1008 lines
42 KiB
Python
1008 lines
42 KiB
Python
from functools import partial
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from abc import abstractmethod
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import torch
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import torch.nn as nn
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import numpy as np
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from einops import rearrange
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import math
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import torch.nn.functional as F
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from ....lvdm.models.utils_diffusion import timestep_embedding
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from ....lvdm.common import checkpoint
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from ....lvdm.basics import (
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zero_module,
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conv_nd,
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linear,
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avg_pool_nd,
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normalization
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)
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from ....lvdm.modules.attention import SpatialTransformer, TemporalTransformer
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import comfy.ops
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ops = comfy.ops.manual_cast
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def exists(x):
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return x is not None
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class TimestepBlock(nn.Module):
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"""
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Any module where forward() takes timestep embeddings as a second argument.
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"""
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@abstractmethod
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def forward(self, x, emb):
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"""
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Apply the module to `x` given `emb` timestep embeddings.
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"""
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class TimestepEmbedSequential(nn.Sequential, TimestepBlock):
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"""
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A sequential module that passes timestep embeddings to the children that
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support it as an extra input.
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"""
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def forward(self, x, emb, context=None, batch_size=None, frame_window_size=None, frame_window_stride=None):
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for layer in self:
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if isinstance(layer, TimestepBlock):
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x = layer(x, emb, batch_size=batch_size)
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elif isinstance(layer, SpatialTransformer):
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x = layer(x, context)
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elif isinstance(layer, TemporalTransformer):
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x = rearrange(x, '(b f) c h w -> b c f h w', b=batch_size)
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x = layer(x, context, frame_window_size=frame_window_size, frame_window_stride=frame_window_stride)
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x = rearrange(x, 'b c f h w -> (b f) c h w')
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else:
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x = layer(x)
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return x
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class Downsample(nn.Module):
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"""
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A downsampling layer with an optional convolution.
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:param channels: channels in the inputs and outputs.
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:param use_conv: a bool determining if a convolution is applied.
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:param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
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downsampling occurs in the inner-two dimensions.
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"""
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def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1):
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super().__init__()
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self.channels = channels
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self.out_channels = out_channels or channels
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self.use_conv = use_conv
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self.dims = dims
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stride = 2 if dims != 3 else (1, 2, 2)
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if use_conv:
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self.op = conv_nd(
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dims, self.channels, self.out_channels, 3, stride=stride, padding=padding
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)
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else:
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assert self.channels == self.out_channels
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self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride)
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def forward(self, x):
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assert x.shape[1] == self.channels
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return self.op(x)
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class Upsample(nn.Module):
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"""
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An upsampling layer with an optional convolution.
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:param channels: channels in the inputs and outputs.
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:param use_conv: a bool determining if a convolution is applied.
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:param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
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upsampling occurs in the inner-two dimensions.
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"""
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def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1):
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super().__init__()
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self.channels = channels
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self.out_channels = out_channels or channels
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self.use_conv = use_conv
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self.dims = dims
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if use_conv:
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self.conv = conv_nd(dims, self.channels, self.out_channels, 3, padding=padding)
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def forward(self, x):
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assert x.shape[1] == self.channels
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if self.dims == 3:
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x = F.interpolate(x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode='nearest')
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else:
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x = F.interpolate(x, scale_factor=2, mode='nearest')
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if self.use_conv:
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x = self.conv(x)
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return x
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class ResBlock(TimestepBlock):
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"""
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A residual block that can optionally change the number of channels.
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:param channels: the number of input channels.
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:param emb_channels: the number of timestep embedding channels.
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:param dropout: the rate of dropout.
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:param out_channels: if specified, the number of out channels.
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:param use_conv: if True and out_channels is specified, use a spatial
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convolution instead of a smaller 1x1 convolution to change the
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channels in the skip connection.
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:param dims: determines if the signal is 1D, 2D, or 3D.
