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| from abc import abstractmethod | |
| import math | |
| import numpy as np | |
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| import torchvision | |
| import torchvision.transforms.functional as TF | |
| import numpy as np | |
| from guided_diffusion.fp16_util import convert_module_to_f16, convert_module_to_f32 | |
| from guided_diffusion.nn import ( | |
| checkpoint, | |
| conv_nd, | |
| linear, | |
| avg_pool_nd, | |
| zero_module, | |
| normalization, | |
| timestep_embedding, | |
| ) | |
| 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: int = None, | |
| ): | |
| super().__init__() | |
| self.positional_embedding = nn.Parameter( | |
| torch.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): | |
| b, c, *_spatial = x.shape | |
| x = x.reshape(b, c, -1) # NC(HW) | |
| x = torch.cat([x.mean(dim=-1, keepdim=True), x], dim=-1) # NC(HW+1) | |
| x = x + self.positional_embedding[None, :, :].to(x.dtype) # NC(HW+1) | |
| 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. | |
| """ | |
| def forward(self, x, emb): | |
| """ | |
| 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, emb): | |
| for layer in self: | |
| if isinstance(layer, TimestepBlock): | |
| x = layer(x, emb) | |
| 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, use_conv, dims=2, out_channels=None): | |
| super().__init__() | |
| self.channels = channels | |
| self.out_channels = out_channels or channels | |
| self.use_conv = use_conv | |
| self.dims = dims | |
| if use_conv: | |
| self.conv = conv_nd(dims, self.channels, self.out_channels, 3, padding=1) | |
| def forward(self, x): | |
| assert x.shape[1] == self.channels | |
| if self.dims == 3: | |
| x = F.interpolate( | |
| x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode="nearest" | |
| ) | |
| else: | |
| x = F.interpolate(x, scale_factor=2, 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, use_conv, dims=2, out_channels=None): | |
| 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 use_conv: | |
| self.op = conv_nd( | |
| dims, self.channels, self.out_channels, 3, stride=stride, padding=1 | |
| ) | |
| else: | |
| assert self.channels == self.out_channels | |
| self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride) | |
| def forward(self, x): | |
| 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, | |
| emb_channels, | |
| dropout, | |
| out_channels=None, | |
| use_conv=False, | |
| use_scale_shift_norm=False, | |
| dims=2, | |
| use_checkpoint=False, | |
| up=False, | |
| down=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.in_layers = nn.Sequential( | |
| normalization(channels), | |
| nn.SiLU(), | |
| conv_nd(dims, channels, self.out_channels, 3, padding=1), | |
| ) | |
| 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.emb_layers = nn.Sequential( | |
| nn.SiLU(), | |
| linear( | |
| emb_channels, | |
| 2 * self.out_channels if use_scale_shift_norm else self.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, 3, padding=1) | |
| ), | |
| ) | |
| if self.out_channels == channels: | |
| self.skip_connection = nn.Identity() | |
| elif use_conv: | |
| self.skip_connection = conv_nd( | |
| dims, channels, self.out_channels, 3, padding=1 | |
| ) | |
| else: | |
| self.skip_connection = conv_nd(dims, channels, self.out_channels, 1) | |
| def forward(self, x, emb): | |
| """ | |
| 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. | |
| """ | |
| return checkpoint( | |
| self._forward, (x, emb), self.parameters(), self.use_checkpoint | |
| ) | |
| def _forward(self, x, emb): | |
| 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) | |
| 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 = torch.chunk(emb_out, 2, dim=1) | |
| h = out_norm(h) * (1 + scale) + shift | |
| h = out_rest(h) | |
| else: | |
| 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, | |
| num_heads=1, | |
| num_head_channels=-1, | |
| use_checkpoint=False, | |
