import torch import torch as th import torch.nn as nn import torch.nn.functional as F from crs_core.modules.diffusionmodules.util import ( checkpoint, conv_nd, linear, zero_module, timestep_embedding, ) from crs_core.modules.attention import SpatialTransformer from crs_core.modules.diffusionmodules.openaimodel import UNetModel, TimestepBlock, TimestepEmbedSequential, ResBlock, Downsample, AttentionBlock from crs_core.utils import exists class LocalTimestepEmbedSequential(nn.Sequential, TimestepBlock): def forward(self, x, emb, context=None, local_features=None): for layer in self: if isinstance(layer, TimestepBlock): x = layer(x, emb) elif isinstance(layer, SpatialTransformer): x = layer(x, context) elif isinstance(layer, LocalResBlock): x = layer(x, emb, local_features) else: x = layer(x) return x class FDN(nn.Module): def __init__(self, norm_nc, label_nc): super().__init__() ks = 3 pw = ks // 2 self.param_free_norm = nn.GroupNorm(32, norm_nc, affine=False) self.conv_gamma = nn.Conv2d(label_nc, norm_nc, kernel_size=ks, padding=pw) self.conv_beta = nn.Conv2d(label_nc, norm_nc, kernel_size=ks, padding=pw) def forward(self, x, local_features): normalized = self.param_free_norm(x) assert local_features.size()[2:] == x.size()[2:] gamma = self.conv_gamma(local_features) beta = self.conv_beta(local_features) out = normalized * (1 + gamma) + beta return out class SelfAttention(nn.Module): def __init__(self, in_dim): super(SelfAttention, self).__init__() # Query, Key, Value transformations self.query_conv = nn.Conv2d(in_dim, in_dim // 8, kernel_size=1) self.key_conv = nn.Conv2d(in_dim, in_dim // 8, kernel_size=1) self.value_conv = nn.Conv2d(in_dim, in_dim, kernel_size=1) # Softmax attention self.softmax = nn.Softmax(dim=-1) def forward(self, x): batch, C, width, height = x.size() query = self.query_conv(x).view(batch, -1, width * height).permute(0, 2, 1) key = self.key_conv(x).view(batch, -1, width * height) value = self.value_conv(x).view(batch, -1, width * height) attention = self.softmax(torch.bmm(query, key)) out = torch.bmm(value, attention.permute(0, 2, 1)) out = out.view(batch, C, width, height) return out + x # Skip connection class EnhancedFDN(nn.Module): def __init__(self, norm_nc, label_nc): super(EnhancedFDN, self).__init__() self.fdn = FDN(norm_nc, label_nc) self.attention = SelfAttention(norm_nc) def forward(self, x, local_features): x = self.attention(x) out = self.fdn(x, local_features) return out class LocalResBlock(nn.Module): def __init__( self, channels, emb_channels, dropout, out_channels=None, dims=2, use_checkpoint=False, inject_channels=None ): super().__init__() self.channels = channels self.emb_channels = emb_channels self.dropout = dropout self.out_channels = out_channels or channels self.use_checkpoint = use_checkpoint self.norm_in = EnhancedFDN(channels, inject_channels) self.norm_out = EnhancedFDN(self.out_channels, inject_channels) self.in_layers = nn.Sequential( nn.Identity(), nn.SiLU(), conv_nd(dims, channels, self.out_channels, 3, padding=1), ) self.emb_layers = nn.Sequential( nn.SiLU(), linear( emb_channels, self.out_channels, ), ) self.out_layers = nn.Sequential( nn.Identity(), 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() else: self.skip_connection = conv_nd(dims, channels, self.out_channels, 1) def forward(self, x, emb, local_conditions): return checkpoint( self._forward, (x, emb, local_conditions), self.parameters(), self.use_checkpoint ) def _forward(self, x, emb, local_conditions): h = self.norm_in(x, local_conditions) h = self.in_layers(h) emb_out = self.emb_layers(emb).type(h.dtype) while len(emb_out.shape) < len(h.shape): emb_out = emb_out[..., None] h = h + emb_out h = self.norm_out(h, local_conditions) h = self.out_layers(h) return self.skip_connection(x) + h class FeatureExtractor(nn.Module): def __init__(self, local_channels, inject_channels, dims=2): super().