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:param up: if True, use this block for upsampling.
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:param down: if True, use this block for downsampling.
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:param use_temporal_conv: if True, use the temporal convolution.
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:param use_image_dataset: if True, the temporal parameters will not be optimized.
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"""
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def __init__(
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self,
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channels,
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emb_channels,
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dropout,
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out_channels=None,
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use_scale_shift_norm=False,
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dims=2,
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use_checkpoint=False,
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use_conv=False,
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up=False,
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down=False,
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use_temporal_conv=False,
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tempspatial_aware=False
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):
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super().__init__()
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self.channels = channels
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self.emb_channels = emb_channels
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self.dropout = dropout
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self.out_channels = out_channels or channels
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self.use_conv = use_conv
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self.use_checkpoint = use_checkpoint
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self.use_scale_shift_norm = use_scale_shift_norm
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self.use_temporal_conv = use_temporal_conv
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self.in_layers = nn.Sequential(
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normalization(channels),
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nn.SiLU(),
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conv_nd(dims, channels, self.out_channels, 3, padding=1),
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)
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self.updown = up or down
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if up:
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self.h_upd = Upsample(channels, False, dims)
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self.x_upd = Upsample(channels, False, dims)
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elif down:
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self.h_upd = Downsample(channels, False, dims)
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self.x_upd = Downsample(channels, False, dims)
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else:
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self.h_upd = self.x_upd = nn.Identity()
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self.emb_layers = nn.Sequential(
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nn.SiLU(),
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ops.Linear(
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emb_channels,
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2 * self.out_channels if use_scale_shift_norm else self.out_channels,
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),
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)
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self.out_layers = nn.Sequential(
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normalization(self.out_channels),
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nn.SiLU(),
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nn.Dropout(p=dropout),
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zero_module(ops.Conv2d(self.out_channels, self.out_channels, 3, padding=1)),
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)
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if self.out_channels == channels:
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self.skip_connection = nn.Identity()
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elif use_conv:
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self.skip_connection = conv_nd(dims, channels, self.out_channels, 3, padding=1)
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else:
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self.skip_connection = conv_nd(dims, channels, self.out_channels, 1)
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if self.use_temporal_conv:
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self.temopral_conv = TemporalConvBlock(
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self.out_channels,
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self.out_channels,
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dropout=0.1,
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spatial_aware=tempspatial_aware
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)
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def forward(self, x, emb, batch_size=None):
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"""
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Apply the block to a Tensor, conditioned on a timestep embedding.
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:param x: an [N x C x ...] Tensor of features.
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:param emb: an [N x emb_channels] Tensor of timestep embeddings.
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:return: an [N x C x ...] Tensor of outputs.
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"""
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input_tuple = (x, emb)
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if batch_size:
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forward_batchsize = partial(self._forward, batch_size=batch_size)
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return checkpoint(forward_batchsize, input_tuple, self.parameters(), self.use_checkpoint)
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return checkpoint(self._forward, input_tuple, self.parameters(), self.use_checkpoint)
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def _forward(self, x, emb, batch_size=None):
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if self.updown:
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in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1]
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h = in_rest(x)
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h = self.h_upd(h)
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x = self.x_upd(x)
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h = in_conv(h)
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else:
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h = self.in_layers(x)
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emb_out = self.emb_layers(emb).type(h.dtype)
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while len(emb_out.shape) < len(h.shape):
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emb_out = emb_out[..., None]
