import math import random from abc import abstractmethod from functools import partial import numpy as np import torch import torch.nn as nn import torch.nn.functional as F from einops import rearrange, repeat from lvdm.basics import (avg_pool_nd, conv_nd, linear, normalization, zero_module) from lvdm.common import checkpoint from lvdm.models.utils_diffusion import timestep_embedding from lvdm.modules.attention import SpatialTransformer, TemporalTransformer 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, context=None, batch_size=None, is_imgbatch=False): for layer in self: if isinstance(layer, TimestepBlock): x = layer(x, emb, batch_size, is_imgbatch=is_imgbatch) elif isinstance(layer, SpatialTransformer): x = layer(x, context) elif isinstance(layer, TemporalTransformer): x = rearrange(x, '(b f) c h w -> b c f h w', b=batch_size) x = layer(x, context, is_imgbatch=is_imgbatch) x = rearrange(x, 'b c f h w -> (b f) c h w') else: x = layer(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, padding=1): 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=padding ) 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 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, padding=1): 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=padding) 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 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 up: if True, use this block for upsampling. :param down: if True, use this block for downsampling. :param use_temporal_conv: if True, use the temporal convolution. :param use_image_dataset: if True, the temporal parameters will not be optimized. """ def __init__( self, channels, emb_channels, dropout, out_channels=None, use_scale_shift_norm=False, dims=2, use_checkpoint=False, use_conv=False, up=False, down=False, use_temporal_conv=False, tempspatial_aware=False, use_image_dataset=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.use_temporal_conv = use_temporal_conv 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(), nn.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(nn.Conv2d(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) if self.use_temporal_conv: self.temopral_conv = TemporalConvBlock( self.out_channels, self.out_channels, dropout=0.1, spatial_aware=tempspatial_aware, use_image_dataset=use_image_dataset ) def forward(self, x, emb, batch_size=None, is_imgbatch=False): """ 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. """ input_tuple = (x, emb) if self.use_temporal_conv: forward_tempconv = partial(self._forward, batch_size=batch_size, is_imgbatch=is_imgbatch) return checkpoint(forward_tempconv, input_tuple, self.parameters(), self.use_checkpoint) return checkpoint(self._forward, input_tuple, self.parameters(), self.use_checkpoint) def _forward(self, x, emb, batch_size=None, is_imgbatch=False): 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) h = self.skip_connection(x) + h if self.use_temporal_conv and batch_size and not is_imgbatch: h = rearrange(h, '(b t) c h w -> b c t h w', b=batch_size) h = self.temopral_conv(h) h = rearrange(h, 'b c t h w -> (b t) c h w') return h class TemporalConvBlock(nn.Module): def __init__(self, in_channels, out_channels=None, dropout=0.0, spatial_aware=False, use_image_dataset=False): super(TemporalConvBlock, self).__init__() if out_channels is None: out_channels = in_channels # int(1.5*in_channels) self.in_channels = in_channels self.out_channels = out_channels self.use_image_dataset = use_image_dataset kernel_shape = (3, 1, 1) if not spatial_aware else (3, 3, 3) padding_shape = (1, 0, 0) if not spatial_aware else (1, 1, 1) # conv layers self.conv1 = nn.Sequential( nn.GroupNorm(32, in_channels), nn.SiLU(), nn.Conv3d(in_channels, out_channels, kernel_shape, padding=padding_shape)) self.conv2 = nn.Sequential( nn.GroupNorm(32, out_channels), nn.SiLU(), nn.Dropout(dropout), nn.Conv3d(out_channels, in_channels, kernel_shape, padding=padding_shape)) self.conv3 = nn.Sequential( nn.GroupNorm(32, out_channels), nn.SiLU(), nn.Dropout(dropout), nn.Conv3d(out_channels, in_channels, (3, 1, 1), padding=(1, 0, 0))) self.conv4 = nn.Sequential( nn.GroupNorm(32, out_channels), nn.SiLU(), nn.Dropout(dropout), nn.Conv3d(out_channels, in_channels, (3, 1, 1), padding=(1, 0, 0))) # zero out the last layer params,so the conv block is identity nn.init.zeros_(self.conv4[-1].weight) nn.init.zeros_(self.conv4[-1].bias) def forward(self, x): identity = x x = self.conv1(x) x = self.conv2(x) x = self.conv3(x) x = self.conv4(x) if self.use_image_dataset: x = identity + 0.0 * x else: x = identity + x return x class UNetModel(nn.Module): """ The full UNet model with attention and timestep embedding. :param in_channels: in_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, model_channels, out_channels, num_res_blocks, attention_resolutions, dropout=0.0, channel_mult=(1, 2, 4, 8), conv_resample=True, dims=2, context_dim=None, use_scale_shift_norm=False, resblock_updown=False, num_heads=-1, num_head_channels=-1, transformer_depth=1, use_linear=False, use_checkpoint=False, temporal_conv=False, tempspatial_aware=False, temporal_attention=True, addition_attention=False, temporal_selfatt_only=True, use_relative_position=True, use_causal_attention=False, temporal_length=None, use_image_dataset=False, use_fp16=False, micro_condition=False, temporal_transformer_depth=1 ): super(UNetModel, self).