| import functools |
| from typing import Iterable, Union |
|
|
| import torch |
| from einops import rearrange, repeat |
|
|
| import comfy.ops |
| ops = comfy.ops.disable_weight_init |
|
|
| from .diffusionmodules.model import ( |
| AttnBlock, |
| Decoder, |
| ResnetBlock, |
| ) |
| from .diffusionmodules.openaimodel import ResBlock, timestep_embedding |
| from .attention import BasicTransformerBlock |
|
|
| def partialclass(cls, *args, **kwargs): |
| class NewCls(cls): |
| __init__ = functools.partialmethod(cls.__init__, *args, **kwargs) |
|
|
| return NewCls |
|
|
|
|
| class VideoResBlock(ResnetBlock): |
| def __init__( |
| self, |
| out_channels, |
| *args, |
| dropout=0.0, |
| video_kernel_size=3, |
| alpha=0.0, |
| merge_strategy="learned", |
| **kwargs, |
| ): |
| super().__init__(out_channels=out_channels, dropout=dropout, *args, **kwargs) |
| if video_kernel_size is None: |
| video_kernel_size = [3, 1, 1] |
| self.time_stack = ResBlock( |
| channels=out_channels, |
| emb_channels=0, |
| dropout=dropout, |
| dims=3, |
| use_scale_shift_norm=False, |
| use_conv=False, |
| up=False, |
| down=False, |
| kernel_size=video_kernel_size, |
| use_checkpoint=False, |
| skip_t_emb=True, |
| ) |
|
|
| self.merge_strategy = merge_strategy |
| if self.merge_strategy == "fixed": |
| self.register_buffer("mix_factor", torch.Tensor([alpha])) |
| elif self.merge_strategy == "learned": |
| self.register_parameter( |
| "mix_factor", torch.nn.Parameter(torch.Tensor([alpha])) |
| ) |
| else: |
| raise ValueError(f"unknown merge strategy {self.merge_strategy}") |
|
|
| def get_alpha(self, bs): |
| if self.merge_strategy == "fixed": |
| return self.mix_factor |
| elif self.merge_strategy == "learned": |
| return torch.sigmoid(self.mix_factor) |
| else: |
| raise NotImplementedError() |
|
|
| def forward(self, x, temb, skip_video=False, timesteps=None): |
| b, c, h, w = x.shape |
| if timesteps is None: |
| timesteps = b |
|
|
| x = super().forward(x, temb) |
|
|
| if not skip_video: |
| x_mix = rearrange(x, "(b t) c h w -> b c t h w", t=timesteps) |
|
|
| x = rearrange(x, "(b t) c h w -> b c t h w", t=timesteps) |
|
|
| x = self.time_stack(x, temb) |
|
|
| alpha = self.get_alpha(bs=b // timesteps).to(x.device) |
| x = alpha * x + (1.0 - alpha) * x_mix |
|
|
| x = rearrange(x, "b c t h w -> (b t) c h w") |
| return x |
|
|
|
|
| class AE3DConv(ops.Conv2d): |
| def __init__(self, in_channels, out_channels, video_kernel_size=3, *args, **kwargs): |
| super().__init__(in_channels, out_channels, *args, **kwargs) |
| if isinstance(video_kernel_size, Iterable): |
| padding = [int(k // 2) for k in video_kernel_size] |
| else: |
| padding = int(video_kernel_size // 2) |
|
|
| self.time_mix_conv = ops.Conv3d( |
| in_channels=out_channels, |
| out_channels=out_channels, |
| kernel_size=video_kernel_size, |
| padding=padding, |
| ) |
|
|
| def forward(self, input, timesteps=None, skip_video=False): |
| if timesteps is None: |
| timesteps = input.shape[0] |
| x = super().forward(input) |
| if skip_video: |
| return x |
| x = rearrange(x, "(b t) c h w -> b c t h w", t=timesteps) |
| x = self.time_mix_conv(x) |
| return rearrange(x, "b c t h w -> (b t) c h w") |
|
|
|
|
| class AttnVideoBlock(AttnBlock): |
| def __init__( |
| self, in_channels: int, alpha: float = 0, merge_strategy: str = "learned" |
| ): |
| super().__init__(in_channels) |
| |
| self.time_mix_block = BasicTransformerBlock( |
| dim=in_channels, |
| n_heads=1, |
| d_head=in_channels, |
| checkpoint=False, |
| ff_in=True, |
| ) |
|
|
| time_embed_dim = self.in_channels * 4 |