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if self.use_scale_shift_norm:
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out_norm, out_rest = self.out_layers[0], self.out_layers[1:]
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scale, shift = torch.chunk(emb_out, 2, dim=1)
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h = out_norm(h) * (1 + scale) + shift
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h = out_rest(h)
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else:
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h = h + emb_out
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h = self.out_layers(h)
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h = self.skip_connection(x) + h
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if self.use_temporal_conv and batch_size:
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h = rearrange(h, '(b t) c h w -> b c t h w', b=batch_size)
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h = self.temopral_conv(h)
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h = rearrange(h, 'b c t h w -> (b t) c h w')
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return h
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class TemporalConvBlock(nn.Module):
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"""
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Adapted from modelscope: https://github.com/modelscope/modelscope/blob/master/modelscope/models/multi_modal/video_synthesis/unet_sd.py
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"""
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def __init__(self, in_channels, out_channels=None, dropout=0.0, spatial_aware=False):
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super(TemporalConvBlock, self).__init__()
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if out_channels is None:
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out_channels = in_channels
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self.in_channels = in_channels
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self.out_channels = out_channels
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th_kernel_shape = (3, 1, 1) if not spatial_aware else (3, 3, 1)
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th_padding_shape = (1, 0, 0) if not spatial_aware else (1, 1, 0)
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tw_kernel_shape = (3, 1, 1) if not spatial_aware else (3, 1, 3)
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tw_padding_shape = (1, 0, 0) if not spatial_aware else (1, 0, 1)
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# conv layers
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self.conv1 = nn.Sequential(
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ops.GroupNorm(32, in_channels), nn.SiLU(),
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ops.Conv3d(in_channels, out_channels, th_kernel_shape, padding=th_padding_shape))
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self.conv2 = nn.Sequential(
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ops.GroupNorm(32, out_channels), nn.SiLU(), nn.Dropout(dropout),
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ops.Conv3d(out_channels, in_channels, tw_kernel_shape, padding=tw_padding_shape))
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self.conv3 = nn.Sequential(
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ops.GroupNorm(32, out_channels), nn.SiLU(), nn.Dropout(dropout),
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ops.Conv3d(out_channels, in_channels, th_kernel_shape, padding=th_padding_shape))
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self.conv4 = nn.Sequential(
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ops.GroupNorm(32, out_channels), nn.SiLU(), nn.Dropout(dropout),
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ops.Conv3d(out_channels, in_channels, tw_kernel_shape, padding=tw_padding_shape))
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# zero out the last layer params,so the conv block is identity
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nn.init.zeros_(self.conv4[-1].weight)
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nn.init.zeros_(self.conv4[-1].bias)
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def forward(self, x):
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identity = x
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x = self.conv1(x)
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x = self.conv2(x)
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x = self.conv3(x)
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x = self.conv4(x)
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return identity + x
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class UNetModel(nn.Module):
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"""
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The full UNet model with attention and timestep embedding.
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:param in_channels: in_channels in the input Tensor.
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:param model_channels: base channel count for the model.
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:param out_channels: channels in the output Tensor.
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:param num_res_blocks: number of residual blocks per downsample.
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:param attention_resolutions: a collection of downsample rates at which
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attention will take place. May be a set, list, or tuple.
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For example, if this contains 4, then at 4x downsampling, attention
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will be used.
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:param dropout: the dropout probability.
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:param channel_mult: channel multiplier for each level of the UNet.
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:param conv_resample: if True, use learned convolutions for upsampling and
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downsampling.
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:param dims: determines if the signal is 1D, 2D, or 3D.
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:param num_classes: if specified (as an int), then this model will be
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class-conditional with `num_classes` classes.
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:param use_checkpoint: use gradient checkpointing to reduce memory usage.
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:param num_heads: the number of attention heads in each attention layer.
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:param num_heads_channels: if specified, ignore num_heads and instead use
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a fixed channel width per attention head.
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:param num_heads_upsample: works with num_heads to set a different number
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of heads for upsampling. Deprecated.
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:param use_scale_shift_norm: use a FiLM-like conditioning mechanism.
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:param resblock_updown: use residual blocks for up/downsampling.
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:param use_new_attention_order: use a different attention pattern for potentially
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increased efficiency.
