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20232026
most citedGMem: A Modular Approach for Ultra-Efficient Generative Models

1 citations · 1 across the 19 of their papers we have counts for

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cs.CV2026

UltraText Bench: A Comprehensive Bilingual Benchmark for Evaluating Visual Text Rendering in Image Generation

Deyuan Liu, Yihao Hu, Jingxuan Zhang +13

Dense visual text requires image generators to reproduce long strings across multiple regions with correct placement and legibility. As short-string rendering improves, evaluation…

cs.CV2026

LLaDA-Image: Building Strong Image Generators with Fully Open Training Recipes

Chuyan Chen, Haoxing Chen, Kun Chen +27

We introduce LLaDA-Image, a unified framework that pairs a 6B Diffusion Transformer (DiT) trained from scratch with a frozen vision-language understanding module built on the LLaDA…

cs.CV2026

Condensing Large-Scale Datasets Directly with Minimal Information Loss

Xinyi Shang, Peng Sun, Bei Shi +2

Recent advancements in scaling dataset distillation rely heavily on decoupled information extraction pipelines, comprising SQUEEZE, RECOVER, and RELABEL stages. Despite their scala…

cs.CV2026

Self-Adversarial One Step Generation via Condition Shifting

Deyuan Liu, Peng Sun, Yansen Han +3

The push for efficient text to image synthesis has moved the field toward one step sampling, yet existing methods still face a three way tradeoff among fidelity, inference speed, a…

cs.CV2026

TwinFlow: Realizing One-step Generation on Large Models with Self-adversarial Flows

Zhenglin Cheng, Peng Sun, Jianguo Li +1

Recent advances in large multi-modal generative models have demonstrated impressive capabilities in multi-modal generation, including image and video generation. These models are t…