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64566e4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 | """AutoencoderKL Decoder — pure MLX implementation.
Decodes latent representations to RGB images without PyTorch/diffusers
dependency. Architecture matches diffusers AutoencoderKL with the
Z-Image-Turbo VAE config:
latent_channels = 16
block_out_channels = [128, 256, 512, 512]
layers_per_block = 2 (decoder uses layers_per_block + 1 = 3)
norm_num_groups = 32
mid_block_add_attention = true
force_upcast = true (all ops in float32)
scaling_factor = 0.3611
shift_factor = 0.1159
Data format: NHWC throughout (MLX convention).
"""
from __future__ import annotations
import math
import mlx.core as mx
import mlx.nn as nn
# Match diffusers VAE GroupNorm: eps=1e-6, pytorch_compatible=True
_GN_EPS = 1e-6
def _gn(groups: int, channels: int) -> nn.GroupNorm:
return nn.GroupNorm(groups, channels, eps=_GN_EPS, pytorch_compatible=True)
# ── Building blocks ──────────────────────────────────────────────
class ResnetBlock2D(nn.Module):
"""Residual block: GroupNorm → SiLU → Conv → GroupNorm → SiLU → Conv + skip."""
def __init__(self, in_channels: int, out_channels: int, groups: int = 32):
super().__init__()
self.norm1 = _gn(groups, in_channels)
self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1)
self.norm2 = _gn(groups, out_channels)
self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1)
self.conv_shortcut = None
if in_channels != out_channels:
self.conv_shortcut = nn.Conv2d(in_channels, out_channels, kernel_size=1)
def __call__(self, x: mx.array) -> mx.array:
residual = x
x = nn.silu(self.norm1(x))
x = self.conv1(x)
x = nn.silu(self.norm2(x))
x = self.conv2(x)
if self.conv_shortcut is not None:
residual = self.conv_shortcut(residual)
return x + residual
class AttentionBlock(nn.Module):
"""Single-head self-attention over spatial positions (NHWC)."""
def __init__(self, channels: int, groups: int = 32):
super().__init__()
self.group_norm = _gn(groups, channels)
self.to_q = nn.Linear(channels, channels)
self.to_k = nn.Linear(channels, channels)
self.to_v = nn.Linear(channels, channels)
# diffusers wraps out-proj in a list (Sequential): to_out.0
self.to_out = [nn.Linear(channels, channels)]
def __call__(self, x: mx.array) -> mx.array:
residual = x
B, H, W, C = x.shape
x = self.group_norm(x)
x = x.reshape(B, H * W, C)
q = self.to_q(x)
k = self.to_k(x)
v = self.to_v(x)
scale = 1.0 / math.sqrt(C)
attn = (q @ k.transpose(0, 2, 1)) * scale
attn = mx.softmax(attn, axis=-1)
x = attn @ v
x = self.to_out[0](x)
x = x.reshape(B, H, W, C)
return x + residual
class Upsample2D(nn.Module):
"""2× nearest-neighbour upsample followed by a 3×3 conv."""
def __init__(self, channels: int):
super().__init__()
self.conv = nn.Conv2d(channels, channels, kernel_size=3, padding=1)
def __call__(self, x: mx.array) -> mx.array:
# Nearest-neighbour 2× in NHWC
B, H, W, C = x.shape
x = mx.repeat(x, 2, axis=1)
x = mx.repeat(x, 2, axis=2)
x = self.conv(x)
return x
class UpDecoderBlock2D(nn.Module):
"""Decoder up-block: N resnet blocks + optional 2× upsample."""
def __init__(
self,
in_channels: int,
out_channels: int,
num_layers: int = 3,
add_upsample: bool = True,
groups: int = 32,
):
super().__init__()
self.resnets = []
for i in range(num_layers):
res_in = in_channels if i == 0 else out_channels
self.resnets.append(ResnetBlock2D(res_in, out_channels, groups))
self.upsamplers = []
if add_upsample:
self.upsamplers.append(Upsample2D(out_channels))
def __call__(self, x: mx.array) -> mx.array:
for resnet in self.resnets:
x = resnet(x)
for up in self.upsamplers:
x = up(x)
return x
class MidBlock2D(nn.Module):
"""Mid block: resnet → self-attention → resnet."""
def __init__(self, channels: int, groups: int = 32):
super().__init__()
self.resnets = [
ResnetBlock2D(channels, channels, groups),
ResnetBlock2D(channels, channels, groups),
]
self.attentions = [AttentionBlock(channels, groups)]
def __call__(self, x: mx.array) -> mx.array:
x = self.resnets[0](x)
x = self.attentions[0](x)
x = self.resnets[1](x)
return x
# ── Decoder ──────────────────────────────────────────────────────
class Decoder(nn.Module):
"""AutoencoderKL Decoder (NHWC, pure MLX).
Module hierarchy matches diffusers weight-key paths after stripping
the ``decoder.`` prefix, so weights can be loaded directly.
"""
def __init__(
self,
latent_channels: int = 16,
block_out_channels: tuple[int, ...] = (128, 256, 512, 512),
layers_per_block: int = 2,
norm_num_groups: int = 32,
):
super().__init__()
reversed_ch = list(reversed(block_out_channels)) # [512, 512, 256, 128]
# Input projection
self.conv_in = nn.Conv2d(latent_channels, reversed_ch[0], kernel_size=3, padding=1)
# Mid block
self.mid_block = MidBlock2D(reversed_ch[0], norm_num_groups)
# Up blocks (3 upsamples → total 8× spatial increase)
self.up_blocks = []
for i, out_ch in enumerate(reversed_ch):
in_ch = reversed_ch[i - 1] if i > 0 else reversed_ch[0]
add_upsample = i < len(reversed_ch) - 1
self.up_blocks.append(
UpDecoderBlock2D(
in_channels=in_ch,
out_channels=out_ch,
num_layers=layers_per_block + 1,
add_upsample=add_upsample,
groups=norm_num_groups,
)
)
# Output
self.conv_norm_out = _gn(norm_num_groups, reversed_ch[-1])
self.conv_out = nn.Conv2d(reversed_ch[-1], 3, kernel_size=3, padding=1)
def __call__(self, z: mx.array) -> mx.array:
"""Decode latents → image.
Args:
z: (B, H, W, C) latent tensor in NHWC, **already scaled**.
Returns:
(B, 8H, 8W, 3) decoded image.
"""
x = self.conv_in(z)
x = self.mid_block(x)
for block in self.up_blocks:
x = block(x)
x = nn.silu(self.conv_norm_out(x))
x = self.conv_out(x)
return x
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