{"list":"other","total":55,"rows":[{"id":"2103.00020","title":"Learning Transferable Visual Models From Natural Language Supervision","created":"2021-02-26","cited_by_count":33744},{"id":"2006.11239","title":"Denoising Diffusion Probabilistic Models","created":"2020-06-19","cited_by_count":21076},{"id":"2112.10752","title":"High-Resolution Image Synthesis with Latent Diffusion Models","created":"2021-12-20","cited_by_count":18863},{"id":"1801.03924","title":"The Unreasonable Effectiveness of Deep Features as a Perceptual Metric","created":"2018-01-11","cited_by_count":11177},{"id":"2010.02502","title":"Denoising Diffusion Implicit Models","created":"2020-10-06","cited_by_count":9781},{"id":"2011.13456","title":"Score-Based Generative Modeling through Stochastic Differential Equations","created":"2020-11-26","cited_by_count":9122},{"id":"2105.05233","title":"Diffusion Models Beat GANs on Image Synthesis","created":"2021-05-11","cited_by_count":8255},{"id":"1503.03585","title":"Deep Unsupervised Learning using Nonequilibrium Thermodynamics","created":"2015-03-12","cited_by_count":7058},{"id":"2301.12597","title":"BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models","created":"2023-01-30","cited_by_count":6137},{"id":"1812.04948","title":"A Style-Based Generator Architecture for Generative Adversarial Networks","created":"2018-12-12","cited_by_count":6013},{"id":"2210.02747","title":"Flow Matching for Generative Modeling","created":"2022-10-06","cited_by_count":5702},{"id":"2403.03206","title":"Scaling Rectified Flow Transformers for High-Resolution Image Synthesis","created":"2024-03-05","cited_by_count":4181},{"id":"1907.05600","title":"Generative Modeling by Estimating Gradients of the Data Distribution","created":"2019-07-12","cited_by_count":4101},{"id":"2204.03458","title":"Video Diffusion Models","created":"2022-04-07","cited_by_count":2305},{"id":"2209.14687","title":"Diffusion Posterior Sampling for General Noisy Inverse Problems","created":"2022-09-29","cited_by_count":1416},{"id":"2111.05826","title":"Palette: Image-to-Image Diffusion Models","created":"2021-11-10","cited_by_count":1300},{"id":"2210.09276","title":"Imagic: Text-Based Real Image Editing with Diffusion Models","created":"2022-10-17","cited_by_count":1178},{"id":"2508.02324","title":"Qwen-Image Technical Report","created":"2025-08-04","cited_by_count":1044},{"id":"2506.15742","title":"FLUX.1 Kontext: Flow Matching for In-Context Image Generation and Editing in Latent Space","created":"2025-06-17","cited_by_count":1006},{"id":"2201.11793","title":"Denoising Diffusion Restoration Models","created":"2022-01-27","cited_by_count":963},{"id":"2111.14818","title":"Blended Diffusion for Text-driven Editing of Natural Images","created":"2021-11-29","cited_by_count":927},{"id":"2212.00490","title":"Zero-Shot Image Restoration Using Denoising Diffusion Null-Space Model","created":"2022-12-01","cited_by_count":583},{"id":"2302.07121","title":"Universal Guidance for Diffusion Models","created":"2023-02-14","cited_by_count":437},{"id":"2206.02779","title":"Blended Latent Diffusion","created":"2022-06-06","cited_by_count":432},{"id":"2305.08995","title":"Denoising Diffusion Models for Plug-and-Play Image Restoration","created":"2023-05-15","cited_by_count":340},{"id":"2206.04119","title":"Diffusion probabilistic modeling of protein backbones in 3D for the motif-scaffolding problem","created":"2022-06-08","cited_by_count":236},{"id":"2212.06909","title":"Imagen Editor and EditBench: Advancing and Evaluating Text-Guided Image Inpainting","created":"2022-12-13","cited_by_count":233},{"id":"2306.17775","title":"Practical and Asymptotically Exact Conditional Sampling in Diffusion Models","created":"2023-06-30","cited_by_count":232},{"id":"2305.04391","title":"A