816 lines
26 KiB
Python
816 lines
26 KiB
Python
from typing import Callable, List, Optional, Tuple, Union
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from einops import rearrange
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from ..dit.joint_model.context_parallel import get_cp_rank_size, local_shard
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from ..vae.cp_conv import cp_pass_frames, gather_all_frames
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def cast_tuple(t, length=1):
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return t if isinstance(t, tuple) else ((t,) * length)
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class GroupNormSpatial(nn.GroupNorm):
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"""
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GroupNorm applied per-frame.
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"""
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def forward(self, x: torch.Tensor, *, chunk_size: int = 8):
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B, C, T, H, W = x.shape
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x = rearrange(x, "B C T H W -> (B T) C H W")
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# Run group norm in chunks.
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output = torch.empty_like(x)
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for b in range(0, B * T, chunk_size):
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output[b : b + chunk_size] = super().forward(x[b : b + chunk_size])
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return rearrange(output, "(B T) C H W -> B C T H W", B=B, T=T)
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class SafeConv3d(torch.nn.Conv3d):
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"""
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NOTE: No support for padding along time dimension.
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Input must already be padded along time.
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"""
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def forward(self, input):
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memory_count = torch.prod(torch.tensor(input.shape)).item() * 2 / 1024**3
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if memory_count > 2:
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part_num = int(memory_count / 2) + 1
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k = self.kernel_size[0]
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input_idx = torch.arange(k - 1, input.size(2))
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input_chunks_idx = torch.chunk(input_idx, part_num, dim=0)
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# assert self.kernel_size == (3, 3, 3), f"kernel_size {self.kernel_size} != (3, 3, 3)"
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assert self.stride[0] == 1, f"stride {self.stride}"
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assert self.dilation[0] == 1, f"dilation {self.dilation}"
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assert self.padding[0] == 0, f"padding {self.padding}"
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# Comptue output size
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assert not input.requires_grad
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B, _, T_in, H_in, W_in = input.shape
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output_size = (
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B,
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self.out_channels,
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T_in - k + 1,
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H_in // self.stride[1],
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W_in // self.stride[2],
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)
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output = torch.empty(output_size, dtype=input.dtype, device=input.device)
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for input_chunk_idx in input_chunks_idx:
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input_s = input_chunk_idx[0] - k + 1
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input_e = input_chunk_idx[-1] + 1
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input_chunk = input[:, :, input_s:input_e, :, :]
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output_chunk = super(SafeConv3d, self).forward(input_chunk)
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output_s = input_s
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output_e = output_s + output_chunk.size(2)
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output[:, :, output_s:output_e, :, :] = output_chunk
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return output
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else:
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return super(SafeConv3d, self).forward(input)
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class StridedSafeConv3d(torch.nn.Conv3d):
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def forward(self, input, local_shard: bool = False):
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assert self.stride[0] == self.kernel_size[0]
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assert self.dilation[0] == 1
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assert self.padding[0] == 0
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kernel_size = self.kernel_size[0]
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stride = self.stride[0]
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T_in = input.size(2)
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T_out = T_in // kernel_size
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# Parallel implementation.
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if local_shard:
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idx = torch.arange(T_out)
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idx = local_shard(idx, dim=0)
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start = idx.min() * stride
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end = idx.max() * stride + kernel_size
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local_input = input[:, :, start:end, :, :]
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return torch.nn.Conv3d.forward(self, local_input)
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raise NotImplementedError
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class ContextParallelConv3d(SafeConv3d):
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def __init__(
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self,
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in_channels,
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out_channels,
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kernel_size: Union[int, Tuple[int, int, int]],
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stride: Union[int, Tuple[int, int, int]],
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causal: bool = True,
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context_parallel: bool = True,
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**kwargs,
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):
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self.causal = causal
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self.context_parallel = context_parallel
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kernel_size = cast_tuple(kernel_size, 3)
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stride = cast_tuple(stride, 3)
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height_pad = (kernel_size[1] - 1) // 2
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width_pad = (kernel_size[2] - 1) // 2
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super().__init__(
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in_channels=in_channels,
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out_channels=out_channels,
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kernel_size=kernel_size,
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stride=stride,
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dilation=(1, 1, 1),
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padding=(0, height_pad, width_pad),
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**kwargs,
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)
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def forward(self, x: torch.Tensor):
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cp_rank, cp_world_size = get_cp_rank_size()
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# Compute padding amounts.
