| import logging |
| import math |
| from abc import abstractmethod |
| from typing import Iterable, List, Optional, Tuple, Union |
|
|
| import torch as th |
| import torch.nn as nn |
| import torch.nn.functional as F |
| from einops import rearrange |
| from torch.utils.checkpoint import checkpoint |
|
|
| from ...modules.attention import SpatialTransformer |
| from ...modules.diffusionmodules.util import (avg_pool_nd, conv_nd, linear, |
| normalization, |
| timestep_embedding, zero_module) |
| from ...modules.video_attention import SpatialVideoTransformer |
| from ...util import exists |
|
|
| logpy = logging.getLogger(__name__) |
|
|
|
|
| class AttentionPool2d(nn.Module): |
| """ |
| Adapted from CLIP: https://github.com/openai/CLIP/blob/main/clip/model.py |
| """ |
|
|
| def __init__( |
| self, |
| spacial_dim: int, |
| embed_dim: int, |
| num_heads_channels: int, |
| output_dim: Optional[int] = None, |
| ): |
| super().__init__() |
| self.positional_embedding = nn.Parameter( |
| th.randn(embed_dim, spacial_dim**2 + 1) / embed_dim**0.5 |
| ) |
| self.qkv_proj = conv_nd(1, embed_dim, 3 * embed_dim, 1) |
| self.c_proj = conv_nd(1, embed_dim, output_dim or embed_dim, 1) |
| self.num_heads = embed_dim // num_heads_channels |
| self.attention = QKVAttention(self.num_heads) |
|
|
| def forward(self, x: th.Tensor) -> th.Tensor: |
| b, c, _ = x.shape |
| x = x.reshape(b, c, -1) |
| x = th.cat([x.mean(dim=-1, keepdim=True), x], dim=-1) |
| x = x + self.positional_embedding[None, :, :].to(x.dtype) |
| x = self.qkv_proj(x) |
| x = self.attention(x) |
| x = self.c_proj(x) |
| return x[:, :, 0] |
|
|
|
|
| class TimestepBlock(nn.Module): |
| """ |
| Any module where forward() takes timestep embeddings as a second argument. |
| """ |
|
|
| @abstractmethod |
| def forward(self, x: th.Tensor, emb: th.Tensor): |
| """ |
| Apply the module to `x` given `emb` timestep embeddings. |
| """ |
|
|
|
|
| class TimestepEmbedSequential(nn.Sequential, TimestepBlock): |
| """ |
| A sequential module that passes timestep embeddings to the children that |
| support it as an extra input. |
| """ |
|
|
| def forward( |
| self, |
| x: th.Tensor, |
| emb: th.Tensor, |
| context: Optional[th.Tensor] = None, |
| image_only_indicator: Optional[th.Tensor] = None, |
| time_context: Optional[int] = None, |
| num_video_frames: Optional[int] = None, |
| ): |
| from ...modules.diffusionmodules.video_model import VideoResBlock |
|
|
| for layer in self: |
| module = layer |
|
|
| if isinstance(module, TimestepBlock) and not isinstance( |
| module, VideoResBlock |
| ): |
| x = layer(x, emb) |
| elif isinstance(module, VideoResBlock): |
| x = layer(x, emb, num_video_frames, image_only_indicator) |
| elif isinstance(module, SpatialVideoTransformer): |
| x = layer( |
| x, |
| context, |
| time_context, |
| num_video_frames, |
| image_only_indicator, |
| ) |
| elif isinstance(module, SpatialTransformer): |
| x = layer(x, context) |
| else: |
| x = layer(x) |
| return x |
|
|
|
|
| class Upsample(nn.Module): |
| """ |
| An upsampling layer with an optional convolution. |
| :param channels: channels in the inputs and outputs. |
| :param use_conv: a bool determining if a convolution is applied. |
| :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then |
| upsampling occurs in the inner-two dimensions. |
| """ |
|
|
| def __init__( |
| self, |
| channels: int, |
| use_conv: bool, |
| dims: int = 2, |
| out_channels: Optional[int] = None, |
| padding: int = 1, |
| third_up: bool = False, |
| kernel_size: int = 3, |
| scale_factor: int = 2, |
| ): |
| super().__init__() |
| self.channels = channels |
| self.out_channels = out_channels or channels |
