{"total":23,"rows":[{"id":"2501.05803","title":"Test-time Alignment of Diffusion Models without Reward Over-optimization","created":"2025-01-10","cited_by_count":93,"area":"diffusion"},{"id":"2410.24060","title":"Understanding Generalizability of Diffusion Models Requires Rethinking the Hidden Gaussian Structure","created":"2024-10-31","cited_by_count":38,"area":"diffusion"},{"id":"2306.04990","title":"Multi-Architecture Multi-Expert Diffusion Models","created":"2023-06-08","cited_by_count":35,"area":"diffusion"},{"id":"2411.16443","title":"SplatFlow: Multi-View Rectified Flow Model for 3D Gaussian Splatting Synthesis","created":"2024-11-25","cited_by_count":34,"area":"diffusion"},{"id":"2403.09176","title":"Switch Diffusion Transformer: Synergizing Denoising Tasks with Sparse Mixture-of-Experts","created":"2024-03-14","cited_by_count":31,"area":"diffusion"},{"id":"2405.17403","title":"A Closer Look at Time Steps is Worthy of Triple Speed-Up for Diffusion Model Training","created":"2024-05-27","cited_by_count":30,"area":"diffusion"},{"id":"2310.07138","title":"Denoising Task Routing for Diffusion Models","created":"2023-10-11","cited_by_count":28,"area":"diffusion"},{"id":"2403.10348","title":"Denoising Task Difficulty-based Curriculum for Training Diffusion Models","created":"2024-03-15","cited_by_count":27,"area":"diffusion"},{"id":"2409.15557","title":"Mixture of Efficient Diffusion Experts Through Automatic Interval and Sub-Network Selection","created":"2024-09-23","cited_by_count":12,"area":"diffusion"},{"id":"2405.17825","title":"Diffusion Model Patching via Mixture-of-Prompts","created":"2024-05-28","cited_by_count":11,"area":"diffusion"},{"id":"2312.09181","title":"Improving Efficiency of Diffusion Models via Multi-Stage Framework and Tailored Multi-Decoder Architectures","created":"2023-12-14","cited_by_count":9,"area":"diffusion"},{"id":"2407.17571","title":"Diffusion Models For Multi-Modal Generative Modeling","created":"2024-07-24","cited_by_count":9,"area":"diffusion"},{"id":"2503.07416","title":"TimeStep Master: Asymmetrical Mixture of Timestep LoRA Experts for Versatile and Efficient Diffusion Models in Vision","created":"2025-03-10","cited_by_count":9,"area":"diffusion"},{"id":"2410.06664","title":"Decouple-Then-Merge: Finetune Diffusion Models as Multi-Task Learning","created":"2024-10-09","cited_by_count":7,"area":"diffusion"},{"id":"2404.02241","title":"Linear Combination of Saved Checkpoints Makes Consistency and Diffusion Models Better","created":"2024-04-02","cited_by_count":6,"area":"diffusion"},{"id":"2504.09606","title":"Early-Bird Diffusion: Investigating and Leveraging Timestep-Aware Early-Bird Tickets in Diffusion Models for Efficient Training","created":"2025-04-13","cited_by_count":5,"area":"diffusion"},{"id":"2504.11457","title":"Aligning Generative Denoising with Discriminative Objectives Unleashes Diffusion for Visual Perception","created":"2025-04-15","cited_by_count":4,"area":"diffusion"},{"id":"2405.13729","title":"ComboStoc: Combinatorial Stochasticity for Diffusion Generative Models","created":"2024-05-22","cited_by_count":2,"area":"diffusion"},{"id":"2503.14868","title":"Efficient Personalization of Quantized Diffusion Model without Backpropagation","created":"2025-03-19","cited_by_count":1,"area":"diffusion"},{"id":"2604.25289","title":"Exploring Time Conditioning in Diffusion Generative Models from Disjoint Noisy Data Manifolds","created":"2026-04-28","cited_by_count":1,"area":"diffusion"},{"id":"2602.01289","title":"Gradient-Aligned Calibration for Post-Training Quantization of Diffusion Models","created":"2026-02-01","cited_by_count":0,"area":"diffusion"},{"id":"2605.19698","title":"Awakening the Hydra: Stabilizing Multi-Concept Backdoor Injection in Text-to-Image Diffusion Models","created":"2026-05-19","cited_by_count":0,"area":"diffusion"},{"id":"2606.30370","title":"MUSE: Unlocking Timestep as Native Task Steering for One-Step Dense Prediction","created":"2026-06-29","cited_by_count":0,"area":"diffusion"}],"next_cursor":null}