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context_size = self.kernel_size[0] - 1
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if self.causal:
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pad_front = context_size
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pad_back = 0
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else:
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pad_front = context_size // 2
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pad_back = context_size - pad_front
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# Apply padding.
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assert self.padding_mode == "replicate" # DEBUG
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mode = "constant" if self.padding_mode == "zeros" else self.padding_mode
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if self.context_parallel and cp_world_size == 1:
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x = F.pad(x, (0, 0, 0, 0, pad_front, pad_back), mode=mode)
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else:
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if cp_rank == 0:
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x = F.pad(x, (0, 0, 0, 0, pad_front, 0), mode=mode)
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elif cp_rank == cp_world_size - 1 and pad_back:
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x = F.pad(x, (0, 0, 0, 0, 0, pad_back), mode=mode)
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if self.context_parallel and cp_world_size == 1:
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return super().forward(x)
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if self.stride[0] == 1:
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# Receive some frames from previous rank.
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x = cp_pass_frames(x, context_size)
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return super().forward(x)
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# Less efficient implementation for strided convs.
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# All gather x, infer and chunk.
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assert (
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x.dtype == torch.bfloat16
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), f"Expected x to be of type torch.bfloat16, got {x.dtype}"
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x = gather_all_frames(x) # [B, C, k - 1 + global_T, H, W]
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return StridedSafeConv3d.forward(self, x, local_shard=True)
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class Conv1x1(nn.Linear):
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"""*1x1 Conv implemented with a linear layer."""
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def __init__(self, in_features: int, out_features: int, *args, **kwargs):
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super().__init__(in_features, out_features, *args, **kwargs)
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def forward(self, x: torch.Tensor):
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"""Forward pass.
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Args:
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x: Input tensor. Shape: [B, C, *] or [B, *, C].
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Returns:
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x: Output tensor. Shape: [B, C', *] or [B, *, C'].
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"""
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x = x.movedim(1, -1)
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x = super().forward(x)
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x = x.movedim(-1, 1)
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return x
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class DepthToSpaceTime(nn.Module):
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def __init__(
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self,
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temporal_expansion: int,
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spatial_expansion: int,
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):
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super().__init__()
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self.temporal_expansion = temporal_expansion
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self.spatial_expansion = spatial_expansion
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# When printed, this module should show the temporal and spatial expansion factors.
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def extra_repr(self):
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return f"texp={self.temporal_expansion}, sexp={self.spatial_expansion}"
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def forward(self, x: torch.Tensor):
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"""Forward pass.
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Args:
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x: Input tensor. Shape: [B, C, T, H, W].
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Returns:
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x: Rearranged tensor. Shape: [B, C/(st*s*s), T*st, H*s, W*s].
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"""
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x = rearrange(
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x,
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"B (C st sh sw) T H W -> B C (T st) (H sh) (W sw)",
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st=self.temporal_expansion,
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sh=self.spatial_expansion,
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sw=self.spatial_expansion,
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)
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cp_rank, _ = get_cp_rank_size()
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if self.temporal_expansion > 1 and cp_rank == 0:
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# Drop the first self.temporal_expansion - 1 frames.
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# This is because we always want the 3x3x3 conv filter to only apply
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# to the first frame, and the first frame doesn't need to be repeated.
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assert all(x.shape)
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x = x[:, :, self.temporal_expansion - 1 :]
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assert all(x.shape)
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return x
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def norm_fn(
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in_channels: int,
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affine: bool = True,
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):
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return GroupNormSpatial(affine=affine, num_groups=32, num_channels=in_channels)
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class ResBlock(nn.Module):
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"""Residual block that preserves the spatial dimensions."""