| self.use_conv = use_conv |
| self.dims = dims |
| self.third_up = third_up |
| self.scale_factor = scale_factor |
| if use_conv: |
| self.conv = conv_nd( |
| dims, self.channels, self.out_channels, kernel_size, padding=padding |
| ) |
|
|
| def forward(self, x: th.Tensor) -> th.Tensor: |
| assert x.shape[1] == self.channels |
|
|
| if self.dims == 3: |
| t_factor = 1 if not self.third_up else self.scale_factor |
| x = F.interpolate( |
| x, |
| ( |
| t_factor * x.shape[2], |
| x.shape[3] * self.scale_factor, |
| x.shape[4] * self.scale_factor, |
| ), |
| mode="nearest", |
| ) |
| else: |
| x = F.interpolate(x, scale_factor=self.scale_factor, mode="nearest") |
| if self.use_conv: |
| x = self.conv(x) |
| return x |
|
|
|
|
| class Downsample(nn.Module): |
| """ |
| A downsampling layer with an optional convolution. |
| :param channels: channels in the inputs and outputs. |
| :param use_conv: a bool determining if a convolution is applied. |
| :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then |
| downsampling occurs in the inner-two dimensions. |
| """ |
|
|
| def __init__( |
| self, |
| channels: int, |
| use_conv: bool, |
| dims: int = 2, |
| out_channels: Optional[int] = None, |
| padding: int = 1, |
| third_down: bool = False, |
| ): |
| super().__init__() |
| self.channels = channels |
| self.out_channels = out_channels or channels |
| self.use_conv = use_conv |
| self.dims = dims |
| stride = 2 if dims != 3 else ((1, 2, 2) if not third_down else (2, 2, 2)) |
| if use_conv: |
| logpy.info(f"Building a Downsample layer with {dims} dims.") |
| logpy.info( |
| f" --> settings are: \n in-chn: {self.channels}, out-chn: {self.out_channels}, " |
| f"kernel-size: 3, stride: {stride}, padding: {padding}" |
| ) |
| if dims == 3: |
| logpy.info(f" --> Downsampling third axis (time): {third_down}") |
| self.op = conv_nd( |
| dims, |
| self.channels, |
| self.out_channels, |
| 3, |
| stride=stride, |
| padding=padding, |
| ) |
| else: |
| assert self.channels == self.out_channels |
| self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride) |
|
|
| def forward(self, x: th.Tensor) -> th.Tensor: |
| assert x.shape[1] == self.channels |
|
|
| return self.op(x) |
|
|
|
|
| class ResBlock(TimestepBlock): |
| """ |
| A residual block that can optionally change the number of channels. |
| :param channels: the number of input channels. |
| :param emb_channels: the number of timestep embedding channels. |
| :param dropout: the rate of dropout. |
| :param out_channels: if specified, the number of out channels. |
| :param use_conv: if True and out_channels is specified, use a spatial |
| convolution instead of a smaller 1x1 convolution to change the |
| channels in the skip connection. |
| :param dims: determines if the signal is 1D, 2D, or 3D. |
| :param use_checkpoint: if True, use gradient checkpointing on this module. |
| :param up: if True, use this block for upsampling. |
| :param down: if True, use this block for downsampling. |
| """ |
|
|
| def __init__( |
| self, |
| channels: int, |
| emb_channels: int, |
| dropout: float, |
| out_channels: Optional[int] = None, |
| use_conv: bool = False, |
| use_scale_shift_norm: bool = False, |
| dims: int = 2, |
| use_checkpoint: bool = False, |
| up: bool = False, |
| down: bool = False, |
| kernel_size: int = 3, |
| exchange_temb_dims: bool = False, |
| skip_t_emb: bool = False, |
| ): |
| super().__init__() |
| self.channels = channels |
| self.emb_channels = emb_channels |
| self.dropout = dropout |
| self.out_channels = out_channels or channels |
| self.use_conv = use_conv |
| self.use_checkpoint = use_checkpoint |
| self.use_scale_shift_norm = use_scale_shift_norm |
| self.exchange_temb_dims = exchange_temb_dims |
|
|
| if isinstance(kernel_size, Iterable): |
| padding = [k // 2 for k in kernel_size] |
| else: |
| padding = kernel_size // 2 |
|
|
| self.in_layers = nn.Sequential( |
| normalization(channels), |
| nn.SiLU(), |
| conv_nd(dims, channels, self.out_channels, kernel_size, padding=padding), |
| ) |
|
|
| self.updown = up or down |
|
|
| if up: |
| self.h_upd = Upsample(channels, False, dims) |
| self.x_upd = Upsample(channels, False, dims) |
| elif down: |
| self.h_upd = Downsample(channels, False, dims) |
| self.x_upd = Downsample(channels, False, dims) |
| else: |
| self.h_upd = self.x_upd = nn.Identity() |
|
|
| self.skip_t_emb = skip_t_emb |
| self.emb_out_channels = ( |
| 2 * self.out_channels if use_scale_shift_norm else self.out_channels |
| ) |
| if self.skip_t_emb: |
| logpy.info(f"Skipping timestep embedding in {self.__class__.__name__}") |
| assert not self.use_scale_shift_norm |
| self.emb_layers = None |
| self.exchange_temb_dims = False |
| else: |
| self.emb_layers = nn.Sequential( |
| nn.SiLU(), |
| linear( |
| emb_channels, |
| self.emb_out_channels, |
| ), |
| ) |
|
|
| self.out_layers = nn.Sequential( |
| normalization(self.out_channels), |
| nn.SiLU(), |
| nn.Dropout(p=dropout), |
| zero_module( |
| conv_nd( |
| dims, |
| self.out_channels, |
| self.out_channels, |
| kernel_size, |
| padding=padding, |
| ) |
| ), |
| ) |
|
|
| if self.out_channels == channels: |
| self.skip_connection = nn.Identity() |
| elif use_conv: |
| self.skip_connection = conv_nd( |
| dims, channels, self.out_channels, kernel_size, padding=padding |
| ) |
| else: |
| self.skip_connection = conv_nd(dims, channels, self.out_channels, 1) |
|
|
| def forward(self, x: th.Tensor, emb: th.Tensor) -> th.Tensor: |
| """ |
| Apply the block to a Tensor, conditioned on a timestep embedding. |
| :param x: an [N x C x ...] Tensor of features. |
| :param emb: an [N x emb_channels] Tensor of timestep embeddings. |
| :return: an [N x C x ...] Tensor of outputs. |
| """ |
| if self.use_checkpoint: |
| return checkpoint(self._forward, x, emb) |
| else: |
| return self._forward(x, emb) |
|
|
| def _forward(self, x: th.Tensor, emb: th.Tensor) -> th.Tensor: |
| if self.updown: |
| in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1] |
| h = in_rest(x) |
| h = self.h_upd(h) |
| x = self.x_upd(x) |
| h = in_conv(h) |
| else: |
| h = self.in_layers(x) |
|
|
| if self.skip_t_emb: |
| emb_out = th.zeros_like(h) |
| else: |
| emb_out = self.emb_layers(emb).type(h.dtype) |
| while len(emb_out.shape) < len(h.shape): |
| emb_out = emb_out[..., None] |
| if self.use_scale_shift_norm: |
| out_norm, out_rest = self.out_layers[0], self.out_layers[1:] |
| scale, shift = th.chunk(emb_out, 2, dim=1) |
| h = out_norm(h) * (1 + scale) + shift |
| h = out_rest(h) |
| else: |
| if self.exchange_temb_dims: |
| emb_out = rearrange(emb_out, "b t c ... -> b c t ...") |
| h = h + emb_out |
| h = self.out_layers(h) |
| return self.skip_connection(x) + h |
|
|
|
|
| class AttentionBlock(nn.Module): |
| """ |
| An attention block that allows spatial positions to attend to each other. |
| Originally ported from here, but adapted to the N-d case. |
| https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/models/unet.py#L66. |
| """ |
|
|
| def __init__( |
| self, |
| channels: int, |
| num_heads: int = 1, |
| num_head_channels: int = -1, |
| use_checkpoint: bool = False, |
| use_new_attention_order: bool = False, |
| ): |
| super().__init__() |
| self.channels = channels |
| if num_head_channels == -1: |
| self.num_heads = num_heads |
| else: |
| assert ( |
| channels % num_head_channels == 0 |
| ), f"q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}" |
| self.num_heads = channels // num_head_channels |
| self.use_checkpoint = use_checkpoint |
| self.norm = normalization(channels) |
| self.qkv = conv_nd(1, channels, channels * 3, 1) |
| if use_new_attention_order: |
| |
| self.attention = QKVAttention(self.num_heads) |
| else: |
| |
| self.attention = QKVAttentionLegacy(self.num_heads) |
|
|
| self.proj_out = zero_module(conv_nd(1, channels, channels, 1)) |
|
|
| def forward(self, x: th.Tensor, **kwargs) -> th.Tensor: |
| return checkpoint(self._forward, x) |
|
|
| def _forward(self, x: th.Tensor) -> th.Tensor: |
| b, c, *spatial = x.shape |
| x = x.reshape(b, c, -1) |
| qkv = self.qkv(self.norm(x)) |
| h = self.attention(qkv) |
| h = self.proj_out(h) |
| return (x + h).reshape(b, c, *spatial) |
|
|
|
|
| class QKVAttentionLegacy(nn.Module): |
| """ |
| A module which performs QKV attention. Matches legacy QKVAttention + input/ouput heads shaping |
| """ |
|
|
| def __init__(self, n_heads: int): |
| super().__init__() |
| self.n_heads = n_heads |
|
|
| def forward(self, qkv: th.Tensor) -> th.Tensor: |
| """ |
| Apply QKV attention. |
| :param qkv: an [N x (H * 3 * C) x T] tensor of Qs, Ks, and Vs. |
| :return: an [N x (H * C) x T] tensor after attention. |
| """ |
| bs, width, length = qkv.shape |
| assert width % (3 * self.n_heads) == 0 |
| ch = width // (3 * self.n_heads) |
| q, k, v = qkv.reshape(bs * self.n_heads, ch * 3, length).split(ch, dim=1) |
| scale = 1 / math.sqrt(math.sqrt(ch)) |
| weight = th.einsum( |
| "bct,bcs->bts", q * scale, k * scale |
| ) |
| weight = th.softmax(weight.float(), dim=-1).type(weight.dtype) |
| a = th.einsum("bts,bcs->bct", weight, v) |
| return a.reshape(bs, -1, length) |
|
|
|
|
| class QKVAttention(nn.Module): |
| """ |
| A module which performs QKV attention and splits in a different order. |
| """ |
|
|
| def __init__(self, n_heads: int): |
| super().__init__() |
| self.n_heads = n_heads |
|
|
| def forward(self, qkv: th.Tensor) -> th.Tensor: |
| """ |
| Apply QKV attention. |
| :param qkv: an [N x (3 * H * C) x T] tensor of Qs, Ks, and Vs. |
| :return: an [N x (H * C) x T] tensor after attention. |
| """ |
| bs, width, length = qkv.shape |
| assert width % (3 * self.n_heads) == 0 |
| ch = width // (3 * self.n_heads) |
| q, k, v = qkv.chunk(3, dim=1) |
| scale = 1 / math.sqrt(math.sqrt(ch)) |
| weight = th.einsum( |
| "bct,bcs->bts", |
| (q * scale).view(bs * self.n_heads, ch, length), |
| (k * scale).view(bs * self.n_heads, ch, length), |
| ) |
| weight = th.softmax(weight.float(), dim=-1).type(weight.dtype) |
| a = th.einsum("bts,bcs->bct", weight, v.reshape(bs * self.n_heads, ch, length)) |
| return a.reshape(bs, -1, length) |
|
|
|
|
| class Timestep(nn.Module): |
| def __init__(self, dim: int): |
| super().__init__() |
| self.dim = dim |
|
|
| def forward(self, t: th.Tensor) -> th.Tensor: |
| return timestep_embedding(t, self.dim) |
|
|
|
|
| class UNetModel(nn.Module): |
| """ |
| The full UNet model with attention and timestep embedding. |
| :param in_channels: channels in the input Tensor. |
| :param model_channels: base channel count for the model. |
| :param out_channels: channels in the output Tensor. |
| :param num_res_blocks: number of residual blocks per downsample. |
| :param attention_resolutions: a collection of downsample rates at which |
| attention will take place. May be a set, list, or tuple. |
| For example, if this contains 4, then at 4x downsampling, attention |
| will be used. |
| :param dropout: the dropout probability. |
