anthology-of-the-sota

flows-and-transport

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43 items with this tag.

Oct 03, 2026
LIT-771: Glow: Generative Flow with Invertible 1x1 Convolutions
Oct 03, 2026
LIT-772: Neural Ordinary Differential Equations
Oct 03, 2026
LIT-773: Improving Video Generation with Human Feedback
Oct 03, 2026
LIT-775: Inductive Moment Matching
Oct 03, 2026
LIT-777: Flow Matching Guide and Code
- generative-modeling
- flows-and-transport
Oct 03, 2026
LIT-779: Flow-GRPO: Training Flow Matching Models via Online RL
Oct 03, 2026
LIT-780: FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Models
- generative-modeling
- flows-and-transport
Oct 03, 2026
LIT-781: Density estimation using Real NVP
Oct 03, 2026
LIT-784: Mean Flows for One-step Generative Modeling
Oct 03, 2026
LIT-785: STARFlow: Scaling Latent Normalizing Flows for High-resolution Image Synthesis
Oct 03, 2026
LIT-787: One Step Diffusion via Shortcut Models
Oct 03, 2026
LIT-789: Multisample Flow Matching: Straightening Flows with Minibatch Couplings
Oct 03, 2026
LIT-790: Simplifying, Stabilizing and Scaling Continuous-Time Consistency Models
Oct 03, 2026
LIT-791: Improving the Training of Rectified Flows
Oct 03, 2026
LIT-792: Discrete Flow Matching
Oct 03, 2026
LIT-793: Flow map matching with stochastic interpolants: A mathematical framework for consistency models
Oct 03, 2026
LIT-794: Normalizing Flows are Capable Generative Models
Oct 03, 2026
LIT-796: InstaFlow: One Step is Enough for High-Quality Diffusion-Based Text-to-Image Generation
Oct 03, 2026
LIT-798: Improving and generalizing flow-based generative models with minibatch optimal transport
Oct 03, 2026
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
Oct 03, 2026
SOTA-444: When a flow will be sampled with a coarse ODE solver, pair noise and data within each minibatch by exact optimal transport
Oct 03, 2026
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
Oct 03, 2026
THEORY-119: Consistency, shortcut, MeanFlow and progressive-distillation models all estimate the two-time flow map of the probability-flow ODE, and differ in which identity of that map they train on
Oct 03, 2026
THEORY-120: A likelihood-trained normalizing flow samples well only when its training data carry Gaussian noise well above the quantization width, because its inverse must be well conditioned over the whole Gaussian latent
- generative-modeling
- flows-and-transport
Oct 03, 2026
THEORY-122: A diffusion or flow sampler can be trained by a likelihood-ratio policy gradient only when each step is stochastic with a tractable density, and a same-marginal SDE built from the trained velocity supplies that without changing the marginals
Oct 03, 2026
THEORY-123: A flow's sampling ODE curves because the noise-data pairs it was trained on cross and the learned velocity averages them, so a coupling whose straight paths do not cross gives a straight flow
Oct 03, 2026
THEORY-125: One affine autoregressive flow block makes every conditional a single Gaussian and cannot approximate an arbitrary density, while two blocks in opposite orders make every conditional but the last a Gaussian mixture and three make all of them one
Sep 26, 2026
LIT-722: Score-Based Generative Modeling through Stochastic Differential Equations
Sep 25, 2026
LIT-678: Diffusion Meets Flow Matching: Two Sides of the Same Coin
Sep 25, 2026
THEORY-106: Gaussian flow matching is a diffusion model; what separates the named recipes is loss weighting, network output and sampling schedule
Sep 24, 2026
LIT-619: Wan: Open and Advanced Large-Scale Video Generative Models
Sep 24, 2026
LIT-626: Movie Gen: A Cast of Media Foundation Models
Sep 24, 2026
LIT-630: Flow Matching for Generative Modeling
- generative-modeling
- flows-and-transport
Sep 24, 2026
LIT-636: Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow
Sep 24, 2026
LIT-644: Building Normalizing Flows with Stochastic Interpolants
Sep 24, 2026
LIT-645: Stochastic Interpolants: A Unifying Framework for Flows and Diffusions
Sep 24, 2026
SOTA-392: 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
Sep 23, 2026
LIT-573: FLUX.1 Kontext: Flow Matching for In-Context Image Generation and Editing in Latent Space
Sep 20, 2026
LIT-447: SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers
- generative-modeling
- flows-and-transport
Sep 20, 2026
LIT-449: Scaling Rectified Flow Transformers for High-Resolution Image Synthesis
Sep 20, 2026
SOTA-263: Shift the timestep schedule when the resolution changes, because more pixels need more noise
Sep 20, 2026
SOTA-265: Tune the stochastic sampler's diffusion coefficient after training; it is not fixed by the forward process
- generative-modeling
- flows-and-transport
Sep 20, 2026
SOTA-266: Connect data and noise on a straight line, and sample the training timesteps from a logit-normal rather than uniformly
- generative-modeling
- flows-and-transport

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