704 lines
25 KiB
Python
704 lines
25 KiB
Python
"""
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MageVAE: DConvEncoder + DConvDenoiser (with CoD Decoder) wrapper.
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Vendored from the reference implementation
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(github.com/microsoft/Mage, mage_flow/models/modules/mage_vae.py); loguru
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swapped for prints, otherwise unchanged. Loads the kl0.1 CoD ckpt layout used
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by ``vae/diffusion_pytorch_model.safetensors`` in the Mage-Flow HF repos:
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encoder weights: 'student.dconv_encoder.*' (packed mean+logvar, out_ch_mult=2)
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decoder weights: 'pipeline.*' (denoiser + y_embedder.decoder)
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Latent shape: [B, 128, H/16, W/16] — no patch packing, no normalization.
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Encode input / decode output are [-1, 1] pixel images with H, W multiples of 16.
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"""
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import math
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import os
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from functools import lru_cache
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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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# ---------------------------------------------------------------------------
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# Primitive layers (vendored from GenCodec, inference subset)
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# ---------------------------------------------------------------------------
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def nonlinearity(x):
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return x * torch.sigmoid(x)
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def Normalize(in_channels):
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return torch.nn.GroupNorm(
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num_groups=32, num_channels=in_channels, eps=1e-6, affine=True
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)
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def modulate(x, shift, scale):
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if x.dim() == 4:
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b, c = x.shape[:2]
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return x * (1 + scale.view(b, c, 1, 1)) + shift.view(b, c, 1, 1)
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return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
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class LayerNorm2d(nn.LayerNorm):
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def __init__(self, num_channels, eps=1e-6, affine=True):
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super().__init__(num_channels, eps=eps, elementwise_affine=affine)
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def forward(self, x):
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# .contiguous() prevents a channels_last-strided NCHW view from
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# propagating into downstream depthwise convs, which would otherwise
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# hit a slow cuDNN path with a per-shape heuristic search.
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x = x.permute(0, 2, 3, 1).contiguous()
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x = F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)
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return x.permute(0, 3, 1, 2).contiguous()
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class _EncoderLayerNorm2d(LayerNorm2d):
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pass
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class RMSNorm(nn.Module):
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def __init__(self, hidden_size, eps=1e-6):
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super().__init__()
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self.weight = nn.Parameter(torch.ones(hidden_size))
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self.variance_epsilon = eps
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def forward(self, x):
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in_dtype = x.dtype
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x = x.to(torch.float32)
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var = x.pow(2).mean(-1, keepdim=True)
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x = x * torch.rsqrt(var + self.variance_epsilon)
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return self.weight * x.to(in_dtype)
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class TimestepEmbedder(nn.Module):
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"""DConv-style timestep MLP (max_period=10000, freq_size=256, hidden=384)."""
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def __init__(self, hidden_size, frequency_embedding_size=256):
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super().__init__()
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self.mlp = nn.Sequential(
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nn.Linear(frequency_embedding_size, hidden_size, bias=True),
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nn.SiLU(),
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nn.Linear(hidden_size, hidden_size, bias=True),
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)
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self.frequency_embedding_size = frequency_embedding_size
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@staticmethod
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def timestep_embedding(t, dim, max_period=10000):
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half = dim // 2
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freqs = torch.exp(
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-math.log(max_period) * torch.arange(0, half, dtype=torch.float32) / half
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).to(t.device)
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args = t[:, None].float() * freqs[None]
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emb = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
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if dim % 2:
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emb = torch.cat([emb, torch.zeros_like(emb[:, :1])], dim=-1)
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return emb
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def forward(self, t):
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emb = self.timestep_embedding(t, self.frequency_embedding_size)
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return self.mlp(emb.to(self.mlp[0].weight.dtype))
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class BottleneckPatchEmbed(nn.Module):
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"""Image patch embed concatenated with a per-patch conditioning vector."""
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def __init__(
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self, patch_size=16, in_chans=3, pca_dim=128, embed_dim=384, bias=True
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):
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super().__init__()
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self.proj1 = nn.Conv2d(
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in_chans, pca_dim, kernel_size=patch_size, stride=patch_size, bias=False
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)
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self.proj2 = nn.Conv2d(pca_dim + embed_dim, embed_dim, kernel_size=1, bias=bias)
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def forward(self, x, cond):
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return self.proj2(torch.cat([self.proj1(x), cond], dim=1))
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class DiCoBlock(nn.Module):
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"""DConv block with adaLN modulation."""
