mirror of
https://github.com/Stability-AI/generative-models.git
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Stable Video Diffusion
This commit is contained in:
Tim Dockhorn
493
sgm/modules/diffusionmodules/video_model.py
Normal file
493
sgm/modules/diffusionmodules/video_model.py
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@@ -0,0 +1,493 @@
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from functools import partial
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from typing import List, Optional, Union
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from einops import rearrange
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from ...modules.diffusionmodules.openaimodel import *
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from ...modules.video_attention import SpatialVideoTransformer
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from ...util import default
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from .util import AlphaBlender
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class VideoResBlock(ResBlock):
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def __init__(
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self,
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channels: int,
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emb_channels: int,
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dropout: float,
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video_kernel_size: Union[int, List[int]] = 3,
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merge_strategy: str = "fixed",
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merge_factor: float = 0.5,
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out_channels: Optional[int] = None,
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use_conv: bool = False,
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use_scale_shift_norm: bool = False,
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dims: int = 2,
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use_checkpoint: bool = False,
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up: bool = False,
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down: bool = False,
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):
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super().__init__(
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channels,
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emb_channels,
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dropout,
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out_channels=out_channels,
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use_conv=use_conv,
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use_scale_shift_norm=use_scale_shift_norm,
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dims=dims,
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use_checkpoint=use_checkpoint,
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up=up,
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down=down,
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)
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self.time_stack = ResBlock(
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default(out_channels, channels),
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emb_channels,
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dropout=dropout,
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dims=3,
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out_channels=default(out_channels, channels),
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use_scale_shift_norm=False,
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use_conv=False,
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up=False,
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down=False,
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kernel_size=video_kernel_size,
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use_checkpoint=use_checkpoint,
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exchange_temb_dims=True,
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)
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self.time_mixer = AlphaBlender(
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alpha=merge_factor,
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merge_strategy=merge_strategy,
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rearrange_pattern="b t -> b 1 t 1 1",
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)
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def forward(
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self,
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x: th.Tensor,
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emb: th.Tensor,
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num_video_frames: int,
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image_only_indicator: Optional[th.Tensor] = None,
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) -> th.Tensor:
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x = super().forward(x, emb)
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x_mix = rearrange(x, "(b t) c h w -> b c t h w", t=num_video_frames)
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x = rearrange(x, "(b t) c h w -> b c t h w", t=num_video_frames)
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x = self.time_stack(
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x, rearrange(emb, "(b t) ... -> b t ...", t=num_video_frames)
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)
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x = self.time_mixer(
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x_spatial=x_mix, x_temporal=x, image_only_indicator=image_only_indicator
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)
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x = rearrange(x, "b c t h w -> (b t) c h w")
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return x
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class VideoUNet(nn.Module):
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def __init__(
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self,
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in_channels: int,
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model_channels: int,
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out_channels: int,
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num_res_blocks: int,
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attention_resolutions: int,
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dropout: float = 0.0,
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channel_mult: List[int] = (1, 2, 4, 8),
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conv_resample: bool = True,
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dims: int = 2,
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num_classes: Optional[int] = None,
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use_checkpoint: bool = False,
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num_heads: int = -1,
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num_head_channels: int = -1,
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num_heads_upsample: int = -1,
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use_scale_shift_norm: bool = False,
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resblock_updown: bool = False,
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transformer_depth: Union[List[int], int] = 1,
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transformer_depth_middle: Optional[int] = None,
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context_dim: Optional[int] = None,
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time_downup: bool = False,
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time_context_dim: Optional[int] = None,
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extra_ff_mix_layer: bool = False,
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use_spatial_context: bool = False,
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merge_strategy: str = "fixed",
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merge_factor: float = 0.5,
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spatial_transformer_attn_type: str = "softmax",
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video_kernel_size: Union[int, List[int]] = 3,
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use_linear_in_transformer: bool = False,
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adm_in_channels: Optional[int] = None,
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disable_temporal_crossattention: bool = False,
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max_ddpm_temb_period: int = 10000,
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):
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super().__init__()
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assert context_dim is not None
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if num_heads_upsample == -1:
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num_heads_upsample = num_heads
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if num_heads == -1:
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assert num_head_channels != -1
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if num_head_channels == -1:
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assert num_heads != -1
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self.in_channels = in_channels
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self.model_channels = model_channels
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self.out_channels = out_channels
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if isinstance(transformer_depth, int):
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transformer_depth = len(channel_mult) * [transformer_depth]
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transformer_depth_middle = default(
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transformer_depth_middle, transformer_depth[-1]
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)
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self.num_res_blocks = num_res_blocks
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self.attention_resolutions = attention_resolutions
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self.dropout = dropout
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self.channel_mult = channel_mult
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self.conv_resample = conv_resample
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self.num_classes = num_classes
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self.use_checkpoint = use_checkpoint
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self.num_heads = num_heads
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self.num_head_channels = num_head_channels
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self.num_heads_upsample = num_heads_upsample
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time_embed_dim = model_channels * 4
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self.time_embed = nn.Sequential(
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linear(model_channels, time_embed_dim),
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nn.SiLU(),
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linear(time_embed_dim, time_embed_dim),
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)
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if self.num_classes is not None:
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if isinstance(self.num_classes, int):
