Fix VAE encoder to load canonical checkpoint key names
This commit is contained in:
Daniel Maddern
parent
807bd64a82
commit
9bec53ac36
1 changed files with 123 additions and 150 deletions
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@ -6,15 +6,18 @@ upcast to FP32 before mean/std normalization (the reference contract).
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Causal-conv semantics: spatial padding is reflect; temporal padding is causal
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(front-only zeros) with a stride grid that starts at the first input frame
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(Comfy autopad "same" / ``causal``). For a single input frame the temporal
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taps of the kernel are truncated (Comfy ``autopad="causal_zero"``) so the frame
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is not convolved against zero frames.
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(Comfy autopad "same" / ``causal``). For a single input frame the temporal taps
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of the kernel are truncated (Comfy ``autopad="causal_zero"``) so a keyframe is
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never convolved against zero frames.
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Weights are read straight from the checkpoint's ``encoder.*`` / ``quant_conv.*``
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keys (a direct name-for-name copy into plain tensors) and applied by the
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stateless kernels below.
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"""
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from __future__ import annotations
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import math
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import os
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from pathlib import Path
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import torch
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@ -28,182 +31,152 @@ IMAGENET_STD = (0.229, 0.224, 0.225)
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LATENTS_MEAN = (0.858090341091156, -0.9606591463088989, 1.0661640167236328, -0.5090325474739075, -0.2727581858634949, -1.3675414323806763, -0.2553254961967468, -0.26907554268836975, -0.5376840829849243, -0.0464097298681736, 0.6657370328903198, 0.19690127670764923, -0.5460608005523682, -0.4035342037677765, -0.23683024942874908, 0.25928452610969543, -0.30133944749832153, 0.211341992020607, -1.1206848621368408, 0.3581933379173279, -0.04225143790245056, 0.2604829967021942, 0.22864092886447906, 0.7056031823158264)
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LATENTS_STD = (1.2223774194717407, 1.2767263650894165, 1.68317747116088865, 1.7549455165863037, 1.5636216402053833, 2.194143533706665, 0.96531379222869875, 1.05698859691619875, 0.841948926448822, 0.7729952931404114, 1.8955937623977661, 0.946841835975647, 0.7996809482574463, 0.44988900423049925, 0.7197399735450745, 0.69362932443618775, 2.961095094680786, 2.7694199085235595, 3.0496184825897215, 2.1088054180145265, 3.276226282119751, 3.1627357006073, 2.28168129920959475, 2.6127843856811525)
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CH = 128
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CH_MULT = (1, 2, 2, 4, 4, 8)
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SPACE_DOWN = (2, 2, 2, 2, 1, 1)
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TIME_DOWN = (1, 2, 2, 1, 1, 1)
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NUM_RES_BLOCKS = 2
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VAE_RATIO = 16
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NIN_LEVELS = frozenset({1, 3, 5})
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DOWNSAMPLE_LEVELS = frozenset({0, 1, 2, 3})
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def _causal_front_padding(t_in: int, kernel: int, stride: int) -> int:
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"""Front-only zero padding matching the reference causal 3D conv.
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``padding = kernel - 1 - (t_out - 1) * stride`` with ``t_out = ceil(t_in /
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stride)`` (the reference computes the output length from the unpadded
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input). The result is non-negative: with a stride-s grid and a (2k+1)
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kernel, ``ceil(t/stride) >= 1 + (t-1) // stride`` for all t, so
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``(t_in - 1) % stride * stride >= (kernel - 1) % (2 * stride)``.
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"""
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"""Front-only zero padding: ``kernel-1-(ceil(t_in/stride)-1)*stride``."""
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t_out = math.ceil(t_in / stride)
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return max(0, kernel - 1 - (t_out - 1) * stride)
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class _CausalConv3d(nn.Module):
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"""3D conv: reflect spatial padding, causal (front-zero) temporal padding."""
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def __init__(self, in_channels: int, out_channels: int, kernel_size: int, stride: int | tuple[int, int, int] = 1, spatial_padding: int = 0):
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super().__init__()
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self.kernel_size = kernel_size
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self.stride = stride if isinstance(stride, tuple) else (stride, stride, stride)
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self.spatial_padding = spatial_padding
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self.conv = nn.Conv3d(in_channels, out_channels, kernel_size, stride=self.stride, padding=(0, spatial_padding, spatial_padding))
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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t = x.shape[2]
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if t == 1:
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# A single input frame never convolves against zero frames: the
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# temporal taps are truncated (Comfy autopad="causal_zero").