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def __init__(self, hidden_size, mlp_ratio=4.0):
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super().__init__()
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self.conv1 = nn.Conv2d(hidden_size, hidden_size, 1, bias=True)
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self.conv2 = nn.Conv2d(
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hidden_size, hidden_size, 3, padding=1, groups=hidden_size, bias=True
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)
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self.conv3 = nn.Conv2d(hidden_size, hidden_size, 1, bias=True)
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self.ca = nn.Sequential(
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nn.AdaptiveAvgPool2d(1),
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nn.Conv2d(hidden_size, hidden_size, 1, bias=True),
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nn.Sigmoid(),
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)
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ffn = int(mlp_ratio * hidden_size)
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self.conv4 = nn.Conv2d(hidden_size, ffn, 1, bias=True)
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self.conv5 = nn.Conv2d(ffn, hidden_size, 1, bias=True)
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self.norm1 = LayerNorm2d(hidden_size, affine=False)
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self.norm2 = LayerNorm2d(hidden_size, affine=False)
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self.adaLN_modulation = nn.Sequential(
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nn.SiLU(),
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nn.Linear(hidden_size, 6 * hidden_size, bias=True),
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)
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def forward(self, inp, c):
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shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (
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self.adaLN_modulation(c).chunk(6, dim=1)
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)
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x = modulate(self.norm1(inp), shift_msa, scale_msa)
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x = F.gelu(self.conv2(self.conv1(x)))
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x = x * self.ca(x)
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x = self.conv3(x)
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x = inp + gate_msa[..., None, None] * x
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x = x + gate_mlp[..., None, None] * self.conv5(
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F.gelu(self.conv4(modulate(self.norm2(x), shift_mlp, scale_mlp)))
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)
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return x
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class _EncoderDiCoBlock(nn.Module):
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"""DiCoBlock without adaLN, for the encoder pathway."""
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def __init__(self, hidden_size, mlp_ratio=4.0):
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super().__init__()
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self.conv1 = nn.Conv2d(hidden_size, hidden_size, 1, bias=True)
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self.conv2 = nn.Conv2d(
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hidden_size, hidden_size, 3, padding=1, groups=hidden_size, bias=True
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)
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self.conv3 = nn.Conv2d(hidden_size, hidden_size, 1, bias=True)
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self.ca = nn.Sequential(
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nn.AdaptiveAvgPool2d(1),
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nn.Conv2d(hidden_size, hidden_size, 1, bias=True),
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nn.Sigmoid(),
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)
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ffn = int(mlp_ratio * hidden_size)
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self.conv4 = nn.Conv2d(hidden_size, ffn, 1, bias=True)
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self.conv5 = nn.Conv2d(ffn, hidden_size, 1, bias=True)
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self.norm1 = _EncoderLayerNorm2d(hidden_size)
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self.norm2 = _EncoderLayerNorm2d(hidden_size)
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def forward(self, inp):
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x = self.norm1(inp)
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x = F.gelu(self.conv2(self.conv1(x)))
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x = x * self.ca(x)
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x = self.conv3(x)
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x = inp + x
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return x + self.conv5(F.gelu(self.conv4(self.norm2(x))))
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class NerfEmbedder(nn.Module):
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"""Patch-position embedder used by the DConv decoder x-pathway."""
