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。
Nanye Ma, Shangyuan Tong, Haolin Jia +8 authors
Inference-time scaling of diffusion models improves sample quality beyond increasing denoising steps through better noise selection and verification.
Patrick Esser, Sumith Kulal, Andreas Blattmann +14 authors
Improved noise sampling techniques and a transformer-based architecture enhance rectified flow models for high-resolution text-to-image synthesis, outperforming existing methods.
Peize Sun, Yi Jiang, Shoufa Chen +4 authors
LlamaGen applies the next-token prediction paradigm from large language models to image generation, achieving state-of-the-art performance with various model sizes and conditions.
Shengming Yin, Zekai Zhang, Zecheng Tang +11 authors
A diffusion model for decomposing RGB images into semantically disentangled RGBA layers enables consistent image editing through independent manipulation of individual layers.
Axel Sauer, Frederic Boesel, Tim Dockhorn +3 authors
Latent Adversarial Diffusion Distillation (LADD) simplifies the training and improves the performance of diffusion models by using generative features from latent diffusion models, enabling fast and high-resolution image synthesis.
David Dinkevich, Matan Levy, Omri Avrahami +2 authors
Story2Board generates expressive storyboards from natural language using a consistency framework that enhances coherence and diversity without fine-tuning.
Zecheng He, Bo Sun, Felix Juefei-Xu +14 authors
Imagine yourself is a tuning-free diffusion model for personalized image generation that enhances identity preservation, text alignment, and visual quality through synthetic data generation, parallel attention architecture, and multi-stage fine-tuning.
Yoad Tewel, Rinon Gal, Dvir Samuel Yuval Atzmon +2 authors
Add-it extends diffusion models with a weighted extended-attention mechanism to seamlessly integrate objects into images based on text instructions, achieving state-of-the-art results on real and generated image insertion benchmarks.
Mehmet Saygin Seyfioglu, Karim Bouyarmane, Suren Kumar +2 authors
Diffuse to Choose is a diffusion-based inpainting model that balances real-time performance with high-fidelity detail preservation and semantic accuracy.
Yan Zeng, Guoqiang Wei, Jiani Zheng +4 authors
PixelDance, a diffusion model-based approach, generates high-dynamic videos by incorporating image instructions for first and last frames alongside text instructions, surpassing current text-to-video methods in complexity and motion.
Ethan Chern, Zhulin Hu, Bohao Tang +4 authors
Real-time multimodal video generation via diffusion is enabled by an improved distillation approach with multimodal conditioning and optimized scheduling, reducing inference latency while maintaining quality for interactive systems.
Sensen Gao, Zhaoqing Wang, Qihang Cao +3 authors
PixWorld presents a unified pixel-space diffusion approach for 3D reconstruction and generation that overcomes limitations of latent-space methods through direct image-level supervision and geometry-aware feature alignment.
Sixiang Chen, Jinbin Bai, Zhuoran Zhao +16 authors
An empirical study of GPT-4o's image generation capabilities across multiple tasks reveals its strengths and limitations compared to other models, highlighting the importance of architectural design and data scaling in unified generative frameworks.
Yiren Song, Cheng Liu, Mike Zheng Shou
OmniConsistency, using large-scale Diffusion Transformers, enhances stylization consistency and generalization in image-to-image pipelines without style degradation.
Zhiheng Liu, Ka Leong Cheng, Xi Chen +7 authors
MangaNinjia, a diffusion model-based approach, enhances line art colorization with patch shuffling and point-driven control for precise detail and color matching.
Zhengfeng Lai, Vasileios Saveris, Chen Chen +9 authors
A study examines the use of synthetic and AltText captions in multimodal models, finding that a hybrid approach using both types improves alignment and performance across various models.
Ziming Liu, Yifan Yang, Chengruidong Zhang +4 authors
RAS, a novel sampling strategy for diffusion transformers, dynamically adjusts sampling ratios based on regions of focus, achieving speedups in diffusion models with minimal quality loss.
Jaehyeong Jo, Sung Ju Hwang
A continuous diffusion model for language modeling that leverages the geometry of discrete distributions outperforms existing discrete models and matches autoregressive models in performance.
Duy-Kien Nguyen, Mahmoud Assran, Unnat Jain +3 authors
Vanilla Transformers achieve high performance in computer vision tasks by treating individual pixels as tokens, challenging the conventional use of local neighborhoods.
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.
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