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if self.spatial_padding > 0:
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half = (self.kernel_size - 1) // 2
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kernel = self.conv.weight[:, :, half:half + 2 * self.spatial_padding + 1]
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return F.conv3d(x, kernel, self.conv.bias, (1, self.stride[1], self.stride[2]), (0, self.spatial_padding, self.spatial_padding))
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return self.conv(x)
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front = _causal_front_padding(t, self.kernel_size, self.stride[0])
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if front > 0:
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x = F.pad(x, (0, 0, 0, 0, front, 0))
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return self.conv(x)
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def _causal_conv3d(x, weight, bias, *, kernel_size, stride, spatial_padding):
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"""Reflect-spatial / causal-temporal 3D conv (single-frame -> truncated taps)."""
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t = x.shape[2]
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if t == 1:
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if spatial_padding > 0:
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half = (kernel_size - 1) // 2
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kernel = weight[:, :, half : half + 2 * spatial_padding + 1]
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return F.conv3d(x, kernel, bias, (1, stride[1], stride[2]), (0, spatial_padding, spatial_padding))
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return F.conv3d(x, weight, bias, stride, (0, spatial_padding, spatial_padding))
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front = _causal_front_padding(t, kernel_size, stride[0])
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if front > 0:
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x = F.pad(x, (0, 0, 0, 0, front, 0))
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return F.conv3d(x, weight, bias, stride, (0, spatial_padding, spatial_padding))
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class TemporalIsolatedGroupNorm(nn.GroupNorm):
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"""GroupNorm with statistics computed per frame (time merged into batch)."""
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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if x.dim() != 5:
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return super().forward(x)
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b, c, t, h, w = x.shape
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x = x.permute(0, 2, 1, 3, 4).contiguous().view(b * t, c, 1, h, w)
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x = super().forward(x)
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return x.view(b, t, c, h, w).permute(0, 2, 1, 3, 4).contiguous()
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def _group_norm_3d(x, weight, bias):
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"""GroupNorm (32 groups, eps 1e-6) with per-frame statistics."""
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b, c, t, h, w = x.shape
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y = F.group_norm(x.permute(0, 2, 1, 3, 4).contiguous().view(b * t, c, 1, h, w), 32, weight, bias, 1e-6)
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return y.view(b, t, c, h, w).permute(0, 2, 1, 3, 4).contiguous()
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def group_norm_3d(num_channels: int) -> TemporalIsolatedGroupNorm:
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return TemporalIsolatedGroupNorm(num_groups=32, num_channels=num_channels, eps=1e-6, affine=True)
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def _resnet(x, p):
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residual = x if p["nin"] is None else _causal_conv3d(x, p["nin"][0], p["nin"][1], kernel_size=1, stride=(1, 1, 1), spatial_padding=0)
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h = _causal_conv3d(F.silu(_group_norm_3d(x, p["norm1_w"], p["norm1_b"])), p["conv1_w"], p["conv1_b"], kernel_size=3, stride=(1, 1, 1), spatial_padding=1)
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h = _causal_conv3d(F.silu(_group_norm_3d(h, p["norm2_w"], p["norm2_b"])), p["conv2_w"], p["conv2_b"], kernel_size=3, stride=(1, 1, 1), spatial_padding=1)
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return h.add_(residual)
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class Downsample3D(nn.Module):
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def __init__(self, in_channels: int, out_channels: int, time_stride: int = 1, space_stride: int = 2):
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super().__init__()
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self.space_stride = space_stride
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self.conv = _CausalConv3d(in_channels, out_channels, kernel_size=3, stride=(time_stride, space_stride, space_stride), spatial_padding=0)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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if self.space_stride == 2:
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x = F.pad(x, (0, 1, 0, 1, 0, 0), mode="reflect")
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return self.conv(x)
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def _downsample(x, p):
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if p["space"] == 2:
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x = F.pad(x, (0, 1, 0, 1, 0, 0), mode="reflect")
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return _causal_conv3d(x, p["w"], p["b"], kernel_size=3, stride=(p["time"], p["space"], p["space"]), spatial_padding=0)
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class ResnetBlock3D(nn.Module):
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def __init__(self, in_channels: int, out_channels: int | None = None):