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"""
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def __init__(self,
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in_channels,
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model_channels,
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out_channels,
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num_res_blocks,
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attention_resolutions,
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dropout=0.0,
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channel_mult=(1, 2, 4, 8),
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conv_resample=True,
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dims=2,
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context_dim=None,
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use_scale_shift_norm=False,
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resblock_updown=False,
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num_heads=-1,
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num_head_channels=-1,
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transformer_depth=1,
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use_linear=False,
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use_checkpoint=False,
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temporal_conv=False,
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tempspatial_aware=False,
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temporal_attention=True,
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use_relative_position=True,
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use_causal_attention=False,
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temporal_length=None,
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use_fp16=False,
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addition_attention=False,
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temporal_selfatt_only=True,
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image_cross_attention=False,
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image_cross_attention_scale_learnable=False,
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default_fs=4,
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fs_condition=False,
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):
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super(UNetModel, self).__init__()
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if num_heads == -1:
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assert num_head_channels != -1, 'Either num_heads or num_head_channels has to be set'
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if num_head_channels == -1:
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assert num_heads != -1, 'Either num_heads or num_head_channels has to be set'
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self.in_channels = in_channels
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self.model_channels = model_channels
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self.out_channels = out_channels
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self.num_res_blocks = num_res_blocks
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self.attention_resolutions = attention_resolutions
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self.dropout = dropout
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self.channel_mult = channel_mult
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self.conv_resample = conv_resample
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self.temporal_attention = temporal_attention
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time_embed_dim = model_channels * 4
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self.use_checkpoint = use_checkpoint
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self.dtype = torch.float16 if use_fp16 else torch.float32
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temporal_self_att_only = True
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self.addition_attention = addition_attention
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self.temporal_length = temporal_length
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self.image_cross_attention = image_cross_attention
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self.image_cross_attention_scale_learnable = image_cross_attention_scale_learnable
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self.default_fs = default_fs
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self.fs_condition = fs_condition
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## Time embedding blocks
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self.time_embed = nn.Sequential(
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linear(model_channels, time_embed_dim),
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nn.SiLU(),
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linear(time_embed_dim, time_embed_dim),
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)
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if fs_condition:
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self.fps_embedding = nn.Sequential(
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linear(model_channels, time_embed_dim),
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nn.SiLU(),
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linear(time_embed_dim, time_embed_dim),
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)
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nn.init.zeros_(self.fps_embedding[-1].weight)
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nn.init.zeros_(self.fps_embedding[-1].bias)
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## Input Block
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self.input_blocks = nn.ModuleList(
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[
