most citedMing-Omni: A Unified Multimodal Model for Perception and Generation

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cs.CV2025

Ming-UniVision: Joint Image Understanding and Generation with a Unified Continuous Tokenizer

Ziyuan Huang, DanDan Zheng, Cheng Zou +13

Visual tokenization remains a core challenge in unifying visual understanding and generation within the autoregressive paradigm. Existing methods typically employ tokenizers in dis…

cs.CV2025

Ming-Flash-Omni: A Sparse, Unified Architecture for Multimodal Perception and Generation

Inclusion AI, :, Bowen Ma +73

We propose Ming-Flash-Omni, an upgraded version of Ming-Omni, built upon a sparser Mixture-of-Experts (MoE) variant of Ling-Flash-2.0 with 100 billion total parameters, of which on…

cs.CV2025

VideoMAR: Autoregressive Video Generatio with Continuous Tokens

Hu Yu, Biao Gong, Hangjie Yuan +5

Masked-based autoregressive models have demonstrated promising image generation capability in continuous space. However, their potential for video generation remains under-explored…

cs.CV2025

Ming-Lite-Uni: Advancements in Unified Architecture for Natural Multimodal Interaction

Inclusion AI, Biao Gong, Cheng Zou +14

We introduce Ming-Lite-Uni, an open-source multimodal framework featuring a newly designed unified visual generator and a native multimodal autoregressive model tailored for unifyi…

cs.CV2023

SpeedUpNet: A Plug-and-Play Adapter Network for Accelerating Text-to-Image Diffusion Models

Weilong Chai, DanDan Zheng, Jiajiong Cao +3

Text-to-image diffusion models (SD) exhibit significant advancements while requiring extensive computational resources. Existing acceleration methods usually require extensive trai…