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。
Xingchao Liu, Xiwen Zhang, Jianzhu Ma +2 authors
Rectified Flow is used to develop an ultra-fast one-step text-to-image generator named InstaFlow, achieving high image quality with significantly reduced inference time compared to existing methods.
Miao Hua, Jiawei Liu, Fei Ding +3 authors
DreamTurner uses a novel approach by injecting reference information through subject encoders and self-subject-attention layers to enhance subject-driven image generation, balancing subject learning and model capabilities.
Quan Sun, Qiying Yu, Yufeng Cui +7 authors
Emu, a Transformer-based multimodal model, generates images and texts in various contexts and demonstrates superior performance across zero-shot and few-shot tasks compared to existing models.
Kangfu Mei, Mauricio Delbracio, Hossein Talebi +3 authors
A novel single-stage distillation method for generative diffusion models reduces sampling time while maintaining performance across tasks like super-resolution and image editing.
Dani Valevski, Danny Wasserman, Yossi Matias +1 authors
A method to condition text-to-image generation models on face embeddings in real-time, enhancing generative capabilities and bias mitigation.
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