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TimestepEmbedSequential(conv_nd(dims, in_channels, model_channels, 3, padding=1))
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]
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)
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if self.addition_attention:
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self.init_attn=TimestepEmbedSequential(
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TemporalTransformer(
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model_channels,
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n_heads=8,
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d_head=num_head_channels,
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depth=transformer_depth,
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context_dim=context_dim,
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use_checkpoint=use_checkpoint, only_self_att=temporal_selfatt_only,
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causal_attention=False, relative_position=use_relative_position,
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temporal_length=temporal_length))
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input_block_chans = [model_channels]
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ch = model_channels
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ds = 1
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for level, mult in enumerate(channel_mult):
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for _ in range(num_res_blocks):
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layers = [
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ResBlock(ch, time_embed_dim, dropout,
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out_channels=mult * model_channels, dims=dims, use_checkpoint=use_checkpoint,
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use_scale_shift_norm=use_scale_shift_norm, tempspatial_aware=tempspatial_aware,
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use_temporal_conv=temporal_conv
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)
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]
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ch = mult * model_channels
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if ds in attention_resolutions:
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if num_head_channels == -1:
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dim_head = ch // num_heads
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else:
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num_heads = ch // num_head_channels
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dim_head = num_head_channels
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layers.append(
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SpatialTransformer(ch, num_heads, dim_head,
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depth=transformer_depth, context_dim=context_dim, use_linear=use_linear,
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use_checkpoint=use_checkpoint, disable_self_attn=False,
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video_length=temporal_length, image_cross_attention=self.image_cross_attention,
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image_cross_attention_scale_learnable=self.image_cross_attention_scale_learnable,
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)
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)
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if self.temporal_attention:
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layers.append(
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TemporalTransformer(ch, num_heads, dim_head,
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depth=transformer_depth, context_dim=context_dim, use_linear=use_linear,
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use_checkpoint=use_checkpoint, only_self_att=temporal_self_att_only,
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causal_attention=use_causal_attention, relative_position=use_relative_position,
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temporal_length=temporal_length
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)
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)
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self.input_blocks.append(TimestepEmbedSequential(*layers))
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input_block_chans.append(ch)
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if level != len(channel_mult) - 1:
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out_ch = ch
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self.input_blocks.append(
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TimestepEmbedSequential(
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ResBlock(ch, time_embed_dim, dropout,
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out_channels=out_ch, dims=dims, use_checkpoint=use_checkpoint,
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use_scale_shift_norm=use_scale_shift_norm,
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down=True
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)
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if resblock_updown
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else Downsample(ch, conv_resample, dims=dims, out_channels=out_ch)
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)
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)
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ch = out_ch
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input_block_chans.append(ch)
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ds *= 2
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if num_head_channels == -1:
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dim_head = ch // num_heads
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else:
|
|
num_heads = ch // num_head_channels
|
|
dim_head = num_head_channels
|
|
layers = [
|
|
ResBlock(ch, time_embed_dim, dropout,
