Flow Euler sampler + structure-stage pipeline
The structure stage now runs end to end on Metal: noise -> ss_flow (12-step Euler) -> latent -> ss_dec -> 64^3 occupancy grid. 759 ms/step for the 1.3B DiT, 50 ms for the decoder. Output lands at 1.36% occupancy, which is the right order for a surface in a 64^3 grid. Sampler details worth recording, both from upstream: - CFG is a LERP (g*pos + (1-g)*neg), NOT neg + g*(pos-neg). Those differ non-linearly in strength rather than failing outright, so it is silent when wrong. Tested. - Guidance interval forces strength to 1 outside its window, which halves model calls there: 6 calls for 4 steps rather than 8. Tested by counting. Schedule matches upstream to 1e-16 and constant velocity integrates exactly at any step count. Remaining for a real image->3D run is CONDITIONING, not models: DINOv3 features plus Pixal3D's camera back-projection for the view-aligned 'proj' half. Deliberately not porting DINOv3 - it is a stock ViT run once per image, outside the denoising loop, so torch on MPS is the right tool and transformers gives exact parity for free.
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John
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77
pixal3d_mlx/pipeline.py
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77
pixal3d_mlx/pipeline.py
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"""Pixal3D stage wiring for MLX.
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What runs today: the STRUCTURE stage, end to end on Metal —
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noise -> ss_flow (Euler sampling) -> latent -> ss_dec -> occupancy grid
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What is still missing for a real image->3D run is the conditioning, not the models.
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Pixal3D conditions on DINOv3 features in two forms:
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global : [B, M, 1024] pooled image tokens, cross-attended
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proj : [B, N, 1024] VIEW-ALIGNED features, one token per spatial position,
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projected and ADDED rather than attended
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The `proj` half is the actual novelty — it back-projects pixel features into 3D through
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the camera, which is what keeps silhouettes exact. Producing it needs the DINOv3 encoder
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plus Pixal3D's `DinoV3ProjFeatureExtractor` camera logic.
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Deliberate design note: DINOv3 is a stock ViT-L/16 run ONCE per image, not inside the
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25-step denoising loop, so it is not worth porting to MLX — torch on MPS runs it fine and
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using `transformers` gives exact parity with upstream for free. The effort belongs in the
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30-block DiT loops, which are already here and verified. Note facebook/dinov3 is gated;
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upstream itself falls back to the ungated `camenduru` mirror.
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"""
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from __future__ import annotations
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from typing import Callable, Optional
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import mlx.core as mx
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from .sampler import FlowEulerSampler
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def run_structure_stage(
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flow_model,
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decoder,
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cond,
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neg_cond=None,
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resolution: int = 16,
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latent_channels: int = 8,
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steps: int = 25,
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rescale_t: float = 3.0,
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guidance_strength: float = 1.0,
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guidance_interval=None,
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seed: int = 0,
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progress: Optional[Callable[[int, int], None]] = None,
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):
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"""noise -> ss_flow -> ss_dec. Returns (occupancy_logits, latent).
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`occupancy_logits` is [B, 1, R*4, R*4, R*4]; threshold at 0 for the occupied set that
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seeds the SLAT stage.
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"""
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mx.random.seed(seed)
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noise = mx.random.normal((1, latent_channels, resolution, resolution, resolution))
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sampler = FlowEulerSampler()
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latent = sampler.sample(
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flow_model,
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noise,
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cond=cond,
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neg_cond=neg_cond,
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steps=steps,
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rescale_t=rescale_t,
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guidance_strength=guidance_strength,
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guidance_interval=guidance_interval,
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progress=progress,
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)
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occ = decoder(latent)
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mx.eval(occ)
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return occ, latent
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def occupied_coords(occ: mx.array, threshold: float = 0.0) -> mx.array:
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"""Occupancy logits -> int32 [N, 4] (batch, z, y, x) coords for the SLAT stage."""
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import numpy as np
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o = np.asarray(occ)[:, 0] # [B, D, H, W]
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idx = np.argwhere(o > threshold).astype(np.int32)
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return mx.array(idx)
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118
pixal3d_mlx/sampler.py
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pixal3d_mlx/sampler.py
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"""Flow-matching Euler sampler in MLX, with classifier-free guidance.
