Diffusion Model

Diffusion models are generative models that create data by reversing a noise-diffusion process, aiming to generate high-quality samples from complex distributions. Current research focuses on improving efficiency through techniques like stochastic Runge-Kutta methods and dynamic model architectures (e.g., Dynamic Diffusion Transformer), as well as enhancing controllability and safety via methods such as classifier-free guidance and reinforcement learning from human feedback. These advancements are significantly impacting various fields, including medical imaging, robotics, and artistic creation, by enabling novel applications in image generation, inverse problem solving, and multi-modal data synthesis.

Papers

October 14, 2024

Automated Filtering of Human Feedback Data for Aligning Text-to-Image Diffusion Models
Yongjin Yang, Sihyeon Kim, Hojung Jung, Sangmin Bae, SangMook Kim, Se-Young Yun, Kimin Lee
Diffusion Model Human Feedback Preference Optimization Image Text Alignment Online Filtering
DINTR: Tracking via Diffusion-based Interpolation
Pha Nguyen, Ngan Le, Jackson Cothren, Alper Yilmaz, Khoa Luu
Diffusion Model Diffusion Based

October 13, 2024

Variational Diffusion Posterior Sampling with Midpoint Guidance
Badr Moufad, Yazid Janati, Lisa Bedin, Alain Durmus, Randal Douc, Eric Moulines, Jimmy Olsson
Diffusion Model Bayesian Inverse Problem Diffusion Posterior Sampling Nonlinear Inverse Problem
Training-Free Adaptive Diffusion with Bounded Difference Approximation Strategy
Hancheng Ye, Jiakang Yuan, Renqiu Xia, Xiangchao Yan, Tao Chen, Junchi Yan, Botian Shi, Bo Zhang
Diffusion Model Denoising Process Video Diffusion Model Strategy Logic Denoising Approach Adaptive Diffusion
Intermediate Representations for Enhanced Text-To-Image Generation Using Diffusion Models
Ran Galun, Sagie Benaim
Diffusion Model Image Generation Text to Image Diffusion Model Text to Image Generation Intermediate Representation Diffusion Based Generative

October 12, 2024

DuoDiff: Accelerating Diffusion Models with a Dual-Backbone Approach
Daniel Gallo Fernández, Rǎzvan-Andrei Matişan, Alejandro Monroy Muñoz, Ana-Maria Vasilcoiu, Janusz Partyka, Tin Hadži Veljković, Metod Jazbec
Neural Network Diffusion Model Denoising Network Multi Exit Backbone Architecture
Enhancing Single Image to 3D Generation using Gaussian Splatting and Hybrid Diffusion Priors
Hritam Basak, Hadi Tabatabaee, Shreekant Gayaka, Ming-Feng Li, Xin Yang, Cheng-Hao Kuo, Arnie Sen, Min Sun, Zhaozheng Yin
Diffusion Model Gaussian Splatting Single Image 3D Generation 3D Diffusion Model 3D Structure 3D Object Generation

October 11, 2024

TD-Paint: Faster Diffusion Inpainting Through Time Aware Pixel Conditioning
Tsiry Mayet, Pourya Shamsolmoali, Simon Bernard, Eric Granger, Romain Hérault, Clement Chatelain
Diffusion Model Consistent Conditioning State of the Art Diffusion Faster Diffusion
Linear Convergence of Diffusion Models Under the Manifold Hypothesis
Peter Potaptchik, Iskander Azangulov, George Deligiannidis
Diffusion Model Generative Model Convergence Guarantee Linear Convergence Manifold Hypothesis Backward Stochastic Differential Equation Step Diffusion Model
Gait Sequence Upsampling using Diffusion Models for Single LiDAR Sensors
Jeongho Ahn, Kazuto Nakashima, Koki Yoshino, Yumi Iwashita, Ryo Kurazume
Diffusion Model Point Cloud Gait Recognition Single LiDAR Human Point Cloud
Diffusion Models Need Visual Priors for Image Generation
Xiaoyu Yue, Zidong Wang, Zeyu Lu, Shuyang Sun, Meng Wei, Wanli Ouyang, Lei Bai, Luping Zhou
Diffusion Model Image Generation Diffusion Explainer Diffusion Sampling Visual Prior

