from functools import partial from typing import List, Optional, Union from einops import rearrange, repeat import copy from ...modules.diffusionmodules.openaimodel import * from ...modules.video_attention import SpatialVideoTransformer from ...modules.diffusionmodules.model import FaceLocator from ...util import default from .util import AlphaBlender class VideoResBlock(ResBlock): def __init__( self, channels: int, emb_channels: int, dropout: float, video_kernel_size: Union[int, List[int]] = 3, merge_strategy: str = "fixed", merge_factor: float = 0.5, out_channels: Optional[int] = None, use_conv: bool = False, use_scale_shift_norm: bool = False, dims: int = 2, use_checkpoint: bool = False, up: bool = False, down: bool = False, skip_time: bool = False, ): super().__init__( channels, emb_channels, dropout, out_channels=out_channels, use_conv=use_conv, use_scale_shift_norm=use_scale_shift_norm, dims=dims, use_checkpoint=use_checkpoint, up=up, down=down, ) self.time_stack = ResBlock( default(out_channels, channels), emb_channels, dropout=dropout, dims=3, out_channels=default(out_channels, channels), use_scale_shift_norm=False, use_conv=False, up=False, down=False, kernel_size=video_kernel_size, use_checkpoint=use_checkpoint, exchange_temb_dims=True, ) self.time_mixer = AlphaBlender( alpha=merge_factor, merge_strategy=merge_strategy, rearrange_pattern="b t -> b 1 t 1 1", ) self.skip_time = skip_time def forward( self, x: th.Tensor, emb: th.Tensor, num_video_frames: int, image_only_indicator: Optional[th.Tensor] = None, ) -> th.Tensor: x = super().forward(x, emb) if self.skip_time: return x x_mix = rearrange(x, "(b t) c h w -> b c t h w", t=num_video_frames) x = rearrange(x, "(b t) c h w -> b c t h w", t=num_video_frames) x = self.time_stack( x, rearrange(emb, "(b t) ... -> b t ...", t=num_video_frames) ) x = self.time_mixer( x_spatial=x_mix, x_temporal=x, image_only_indicator=image_only_indicator ) x = rearrange(x, "b c t h w -> (b t) c h w") return x class VideoUNet(nn.Module): def __init__( self, in_channels: int, model_channels: int, out_channels: int, num_res_blocks: int, attention_resolutions: int, dropout: float = 0.0, channel_mult: List[int] = (1, 2, 4, 8), conv_resample: bool = True, dims: int = 2, num_classes: Optional[int] = None, use_checkpoint: bool = False, num_heads: int = -1, num_head_channels: int = -1, num_heads_upsample: int = -1, use_scale_shift_norm: bool = False, resblock_updown: bool = False, transformer_depth: Union[List[int], int] = 1, transformer_depth_middle: Optional[int] = None, context_dim: Optional[int] = None, time_downup: bool = False, time_context_dim: Optional[int] = None, extra_ff_mix_layer: bool = False, use_spatial_context: bool = False, merge_strategy: str = "fixed", merge_factor: float = 0.5, spatial_transformer_attn_type: str = "softmax", video_kernel_size: Union[int, List[int]] = 3, use_linear_in_transformer: bool = False, adm_in_channels: Optional[int] = None, disable_temporal_crossattention: bool = False, max_ddpm_temb_period: int = 10000, fine_tuning_method: str = None, unfreeze_blocks: Optional[List[str]] = None, adapter_kwargs: Optional[dict] = {}, audio_cond_method: str = None, audio_dim: Optional[int] = 0, additional_audio_frames: Optional[int] = 0, skip_time: bool = False, use_ada_aug: bool = False, encode_landmarks: bool = False, reference_to: str = None, ): 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 if num_head_channels == -1: assert num_heads != -1 self.additional_audio_frames = additional_audio_frames audio_multiplier = additional_audio_frames * 2 + 1 audio_dim = audio_dim * audio_multiplier self.audio_is_context = "both" in audio_cond_method if "both" == audio_cond_method: audio_cond_method = "to_time_emb_image" elif "both_keyframes" == audio_cond_method: audio_cond_method = "to_time_emb" if "to_time_emb" in audio_cond_method: adm_in_channels += audio_dim print(adm_in_channels, audio_dim, audio_cond_method) self.adapter = None self.audio_cond_method = audio_cond_method self.in_channels = in_channels self.model_channels = model_channels self.out_channels = out_channels if isinstance(transformer_depth, int): transformer_depth = len(channel_mult) * [transformer_depth] transformer_depth_middle = default( transformer_depth_middle, transformer_depth[-1] ) 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.