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。
Jinbin Bai, Tian Ye, Wei Chow +6 authors
Meissonic, a non-autoregressive masked image model, achieves high-quality image generation by incorporating advanced techniques and outperforms existing models like SDXL.
Yu Lu, Yuanzhi Liang, Linchao Zhu +1 authors
FreeLong enhances long video generation by balancing global and local video features during denoising, improving quality and consistency.
Cong Wei, Zheyang Xiong, Weiming Ren +3 authors
An omnipotent image editor trained on multisource supervision, importance sampling, and a versatile architecture excels in multiple tasks with diverse aspect ratios and high data quality.
Bram Wallace, Meihua Dang, Rafael Rafailov +7 authors
A method called Diffusion-DPO aligns text-to-image diffusion models to human preferences using direct optimization on comparison data, improving visual appeal and prompt alignment.
Junsong Chen, Yue Wu, Simian Luo +5 authors
PIXART-{\delta} integrates Latent Consistency Model (LCM) and ControlNet into PIXART-{\alpha} for accelerated, high-quality text-to-image synthesis with efficient training and inference.
Fu-Yun Wang, Zhaoyang Huang, Alexander William Bergman +9 authors
The Phased Consistency Model (PCM) addresses limitations in Latent Consistency Models (LCM) and outperforms them in high-resolution, text-conditioned image and few-step text-to-video generation.
Zeyu Lu, Zidong Wang, Di Huang +4 authors
The Flexible Vision Transformer adapts to varied image resolutions and aspect ratios through dynamic tokenization and extrapolation techniques, outperforming traditional methods.
Yanwu Xu, Yang Zhao, Zhisheng Xiao +1 authors
UFOGen, a hybrid diffusion-GAN model, achieves efficient one-step text-to-image synthesis at high quality.
Jiatao Gu, Shuangfei Zhai, Yizhe Zhang +2 authors
Matryoshka Diffusion Models use a NestedUNet architecture for joint denoising at multiple resolutions, enabling efficient high-resolution image and video synthesis.
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.
Jooyoung Choi, Chaehun Shin, Yeongtak Oh +2 authors
The Style-friendly SNR sampler modifies the noise level distribution during fine-tuning to improve style alignment in diffusion models, enabling better capture of unique artistic styles.
Donghyun Lee, Jitesh Chavan, Duy Nguyen +5 authors
OrbitQuant enables efficient post-training quantization for diffusion transformers by using a normalized rotated basis that eliminates the need for recalibration across different timesteps and modalities.
Huizhuo Yuan, Zixiang Chen, Kaixuan Ji +1 authors
Self-play fine-tuning improves diffusion models' performance and alignment with human preferences using competition between model versions, outperforming supervised and RL-based methods with less data.
Chong Mou, Xintao Wang, Jiechong Song +2 authors
DragonDiffusion, a novel image editing method, enables precise manipulation of generated or real images using classifier guidance and multi-scale attention mechanisms without requiring fine-tuning.
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.
Zehua Chen, Guande He, Kaiwen Zheng +2 authors
Bridge-TTS introduces a novel text-to-speech system using a clean deterministic prior in diffusion models, significantly improving synthesis quality and sampling efficiency over existing methods.
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.
Shiwei Zhang, Jiayu Wang, Yingya Zhang +6 authors
The cascaded I2VGen-XL approach uses hierarchical encoders and text-image pairs to improve the semantic accuracy, continuity, and clarity of generated videos.
Jiahao Li, Hao Tan, Kai Zhang +7 authors
Instant3D rapidly generates high-quality 3D assets from text using a two-stage approach involving a fine-tuned 2D diffusion model and a transformer-based reconstructor.
Tero Karras, Miika Aittala, Jaakko Lehtinen +3 authors
Modifications to network layers in the ADM diffusion model architecture improve training stability and synthesis quality, reducing FID from 2.41 to 1.81, and a novel method for post-hoc EMA parameter tuning is introduced.
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