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one-step generation

7 articles · Page 1 of 1
Data Party THU
Data Party THU
Nov 10, 2026 · Artificial Intelligence

One-Step Text Generation with Seq-Drifting: Diffusion LMs Break Multi-Step Barrier

Researchers from City University of Hong Kong, Shenzhen University, USTC, and Oxford propose Seq-Drifting, a context-dependent drifting field method enabling one-step text generation for diffusion language models, achieving 11.2x speedup over 4-step baselines with Gen-PPL of 36.01 on 128-token generation, and validated on translation, summarization, and reasoning tasks.

Seq-Driftingcontext-dependent drifting fielddiffusion language models
0 likes · 11 min read
One-Step Text Generation with Seq-Drifting: Diffusion LMs Break Multi-Step Barrier
SmartApe AI
SmartApe AI
Oct 9, 2026 · Artificial Intelligence

CMU PhD Zhengyang Geng Joins OpenAI After One-Step Generation Breakthrough

CMU PhD student Zhengyang Geng announced he has joined OpenAI; his research includes MeanFlow (one-step image generation achieving FID 3.43 on ImageNet 256×256, NeurIPS 2025 Oral), Improved MeanFlow (FID 1.72, CVPR 2026 Highlight), and Equilibrium Reasoners (latent-space iterative reasoning boosting Sudoku-Extreme accuracy from 2.6% to 99%+, ICML 2026).

CMUEquilibrium ReasonersImproved MeanFlow
0 likes · 5 min read
CMU PhD Zhengyang Geng Joins OpenAI After One-Step Generation Breakthrough
Machine Learning Algorithms & Natural Language Processing
Machine Learning Algorithms & Natural Language Processing
Aug 10, 2026 · Artificial Intelligence

Training One‑Step Generative Models Without CFG, DMD, GAN, Drifting, or MeanFlow

The paper introduces TBSM, a lightweight direction‑tracking network that learns fake‑to‑real movement fields from three‑sample scattering events, enabling single‑forward‑pass generation and achieving state‑of‑the‑art FID scores on ImageNet and a 20B text‑to‑image model without relying on CFG, DMD, GAN, drifting, or mean‑flow techniques.

Generative AIPixelDiTQwen-Image
0 likes · 12 min read
Training One‑Step Generative Models Without CFG, DMD, GAN, Drifting, or MeanFlow
Machine Heart
Machine Heart
Aug 9, 2026 · Artificial Intelligence

How to Train a One‑Step Generative Model Without CFG, DMD, GAN, or Drifting

The paper introduces TBSM, a lightweight direction‑tracking network that learns per‑sample Fake‑to‑Real vectors to guide a generator, enabling single‑forward (NFE=1) image synthesis with FID 1.92 on ImageNet‑512 and high‑quality text‑to‑image results, all without CFG, DMD, GAN, or drifting methods.

Generative AITBSMdiffusion models
0 likes · 11 min read
How to Train a One‑Step Generative Model Without CFG, DMD, GAN, or Drifting
Machine Learning Algorithms & Natural Language Processing
Machine Learning Algorithms & Natural Language Processing
Jul 8, 2026 · Artificial Intelligence

Why the One‑Step “Drift Model” by He Kaiming’s Team Was Rejected at ICML (Scores 5‑4‑4‑3)

The drift model moves the distribution evolution into training to achieve single‑step image synthesis with near‑SOTA FID scores, but ICML reviewers gave it 5/4/4/3 and rejected it, citing heavy reliance on a pretrained feature encoder, unclear contribution of the drift field versus classifier‑free guidance, and insufficient comparison to prior work.

Classifier-Free GuidanceFIDICML rejection
0 likes · 9 min read
Why the One‑Step “Drift Model” by He Kaiming’s Team Was Rejected at ICML (Scores 5‑4‑4‑3)
AIWalker
AIWalker
Mar 4, 2026 · Artificial Intelligence

Drifting Models Enable One‑Step Generation, Shattering Speed Records

The paper introduces Drifting Models, a new generative paradigm that moves the distribution evolution to the training phase, achieving true one‑step (1‑NFE) generation with state‑of‑the‑art ImageNet FID scores of 1.54 in latent space and 1.61 in pixel space, while eliminating the need for distillation or classifier‑free guidance.

Drifting ModelsImageNetdiffusion
0 likes · 24 min read
Drifting Models Enable One‑Step Generation, Shattering Speed Records