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import gc
import json
import logging
import math
from pathlib import Path
import mlx.core as mx
import numpy as np
from PIL import Image
from .ops import attention, layer_norm, rms_norm, rotate, swiglu
from .weights import Linear, WeightStore
log = logging.getLogger(__name__)
def split_tiles(length, tile=256, overlap=64, ratio=16):
if tile <= overlap or tile % ratio or overlap % ratio:
raise ValueError(
"Tile size/overlap must be multiples of 16, with tile > overlap"
)
if length <= tile:
return [0], [length], []
n = math.ceil(length / tile)
while tile * n - overlap * (n - 1) < length:
n += 1
overlaps = [overlap] * (n - 1)
for i in range((tile * n - sum(overlaps) - length) // ratio):
overlaps[i % (n - 1)] += ratio
starts = [0]
for ov in overlaps:
starts.append(starts[-1] + tile - ov)
return starts, [tile] * n, overlaps
def blend(a, b, extent, axis):
extent = min(a.shape[axis], b.shape[axis], extent)
if extent == 0:
return b
shape = [1] * b.ndim
shape[axis] = extent
weight = mx.arange(extent, dtype=b.dtype).reshape(shape) / extent
aa, bb, rest = (
[slice(None)] * b.ndim,
[slice(None)] * b.ndim,
[slice(None)] * b.ndim,
)
aa[axis], bb[axis], rest[axis] = (
slice(-extent, None),
slice(0, extent),
slice(extent, None),
)
mixed = a[tuple(aa)] * (1 - weight) + b[tuple(bb)] * weight
return mx.concatenate((mixed, b[tuple(rest)]), axis=axis)
def assemble_tiles(rows, y_overlap, x_overlap):
assembled = []
for i, row in enumerate(rows):
pieces = []
for j, tile in enumerate(row):
if i:
tile = blend(rows[i - 1][j], tile, y_overlap[i - 1], -2)
if j:
tile = blend(row[j - 1], tile, x_overlap[j - 1], -1)
if i < len(rows) - 1:
tile = tile[..., : -y_overlap[i], :]
if j < len(row) - 1:
tile = tile[..., : -x_overlap[j]]
pieces.append(tile)
assembled.append(mx.concatenate(pieces, axis=-1))
return mx.concatenate(assembled, axis=-2)
class DecoderBlock:
def __init__(self, store, index, dtype):
p = f"decoder.transformer_blocks.{index}"
self.n1, self.n2 = (store.get(p + f".norm{i}.weight") for i in (1, 2))
self.s1, self.s2 = (store.get(p + f".scale{i}") for i in (1, 2))
self.qkv = Linear.load(store, p + ".attn.to_qkv")
self.out = Linear.load(store, p + ".attn.to_out")
self.w1 = Linear.load(store, p + ".ff.w1")
self.w2 = Linear.load(store, p + ".ff.w2")
def __call__(self, x, rope):
b, n, _ = x.shape
h = rms_norm(x, self.n1)
# VAE QKV is head-interleaved too, but keep its native layout here.
qkv = self.qkv(h).reshape(b, n, 16, 3, 64)
q, k, v = (qkv[:, :, :, i] for i in range(3))
q, k = rms_norm(q), rms_norm(k)
q, k = rotate(q, *rope), rotate(k, *rope)
a = self.out(attention(q, k, v).reshape(b, n, -1))
x = x + a * self.s1
h = rms_norm(x, self.n2)
return x + self.w2(swiglu(self.w1(h))) * self.s2
class VAE:
def __init__(
self,
path,
*,
tile_batch=1,
dtype="bfloat16",
tile_size=256,
compile_blocks=False,
):
self.path = Path(path)
self.config = json.loads((self.path / "config.json").read_text())
self.dtype = {"bfloat16": mx.bfloat16, "float32": mx.float32}[dtype]
self.store = WeightStore(self.path, cast=self.dtype)
self.tile_batch, self.tile_size = tile_batch, tile_size
self.blocks = None
self.compile_blocks = compile_blocks
self.runners = []
def _load(self):
if self.blocks is not None:
return
s = self.store
self.post = Linear(
s.get("post_quant_conv.weight").reshape(24, 24),
s.get("post_quant_conv.bias"),
)
self.embed = Linear.load(s, "decoder.x_embedder")
self.register = s.get("decoder.register_tokens")
self.norm_w, self.norm_b = (
s.get("decoder.norm_out.weight"),
s.get("decoder.norm_out.bias"),
)
self.out = Linear.load(s, "decoder.proj_out")
self.blocks = [DecoderBlock(s, i, self.dtype) for i in range(24)]
if self.compile_blocks:
for block in self.blocks:
def forward(x, rope, block=block):
return block(x, rope)
self.runners.append(mx.compile(forward))
def decode_tiles(self, raw):
self._load()