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 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.use_ada_aug = use_ada_aug if use_ada_aug: self.map_aug = linear(9, time_embed_dim) if self.num_classes is not None: if isinstance(self.num_classes, int): self.label_emb = nn.Embedding(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 == "timestep": self.label_emb = nn.Sequential( Timestep(model_channels), nn.Sequential( linear(model_channels, time_embed_dim), nn.SiLU(), linear(time_embed_dim, time_embed_dim), ), ) elif self.num_classes == "sequential": if adm_in_channels > 0: assert adm_in_channels is not None self.label_emb = nn.Sequential( nn.Sequential( linear(adm_in_channels, time_embed_dim), nn.SiLU(), linear(time_embed_dim, time_embed_dim), ) ) else: # Disabling the label embedding self.num_classes = None else: raise ValueError() self.encode_landmarks = encode_landmarks if encode_landmarks: self.face_locator = FaceLocator( 320, conditioning_channels=3, block_out_channels=(16, 32, 96, 256) ) self.input_blocks = nn.ModuleList( [ TimestepEmbedSequential( 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 def get_attention_layer( ch, num_heads, dim_head, depth=1, context_dim=None, use_checkpoint=False, disabled_sa=False, audio_context_dim=None, ): return SpatialVideoTransformer( ch, num_heads, dim_head, depth=depth, context_dim=context_dim, audio_context_dim=audio_context_dim, time_context_dim=time_context_dim, dropout=dropout, ff_in=extra_ff_mix_layer, use_spatial_context=use_spatial_context, merge_strategy=merge_strategy, merge_factor=merge_factor, checkpoint=use_checkpoint, use_linear=use_linear_in_transformer, attn_mode=spatial_transformer_attn_type, disable_self_attn=disabled_sa, disable_temporal_crossattention=disable_temporal_crossattention, max_time_embed_period=max_ddpm_temb_period, skip_time=skip_time, reference_to=reference_to, ) def get_resblock( merge_factor, merge_strategy, video_kernel_size, ch, time_embed_dim, dropout, out_ch, dims, use_checkpoint, use_scale_shift_norm, down=False, up=False, ): return VideoResBlock( merge_factor=merge_factor, merge_strategy=merge_strategy, video_kernel_size=video_kernel_size, channels=ch, emb_channels=time_embed_dim, dropout=dropout, out_channels=out_ch, dims=dims, use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm, down=down, up=up, skip_time=skip_time, ) for level, mult in enumerate(channel_mult): for _ in range(num_res_blocks): layers = [ get_resblock( merge_factor=merge_factor, merge_strategy=merge_strategy, video_kernel_size=video_kernel_size, ch=ch, time_embed_dim=time_embed_dim, dropout=dropout, out_ch=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 layers.append( get_attention_layer( ch, num_heads, dim_head, depth=transformer_depth[level], context_dim=context_dim, audio_context_dim=audio_dim if "cross_attention" in audio_cond_method else None, use_checkpoint=use_checkpoint, disabled_sa=False, ) ) self.input_blocks.append(TimestepEmbedSequential(*layers)) self._feature_size += ch input_block_chans.append(ch) if level != len(channel_mult) - 1: ds *= 2 out_ch = ch self.input_blocks.append( TimestepEmbedSequential( get_resblock( merge_factor=merge_factor, merge_strategy=merge_strategy, video_kernel_size=video_kernel_size, ch=ch, time_embed_dim=time_embed_dim, dropout=dropout, out_ch=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, third_down=time_downup, ) ) ) ch = out_ch input_block_chans.append(ch) 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 = TimestepEmbedSequential( get_resblock( merge_factor=merge_factor, merge_strategy=merge_strategy, video_kernel_size=video_kernel_size, ch=ch, time_embed_dim=time_embed_dim, out_ch=None, dropout=dropout, dims=dims, use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm, ), get_attention_layer( ch, num_heads, dim_head, depth=transformer_depth_middle, context_dim=context_dim, audio_context_dim=audio_dim if "new_cross_attention" in audio_cond_method