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if hasattr(upsample_block, "skip_conv"): |
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sample, skip_sample = upsample_block(sample, res_samples, emb, skip_sample) |
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else: |
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sample = upsample_block(sample, res_samples, emb) |
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sample = self.conv_norm_out(sample) |
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sample = self.conv_act(sample) |
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sample = self.conv_out(sample) |
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if skip_sample is not None: |
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sample += skip_sample |
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if self.config.time_embedding_type == "fourier": |
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timesteps = timesteps.reshape((sample.shape[0], *([1] * len(sample.shape[1:])))) |
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sample = sample / timesteps |
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if not return_dict: |
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return (sample,) |
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return UNet2DOutput(sample=sample) |
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