|
|
dims=dims, use_checkpoint=use_checkpoint,
|
|
use_scale_shift_norm=use_scale_shift_norm, tempspatial_aware=tempspatial_aware,
|
|
use_temporal_conv=temporal_conv
|
|
),
|
|
SpatialTransformer(ch, num_heads, dim_head,
|
|
depth=transformer_depth, context_dim=context_dim, use_linear=use_linear,
|
|
use_checkpoint=use_checkpoint, disable_self_attn=False, video_length=temporal_length,
|
|
image_cross_attention=self.image_cross_attention,image_cross_attention_scale_learnable=self.image_cross_attention_scale_learnable
|
|
)
|
|
]
|
|
if self.temporal_attention:
|
|
layers.append(
|
|
TemporalTransformer(ch, num_heads, dim_head,
|
|
depth=transformer_depth, context_dim=context_dim, use_linear=use_linear,
|
|
use_checkpoint=use_checkpoint, only_self_att=temporal_self_att_only,
|
|
causal_attention=use_causal_attention, relative_position=use_relative_position,
|
|
temporal_length=temporal_length
|
|
)
|
|
)
|
|
layers.append(
|
|
ResBlock(ch, time_embed_dim, dropout,
|
|
dims=dims, use_checkpoint=use_checkpoint,
|
|
use_scale_shift_norm=use_scale_shift_norm, tempspatial_aware=tempspatial_aware,
|
|
use_temporal_conv=temporal_conv
|
|
)
|
|
)
|
|
|
|
## Middle Block
|
|
self.middle_block = TimestepEmbedSequential(*layers)
|
|
|
|
## Output Block
|
|
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=mult * model_channels, dims=dims, use_checkpoint=use_checkpoint,
|
|
use_scale_shift_norm=use_scale_shift_norm, tempspatial_aware=tempspatial_aware,
|
|
use_temporal_conv=temporal_conv
|
|
)
|
|
]
|
|
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
|
|
layers.append(
|
|
SpatialTransformer(ch, num_heads, dim_head,
|
|
depth=transformer_depth, context_dim=context_dim, use_linear=use_linear,
|
|
use_checkpoint=use_checkpoint, disable_self_attn=False, video_length=temporal_length,
|
|
image_cross_attention=self.image_cross_attention,image_cross_attention_scale_learnable=self.image_cross_attention_scale_learnable
|
|
)
|
|
)
|
|
if self.temporal_attention:
|
|
layers.append(
|
|
TemporalTransformer(ch, num_heads, dim_head,
|
|
depth=transformer_depth, context_dim=context_dim, use_linear=use_linear,
|
|
use_checkpoint=use_checkpoint, only_self_att=temporal_self_att_only,
|
|
causal_attention=use_causal_attention, relative_position=use_relative_position,
|
|
temporal_length=temporal_length
|
|
)
|
|
)
|
|
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.out = nn.Sequential(
|
|
normalization(ch),
|
|
nn.SiLU(),
|
|
zero_module(conv_nd(dims, model_channels, out_channels, 3, padding=1)),
|
|
)
|
|
|
|
def forward(self, x, timesteps, context=None, features_adapter=None, fs=None, frame_window_size=None, frame_window_stride=None, control=None, **kwargs):
|
|
b,_,t,_,_ = x.shape
|
|
t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False).type(x.dtype)
|
|
emb = self.time_embed(t_emb)
|
|
|
|
## repeat t times for context [(b t) 77 768] & time embedding
|
|
## check if we use per-frame image conditioning
|
|
_, l_context, _ = context.shape
|
|
if l_context == 77 + t*16: ## !!! HARD CODE here
|
|
context_text, context_img = context[:,:77,:], context[:,77:,:]
|
|
context_text = context_text.repeat_interleave(repeats=t, dim=0)
|
|
context_img = rearrange(context_img, 'b (t l) c -> (b t) l c', t=t)
|
|
context = torch.cat([context_text, context_img], dim=1)
|
|
else:
|
|
context = context.repeat_interleave(repeats=t, dim=0)
|
|
emb = emb.repeat_interleave(repeats=t, dim=0)
|
|
|
|
## always in shape (b t) c h w, except for temporal layer
|
|
x = rearrange(x, 'b c t h w -> (b t) c h w')
|
|
|
|
## combine emb
|
|
if self.fs_condition:
|
|
if fs is None:
|
|
fs = torch.tensor(
|
|
[self.default_fs] * b, dtype=torch.long, device=x.device)
|
|
fs_emb = timestep_embedding(fs, self.model_channels, repeat_only=False).type(x.dtype)
|
|
|
|
fs_embed = self.fps_embedding(fs_emb)
|
|
fs_embed = fs_embed.repeat_interleave(repeats=t, dim=0)
|
|
emb = emb + fs_embed
|
|
|
|
h = x.type(self.dtype)
|
|
adapter_idx = 0
|
|
hs = []
|
|
for id, module in enumerate(self.input_blocks):
|
|
h = module(h, emb, context=context, batch_size=b, frame_window_size=frame_window_size, frame_window_stride=frame_window_stride)
|
|
if id ==0 and self.addition_attention:
|
|
h = self.init_attn(h, emb, context=context, batch_size=b, frame_window_size=frame_window_size, frame_window_stride=frame_window_stride)
|
|
## plug-in adapter features
|
|
if ((id+1)%3 == 0) and features_adapter is not None:
|
|
h = h + features_adapter[adapter_idx]
|
|
adapter_idx += 1
|
|
hs.append(h)
|
|
if features_adapter is not None:
|
|
assert len(features_adapter)==adapter_idx, 'Wrong features_adapter'
|
|
|
|
h = self.middle_block(h, emb, context=context, batch_size=b, frame_window_size=frame_window_size, frame_window_stride=frame_window_stride)
|
|
|
|
if control is not None:
|
|
h += control.pop()
|
|
|
|
for module in self.output_blocks:
|
|
if control is None:
|
|
h = torch.cat([h, hs.pop()], dim=1)
|
|
else:
|
|
h = torch.cat([h, hs.pop() + control.pop()], dim=1)
|
|
h = module(h, emb, context=context, batch_size=b, frame_window_size=frame_window_size, frame_window_stride=frame_window_stride)
|
|
h = h.type(x.dtype)
|
|
y = self.out(h)
|
|
|
|
# reshape back to (b c t h w)
|
|
y = rearrange(y, '(b t) c h w -> b c t h w', b=b)
|
|
return y
|
|
|
|
class ControlNet(nn.Module):
|
|
def __init__(
|
|
self,
|
|
image_size,
|
|
in_channels,
|
|
model_channels,
|
|
hint_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,
|
|
use_spatial_transformer=False, # custom transformer support
|
|
transformer_depth=1, # custom transformer support
|
|
context_dim=None, # custom transformer support
|
|
n_embed=None, # custom support for prediction of discrete ids into codebook of first stage vq model
|
|
legacy=True,
|
|
disable_self_attentions=None,
|
|
num_attention_blocks=None,
|
|
disable_middle_self_attn=False,
|
|
use_linear_in_transformer=False,
|
|
):
|
|
super().__init__()
|
|
if use_spatial_transformer:
|
|
assert context_dim is not None, 'Fool!! You forgot to include the dimension of your cross-attention conditioning...'
