Poster "image generation" Papers
130 papers found • Page 3 of 3
Conference
DiffiT: Diffusion Vision Transformers for Image Generation
Ali Hatamizadeh, Jiaming Song, Guilin Liu et al.
Directly Denoising Diffusion Models
Dan Zhang, Jingjing Wang, Feng Luo
Eta Inversion: Designing an Optimal Eta Function for Diffusion-based Real Image Editing
Wonjun Kang, Kevin Galim, Hyung Il Koo
Feedback Efficient Online Fine-Tuning of Diffusion Models
Masatoshi Uehara, Yulai Zhao, Kevin Black et al.
Fixed Point Diffusion Models
Luke Melas-Kyriazi, Xingjian Bai
FRDiff : Feature Reuse for Universal Training-free Acceleration of Diffusion Models
Junhyuk So, Jungwon Lee, Eunhyeok Park
Going beyond Compositions, DDPMs Can Produce Zero-Shot Interpolations
Justin Deschenaux, Igor Krawczuk, Grigorios Chrysos 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.
Learning Quantized Adaptive Conditions for Diffusion Models
Yuchen Liang, Yuchuan Tian, Lei Yu et al.
LightIt: Illumination Modeling and Control for Diffusion Models
Peter Kocsis, Kalyan Sunkavalli, Julien Philip et al.
LLMGA: Multimodal Large Language Model based Generation Assistant
Bin Xia, Shiyin Wang, Yingfan Tao et al.
Mixture of Efficient Diffusion Experts Through Automatic Interval and Sub-Network Selection
Alireza Ganjdanesh, Yan Kang, Yuchen Liu et al.
MS$^3$D: A RG Flow-Based Regularization for GAN Training with Limited Data
Jian Wang, Xin Lan, Yuxin Tian et al.
Neural Diffusion Models
Grigory Bartosh, Dmitry Vetrov, Christian Andersson Naesseth
One-step Diffusion with Distribution Matching Distillation
Tianwei Yin, Michaël Gharbi, Richard Zhang et al.
On the Trajectory Regularity of ODE-based Diffusion Sampling
Defang Chen, Zhenyu Zhou, Can Wang et al.
Quantum Implicit Neural Representations
Jiaming Zhao, Wenbo Qiao, Peng Zhang et al.
ReGround: Improving Textual and Spatial Grounding at No Cost
Phillip (Yuseung) Lee, Minhyuk Sung
Residual Denoising Diffusion Models
Jiawei Liu, Qiang Wang, Huijie Fan 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.
Switch Diffusion Transformer: Synergizing Denoising Tasks with Sparse Mixture-of-Experts
Byeongjun Park, Hyojun Go, Jin-Young Kim 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.
UDiffText: A Unified Framework for High-quality Text Synthesis in Arbitrary Images via Character-aware Diffusion Models
Yiming Zhao, Zhouhui Lian
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.
Watermark-embedded Adversarial Examples for Copyright Protection against Diffusion Models
Peifei Zhu, Tsubasa Takahashi, Hirokatsu Kataoka
Wear-Any-Way: Manipulable Virtual Try-on via Sparse Correspondence Alignment
Mengting Chen, Xi Chen, Zhonghua Zhai et al.