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def __init__(
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self,
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channels: int,
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*,
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affine: bool = True,
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attn_block: Optional[nn.Module] = None,
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padding_mode: str = "replicate",
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causal: bool = True,
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):
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super().__init__()
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self.channels = channels
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assert causal
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self.stack = nn.Sequential(
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norm_fn(channels, affine=affine),
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nn.SiLU(inplace=True),
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ContextParallelConv3d(
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in_channels=channels,
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out_channels=channels,
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kernel_size=(3, 3, 3),
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stride=(1, 1, 1),
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padding_mode=padding_mode,
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bias=True,
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causal=causal,
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),
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norm_fn(channels, affine=affine),
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nn.SiLU(inplace=True),
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ContextParallelConv3d(
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in_channels=channels,
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out_channels=channels,
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kernel_size=(3, 3, 3),
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stride=(1, 1, 1),
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padding_mode=padding_mode,
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bias=True,
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causal=causal,
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),
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)
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self.attn_block = attn_block if attn_block else nn.Identity()
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def forward(self, x: torch.Tensor):
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"""Forward pass.
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Args:
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x: Input tensor. Shape: [B, C, T, H, W].
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"""
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residual = x
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x = self.stack(x)
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x = x + residual
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del residual
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return self.attn_block(x)
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def prepare_for_attention(qkv: torch.Tensor, head_dim: int, qk_norm: bool = True):
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"""Prepare qkv tensor for attention and normalize qk.
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Args:
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qkv: Input tensor. Shape: [B, L, 3 * num_heads * head_dim].
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Returns:
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q, k, v: qkv tensor split into q, k, v. Shape: [B, num_heads, L, head_dim].
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"""
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assert qkv.ndim == 3 # [B, L, 3 * num_heads * head_dim]
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assert qkv.size(2) % (3 * head_dim) == 0
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num_heads = qkv.size(2) // (3 * head_dim)
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qkv = qkv.unflatten(2, (3, num_heads, head_dim))
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q, k, v = qkv.unbind(2) # [B, L, num_heads, head_dim]
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q = q.transpose(1, 2) # [B, num_heads, L, head_dim]
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k = k.transpose(1, 2) # [B, num_heads, L, head_dim]
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v = v.transpose(1, 2) # [B, num_heads, L, head_dim]
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if qk_norm:
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q = F.normalize(q, p=2, dim=-1)
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k = F.normalize(k, p=2, dim=-1)
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# Mixed precision can change the dtype of normed q/k to float32.
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q = q.to(dtype=qkv.dtype)
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k = k.to(dtype=qkv.dtype)
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return q, k, v
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class Attention(nn.Module):
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def __init__(
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self,
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dim: int,
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head_dim: int = 32,
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qkv_bias: bool = False,
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out_bias: bool = True,
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qk_norm: bool = True,
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) -> None:
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super().__init__()
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self.head_dim = head_dim
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self.num_heads = dim // head_dim
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self.qk_norm = qk_norm
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self.qkv = nn.Linear(dim, 3 * dim, bias=qkv_bias)
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self.out = nn.Linear(dim, dim, bias=out_bias)
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def forward(
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self,
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x: torch.Tensor,
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*,
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chunk_size=2**15,
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) -> torch.Tensor:
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"""Compute temporal self-attention.
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Args:
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x: Input tensor. Shape: [B, C, T, H, W].
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chunk_size: Chunk size for large tensors.
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Returns:
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x: Output tensor. Shape: [B, C, T, H, W].
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"""
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B, _, T, H, W = x.shape
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if T == 1:
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# No attention for single frame.
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x = x.movedim(1, -1) # [B, C, T, H, W] -> [B, T, H, W, C]
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qkv = self.qkv(x)
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_, _, x = qkv.chunk(3, dim=-1) # Throw away queries and keys.
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x = self.out(x)
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return x.movedim(-1, 1) # [B, T, H, W, C] -> [B, C, T, H, W]
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# 1D temporal attention.
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x = rearrange(x, "B C t h w -> (B h w) t C")
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qkv = self.qkv(x)
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# Input: qkv with shape [B, t, 3 * num_heads * head_dim]
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# Output: x with shape [B, num_heads, t, head_dim]
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q, k, v = prepare_for_attention(qkv, self.head_dim, qk_norm=self.qk_norm)
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attn_kwargs = dict(
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attn_mask=None,
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dropout_p=0.0,
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is_causal=True,
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scale=self.head_dim**-0.5,
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)
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if q.size(0) <= chunk_size:
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x = F.scaled_dot_product_attention(
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q, k, v, **attn_kwargs
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) # [B, num_heads, t, head_dim]
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else:
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# Evaluate in chunks to avoid `RuntimeError: CUDA error: invalid configuration argument.`
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# Chunks of 2**16 and up cause an error.
