from __future__ import annotations from typing import Union from ...util import default, repeat_as_img_seq from ..video_attention import SpatialVideoTransformer from .openaimodel import * 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 ): 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, causal=False ) self.time_mixer = AlphaBlender( alpha=merge_factor, merge_strategy=merge_strategy, rearrange_pattern="b t -> b 1 t 1 1" ) def forward( self, x: torch.Tensor, emb: torch.Tensor, num_frames: int ) -> torch.Tensor: x = super().forward(x, emb) x_mix = rearrange(x, "(b t) c h w -> b c t h w", t=num_frames) x = rearrange(x, "(b t) c h w -> b c t h w", t=num_frames) x = self.time_stack( x, rearrange(emb, "(b t) ... -> b t ...", t=num_frames) ) x = self.time_mixer(x_spatial=x_mix, x_temporal=x) 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 = "learned_with_images", 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, add_lora: bool = False, action_control: bool = False ): 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.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.cond_time_stack_embed = nn.Sequential( linear(model_channels, time_embed_dim), nn.SiLU(), 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(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": # this way 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: raise ValueError 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, add_lora=False, action_control=False ): return SpatialVideoTransformer( ch, num_heads, dim_head, depth=depth, context_dim=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, use_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, add_lora=add_lora, action_control=action_control ) 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 ) 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, use_checkpoint=use_checkpoint, disabled_sa=False, add_lora=add_lora, action_control=action_control ) ) 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, use_checkpoint=use_checkpoint, add_lora=add_lora, action_control=action_control ), 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(list()) 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, use_checkpoint=use_checkpoint, disabled_sa=False, add_lora=add_lora, action_control=action_control ) ) 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) ) ) def forward( self, x: torch.Tensor, timesteps: torch.Tensor, context: Optional[torch.Tensor] = None, y: Optional[torch.Tensor] = None, time_context: Optional[torch.Tensor] = None, cond_mask: Optional[torch.Tensor] = None, num_frames: Optional[int] = None ): assert (y is not None) == ( self.num_classes is not None ), "Must specify y if and only if the model is class-conditional" hs = list() t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False) if cond_mask is not None and cond_mask.any(): cond_mask_ = cond_mask[..., None].float() emb = self.cond_time_stack_embed(t_emb) * cond_mask_ + self.time_embed(t_emb) * (1 - cond_mask_) else: emb = self.time_embed(t_emb) if num_frames > 1 and context.shape[0] != x.shape[0]: assert context.shape[0] == x.shape[0] // num_frames, f"{context.shape} {x.shape}" context = repeat_as_img_seq(context, num_frames) if self.num_classes is not None: if num_frames > 1 and y.shape[0] != x.shape[0]: assert y.shape[0] == x.shape[0] // num_frames, f"{y.shape} {x.shape}" y = repeat_as_img_seq(y, num_frames) emb = emb + self.label_emb(y) h = x for module in self.input_blocks: h = module( h, emb, context=context, time_context=time_context, num_frames=num_frames ) hs.append(h) h = self.middle_block( h, emb, context=context, time_context=time_context, num_frames=num_frames ) for module in self.output_blocks: h = torch.cat((h, hs.pop()), dim=1) h = module( h, emb, context=context, time_context=time_context, num_frames=num_frames ) h = h.type(x.dtype) return self.out(h)