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super().__init__()
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self.in_channels = in_channels
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self.out_channels = in_channels if out_channels is None else out_channels
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self.norm1 = group_norm_3d(in_channels)
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self.norm2 = group_norm_3d(self.out_channels)
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self.conv1 = _CausalConv3d(in_channels, self.out_channels, kernel_size=3, spatial_padding=1)
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self.conv2 = _CausalConv3d(self.out_channels, self.out_channels, kernel_size=3, spatial_padding=1)
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if self.in_channels != self.out_channels:
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self.nin_shortcut = _CausalConv3d(self.in_channels, self.out_channels, kernel_size=1, spatial_padding=0)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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h = self.conv1(F.silu(self.norm1(x), inplace=True))
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h = self.conv2(F.silu(self.norm2(h), inplace=True))
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if self.in_channels != self.out_channels:
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x = self.nin_shortcut(x)
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return h.add_(x)
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class EncoderFCN3D(nn.Module):
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def __init__(self, ch: int, ch_mult: tuple[int, ...], space_down: tuple[int, ...], time_down: tuple[int, ...], num_res_blocks: int, in_channels: int, z_channels: int, double_z: bool = True):
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super().__init__()
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self.num_levels = len(ch_mult)
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self.num_res_blocks = [num_res_blocks] * self.num_levels
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block_mid = [ch * ch_mult[i] for i in range(self.num_levels)]
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block_in = [block_mid[0]] + block_mid[:-1]
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self.conv_in = _CausalConv3d(in_channels, block_in[0], kernel_size=3, spatial_padding=1)
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self.down = nn.ModuleList()
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for i_level in range(self.num_levels):
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down = nn.Module()
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down.block = nn.ModuleList(
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ResnetBlock3D(block_in[i_level] if i == 0 else block_mid[i_level], block_mid[i_level])
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for i in range(self.num_res_blocks[i_level])
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)
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if space_down[i_level] * time_down[i_level] > 1:
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down.downsample = Downsample3D(block_mid[i_level], block_mid[i_level], time_stride=time_down[i_level], space_stride=space_down[i_level])
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self.down.append(down)
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self.norm_out = group_norm_3d(block_mid[-1])
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self.conv_out = _CausalConv3d(block_mid[-1], 2 * z_channels if double_z else z_channels, kernel_size=3, spatial_padding=1)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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h = self.conv_in(x)
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for i_level in range(self.num_levels):
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for i_block in range(self.num_res_blocks[i_level]):
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h = self.down[i_level].block[i_block](h)
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if hasattr(self.down[i_level], "downsample"):
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h = self.down[i_level].downsample(h)
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h = F.silu(self.norm_out(h), inplace=True)
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return self.conv_out(h)
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def _encoder_run(x, E):
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h = _causal_conv3d(x, E["conv_in"][0], E["conv_in"][1], kernel_size=3, stride=(1, 1, 1), spatial_padding=1)
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for level in E["down"]:
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for blk in level["blocks"]:
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h = _resnet(h, blk)
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if level["down"] is not None:
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h = _downsample(h, level["down"])
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h = F.silu(_group_norm_3d(h, E["norm_out_w"], E["norm_out_b"]))
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return _causal_conv3d(h, E["conv_out"][0], E["conv_out"][1], kernel_size=3, stride=(1, 1, 1), spatial_padding=1)
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class MiniMaxH3VideoVAEEncoder(nn.Module):
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"""Encoder-only H3 VAE. ``encode`` matches the public contract of upstream_vae.py."""
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"""Encoder-only H3 VAE. ``encode`` matches the public contract of upstream_vae.py.
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def __init__(self, *, device: torch.device | str | None = None, tiling: bool = True):
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Weights are plain tensors loaded from the checkpoint by canonical name into
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``self.W`` (a dict), so no ``nn.Module`` sub-hierarchy is needed.