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def __init__(self, in_channels, hidden_size_input, max_freqs=8):
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super().__init__()
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self.max_freqs = max_freqs
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self.embedder = nn.Sequential(
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nn.Linear(in_channels + max_freqs**2, hidden_size_input, bias=True),
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)
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@lru_cache
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def fetch_pos(self, patch_size, device, dtype):
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pos = torch.linspace(0, 1, patch_size, device=device, dtype=dtype)
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pos_y, pos_x = torch.meshgrid(pos, pos, indexing="ij")
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pos_x = pos_x.reshape(-1, 1, 1)
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pos_y = pos_y.reshape(-1, 1, 1)
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freqs = torch.linspace(
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0, self.max_freqs, self.max_freqs, dtype=dtype, device=device
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)
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fx = freqs[None, :, None]
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fy = freqs[None, None, :]
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coeffs = (1 + fx * fy) ** -1
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dct_x = torch.cos(pos_x * fx * torch.pi)
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dct_y = torch.cos(pos_y * fy * torch.pi)
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return (dct_x * dct_y * coeffs).view(1, -1, self.max_freqs**2)
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def forward(self, x):
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B, P2, _ = x.shape
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ps = int(P2**0.5)
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dct = self.fetch_pos(ps, x.device, x.dtype).expand(B, -1, -1)
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return self.embedder(torch.cat([x, dct], dim=-1))
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class NerfFinalLayer(nn.Module):
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def __init__(self, hidden_size, out_channels):
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super().__init__()
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self.norm = RMSNorm(hidden_size)
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self.linear = nn.Linear(hidden_size, out_channels, bias=True)
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def forward(self, x):
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return self.linear(self.norm(x))
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class SimpleMLPAdaLN(nn.Module):
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"""Final small MLP that maps NerfEmbedder features to per-patch RGB."""
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def __init__(
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self,
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in_channels,
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model_channels,
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out_channels,
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z_channels,
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num_res_blocks,
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patch_size,
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):
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super().__init__()
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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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self.num_res_blocks = num_res_blocks
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self.patch_size = patch_size
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self.cond_embed = nn.Linear(z_channels, patch_size**2 * model_channels)
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self.input_proj = nn.Linear(in_channels, model_channels)
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self.res_blocks = nn.ModuleList(
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_MLPResBlock(model_channels) for _ in range(num_res_blocks)
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)
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def forward(self, x, c):
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x = self.input_proj(x)
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c = self.cond_embed(c).reshape(c.shape[0], self.patch_size**2, -1)
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for block in self.res_blocks:
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x = block(x, c)
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return x
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class _MLPResBlock(nn.Module):
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def __init__(self, channels):
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super().__init__()
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self.in_ln = nn.LayerNorm(channels, eps=1e-6)
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self.mlp = nn.Sequential(
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nn.Linear(channels, channels, bias=True),
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nn.SiLU(),
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nn.Linear(channels, channels, bias=True),
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)
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self.adaLN_modulation = nn.Sequential(
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nn.SiLU(),
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nn.Linear(channels, 3 * channels, bias=True),
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)
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def forward(self, x, y):
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shift, scale, gate = self.adaLN_modulation(y).chunk(3, dim=-1)
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h = self.in_ln(x) * (1 + scale) + shift
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return x + gate * self.mlp(h)
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class ResnetBlock(nn.Module):
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"""GroupNorm + Conv ResBlock used by the CoD Decoder."""
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def __init__(self, *, in_channels, out_channels=None, dropout=0.0):
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super().__init__()
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out_channels = out_channels or in_channels
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self.in_channels = in_channels
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self.out_channels = out_channels
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self.norm1 = Normalize(in_channels)
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self.conv1 = nn.Conv2d(in_channels, out_channels, 3, padding=1)
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self.norm2 = Normalize(out_channels)
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self.dropout = nn.Dropout(dropout)
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self.conv2 = nn.Conv2d(out_channels, out_channels, 3, padding=1)
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if in_channels != out_channels:
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self.nin_shortcut = nn.Conv2d(in_channels, out_channels, 1)
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def forward(self, x):
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h = self.conv1(nonlinearity(self.norm1(x)))
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h = self.conv2(self.dropout(nonlinearity(self.norm2(h))))
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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 x + h
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class AttnBlock(nn.Module):
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"""Patched self-attention used at inference (eval mode of the original)."""
