Topics — The twenty-eight kinds of claim this anthology files against — the primary axis of both the practice registry and the reading list, and the same twenty-eight for each (ADR-026). The filing rule is the point: take a domain topic when the claim is ABOUT the domain; a claim merely discovered in one still takes its kind. The axis organizes the record, it does not bound it: content the list cannot place is evidence the list is short (ADR-059).
Flows and transport — generative models as learned transport between distributions — flow matching, rectified flow, stochastic interpolants, normalizing flows and neural ODEs, and the paths, couplings and time schedules they are trained on; Gaussian diffusion is the special case (THEORY-106). A subtopic, worn beside generative-modeling.
7 of 449 SOTA documents. Back to the full index.
| # | Title | Summary | Status |
|---|---|---|---|
| SOTA-263 v4 | Shift the timestep schedule when the resolution changes, because more pixels need more noise | Hoogeboom et al. (2023), LIT-660, and Chen (2023), LIT-659. A timestep is not a fixed amount of corruption. Destroying the signal in an image with more pixels takes more noise, so a schedule set at one resolution under-corrupts at a higher one. Shifting log-SNR down with resolution takes ImageNet 256 FID from 7.65 to 3.76 in pixel space. The direction is measured. The magnitude is tuned in every source. | Active |
| SOTA-265 v3 | Tune the stochastic sampler’s diffusion coefficient after training; it is not fixed by the forward process | Albergo, Boffi and Vanden-Eijnden (2023), LIT-645, state it and Ma et al. (2024), LIT-447, measure it — score-based diffusion conventionally takes the reverse SDE’s diffusion coefficient from the forward process, and presents the two as intrinsically tied. They are not: the coefficient affects neither the velocity nor the score, only the integration. So it is a sampler hyperparameter, tunable on a frozen model, and tuning it tightens the KL divergence to the target. | Active |
| SOTA-266 v5 | Connect data and noise on a straight line, and sample the training timesteps from a logit-normal rather than uniformly | Esser et al. (2024), LIT-449, and Ma et al. (2024), LIT-447 — the straight-line path between data and noise beats the curved variance-preserving one at fixed architecture and compute, and the advantage is largest at few sampling steps. Movie Gen (LIT-626) confirms it for video at 5B. In SD3’s latent text-to-image sweep, the timestep distribution decides the ranking: uniform does not beat tuned ε-prediction and logit-normal does. | Active |
| SOTA-392 v3 | To get a one-step sampler from a rectified flow, distil it; treat reflow as an optional extra pass, and keep the pre-reflow model for many steps | Liu, Gong and Liu (2022), LIT-636. On CIFAR-10, one-step FID is 378 from the base model, 6.18 after distillation alone, 12.21 after one reflow, and 4.85 after reflow and distillation (Table 1a). Distillation does most of the work. Reflow adds a further gain at the cost of a second training pass, and worsens many-step quality from 2.58 to 3.36. InstaFlow (LIT-796) contests it at text-to-image scale, starting from a diffusion model rather than a rectified flow. | Proposed |
| SOTA-442 | To RL-tune a flow-matching generator, roll it out as a same-marginal SDE built from its own velocity, and anchor each step with the closed-form KL to the reference | Liu et al. (2025), LIT-779. A flow model’s ODE sampler is not a stochastic policy. Swapping it for an SDE with the same marginals, built from the trained velocity alone, makes each step an isotropic Gaussian, so GRPO’s ratio and the KL to the reference model are both closed form. On SD3.5-M, GenEval goes 0.63 → 0.95 either way. Without the KL term, DrawBench aesthetic falls to 4.93 from a base of 5.39, against 5.25 with it (Table 2). Quality is judged by reward models only, one run each. | Proposed |
| SOTA-444 | When a flow will be sampled with a coarse ODE solver, pair noise and data within each minibatch by exact optimal transport | Pooladian et al. (2023), LIT-789, and Tong et al. (2023), LIT-798. Re-pair each minibatch’s noise and data by exact discrete OT before the flow-matching regression. The marginals stay exact, the learned paths straighten, and on ImageNet-64 reaching FID 20 with Euler takes 12 steps instead of 29, at equal likelihood and 4% more time per iteration. It lowers the step count of an ODE sampler. It does not make one: four-step FID stays near 39 on ImageNet-32. Unconditional, 64 px and below. | Proposed |
| SOTA-448 | Without a pretrained teacher, train a one-step generator as a MeanFlow average-velocity model rather than by consistency training or a shortcut model | Geng et al. (2025), LIT-784. Train a network for the average velocity over an interval, regressed onto v − (t − r)·du/dt under stop-gradient, with du/dt from a Jacobian-vector product. From scratch on ImageNet-256 with DiT-XL/2, one-step FID is 3.43 after 240 epochs, against 10.60 for a shortcut model and 34.24 for iCT as Inductive Moment Matching re-ran it (Table 2). Those rows are copied, at other budgets. No arm compares distilling a teacher. | Proposed |