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。
Team Seawead, Ceyuan Yang, Zhijie Lin +51 authors
A mid-sized video generation model with 7 billion parameters, trained efficiently with substantial GPU hours, achieves competitive performance and strong generalization across various applications.
Shen Nie, Fengqi Zhu, Zebin You +7 authors
LLaDA, a diffusion model trained from scratch, outperforms autoregressive models in benchmarks and demonstrates strong instruction-following capabilities, challenging the dominance of ARMs in LLMs.
Dani Valevski, Yaniv Leviathan, Moab Arar +1 authors
GameNGen, a neural model-powered game engine, simulates high-quality gameplay in real-time using a diffusion model conditioned on past frames and actions.
Yukang Chen, Luozhou Wang, Wei Huang +13 authors
LongLive-2.0 presents an NVFP4-based parallel infrastructure for long video generation that addresses training and inference bottlenecks through sequence-parallel autoregressive training and diffusion model tuning.
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.
Jianhong Bai, Xiaoshi Wu, Xintao Wang +9 authors
SemanticGen addresses slow convergence and computational costs in video generation by using a two-stage diffusion model approach that first generates semantic features and then VAE latents, leading to faster convergence and high-quality results.
Omer Bar-Tal, Hila Chefer, Omer Tov +11 authors
A text-to-video diffusion model using Space-Time U-Net architecture generates realistic, diverse, and coherent videos through a single pass, achieving state-of-the-art results and supporting various content creation and editing tasks.
Jiacheng Chen, Ramin Mehran, Xuhui Jia +2 authors
A generative visual compositing framework using a diffusion model for scene editing and composition with source masking and simulated object jittering.
Junsong Chen, Jincheng Yu, Chongjian Ge +8 authors
PIXART-$\alpha$ is a Transformer-based text-to-image diffusion model that achieves high-quality image synthesis with low training costs using advanced training strategies and efficient architecture.
Qihang Yu, Mark Weber, Xueqing Deng +3 authors
Transformer-based 1D Tokenizer (TiTok) reduces computational demands and enhances image synthesis efficiency by significantly decreasing token count and outperforming state-of-the-art methods in both speed and quality.
Qixun Wang, Xu Bai, Haofan Wang +2 authors
InstantID is a diffusion model-based solution for personalized image synthesis that uses a single facial image and integrates with pre-trained models while maintaining high face fidelity and efficiency.
Jihwan Kim, Junoh Kang, Jinyoung Choi +1 authors
FIFO-Diffusion generates long text-conditional videos through iterative denoising with latent partitioning and lookahead strategies to minimize the training-inference gap.
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.
Nataniel Ruiz, Yuanzhen Li, Varun Jampani +6 authors
HyperDreamBooth leverages hypernetworks for efficient personalized face generation, significantly speeding up the process and reducing model size compared to existing methods.
Boyuan Chen, Diego Marti Monso, Yilun Du +3 authors
Diffusion Forcing trains diffusion models to denoise tokens with independent noise levels, combining next-token prediction and full-sequence diffusion strengths for sequence generation and decision-making tasks.
Jingxiang Sun, Bo Zhang, Ruizhi Shao +4 authors
DreamCraft3D uses a hierarchical approach with diffusion models and tailored 3D priors to generate high-fidelity and coherent 3D objects from 2D references, addressing geometry consistency and texture fidelity.
Peike Li, Boyu Chen, Yao Yao +3 authors
JEN-1, a diffusion model combining autoregressive and non-autoregressive training, achieves superior performance in text-to-music generation, music inpainting, and continuation, while maintaining computational efficiency.
Saurabh Saxena, Junhwa Hur, Charles Herrmann +2 authors
A generic diffusion model with log-scale depth parameterization and FOV conditioning achieves state-of-the-art zero-shot metric depth estimation by handling indoor and outdoor scenes effectively and reducing relative error significantly.
Lingmin Ran, Xiaodong Cun, JiaWei Liu +5 authors
X-Adapter upgrades text-to-image diffusion models like SDXL to work with existing plugins without retraining, through feature remapping and a null-text training strategy.
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