arXiv AI September 25, 2026 By Yike Xu, Yue Shi, Yong Guo, Jiezhang Cao

TOLA: Text-aware One-Step Latent Adaptation for Diffusion-based Text Image Super-Resolution

TOLA is a diffusion‑based text image super‑resolution method that eliminates iterative image‑text diffusion by using a one‑step latent adaptation framework. It employs a confidence‑weighted text conditioning module to build a reliable semantic condition and a lightweight latent residual correction module to fix structured residual errors, thereby preserving text fidelity. Experiments show TOLA outperforms existing diffusion‑based TSR methods, achieving at least 2.72 dB higher PSNR on the CTR‑TSR‑Test benchmark.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

Hugging Face Trending Papers

Aug 10

Preserve More Details: Mitigating Content Drift in Real-World Image Super-Resolution

Real-world image super-resolution (Real-ISR) aims to reconstruct high-quality (HQ) images from low-quality (LQ) inputs subject to diverse real-world degradations. Recent advances have leveraged the LQ inputs and natural image priors learned by Stable Diffusion models to achieve impressive results.

diffusion benchmarks

arXiv AI

Sep 17

From Alignment to Synthesis: Contrastive Volumetric Grounding for Text-to-CT Generation

The paper introduces a 3D-CLIP encoder trained with structured hard negatives to improve vision‑language alignment for text‑to‑CT generation. This encoder drives a latent diffusion model that operates directly in 3D latent space, eliminating spatial artifacts from super‑resolution pipelines. Experiments on the CT‑RATE dataset show state‑of‑the‑art image fidelity and factual correctness across 18 pathological conditions, with lower inference time and GPU memory usage than competing methods.

By Daniele Molino, Camillo Maria Caruso, Filippo Ruffini, Paolo Soda, Valerio Guarrasi

diffusion multimodal benchmarks safety

arXiv AI

Aug 11

From Alignment to Synthesis Contrastive Volumetric Grounding for Text-to-CT Generation

arXiv:2506. 00633v3 Announce Type: replace-cross Abstract: Generating semantically controllable 3D CT volumes from radiology reports requires more than a rich text encoder, it requires vision-language alignment grounded in volumetric space.

By Daniele Molino, Camillo Maria Caruso, Filippo Ruffini, Paolo Soda, Valerio Guarrasi

diffusion multimodal benchmarks safety

arXiv AI

Jul 31

Dual Inversion for Text-to-Image Diffusion Models: From Both Prompt and Noise Perspectives

arXiv:2607. 26735v1 Announce Type: cross Abstract: Prompt inversion, as a typical reverse engineering technique, enables text-to-image (T2I) diffusion models to generate the desired target images without extensive prompt engineering.

By Xiaolong Liu, Junjian Li, Yuan Xiao, Jiaqi Deng, Dayong Ye, Tianqing Zhu, Huan Huo

llms diffusion multimodal benchmarks safety

arXiv Computer Vision

Sep 14

Lumina-OmniLV: A Unified Multimodal Framework for General Low-Level Vision

arXiv:2504.04903v3 Announce Type: replace Abstract: We present Lunima-OmniLV (abbreviated as OmniLV), a universal multimodal multi-task framework for low-level vision that addresses over 100 sub-task...

By Yuandong Pu, Le Zhuo, Kaiwen Zhu, Liangbin Xie, Wenlong Zhang, Xiangyu Chen, Peng Gao, Yu Qiao, Chao Dong, Yihao Liu

llms diffusion multimodal

arXiv Computer Vision

Aug 28

Zero-Shot Video Restoration and Enhancement with Text-to-Image Latent Diffusion Models and Multi-Modal References

The paper introduces a zero‑shot video restoration and enhancement framework that leverages a text‑to‑image latent diffusion model along with multi‑modal references. It employs dual prompt tuning inversion and sampling to cut inference time to about one‑third of the original, while also strengthening performance and temporal consistency. Additional techniques such as texture‑aware video token merging, referenced self‑attention, and referenced token merging further improve temporal coherence across frames.

By Cong Cao, Huanjing Yue, Xin Liu, Jingyu Yang

diffusion multimodal

One email a morning, machine-written

Related stories

Preserve More Details: Mitigating Content Drift in Real-World Image Super-Resolution

From Alignment to Synthesis: Contrastive Volumetric Grounding for Text-to-CT Generation

From Alignment to Synthesis Contrastive Volumetric Grounding for Text-to-CT Generation

Dual Inversion for Text-to-Image Diffusion Models: From Both Prompt and Noise Perspectives

Lumina-OmniLV: A Unified Multimodal Framework for General Low-Level Vision

Zero-Shot Video Restoration and Enhancement with Text-to-Image Latent Diffusion Models and Multi-Modal References