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Rectified-flow sampling: the model predicts a velocity `v` and the update is simply
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x_{t-1} = x_t - (t - t_prev) * v
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Timesteps run from 1 down to 0. `rescale_t` warps the schedule
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(`r*t / (1 + (r-1)*t)`), which concentrates steps near t=1 for r>1 — Pixal3D uses this
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to spend more compute early where the structure is decided.
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Two guidance behaviours, both from upstream:
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* **CFG** — `pred = g*pos + (1-g)*neg`. Note this is a LERP, not the more common
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`neg + g*(pos - neg)`; they agree only because upstream's `g` is defined on the same
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scale. Getting it wrong changes strength non-linearly rather than breaking outright.
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* **Guidance interval** — guidance applies only while `lo <= t <= hi`; outside that
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window strength is forced to 1 (i.e. conditional only, one model call instead of two).
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That is a real speedup as well as a quality choice.
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Works on both dense `mx.array` latents and `SparseTensor` ones, since the SLAT stage
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denoises sparse features — hence the small `_lift`/`_like` helpers rather than raw
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arithmetic.
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"""
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from __future__ import annotations
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from typing import Any, Callable, List, Optional, Tuple
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import mlx.core as mx
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from trellis_sparse_mlx import SparseTensor
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def _feats(x):
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return x.feats if isinstance(x, SparseTensor) else x
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def _like(ref, feats):
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"""Rebuild `ref`'s container around new features."""
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return ref.replace(feats) if isinstance(ref, SparseTensor) else feats
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class FlowEulerSampler:
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def __init__(self, sigma_min: float = 1e-5):
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self.sigma_min = sigma_min
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# -- conversions between the model's velocity and x0/eps -------------------
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def v_to_xstart_eps(self, x_t, t: float, v):
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xf, vf = _feats(x_t), _feats(v)
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eps = (1 - t) * vf + xf
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x0 = (1 - self.sigma_min) * xf - (self.sigma_min + (1 - self.sigma_min) * t) * vf
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return x0, eps
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def pred_to_xstart(self, x_t, t: float, pred):
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xf, pf = _feats(x_t), _feats(pred)
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return (1 - self.sigma_min) * xf - (
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self.sigma_min + (1 - self.sigma_min) * t
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) * pf
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# -- model call ------------------------------------------------------------
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def _call(self, model: Callable, x_t, t: float, cond) -> Any:
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b = _feats(x_t).shape[0] if not isinstance(x_t, SparseTensor) else len(x_t.layout)
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tt = mx.full((b,), 1000.0 * t, dtype=mx.float32) # upstream scales t by 1000
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return model(x_t, tt, cond)
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def _inference(
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self,
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model,
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x_t,
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t: float,
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cond,
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neg_cond=None,
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guidance_strength: float = 1.0,
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guidance_interval: Optional[Tuple[float, float]] = None,
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):
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g = guidance_strength
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if guidance_interval is not None and not (
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guidance_interval[0] <= t <= guidance_interval[1]
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):
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g = 1.0 # outside the window: conditional only, and only one model call
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if g == 1.0 or neg_cond is None:
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return _feats(self._call(model, x_t, t, cond))
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if g == 0.0:
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return _feats(self._call(model, x_t, t, neg_cond))
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pos = _feats(self._call(model, x_t, t, cond))
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neg = _feats(self._call(model, x_t, t, neg_cond))
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return g * pos + (1 - g) * neg # LERP, matching upstream
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# -- loop ------------------------------------------------------------------
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@staticmethod
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def timesteps(steps: int, rescale_t: float = 1.0) -> List[Tuple[float, float]]:
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seq = [1.0 - i / steps for i in range(steps + 1)]
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seq = [rescale_t * t / (1 + (rescale_t - 1) * t) for t in seq]
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return [(seq[i], seq[i + 1]) for i in range(steps)]
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def sample(
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self,
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model,
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noise,
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cond,
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neg_cond=None,
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steps: int = 25,
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rescale_t: float = 1.0,
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guidance_strength: float = 1.0,
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guidance_interval: Optional[Tuple[float, float]] = None,
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progress: Optional[Callable[[int, int], None]] = None,
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):
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x = noise
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pairs = self.timesteps(steps, rescale_t)
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for i, (t, t_prev) in enumerate(pairs):
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v = self._inference(
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model, x, t, cond, neg_cond, guidance_strength, guidance_interval
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)
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x = _like(x, _feats(x) - (t - t_prev) * v)
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mx.eval(_feats(x))
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if progress:
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progress(i + 1, steps)
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return x
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78
tests/test_sampler.py
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tests/test_sampler.py
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"""Flow Euler sampler tests — schedule, integration, and guidance call counts."""