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def __init__(self, in_channels, patch_size=32):
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super().__init__()
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self.in_channels = in_channels
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self.patch_size = patch_size
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self.norm = Normalize(in_channels)
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self.q = nn.Conv2d(in_channels, in_channels, 1)
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self.k = nn.Conv2d(in_channels, in_channels, 1)
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self.v = nn.Conv2d(in_channels, in_channels, 1)
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self.proj_out = nn.Conv2d(in_channels, in_channels, 1)
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def forward(self, x):
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h_ = self.norm(x)
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Q = self.q(h_)
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K = self.k(h_)
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V = self.v(h_)
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d = self.patch_size
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b, c, H, W = Q.shape
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pad_h = (d - H % d) % d
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pad_w = (d - W % d) % d
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if pad_h or pad_w:
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Q = F.pad(Q, (0, pad_w, 0, pad_h), mode="replicate")
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K = F.pad(K, (0, pad_w, 0, pad_h), mode="replicate")
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V = F.pad(V, (0, pad_w, 0, pad_h), mode="replicate")
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_, _, H_pad, W_pad = Q.shape
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nph, npw = H_pad // d, W_pad // d
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np_ = nph * npw
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def to_patches(t):
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return (
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t.reshape(b, c, nph, d, npw, d)
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.permute(0, 2, 4, 1, 3, 5)
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.reshape(b * np_, c, d * d)
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)
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Q = to_patches(Q)
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K = to_patches(K)
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V = to_patches(V)
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w_ = torch.bmm(Q.permute(0, 2, 1), K) * (c**-0.5)
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w_ = F.softmax(w_, dim=2).permute(0, 2, 1)
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h_ = (
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torch.bmm(V, w_)
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.reshape(b, nph, npw, c, d, d)
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.permute(0, 3, 1, 4, 2, 5)
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.reshape(b, c, H_pad, W_pad)
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)
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if pad_h or pad_w:
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h_ = h_[:, :, :H, :W]
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return x + self.proj_out(h_)
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# ---------------------------------------------------------------------------
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# adaLN constant-folding: at fixed t=0, adaLN_modulation(c) is constant.
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# Replace the MLP with a buffer so DiCoBlock.forward stays unchanged.
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# ---------------------------------------------------------------------------
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class _ConstAdaLN(nn.Module):
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def __init__(self, modulation: torch.Tensor):
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super().__init__()
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self.register_buffer("modulation", modulation.detach().clone())
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def forward(self, c):
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b = c.shape[0]
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if self.modulation.shape[0] != b:
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return self.modulation.expand(b, *self.modulation.shape[1:])
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return self.modulation
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def _replace_adaln_with_const(module: nn.Module, c: torch.Tensor) -> int:
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# Only DiCoBlock is targeted: its adaLN is conditioned solely on t.
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# Other adaLN_modulation submodules (e.g. _MLPResBlock in the decoder MLP)
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# take a per-position latent and must not be folded.
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n = 0
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for child in module.modules():
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if not isinstance(child, DiCoBlock):
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continue
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adaln = child.adaLN_modulation
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if isinstance(adaln, _ConstAdaLN):
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continue
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with torch.no_grad():
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mod = adaln(c)
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child.adaLN_modulation = _ConstAdaLN(mod)
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n += 1
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return n
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# ---------------------------------------------------------------------------
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# CoD Decoder: latent → conditioning features for the denoiser
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# ---------------------------------------------------------------------------
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class _Decoder(nn.Module):
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"""ds=16, up2x=True, light=True only."""