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self.label_emb = nn.Embedding(num_classes, time_embed_dim)
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elif self.num_classes == "continuous":
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print("setting up linear c_adm embedding layer")
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self.label_emb = nn.Linear(1, time_embed_dim)
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elif self.num_classes == "timestep":
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self.label_emb = nn.Sequential(
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Timestep(model_channels),
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nn.Sequential(
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linear(model_channels, time_embed_dim),
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nn.SiLU(),
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linear(time_embed_dim, time_embed_dim),
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),
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)
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elif self.num_classes == "sequential":
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assert adm_in_channels is not None
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self.label_emb = nn.Sequential(
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nn.Sequential(
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linear(adm_in_channels, time_embed_dim),
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nn.SiLU(),
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linear(time_embed_dim, time_embed_dim),
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)
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)
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else:
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raise ValueError()
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self.input_blocks = nn.ModuleList(
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[
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TimestepEmbedSequential(
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conv_nd(dims, in_channels, model_channels, 3, padding=1)
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)
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]
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)
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self._feature_size = model_channels
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input_block_chans = [model_channels]
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ch = model_channels
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ds = 1
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def get_attention_layer(
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ch,
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num_heads,
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dim_head,
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depth=1,
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context_dim=None,
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use_checkpoint=False,
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disabled_sa=False,
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):
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return SpatialVideoTransformer(
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ch,
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num_heads,
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dim_head,
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depth=depth,
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context_dim=context_dim,
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time_context_dim=time_context_dim,
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dropout=dropout,
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ff_in=extra_ff_mix_layer,
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use_spatial_context=use_spatial_context,
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merge_strategy=merge_strategy,
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merge_factor=merge_factor,
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checkpoint=use_checkpoint,
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use_linear=use_linear_in_transformer,
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attn_mode=spatial_transformer_attn_type,
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disable_self_attn=disabled_sa,
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disable_temporal_crossattention=disable_temporal_crossattention,
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max_time_embed_period=max_ddpm_temb_period,
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)
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def get_resblock(
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merge_factor,
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merge_strategy,
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video_kernel_size,
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ch,
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time_embed_dim,
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dropout,
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out_ch,
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dims,
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use_checkpoint,
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use_scale_shift_norm,
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down=False,
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up=False,
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):
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return VideoResBlock(
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merge_factor=merge_factor,
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merge_strategy=merge_strategy,
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video_kernel_size=video_kernel_size,
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channels=ch,
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emb_channels=time_embed_dim,
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dropout=dropout,
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out_channels=out_ch,
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dims=dims,
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use_checkpoint=use_checkpoint,
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use_scale_shift_norm=use_scale_shift_norm,
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down=down,
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up=up,
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)
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for level, mult in enumerate(channel_mult):
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for _ in range(num_res_blocks):
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layers = [
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get_resblock(
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merge_factor=merge_factor,
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merge_strategy=merge_strategy,
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video_kernel_size=video_kernel_size,
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ch=ch,
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time_embed_dim=time_embed_dim,
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dropout=dropout,
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out_ch=mult * model_channels,
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dims=dims,
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use_checkpoint=use_checkpoint,
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use_scale_shift_norm=use_scale_shift_norm,
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)
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]
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ch = mult * model_channels
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if ds in attention_resolutions:
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if num_head_channels == -1:
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dim_head = ch // num_heads
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else:
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num_heads = ch // num_head_channels
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dim_head = num_head_channels
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layers.append(
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get_attention_layer(
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ch,
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num_heads,
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dim_head,
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depth=transformer_depth[level],
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context_dim=context_dim,
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use_checkpoint=use_checkpoint,
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disabled_sa=False,
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)
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)
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self.input_blocks.append(TimestepEmbedSequential(*layers))
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self._feature_size += ch
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input_block_chans.append(ch)
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if level != len(channel_mult) - 1:
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ds *= 2
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out_ch = ch
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self.input_blocks.append(
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TimestepEmbedSequential(
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get_resblock(
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merge_factor=merge_factor,
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merge_strategy=merge_strategy,
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video_kernel_size=video_kernel_size,
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ch=ch,
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time_embed_dim=time_embed_dim,
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dropout=dropout,
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out_ch=out_ch,
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dims=dims,
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use_checkpoint=use_checkpoint,
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use_scale_shift_norm=use_scale_shift_norm,
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down=True,
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)
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if resblock_updown
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else Downsample(
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ch,
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conv_resample,
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dims=dims,
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out_channels=out_ch,
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third_down=time_downup,
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)
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)
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)
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ch = out_ch
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input_block_chans.append(ch)
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self._feature_size += ch
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if num_head_channels == -1:
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dim_head = ch // num_heads
