{"total":65,"rows":[{"id":"2307.11410","title":"Subject-Diffusion:Open Domain Personalized Text-to-Image Generation without Test-time Fine-tuning","created":"2023-07-21","cited_by_count":191,"area":"diffusion"},{"id":"2312.04433","title":"DreamVideo: Composing Your Dream Videos with Customized Subject and Motion","created":"2023-12-07","cited_by_count":188,"area":"diffusion"},{"id":"2310.08465","title":"MotionDirector: Motion Customization of Text-to-Video Diffusion Models","created":"2023-10-12","cited_by_count":173,"area":"diffusion"},{"id":"2305.18264","title":"Gen-L-Video: Multi-Text to Long Video Generation via Temporal Co-Denoising","created":"2023-05-29","cited_by_count":139,"area":"diffusion"},{"id":"2309.14859","title":"Navigating Text-To-Image Customization: From LyCORIS Fine-Tuning to Model Evaluation","created":"2023-09-26","cited_by_count":109,"area":"diffusion"},{"id":"2403.04279","title":"Controllable Generation with Text-to-Image Diffusion Models: A Survey","created":"2024-03-07","cited_by_count":83,"area":"diffusion"},{"id":"2312.06354","title":"PortraitBooth: A Versatile Portrait Model for Fast Identity-preserved Personalization","created":"2023-12-11","cited_by_count":77,"area":"diffusion"},{"id":"2403.10983","title":"OMG: Occlusion-friendly Personalized Multi-concept Generation in Diffusion Models","created":"2024-03-16","cited_by_count":64,"area":"diffusion"},{"id":"2306.00926","title":"Inserting Anybody in Diffusion Models via Celeb Basis","created":"2023-06-01","cited_by_count":63,"area":"diffusion"},{"id":"2403.11627","title":"LoRA-Composer: Leveraging Low-Rank Adaptation for Multi-Concept Customization in Training-Free Diffusion Models","created":"2024-03-18","cited_by_count":55,"area":"diffusion"},{"id":"2402.05195","title":"$\\lambda$-ECLIPSE: Multi-Concept Personalized Text-to-Image Diffusion Models by Leveraging CLIP Latent Space","created":"2024-02-07","cited_by_count":51,"area":"diffusion"},{"id":"2310.18840","title":"Customizing 360-Degree Panoramas through Text-to-Image Diffusion Models","created":"2023-10-28","cited_by_count":47,"area":"diffusion"},{"id":"2409.09347","title":"Schr\\\"odinger Bridge Flow for Unpaired Data Translation","created":"2024-09-14","cited_by_count":47,"area":"diffusion"},{"id":"2401.01827","title":"Moonshot: Towards Controllable Video Generation and Editing with Multimodal Conditions","created":"2024-01-03","cited_by_count":42,"area":"diffusion"},{"id":"2410.11795","title":"Efficient Diffusion Models: A Comprehensive Survey from Principles to Practices","created":"2024-10-15","cited_by_count":41,"area":"diffusion"},{"id":"2410.17594","title":"How to Continually Adapt Text-to-Image Diffusion Models for Flexible Customization?","created":"2024-10-23","cited_by_count":33,"area":"diffusion"},{"id":"2403.13535","title":"IDAdapter: Learning Mixed Features for Tuning-Free Personalization of Text-to-Image Models","created":"2024-03-20","cited_by_count":32,"area":"diffusion"},{"id":"2405.05538","title":"A Survey on Personalized Content Synthesis with Diffusion Models","created":"2024-05-09","cited_by_count":29,"area":"diffusion"},{"id":"2401.13974","title":"BootPIG: Bootstrapping Zero-shot Personalized Image Generation Capabilities in Pretrained Diffusion Models","created":"2024-01-25","cited_by_count":27,"area":"diffusion"},{"id":"2403.02084","title":"ResAdapter: Domain Consistent Resolution Adapter for Diffusion Models","created":"2024-03-04","cited_by_count":23,"area":"diffusion"},{"id":"2410.15027","title":"Group Diffusion Transformers are Unsupervised Multitask Learners","created":"2024-10-19","cited_by_count":22,"area":"diffusion"},{"id":"2311.14028","title":"Continual