用於生成式建模的三體散射

摘要

現今的生成模型通常依賴對抗性判別器、預設的噪聲到資料路徑，或自回歸分解。然而，我們證明適當的分布能量可以誘導樣本層級的運動，並為單步生成器提供直接回歸監督。用於生成的三體散射建模（TBSM）將能量距離轉化為每個拋射體與固定大小相互作用的關係：每個拋射體被一個真實來源吸引，並被一個獨立生成的來源排斥。以拋射體及其條件為基礎，其期望值等於½D_E²(P_θ, Q)的2-Wasserstein梯度流速度。一批B個凍結目標事件會產生O(B)個樣本層級損失，每個損失使用一個參考作為其條件，而非如漂移模型（Drifting Models）這類方法所使用的全批次成對場域。在線追蹤此條件期望值可降低場域噪聲。透過在凍結影像特徵中進行散射，TBSM在ImageNet-256上訓練單步生成器，在像素空間PixelDiT-XL中達到FID=2.23，在潛在空間DiT-XL中達到FID=1.63（NFE=1）。我們提供了一個設計圖，將擴散相關監督、漂移式動力學及GAN式目標函數聯繫起來。這些結果確立了追蹤散射作為實現高維度單步生成的有效途徑。程式碼：https://github.com/sp12138/TBSM。

English

Modern generative models typically rely on an adversarial critic, a prescribed noise-to-data path, or an autoregressive factorization. Instead, we show that a proper distributional energy can induce sample-level motion and provide direct regression supervision for a one-step generator. Three-Body Scattering Modeling (TBSM) for generation turns the energy distance into a constant-size per-projectile interaction: each projectile is attracted toward one real source and repelled from one independently generated source. Conditioned on the projectile and its condition, its expectation equals the 2-Wasserstein gradient-flow velocity of frac12D_E^2(P_θ,Q). A batch of B frozen-target events yields O(B) sample-level losses, each using one reference for its condition instead of the minibatch-wide all-pairs field used by methods such as Drifting Models. Tracking this conditional expectation online can reduce field noise. Using scattering in frozen image features, TBSM trains one-step generators on ImageNet-256, achieving FID{}=2.23 with pixel-space PixelDiT-XL and FID{}=1.63 with latent-space DiT-XL at NFE{}=1. We provide a design map relating diffusion-related supervision, Drift-like dynamics, and GAN-like objectives. These results establish tracked scattering as a route to high-dimensional one-step generation. Code: https://github.com/sp12138/TBSM.