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def __init__(self, out_ch=384, z_ch=128):
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super().__init__()
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self.conv_in = nn.Conv2d(z_ch, out_ch, kernel_size=3, stride=1, padding=1)
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self.block = nn.Sequential(
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ResnetBlock(in_channels=out_ch, out_channels=out_ch),
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AttnBlock(out_ch, patch_size=32),
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ResnetBlock(in_channels=out_ch, out_channels=out_ch),
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AttnBlock(out_ch, patch_size=32),
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ResnetBlock(in_channels=out_ch, out_channels=out_ch),
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)
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self.norm_out = Normalize(out_ch)
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self.conv_out = nn.Conv2d(out_ch, out_ch, kernel_size=3, stride=1, padding=1)
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self.ada = nn.Identity()
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def forward(self, z):
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h = self.block(self.conv_in(z))
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h = self.conv_out(nonlinearity(self.norm_out(h)))
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return self.ada(h)
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# ---------------------------------------------------------------------------
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# DConvEncoder: image → packed (mean, logvar) latent
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# ---------------------------------------------------------------------------
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class _DConvEncoder(nn.Module):
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def __init__(
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self,
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z_ch=128,
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hidden_size=384,
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num_blocks=21,
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patch_size=16,
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mlp_ratio=4.0,
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head_size=768,
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num_head_blocks=2,
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out_ch_mult=2,
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):
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super().__init__()
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self.z_ch = z_ch
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self.patch_size = patch_size
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self.patch_cond_embed = nn.Conv2d(
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3, head_size, kernel_size=patch_size, stride=patch_size, bias=True
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)
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self.head_blocks = nn.ModuleList(
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[
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_EncoderDiCoBlock(head_size, mlp_ratio=mlp_ratio)
|
|
for _ in range(num_head_blocks)
|
|
]
|
|
)
|
|
self.proj_down = nn.Conv2d(head_size, hidden_size, kernel_size=1, bias=True)
|
|
self.z_proj = nn.Conv2d(z_ch, hidden_size, kernel_size=1, bias=True)
|
|
self.fuse_proj = nn.Conv2d(
|
|
hidden_size * 2, hidden_size, kernel_size=1, bias=True
|
|
)
|
|
self.t_embedder = TimestepEmbedder(hidden_size)
|
|
self.blocks = nn.ModuleList(
|
|
[DiCoBlock(hidden_size, mlp_ratio=mlp_ratio) for _ in range(num_blocks)]
|
|
)
|
|
self.norm_out = LayerNorm2d(hidden_size)
|
|
self.proj_out = nn.Conv2d(
|
|
hidden_size, z_ch * out_ch_mult, kernel_size=1, bias=True
|
|
)
|
|
|
|
def forward_pred(self, z_t, t, y):
|
|
cond = self.patch_cond_embed(y)
|
|
for block in self.head_blocks:
|
|
cond = block(cond)
|
|
cond = self.proj_down(cond)
|
|
|
|
s = self.fuse_proj(torch.cat([cond, self.z_proj(z_t)], dim=1))
|
|
c = self.t_embedder(t.view(-1))