| use_new_attention_order=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: | |
| # split qkv before split heads | |
| self.attention = QKVAttention(self.num_heads) | |
| else: | |
| # split heads before split qkv | |
| self.attention = QKVAttentionLegacy(self.num_heads) | |
| self.proj_out = zero_module(conv_nd(1, channels, channels, 1)) | |
| def forward(self, x): | |
| return checkpoint(self._forward, (x,), self.parameters(), True) | |
| def _forward(self, x): | |
| 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) | |
| def count_flops_attn(model, _x, y): | |
| """ | |
| A counter for the `thop` package to count the operations in an | |
| attention operation. | |
| Meant to be used like: | |
| macs, params = thop.profile( | |
| model, | |
| inputs=(inputs, timestamps), | |
| custom_ops={QKVAttention: QKVAttention.count_flops}, | |
| ) | |
| """ | |
| b, c, *spatial = y[0].shape | |
| num_spatial = int(np.prod(spatial)) | |
| # We perform two matmuls with the same number of ops. | |
| # The first computes the weight matrix, the second computes | |
| # the combination of the value vectors. | |
| matmul_ops = 2 * b * (num_spatial ** 2) * c | |
| model.total_ops += torch.DoubleTensor([matmul_ops]) | |
| class QKVAttentionLegacy(nn.Module): | |
| """ | |
| A module which performs QKV attention. Matches legacy QKVAttention + input/ouput heads shaping | |
| """ | |
| def __init__(self, n_heads): | |
| super().__init__() | |
| self.n_heads = n_heads | |
| def forward(self, qkv): | |
| """ | |
| 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 = torch.einsum( | |
| "bct,bcs->bts", q * scale, k * scale | |
| ) # More stable with f16 than dividing afterwards | |
| weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype) | |
| a = torch.einsum("bts,bcs->bct", weight, v) | |
| return a.reshape(bs, -1, length) | |
| def count_flops(model, _x, y): | |
| return count_flops_attn(model, _x, y) | |
| class QKVAttention(nn.Module): | |
| """ | |
| A module which performs QKV attention and splits in a different order. | |
| """ | |
| def __init__(self, n_heads): | |
| super().__init__() | |
| self.n_heads = n_heads | |
| def forward(self, qkv): | |
| """ | |
| 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 = torch.einsum( | |
| "bct,bcs->bts", | |
| (q * scale).view(bs * self.n_heads, ch, length), | |
| (k * scale).view(bs * self.n_heads, ch, length), | |
| ) # More stable with f16 than dividing afterwards | |
| weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype) | |
| a = torch.einsum("bts,bcs->bct", weight, v.reshape(bs * self.n_heads, ch, length)) | |
| return a.reshape(bs, -1, length) | |
| def count_flops(model, _x, y): | |
| return count_flops_attn(model, _x, y) | |
| 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, | |
| out_channels, | |
| model_channels, | |
| num_res_blocks, | |
| attention_resolutions, | |
| dropout=0, | |
| channel_mult=(1, 2, 4, 8), | |
| conv_resample=True, | |
| dims=2, | |
| num_classes=None, | |
| use_checkpoint=False, | |
| use_fp16=False, | |
| num_heads=1, | |
| num_head_channels=-1, | |
| num_heads_upsample=-1, | |
| use_scale_shift_norm=False, | |
| resblock_updown=False, | |
| use_new_attention_order=False, | |
| ): | |
| super().__init__() | |
| if num_heads_upsample == -1: | |
| num_heads_upsample = num_heads | |
| self.in_channels = in_channels | |
| self.model_channels = model_channels | |
| self.out_channels = out_channels | |
| self.num_res_blocks = num_res_blocks | |
| 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.dtype = torch.float16 if use_fp16 else torch.float32 | |
| 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.dt_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: | |
| self.label_emb = nn.Embedding(num_classes, time_embed_dim) | |
| ch = input_ch = int(channel_mult[0] * model_channels) | |
| self.input_blocks = nn.ModuleList( | |
| [TimestepEmbedSequential(conv_nd(dims, in_channels, ch, 3, padding=1))] | |
| ) | |