| self.video_time_embed = torch.nn.Sequential( |
| ops.Linear(self.in_channels, time_embed_dim), |
| torch.nn.SiLU(), |
| ops.Linear(time_embed_dim, self.in_channels), |
| ) |
|
|
| self.merge_strategy = merge_strategy |
| if self.merge_strategy == "fixed": |
| self.register_buffer("mix_factor", torch.Tensor([alpha])) |
| elif self.merge_strategy == "learned": |
| self.register_parameter( |
| "mix_factor", torch.nn.Parameter(torch.Tensor([alpha])) |
| ) |
| else: |
| raise ValueError(f"unknown merge strategy {self.merge_strategy}") |
|
|
| def forward(self, x, timesteps=None, skip_time_block=False): |
| if skip_time_block: |
| return super().forward(x) |
|
|
| if timesteps is None: |
| timesteps = x.shape[0] |
|
|
| x_in = x |
| x = self.attention(x) |
| h, w = x.shape[2:] |
| x = rearrange(x, "b c h w -> b (h w) c") |
|
|
| x_mix = x |
| num_frames = torch.arange(timesteps, device=x.device) |
| num_frames = repeat(num_frames, "t -> b t", b=x.shape[0] // timesteps) |
| num_frames = rearrange(num_frames, "b t -> (b t)") |
| t_emb = timestep_embedding(num_frames, self.in_channels, repeat_only=False) |
| emb = self.video_time_embed(t_emb) |
| emb = emb[:, None, :] |
| x_mix = x_mix + emb |
|
|
| alpha = self.get_alpha().to(x.device) |
| x_mix = self.time_mix_block(x_mix, timesteps=timesteps) |
| x = alpha * x + (1.0 - alpha) * x_mix |
|
|
| x = rearrange(x, "b (h w) c -> b c h w", h=h, w=w) |
| x = self.proj_out(x) |
|
|
| return x_in + x |
|
|
| def get_alpha( |
| self, |
| ): |
| if self.merge_strategy == "fixed": |
| return self.mix_factor |
| elif self.merge_strategy == "learned": |
| return torch.sigmoid(self.mix_factor) |
| else: |
| raise NotImplementedError(f"unknown merge strategy {self.merge_strategy}") |
|
|
|
|
|
|
| def make_time_attn( |
| in_channels, |
| attn_type="vanilla", |
| attn_kwargs=None, |
| alpha: float = 0, |
| merge_strategy: str = "learned", |
| conv_op=ops.Conv2d, |
| ): |
| return partialclass( |
| AttnVideoBlock, in_channels, alpha=alpha, merge_strategy=merge_strategy |
| ) |
|
|
|
|
| class Conv2DWrapper(torch.nn.Conv2d): |
| def forward(self, input: torch.Tensor, **kwargs) -> torch.Tensor: |
| return super().forward(input) |
|
|
|
|
| class VideoDecoder(Decoder): |
| available_time_modes = ["all", "conv-only", "attn-only"] |
|
|
| def __init__( |
| self, |
| *args, |
| video_kernel_size: Union[int, list] = 3, |
| alpha: float = 0.0, |
| merge_strategy: str = "learned", |
| time_mode: str = "conv-only", |
| **kwargs, |
| ): |
| self.video_kernel_size = video_kernel_size |
| self.alpha = alpha |
| self.merge_strategy = merge_strategy |
| self.time_mode = time_mode |
| assert ( |
| self.time_mode in self.available_time_modes |
| ), f"time_mode parameter has to be in {self.available_time_modes}" |
|
|
| if self.time_mode != "attn-only": |
| kwargs["conv_out_op"] = partialclass(AE3DConv, video_kernel_size=self.video_kernel_size) |
| if self.time_mode not in ["conv-only", "only-last-conv"]: |
| kwargs["attn_op"] = partialclass(make_time_attn, alpha=self.alpha, merge_strategy=self.merge_strategy) |
| if self.time_mode not in ["attn-only", "only-last-conv"]: |
| kwargs["resnet_op"] = partialclass(VideoResBlock, video_kernel_size=self.video_kernel_size, alpha=self.alpha, merge_strategy=self.merge_strategy) |
|
|
| super().__init__(*args, **kwargs) |
|
|
| def get_last_layer(self, skip_time_mix=False, **kwargs): |
| if self.time_mode == "attn-only": |
| raise NotImplementedError("TODO") |
| else: |
| return ( |
| self.conv_out.time_mix_conv.weight |
| if not skip_time_mix |
| else self.conv_out.weight |
| ) |
|
|