Variational Perspective on Solving Inverse Problems with Diffusion Models","created":"2023-05-07","cited_by_count":222},{"id":"2402.17525","title":"Diffusion Model-Based Image Editing: A Survey","created":"2024-02-27","cited_by_count":212},{"id":"2105.14951","title":"SNIPS: Solving Noisy Inverse Problems Stochastically","created":"2021-05-31","cited_by_count":191},{"id":"2307.08123","title":"Solving Inverse Problems with Latent Diffusion Models via Hard Data Consistency","created":"2023-07-16","cited_by_count":182},{"id":"2410.00083","title":"A Survey on Diffusion Models for Inverse Problems","created":"2024-09-30","cited_by_count":180},{"id":"2410.10792","title":"Semantic Image Inversion and Editing using Rectified Stochastic Differential Equations","created":"2024-10-14","cited_by_count":165},{"id":"2407.01521","title":"Improving Diffusion Inverse Problem Solving with Decoupled Noise Annealing","created":"2024-07-01","cited_by_count":163},{"id":"2307.00619","title":"Solving Linear Inverse Problems Provably via Posterior Sampling with Latent Diffusion Models","created":"2023-07-02","cited_by_count":157},{"id":"2410.02423","title":"PnP-Flow: Plug-and-Play Image Restoration with Flow Matching","created":"2024-10-03","cited_by_count":97},{"id":"2007.13640","title":"Solving Linear Inverse Problems Using the Prior Implicit in a Denoiser","created":"2020-07-27","cited_by_count":79},{"id":"2204.07156","title":"Any-resolution Training for High-resolution Image Synthesis","created":"2022-04-14","cited_by_count":72},{"id":"2503.08136","title":"FlowDPS: Flow-Driven Posterior Sampling for Inverse Problems","created":"2025-03-11","cited_by_count":68},{"id":"2308.07983","title":"Monte Carlo guided Diffusion for Bayesian linear inverse problems","created":"2023-08-15","cited_by_count":66},{"id":"2111.04726","title":"Estimating High Order Gradients of the Data Distribution by Denoising","created":"2021-11-08","cited_by_count":64},{"id":"2405.13712","title":"Learning Diffusion Priors from Observations by Expectation Maximization","created":"2024-05-22","cited_by_count":61},{"id":"2412.00100","title":"Steering Rectified Flow Models in the Vector Field for Controlled Image Generation","created":"2024-11-27","cited_by_count":39},{"id":"2410.09945","title":"Variational Diffusion Posterior Sampling with Midpoint Guidance","created":"2024-10-13","cited_by_count":32},{"id":"2306.07526","title":"User-defined Event Sampling and Uncertainty Quantification in Diffusion Models for Physical Dynamical Systems","created":"2023-06-13","cited_by_count":30},{"id":"2403.11407","title":"Divide-and-Conquer Posterior Sampling for Denoising Diffusion Priors","created":"2024-03-18","cited_by_count":28},{"id":"2502.03686","title":"Variational Control for Guidance in Diffusion Models","created":"2025-02-06","cited_by_count":22},{"id":"2502.03332","title":"A Mixture-Based Framework for Guiding Diffusion Models","created":"2025-02-05","cited_by_count":18},{"id":"2506.02680","title":"Solving Inverse Problems with FLAIR","created":"2025-06-03","cited_by_count":17},{"id":"2503.12615","title":"LATINO-PRO: LAtent consisTency INverse sOlver with PRompt Optimization","created":"2025-03-16","cited_by_count":16},{"id":"2508.01975","title":"Diffusion models for inverse problems","created":"2025-08-04","cited_by_count":16},{"id":"2406.16683","title":"Repulsive Latent Score Distillation for Solving Inverse Problems","created":"2024-06-24","cited_by_count":15},{"id":"2405.13794","title":"Conditioning diffusion models by explicit forward-backward bridging","created":"2024-05-22","cited_by_count":11},{"id":"2506.01083","title":"Generative diffusion posterior sampling for informative likelihoods","created":"2025-06-01","cited_by_count":4}],"next_cursor":null}