| """From https://raw.githubusercontent.com/openai/guided-diffusion/main/guided_diffusion/unet.py."""
|
|
|
| import math
|
| from abc import abstractmethod
|
|
|
| import numpy as np
|
| import torch as th
|
| import torch.nn as nn
|
| import torch.nn.functional as F
|
|
|
| from .fp16_util import convert_module_to_f16, convert_module_to_f32
|
| from .nn import (
|
| avg_pool_nd,
|
| checkpoint,
|
| conv_nd,
|
| linear,
|
| normalization,
|
| timestep_embedding,
|
| zero_module,
|
| )
|
|
|
|
|
| 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(
|
| 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):
|
| b, c, *_spatial = 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, 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 = th.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:
|
|
|
| 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):
|
| 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))
|
|
|
|
|
|
|
| matmul_ops = 2 * b * (num_spatial**2) * c
|
| model.total_ops += th.DoubleTensor([matmul_ops])
|
|
|
|
|
| class QKVAttentionLegacy(nn.Module):
|
| """A module which performs QKV attention.
|
|
|
| Matches legacy QKVAttention + input/output 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 = 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)
|
|
|
| @staticmethod
|
| 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 = 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)
|
|
|
| @staticmethod
|
| 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,
|
| image_size,
|
| in_channels,
|
| model_channels,
|
| out_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.image_size = image_size
|
| 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 = th.float16 if use_fp16 else th.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, t, x, 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.
|
| """
|
| timesteps = t
|
| assert (y is not None) == (
|
| self.num_classes is not None
|
| ), "must specify y if and only if the model is class-conditional"
|
| while timesteps.dim() > 1:
|
| print(timesteps.shape)
|
| timesteps = timesteps[:, 0]
|
| if timesteps.dim() == 0:
|
| timesteps = timesteps.repeat(x.shape[0])
|
|
|
| hs = []
|
| emb = self.time_embed(timestep_embedding(timesteps, 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 = th.cat([h, hs.pop()], dim=1)
|
| h = module(h, emb)
|
| h = h.type(x.dtype)
|
| return self.out(h)
|
|
|
|
|
| class SuperResModel(UNetModel):
|
| """A UNetModel that performs super-resolution.
|
|
|
| Expects an extra kwarg `low_res` to condition on a low-resolution image.
|
| """
|
|
|
| def __init__(self, image_size, in_channels, *args, **kwargs):
|
| super().__init__(image_size, in_channels * 2, *args, **kwargs)
|
|
|
| def forward(self, x, timesteps, low_res=None, **kwargs):
|
| _, _, new_height, new_width = x.shape
|
| upsampled = F.interpolate(low_res, (new_height, new_width), mode="bilinear")
|
| x = th.cat([x, upsampled], dim=1)
|
| return super().forward(x, timesteps, **kwargs)
|
|
|
|
|
| class EncoderUNetModel(nn.Module):
|
| """The half UNet model with attention and timestep embedding.
|
|
|
| For usage, see UNet.
|
| """
|
|
|
| def __init__(
|
| self,
|
| image_size,
|
| in_channels,
|
| model_channels,
|
| out_channels,
|
| num_res_blocks,
|
| attention_resolutions,
|
| dropout=0,
|
| channel_mult=(1, 2, 4, 8),
|
| conv_resample=True,
|
| dims=2,
|
| 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,
|
| pool="adaptive",
|
| ):
|
| 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.use_checkpoint = use_checkpoint
|
| self.dtype = th.float16 if use_fp16 else th.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),
|
| )
|
|
|
| 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.pool = pool
|
| if pool == "adaptive":
|
| self.out = nn.Sequential(
|
| normalization(ch),
|
| nn.SiLU(),
|
| nn.AdaptiveAvgPool2d((1, 1)),
|
| zero_module(conv_nd(dims, ch, out_channels, 1)),
|
| nn.Flatten(),
|
| )
|
| elif pool == "attention":
|
| assert num_head_channels != -1
|
| self.out = nn.Sequential(
|
| normalization(ch),
|
| nn.SiLU(),
|
| AttentionPool2d((image_size // ds), ch, num_head_channels, out_channels),
|
| )
|
| elif pool == "spatial":
|
| self.out = nn.Sequential(
|
| nn.Linear(self._feature_size, 2048),
|
| nn.ReLU(),
|
| nn.Linear(2048, self.out_channels),
|
| )
|
| elif pool == "spatial_v2":
|
| self.out = nn.Sequential(
|
| nn.Linear(self._feature_size, 2048),
|
| normalization(2048),
|
| nn.SiLU(),
|
| nn.Linear(2048, self.out_channels),
|
| )
|
| else:
|
| raise NotImplementedError(f"Unexpected {pool} pooling")
|
|
|
| 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)
|
|
|
| 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)
|
|
|
| def forward(self, x, timesteps):
|
| """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.
