Trends · 研究趋势
数据来自 Hugging Face 论文的 AI 提取关键词,按月统计研究方向的增长与热度。
Guoxuan Chen, Chufeng Xiao, Haoran Yang +30 authors
Boogu-Image-0.1 is an open-source unified multimodal model family that achieves strong text-to-image generation, editing, and bilingual rendering with low training cost by improving multimodal understanding, data quality, and agentic inference scaling.
Xiangyan Qu, Zhenlong Yuan, Jing Tang +9 authors
Image-CoT methods are extended to image editing with ADE-CoT, which improves efficiency and performance through adaptive resource allocation, edit-specific verification, and opportunistic stopping mechanisms.
Shitao Xiao, Yueze Wang, Junjie Zhou +6 authors
OmniGen is a unified diffusion model for image generation that supports diverse tasks without additional modules, emphasizing simplicity, knowledge transfer, and reasoning capabilities.
Shoufa Chen, Chongjian Ge, Yuqi Zhang +19 authors
Goku, a state-of-the-art family of joint image-and-video generation models using rectified flow Transformers, sets new benchmarks in text-to-image and text-to-video tasks.
Haozhe Wang, Cong Wei, Weiming Ren +3 authors
Training reward models to generate multi-dimensional critiques improves visual generation through both enhanced reinforcement learning rewards and test-time refinement loops, achieving state-of-the-art performance with reduced training data requirements.
Ling Yang, Ye Tian, Bowen Li +4 authors
MMaDA, a multimodal diffusion foundation model, achieves superior performance through a unified architecture, mixed long chain-of-thought fine-tuning, and a unified policy-gradient-based RL algorithm.
Dianyi Wang, Chaofan Ma, Feng Han +8 authors
UniReason integrates text-to-image generation and image editing through a dual reasoning paradigm that enhances planning with world knowledge and uses editing for visual refinement, achieving superior performance on reasoning-intensive benchmarks.
Anton Razzhigaev, Arseniy Shakhmatov, Anastasia Maltseva +7 authors
Kandinsky1, a latent diffusion architecture, achieves high-quality text-to-image generation by integrating image prior models and modified MoVQ autoencoders, outperforming other open-source models.
Guangting Zheng, Yiyuan Zhang, Tao Yang +4 authors
Direct end-to-end training of latent generative models avoids collapse via entropy preservation and asymmetric dynamics, using a generation-first strategy to achieve state-of-the-art image synthesis and unified multimodal generation.
Peiyu Wang, Yi Peng, Yimeng Gan +12 authors
Skywork UniPic, a 1.5 billion-parameter autoregressive model, unifies image understanding, text-to-image generation, and image editing with state-of-the-art performance on commodity hardware.
Zeyi Sun, Ziyang Chu, Pan Zhang +6 authors
X-Prompt, an auto-regressive large-vision language model, uses in-context learning to achieve competitive performance in seen and unseen image generation tasks.
Guo-Hua Wang, Shanshan Zhao, Xinjie Zhang +9 authors
Ovis-U1, a 3-billion-parameter unified model, integrates multimodal understanding, text-to-image generation, and image editing using a diffusion-based visual decoder and bidirectional token refiner, achieving state-of-the-art performance across various benchmarks.
Zhen Li, Mingdeng Cao, Xintao Wang +3 authors
PhotoMaker is an efficient personalized text-to-image generation method that encodes ID images into a unified embedding, achieving high ID fidelity, speed improvements, and generalization capabilities with an ID-oriented data pipeline.
Rongyao Fang, Chengqi Duan, Kun Wang +7 authors
PUMA, a unified multimodal large language model, addresses varying granularity demands in visual content generation tasks through the integration of multi-granular visual features.
Rongyao Fang, Chengqi Duan, Kun Wang +9 authors
A new reasoning-driven paradigm, GoT, improves text-to-image generation and editing through language understanding and reasoning chains, integrating Qwen2.5-VL and an enhanced diffusion model with a Semantic-Spatial Guidance Module.
Jinheng Xie, Weijia Mao, Zechen Bai +7 authors
Show-o, a unified transformer model, combines autoregressive and diffusion modeling for versatile vision-language tasks, achieving competitive performance.
Xiwei Hu, Rui Wang, Yixiao Fang +3 authors
ELLA, an Efficient Large Language Model Adapter, enhances text-to-image diffusion models by integrating powerful Large Language Models through a Timestep-Aware Semantic Connector, improving dense prompt comprehension and generation quality.
Chunyuan Li, Zhe Gan, Zhengyuan Yang +4 authors
The survey covers the development and advancements in multimodal foundation models that integrate vision and language, transitioning from specialized models to versatile assistants through various training methods and unified architectures.
Chaehun Shin, Jooyoung Choi, Heeseung Kim +1 authors
Diptych Prompting is a zero-shot text-to-image generation method that enhances subject alignment and attention between panels during inpainting, resulting in visually preferable images across various applications.
Dongzhi Jiang, Guanglu Song, Xiaoshi Wu +5 authors
CoMat, an end-to-end diffusion model fine-tuning strategy with an image-to-text concept matching mechanism, improves text-to-image alignment in SDXL without additional data.
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