The paper introduces an adaptive step schedule controller for text‑to‑image diffusion models, allowing the number of denoising steps to vary based on the complexity of the input prompt. By mixing step schedules of different sizes and monitoring error discrepancies at each timestep, the method switches schedules to maintain image quality while reducing inference time. Experiments on COCO and DiffusionDB demonstrate that this approach achieves faster generation without sacrificing visual fidelity.
By Kuluhan Binici, Cihan Acar, Shivam Aggarwal, Siying Liu, Tulika Mitra
arXiv:2603. 12506v2 Announce Type: replace-cross Abstract: Text-to-Image (T2I) generation is primarily driven by Diffusion Models (DM) which rely on random Gaussian noise.
By Joong Ho Kim, Nicholas Thai, Souhardya Saha Dip, Dong Lao, Keith G. Mills
arXiv:2608.27885v1 Announce Type: new
Abstract: Multimodality translation (e.g., text-to-image) is a core generative AI task. However, existing approaches (1) follow generative paths that do not dire...
By Gabe Guo, Elon Litman, Thanawat Sornwanee, Jose Blanchet, Stefano Ermon
arXiv:2511.19811v2 Announce Type: replace-cross
Abstract: Image diversity remains a fundamental challenge for text-to-image diffusion models. Low-diversity generation often leads to repetitive output...
By Debin Meng, Chen Jin, Zheng Gao, Yanran Li, Ioannis Patras, Georgios Tzimiropoulos
ReGain is a training‑free correction that improves subject fidelity in text‑to‑image diffusion models personalized with synthetic images. The authors show that fine‑tuning on synthetic images degrades fidelity due to inflated classifier‑free guidance, especially at high frequencies. ReGain measures this inflation per frequency band and scales it down during sampling, closing 51‑64% of the fidelity gap on Stable Diffusion v1.5 and improving performance on SDXL and SD 3.5 while preserving text alignment.
By Shubhang Bhatnagar, Ishan Bhatnagar, Viraj Shah, Narendra Ahuja
arXiv:2607. 15711v1 Announce Type: cross Abstract: Diffusion-based methods have achieved impressive performance in real-world image super-resolution (Real-ISR) by leveraging large pre-trained stable diffusion (SD) models as powerful generative priors.
By Xue Wu, Kang Zhao, Kafeng Wang, Jianfei Chen, Jingwei Xin, Nannan Wang, Xinbo Gao
LLaDA-Image is a unified framework that couples a 6B Diffusion Transformer (DiT) trained from scratch with a frozen vision‑language module based on the LLaDA2.0‑Mini diffusion language model. The approach first builds a strong visual generative prior through image‑only pre‑training and mid‑training, then fine‑tunes with a 220M‑sample generation pipeline that includes 98 real images. The resulting model produces highly photorealistic images that accurately follow fine‑grained editing instructions, and a distilled version, LLaDA‑Image‑Turbo, enables fast inference in 2–4 sampling steps. On Qwen‑Image‑Bench, LLaDA‑Image sets new state‑of‑the‑art scores for open‑source models in both English and Chinese tracks, and the authors release weights, code, and detailed recipes to support further research.
By Chuyan Chen, Haoxing Chen, Kun Chen, Zhenglin Cheng, Long Cui, Ruishan Fang, Zhangxuan Gu, Zhicheng Huang, Zhenzhong Lan, Yuanting Lei, Haoquan Li, Jianguo Li, Rongchuan Li, Sidu Li, Tao Lin, Deyuan Liu, Jiacheng Liu, Lin Liu, Yuxuan Lou, Zhisheng Lu, Yuxin Ma, Shuheng Shen, Peng Sun, Chaoyang Wang, Hongjun Wang, Xiaomei Wang, Yongxin Wang, Chengzhang Wu, Hongru Wu, Jun Xie
arXiv:2608.29997v1 Announce Type: new
Abstract: We propose Discrete Diffusion Bridges (DDB), a novel framework designed to resolve the fundamental spatiotemporal misalignment of standard discrete dif...
By Xing Xie, Jiawei Liu, Shijun Zhou, Huijie Fan, Zhi Han, Yandong Tang, Liangqiong Qu
arXiv:2607. 19332v1 Announce Type: new Abstract: Generative models have undergone many generations of evolution, from VAEs/GANs to diffusion/flow matching.
By Chirag Vashist, Ke Li
The paper introduces Abra, a family of flow‑matching transformers used to systematically study scaling laws for text‑to‑image diffusion models across three orders of magnitude in compute. It finds that diffusion models scale predictably like language models but need far more data, with compute optimality occurring at roughly 200 image tokens per parameter—ten times the optimal ratio for large language models. The study also shows that diffusion models are robust to overtraining, that more data is preferable to larger models, and that scaling predictability extends to generative quality, optimal CFG settings, representation quality, and training curve shapes.
By Kyle Chickering, Wei-An Lin, Swayam Bhanded, Dan Saunders, Akshat Tripathi, Jiaming Song, Shyamal Buch, Xinchen Yan
arXiv:2502.03726v3 Announce Type: replace
Abstract: Text-to-image diffusion models are capable of generating high-quality images, but suboptimal pre-trained text representations often result in these...
By Zhenyu Zhou, Defang Chen, Can Wang, Chun Chen, Siwei Lyu
arXiv:2606. 31683v1 Announce Type: cross Abstract: Diffusion models have emerged as a dominant paradigm in generative modeling, enabling high-fidelity sampling from complex data distributions.
By Haoming Liu, Yuanhe Guo, Yijia Cao, Shenji Wan, Hongyi Wen