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"""
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def __init__(self, *, tiling: bool = True):
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super().__init__()
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self.vae_ratio, self.vae_ratio_t = 16, 4
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self.vae_ratio, self.vae_ratio_t = VAE_RATIO, 4
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self.clip_length, self.token_drop = 17, 3
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self.tiling, self.tile_size, self.tile_overlap_min = tiling, 256, 64
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self.encoder = EncoderFCN3D(ch=128, ch_mult=(1, 2, 2, 4, 4, 8), space_down=(2, 2, 2, 2, 1, 1), time_down=(1, 2, 2, 1, 1, 1), num_res_blocks=2, in_channels=3, z_channels=24, double_z=True)
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self.quant_conv = nn.Conv3d(48, 48, 1, device=device)
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self.quant_conv = nn.Conv3d(48, 48, 1)
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self.register_buffer("latents_mean", torch.tensor(LATENTS_MEAN), persistent=False)
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self.register_buffer("latents_std", torch.tensor(LATENTS_STD), persistent=False)
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self.register_buffer("pixel_mean", torch.tensor(IMAGENET_MEAN).view(1, 3, 1, 1, 1), persistent=False)
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self.register_buffer("pixel_std", torch.tensor(IMAGENET_STD).view(1, 3, 1, 1, 1), persistent=False)
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def _required_encoder_names(self) -> list[str]:
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names = ["encoder.conv_in.weight", "encoder.conv_in.bias", "encoder.norm_out.weight", "encoder.norm_out.bias", "encoder.conv_out.weight", "encoder.conv_out.bias"]
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for i in range(len(CH_MULT)):
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for b in range(NUM_RES_BLOCKS):
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base = f"encoder.down.{i}.block.{b}."
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names += [
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base + "conv1.weight", base + "conv1.bias",
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base + "conv2.weight", base + "conv2.bias",
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base + "norm1.weight", base + "norm1.bias",
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base + "norm2.weight", base + "norm2.bias",
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]
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if b == 0 and i in NIN_LEVELS:
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names += [base + "nin_shortcut.weight", base + "nin_shortcut.bias"]
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if i in DOWNSAMPLE_LEVELS:
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names += [f"encoder.down.{i}.downsample.conv.weight", f"encoder.down.{i}.downsample.conv.bias"]
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return names
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@classmethod
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def from_safetensors(cls, path: str | Path, *, device: str | torch.device = "cuda", tiling: bool = True) -> "MiniMaxH3VideoVAEEncoder":
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model = cls(device="meta", tiling=tiling)
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expected = model.state_dict()
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if os.getenv("H3_FAST_SAFETENSORS", "").lower() in {"1", "true", "yes", "on"}:
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from fastsafetensors import fastsafe_open
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fast_device = "cuda:0" if str(device) == "cuda" else str(device)
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available = set()
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with fastsafe_open(filenames=[str(path)], nogds=True, device=fast_device) as checkpoint:
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available = set(checkpoint.keys())
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missing = sorted(set(expected) - available)
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weights = {name: checkpoint.get_tensor(name).clone().detach() for name in expected}
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model = cls(tiling=tiling)
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names = model._required_encoder_names()
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with safe_open(str(path), framework="pt", device=str(device)) as ck:
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available = set(ck.keys())
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missing = [n for n in names if n not in available]
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if missing:
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raise ValueError(f"incompatible H3 VAE checkpoint; missing: {', '.join(missing)}")
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weights = {name: t.to(device=device, dtype=torch.float32) for name, t in weights.items()}
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elif os.getenv("H3_DISABLE_MMAP", "").lower() in {"1", "true", "yes", "on"}:
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from safetensors.torch import load
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raise ValueError(f"incompatible H3 VAE checkpoint; missing: {', '.join(sorted(missing)[:16])}")
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W = {n: ck.get_tensor(n).to(dtype=torch.float32).to(device) for n in names}
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with open(path, "rb") as file:
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available_weights = load(file.read())
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available = set(available_weights)
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missing = sorted(set(expected) - available)
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if missing:
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raise ValueError(f"incompatible H3 VAE checkpoint; missing: {', '.join(missing)}")
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weights = {name: available_weights[name].to(device=device, dtype=torch.float32) for name in expected}
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else:
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with safe_open(str(path), framework="pt", device=str(device)) as checkpoint:
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available = set(checkpoint.keys())
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shape_errors = [(name, tuple(expected[name].shape), tuple(checkpoint.get_slice(name).get_shape())) for name in expected if name in available and tuple(expected[name].shape) != tuple(checkpoint.get_slice(name).get_shape())]
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if shape_errors:
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raise ValueError(f"incompatible H3 VAE checkpoint; shape mismatch: {shape_errors}")
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weights = {name: checkpoint.get_tensor(name).to(dtype=torch.float32) for name in expected}
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missing = sorted(set(expected) - available)
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if missing:
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raise ValueError(f"incompatible H3 VAE checkpoint; missing: {', '.join(missing)}")
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model.load_state_dict(weights, strict=True, assign=True)
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# Build the structured params dict.
