Hugging Face Trending Papers

Nexus: Structured Synergy for Efficient Text-to-Image Generation using Rectified Flow Model

Diffusion and flow matching models have made significant progress in text-to-image generation, yet high computation, quadratic complexity, and large memory footprint hinder high-resolution synthesis and edge deployment. We propose Nexus, which integrates sparse architecture, linear complexity, and low-bit quantization.

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

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 Machine Learning

Jun 10

Cost-Aware Routing for Efficient Text-To-Image Generation

arXiv:2506. 14753v3 Announce Type: replace-cross Abstract: Diffusion models are well known for their ability to generate a high-fidelity image for an input prompt through an iterative denoising process.

By Qinchan Li, Kenneth Chen, Changyue Su, Wittawat Jitkrittum, Qi Sun, Patsorn Sangkloy

diffusion efficiency

arXiv AI

Jun 2

Better Source, Better Flow: Learning Condition-Dependent Source Distribution for Flow Matching

arXiv:2602. 05951v2 Announce Type: replace-cross Abstract: Flow matching has recently emerged as a promising alternative to diffusion-based generative models, particularly for text-to-image generation.

By Junwan Kim, Jiho Park, Seonghu Jeon, Seungryong Kim

diffusion benchmarks safety

Hugging Face Trending Papers

Jul 14

DiTailed: Ensuring Visual Object Consistency in Text-Image-to-Image Flow Matching Models

Despite remarkable progress in text-guided image editing, generative models frequently fail to preserve visual object consistency, defined as the preservation of a subject's key attributes throughout the editing process. We address this limitation through three contributions.

rag diffusion

arXiv AI

Jul 22

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.

By Xinjie Zhang, Peng Zhang, Shicheng Zheng, Jinghao Guo, Zhaoyang Jia, Yifei Shen, Xun Guo, Yuxuan Luo, Jiahao Li, Wenxuan Xie, Fanyi Pu, Xiaoyi Zhang, Kaichen Zhang, Zongyu Guo, Tianci Bi, Dongnan Gui, Zhening Liu, Zimo Wen, Zihan Zheng, Senqiao Yang, Xiao Li, Jinglu Wang, Bin Li, Yan Lu

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

arXiv Machine Learning

22h ago

From Diffusion to Flow: Efficient Motion Generation in MotionGPT3

arXiv:2603. 26747v3 Announce Type: replace-cross Abstract: Recent text-driven motion generation methods span both discrete token-based approaches and continuous-latent formulations.

By Jaymin Bhan, JiHong Jeon, SangYeop Jeong

diffusion

Hugging Face Trending Papers

Aug 6

Energy-Guided Flow Matching

Pixel-space generative models bypass lossy latent compression, yet necessitate joint learning of global structure and fine-grained details in a high-dimensional space. Standard flow matching interpolates noise toward a fixed clean-image endpoint, leaving the spectral evolution to be learned implicitly.

diffusion

arXiv Machine Learning

Jun 16

Semantic Editing with Coupled Stochastic Differential Equations

arXiv:2509. 24223v2 Announce Type: replace Abstract: Editing the content of an image with a pretrained text-to-image model remains challenging.

By Jianxin Zhang, Clayton Scott

diffusion

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

arXiv AI

Jun 9

BLM-SGAN: Bidirectional Language Modeling for Semantic-Spatial Text-to-Image Generation

arXiv:2606. 08847v1 Announce Type: cross Abstract: Despite the success of image generation from text descriptions, it still faces challenges that are difficult to overcome in domains such as natural language processing (NLP) and computer vision (CV).

By Ahmed Abdelmoneim Mazrou, Haidy Maher El-Amir, Ali Hamdi

diffusion computer-vision nlp benchmarks safety

Hugging Face Trending Papers

Jun 11

InterleaveThinker: Reinforcing Agentic Interleaved Generation

Recent image generators have demonstrated impressive photorealism and instruction-following capabilities in single-image generation and editing. However, constrained by their architectures, they cannot achieve interleaved generation (text-image sequence), which has crucial applications in visual narratives, guidance, and embodied manipulation.

llms agents diffusion reinforcement-learning robotics fine-tuning multimodal benchmarks

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