| :param channel_mult: channel multiplier for each level of the UNet. |
| :param conv_resample: if True, use learned convolutions for upsampling and |
| downsampling. |
| :param dims: determines if the signal is 1D, 2D, or 3D. |
| :param num_classes: if specified (as an int), then this model will be |
| class-conditional with `num_classes` classes. |
| :param use_checkpoint: use gradient checkpointing to reduce memory usage. |
| :param num_heads: the number of attention heads in each attention layer. |
| :param num_heads_channels: if specified, ignore num_heads and instead use |
| a fixed channel width per attention head. |
| :param num_heads_upsample: works with num_heads to set a different number |
| of heads for upsampling. Deprecated. |
| :param use_scale_shift_norm: use a FiLM-like conditioning mechanism. |
| :param resblock_updown: use residual blocks for up/downsampling. |
| :param use_new_attention_order: use a different attention pattern for potentially |
| increased efficiency. |
| """ |
|
|
| def __init__( |
| self, |
| in_channels: int, |
| model_channels: int, |
| out_channels: int, |
| num_res_blocks: int, |
| attention_resolutions: int, |
| dropout: float = 0.0, |
| channel_mult: Union[List, Tuple] = (1, 2, 4, 8), |
| conv_resample: bool = True, |
| dims: int = 2, |
| num_classes: Optional[Union[int, str]] = None, |
| use_checkpoint: bool = False, |
| num_heads: int = -1, |
| num_head_channels: int = -1, |
| num_heads_upsample: int = -1, |
| use_scale_shift_norm: bool = False, |
| resblock_updown: bool = False, |
| transformer_depth: int = 1, |
| context_dim: Optional[int] = None, |
| disable_self_attentions: Optional[List[bool]] = None, |
| num_attention_blocks: Optional[List[int]] = None, |
| disable_middle_self_attn: bool = False, |
| disable_middle_transformer: bool = False, |
| use_linear_in_transformer: bool = False, |
| spatial_transformer_attn_type: str = "softmax", |
| adm_in_channels: Optional[int] = None, |
| ): |
| super().__init__() |
|
|
| if num_heads_upsample == -1: |
| num_heads_upsample = num_heads |
|
|
| if num_heads == -1: |
| assert ( |
| num_head_channels != -1 |
| ), "Either num_heads or num_head_channels has to be set" |
|
|
| if num_head_channels == -1: |
| assert ( |
| num_heads != -1 |
| ), "Either num_heads or num_head_channels has to be set" |
|
|
| self.in_channels = in_channels |
| self.model_channels = model_channels |
| self.out_channels = out_channels |
| if isinstance(transformer_depth, int): |
| transformer_depth = len(channel_mult) * [transformer_depth] |
| transformer_depth_middle = transformer_depth[-1] |
|
|
| if isinstance(num_res_blocks, int): |
| self.num_res_blocks = len(channel_mult) * [num_res_blocks] |
| else: |
| if len(num_res_blocks) != len(channel_mult): |
| raise ValueError( |
| "provide num_res_blocks either as an int (globally constant) or " |
| "as a list/tuple (per-level) with the same length as channel_mult" |
| ) |
| self.num_res_blocks = num_res_blocks |
|
|
| if disable_self_attentions is not None: |
| assert len(disable_self_attentions) == len(channel_mult) |
| if num_attention_blocks is not None: |
| assert len(num_attention_blocks) == len(self.num_res_blocks) |
| assert all( |
| map( |
| lambda i: self.num_res_blocks[i] >= num_attention_blocks[i], |
| range(len(num_attention_blocks)), |
| ) |
| ) |
| logpy.info( |
| f"Constructor of UNetModel received num_attention_blocks={num_attention_blocks}. " |
| f"This option has LESS priority than attention_resolutions {attention_resolutions}, " |
| f"i.e., in cases where num_attention_blocks[i] > 0 but 2**i not in attention_resolutions, " |
| f"attention will still not be set." |
| ) |
|
|
