import torch from torch import nn from imaginairy.modules.attention import SpatialTransformer from imaginairy.modules.diffusion.ddpm import LatentDiffusion # type: ignore from imaginairy.modules.diffusion.openaimodel import ( AttentionBlock, Downsample, ResBlock, TimestepEmbedSequential, UNetModel, ) from imaginairy.modules.diffusion.util import ( conv_nd, linear, timestep_embedding, zero_module, ) class ControlledUnetModel(UNetModel): def forward( self, x, timesteps=None, context=None, control=None, only_mid_control=False, **kwargs, ): hs = [] with torch.no_grad(): t_emb = timestep_embedding( timesteps, self.model_channels, repeat_only=False ) emb = self.time_embed(t_emb) 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) if control is not None: h += control.pop() for i, module in enumerate(self.output_blocks): # allows us to work with multiples of 8 instead of just 32 if h.shape[-2:] != hs[-1].shape[-2:]: h = nn.functional.interpolate(h, hs[-1].shape[-2:], mode="nearest") if only_mid_control or control is None: h = torch.cat([h, hs.pop()], dim=1) else: ctrl = control.pop() if ctrl.shape[-2:] != hs[-1].shape[-2:]: ctrl = nn.functional.interpolate( ctrl, hs[-1].shape[-2:], mode="nearest" ) h = torch.cat([h, hs.pop() + ctrl], dim=1) del ctrl h = module(h, emb, context) h = h.type(x.dtype) return self.out(h) 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 isinstance(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): msg = "provide num_res_blocks either as an int (globally constant) or as a list/tuple (per-level) with the same length as channel_mult" raise ValueError(msg) 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( self.num_res_blocks[i] >= num_attention_blocks[i] for i in 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 disable_self_attentions is not None: disabled_sa = disable_self_attentions[level] else: disabled_sa = False if num_attention_blocks is None 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) hint = hint.to(dtype=emb.dtype).to(device=emb.device) 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) # for wider img resolution handling? if h.shape[-2:] != guided_hint[-1].shape[-2:]: guided_hint = nn.functional.interpolate( guided_hint, h[-1].shape[-2:], mode="nearest" ) h += guided_hint guided_hint = None 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 ControlLDM(LatentDiffusion): def __init__( self, control_stage_config, control_key, only_mid_control, *args, global_average_pooling=False, **kwargs, ): super().__init__(*args, **kwargs) self.control_stage_config = control_stage_config # self.control_model = instantiate_from_config(control_stage_config) self.control_models = [] self.control_key = control_key self.only_mid_control = only_mid_control self.control_scales = [1.0] * 13 self.global_average_pooling = global_average_pooling def set_control_models(self, control_models): self.control_models = control_models def apply_model(self, x_noisy, t, cond, *args, **kwargs): assert isinstance(cond, dict) diffusion_model = self.model.diffusion_model merged_control = None cond_txt = torch.cat(cond["c_crossattn"], 1) for control_model, c_concat, control_strength in zip( self.control_models, cond["c_concat"], cond["control_strengths"] ): cond_hint = torch.cat([c_concat], 1) control = control_model( x=x_noisy, hint=cond_hint, timesteps=t, context=cond_txt ) control_scales = [control_strength] * 13 control = [c * scale for c, scale in zip(control, control_scales)] if self.global_average_pooling: control = [torch.mean(c, dim=(2, 3), keepdim=True) for c in control] if merged_control is None: merged_control = control else: merged_control = [mc + c for mc, c in zip(merged_control, control)] eps = diffusion_model( x=x_noisy, timesteps=t, context=cond_txt, control=merged_control, only_mid_control=self.only_mid_control, ) return eps def low_vram_shift(self, is_diffusing): if is_diffusing: self.model = self.model.cuda() self.control_models = [cm.cuda() for cm in self.control_models] self.first_stage_model = self.first_stage_model.cpu() self.cond_stage_model = self.cond_stage_model.cpu() else: self.model = self.model.cpu() self.control_models = [cm.cpu() for cm in self.control_models] self.first_stage_model = self.first_stage_model.cuda() self.cond_stage_model = self.cond_stage_model.cuda()