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Running
on
Zero
| import functools | |
| from typing import Callable, 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) | |
| # no context, single headed, as in base class | |
| 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) # b, n_channels | |
| 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 # alpha merge | |
| 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", | |
| ): | |
| 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 | |
| ) | |