2024 "image generation" Papers

A Bias-Variance-Covariance Decomposition of Kernel Scores for Generative Models

Sebastian Gregor Gruber, Florian Buettner

Accelerating Parallel Sampling of Diffusion Models

Zhiwei Tang, Jiasheng Tang, Hao Luo et al.

ArtBank: Artistic Style Transfer with Pre-trained Diffusion Model and Implicit Style Prompt Bank

Zhanjie Zhang, Quanwei Zhang, Wei Xing et al.

ArtWhisperer: A Dataset for Characterizing Human-AI Interactions in Artistic Creations

Kailas Vodrahalli, James Zou

Auto-GAS: Automated Proxy Discovery for Training-free Generative Architecture Search

Lujun Li, Haosen SUN, Shiwen Li et al.

Characteristic Guidance: Non-linear Correction for Diffusion Model at Large Guidance Scale

Candi Zheng, Yuan LAN

Completing Visual Objects via Bridging Generation and Segmentation

Xiang Li, Yinpeng Chen, Chung-Ching Lin et al.

Critical windows: non-asymptotic theory for feature emergence in diffusion models

Marvin Li, Sitan Chen

Directly Denoising Diffusion Models

Dan Zhang, Jingjing Wang, Feng Luo

Feedback Efficient Online Fine-Tuning of Diffusion Models

Masatoshi Uehara, Yulai Zhao, Kevin Black et al.

Going beyond Compositions, DDPMs Can Produce Zero-Shot Interpolations

Justin Deschenaux, Igor Krawczuk, Grigorios Chrysos et al.

Image Content Generation with Causal Reasoning

Xiaochuan Li, Baoyu Fan, Run Zhang et al.

Kepler codebook

Junrong Lian, Ziyue Dong, Pengxu Wei et al.

LayerMerge: Neural Network Depth Compression through Layer Pruning and Merging

Jinuk Kim, Marwa El Halabi, Mingi Ji et al.

MS$^3$D: A RG Flow-Based Regularization for GAN Training with Limited Data

Jian Wang, Xin Lan, Yuxin Tian et al.

Multi-Architecture Multi-Expert Diffusion Models

Yunsung Lee, Jin-Young Kim, Hyojun Go et al.

Neural Diffusion Models

Grigory Bartosh, Dmitry Vetrov, Christian Andersson Naesseth

On Inference Stability for Diffusion Models

Viet Nguyen, Giang Vu, Tung Nguyen Thanh et al.

On the Trajectory Regularity of ODE-based Diffusion Sampling

Defang Chen, Zhenyu Zhou, Can Wang et al.

Operator-Learning-Inspired Modeling of Neural Ordinary Differential Equations

Woojin Cho, Seunghyeon Cho, Hyundong Jin et al.

Quantum Implicit Neural Representations

Jiaming Zhao, Wenbo Qiao, Peng Zhang et al.

Reducing Spatial Fitting Error in Distillation of Denoising Diffusion Models

Shengzhe Zhou, Zejian Li, Shengyuan Zhang et al.

ReGround: Improving Textual and Spatial Grounding at No Cost

Phillip (Yuseung) Lee, Minhyuk Sung

S2WAT: Image Style Transfer via Hierarchical Vision Transformer Using Strips Window Attention

Chiyu Zhang, Xiaogang Xu, Lei Wang et al.

Scalable Wasserstein Gradient Flow for Generative Modeling through Unbalanced Optimal Transport

Jaemoo Choi, Jaewoong Choi, Myungjoo Kang

ShoeModel: Learning to Wear on the User-specified Shoes via Diffusion Model

Wenyu Li, Binghui Chen, Yifeng Geng et al.

The Surprising Effectiveness of Skip-Tuning in Diffusion Sampling

Jiajun Ma, Shuchen Xue, Tianyang Hu et al.

Trainable Highly-expressive Activation Functions

Irit Chelly, Shahaf Finder, Shira Ifergane et al.

Turning Waste into Wealth: Leveraging Low-Quality Samples for Enhancing Continuous Conditional Generative Adversarial Networks

Understanding Diffusion Models by Feynman's Path Integral

Yuji Hirono, Akinori Tanaka, Kenji Fukushima

Unifying Bayesian Flow Networks and Diffusion Models through Stochastic Differential Equations

Kaiwen Xue, Yuhao Zhou, Shen Nie et al.