Conditional Diffusion Models

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Overview

Conditional diffusion models learn to generate samples from a distribution specified by inputs such as images, labels, text, measurements, or physical parameters. Common approaches inject conditions into U-Nets or diffusion transformers through cross-attention, adaptive normalization, ControlNet branches, or inpainting, while classifier-free guidance adjusts conditional influence during sampling. They are used for image and video editing, medical reconstruction, time-series forecasting, scientific simulation, and molecular or engineering design, with evaluation spanning fidelity, condition accuracy, uncertainty, and physical validity. A central challenge is balancing faithful control with diversity and realism while keeping sampling efficient and ensuring models generalize to unseen conditions or domains.

Latest papers

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279 papers32 papers in the last four weeks, up 23% on the four weeks before. 0.2% of all new papers.

  1. CanvasComposer: Personalized Group Photo Generation via a Multi-Reference Canvas

    Oct 23, 2025Gordon Guocheng Qian, Ruihang Zhang, Tsai-Shien Chen +11Subject-Driven Image GenerationImage Generation

    Snap Inc., USA · University of Toronto, Canada · University of California, Merced, USA +1

  2. scTREAT: Latent Diffusion for Single-Cell Drug-Response Prediction

    Oct 8, 2025Zhaokang Liang, Shuyang Zhuang, Xiaoran Jiao +2Single-Cell Perturbation ModelingCancer Drug Response Prediction

    Zhejiang University, China · Massachusetts Institute of Technology, USA

  3. Multi-Channel Uncertainty-Weighted Score Matching for Conditional Diffusion in Medical UDA

    Sep 26, 2025Chen Li, Meilong Xu, Xiaoling Hu +2Cross-Modality Medical Image SegmentationUnsupervised Domain Adaptation

    Stony Brook University, Stony Brook, NY, USA · Massachusetts General Hospital and Harvard Medical School, MA, USA

  4. Guided Unconditional and Conditional Generative Models for Super-Resolution and Inference of Quasi-Geostrophic Turbulence

    Jul 1, 2025Anantha Narayanan Suresh Babu, Akhil Sadam, Pierre F. J. LermusiauxDiffusion Model GuidanceData Assimilation

    Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, USA · Center for Computational Science and Engineering, Massachusetts Institute of Technology, Cambridge, USA

  5. Stochastic and Non-local Closure Modeling for Nonlinear Dynamical Systems via Latent Score-based Generative Models

    Jun 25, 2025Xinghao Dong, Huchen Yang, Jin-Long WuDynamical SystemsLatent Diffusion Models

    Department of Mechanical Engineering, University of Wisconsin-Madison, Madison, WI, 53706, USA

  6. Ctrl-Z Sampling: Scaling Diffusion Sampling with Controlled Random Zigzag Explorations

    Jun 25, 2025Shunqi Mao, Wei Guo, Chaoyi Zhang +3Diffusion SamplingConditional Diffusion Models

    School of Computer Science, The University of Sydney, Australia

  7. FlexPainter: Flexible and Multi-View Consistent Texture Generation

    Jun 3, 2025Dongyu Yan, Leyi Wu, Jiantao Lin +7Multi-View Image Synthesis3D Texture Generation

    HKUST(GZ) · Quwan · HKUST

  8. Conditioning Residuals for Diffusion Models via Representation Feedback

    May 16, 2025Weilai Xiang, Hongyu Yang, Di Huang +1Representation LearningDiffusion Transformer

    State Key Laboratory of Virtual Reality Technology and Systems, School of Computer Science and Engineering, Beihang University, Beijing 100191, China · Institute of Artificial Intelligence, Beihang University, Beijing 100191, China · School of Computer Science and Engineering, Beihang University, Beijing 100191, China

  9. Bifidelity Parameter Estimation Using Conditional Diffusion Models

    Apr 2, 2025Caroline Tatsuoka, Minglei Yang, Dongbin Xiu +1Simulation-Based InferenceSurrogate Modeling

    Department of Mathematics, The Ohio State University, Columbus, OH 43210, USA · Fusion Energy Science Division, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA · Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA