import torch import torch.nn as nn import torch.nn.functional as F from diffusers.configuration_utils import ConfigMixin from diffusers.models.modeling_utils import ModelMixin from typing import Any, List, Optional from torch import Tensor from .util import ( checkpoint, conv_nd, avg_pool_nd, zero_module, timestep_embedding, ) from .attention import SpatialTransformer3D from .adaptor import Resampler, ImageProjModel import kiui class CondSequential(nn.Sequential): """ A sequential module that passes timestep embeddings to the children that support it as an extra input. """ def forward(self, x, emb, context=None, num_frames=1): for layer in self: if isinstance(layer, ResBlock): x = layer(x, emb) elif isinstance(layer, SpatialTransformer3D): x = layer(x, context, num_frames=num_frames) 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, 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 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 ResBlock(nn.Module): """ 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( nn.GroupNorm(32, 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( nn.GroupNorm(32, self.out_channels), nn.SiLU(), nn.Dropout(p=dropout), zero_module( conv_nd(dims, self.out_channels, self.out_channels, 3, padding=1) ), ) if self.out_channels == channels: self.skip_connection = nn.Identity() elif use_conv: self.skip_connection = conv_nd( dims, channels, self.out_channels, 3, padding=1 ) else: self.skip_connection = conv_nd(dims, channels, self.out_channels, 1) def forward(self, x, emb): """ Apply the block to a Tensor, conditioned on a timestep embedding. :param x: an [N x C x ...] Tensor of features. :param emb: an [N x emb_channels] Tensor of timestep embeddings. :return: an [N x C x ...] Tensor of outputs. """ return checkpoint( self._forward, (x, emb), self.parameters(), self.use_checkpoint ) def _forward(self, x, emb): if self.updown: in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1] h = in_rest(x) h = self.h_upd(h) x = self.x_upd(x) h = in_conv(h) else: h = self.in_layers(x) emb_out = self.emb_layers(emb).type(h.dtype) while len(emb_out.shape) < len(h.shape): emb_out = emb_out[..., None] if self.use_scale_shift_norm: out_norm, out_rest = self.out_layers[0], self.out_layers[1:] scale, shift = torch.chunk(emb_out, 2, dim=1) h = out_norm(h) * (1 + scale) + shift h = out_rest(h) else: h = h + emb_out h = self.out_layers(h) return self.skip_connection(x) + h class MultiViewUNetModel(ModelMixin, ConfigMixin): """ The full multi-view UNet model with attention, timestep embedding and camera 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. :param camera_dim: dimensionality of camera input. """ 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, num_heads=-1, num_head_channels=-1, num_heads_upsample=-1, use_scale_shift_norm=False, resblock_updown=False, 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 disable_self_attentions=None, num_attention_blocks=None, disable_middle_self_attn=False, adm_in_channels=None, camera_dim=None, ip_dim=0, ip_weight=1.0, **kwargs, ): super().__init__() assert context_dim is not None 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.image_size = image_size self.in_channels = in_channels self.model_channels = model_channels self.out_channels = out_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 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.num_classes = num_classes self.use_checkpoint = use_checkpoint self.num_heads = num_heads self.num_head_channels = num_head_channels self.num_heads_upsample = num_heads_upsample self.predict_codebook_ids = n_embed is not None self.ip_dim = ip_dim self.ip_weight = ip_weight if self.ip_dim > 0: self.image_embed = Resampler( dim=context_dim, depth=4, dim_head=64, heads=12, num_queries=ip_dim, # num token embedding_dim=1280, output_dim=context_dim, ff_mult=4, ) time_embed_dim = model_channels * 4 self.time_embed = nn.Sequential( nn.Linear(model_channels, time_embed_dim), nn.SiLU(), nn.Linear(time_embed_dim, time_embed_dim), ) if camera_dim is not None: time_embed_dim = model_channels * 4 self.camera_embed = nn.Sequential( nn.Linear(camera_dim, time_embed_dim), nn.SiLU(), nn.Linear(time_embed_dim, time_embed_dim), ) if self.num_classes is not None: if isinstance(self.num_classes, int): self.label_emb = nn.Embedding(self.num_classes, time_embed_dim) elif self.num_classes == "continuous": # print("setting up linear c_adm embedding layer") self.label_emb = nn.Linear(1, time_embed_dim) elif self.num_classes == "sequential": assert adm_in_channels is not None self.label_emb = nn.Sequential( nn.Sequential( nn.Linear(adm_in_channels, time_embed_dim), nn.SiLU(), nn.Linear(time_embed_dim, time_embed_dim), ) ) else: raise ValueError() self.input_blocks = nn.ModuleList( [ CondSequential( conv_nd(dims, in_channels, model_channels, 