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x = torch.empty_like(q)
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for i in range(0, q.size(0), chunk_size):
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qc = q[i : i + chunk_size]
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kc = k[i : i + chunk_size]
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vc = v[i : i + chunk_size]
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chunk = F.scaled_dot_product_attention(qc, kc, vc, **attn_kwargs)
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x[i : i + chunk_size].copy_(chunk)
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assert x.size(0) == q.size(0)
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x = x.transpose(1, 2) # [B, t, num_heads, head_dim]
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x = x.flatten(2) # [B, t, num_heads * head_dim]
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x = self.out(x)
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x = rearrange(x, "(B h w) t C -> B C t h w", B=B, h=H, w=W)
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return x
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class AttentionBlock(nn.Module):
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def __init__(
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self,
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dim: int,
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**attn_kwargs,
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) -> None:
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super().__init__()
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self.norm = norm_fn(dim)
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self.attn = Attention(dim, **attn_kwargs)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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return x + self.attn(self.norm(x))
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class CausalUpsampleBlock(nn.Module):
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def __init__(
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self,
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in_channels: int,
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out_channels: int,
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num_res_blocks: int,
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*,
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temporal_expansion: int = 2,
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spatial_expansion: int = 2,
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**block_kwargs,
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):
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super().__init__()
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blocks = []
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for _ in range(num_res_blocks):
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blocks.append(block_fn(in_channels, **block_kwargs))
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self.blocks = nn.Sequential(*blocks)
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self.temporal_expansion = temporal_expansion
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self.spatial_expansion = spatial_expansion
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# Change channels in the final convolution layer.
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self.proj = Conv1x1(
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in_channels,
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out_channels * temporal_expansion * (spatial_expansion**2),
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)
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self.d2st = DepthToSpaceTime(
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temporal_expansion=temporal_expansion, spatial_expansion=spatial_expansion
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)
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def forward(self, x):
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x = self.blocks(x)
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x = self.proj(x)
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x = self.d2st(x)
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return x
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def block_fn(channels, *, has_attention: bool = False, **block_kwargs):
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attn_block = AttentionBlock(channels) if has_attention else None
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return ResBlock(
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channels, affine=True, attn_block=attn_block, **block_kwargs
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)
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class DownsampleBlock(nn.Module):
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def __init__(
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self,
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in_channels: int,
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out_channels: int,
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num_res_blocks,
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*,
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temporal_reduction=2,
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spatial_reduction=2,
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**block_kwargs,
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):
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"""
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Downsample block for the VAE encoder.
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Args:
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in_channels: Number of input channels.
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out_channels: Number of output channels.
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num_res_blocks: Number of residual blocks.
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temporal_reduction: Temporal reduction factor.
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spatial_reduction: Spatial reduction factor.
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"""
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super().__init__()
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layers = []
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# Change the channel count in the strided convolution.
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# This lets the ResBlock have uniform channel count,
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# as in ConvNeXt.
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assert in_channels != out_channels
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layers.append(
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ContextParallelConv3d(
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in_channels=in_channels,
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out_channels=out_channels,
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kernel_size=(temporal_reduction, spatial_reduction, spatial_reduction),
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stride=(temporal_reduction, spatial_reduction, spatial_reduction),
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padding_mode="replicate",
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bias=True,
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)
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)
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for _ in range(num_res_blocks):
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layers.append(block_fn(out_channels, **block_kwargs))
|
|
|
|
self.layers = nn.Sequential(*layers)
|
|
|
|
def forward(self, x):
|
|
return self.layers(x)
|
|
|
|
|
|
def add_fourier_features(inputs: torch.Tensor, start=6, stop=8, step=1):
|
|
num_freqs = (stop - start) // step
|
|
assert inputs.ndim == 5
|
|
C = inputs.size(1)