b, c, h, w = raw.shape
# Native image decoder is BF16, including its pointwise convolution.
x = raw.transpose(0, 2, 3, 1).astype(self.dtype)
x = self.embed(self.post(x)).reshape(b, h * w, 1024)
x = mx.concatenate(
(
x,
mx.broadcast_to(self.register, (b, 4, 1024)),
mx.zeros((b, 1, 1024), self.dtype),
),
axis=1,
)
# Coordinates start in latent dtype (BF16), as in released ViT2D.
yy = 2 * ((mx.arange(h, dtype=self.dtype) + mx.array(0.5, self.dtype)) / h) - 1
xx = 2 * ((mx.arange(w, dtype=self.dtype) + mx.array(0.5, self.dtype)) / w) - 1
yy, xx = mx.meshgrid(yy, xx, indexing="ij")
positions = mx.stack((yy, xx), axis=-1).reshape(-1, 2)
positions = mx.concatenate((positions, mx.zeros((5, 2), self.dtype)), axis=0)
inv = 1 / (100 ** mx.arange(0, 1, 0.125, dtype=mx.float32))
scaled = (2 * math.pi * positions.astype(mx.float32)).astype(self.dtype)
angles = (scaled.astype(mx.float32)[..., None] * inv).reshape(h * w + 5, 16)
angles = mx.concatenate((angles, angles), axis=-1)
rope = (
mx.cos(angles).astype(self.dtype)[None, :, None],
mx.sin(angles).astype(self.dtype)[None, :, None],
)
for block in self.runners if self.compile_blocks else self.blocks:
x = block(x, rope)
if not self.compile_blocks:
mx.eval(x)
x = layer_norm(x, self.norm_w, self.norm_b)
x = self.out(x)[:, : h * w]
# Output channels are [C, patchH, patchW], matching the VAE checkpoint.
return (
x.reshape(b, h, w, 3, 16, 16)
.transpose(0, 3, 1, 4, 2, 5)
.reshape(b, 3, h * 16, w * 16)
)
def decode(self, latent, *, on_batch=None):
c = self.config
mean = mx.array(c["latents_mean"], mx.float32)[None, :, None, None]
std = mx.array(c["latents_std"], mx.float32)[None, :, None, None]
raw = latent.astype(mx.float32) * std + mean
h, w = raw.shape[-2:]
ys, ylen, yo = split_tiles(h * 16, self.tile_size)
xs, xlen, xo = split_tiles(w * 16, self.tile_size)
tasks = [
(i, j, raw[..., y // 16 : (y + yl) // 16, x // 16 : (x + xl) // 16])
for i, (y, yl) in enumerate(zip(ys, ylen, strict=False))
for j, (x, xl) in enumerate(zip(xs, xlen, strict=False))
]
rows = [[None for _ in xs] for _ in ys]
for start in range(0, len(tasks), self.tile_batch):
group = tasks[start : start + self.tile_batch]
tiles = mx.concatenate([t for _, _, t in group], axis=0)
decoded = self.decode_tiles(tiles)
mx.eval(decoded)
for k, (i, j, _) in enumerate(group):
rows[i][j] = decoded[k : k + 1]
if on_batch:
on_batch(start + len(group), len(tasks))
# Decode tile overlap arithmetic stays in the decoder dtype.
out = assemble_tiles(rows, yo, xo)
# torchvision's inverse Normalize subtracts its inverse mean, then divides.
inv_std = mx.array([1 / 0.229, 1 / 0.224, 1 / 0.225], self.dtype)[
None, :, None, None
]
inv_mean = mx.array(
[-0.485 / 0.229, -0.456 / 0.224, -0.406 / 0.225], self.dtype
)[None, :, None, None]
out = mx.clip((out - inv_mean) / inv_std, 0, 1).astype(mx.float32)
mx.eval(out)
return out
def clear(self):
self.runners.clear()
self.blocks = None
for name in ("post", "embed", "register", "norm_w", "norm_b", "out"):
if hasattr(self, name):
delattr(self, name)
self.store.clear()
gc.collect()
mx.clear_cache()
def to_image(pixels, width, height):
array = np.asarray(
mx.clip(pixels[0].transpose(1, 2, 0) * 255, 0, 255).astype(mx.uint8)
)
image = Image.fromarray(array)
if image.size != (width, height):
image = image.resize((width, height), Image.Resampling.LANCZOS)
return image
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