| self._feature_size = ch | |
| input_block_chans = [ch] | |
| ds = 1 | |
| for level, mult in enumerate(channel_mult): | |
| for _ in range(num_res_blocks): | |
| layers = [ | |
| ResBlock( | |
| ch, | |
| time_embed_dim, | |
| dropout, | |
| out_channels=int(mult * model_channels), | |
| dims=dims, | |
| use_checkpoint=use_checkpoint, | |
| use_scale_shift_norm=use_scale_shift_norm, | |
| ) | |
| ] | |
| ch = int(mult * model_channels) | |
| if ds in attention_resolutions: | |
| layers.append( | |
| AttentionBlock( | |
| ch, | |
| use_checkpoint=use_checkpoint, | |
| num_heads=num_heads, | |
| num_head_channels=num_head_channels, | |
| use_new_attention_order=use_new_attention_order, | |
| ) | |
| ) | |
| 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 | |
| self.middle_block = TimestepEmbedSequential( | |
| ResBlock( | |
| ch, | |
| time_embed_dim, | |
| dropout, | |
| dims=dims, | |
| use_checkpoint=use_checkpoint, | |
| use_scale_shift_norm=use_scale_shift_norm, | |
| ), | |
| AttentionBlock( | |
| ch, | |
| use_checkpoint=use_checkpoint, | |
| num_heads=num_heads, | |
| num_head_channels=num_head_channels, | |
| use_new_attention_order=use_new_attention_order, | |
| ), | |
| 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(num_res_blocks + 1): | |
| ich = input_block_chans.pop() | |
| layers = [ | |
| ResBlock( | |
| ch + ich, | |
| time_embed_dim, | |
| dropout, | |
| out_channels=int(model_channels * mult), | |
| dims=dims, | |
| use_checkpoint=use_checkpoint, | |
| use_scale_shift_norm=use_scale_shift_norm, | |
| ) | |
| ] | |
| ch = int(model_channels * mult) | |
| if ds in attention_resolutions: | |
| layers.append( | |
| AttentionBlock( | |
| ch, | |
| use_checkpoint=use_checkpoint, | |
| num_heads=num_heads_upsample, | |
| num_head_channels=num_head_channels, | |
| use_new_attention_order=use_new_attention_order, | |
| ) | |
| ) | |
| if level and i == num_res_blocks: | |
| 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, input_ch, out_channels, 3, padding=1)), | |
| ) | |
| def convert_to_fp16(self): | |
| """ | |
| Convert the torso of the model to float16. | |
| """ | |
| self.input_blocks.apply(convert_module_to_f16) | |
| self.middle_block.apply(convert_module_to_f16) | |
| self.output_blocks.apply(convert_module_to_f16) | |
| def convert_to_fp32(self): | |
| """ | |
| Convert the torso of the model to float32. | |
| """ | |
| self.input_blocks.apply(convert_module_to_f32) | |
| self.middle_block.apply(convert_module_to_f32) | |
| self.output_blocks.apply(convert_module_to_f32) | |
| def forward(self, x, dt, y=None): | |
| """ | |
| 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 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 = [] | |
| emb = self.dt_embed(timestep_embedding(dt, self.model_channels)) | |
| if self.num_classes is not None: | |
| assert y.shape == (x.shape[0],) | |
| emb = emb + self.label_emb(y) | |
| h = x.type(self.dtype) | |
| for module in self.input_blocks: | |
| h = module(h, emb) | |
| hs.append(h) | |
| h = self.middle_block(h, emb) | |
| for module in self.output_blocks: | |
| h = torch.cat([h, hs.pop()], dim=1) | |
| h = module(h, emb) | |
| h = h.type(x.dtype) | |
| return self.out(h) | |
| class ConditionalUNetModel(UNetModel): | |
| def __init__(self, in_channels, *args, **kwargs): | |
| super().__init__(in_channels * 2, *args, **kwargs) | |
| def forward(self, x, timesteps, lq=None, **kwargs): | |
| x = torch.cat([x, lq], dim=1) | |
| return super().forward(x, timesteps, **kwargs) | |
| class ILVRUNetModel(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, | |
| out_channels, | |
| model_channels, | |
| num_res_blocks, | |
| attention_resolutions, | |
| dropout=0, | |
| channel_mult=(1, 2, 4, 8), | |
| conv_resample=True, | |
| dims=2, | |
| num_classes=None, | |
| use_checkpoint=False, | |
| use_fp16=False, | |
| num_heads=1, | |
| num_head_channels=-1, | |
| num_heads_upsample=-1, | |
| use_scale_shift_norm=False, | |