|
|
|
|
# Create Base 2 Fourier features.
|
|
freqs = torch.arange(start, stop, step, dtype=inputs.dtype, device=inputs.device)
|
|
assert num_freqs == len(freqs)
|
|
w = torch.pow(2.0, freqs) * (2 * torch.pi) # [num_freqs]
|
|
C = inputs.shape[1]
|
|
w = w.repeat(C)[None, :, None, None, None] # [1, C * num_freqs, 1, 1, 1]
|
|
|
|
# Interleaved repeat of input channels to match w.
|
|
h = inputs.repeat_interleave(num_freqs, dim=1) # [B, C * num_freqs, T, H, W]
|
|
# Scale channels by frequency.
|
|
h = w * h
|
|
|
|
return torch.cat(
|
|
[
|
|
inputs,
|
|
torch.sin(h),
|
|
torch.cos(h),
|
|
],
|
|
dim=1,
|
|
)
|
|
|
|
|
|
class FourierFeatures(nn.Module):
|
|
def __init__(self, start: int = 6, stop: int = 8, step: int = 1):
|
|
super().__init__()
|
|
self.start = start
|
|
self.stop = stop
|
|
self.step = step
|
|
|
|
def forward(self, inputs):
|
|
"""Add Fourier features to inputs.
|
|
|
|
Args:
|
|
inputs: Input tensor. Shape: [B, C, T, H, W]
|
|
|
|
Returns:
|
|
h: Output tensor. Shape: [B, (1 + 2 * num_freqs) * C, T, H, W]
|
|
"""
|
|
return add_fourier_features(inputs, self.start, self.stop, self.step)
|
|
|
|
|
|
class Decoder(nn.Module):
|
|
def __init__(
|
|
self,
|
|
*,
|
|
out_channels: int = 3,
|
|
latent_dim: int,
|
|
base_channels: int,
|
|
channel_multipliers: List[int],
|
|
num_res_blocks: List[int],
|
|
temporal_expansions: Optional[List[int]] = None,
|
|
spatial_expansions: Optional[List[int]] = None,
|
|
has_attention: List[bool],
|
|
output_norm: bool = True,
|
|
nonlinearity: str = "silu",
|
|
output_nonlinearity: str = "silu",
|
|
causal: bool = True,
|
|
**block_kwargs,
|
|
):
|
|
super().__init__()
|
|
self.input_channels = latent_dim
|
|
self.base_channels = base_channels
|
|
self.channel_multipliers = channel_multipliers
|
|
self.num_res_blocks = num_res_blocks
|
|
self.output_nonlinearity = output_nonlinearity
|
|
assert nonlinearity == "silu"
|
|
assert causal
|
|
|
|
ch = [mult * base_channels for mult in channel_multipliers]
|
|
self.num_up_blocks = len(ch) - 1
|
|
assert len(num_res_blocks) == self.num_up_blocks + 2
|
|
|
|
blocks = []
|
|
|
|
first_block = [
|
|
nn.Conv3d(latent_dim, ch[-1], kernel_size=(1, 1, 1))
|
|
] # Input layer.
|
|
# First set of blocks preserve channel count.
|
|
for _ in range(num_res_blocks[-1]):
|
|
first_block.append(
|
|
block_fn(
|
|
ch[-1],
|
|
has_attention=has_attention[-1],
|
|
causal=causal,
|
|
**block_kwargs,
|
|
)
|
|
)
|
|
blocks.append(nn.Sequential(*first_block))
|
|
|
|
assert len(temporal_expansions) == len(spatial_expansions) == self.num_up_blocks
|
|
assert len(num_res_blocks) == len(has_attention) == self.num_up_blocks + 2
|
|
|
|
upsample_block_fn = CausalUpsampleBlock
|
|
|
|
for i in range(self.num_up_blocks):
|
|
block = upsample_block_fn(
|
|
ch[-i - 1],
|
|
ch[-i - 2],
|
|
num_res_blocks=num_res_blocks[-i - 2],
|
|
has_attention=has_attention[-i - 2],
|
|
temporal_expansion=temporal_expansions[-i - 1],
|
|
spatial_expansion=spatial_expansions[-i - 1],
|
|
causal=causal,
|
|
**block_kwargs,
|
|
)
|
|
blocks.append(block)
|
|
|
|
assert not output_norm
|
|
|
|
# Last block. Preserve channel count.
|
|
last_block = []
|
|
for _ in range(num_res_blocks[0]):
|
|
last_block.append(
|
|
block_fn(
|
|
ch[0], has_attention=has_attention[0], causal=causal, **block_kwargs
|
|
)
|
|
)
|
|
blocks.append(nn.Sequential(*last_block))
|
|
|
|
self.blocks = nn.ModuleList(blocks)
|
|
self.output_proj = Conv1x1(ch[0], out_channels)
|
|
|
|
def forward(self, x):
|
|
"""Forward pass.