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down = []
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for i in range(len(CH_MULT)):
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mid = CH * CH_MULT[i]
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blocks = []
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for b in range(NUM_RES_BLOCKS):
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base = f"encoder.down.{i}.block.{b}."
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blk = {
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"conv1_w": W[base + "conv1.weight"], "conv1_b": W[base + "conv1.bias"],
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"conv2_w": W[base + "conv2.weight"], "conv2_b": W[base + "conv2.bias"],
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"norm1_w": W[base + "norm1.weight"], "norm1_b": W[base + "norm1.bias"],
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"norm2_w": W[base + "norm2.weight"], "norm2_b": W[base + "norm2.bias"],
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"nin": None,
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}
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if base + "nin_shortcut.weight" in W:
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blk["nin"] = (W[base + "nin_shortcut.weight"], W[base + "nin_shortcut.bias"])
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blocks.append(blk)
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level_down = None
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if i in DOWNSAMPLE_LEVELS:
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ds = f"encoder.down.{i}.downsample.conv."
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level_down = {"w": W[ds + "weight"], "b": W[ds + "bias"], "time": TIME_DOWN[i], "space": SPACE_DOWN[i]}
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down.append({"blocks": blocks, "down": level_down})
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E = {
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"conv_in": (W["encoder.conv_in.weight"], W["encoder.conv_in.bias"]),
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"down": down,
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"norm_out_w": W["encoder.norm_out.weight"], "norm_out_b": W["encoder.norm_out.bias"],
|
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"conv_out": (W["encoder.conv_out.weight"], W["encoder.conv_out.bias"]),
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}
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model.W = E
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model.quant_conv.to(device, torch.float32)
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for b in ("latents_mean", "latents_std", "pixel_mean", "pixel_std"):
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getattr(model, b).to(device)
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return model
|
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@torch.inference_mode()
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def _encode_moments(self, x: torch.Tensor) -> torch.Tensor:
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return self.quant_conv(self.encoder(x))
|
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return F.conv3d(_encoder_run(x.to(torch.float32), self.W), self.quant_conv.weight, self.quant_conv.bias)
|
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|
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def _adaptive_encode(self, x: torch.Tensor) -> torch.Tensor:
|
||||
if self.tiling:
|
||||
|
|
@ -259,7 +232,7 @@ class MiniMaxH3VideoVAEEncoder(nn.Module):
|
|||
if i > 0:
|
||||
tile = self.blend(rows[i - 1][j], tile, latent_y_overlap[i - 1], dim=-2)
|
||||
if j > 0:
|
||||
tile = self.blend(row[j - 1], tile, latent_x_overlap[j - 1], dim=-1)
|
||||
tile = self.blend(row[j - 1], tile, latent_x_overlap[j], dim=-1)
|
||||
if i < len(rows) - 1:
|
||||
tile = tile[..., :-latent_y_overlap[i], :]
|
||||
if j < len(row) - 1:
|
||||
|
|
@ -273,14 +246,14 @@ class MiniMaxH3VideoVAEEncoder(nn.Module):
|
|||
pad_size = (-x.shape[2]) % self.clip_length
|
||||
x = torch.cat([x, x[:, :, -1:].repeat(1, 1, pad_size, 1, 1)], dim=2)
|
||||
num_chunks = x.shape[2] // self.clip_length
|
||||
z_list = [self._adaptive_encode(x[:, :, i * self.clip_length:(i + 1) * self.clip_length, :, :]) for i in range(num_chunks)]
|
||||
z_list = [self._adaptive_encode(x[:, :, i * self.clip_length : (i + 1) * self.clip_length, :, :]) for i in range(num_chunks)]
|
||||
z = torch.cat(z_list, dim=2)
|
||||
if self.token_drop > 0:
|
||||
z = z[:, :, :-self.token_drop]
|
||||
return z
|
||||
|
||||
def encode(self, x: torch.Tensor) -> torch.Tensor:
|
||||
"""``[B, 3, T, H, W]`` pixels in ``[-1, 1]`` -> normalized latents ``[B, 24, T_lat, H//16, W//16]``."""
|
||||
"""``[B,3,H,W]`` or ``[B,3,T,H,W]`` pixels in ``[-1, 1]`` -> normalized latents ``[B,24,T_lat,H//16,W//16]``."""
|
||||
if x.ndim == 4:
|
||||
x = x.unsqueeze(2)
|
||||
x = x.add(1.0).mul_(0.5).sub_(self.pixel_mean.to(x)).div_(self.pixel_std.to(x))
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue