89 lines
4.2 KiB
Python
89 lines
4.2 KiB
Python
"""Samplers as a small library; model families compose them.
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Flow-match Euler is shared by the Wan and LTX-2 denoise step bodies. On GPU the real UniPC
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multistep / distilled schedulers are wrapped by the torch component adapters; this numpy form
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is the CPU-testable, bit-reproducible reference used by the loop tests.
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Flow-match interpolation: ``x_t = (1-σ_t)·x0 + σ_t·ε``; the model predicts velocity
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``v = ε - x0``. A deterministic Euler step to σ_next is ``x_next = x_t + (σ_next-σ_t)·v``
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(exactly LTX-2's ``velocity=(latents-denoised)/σ; latents += velocity·dt``).
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"""
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from __future__ import annotations
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import numpy as np
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def build_flow_sigmas(num_steps: int, shift: float = 1.0, terminal: float = 0.0) -> np.ndarray:
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"""σ schedule from 1→terminal over ``num_steps+1`` points, with flow-shift applied.
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Flow-shift: ``σ' = shift·σ / (1 + (shift-1)·σ)`` (Wan/LTX). shift=1 is the identity.
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"""
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base = np.linspace(1.0, terminal, num_steps + 1, dtype=np.float64)
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if shift != 1.0:
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base = shift * base / (1.0 + (shift - 1.0) * base)
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return base
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def x0_from_velocity(x_t: np.ndarray, velocity: np.ndarray, sigma_t: float) -> np.ndarray:
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"""x0 = x_t - σ_t·v (flow prediction → clean sample; Wan ``x0 = x_t - σ·model_output``)."""
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return x_t - sigma_t * velocity
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def flow_match_euler_step(x_t: np.ndarray, velocity: np.ndarray, sigma_t: float, sigma_next: float) -> np.ndarray:
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"""One deterministic Euler step along the flow ODE."""
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return x_t + (sigma_next - sigma_t) * velocity
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def add_noise(x0: np.ndarray, noise: np.ndarray, sigma: float) -> np.ndarray:
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"""Forward flow-match interpolation: x_σ = (1-σ)·x0 + σ·noise."""
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return (1.0 - sigma) * x0 + sigma * noise
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def flow_sde_step_with_logprob(x_t,
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velocity,
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sigma_t: float,
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sigma_next: float,
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*,
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noise=None,
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prev_sample=None,
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noise_scale: float = 0.7):
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"""Stochastic diffusion step + Gaussian log-prob metadata.
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This is the stochastic counterpart to deterministic ``flow_match_euler_step``. ``prev_sample=None``
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samples a new step; passing ``prev_sample`` recomputes its log-prob under the current velocity.
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Returns ``(prev_sample, log_prob, mean, eff_std)``.
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"""
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x_t = np.asarray(x_t, dtype=np.float64)
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velocity = np.asarray(velocity, dtype=np.float64)
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s = min(float(sigma_t), 0.9999)
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dt = float(sigma_next) - float(sigma_t) # negative (σ decreases)
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std = float(np.sqrt(s / (1.0 - s)) * noise_scale)
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denom = 2.0 * max(s, 1e-6)
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mean = x_t * (1.0 + std**2 / denom * dt) + velocity * (1.0 + std**2 * (1.0 - s) / denom) * dt
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eff_std = max(std * np.sqrt(max(-dt, 1e-12)), 1e-6)
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if prev_sample is None:
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n = noise if noise is not None else np.zeros_like(x_t)
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prev_sample = mean + eff_std * np.asarray(n, dtype=np.float64)
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prev_sample = np.asarray(prev_sample, dtype=np.float64)
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var = eff_std**2
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log_prob = float(np.mean(-((prev_sample - mean)**2) / (2.0 * var) - np.log(eff_std) - 0.5 * np.log(2.0 * np.pi)))
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return prev_sample.astype("float32"), log_prob, mean.astype("float32"), float(eff_std)
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def flow_sde_ml_velocity(x_t, sample, sigma_t: float, sigma_next: float, *, noise_scale: float = 0.7):
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"""The velocity whose deterministic SDE mean lands exactly on ``sample`` (the max-likelihood
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velocity for a realized FlowGRPO sample). Moving the policy toward it, advantage-weighted, is the
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policy-gradient direction — and is nonzero even at ratio==1, so it is the correct FlowGRPO update
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surrogate (nudging toward the velocity the model already produced would be a no-op).
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"""
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x_t = np.asarray(x_t, dtype=np.float64)
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sample = np.asarray(sample, dtype=np.float64)
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s = min(float(sigma_t), 0.9999)
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dt = float(sigma_next) - float(sigma_t)
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std = float(np.sqrt(s / (1.0 - s)) * noise_scale)
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denom = 2.0 * max(s, 1e-6)
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a = 1.0 + std**2 / denom * dt
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b = (1.0 + std**2 * (1.0 - s) / denom) * dt
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b = b if abs(b) > 1e-8 else (1e-8 if b >= 0.0 else -1e-8)
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return ((sample - x_t * a) / b).astype("float32")
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