|
|
|
|
if context_dim is not None:
|
|
assert use_spatial_transformer, 'Fool!! You forgot to use the spatial transformer for your cross-attention conditioning...'
|
|
from omegaconf.listconfig import ListConfig
|
|
if type(context_dim) == ListConfig:
|
|
context_dim = list(context_dim)
|
|
|
|
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.dims = dims
|
|
self.image_size = image_size
|
|
self.in_channels = in_channels
|
|
self.model_channels = model_channels
|
|
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:
|
|
# should be a list of booleans, indicating whether to disable self-attention in TransformerBlocks or not
|
|
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))))
|
|
print(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.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
|
|
self.predict_codebook_ids = n_embed is not None
|
|
|
|
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),
|
|
)
|
|
|
|
self.input_blocks = nn.ModuleList(
|
|
[
|
|
TimestepEmbedSequential(
|
|
conv_nd(dims, in_channels, model_channels, 3, padding=1)
|
|
)
|
|
]
|
|
)
|
|
self.zero_convs = nn.ModuleList([self.make_zero_conv(model_channels)])
|
|
|
|
self.input_hint_block = TimestepEmbedSequential(
|
|
conv_nd(dims, hint_channels, 16, 3, padding=1),
|
|
nn.SiLU(),
|
|
conv_nd(dims, 16, 16, 3, padding=1),
|
|
nn.SiLU(),
|
|
conv_nd(dims, 16, 32, 3, padding=1, stride=2),
|
|
nn.SiLU(),
|
|
conv_nd(dims, 32, 32, 3, padding=1),
|
|
nn.SiLU(),
|
|
conv_nd(dims, 32, 96, 3, padding=1, stride=2),
|
|
nn.SiLU(),
|
|
conv_nd(dims, 96, 96, 3, padding=1),
|
|
nn.SiLU(),
|
|
conv_nd(dims, 96, 256, 3, padding=1, stride=2),
|
|
nn.SiLU(),
|
|
zero_module(conv_nd(dims, 256, 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 legacy:
|
|
# num_heads = 1
|
|
dim_head = ch // num_heads if use_spatial_transformer else 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 nr < num_attention_blocks[level]:
|
|
layers.append(
|
|
AttentionBlock(
|
|
ch,
|
|
use_checkpoint=use_checkpoint,
|
|
num_heads=num_heads,
|
|
num_head_channels=dim_head,
|
|
use_new_attention_order=use_new_attention_order,
|
|
) if not use_spatial_transformer else SpatialTransformer(
|
|
ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim,
|
|
disable_self_attn=disabled_sa, use_linear=use_linear_in_transformer,
|
|
use_checkpoint=use_checkpoint
|
|
)
|
|
)
|
|
self.input_blocks.append(TimestepEmbedSequential(*layers))
|
|
self.zero_convs.append(self.make_zero_conv(ch))
|
|
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)
|
|
self.zero_convs.append(self.make_zero_conv(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
|
|
if legacy:
|
|
# num_heads = 1
|
|
dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
|
|
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=dim_head,
|
|
use_new_attention_order=use_new_attention_order,
|
|
) if not use_spatial_transformer else SpatialTransformer( # always uses a self-attn
|
|
ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim,
|
|
disable_self_attn=disable_middle_self_attn, use_linear=use_linear_in_transformer,
|
|
use_checkpoint=use_checkpoint
|
|
),
|
|
ResBlock(
|
|
ch,
|
|
time_embed_dim,
|
|
dropout,
|
|
dims=dims,
|
|
use_checkpoint=use_checkpoint,
|
|
use_scale_shift_norm=use_scale_shift_norm,
|
|
),
|
|
)
|
|
self.middle_block_out = self.make_zero_conv(ch)
|
|
self._feature_size += ch
|
|
|
|
def make_zero_conv(self, channels):
|
|
return TimestepEmbedSequential(zero_module(conv_nd(self.dims, channels, channels, 1, padding=0)))
|
|
|
|
def forward(self, x, hint, timesteps, context, **kwargs):
|
|
t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False)
|
|
emb = self.time_embed(t_emb)
|
|
|
|
guided_hint = self.input_hint_block(hint, emb, context)
|
|
|
|
outs = []
|
|
|
|
h = x.type(self.dtype)
|
|
|
|
for module, zero_conv in zip(self.input_blocks, self.zero_convs):
|
|
if guided_hint is not None:
