{"total":39,"rows":[{"id":"2209.12152","title":"All are Worth Words: A ViT Backbone for Diffusion Models","created":"2022-09-25","cited_by_count":502,"area":"diffusion"},{"id":"2211.13227","title":"Paint by Example: Exemplar-based Image Editing with Diffusion Models","created":"2022-11-23","cited_by_count":489,"area":"diffusion"},{"id":"2212.06135","title":"Rodin: A Generative Model for Sculpting 3D Digital Avatars Using Diffusion","created":"2022-12-12","cited_by_count":309,"area":"diffusion"},{"id":"2312.06662","title":"Photorealistic Video Generation with Diffusion Models","created":"2023-12-11","cited_by_count":306,"area":"diffusion"},{"id":"2306.04675","title":"Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models","created":"2023-06-07","cited_by_count":205,"area":"diffusion"},{"id":"2205.12952","title":"Pretraining is All You Need for Image-to-Image Translation","created":"2022-05-25","cited_by_count":160,"area":"diffusion"},{"id":"2210.00939","title":"Improving Sample Quality of Diffusion Models Using Self-Attention Guidance","created":"2022-10-03","cited_by_count":154,"area":"diffusion"},{"id":"2401.11605","title":"Scalable High-Resolution Pixel-Space Image Synthesis with Hourglass Diffusion Transformers","created":"2024-01-21","cited_by_count":118,"area":"diffusion"},{"id":"2211.16152","title":"Wavelet Diffusion Models are fast and scalable Image Generators","created":"2022-11-29","cited_by_count":117,"area":"diffusion"},{"id":"2308.09388","title":"Diffusion Models for Image Restoration and Enhancement: A Comprehensive Survey","created":"2023-08-18","cited_by_count":109,"area":"diffusion"},{"id":"2312.02139","title":"DiffiT: Diffusion Vision Transformers for Image Generation","created":"2023-12-04","cited_by_count":106,"area":"diffusion"},{"id":"2309.03350","title":"Relay Diffusion: Unifying diffusion process across resolutions for image synthesis","created":"2023-09-04","cited_by_count":79,"area":"diffusion"},{"id":"2210.05559","title":"Unifying Diffusion Models' Latent Space, with Applications to CycleDiffusion and Guidance","created":"2022-10-11","cited_by_count":67,"area":"diffusion"},{"id":"2403.17870","title":"Boosting Diffusion Models with Moving Average Sampling in Frequency Domain","created":"2024-03-26","cited_by_count":55,"area":"diffusion"},{"id":"2505.12335","title":"Is Artificial Intelligence Generated Image Detection a Solved Problem?","created":"2025-05-18","cited_by_count":51,"area":"diffusion"},{"id":"2407.06938","title":"RodinHD: High-Fidelity 3D Avatar Generation with Diffusion Models","created":"2024-07-09","cited_by_count":36,"area":"diffusion"},{"id":"2507.23785","title":"Gaussian Variation Field Diffusion for High-fidelity Video-to-4D Synthesis","created":"2025-07-31","cited_by_count":33,"area":"diffusion"},{"id":"2405.19572","title":"Blind Image Restoration via Fast Diffusion Inversion","created":"2024-05-29","cited_by_count":30,"area":"diffusion"},{"id":"2303.18242","title":"$\\infty$-Diff: Infinite Resolution Diffusion with Subsampled Mollified States","created":"2023-03-31","cited_by_count":21,"area":"diffusion"},{"id":"2304.04820","title":"Binary Latent Diffusion","created":"2023-04-10","cited_by_count":18,"area":"diffusion"},{"id":"2210.12100","title":"Boomerang: Local sampling on image manifolds using diffusion models","created":"2022-10-21","cited_by_count":16,"area":"diffusion"},{"id":"2407.02549","title":"Diffusion Models for Tabular Data Imputation and Synthetic Data Generation","created":"2024-07-02","cited_by_count":15,"area":"diffusion"},{"id":"2405.17035","title":"Glauber Generative Model: Discrete Diffusion Models via Binary Classification","created":"2024-05-27","cited_by_count":11,"area":"diffusion"},{"id":"2306.00914","title":"Conditioning Diffusion Models via Attributes and Semantic Masks for Face Generation","created":"2023-06-01","cited_by_count":9,"area":"diffusion"},{"id":"2404.04544","title":"BeyondScene: Higher-Resolution Human-Centric Scene Generation With Pretrained Diffusion","created":"2024-04-06","cited_by_count":9,"area":"diffusion"},{"id":"2504.09454","title":"D$^2$iT: Dynamic Diffusion Transformer for Accurate Image Generation","created":"2025-04-13","cited_by_count":9,"area":"diffusion"},{"id":"2508.01248","title":"NS-Net: Decoupling CLIP Semantic Information through NULL-Space for Generalizable AI-Generated Image Detection","created":"2025-08-02","cited_by_count":8,"area":"diffusion"},{"id":"2303.05456","title":"Restoration based Generative Models","created":"2023-02-20","cited_by_count":6,"area":"diffusion"},{"id":"2402.00250","title":"LRDif: Diffusion Models for Under-Display Camera Emotion Recognition","created":"2024-02-01","cited_by_count":6,"area":"diffusion"},{"id":"2507.10072","title":"Frequency Regulation for Exposure Bias Mitigation in Diffusion Models","created":"2025-07-14","cited_by_count":6,"area":"diffusion"},{"id":"2605.21207","title":"PGC: Peak-Guided Calibration for Generalizable AI-Generated Image Detection","created":"2026-05-20","cited_by_count":6,"area":"diffusion"},{"id":"2511.13108","title":"DGS-Net: Distillation-Guided Gradient Surgery for CLIP Fine-Tuning in AI-Generated Image Detection","created":"2025-11-17","cited_by_count":5,"area":"diffusion"},{"id":"2310.17261","title":"Attribute Based Interpretable Evaluation Metrics for Generative Models","created":"2023-10-26","cited_by_count":4,"area":"diffusion"},{"id":"2411.03505","title":"SynthSet: Generative Diffusion Model for Semantic Segmentation in Precision Agriculture","created":"2024-11-05","cited_by_count":4,"area":"diffusion"},{"id":"2401.09596","title":"Efficient generative adversarial networks using linear additive-attention Transformers","created":"2024-01-17","cited_by_count":2,"area":"diffusion"},{"id":"2412.03791","title":"INRFlow: Flow Matching for INRs in Ambient Space","created":"2024-12-05","cited_by_count":2,"area":"diffusion"},{"id":"2403.12915","title":"Ultra-High-Resolution Image Synthesis with Pyramid Diffusion Model","created":"2024-03-19","cited_by_count":1,"area":"diffusion"},{"id":"2605.07074","title":"Decoupling Semantics and Fingerprints: A Universal Representation for AI-Generated Image Detection","created":"2026-05-08","cited_by_count":0,"area":"diffusion"},{"id":"2607.19332","title":"ROMS-IMLE: A Minimalist Approach to Competitive Single-Step Generative Modelling","created":"2026-07-21","cited_by_count":0,"area":"diffusion"}],"next_cursor":null}