__init__() if num_heads == -1: assert num_head_channels != -1, 'Either num_heads or num_head_channels has to be set' if num_head_channels == -1: assert num_heads != -1, 'Either num_heads or num_head_channels has to be set' self.in_channels = in_channels self.model_channels = model_channels self.out_channels = out_channels 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.temporal_attention = temporal_attention time_embed_dim = model_channels * 4 self.use_checkpoint = use_checkpoint self.dtype = torch.float16 if use_fp16 else torch.float32 #temporal_selfatt_only = True self.addition_attention=addition_attention self.time_embed = nn.Sequential( linear(model_channels, time_embed_dim), nn.SiLU(), linear(time_embed_dim, time_embed_dim), ) if micro_condition: self.micro_embed = nn.Sequential( linear(model_channels, time_embed_dim), nn.SiLU(), linear(time_embed_dim, time_embed_dim), ) self.micro_condition = micro_condition self.input_blocks = nn.ModuleList( [ TimestepEmbedSequential(conv_nd(dims, in_channels, model_channels, 3, padding=1)) ] ) if self.addition_attention: self.init_attn=TimestepEmbedSequential( TemporalTransformer( model_channels, n_heads=8, d_head=num_head_channels, depth=transformer_depth, context_dim=context_dim, use_checkpoint=use_checkpoint, only_self_att=temporal_selfatt_only, causal_attention=use_causal_attention, relative_position=use_relative_position, temporal_length=temporal_length, use_image_dataset=use_image_dataset)) input_block_chans = [model_channels] ch = model_channels ds = 1 for level, mult in enumerate(channel_mult): for _ in range(num_res_blocks): 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, tempspatial_aware=tempspatial_aware, use_temporal_conv=temporal_conv, use_image_dataset=use_image_dataset ) ] 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 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 ) ) if self.temporal_attention: layers.append( TemporalTransformer(ch, num_heads, dim_head, depth=temporal_transformer_depth, context_dim=context_dim, use_linear=use_linear, use_checkpoint=use_checkpoint, only_self_att=temporal_selfatt_only, causal_attention=use_causal_attention, relative_position=use_relative_position, temporal_length=temporal_length, use_image_dataset=use_image_dataset ) ) self.input_blocks.append(TimestepEmbedSequential(*layers)) 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 if num_head_channels == -1: dim_head = ch // num_heads 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, use_image_dataset=use_image_dataset ), 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 ) ] if self.temporal_attention: layers.append( TemporalTransformer(ch, num_heads, dim_head, depth=temporal_transformer_depth, context_dim=context_dim, use_linear=use_linear, use_checkpoint=use_checkpoint, only_self_att=temporal_selfatt_only, causal_attention=use_causal_attention, relative_position=use_relative_position, temporal_length=temporal_length, use_image_dataset=use_image_dataset ) ) 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, use_image_dataset=use_image_dataset ) ) self.middle_block = TimestepEmbedSequential(*layers) 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, use_image_dataset=use_image_dataset ) ] 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 ) ) if self.temporal_attention: layers.append( TemporalTransformer(ch, num_heads, dim_head, depth=temporal_transformer_depth, context_dim=context_dim, use_linear=use_linear, use_checkpoint=use_checkpoint, only_self_att=temporal_selfatt_only, causal_attention=use_causal_attention, relative_position=use_relative_position, temporal_length=temporal_length, use_image_dataset=use_image_dataset ) ) 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, y=None, features_adapter=None, is_imgbatch=False, **kwargs): b,_,t,_,_ = x.shape t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False) emb = self.time_embed(t_emb) if self.micro_condition and y is not None: micro_emb = timestep_embedding(y, self.model_channels, repeat_only=False) emb = emb + self.micro_embed(micro_emb) ## repeat t times for context [(b t) 77 768] & time embedding if not is_imgbatch: 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') if features_adapter is not None: features_adapter = [rearrange(feature, 'b c t h w -> (b t) c h w') for feature in features_adapter] 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,is_imgbatch=is_imgbatch) if id ==0 and self.addition_attention: h = self.init_attn(h, emb, context=context, batch_size=b,is_imgbatch=is_imgbatch) ## 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, is_imgbatch=is_imgbatch) for module in self.output_blocks: h = torch.cat([h, hs.pop()], dim=1) h = module(h, emb, context=context, batch_size=b, is_imgbatch=is_imgbatch) 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