| self.attention_resolutions = attention_resolutions |
| self.dropout = dropout |
| self.channel_mult = channel_mult |
| self.conv_resample = conv_resample |
| self.num_classes = num_classes |
| self.use_checkpoint = use_checkpoint |
| self.num_heads = num_heads |
| self.num_head_channels = num_head_channels |
| self.num_heads_upsample = num_heads_upsample |
|
|
| time_embed_dim = model_channels * 4 |
| self.time_embed = nn.Sequential( |
| linear(model_channels, time_embed_dim), |
| nn.SiLU(), |
| linear(time_embed_dim, time_embed_dim), |
| ) |
|
|
| if self.num_classes is not None: |
| if isinstance(self.num_classes, int): |
| self.label_emb = nn.Embedding(num_classes, time_embed_dim) |
| elif self.num_classes == "continuous": |
| logpy.info("setting up linear c_adm embedding layer") |
| self.label_emb = nn.Linear(1, time_embed_dim) |
| elif self.num_classes == "timestep": |
| self.label_emb = nn.Sequential( |
| Timestep(model_channels), |
| nn.Sequential( |
| linear(model_channels, time_embed_dim), |
| nn.SiLU(), |
| linear(time_embed_dim, time_embed_dim), |
| ), |
| ) |
| elif self.num_classes == "sequential": |
| assert adm_in_channels is not None |
| self.label_emb = nn.Sequential( |
| nn.Sequential( |
| linear(adm_in_channels, time_embed_dim), |
| nn.SiLU(), |
| linear(time_embed_dim, time_embed_dim), |
| ) |
| ) |
| else: |
| raise ValueError |
|
|
| self.input_blocks = nn.ModuleList( |
| [ |
| TimestepEmbedSequential( |
| conv_nd(dims, in_channels, model_channels, 3, padding=1) |
| ) |
| ] |
| ) |
| self._feature_size = model_channels |
| input_block_chans = [model_channels] |
| ch = model_channels |
| ds = 1 |
| for level, mult in enumerate(channel_mult): |
| for nr in range(self.num_res_blocks[level]): |
| layers = [ |
| ResBlock( |
| ch, |
| time_embed_dim, |
| dropout, |
| out_channels=mult * model_channels, |
| dims=dims, |
| use_checkpoint=use_checkpoint, |
| use_scale_shift_norm=use_scale_shift_norm, |
| ) |
| ] |
| ch = mult * model_channels |
| if ds in attention_resolutions: |
| if num_head_channels == -1: |
| dim_head = ch // num_heads |
| else: |
| num_heads = ch // num_head_channels |
| dim_head = num_head_channels |
|
|
| if context_dim is not None and exists(disable_self_attentions): |
| disabled_sa = disable_self_attentions[level] |
| else: |
| disabled_sa = False |
|
|
| if ( |
| not exists(num_attention_blocks) |
| or nr < num_attention_blocks[level] |
| ): |
| layers.append( |
| SpatialTransformer( |
| ch, |
| num_heads, |
| dim_head, |
| depth=transformer_depth[level], |
| context_dim=context_dim, |
| disable_self_attn=disabled_sa, |
| use_linear=use_linear_in_transformer, |
| attn_type=spatial_transformer_attn_type, |
| use_checkpoint=use_checkpoint, |
| ) |
| ) |
| self.input_blocks.append(TimestepEmbedSequential(*layers)) |
| self._feature_size += ch |
| input_block_chans.append(ch) |
| if level != len(channel_mult) - 1: |
| out_ch = ch |
| self.input_blocks.append( |
| TimestepEmbedSequential( |
| ResBlock( |
| ch, |
| time_embed_dim, |
| dropout, |
| out_channels=out_ch, |
| dims=dims, |
| use_checkpoint=use_checkpoint, |
| use_scale_shift_norm=use_scale_shift_norm, |
| down=True, |
| ) |
| if resblock_updown |
| else Downsample( |
| ch, conv_resample, dims=dims, out_channels=out_ch |
| ) |
| ) |
| ) |
| ch = out_ch |
| input_block_chans.append(ch) |
| ds *= 2 |
| self._feature_size += ch |
|
|
| if num_head_channels == -1: |
| dim_head = ch // num_heads |
| else: |
| num_heads = ch // num_head_channels |
| dim_head = num_head_channels |
|
|
| self.middle_block = TimestepEmbedSequential( |
| ResBlock( |
| ch, |
| time_embed_dim, |
| dropout, |
| out_channels=ch, |