|
|
for block in self.blocks:
|
|
s = block(s, c)
|
|
return self.proj_out(self.norm_out(s))
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# DConv denoiser: latent (via cond) + zero noise → reconstructed image
|
|
# ---------------------------------------------------------------------------
|
|
class _YEmbedder(nn.Module):
|
|
"""Holds only the CoD decoder; the original Flux2 VAE encoder side is omitted."""
|
|
|
|
def __init__(self, ch=384, z_ch=128):
|
|
super().__init__()
|
|
self.decoder = _Decoder(out_ch=ch, z_ch=z_ch)
|
|
|
|
|
|
class _DConvDenoiser(nn.Module):
|
|
def __init__(
|
|
self,
|
|
patch_size=16,
|
|
in_channels=3,
|
|
hidden_size=384,
|
|
hidden_size_x=32,
|
|
mlp_ratio=4.0,
|
|
num_blocks=24,
|
|
num_cond_blocks=21,
|
|
bottleneck_dim=128,
|
|
):
|
|
super().__init__()
|
|
self.in_channels = in_channels
|
|
self.patch_size = patch_size
|
|
self.hidden_size = hidden_size
|
|
self.num_cond_blocks = num_cond_blocks
|
|
|
|
self.t_embedder = TimestepEmbedder(hidden_size)
|
|
self.y_embedder_x = nn.Conv2d(
|
|
hidden_size, hidden_size_x * patch_size**2, 1, 1, 0
|
|
)
|
|
self.x_embedder = NerfEmbedder(
|
|
in_channels + hidden_size_x, hidden_size_x, max_freqs=8
|
|
)
|
|
self.s_embedder = BottleneckPatchEmbed(
|
|
patch_size, in_channels, bottleneck_dim, hidden_size, bias=True
|
|
)
|
|
self.blocks = nn.ModuleList(
|
|
[
|
|
DiCoBlock(hidden_size, mlp_ratio=mlp_ratio)
|
|
for _ in range(num_cond_blocks)
|
|
]
|
|
)
|
|
self.dec_net = SimpleMLPAdaLN(
|
|
in_channels=hidden_size_x,
|
|
model_channels=hidden_size_x,
|
|
out_channels=in_channels,
|
|
z_channels=hidden_size,
|
|
num_res_blocks=num_blocks - num_cond_blocks,
|
|
patch_size=patch_size,
|
|
)
|
|
self.final_layer = NerfFinalLayer(hidden_size_x, in_channels)
|
|
self.y_embedder = _YEmbedder(ch=hidden_size, z_ch=bottleneck_dim)
|
|
|
|
def forward(self, x, t, cond):
|
|
b, _, h, w = x.shape
|
|
c = self.t_embedder(t.view(-1))
|
|
|
|
s = self.s_embedder(x, cond)
|
|
for block in self.blocks:
|
|
s = block(s, c)
|
|
|
|
length = s.shape[-2] * s.shape[-1]
|
|
s = s.permute(0, 2, 3, 1).reshape(-1, self.hidden_size)
|
|
|
|
x = torch.nn.functional.unfold(
|
|
x, kernel_size=self.patch_size, stride=self.patch_size
|
|
)
|
|
x = torch.cat([x, self.y_embedder_x(cond).flatten(2)], dim=1)
|
|
x = (
|
|
x.reshape(b, -1, self.patch_size**2, length)
|
|
.permute(0, 3, 2, 1)
|
|
.flatten(0, 1)
|
|
)
|
|
x = self.x_embedder(x)
|
|
|
|
x = self.dec_net(x, s)
|
|
x = self.final_layer(x)
|
|
x = x.transpose(1, 2).reshape(b, length, -1)
|
|
return torch.nn.functional.fold(
|
|
x.transpose(1, 2).contiguous(),
|
|
(h, w),
|
|
kernel_size=self.patch_size,
|
|
stride=self.patch_size,
|
|
)
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# Wrapper
|
|
# ---------------------------------------------------------------------------
|
|
def _load_state_dict(ckpt_path: str):
|
|
if ckpt_path.endswith(".safetensors"):
|
|
from safetensors.torch import load_file
|
|
|
|
return load_file(ckpt_path, device="cpu")
|
|
if os.path.exists(os.path.join(ckpt_path, "checkpoint-state_dict.pt")):
|
|
ckpt_path = os.path.join(ckpt_path, "checkpoint-state_dict.pt")
|
|
elif os.path.isdir(ckpt_path):
|
|
ckpt_path = os.path.join(ckpt_path, "checkpoint", "mp_rank_00_model_states.pt")
|
|
state = torch.load(ckpt_path, map_location="cpu")
|
|
if "module" in state:
|
|
return state["module"]
|
|
if "state_dict" in state:
|
|
return state["state_dict"]
|
|
return state
|
|
|
|
|
|
class MageVAE(nn.Module):
|
|
"""
|
|
Encode: DConvEncoder (one-step diffusion) → latent [B, 128, H/16, W/16]
|
|
Decode: DConvDenoiser + CoD Decoder → image [B, 3, H, W] in [-1, 1]
|
|
"""
|
|
|
|
latent_channels = 128
|
|
downsample_factor = 16
|
|
|
|
def __init__(self, ckpt_path: str, sample_posterior: bool = True):
|
|
super().__init__()
|
|
self.sample_posterior = sample_posterior
|
|
|
|
self.dconv_encoder = _DConvEncoder()
|
|
self.decoder_model = _DConvDenoiser()
|
|
|
|
sd = _load_state_dict(ckpt_path)
|
|
self._load_encoder(sd, ckpt_path)
|
|
self._load_decoder(sd, ckpt_path)
|
|
|
|
# adaLN modulation depends only on t, and we always run at t=0.
|
|
# Precompute and drop the MLPs once at construction (~37M params saved).
|
|
self._freeze_adaln_cache()
|
|
|
|
def _load_encoder(self, sd, ckpt_path):
|
|
prefix = "student.dconv_encoder."