else None, use_checkpoint=use_checkpoint, ), get_resblock( merge_factor=merge_factor, merge_strategy=merge_strategy, video_kernel_size=video_kernel_size, ch=ch, out_ch=None, time_embed_dim=time_embed_dim, dropout=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(num_res_blocks + 1): ich = input_block_chans.pop() layers = [ get_resblock( merge_factor=merge_factor, merge_strategy=merge_strategy, video_kernel_size=video_kernel_size, ch=ch + ich, time_embed_dim=time_embed_dim, dropout=dropout, out_ch=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 layers.append( get_attention_layer( ch, num_heads, dim_head, depth=transformer_depth[level], context_dim=context_dim, audio_context_dim=audio_dim if "new_cross_attention" == audio_cond_method else None, use_checkpoint=use_checkpoint, disabled_sa=False, ) ) if level and i == num_res_blocks: out_ch = ch ds //= 2 layers.append( get_resblock( merge_factor=merge_factor, merge_strategy=merge_strategy, video_kernel_size=video_kernel_size, ch=ch, time_embed_dim=time_embed_dim, dropout=dropout, out_ch=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, third_up=time_downup, ) ) self.output_blocks.append(TimestepEmbedSequential(*layers)) self._feature_size += ch self.out = nn.Sequential( normalization(ch), nn.SiLU(), zero_module(conv_nd(dims, model_channels, out_channels, 3, padding=1)), ) if fine_tuning_method is not None: # Freeze everything except the adapter for param in self.parameters(): param.requires_grad = False if self.adapter is not None: for param in self.adapter.parameters(): param.requires_grad = True if len(unfreeze_blocks): if "input" in unfreeze_blocks: for param in self.input_blocks[0].parameters(): param.requires_grad = True # break # only unfreeze the first input block if "label_emb" in unfreeze_blocks: for param in self.label_emb.parameters(): param.requires_grad = True def get_skip_attention_at( self, skip_attention_at: List[int], curr_layer: int, batch_size: int, num_video_frames: int, ): if skip_attention_at is None: return None skip_attention = th.zeros(len(skip_attention_at), 1, dtype=th.bool) for i, layer in enumerate(skip_attention_at): skip_attention[i] = layer == curr_layer skip_attention = repeat( skip_attention, "b ... -> (b t) ...", t=num_video_frames ) assert skip_attention.shape[0] == batch_size, ( f"{skip_attention.shape[0]} != {batch_size}" ) return skip_attention def forward( self, x: th.Tensor, timesteps: th.Tensor, context: Optional[th.Tensor] = None, reference_context: Optional[th.Tensor] = None, y: Optional[th.Tensor] = None, audio_emb: Optional[th.Tensor] = None, landmarks: Optional[th.Tensor] = None, aug_labels: Optional[th.Tensor] = None, time_context: Optional[th.Tensor] = None, num_video_frames: Optional[int] = 1, image_only_indicator: Optional[th.Tensor] = None, skip_spatial_attention_at: Optional[List[int]] = None, skip_temporal_attention_at: Optional[List[int]] = None, ): if self.audio_is_context: assert audio_emb is None audio_emb = context.clone() curr_context_idx = 0 num_video_frames = ( num_video_frames if isinstance(num_video_frames, int) else num_video_frames[0] ) if reference_context is not None: copy_context = copy.deepcopy(reference_context) mid = copy_context.pop(-1) copy_context.insert((len(copy_context) // 2) - 1, mid) reference_context = copy_context curr_context_idx = 0 if num_video_frames > 1: reference_context = [ repeat(ref_context, "b h w -> (b t) h w", t=num_video_frames) for ref_context in reference_context ] or_batch_size = x.shape[0] // num_video_frames if ( image_only_indicator is not None and image_only_indicator.shape[0] != or_batch_size ): # TODO: fix this image_only_indicator = repeat( image_only_indicator, "b ... -> (b t) ...", t=2 ) if context is not None and x.shape[0] != context.shape[0]: context = repeat(context, "b ... -> b t ...", t=num_video_frames) context = rearrange(context, "b t ... -> (b t) ...", t=num_video_frames) if "cross_attention" in self.audio_cond_method: assert audio_emb is not None if audio_emb.ndim == 4: audio_emb = rearrange(audio_emb, "b t d c -> b (t d) c") # context = th.cat([context, audio_emb], dim=1) if