Learning of Diffusion Models with Generative Distillation","created":"2023-11-23","cited_by_count":20,"area":"diffusion"},{"id":"2311.18158","title":"HiPA: Enabling One-Step Text-to-Image Diffusion Models via High-Frequency-Promoting Adaptation","created":"2023-11-30","cited_by_count":16,"area":"diffusion"},{"id":"2507.05964","title":"T-LoRA: Single Image Diffusion Model Customization Without Overfitting","created":"2025-07-08","cited_by_count":15,"area":"diffusion"},{"id":"2311.18763","title":"Continual Diffusion with STAMINA: STack-And-Mask INcremental Adapters","created":"2023-11-30","cited_by_count":10,"area":"diffusion"},{"id":"2312.15905","title":"Cross Initialization for Personalized Text-to-Image Generation","created":"2023-12-26","cited_by_count":10,"area":"diffusion"},{"id":"2405.12978","title":"Personalized Residuals for Concept-Driven Text-to-Image Generation","created":"2024-05-21","cited_by_count":9,"area":"diffusion"},{"id":"2308.01127","title":"DiffusePast: Diffusion-based Generative Replay for Class Incremental Semantic Segmentation","created":"2023-08-02","cited_by_count":8,"area":"diffusion"},{"id":"2312.08195","title":"Image is All You Need to Empower Large-scale Diffusion Models for In-Domain Generation","created":"2023-12-13","cited_by_count":7,"area":"diffusion"},{"id":"2410.03941","title":"AutoLoRA: AutoGuidance Meets Low-Rank Adaptation for Diffusion Models","created":"2024-10-04","cited_by_count":7,"area":"diffusion"},{"id":"2405.03958","title":"Simple Drop-in LoRA Conditioning on Attention Layers Will Improve Your Diffusion Model","created":"2024-05-07","cited_by_count":6,"area":"diffusion"},{"id":"2411.08224","title":"Joint Diffusion models in Continual Learning","created":"2024-11-12","cited_by_count":6,"area":"diffusion"},{"id":"2403.03938","title":"GUIDE: Guidance-based Incremental Learning with Diffusion Models","created":"2024-03-06","cited_by_count":5,"area":"diffusion"},{"id":"2504.03738","title":"Attention in Diffusion Model: A Survey","created":"2025-04-01","cited_by_count":4,"area":"diffusion"},{"id":"2504.14815","title":"What Lurks Within? Concept Auditing for Shared Diffusion Models at Scale","created":"2025-04-21","cited_by_count":4,"area":"diffusion"},{"id":"2410.00700","title":"Mining Your Own Secrets: Diffusion Classifier Scores for Continual Personalization of Text-to-Image Diffusion Models","created":"2024-10-01","cited_by_count":3,"area":"diffusion"},{"id":"2410.04891","title":"Low-Rank Continual Personalization of Diffusion Models","created":"2024-10-07","cited_by_count":3,"area":"diffusion"},{"id":"2510.02296","title":"Continual Personalization for Diffusion Models","created":"2025-10-02","cited_by_count":3,"area":"diffusion"},{"id":"2510.04668","title":"ConceptSplit: Decoupled Multi-Concept Personalization of Diffusion Models via Token-wise Adaptation and Attention Disentanglement","created":"2025-10-06","cited_by_count":3,"area":"diffusion"},{"id":"2402.00631","title":"Beyond Inserting: Learning Identity Embedding for Semantic-Fidelity Personalized Diffusion Generation","created":"2024-01-31","cited_by_count":2,"area":"diffusion"},{"id":"2406.04814","title":"Lifelong Learning of Video Diffusion Models From a Single Video Stream","created":"2024-06-07","cited_by_count":2,"area":"diffusion"},{"id":"2407.12579","title":"The Fabrication of Reality and Fantasy: Scene Generation with LLM-Assisted Prompt Interpretation","created":"2024-07-17","cited_by_count":2,"area":"diffusion"},{"id":"2410.14159","title":"Assessing Open-world Forgetting in Generative Image Model Customization","created":"2024-10-18","cited_by_count":2,"area":"diffusion"},{"id":"2411.05544","title":"Towards Lifelong