|
|
h = module(h, emb, context)
|
|
h += guided_hint
|
|
guided_hint = None
|
|
else:
|
|
h = module(h, emb, context)
|
|
outs.append(zero_conv(h, emb, context, True))
|
|
|
|
h = self.middle_block(h, emb, context)
|
|
outs.append(self.middle_block_out(h, emb, context))
|
|
|
|
return outs
|
|
|
|
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) # TODO: check checkpoint usage, is True # TODO: fix the .half call!!!
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#return pt_checkpoint(self._forward, x) # pytorch
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|
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def _forward(self, x):
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b, c, *spatial = x.shape
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x = x.reshape(b, c, -1)
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qkv = self.qkv(self.norm(x))
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h = self.attention(qkv)
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h = self.proj_out(h)
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return (x + h).reshape(b, c, *spatial)
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|
|
|
class QKVAttention(nn.Module):
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"""
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|
A module which performs QKV attention and splits in a different order.
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"""
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|
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def __init__(self, n_heads):
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super().__init__()
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self.n_heads = n_heads
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|
|
|
def forward(self, qkv):
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|
"""
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|
Apply QKV attention.
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:param qkv: an [N x (3 * H * C) x T] tensor of Qs, Ks, and Vs.
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:return: an [N x (H * C) x T] tensor after attention.
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"""
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bs, width, length = qkv.shape
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assert width % (3 * self.n_heads) == 0
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ch = width // (3 * self.n_heads)
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q, k, v = qkv.chunk(3, dim=1)
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scale = 1 / math.sqrt(math.sqrt(ch))
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|
weight = torch.einsum(
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"bct,bcs->bts",
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|
(q * scale).view(bs * self.n_heads, ch, length),
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|
(k * scale).view(bs * self.n_heads, ch, length),
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|
) # More stable with f16 than dividing afterwards
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weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype)
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a = torch.einsum("bts,bcs->bct", weight, v.reshape(bs * self.n_heads, ch, length))
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return a.reshape(bs, -1, length)
|
|
|
|
@staticmethod
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|
def count_flops(model, _x, y):
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|
return count_flops_attn(model, _x, y)
|
|
|
|
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)
|
|
|
|
@staticmethod
|
|
def count_flops(model, _x, y):
|
|
return count_flops_attn(model, _x, y)
|
|
|
|
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]) |