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else:
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num_heads = ch // num_head_channels
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dim_head = num_head_channels
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self.middle_block = TimestepEmbedSequential(
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get_resblock(
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merge_factor=merge_factor,
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merge_strategy=merge_strategy,
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video_kernel_size=video_kernel_size,
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ch=ch,
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time_embed_dim=time_embed_dim,
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out_ch=None,
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dropout=dropout,
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dims=dims,
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use_checkpoint=use_checkpoint,
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use_scale_shift_norm=use_scale_shift_norm,
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),
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get_attention_layer(
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ch,
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num_heads,
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dim_head,
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depth=transformer_depth_middle,
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context_dim=context_dim,
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use_checkpoint=use_checkpoint,
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),
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get_resblock(
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merge_factor=merge_factor,
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merge_strategy=merge_strategy,
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video_kernel_size=video_kernel_size,
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ch=ch,
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out_ch=None,
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time_embed_dim=time_embed_dim,
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dropout=dropout,
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dims=dims,
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use_checkpoint=use_checkpoint,
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use_scale_shift_norm=use_scale_shift_norm,
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),
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)
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self._feature_size += ch
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self.output_blocks = nn.ModuleList([])
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for level, mult in list(enumerate(channel_mult))[::-1]:
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for i in range(num_res_blocks + 1):
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ich = input_block_chans.pop()
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layers = [
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get_resblock(
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merge_factor=merge_factor,
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merge_strategy=merge_strategy,
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video_kernel_size=video_kernel_size,
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ch=ch + ich,
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time_embed_dim=time_embed_dim,
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dropout=dropout,
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out_ch=model_channels * mult,
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dims=dims,
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use_checkpoint=use_checkpoint,
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use_scale_shift_norm=use_scale_shift_norm,
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)
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]
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ch = model_channels * mult
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if ds in attention_resolutions:
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if num_head_channels == -1:
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dim_head = ch // num_heads
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else:
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num_heads = ch // num_head_channels
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dim_head = num_head_channels
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layers.append(
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get_attention_layer(
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ch,
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num_heads,
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dim_head,
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depth=transformer_depth[level],
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context_dim=context_dim,
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use_checkpoint=use_checkpoint,
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disabled_sa=False,
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)
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)
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if level and i == num_res_blocks:
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out_ch = ch
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ds //= 2
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layers.append(
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get_resblock(
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merge_factor=merge_factor,
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merge_strategy=merge_strategy,
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video_kernel_size=video_kernel_size,
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ch=ch,
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time_embed_dim=time_embed_dim,
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dropout=dropout,
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out_ch=out_ch,
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dims=dims,
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use_checkpoint=use_checkpoint,
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use_scale_shift_norm=use_scale_shift_norm,
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up=True,
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)
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if resblock_updown
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else Upsample(
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ch,
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conv_resample,
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dims=dims,
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out_channels=out_ch,
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third_up=time_downup,
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)
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)
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self.output_blocks.append(TimestepEmbedSequential(*layers))
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self._feature_size += ch
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self.out = nn.Sequential(
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normalization(ch),
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nn.SiLU(),
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zero_module(conv_nd(dims, model_channels, out_channels, 3, padding=1)),
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)
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def forward(
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self,
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x: th.Tensor,
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timesteps: th.Tensor,
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context: Optional[th.Tensor] = None,
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y: Optional[th.Tensor] = None,
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time_context: Optional[th.Tensor] = None,
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num_video_frames: Optional[int] = None,
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image_only_indicator: Optional[th.Tensor] = None,
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):
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assert (y is not None) == (
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self.num_classes is not None
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), "must specify y if and only if the model is class-conditional -> no, relax this TODO"
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hs = []
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t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False)
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emb = self.time_embed(t_emb)
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if self.num_classes is not None:
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assert y.shape[0] == x.shape[0]
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emb = emb + self.label_emb(y)
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h = x
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for module in self.input_blocks:
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h = module(
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h,
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emb,
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context=context,
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image_only_indicator=image_only_indicator,
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time_context=time_context,
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num_video_frames=num_video_frames,
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)
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hs.append(h)
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h = self.middle_block(
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h,
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emb,
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context=context,
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image_only_indicator=image_only_indicator,
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time_context=time_context,
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num_video_frames=num_video_frames,
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)
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for module in self.output_blocks:
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h = th.cat([h, hs.pop()], dim=1)
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h = module(
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h,
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emb,
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context=context,
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image_only_indicator=image_only_indicator,
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time_context=time_context,
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num_video_frames=num_video_frames,
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)
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h = h.type(x.dtype)
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return self.out(h)
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