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ed66dc1 74728e5 ed66dc1 74728e5 ed66dc1 74728e5 ed66dc1 74728e5 ed66dc1 74728e5 ed66dc1 74728e5 ed66dc1 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 | """The phone pipeline in Python: the shipped LiteRT graphs + the host glue, step for step what the Kotlin app does.
Used for the Mac stand-in of CONTRACT.md lines (a), (a') and (b) (graphs on Metal = ML Drift fp16 on the Mac).
stages: matting (the author's BiRefNet on the CPU; IS-Net DIS as an option) -> preprocessing -> DINOv3 (4 graphs) + VAE (4 graphs) -> 5 Euler steps (den_ctx once per
LoRA phase, den_in, 24 joint graphs) -> octree (8 levels, row buckets) -> Gaussian decoder (8 graphs) -> PLY
"""
import gc
import json
import os
import time
import numpy as np
import torch
import idol_glue as GL
import idol_graphs as IG
from lrt import Graph
class IdolLiteRT:
def __init__(self, models, accel='gpu', dis_path=None, enforce_f32=False, stream=False, dino_dir=None,
matting='birefnet'):
self.dir = models
self.matting = matting # 'birefnet' = the author's background remover (bir.tflite, CPU, the default);
# 'dis' = IS-Net DIS + the alpha floor (dis.tflite, GPU; CONTRACT.md: its edges fail line (b) on input 3)
self.dino_dir = dino_dir or models # the DINOv3 graphs and RoPE tables ship in their own (gated) repo
self.accel, self.f32 = accel, enforce_f32
self.stream = stream # load each graph for one call and release it (fp32 on the Mac: 26 resident graphs > 32 GB)
self.g = {}
f = lambda n: os.path.join(models, n) # noqa: E731
if os.path.exists(f('den_pos_emb.f32')):
self.pos_emb = np.fromfile(f('den_pos_emb.f32'), np.float32).reshape(1, 8192, 1024)
rope = lambda n: os.path.join(self.dino_dir, n) # noqa: E731
if os.path.exists(rope('dino_rope_cos.f32')):
self.rope_cos = np.fromfile(rope('dino_rope_cos.f32'), np.float32).reshape(1, 1, 4101, 64)
self.rope_sin = np.fromfile(rope('dino_rope_sin.f32'), np.float32).reshape(1, 1, 4101, 64)
if os.path.exists(f('vae_bn.json')):
bn = json.load(open(f('vae_bn.json')))
self.bn_mean = np.asarray(bn['bn_mean'], np.float32).reshape(1, 128, 1, 1)
self.bn_std = np.sqrt(np.asarray(bn['bn_var'], np.float32) + bn['bn_eps']).reshape(1, 128, 1, 1)
if os.path.exists(f('gs_rep.json')):
rep = json.load(open(f('gs_rep.json')))
self.rep, self.base_offset_scale = rep['rep'], rep['base_offset_scale']
if os.path.exists(f('gs_perturbation.npy')):
pert = np.load(f('gs_perturbation.npy'))
else: # the released folder carries the raw little-endian float32 file only
pert = np.fromfile(f('gs_perturbation.f32'), '<f4').reshape(-1, 3)
self.perturbation = torch.from_numpy(pert)
self.dis_path = dis_path
self.times = {}
# graphs that run in fp32 precision (their fp16 form fails a CONTRACT.md line): the manifest's, as the app does
self.fp32_graphs = set()
# anchor points of the octree / Gaussian decoder: the first input of gs_0 is [1, anchors, 3] (8,192 by default)
self.num_points = 8192
# shared graphs (den_joint_mid, gs_mid: the middle blocks' weights as runtime inputs) and their weights files
self.shared = {}
if os.path.exists(f('manifest.json')):
man = json.load(open(f('manifest.json')))
self.fp32_graphs = {n for n, g in man['graphs'].items() if g['precision'] == 'fp32'}
if 'gs_0' in man['graphs']:
self.num_points = int(next(iter(man['graphs']['gs_0']['signatures'].values()))['inputs'][0][1][1])
for n, g in man['graphs'].items():
if g.get('shared_blocks'):
ins = next(iter(g['signatures'].values()))['inputs']
first = g.get('first_weight_input', 3) # the inputs before the weights
shapes = [s for _, s in sorted(ins, key=lambda t: int(t[0].split('_')[-1]))][first:]
w = np.memmap(f(g['block_weights']), '<f4', 'r').reshape(len(g['shared_blocks']), -1)
self.shared[n] = (g['shared_blocks'], shapes, w)
self.joint_steps = self._steps(lambda k: f'den_joint_{k:02d}', range(1, 23), 'den_joint_')
self.gs_steps = self._steps(lambda k: f'gs_{k}', range(1, 8), 'gs_')
def _steps(self, own_name, blocks, prefix):
"""Blocks in run order as (graph, block): a shared graph whose name starts with prefix stands for each of its
blocks (block weights written before the call), an ordinary graph has block -1."""
