{"total":225,"rows":[{"id":"2307.01952","title":"SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis","created":"2023-07-04","cited_by_count":4356,"area":"diffusion"},{"id":"2302.08453","title":"T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models","created":"2023-02-16","cited_by_count":1351,"area":"diffusion"},{"id":"2310.11513","title":"GenEval: An Object-Focused Framework for Evaluating Text-to-Image Alignment","created":"2023-10-17","cited_by_count":972,"area":"diffusion"},{"id":"2301.13826","title":"Attend-and-Excite: Attention-Based Semantic Guidance for Text-to-Image Diffusion Models","created":"2023-01-31","cited_by_count":702,"area":"diffusion"},{"id":"2305.07015","title":"Exploiting Diffusion Prior for Real-World Image Super-Resolution","created":"2023-05-11","cited_by_count":489,"area":"diffusion"},{"id":"2403.05135","title":"ELLA: Equip Diffusion Models with LLM for Enhanced Semantic Alignment","created":"2024-03-08","cited_by_count":475,"area":"diffusion"},{"id":"2305.16381","title":"DPOK: Reinforcement Learning for Fine-tuning Text-to-Image Diffusion Models","created":"2023-05-25","cited_by_count":450,"area":"diffusion"},{"id":"2303.11305","title":"SVDiff: Compact Parameter Space for Diffusion Fine-Tuning","created":"2023-03-20","cited_by_count":338,"area":"diffusion"},{"id":"2303.09833","title":"FreeDoM: Training-Free Energy-Guided Conditional Diffusion Model","created":"2023-03-17","cited_by_count":294,"area":"diffusion"},{"id":"2310.03739","title":"Aligning Text-to-Image Diffusion Models with Reward Backpropagation","created":"2023-10-05","cited_by_count":288,"area":"diffusion"},{"id":"2303.14420","title":"Human Preference Score: Better Aligning Text-to-Image Models with Human Preference","created":"2023-03-25","cited_by_count":287,"area":"diffusion"},{"id":"2307.10816","title":"BoxDiff: Text-to-Image Synthesis with Training-Free Box-Constrained Diffusion","created":"2023-07-20","cited_by_count":282,"area":"diffusion"},{"id":"2305.18292","title":"Mix-of-Show: Decentralized Low-Rank Adaptation for Multi-Concept Customization of Diffusion Models","created":"2023-05-29","cited_by_count":261,"area":"diffusion"},{"id":"2405.01434","title":"StoryDiffusion: Consistent Self-Attention for Long-Range Image and Video Generation","created":"2024-05-02","cited_by_count":247,"area":"diffusion"},{"id":"2307.02421","title":"DragonDiffusion: Enabling Drag-style Manipulation on Diffusion Models","created":"2023-07-05","cited_by_count":195,"area":"diffusion"},{"id":"2307.12493","title":"TF-ICON: Diffusion-Based Training-Free Cross-Domain Image Composition","created":"2023-07-24","cited_by_count":182,"area":"diffusion"},{"id":"2402.03290","title":"InstanceDiffusion: Instance-level Control for Image Generation","created":"2024-02-05","cited_by_count":175,"area":"diffusion"},{"id":"2306.08877","title":"Linguistic Binding in Diffusion Models: Enhancing Attribute Correspondence through Attention Map Alignment","created":"2023-06-15","cited_by_count":171,"area":"diffusion"},{"id":"2311.09257","title":"UFOGen: You Forward Once Large Scale Text-to-Image Generation via Diffusion GANs","created":"2023-11-14","cited_by_count":166,"area":"diffusion"},{"id":"2306.05427","title":"Grounded Text-to-Image Synthesis with Attention Refocusing","created":"2023-06-08","cited_by_count":164,"area":"diffusion"},{"id":"2308.12964","title":"Dense Text-to-Image Generation with Attention Modulation","created":"2023-08-24","cited_by_count":163,"area":"diffusion"},{"id":"2303.05125","title":"Cones: Concept Neurons in Diffusion Models for Customized Generation","created":"2023-03-09","cited_by_count":150,"area":"diffusion"},{"id":"2310.01506","title":"Direct Inversion: Boosting Diffusion-based Editing with 3 Lines of Code","created":"2023-10-02","cited_by_count":145,"area":"diffusion"},{"id":"2303.15233","title":"Text-to-Image Diffusion Models are Zero-Shot Classifiers","created":"2023-03-27","cited_by_count":143,"area":"diffusion"},{"id":"2404.04650","title":"InitNO: Boosting Text-to-Image Diffusion Models via Initial Noise Optimization","created":"2024-04-06","cited_by_count":123,"area":"diffusion"},{"id":"2407.08737","title":"Video