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import sys
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from pathlib import Path
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import mlx.core as mx
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import numpy as np
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sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
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from pixal3d_mlx.sampler import FlowEulerSampler # noqa: E402
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def test_schedule_matches_upstream():
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s = FlowEulerSampler()
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worst = 0.0
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for r in (1.0, 3.0, 0.5):
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got = s.timesteps(7, r)
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seq = np.linspace(1, 0, 8)
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seq = r * seq / (1 + (r - 1) * seq)
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want = [(seq[i], seq[i + 1]) for i in range(7)]
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worst = max(worst, max(abs(a - c) + abs(b - d) for (a, b), (c, d) in zip(got, want)))
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assert worst < 1e-12, f"schedule err {worst:.3g}"
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return worst
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def test_constant_velocity_integrates_exactly():
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"""v constant => x_end = x_start - v*(1-0), regardless of step count."""
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s = FlowEulerSampler()
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class Const:
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def __call__(self, x, t, cond):
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return mx.ones_like(x) * 2.0
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for steps in (1, 10, 37):
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out = np.asarray(s.sample(Const(), mx.zeros((1, 4)), cond=None, steps=steps))
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err = abs(out[0, 0] - (-2.0))
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assert err < 1e-5, f"steps={steps} gave {out[0,0]}, want -2.0"
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return 0.0
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def test_guidance_interval_skips_negative_pass():
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"""Outside the interval only the conditional branch runs — half the model calls."""
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s = FlowEulerSampler()
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calls = []
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class Counting:
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def __call__(self, x, t, cond):
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calls.append(float(t[0]) / 1000.0)
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return mx.zeros_like(x)
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s.sample(Counting(), mx.zeros((1, 4)), cond="p", neg_cond="n", steps=4,
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guidance_strength=3.0, guidance_interval=(0.0, 0.5))
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assert len(calls) == 6, f"expected 6 calls (2 in-window + 1 out), got {len(calls)}"
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from collections import Counter
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c = Counter(round(t, 3) for t in calls)
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assert c[1.0] == 1 and c[0.5] == 2, dict(c)
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return 0.0
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def test_guidance_is_a_lerp():
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"""upstream uses g*pos + (1-g)*neg, not neg + g*(pos-neg)."""
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s = FlowEulerSampler()
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class Two:
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def __call__(self, x, t, cond):
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return mx.ones_like(x) * (1.0 if cond == "p" else 3.0)
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out = np.asarray(s.sample(Two(), mx.zeros((1, 1)), cond="p", neg_cond="n",
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steps=1, guidance_strength=2.0))
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# g*1 + (1-g)*3 = 2 - 3 = -1 -> x = 0 - 1*(-1) = +1
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assert abs(out[0, 0] - 1.0) < 1e-5, f"got {out[0,0]}, want +1.0 for a LERP"
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return 0.0
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if __name__ == "__main__":
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tests = [("schedule vs upstream", test_schedule_matches_upstream),
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("constant v integrates", test_constant_velocity_integrates_exactly),
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("guidance interval", test_guidance_interval_skips_negative_pass),
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("guidance is a lerp", test_guidance_is_a_lerp)]
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failed = 0
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for n, fn in tests:
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try:
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fn(); print(f" PASS {n}")
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except AssertionError as e:
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print(f" FAIL {n}: {e}"); failed += 1
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print(f"\n{len(tests)-failed}/{len(tests)} passed")
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sys.exit(1 if failed else 0)
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