from re import I import torch import torch as th import torch.nn as nn from model_lib.ControlNet.ldm.modules.diffusionmodules.util import ( conv_nd, linear, zero_module, timestep_embedding, ) from model_lib.ControlNet.ldm.modules.attention import SpatialTransformer from model_lib.ControlNet.ldm.modules.diffusionmodules.openaimodel import TimestepEmbedSequential, ResBlock, Downsample, AttentionBlock,Upsample, UNetModel_Temporal from model_lib.ControlNet.ldm.models.diffusion.ddpm import LatentDiffusionReferenceOnly from model_lib.ControlNet.ldm.util import exists, instantiate_from_config ## TODO: here UNet class ControlledUnetModelAttn_Temporal_Pose_Local(UNetModel_Temporal): def forward(self, x, timesteps=None, context=None, control=None, pose_control=None,local_pose_control=None,only_mid_control=False, attention_mode=None,uc=False, **kwargs): hs = [] bank_attn = control attn_index = 0 t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False) emb = self.time_embed(t_emb) h = x.type(self.dtype) num_input_motion_module = 0 if uc: for i, module in enumerate(self.input_blocks): if i in [1,2,4,5,7,8,10,11]: motion_module = self.input_blocks_motion_module[num_input_motion_module] h = module(h, emb, context,uc=uc) # Attn here h = motion_module(h, emb, context) num_input_motion_module += 1 else: h = module(h, emb, context,uc=uc) # Attn here hs.append(h) h = self.middle_block(h, emb, context,uc=uc) # Attn here for i, module in enumerate(self.output_blocks): output_block_motion_module = self.output_blocks_motion_module[i] if only_mid_control: h = torch.cat([h, hs.pop()], dim=1) h = module(h, emb, context,uc=uc) else: h = torch.cat([h, hs.pop()], dim=1) h = module(h, emb, context,uc=uc) # Attn here h = output_block_motion_module(h, emb, context) else: num_input_motion_module = 0 for i, module in enumerate(self.input_blocks): if i in [1,2,4,5,7,8,10,11]: motion_module = self.input_blocks_motion_module[num_input_motion_module] h, attn_index = module(h, emb, context, bank_attn, attention_mode, attn_index) h = motion_module(h, emb, context) num_input_motion_module += 1 else: h, attn_index = module(h, emb, context, bank_attn, attention_mode, attn_index) # Attn here hs.append(h) h, attn_index = self.middle_block(h, emb, context, bank_attn, attention_mode, attn_index) # Attn here amplify_f = 1. if pose_control is not None: h += pose_control.pop() * amplify_f if local_pose_control is not None: h += local_pose_control.pop() * amplify_f for i, module in enumerate(self.output_blocks): output_block_motion_module = self.output_blocks_motion_module[i] if only_mid_control or (bank_attn is None): h = torch.cat([h, hs.pop()], dim=1) h = module(h, emb, context) else: if pose_control is not None and local_pose_control is not None: h = torch.cat([h, hs.pop() + pose_control.pop() * amplify_f + local_pose_control.pop() * amplify_f], dim=1) elif pose_control is not None: h = torch.cat([h, hs.pop() + pose_control.pop() * amplify_f], dim=1) elif local_pose_control is not None: h = torch.cat([h, hs.pop() + local_pose_control.pop() * amplify_f], dim=1) else: h = torch.cat([h, hs.pop()], dim=1) h, attn_index = module(h, emb, context, bank_attn, attention_mode, attn_index) # Attn here h = output_block_motion_module(h, emb, context) h = h.type(x.dtype) return self.out(h) ## ControlNet Reference Only-Like Attention class ControlNetReferenceOnly(nn.Module): def __init__( self, image_size, in_channels, model_channels, hint_channels, out_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.out_channels = out_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 = 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.input_blocks = nn.ModuleList( [ TimestepEmbedSequential( conv_nd(dims, in_channels, model_channels, 3, padding=1) ) ] ) 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: 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._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) 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 = 