| dims=dims, |
| use_checkpoint=use_checkpoint, |
| use_scale_shift_norm=use_scale_shift_norm, |
| ), |
| SpatialTransformer( |
| ch, |
| num_heads, |
| dim_head, |
| depth=transformer_depth_middle, |
| context_dim=context_dim, |
| disable_self_attn=disable_middle_self_attn, |
| use_linear=use_linear_in_transformer, |
| attn_type=spatial_transformer_attn_type, |
| use_checkpoint=use_checkpoint, |
| ) |
| if not disable_middle_transformer |
| else th.nn.Identity(), |
| ResBlock( |
| ch, |
| time_embed_dim, |
| dropout, |
| dims=dims, |
| use_checkpoint=use_checkpoint, |
| use_scale_shift_norm=use_scale_shift_norm, |
| ), |
| ) |
| self._feature_size += ch |
|
|
| self.output_blocks = nn.ModuleList([]) |
| for level, mult in list(enumerate(channel_mult))[::-1]: |
| for i in range(self.num_res_blocks[level] + 1): |
| ich = input_block_chans.pop() |
| layers = [ |
| ResBlock( |
| ch + ich, |
| time_embed_dim, |
| dropout, |
| out_channels=model_channels * mult, |
| dims=dims, |
| use_checkpoint=use_checkpoint, |
| use_scale_shift_norm=use_scale_shift_norm, |
| ) |
| ] |
| ch = model_channels * mult |
| if ds in attention_resolutions: |
| if num_head_channels == -1: |
| dim_head = ch // num_heads |
| else: |
| num_heads = ch // num_head_channels |
| dim_head = num_head_channels |
|
|
| if exists(disable_self_attentions): |
| disabled_sa = disable_self_attentions[level] |
| else: |
| disabled_sa = False |
|
|
| if ( |
| not exists(num_attention_blocks) |
| or i < num_attention_blocks[level] |
| ): |
| layers.append( |
| SpatialTransformer( |
| ch, |
| num_heads, |
| dim_head, |
| depth=transformer_depth[level], |
| context_dim=context_dim, |
| disable_self_attn=disabled_sa, |
| use_linear=use_linear_in_transformer, |
| attn_type=spatial_transformer_attn_type, |
| use_checkpoint=use_checkpoint, |
| ) |
| ) |
| if level and i == self.num_res_blocks[level]: |
| out_ch = ch |
| layers.append( |
| ResBlock( |
| ch, |
| time_embed_dim, |
| dropout, |
| out_channels=out_ch, |
| dims=dims, |
| use_checkpoint=use_checkpoint, |
| use_scale_shift_norm=use_scale_shift_norm, |
| up=True, |
| ) |
| if resblock_updown |
| else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch) |
| ) |
| ds //= 2 |
| self.output_blocks.append(TimestepEmbedSequential(*layers)) |
| self._feature_size += ch |
|
|
| self.out = nn.Sequential( |
| normalization(ch), |
| nn.SiLU(), |
| zero_module(conv_nd(dims, model_channels, out_channels, 3, padding=1)), |
| ) |
|
|
| def forward( |
| self, |
| x: th.Tensor, |
| timesteps: Optional[th.Tensor] = None, |
| context: Optional[th.Tensor] = None, |
| y: Optional[th.Tensor] = None, |
| **kwargs, |
| ) -> th.Tensor: |
| """ |
| Apply the model to an input batch. |
| :param x: an [N x C x ...] Tensor of inputs. |
| :param timesteps: a 1-D batch of timesteps. |
| :param context: conditioning plugged in via crossattn |
| :param y: an [N] Tensor of labels, if class-conditional. |
| :return: an [N x C x ...] Tensor of outputs. |
| """ |
| assert (y is not None) == ( |
| self.num_classes is not None |
| ), "must specify y if and only if the model is class-conditional" |
| hs = [] |
| t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False) |
| emb = self.time_embed(t_emb) |
|
|
| if self.num_classes is not None: |
| assert y.shape[0] == x.shape[0] |
| emb = emb + self.label_emb(y) |
|
|
| h = x |
| for module in self.input_blocks: |
| h = module(h, emb, context) |
| hs.append(h) |
| h = self.middle_block(h, emb, context) |
| for module in self.output_blocks: |
| h = th.cat([h, hs.pop()], dim=1) |
| h = module(h, emb, context) |
| h = h.type(x.dtype) |
|
|
| return self.out(h) |
|
|
