arXiv AI

Mage-Flow: An Efficient Native-Resolution Foundation Model for Image Generation and Editing

arXiv:2607. 19064v1 Announce Type: cross Abstract: Large-scale visual generators are increasingly capable but costly to train, fine-tune, and deploy.

Hugging Face Trending Papers

Jul 21

Mage-Flow: An Efficient Native-Resolution Foundation Model for Image Generation and Editing

Large-scale visual generators are increasingly capable but costly to train, fine-tune, and deploy. We introduce Mage-Flow, a compact 4B-scale generative stack for efficient text-to-image generation and instruction-based image editing.

llms diffusion nlp reinforcement-learning fine-tuning efficiency multimodal benchmarks safety

arXiv AI

Jun 15

HiLo-Token: Input-Adaptive High-Low Frequency Token Compression for Efficient Image Editing

arXiv:2606. 13898v1 Announce Type: cross Abstract: Creative image editing tools, such as Photoshop's Remove or Generative Fill buttons, are central to everyday customer use and account for a major share of traffic in Photoshop and Lightroom.

By Haoran You, Yotam Nitzan, Lingzhi Zhang, Yifan Gong, Mang-Tik Chiu, Connelly Barnes, Yan Kang, Yuqian Zhou, Eli Shechtman, Sohrab Amirghodsi

llms diffusion

arXiv AI

Jul 16

Boogu-Image-0.1: Boosting Open-Source Unified Multimodal Understanding and Generation

arXiv:2607. 13125v1 Announce Type: cross Abstract: We introduce Boogu-Image-0.

By Guoxuan Chen, Chufeng Xiao, Haoran Yang, Siyue Xie, Binxiao Huang, Ming Zhang, Cheuk Him Chau, Xinyu Fu, Yingzhao Lian, Tom S. Y. Li, Jintao Lin, Bowen Dong, Zian Qian, Yuhao Liu, Yuxuan Hu, Weikang Shi, Bin Zou, Bowen Zheng, Haoxuan Che, Chang Chen, Yuyang He, Heyang Sun, Tianyu Huang, Chong Hou Choi, Cheng Gong, Han Shi, Haoli Bai, Xihui Liu, Hongsheng Li, Qifeng Chen, Chao Huang, Rui Liu, Chenyang Lei

llms agents diffusion multimodal benchmarks data-engineering

arXiv AI

Jul 2

UltraFlux: Data-Model Co-Design for High-quality Native 4K Text-to-Image Generation across Diverse Aspect Ratios

arXiv:2511. 18050v1 Announce Type: cross Abstract: Diffusion transformers have recently delivered strong text-to-image generation around 1K resolution, but we show that extending them to native 4K across diverse aspect ratios exposes a tightly coupled failure mode spanning positional encoding, VAE compression, and optimization.

By Tian Ye, Song Fei, Lei Zhu

llms diffusion multimodal benchmarks safety

arXiv Machine Learning

Aug 4

UDT: Reconciling U-Nets and Diffusion Transformers with Data-Adaptive Token Reduction

arXiv:2608. 01298v1 Announce Type: cross Abstract: Diffusion Transformers (DiTs) have emerged as a core architecture in generative modeling due to their scalability and adaptability to multimodal tasks.

By Junno Yun, Ya\c{s}ar Utku Al\c{c}alar, Mehmet Ak\c{c}akaya

llms diffusion multimodal safety

arXiv AI

Jul 13

M4V: Multimodal Mamba for Efficient Text-to-Video Generation

arXiv:2506. 10915v2 Announce Type: replace-cross Abstract: Text-to-video generation has significantly enriched content creation and holds the potential to evolve into powerful world simulators.

By Jiancheng Huang, Gengwei Zhang, Zequn Jie, Siyu Jiao, Yinlong Qian, Ling Chen, Yunchao Wei, Lin Ma

llms diffusion multimodal benchmarks

arXiv AI

Jul 21

Boogu-Image-0.1: Boosting Open Agentic Multimodal Generation via Understanding under a Minimal Budget

arXiv:2607. 13125v2 Announce Type: replace-cross Abstract: We introduce Boogu-Image-0.

llms agents diffusion multimodal benchmarks data-engineering

Hugging Face Trending Papers

2d ago

PixRestore: Unified Image Restoration via Pixel Diffusion Transformer

Unified image restoration (UIR) aims to recover high-quality (HQ) content from low-quality (LQ) images with different degradations using a single model. Most recent methods adapt large pretrained text-to-image (T2I) latent diffusion models for their strong capacity and generative priors.

llms diffusion benchmarks safety

Hugging Face Trending Papers

Jul 27

UniGen-AR: Unifying Visual Generation with Auto-Regressive Modeling

Modern computer vision pipelines remain fragmented, with tasks such as text-to-image generation, editing, restoration, and classical perception handled by separate models. We study Unified Visual Generation (UVG), where a single model produces diverse image-valued outputs through a unified multimodal interface.

llms diffusion computer-vision nlp efficiency multimodal

arXiv AI

Aug 11

When Latents Forget Pixels: Restoring Fidelity in Diffusion Transformer Super-Resolution

arXiv:2608. 09133v1 Announce Type: cross Abstract: Image super-resolution (SR) with large generative models has recently achieved remarkable perceptual quality, yet maintaining fidelity to the LR observation remains challenging.

By Yu Shi, Yuyao Zhang, Yu-wing Tai

llms diffusion

arXiv AI

Aug 11

UniSpace: Unified Visual Representation and Scalable Multimodal Modeling

arXiv:2608. 08676v1 Announce Type: cross Abstract: Semantic vision encoders have become a central visual interface for multimodal understanding and semantic conditioning in image generation.

By Jinbo Yan, Limeng Qiao, Jie Qin, Junyan He, Feize Wu, Guanglu Wan

llms rag diffusion multimodal

arXiv Machine Learning

Jul 31

Explorative Modeling: Unlocking a Third Pretraining Axis and End-to-End Generation

arXiv:2607. 27372v1 Announce Type: new Abstract: The deep learning revolution, kicked off by AlexNet, taught us that end-to-end training beats decomposing a problem into hand-designed stages.

By Alexi Gladstone, Heng Ji, Yilun Du

diffusion benchmarks

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