Few-Shot Customization of Text-to-Image Diffusion","created":"2024-11-08","cited_by_count":2,"area":"diffusion"},{"id":"2505.16875","title":"T2I-ConBench: Text-to-Image Benchmark for Continual Post-training","created":"2025-05-22","cited_by_count":1,"area":"diffusion"},{"id":"2510.20512","title":"Adversarial Concept Distillation for One-Step Diffusion Personalization","created":"2025-10-23","cited_by_count":1,"area":"diffusion"},{"id":"2511.08416","title":"Generative AI Meets 6G and Beyond: Diffusion Models for Semantic Communications","created":"2025-11-11","cited_by_count":1,"area":"diffusion"},{"id":"2601.01963","title":"Forget Less by Learning Together through Concept Consolidation","created":"2026-01-05","cited_by_count":1,"area":"diffusion"},{"id":"2606.04797","title":"Crafting Your Evolving Dreams: Concept-Incremental Versatile Customization","created":"2026-06-03","cited_by_count":1,"area":"diffusion"},{"id":"2607.00183","title":"DriftScope: Measuring The Hidden Effects of Diffusion Model Adaptation","created":"2026-06-30","cited_by_count":1,"area":"diffusion"},{"id":"2403.06394","title":"FSViewFusion: Few-Shots View Generation of Novel Objects","created":"2024-03-11","cited_by_count":0,"area":"diffusion"},{"id":"2410.01262","title":"Improving Fine-Grained Control via Aggregation of Multiple Diffusion Models","created":"2024-10-02","cited_by_count":0,"area":"diffusion"},{"id":"2505.06995","title":"KDC-Diff: A Latent-Aware Diffusion Model with Knowledge Retention for Memory-Efficient Image Generation","created":"2025-05-11","cited_by_count":0,"area":"diffusion"},{"id":"2505.11936","title":"CCD: Continual Consistency Diffusion for Lifelong Generative Modeling","created":"2025-05-17","cited_by_count":0,"area":"diffusion"},{"id":"2510.02631","title":"Deep Generative Continual Learning using Functional LoRA: FunLoRA","created":"2025-10-03","cited_by_count":0,"area":"diffusion"},{"id":"2601.06605","title":"Sissi: Zero-shot Style-guided Image Synthesis via Semantic-style Integration","created":"2026-01-10","cited_by_count":0,"area":"diffusion"},{"id":"2605.10302","title":"Follow the Mean: Reference-Guided Flow Matching","created":"2026-05-11","cited_by_count":0,"area":"diffusion"},{"id":"2605.20237","title":"AnimeAdapter: A Modular Adapter for Appearance-Consistent Anime Character Generation","created":"2026-05-17","cited_by_count":0,"area":"diffusion"},{"id":"2605.27975","title":"Continual Learning in Modern Hopfield Networks with an Application to Diffusion Models","created":"2026-05-27","cited_by_count":0,"area":"diffusion"},{"id":"2607.16267","title":"HyperDCM: Dynamic Cluster Memory Replay in Hyperbolic Space for Continual Robotic Navigation Across Scenes","created":"2026-07-04","cited_by_count":0,"area":"diffusion"},{"id":"2609.10723","title":"AcFlow: Controlling Text-to-Image Diffusion Transformers via Learned Conditional Activation Flow","created":"2026-09-09","cited_by_count":0,"area":"diffusion"},{"id":"2610.01331","title":"CLASP: Continual Low-rank Adapters for Spatially Placed Concepts from One Hypernetwork","created":"2026-10-01","cited_by_count":0,"area":"diffusion"},{"id":"2610.06940","title":"Stabilizing language models under continual learning via condition-anchored distillation","created":"2026-10-03","cited_by_count":0,"area":"diffusion"},{"id":"2610.07250","title":"Internalizing Agent Experience into Diffusion Model Weights via On-Policy Context Distillation","created":"2026-10-05","cited_by_count":0,"area":"diffusion"},{"id":"2610.18990","title":"Discovering Undisclosed Concepts in LoRA Adapters for Diffusion-Based AIGC Models","created":"2026-10-13","cited_by_count":0,"area":"diffusion"}],"next_cursor":null}