|
|
enc_sd = {k[len(prefix) :]: v for k, v in sd.items() if k.startswith(prefix)}
|
|
if not enc_sd:
|
|
raise RuntimeError(f"MageVAE encoder: no '{prefix}*' keys in {ckpt_path}")
|
|
proj = enc_sd.get("proj_out.weight")
|
|
if proj is None or proj.shape[0] != 2 * self.latent_channels:
|
|
raise RuntimeError(
|
|
f"MageVAE encoder: expected packed mean+logvar (proj_out out_channels="
|
|
f"{2 * self.latent_channels}), got {None if proj is None else tuple(proj.shape)}"
|
|
)
|
|
missing, unexpected = self.dconv_encoder.load_state_dict(enc_sd, strict=False)
|
|
if missing:
|
|
print(f"MageVAE encoder missing keys: {missing[:10]}")
|
|
|
|
def _load_decoder(self, sd, ckpt_path):
|
|
prefix = "pipeline."
|
|
if not any(k.startswith(prefix) for k in sd):
|
|
raise RuntimeError(f"MageVAE decoder: no '{prefix}*' keys in {ckpt_path}")
|
|
model_dict = self.decoder_model.state_dict()
|
|
matched = {}
|
|
for k, v in sd.items():
|
|
if not k.startswith(prefix):
|
|
continue
|
|
new_k = k[len(prefix) :]
|
|
if new_k.startswith("y_embedder.encoder.") or new_k.startswith(
|
|
"y_embedder.bottleneck."
|
|
):
|
|
continue
|
|
if new_k in model_dict and model_dict[new_k].shape == v.shape:
|
|
matched[new_k] = v
|
|
self.decoder_model.load_state_dict(matched, strict=False)
|
|
if not matched:
|
|
raise RuntimeError(f"MageVAE decoder: 0 params matched from {ckpt_path}")
|
|
|
|
@torch.no_grad()
|
|
def _moments(self, x: torch.Tensor):
|
|
B, _, H, W = x.shape
|
|
ps = self.dconv_encoder.patch_size
|
|
z_t = torch.zeros(
|
|
B, self.dconv_encoder.z_ch, H // ps, W // ps, device=x.device, dtype=x.dtype
|
|
)
|
|
t = torch.zeros(B, device=x.device, dtype=x.dtype)
|
|
out = self.dconv_encoder.forward_pred(z_t, t, x)
|
|
mean = out[:, : self.latent_channels]
|
|
logvar = out[:, self.latent_channels :].clamp(min=-20.0, max=10.0)
|
|
return mean, logvar
|
|
|
|
@torch.no_grad()
|
|
def encode(self, x: torch.Tensor) -> torch.Tensor:
|
|
ps = self.dconv_encoder.patch_size
|
|
H, W = x.shape[-2], x.shape[-1]
|
|
if H % ps or W % ps:
|
|
raise ValueError(f"H, W must be multiples of {ps}, got ({H}, {W})")
|
|
mean, logvar = self._moments(x)
|
|
if self.sample_posterior:
|
|
return mean + torch.exp(0.5 * logvar) * torch.randn_like(mean)
|
|
return mean
|
|
|
|
@torch.no_grad()
|
|
def decode(self, z: torch.Tensor) -> torch.Tensor:
|
|
cond = self.decoder_model.y_embedder.decoder(z)
|
|
B = z.shape[0]
|
|
H = z.shape[2] * self.downsample_factor
|
|
W = z.shape[3] * self.downsample_factor
|
|
noise = torch.zeros(B, 3, H, W, device=z.device, dtype=z.dtype)
|
|
t = torch.zeros(B, device=z.device, dtype=z.dtype)
|
|
return self.decoder_model.forward(noise, t, cond)
|
|
|
|
@property
|
|
def device(self):
|
|
return next(self.parameters()).device
|
|
|
|
@property
|
|
def dtype(self):
|
|
return next(self.parameters()).dtype
|
|
|
|
def _freeze_adaln_cache(self):
|
|
"""Constant-fold adaLN_modulation MLPs at t=0 (encoder + decoder)."""
|
|
device = next(self.parameters()).device
|
|
dtype = next(self.parameters()).dtype
|
|
t = torch.zeros(1, device=device, dtype=dtype)
|
|
c_enc = self.dconv_encoder.t_embedder(t)
|
|
_replace_adaln_with_const(self.dconv_encoder, c_enc)
|
|
c_dec = self.decoder_model.t_embedder(t)
|
|
_replace_adaln_with_const(self.decoder_model, c_dec)
|