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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…
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…
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…
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…
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…
GMem: A Modular Approach for Ultra-Efficient Generative Models
Yi Tang, Peng Sun, Zhenglin Cheng +1
Recent studies indicate that the denoising process in deep generative diffusion models implicitly learns and memorizes semantic information from the data distribution. These findin…
GIFT: Unlocking Full Potential of Labels in Distilled Dataset at Near-zero Cost
Xinyi Shang, Peng Sun, Tao Lin
Recent advancements in dataset distillation have demonstrated the significant benefits of employing soft labels generated by pre-trained teacher models. In this paper, we introduce…
On the Diversity and Realism of Distilled Dataset: An Efficient Dataset Distillation Paradigm
Peng Sun, Bei Shi, Daiwei Yu +1
Contemporary machine learning requires training large neural networks on massive datasets and thus faces the challenges of high computational demands. Dataset distillation, as a re…