October 10, 2024

Avoiding mode collapse in diffusion models fine-tuned with reinforcement learning
Roberto Barceló, Cristóbal Alcázar, Felipe Tobar
Reinforcement Learning LeArning Abstract Diffusion Model Positive Reinforcement Diffusion Policy Robust Fine Tuning Mode Collapse Reward Fine Tuning
Dynamics of Concept Learning and Compositional Generalization
Yongyi Yang, Core Francisco Park, Ekdeep Singh Lubana, Maya Okawa, Wei Hu, Hidenori Tanaka
Diffusion Model Strong Generalization Compositional Generalization Concept Learning
DICE: Discrete Inversion Enabling Controllable Editing for Multinomial Diffusion and Masked Generative Models
Xiaoxiao He, Ligong Han, Quan Dao, Song Wen, Minhao Bai, Di Liu, Han Zhang, Martin Renqiang Min, Felix Juefei-Xu, Chaowei Tan, Bo Liu, Kang Li, Hongdong Li, Junzhou Huang, Faez Ahmed, Akash Srivastava, Dimitris Metaxas
Diffusion Model Generative Model Discrete Diffusion Model Masked Generative Controllable Editing Improved Box Dice
Meissonic: Revitalizing Masked Generative Transformers for Efficient High-Resolution Text-to-Image Synthesis
Jinbin Bai, Tian Ye, Wei Chow, Enxin Song, Qing-Guo Chen, Xiangtai Li, Zhen Dong, Lei Zhu, Shuicheng Yan
Diffusion Model High Efficiency Image Generation Text to Image Synthesis Image Fidelity Masked Generative Autoregressive Image Generation
DART: Denoising Autoregressive Transformer for Scalable Text-to-Image Generation
Jiatao Gu, Yuyang Wang, Yizhe Zhang, Qihang Zhang, Dinghuai Zhang, Navdeep Jaitly, Josh Susskind, Shuangfei Zhai
Diffusion Model Image Generation Image Synthesis Image Modeling Autoregressive Transformer Text to Image Generation Task Large Scale Text to Image
Unstable Unlearning: The Hidden Risk of Concept Resurgence in Diffusion Models
Vinith M. Suriyakumar, Rohan Alur, Ayush Sekhari, Manish Raghavan, Ashia C. Wilson
Diffusion Model Text to Image Diffusion Model Concept Erasure Adversarial Information Hidden Risk
Generated Bias: Auditing Internal Bias Dynamics of Text-To-Image Generative Models
Abhishek Mandal, Susan Leavy, Suzanne Little
Diffusion Model Text to Image Text to Image Generative Model Source Bias Bias Auditing
$\textit{Jump Your Steps}$: Optimizing Sampling Schedule of Discrete Diffusion Models
Yong-Hyun Park, Chieh-Hsin Lai, Satoshi Hayakawa, Yuhta Takida, Yuki Mitsufuji
Diffusion Model Sample STEP Fast Sampling Discrete Diffusion Model Sampling Technique Parallel Sampling
Synthesizing Multi-Class Surgical Datasets with Anatomy-Aware Diffusion Models
Danush Kumar Venkatesh, Dominik Rivoir, Micha Pfeiffer, Fiona Kolbinger, Stefanie Speidel
Diffusion Model Synthetic Data Surgical Datasets Target Mask

Diffusion Model

Papers

Automated Filtering of Human Feedback Data for Aligning Text-to-Image Diffusion Models

DINTR: Tracking via Diffusion-based Interpolation

Variational Diffusion Posterior Sampling with Midpoint Guidance

Training-Free Adaptive Diffusion with Bounded Difference Approximation Strategy

Intermediate Representations for Enhanced Text-To-Image Generation Using Diffusion Models

DuoDiff: Accelerating Diffusion Models with a Dual-Backbone Approach

Enhancing Single Image to 3D Generation using Gaussian Splatting and Hybrid Diffusion Priors

TD-Paint: Faster Diffusion Inpainting Through Time Aware Pixel Conditioning

Linear Convergence of Diffusion Models Under the Manifold Hypothesis

Gait Sequence Upsampling using Diffusion Models for Single LiDAR Sensors

Diffusion Models Need Visual Priors for Image Generation

Avoiding mode collapse in diffusion models fine-tuned with reinforcement learning

Dynamics of Concept Learning and Compositional Generalization

DICE: Discrete Inversion Enabling Controllable Editing for Multinomial Diffusion and Masked Generative Models

Meissonic: Revitalizing Masked Generative Transformers for Efficient High-Resolution Text-to-Image Synthesis

DART: Denoising Autoregressive Transformer for Scalable Text-to-Image Generation

Unstable Unlearning: The Hidden Risk of Concept Resurgence in Diffusion Models

Generated Bias: Auditing Internal Bias Dynamics of Text-To-Image Generative Models

$\textit{Jump Your Steps}$: Optimizing Sampling Schedule of Discrete Diffusion Models

Synthesizing Multi-Class Surgical Datasets with Anatomy-Aware Diffusion Models