|
| :return: an [N x K] Tensor of outputs.
|
| """
|
| emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))
|
|
|
| results = []
|
| h = x.type(self.dtype)
|
| for module in self.input_blocks:
|
| h = module(h, emb)
|
| if self.pool.startswith("spatial"):
|
| results.append(h.type(x.dtype).mean(dim=(2, 3)))
|
| h = self.middle_block(h, emb)
|
| if self.pool.startswith("spatial"):
|
| results.append(h.type(x.dtype).mean(dim=(2, 3)))
|
| h = th.cat(results, axis=-1)
|
| return self.out(h)
|
| else:
|
| h = h.type(x.dtype)
|
| return self.out(h)
|
|
|
|
|
| NUM_CLASSES = 1000
|
|
|
|
|
| class UNetModelWrapper(UNetModel):
|
| def __init__(
|
| self,
|
| dim,
|
| num_channels,
|
| num_res_blocks,
|
| channel_mult=None,
|
| learn_sigma=False,
|
| class_cond=False,
|
| use_checkpoint=False,
|
| attention_resolutions="16",
|
| num_heads=1,
|
| num_head_channels=-1,
|
| num_heads_upsample=-1,
|
| use_scale_shift_norm=False,
|
| dropout=0,
|
| resblock_updown=False,
|
| use_fp16=False,
|
| use_new_attention_order=False,
|
| ):
|
| image_size = dim[-1]
|
| if channel_mult is None:
|
| if image_size == 512:
|
| channel_mult = (0.5, 1, 1, 2, 2, 4, 4)
|
| elif image_size == 256:
|
| channel_mult = (1, 1, 2, 2, 4, 4)
|
| elif image_size == 128:
|
| channel_mult = (1, 1, 2, 3, 4)
|
| elif image_size == 64:
|
| channel_mult = (1, 2, 3, 4)
|
| elif image_size == 32:
|
| channel_mult = (1, 2, 2, 2)
|
| elif image_size == 28:
|
| channel_mult = (1, 2, 2, 2)
|
| else:
|
| raise ValueError(f"unsupported image size: {image_size}")
|
| else:
|
| channel_mult = list(channel_mult)
|
|
|
| attention_ds = []
|
| for res in attention_resolutions.split(","):
|
| attention_ds.append(image_size // int(res))
|
|
|
| return super().__init__(
|
| image_size=image_size,
|
| in_channels=3,
|
| model_channels=num_channels,
|
| out_channels=(3 if not learn_sigma else 6),
|
| num_res_blocks=num_res_blocks,
|
| attention_resolutions=tuple(attention_ds),
|
| dropout=dropout,
|
| channel_mult=channel_mult,
|
| num_classes=(NUM_CLASSES if class_cond else None),
|
| use_checkpoint=use_checkpoint,
|
| use_fp16=use_fp16,
|
| num_heads=num_heads,
|
| num_head_channels=num_head_channels,
|
| num_heads_upsample=num_heads_upsample,
|
| use_scale_shift_norm=use_scale_shift_norm,
|
| resblock_updown=resblock_updown,
|
| use_new_attention_order=use_new_attention_order,
|
| )
|
|
|