steps = []
for k in blocks:
own = os.path.exists(os.path.join(self.dir, f'{own_name(k)}.tflite'))
name = None if own else next(
(n for n, (bl, _, _) in self.shared.items() if n.startswith(prefix) and k in bl), None)
steps.append((name, k) if name else (own_name(k), -1))
return steps
def block_weights(self, name, block):
blocks, shapes, w = self.shared[name]
row = np.asarray(w[blocks.index(block)])
out, o = [], 0
for s in shapes:
n = int(np.prod(s))
out.append(row[o:o + n].reshape(s))
o += n
assert o == row.size, (name, o, row.size)
return out
def graph(self, name):
if name not in self.g:
base = self.dino_dir if name.startswith('dino_') else self.dir
path = self.dis_path if name == 'dis' else os.path.join(base, f'{name}.tflite')
# BiRefNet's deformable convolutions sample at data-dependent positions (GATHER_ND): no GPU path, so CPU
accel = 'cpu' if name == 'bir' else self.accel
self.g[name] = Graph(path, accel, self.f32 or name in self.fp32_graphs)
return self.g[name]
def release(self, *prefixes):
"""Close the graphs whose names start with any of the prefixes: one stage's graphs at a time, as the app's
preload = false mode does (all 46 graphs loaded at once pass 32 GB of footprint on the Mac)."""
for n in [n for n in self.g if n.startswith(prefixes)]:
del self.g[n]
gc.collect()
def run1(self, name, inputs, key=None):
out = self.graph(name).run(inputs, key)
if self.stream and name != 'oct':
self.release(name)
return [out[k] for k in sorted(out, key=lambda s: int(s.split('_')[-1]))]
def _t(self, stage, t0):
self.times[stage] = self.times.get(stage, 0.0) + time.time() - t0
# ---------------------------------------------------------------- stages
def matting_alpha(self, rgb):
if self.matting == 'birefnet':
return GL.birefnet_alpha(rgb, lambda x: self.run1('bir', [x])[0])
return GL.dis_alpha(rgb, lambda x: self.run1('dis', [x])[0])
def dinov3(self, pixels):
t0 = time.time()
h = pixels
for i in range(4):
h = self.run1(f'dino_{i}', [h, self.rope_cos, self.rope_sin])[0]
self._t('dinov3', t0)
return h
def vae_moments(self, pixels):
t0 = time.time()
h = pixels
for s in IG.Vae.PARTS:
h = self.run1(f'vae_{s}', [h])[0]
self._t('vae', t0)
return h
def vae_tokens(self, moments, noise):
m, logvar = moments[:, :32], moments[:, 32:]
lat = m + np.exp(0.5 * np.clip(logvar, -30.0, 20.0)) * noise
b, c, h, w = lat.shape
lat = lat.reshape(b, c, h // 2, 2, w // 2, 2).transpose(0, 1, 3, 5, 2, 4).reshape(b, c * 4, h // 2, w // 2)
tok = ((lat - self.bn_mean) / self.bn_std).reshape(b, c * 4, -1).transpose(0, 2, 1)
return np.ascontiguousarray(tok, np.float32)
def cond(self, pixels, vae_noise):
f1 = self.dinov3(pixels)
tok = self.vae_tokens(self.vae_moments(pixels), vae_noise)
f2 = np.concatenate([np.zeros((1, 5, 128), np.float32), tok], axis=1)
return f1, f2
def velocity(self, x_lat, x_cam, t, f1, f2, ctx_cache):
phase = 'slow' if t >= GL.PHASE_THRESHOLD else 'fast'
gate = IG.lora_gate(phase).numpy()
t0 = time.time()
if phase not in ctx_cache:
ctx_cache[phase] = self.run1('den_ctx', [f1, f2, gate])[0]
h_cond = ctx_cache[phase]
h_x, h_cam, t_mod, t_emb = self.run1('den_in', [x_lat, x_cam, IG.sinusoid(1000 * t).numpy(), self.pos_emb,
gate])
h = self.run1('den_joint_00', [h_x, h_cond, h_cam, t_mod, gate])[0]
for name, block in self.joint_steps:
if block >= 0: # by name: the signature lists 33 inputs as args_0, args_1, args_10, ...