| resblock_updown=False, | |
| use_new_attention_order=False, | |
| ): | |
| super().__init__() | |
| if num_heads_upsample == -1: | |
| num_heads_upsample = num_heads | |
| self.in_channels = in_channels | |
| self.model_channels = model_channels | |
| self.out_channels = out_channels | |
| self.num_res_blocks = num_res_blocks | |
| 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.dtype = torch.float16 if use_fp16 else torch.float32 | |
| 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: | |
| self.label_emb = nn.Embedding(num_classes, time_embed_dim) | |
| ch = input_ch = int(channel_mult[0] * model_channels) | |
| self.input_blocks = nn.ModuleList( | |
| [TimestepEmbedSequential(conv_nd(dims, in_channels, ch, 3, padding=1))] | |
| ) | |
| self._feature_size = ch | |
| input_block_chans = [ch] | |
| ds = 1 | |
| for level, mult in enumerate(channel_mult): | |
| for _ in range(num_res_blocks): | |
| layers = [ | |
| ResBlock( | |
| ch, | |
| time_embed_dim, | |
| dropout, | |
| out_channels=int(mult * model_channels), | |
| dims=dims, | |
| use_checkpoint=use_checkpoint, | |
| use_scale_shift_norm=use_scale_shift_norm, | |
| ) | |
| ] | |
| ch = int(mult * model_channels) | |
| if ds in attention_resolutions: | |
| layers.append( | |
| AttentionBlock( | |
| ch, | |
| use_checkpoint=use_checkpoint, | |
| num_heads=num_heads, | |
| num_head_channels=num_head_channels, | |
| use_new_attention_order=use_new_attention_order, | |
| ) | |
| ) | |
| 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 | |
| self.middle_block = TimestepEmbedSequential( | |
| ResBlock( | |
| ch, | |
| time_embed_dim, | |
| dropout, | |
| dims=dims, | |
| use_checkpoint=use_checkpoint, | |
| use_scale_shift_norm=use_scale_shift_norm, | |
| ), | |
| AttentionBlock( | |
| ch, | |
| use_checkpoint=use_checkpoint, | |
| num_heads=num_heads, | |
| num_head_channels=num_head_channels, | |
| use_new_attention_order=use_new_attention_order, | |
| ), | |
| 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(num_res_blocks + 1): | |
| ich = input_block_chans.pop() | |
| layers = [ | |
| ResBlock( | |
| ch + ich, | |
| time_embed_dim, | |
| dropout, | |
| out_channels=int(model_channels * mult), | |
| dims=dims, | |
| use_checkpoint=use_checkpoint, | |
| use_scale_shift_norm=use_scale_shift_norm, | |
| ) | |
| ] | |
| ch = int(model_channels * mult) | |
| if ds in attention_resolutions: | |
| layers.append( | |
| AttentionBlock( | |
| ch, | |
| use_checkpoint=use_checkpoint, | |
| num_heads=num_heads_upsample, | |
| num_head_channels=num_head_channels, | |
| use_new_attention_order=use_new_attention_order, | |
| ) | |
| ) | |
| if level and i == num_res_blocks: | |
| 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, input_ch, out_channels, 3, padding=1)), | |
| ) | |
| def convert_to_fp16(self): | |
| """ | |
| Convert the torso of the model to float16. | |
| """ | |
| self.input_blocks.apply(convert_module_to_f16) | |
| self.middle_block.apply(convert_module_to_f16) | |
| self.output_blocks.apply(convert_module_to_f16) | |
| def convert_to_fp32(self): | |
| """ | |
| Convert the torso of the model to float32. | |
| """ | |
| self.input_blocks.apply(convert_module_to_f32) | |
| self.middle_block.apply(convert_module_to_f32) | |
| self.output_blocks.apply(convert_module_to_f32) | |
| def forward(self, x, dt, y=None): | |
| """ | |
| 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 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 = [] | |
| emb = self.time_embed(timestep_embedding(dt, self.model_channels)) | |
| if self.num_classes is not None: | |
| assert y.shape == (x.shape[0],) | |
| emb = emb + self.label_emb(y) | |
| h = x.type(self.dtype) | |
| for module in self.input_blocks: | |
| h = module(h, emb) | |
| hs.append(h) | |
| h = self.middle_block(h, emb) | |
| for module in self.output_blocks: | |
| h = torch.cat([h, hs.pop()], dim=1) | |
| h = module(h, emb) | |
| h = h.type(x.dtype) | |
| return self.out(h) | |