__init__() self.pre_extractor = LocalTimestepEmbedSequential( conv_nd(dims, local_channels, 32, 3, padding=1), nn.SiLU(), conv_nd(dims, 32, 64, 3, padding=1, stride=2), nn.SiLU(), conv_nd(dims, 64, 64, 3, padding=1), nn.SiLU(), conv_nd(dims, 64, 128, 3, padding=1, stride=2), nn.SiLU(), conv_nd(dims, 128, 128, 3, padding=1), nn.SiLU(), ) self.extractors = nn.ModuleList([ LocalTimestepEmbedSequential( conv_nd(dims, 128, inject_channels[0], 3, padding=1, stride=2), nn.SiLU() ), LocalTimestepEmbedSequential( conv_nd(dims, inject_channels[0], inject_channels[1], 3, padding=1, stride=2), nn.SiLU() ), LocalTimestepEmbedSequential( conv_nd(dims, inject_channels[1], inject_channels[2], 3, padding=1, stride=2), nn.SiLU() ), LocalTimestepEmbedSequential( conv_nd(dims, inject_channels[2], inject_channels[3], 3, padding=1, stride=2), nn.SiLU() ) ]) self.zero_convs = nn.ModuleList([ zero_module(conv_nd(dims, inject_channels[0], inject_channels[0], 3, padding=1)), zero_module(conv_nd(dims, inject_channels[1], inject_channels[1], 3, padding=1)), zero_module(conv_nd(dims, inject_channels[2], inject_channels[2], 3, padding=1)), zero_module(conv_nd(dims, inject_channels[3], inject_channels[3], 3, padding=1)) ]) def forward(self, local_conditions): local_features = self.pre_extractor(local_conditions, None) assert len(self.extractors) == len(self.zero_convs) output_features = [] for idx in range(len(self.extractors)): local_features = self.extractors[idx](local_features, None) output_features.append(self.zero_convs[idx](local_features)) return output_features class LocalAdapter(nn.Module): def __init__( self, in_channels, model_channels, local_channels, inject_channels, inject_layers, 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, transformer_depth=1, context_dim=None, n_embed=None, legacy=True, disable_self_attentions=None, num_attention_blocks=None, disable_middle_self_attn=False, use_linear_in_transformer=False, ): super().__init__() if context_dim is not None: 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 self.dims = dims self.in_channels = in_channels self.model_channels = model_channels self.inject_layers = inject_layers 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 = th.float16 if use_fp16 else th.float32 self.num_heads = num_heads self.num_head_channels = num_head_channels self.num_heads_upsample = num_heads_upsample 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.feature_extractor = FeatureExtractor(local_channels, inject_channels) self.input_blocks = nn.ModuleList( [ LocalTimestepEmbedSequential( conv_nd(dims, in_channels, model_channels, 3, padding=1) ) ] ) self.zero_convs = nn.ModuleList([self.make_zero_conv(model_channels)]) 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]): if (1 + 3*level + nr) in self.inject_layers: layers = [ LocalResBlock( ch, time_embed_dim, dropout, out_channels=mult * model_channels, dims=dims, use_checkpoint=use_checkpoint, inject_channels=inject_channels[level] ) ] else: 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: 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(LocalTimestepEmbedSequential(*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( LocalTimestepEmbedSequential( 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: dim_head = ch // num_heads if use_spatial_transformer else num_head_channels self.middle_block = LocalTimestepEmbedSequential( 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( 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 LocalTimestepEmbedSequential(zero_module(conv_nd(self.dims, channels, channels, 1, padding=0))) def forward(self, x, timesteps, context, local_conditions, **kwargs): t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False) emb = self.time_embed(t_emb) local_features = self.feature_extractor(local_conditions) outs = [] h = x.type(self.dtype) for layer_idx, (module, zero_conv) in enumerate(zip(self.input_blocks, self.zero_convs)): if layer_idx in self.inject_layers: h = module(h, emb, context, local_features[self.inject_layers.index(layer_idx)]) else: h = module(h, emb, context) outs.append(zero_conv(h, emb, context)) h = self.middle_block(h, emb, context) outs.append(self.middle_block_out(h, emb, context)) return outs class LocalControlUNetModel(UNetModel): def forward(self, x, timesteps=None, metadata=None,context=None, local_control=None,meta=False, **kwargs): hs = [] t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False) emb = self.time_embed(t_emb)+metadata h = x.type(self.dtype) for module in self.input_blocks: h = module(h, emb, context) hs.append(h) h = self.middle_block(h, emb, context) h += local_control.pop() for module in self.output_blocks: h = torch.cat([h, hs.pop() + local_control.pop()], dim=1) h = module(h, emb, context) h = h.type(x.dtype) return self.out(h)