|
|
|
|
Args:
|
|
x: Latent tensor. Shape: [B, input_channels, t, h, w]. Scaled [-1, 1].
|
|
|
|
Returns:
|
|
x: Reconstructed video tensor. Shape: [B, C, T, H, W]. Scaled to [-1, 1].
|
|
T + 1 = (t - 1) * 4.
|
|
H = h * 16, W = w * 16.
|
|
"""
|
|
for block in self.blocks:
|
|
x = block(x)
|
|
|
|
if self.output_nonlinearity == "silu":
|
|
x = F.silu(x, inplace=not self.training)
|
|
else:
|
|
assert (
|
|
not self.output_nonlinearity
|
|
) # StyleGAN3 omits the to-RGB nonlinearity.
|
|
|
|
return self.output_proj(x).contiguous()
|
|
|
|
|
|
def make_broadcastable(
|
|
tensor: torch.Tensor,
|
|
axis: int,
|
|
ndim: int,
|
|
) -> torch.Tensor:
|
|
"""
|
|
Reshapes the input tensor to have singleton dimensions in all axes except the specified axis.
|
|
|
|
Args:
|
|
tensor (torch.Tensor): The tensor to reshape. Typically 1D.
|
|
axis (int): The axis along which the tensor should retain its original size.
|
|
ndim (int): The total number of dimensions the reshaped tensor should have.
|
|
|
|
Returns:
|
|
torch.Tensor: The reshaped tensor with shape suitable for broadcasting.
|
|
"""
|
|
if tensor.dim() != 1:
|
|
raise ValueError(f"Expected tensor to be 1D, but got {tensor.dim()}D tensor.")
|
|
|
|
axis = (axis + ndim) % ndim # Ensure the axis is within the tensor dimensions
|
|
shape = [1] * ndim # Start with all dimensions as 1
|
|
shape[axis] = tensor.size(0) # Set the specified axis to the size of the tensor
|
|
return tensor.view(*shape)
|
|
|
|
|
|
def blend(a: torch.Tensor, b: torch.Tensor, axis: int) -> torch.Tensor:
|
|
"""
|
|
Blends two tensors `a` and `b` along the specified axis using linear interpolation.
|
|
|
|
Args:
|
|
a (torch.Tensor): The first tensor.
|
|
b (torch.Tensor): The second tensor. Must have the same shape as `a`.
|
|
axis (int): The axis along which to perform the blending.
|
|
|
|
Returns:
|
|
torch.Tensor: The blended tensor.
|
|
"""
|
|
assert (
|
|
a.shape == b.shape
|
|
), f"Tensors must have the same shape, got {a.shape} and {b.shape}"
|
|
steps = a.size(axis)
|
|
|
|
# Create a weight tensor that linearly interpolates from 0 to 1
|
|
start = 1 / (steps + 1)
|
|
end = steps / (steps + 1)
|
|
weight = torch.linspace(start, end, steps=steps, device=a.device, dtype=a.dtype)
|
|
|
|
# Make the weight tensor broadcastable across all dimensions
|
|
weight = make_broadcastable(weight, axis, a.dim())
|
|
|
|
# Perform the blending
|
|
return a * (1 - weight) + b * weight
|
|
|
|
|
|
def blend_horizontal(a: torch.Tensor, b: torch.Tensor, overlap: int) -> torch.Tensor:
|
|
if overlap == 0:
|
|
return torch.cat([a, b], dim=-1)
|
|
|
|
assert a.size(-1) >= overlap
|
|
assert b.size(-1) >= overlap
|
|
a_left, a_overlap = a[..., :-overlap], a[..., -overlap:]
|
|
b_overlap, b_right = b[..., :overlap], b[..., overlap:]
|
|
return torch.cat([a_left, blend(a_overlap, b_overlap, -1), b_right], dim=-1)
|
|
|
|
|
|
def blend_vertical(a: torch.Tensor, b: torch.Tensor, overlap: int) -> torch.Tensor:
|
|
if overlap == 0:
|
|
return torch.cat([a, b], dim=-2)
|
|
|
|
assert a.size(-2) >= overlap
|
|
assert b.size(-2) >= overlap
|
|
a_top, a_overlap = a[..., :-overlap, :], a[..., -overlap:, :]
|
|
b_overlap, b_bottom = b[..., :overlap, :], b[..., overlap:, :]
|
|
return torch.cat([a_top, blend(a_overlap, b_overlap, -2), b_bottom], dim=-2)
|
|
|
|
|
|
def nearest_multiple(x: int, multiple: int) -> int:
|
|
return round(x / multiple) * multiple
|
|
|
|
|
|
def apply_tiled(
|
|
fn: Callable[[torch.Tensor], torch.Tensor],
|
|
x: torch.Tensor,
|
|
num_tiles_w: int,
|
|
num_tiles_h: int,
|
|
overlap: int = 0, # Number of pixel of overlap between adjacent tiles.