self.audio_cond_method == "cross_time": assert audio_emb is not None time_context = audio_emb if y is not None and y.shape[0] != x.shape[0]: y = repeat(y, "b ... -> b t ...", t=num_video_frames) y = rearrange(y, "b t ... -> (b t) ...", t=num_video_frames) hs = [] t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False) emb = self.time_embed(t_emb) if self.num_classes is not None: assert y is not None or "to_time_emb" in self.audio_cond_method if self.audio_cond_method == "to_time_emb": assert audio_emb is not None audio_emb = rearrange(audio_emb, "b t c -> (b t) c") if y is not None: y = th.cat([y, audio_emb], dim=1) else: y = audio_emb elif self.audio_cond_method == "to_time_emb_image": assert audio_emb is not None audio_emb = rearrange(audio_emb, "b t c -> b (t c)") if y is not None: y = th.cat([y, audio_emb], dim=1) else: y = audio_emb assert y.shape[0] == x.shape[0], ( f"{y.shape} != {x.shape} and audio_emb.shape: {audio_emb.shape}" ) emb = emb + self.label_emb(y) if self.use_ada_aug: assert aug_labels is not None, ( "must provide aug_labels if use_ada_aug is True" ) emb = emb + self.map_aug(aug_labels) h = x if self.encode_landmarks: landmarks_emb = self.face_locator(landmarks) landmarks_emb = rearrange(landmarks_emb, "b c t h w -> (b t) c h w") # print("landmarks_emb:", landmarks_emb.shape) for i, module in enumerate(self.input_blocks): # print(image_only_indicator.shape, num_video_frames, h.shape) if i == 1 and self.encode_landmarks: h = h + landmarks_emb # print("h.shape:", h.shape, i) skip_spatial_attention = self.get_skip_attention_at( skip_spatial_attention_at, curr_context_idx, x.shape[0], num_video_frames, ) skip_temporal_attention = self.get_skip_attention_at( skip_temporal_attention_at, curr_context_idx, x.shape[0], num_video_frames, ) h, is_attention = module( h, emb, context=context, reference_context=reference_context[curr_context_idx] if reference_context is not None else None, audio_context=audio_emb if "cross_attention" in self.audio_cond_method else None, image_only_indicator=image_only_indicator, time_context=time_context, num_video_frames=num_video_frames, skip_spatial_attention=skip_spatial_attention, skip_temporal_attention=skip_temporal_attention, ) if is_attention: curr_context_idx = ( None if curr_context_idx is None else curr_context_idx + 1 ) hs.append(h) skip_spatial_attention = self.get_skip_attention_at( skip_spatial_attention_at, curr_context_idx, x.shape[0], num_video_frames ) skip_temporal_attention = self.get_skip_attention_at( skip_temporal_attention_at, curr_context_idx, x.shape[0], num_video_frames ) h, is_attention = self.middle_block( h, emb, context=context, reference_context=reference_context[curr_context_idx] if reference_context is not None else None, audio_context=audio_emb if "cross_attention" in self.audio_cond_method else None, image_only_indicator=image_only_indicator, time_context=time_context, num_video_frames=num_video_frames, skip_spatial_attention=skip_spatial_attention, skip_temporal_attention=skip_temporal_attention, ) curr_context_idx = None if curr_context_idx is None else curr_context_idx + 1 for i, module in enumerate(self.output_blocks): skip_x = hs.pop() if self.adapter is not None: skip_x = self.adapter[i]( skip_x, n_frames=num_video_frames, condition=audio_emb ) h = th.cat([h, skip_x], dim=1) skip_spatial_attention = self.get_skip_attention_at( skip_spatial_attention_at, curr_context_idx, x.shape[0], num_video_frames, ) skip_temporal_attention = self.get_skip_attention_at( skip_temporal_attention_at, curr_context_idx, x.shape[0], num_video_frames, ) h, is_attention = module( h, emb, context=context, reference_context=reference_context[curr_context_idx] if reference_context is not None else None, audio_context=audio_emb if "cross_attention" in self.audio_cond_method else None, image_only_indicator=image_only_indicator, time_context=time_context, num_video_frames=num_video_frames, skip_spatial_attention=skip_spatial_attention, skip_temporal_attention=skip_temporal_attention, ) if is_attention: curr_context_idx = ( None if curr_context_idx is None else curr_context_idx + 1 ) # h = h.type(x.dtype) return self.out(h)