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。
Z-Image Team, Huanqia Cai, Sihan Cao +18 authors
Z-Image, a 6B-parameter Scalable Single-Stream Diffusion Transformer (S3-DiT) model, achieves high-performance image generation with reduced computational cost, offering sub-second inference and compatibility with consumer hardware.
Chuyan Chen, Haoxing Chen, Kun Chen +27 authors
LLaDA-Image unifies a 6B diffusion transformer with a frozen vision-language module, using image-only pre-training and a Muon optimizer to generate photorealistic images with precise editing, and is distilled into a fast 2-4 step variant that achieves state-of-the-art open-source results.
Gaojie Lin, Jianwen Jiang, Jiaqi Yang +2 authors
OmniHuman, a Diffusion Transformer-based framework, achieves realistic human video generation by integrating motion-related conditions into its training process, supporting various inputs and driving modalities.
Jintao Zhang, Haoxu Wang, Kai Jiang +10 authors
SLA, a trainable attention method combining sparse and linear attention, accelerates Diffusion Transformer models for video generation with minimal quality loss.
Jiuhai Chen, Zhiyang Xu, Xichen Pan +10 authors
A diffusion transformer is used in a unified multimodal model framework to improve image generation while maintaining image understanding capabilities.
Xiangyang Luo, Xiaozhe Xin, Tao Feng +3 authors
CoInteract presents an end-to-end framework for human-object interaction video synthesis using a Diffusion Transformer backbone with specialized modules for structural stability and physical plausibility.
Team Seedream, Yunpeng Chen, Yu Gao +47 authors
Seedream 4.0 is a high-performance multimodal image generation system that integrates text-to-image synthesis, image editing, and multi-image composition using a diffusion transformer and VAE, achieving state-of-the-art results with efficient training and inference.
Zongyu Lin, Wei Liu, Chen Chen +14 authors
STIV, a text-image-conditioned video generation method integrating Diffusion Transformer and classifier-free guidance, achieves state-of-the-art performance in text-to-video, text-image-to-video, and image-to-video tasks.
Yuxuan Luo, Zhengkun Rong, Lizhen Wang +3 authors
A diffusion transformer framework, DreamActor-M1, enhances image-based human animation through hybrid guidance, achieving robust control, multi-scale adaptability, and long-term temporal coherence.
WanTeam, Ang Wang, Baole Ai +59 authors
Wan, a comprehensive suite of video foundation models built on the diffusion transformer paradigm, advannces video generation by introducing a novel VAE, scalable pre-training strategies, and large-scale data curation, offering superior performance and versatility across various applications with both large and efficient models.
Zhenxiong Tan, Songhua Liu, Xingyi Yang +2 authors
OminiControl, a parameter-efficient framework, integrates image conditions into Diffusion Transformer models using a parameter reuse mechanism, excelling in subject-driven and spatially-aligned conditional generation.
Zeyinzi Jiang, Zhen Han, Chaojie Mao +3 authors
VACE, an all-in-one framework for video creation and editing, integrates multiple tasks within a unified model using a Video Condition Unit and Context Adapter for flexible and consistent video synthesis.
Igor Pavlovic, Thiemo Wandel, Anton Obukhov +6 authors
Marigold V2 repurposes diffusion transformers for monocular depth estimation via single-step flow-matching inference, semantic alignment, and a Sinkhorn-based two-stage fine-tuning protocol, yielding sharper out-of-distribution depth maps and strong results on related dense regression tasks.
Junsong Chen, Chongjian Ge, Enze Xie +7 authors
PixArt-\Sigma, a Diffusion Transformer model, generates high-quality 4K images more efficiently than existing models using improved data and novel token compression techniques.
Zhuoyi Yang, Jiayan Teng, Wendi Zheng +16 authors
CogVideoX is a large-scale diffusion transformer model using a 3D Variational Autoencoder and expert transformer for generating high-quality, coherent videos from text prompts.
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