3, padding=1) ) ] ) self._feature_size = model_channels input_block_chans = [model_channels] ch = model_channels ds = 1 for level, mult in enumerate(channel_mult): for nr in range(self.num_res_blocks[level]): layers: List[Any] = [ 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 num_attention_blocks is None or nr < num_attention_blocks[level]: layers.append( SpatialTransformer3D( ch, num_heads, dim_head, context_dim=context_dim, depth=transformer_depth, use_checkpoint=use_checkpoint, ip_dim=self.ip_dim, ip_weight=self.ip_weight, ) ) self.input_blocks.append(CondSequential(*layers)) self._feature_size += ch input_block_chans.append(ch) if level != len(channel_mult) - 1: out_ch = ch self.input_blocks.append( CondSequential( ResBlock( ch, time_embed_dim, dropout, out_channels=out_ch, dims=dims, use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm, down=True, ) if resblock_updown else Downsample( ch, conv_resample, dims=dims, out_channels=out_ch ) ) ) ch = out_ch input_block_chans.append(ch) ds *= 2 self._feature_size += ch if num_head_channels == -1: dim_head = ch // num_heads else: num_heads = ch // num_head_channels dim_head = num_head_channels self.middle_block = CondSequential( ResBlock( ch, time_embed_dim, dropout, dims=dims, use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm, ), SpatialTransformer3D( ch, num_heads, dim_head, context_dim=context_dim, depth=transformer_depth, use_checkpoint=use_checkpoint, ip_dim=self.ip_dim, ip_weight=self.ip_weight, ), ResBlock( ch, time_embed_dim, dropout, dims=dims, use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm, ), ) self._feature_size += ch self.output_blocks = nn.ModuleList([]) for level, mult in list(enumerate(channel_mult))[::-1]: for i in range(self.num_res_blocks[level] + 1): ich = input_block_chans.pop() layers = [ ResBlock( ch + ich, time_embed_dim, dropout, out_channels=model_channels * mult, dims=dims, use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm, ) ] ch = model_channels * mult if ds in attention_resolutions: if num_head_channels == -1: dim_head = ch // num_heads else: num_heads = ch // num_head_channels dim_head = num_head_channels if num_attention_blocks is None or i < num_attention_blocks[level]: layers.append( SpatialTransformer3D( ch, num_heads, dim_head, context_dim=context_dim, depth=transformer_depth, use_checkpoint=use_checkpoint, ip_dim=self.ip_dim, ip_weight=self.ip_weight, ) ) if level and i == self.num_res_blocks[level]: out_ch = ch layers.append( ResBlock( ch, time_embed_dim, dropout, out_channels=out_ch, dims=dims, use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm, up=True, ) if resblock_updown else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch) ) ds //= 2 self.output_blocks.append(CondSequential(*layers)) self._feature_size += ch self.out = nn.Sequential( nn.GroupNorm(32, ch), nn.SiLU(), zero_module(conv_nd(dims, model_channels, out_channels, 3, padding=1)), ) if self.predict_codebook_ids: self.id_predictor = nn.Sequential( nn.GroupNorm(32, ch), conv_nd(dims, model_channels, n_embed, 1), # nn.LogSoftmax(dim=1) # change to cross_entropy and produce non-normalized logits ) def forward( self, x, timesteps=None, context=None, y=None, camera=None, num_frames=1, ip=None, ip_img=None, **kwargs, ): """ Apply the model to an input batch. :param x: an [(N x F) x C x ...] Tensor of inputs. F is the number of frames (views). :param timesteps: a 1-D batch of timesteps. :param context: conditioning plugged in via crossattn :param y: an [N] Tensor of labels, if class-conditional. :param num_frames: a integer indicating number of frames for tensor reshaping. :return: an [(N x F) x C x ...] Tensor of outputs. F is the number of frames (views). """ assert ( x.shape[0] % num_frames == 0 ), "input batch size must be dividable by num_frames!" assert (y is not None) == ( self.num_classes is not None ), "must specify y if and only if the model is class-conditional" hs = [] t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False).to(x.dtype) emb = self.time_embed(t_emb) if self.num_classes is not None: assert y is not None assert y.shape[0] == x.shape[0] emb = emb + self.label_emb(y) # Add camera embeddings if camera is not None: emb = emb + self.camera_embed(camera) # imagedream variant if self.ip_dim > 0: x[(num_frames - 1) :: num_frames, :, :, :] = ip_img ip_emb = self.image_embed(ip) context = torch.cat((context, ip_emb), 1) h = x for module in self.input_blocks: h = module(h, emb, context, num_frames=num_frames) hs.append(h) h = self.middle_block(h, emb, context, num_frames=num_frames) for module in self.output_blocks: h = torch.cat([h, hs.pop()], dim=1) h = module(h, emb, context, num_frames=num_frames) h = h.type(x.dtype) if self.predict_codebook_ids: return self.id_predictor(h) else: return self.out(h)