args = [h, t_mod, gate] + self.block_weights(name, block)
h = self.run1(name, {f'args_{i}': a for i, a in enumerate(args)})[0]
else:
h = self.run1(name, [h, t_mod, gate])[0]
v, vc = self.run1('den_joint_23', [h, t_mod, t_emb, gate])
self._t('denoiser', t0)
return v, vc
def sample(self, noise, f1, f2, steps=5, rescale_t=3.0, rec=None):
x_lat, x_cam = noise['latent_noise'].copy(), noise['camera_noise'].copy()
ts = GL.time_grid(steps, rescale_t)
cache = {}
for i in range(steps):
v, vc = self.velocity(x_lat, x_cam, float(ts[i]), f1, f2, cache)
if rec is not None:
rec[f'v_latent_{i}'], rec[f'v_camera_{i}'] = v, vc
dt = np.float32(ts[i] - ts[i + 1])
x_lat = (x_lat - v * dt).astype(np.float32)
x_cam = (x_cam - vc * dt).astype(np.float32)
return x_lat, x_cam
def octree_logits(self, cells, level, latent):
n = cells.shape[1]
b = next(bb for bb in IG.OCT_BUCKETS if n <= bb)
c = np.zeros((1, b, 3), np.float32)
c[:, :n] = cells
pe = IG.point_pe(torch.from_numpy(c[0])).numpy()[None]
lf = IG.sinusoid(float(1 << level), max_period=1024.0, two_pi=True).numpy()
return self.run1('oct', [c, pe, lf, latent], key=f'L{b}')[0][:, :n]
def anchors(self, latent, noise, rec=None):
t0 = time.time()
pool = torch.from_numpy(noise['octree_pool'])
leaf = torch.from_numpy(noise['leaf_pool'][:, :self.num_points])
def probs_fn(cells, level):
logits = torch.from_numpy(self.octree_logits(cells.numpy(), level, latent))
return torch.softmax(logits.float(), dim=-1)
out = GL.octree_descend(probs_fn, pool, leaf, num_points=self.num_points, rec=rec).numpy()
self._t('octree', t0)
return out
def gaussians(self, anchors, latent):
t0 = time.time()
pe = IG.point_pe(torch.from_numpy(anchors[0])).numpy()[None]
h = self.run1('gs_0', [anchors, pe, latent])[0]
for name, block in self.gs_steps:
if block >= 0: # by name: the signature lists the inputs as args_0, args_1, args_10, ...
args = [h, latent] + self.block_weights(name, block)
h = self.run1(name, {f'args_{i}': a for i, a in enumerate(args)})[0]
else:
h = self.run1(name, [h, latent])[0]
self._t('gaussian_decoder', t0)
g = GL.to_gaussians(torch.from_numpy(anchors), torch.from_numpy(h), self.rep, self.perturbation,
torch.tensor(self.base_offset_scale))
return h, g
def run(self, image, mode, noise, out_dir, rec=None):
"""raw image -> PLY, the app's whole pipeline."""
from PIL import Image
t_all = time.time()
raw = Image.open(image)
t0 = time.time()
composite, alpha, box = GL.preprocess(raw, self.matting_alpha, GL.IMAGE_SIZE, GL.CROP_MARGIN[mode])
self._t('preprocess', t0)
self.release('dis', 'bir')
os.makedirs(out_dir, exist_ok=True)
composite.save(os.path.join(out_dir, 'input.png'))
Image.fromarray(alpha, mode='L').save(os.path.join(out_dir, f'alpha_{self.matting}.png'))
pixels = GL.pixels_of(composite)
t_gen = time.time()
f1, f2 = self.cond(pixels, noise['vae_noise'])
self.release('dino_', 'vae_')
lat, cam = self.sample(noise, f1, f2, rec=rec)
self.release('den_')
anchors = self.anchors(lat, noise)
self.release('oct')
_, g = self.gaussians(anchors, lat)
self.release('gs_')
t0 = time.time()
GL.write_ply(GL.ply_rows(g), os.path.join(out_dir, 'gaussian.ply'))
self._t('ply', t0)
self.times['generate_total'] = time.time() - t_gen
self.times['all'] = time.time() - t_all
return {'box': box, 'times': {k: round(v, 2) for k, v in self.times.items()}}
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