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._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 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 i < num_attention_blocks[level]: layers.append( AttentionBlock( ch, use_checkpoint=use_checkpoint, num_heads=num_heads_upsample, 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 ) ) 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(TimestepEmbedSequential(*layers)) self._feature_size += ch def forward(self, x, hint, timesteps, context, attention_bank=None, attention_mode=None,uc=False, **kwargs): hs = [] t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False) emb = self.time_embed(t_emb) banks = attention_bank outs = [] h = x.type(self.dtype) for module in self.input_blocks: h = module(h, emb, context, banks, attention_mode,uc) hs.append(h) h = self.middle_block(h, emb, context, banks, attention_mode,uc) for module in self.output_blocks: h = th.cat([h, hs.pop()], dim=1) h = module(h, emb, context, banks, attention_mode,uc) return outs ### ControlNet Origin 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 = 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.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: 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: 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)) h = self.middle_block(h, emb, context) outs.append(self.middle_block_out(h, emb, context)) return outs class ControlLDMReferenceOnly_Temporal_Pose_Local(LatentDiffusionReferenceOnly): def __init__(self, control_key, only_mid_control,appearance_control_stage_config, pose_control_stage_config, local_pose_control_stage_config, *args, **kwargs): super().__init__(*args, **kwargs) print(args) print(kwargs) self.control_key = control_key self.only_mid_control = only_mid_control self.control_enabled = True self.appearance_control_model = instantiate_from_config(appearance_control_stage_config) self.pose_control_model = instantiate_from_config(pose_control_stage_config) self.local_pose_control_model = instantiate_from_config(local_pose_control_stage_config) def apply_model(self, x_noisy, t, cond, reference_image_noisy, more_reference_image_noisy=[], uc=False,*args, **kwargs): assert isinstance(cond, dict) diffusion_model = self.model.diffusion_model cond_txt = torch.cat(cond['c_crossattn'], 1) if self.control_enabled and 'c_crossattn_void' in cond and cond['c_crossattn_void'] is not None: cond_txt_void = torch.cat(cond['c_crossattn_void'], 1) else: cond_txt_void = cond_txt attention_bank = [] if reference_image_noisy is not None: empty_outs = self.appearance_control_model(x=reference_image_noisy, hint=None, timesteps=t, context=cond_txt_void, attention_bank=attention_bank, attention_mode='write',uc=uc) for m_reference_image_noisy in more_reference_image_noisy: l_attention_bank = [] empty_outs = self.appearance_control_model(x=m_reference_image_noisy, hint=None, timesteps=t, context=cond_txt_void, attention_bank=l_attention_bank, attention_mode='write',uc=uc) for j in range(len(attention_bank)): for k in range(len(attention_bank[j])): attention_bank[j][k] = torch.concat([attention_bank[j][k], l_attention_bank[j][k]], dim=1) if not uc: if self.control_enabled and 'c_concat' in cond and cond['c_concat'] is not None: cond_hint = torch.cat(cond['c_concat'], 1) pose_control = self.pose_control_model(x=x_noisy, hint=cond_hint, timesteps=t, context=cond_txt_void) if self.control_enabled and 'local_c_concat' in cond and cond['local_c_concat'] is not None: cond_hint = torch.cat(cond['local_c_concat'], 1) local_pose_control = self.local_pose_control_model(x=x_noisy, hint=cond_hint, timesteps=t, context=cond_txt_void) else: pose_control = None local_pose_control = None eps = diffusion_model(x=x_noisy, timesteps=t, context=cond_txt, control=attention_bank, pose_control=pose_control, local_pose_control=local_pose_control, only_mid_control=self.only_mid_control, attention_mode='read',uc=uc) return eps @torch.no_grad() def get_unconditional_conditioning(self, N): return self.get_learned_conditioning([""] * N)