|
|
# Use a factor of 2 times the latent downsample factor.
|
|
min_block_size: int = 1, # Minimum number of pixels in each dimension when subdividing.
|
|
):
|
|
if num_tiles_w == 1 and num_tiles_h == 1:
|
|
return fn(x)
|
|
|
|
assert (
|
|
num_tiles_w & (num_tiles_w - 1) == 0
|
|
), f"num_tiles_w={num_tiles_w} must be a power of 2"
|
|
assert (
|
|
num_tiles_h & (num_tiles_h - 1) == 0
|
|
), f"num_tiles_h={num_tiles_h} must be a power of 2"
|
|
|
|
H, W = x.shape[-2:]
|
|
assert H % min_block_size == 0
|
|
assert W % min_block_size == 0
|
|
ov = overlap // 2
|
|
assert ov % min_block_size == 0
|
|
|
|
if num_tiles_w >= 2:
|
|
# Subdivide horizontally.
|
|
half_W = nearest_multiple(W // 2, min_block_size)
|
|
left = x[..., :, : half_W + ov]
|
|
right = x[..., :, half_W - ov :]
|
|
|
|
assert num_tiles_w % 2 == 0, f"num_tiles_w={num_tiles_w} must be even"
|
|
left = apply_tiled(
|
|
fn, left, num_tiles_w // 2, num_tiles_h, overlap, min_block_size
|
|
)
|
|
right = apply_tiled(
|
|
fn, right, num_tiles_w // 2, num_tiles_h, overlap, min_block_size
|
|
)
|
|
if left is None or right is None:
|
|
return None
|
|
|
|
# If `fn` changed the resolution, adjust the overlap.
|
|
resample_factor = left.size(-1) / (half_W + ov)
|
|
out_overlap = int(overlap * resample_factor)
|
|
|
|
return blend_horizontal(left, right, out_overlap)
|
|
|
|
if num_tiles_h >= 2:
|
|
# Subdivide vertically.
|
|
half_H = nearest_multiple(H // 2, min_block_size)
|
|
top = x[..., : half_H + ov, :]
|
|
bottom = x[..., half_H - ov :, :]
|
|
|
|
assert num_tiles_h % 2 == 0, f"num_tiles_h={num_tiles_h} must be even"
|
|
top = apply_tiled(
|
|
fn, top, num_tiles_w, num_tiles_h // 2, overlap, min_block_size
|
|
)
|
|
bottom = apply_tiled(
|
|
fn, bottom, num_tiles_w, num_tiles_h // 2, overlap, min_block_size
|
|
)
|
|
if top is None or bottom is None:
|
|
return None
|
|
|
|
# If `fn` changed the resolution, adjust the overlap.
|
|
resample_factor = top.size(-2) / (half_H + ov)
|
|
out_overlap = int(overlap * resample_factor)
|
|
|
|
return blend_vertical(top, bottom, out_overlap)
|
|
|
|
raise ValueError(f"Invalid num_tiles_w={num_tiles_w} and num_tiles_h={num_tiles_h}")
|