Diffusion Alignment via Reward Gradients","created":"2024-07-11","cited_by_count":107,"area":"diffusion"},{"id":"2304.05390","title":"HRS-Bench: Holistic, Reliable and Scalable Benchmark for Text-to-Image Models","created":"2023-04-11","cited_by_count":95,"area":"diffusion"},{"id":"2310.09336","title":"Compositional Abilities Emerge Multiplicatively: Exploring Diffusion Models on a Synthetic Task","created":"2023-10-13","cited_by_count":93,"area":"diffusion"},{"id":"2308.05095","title":"LayoutLLM-T2I: Eliciting Layout Guidance from LLM for Text-to-Image Generation","created":"2023-08-09","cited_by_count":92,"area":"diffusion"},{"id":"2402.02583","title":"DiffEditor: Boosting Accuracy and Flexibility on Diffusion-based Image Editing","created":"2024-02-04","cited_by_count":88,"area":"diffusion"},{"id":"2305.13921","title":"Compositional Text-to-Image Synthesis with Attention Map Control of Diffusion Models","created":"2023-05-23","cited_by_count":83,"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":"2307.10864","title":"Divide & Bind Your Attention for Improved Generative Semantic Nursing","created":"2023-07-20","cited_by_count":81,"area":"diffusion"},{"id":"2304.14530","title":"Generating images of rare concepts using pre-trained diffusion models","created":"2023-04-27","cited_by_count":79,"area":"diffusion"},{"id":"2306.14544","title":"A-STAR: Test-time Attention Segregation and Retention for Text-to-image Synthesis","created":"2023-06-26","cited_by_count":78,"area":"diffusion"},{"id":"2401.12244","title":"Large-scale Reinforcement Learning for Diffusion Models","created":"2024-01-20","cited_by_count":78,"area":"diffusion"},{"id":"2311.17002","title":"Ranni: Taming Text-to-Image Diffusion for Accurate Instruction Following","created":"2023-11-28","cited_by_count":77,"area":"diffusion"},{"id":"2309.02773","title":"Diffusion Model is Secretly a Training-free Open Vocabulary Semantic Segmenter","created":"2023-09-06","cited_by_count":76,"area":"diffusion"},{"id":"2302.13153","title":"Directed Diffusion: Direct Control of Object Placement through Attention Guidance","created":"2023-02-25","cited_by_count":69,"area":"diffusion"},{"id":"2403.16990","title":"Be Yourself: Bounded Attention for Multi-Subject Text-to-Image Generation","created":"2024-03-25","cited_by_count":69,"area":"diffusion"},{"id":"2306.13754","title":"Zero-shot spatial layout conditioning for text-to-image diffusion models","created":"2023-06-23","cited_by_count":64,"area":"diffusion"},{"id":"2305.03382","title":"Guided Image Synthesis via Initial Image Editing in Diffusion Model","created":"2023-05-05","cited_by_count":63,"area":"diffusion"},{"id":"2404.03653","title":"CoMat: Aligning Text-to-Image Diffusion Model with Image-to-Text Concept Matching","created":"2024-04-04","cited_by_count":61,"area":"diffusion"},{"id":"2303.09319","title":"Unified Multi-Modal Latent Diffusion for Joint Subject and Text Conditional Image Generation","created":"2023-03-16","cited_by_count":60,"area":"diffusion"},{"id":"2306.04542","title":"On the Design Fundamentals of Diffusion Models: A Survey","created":"2023-06-07","cited_by_count":60,"area":"diffusion"},{"id":"2312.04884","title":"UDiffText: A Unified Framework for High-quality Text Synthesis in Arbitrary Images via Character-aware Diffusion Models","created":"2023-12-08","cited_by_count":59,"area":"diffusion"},{"id":"2303.13843","title":"CompoNeRF: Text-guided Multi-object Compositional NeRF with Editable 3D Scene Layout","created":"2023-03-24","cited_by_count":57,"area":"diffusion"},{"id":"2305.16283","title":"CommonScenes: Generating Commonsense 3D Indoor Scenes with Scene Graph Diffusion","created":"2023-05-25","cited_by_count":54,"area":"diffusion"},{"id":"2312.06059","title":"CONFORM: Contrast is All You Need For High-Fidelity Text-to-Image Diffusion Models","created":"2023-12-11","cited_by_count":54,"area":"diffusion"},{"id":"2410.05677","title":"T2V-Turbo-v2: Enhancing Video Generation Model Post-Training through Data, Reward, and Conditional Guidance Design","created":"2024-10-08","cited_by_count":54,"area":"diffusion"},{"id":"2310.11784","title":"Progressive3D: Progressively Local Editing for Text-to-3D Content Creation with Complex Semantic Prompts","created":"2023-10-18","cited_by_count":52,"area":"diffusion"},{"id":"2304.03869","title":"Harnessing the Spatial-Temporal Attention of Diffusion Models for High-Fidelity Text-to-Image Synthesis","created":"2023-04-07","cited_by_count":51,"area":"diffusion"},{"id":"2406.02965","title":"Understanding the Impact of Negative Prompts: When and How Do They Take Effect?","created":"2024-06-05","cited_by_count":51,"area":"diffusion"},{"id":"2406.04277","title":"VideoTetris: Towards Compositional Text-to-Video Generation","created":"2024-06-06","cited_by_count":51,"area":"diffusion"},{"id":"2309.04430","title":"Create Your World: Lifelong Text-to-Image Diffusion","created":"2023-09-08","cited_by_count":46,"area":"diffusion"},{"id":"2407.07614","title":"MARS: Mixture of Auto-Regressive Models for Fine-grained Text-to-image Synthesis","created":"2024-07-10","cited_by_count":44,"area":"diffusion"},{"id":"2306.08687","title":"Norm-guided latent space exploration for text-to-image generation","created":"2023-06-14","cited_by_count":43,"area":"diffusion"},{"id":"2305.13308","title":"If at First You Don't Succeed, Try, Try Again: Faithful Diffusion-based Text-to-Image Generation by Selection","created":"2023-05-22","cited_by_count":40,"area":"diffusion"},{"id":"2312.03626","title":"TokenCompose: Text-to-Image Diffusion with Token-level Supervision","created":"2023-12-06","cited_by_count":40,"area":"diffusion"},{"id":"2407.15861","title":"Adversarial Attacks and Defenses on Text-to-Image Diffusion Models: A Survey","created":"2024-07-10","cited_by_count":35,"area":"diffusion"},{"id":"2307.04749","title":"Divide, Evaluate, and Refine: Evaluating and Improving Text-to-Image Alignment with Iterative VQA Feedback","created":"2023-07-10","cited_by_count":32,"area":"diffusion"},{"id":"2305.14724","title":"I Spy a Metaphor: Large Language Models and Diffusion Models Co-Create Visual Metaphors","created":"2023-05-24","cited_by_count":31,"area":"diffusion"},{"id":"2311.11919","title":"An Image is Worth Multiple Words: Multi-attribute Inversion for Constrained Text-to-Image Synthesis","created":"2023-11-20","cited_by_count":31,"area":"diffusion"},{"id":"2311.17695","title":"Fair Text-to-Image Diffusion via Fair Mapping","created":"2023-11-29","cited_by_count":31,"area":"diffusion"},{"id":"2306.09869","title":"Energy-Based Cross Attention for Bayesian Context Update in Text-to-Image Diffusion Models","created":"2023-06-16","cited_by_count":30,"area":"diffusion"},{"id":"2406.09292","title":"Neural Assets: 3D-Aware Multi-Object Scene Synthesis with Image Diffusion Models","created":"2024-06-13","cited_by_count":30,"area":"diffusion"},{"id":"2410.22376","title":"Rare-to-Frequent: Unlocking Compositional Generation Power of Diffusion Models on Rare Concepts with LLM Guidance","created":"2024-10-29","cited_by_count":30,"area":"diffusion"},{"id":"2503.00897","title":"A Simple and Effective Reinforcement Learning Method for Text-to-Image Diffusion Fine-tuning","created":"2025-03-02","cited_by_count":30,"area":"diffusion"},{"id":"2407.01606","title":"On Discrete Prompt Optimization for Diffusion Models","created":"2024-06-27","cited_by_count":29,"area":"diffusion"},{"id":"2306.02583","title":"Stable Diffusion is Unstable","created":"2023-06-05","cited_by_count":27,"area":"diffusion"},{"id":"2403.06381","title":"Enhancing Semantic Fidelity in Text-to-Image Synthesis: Attention Regulation in Diffusion Models","created":"2024-03-11","cited_by_count":27,"area":"diffusion"},{"id":"2306.00974","title":"Discovering Failure Modes of Text-guided Diffusion Models via Adversarial Search","created":"2023-06-01","cited_by_count":25,"area":"diffusion"},{"id":"2308.13142","title":"A Survey of Diffusion Based Image Generation Models: Issues and Their Solutions","created":"2023-08-25","cited_by_count":25,"area":"diffusion"},{"id":"2410.20474","title":"GrounDiT: Grounding Diffusion Transformers via Noisy Patch Transplantation","created":"2024-10-27","cited_by_count":24,"area":"diffusion"},{"id":"2312.08872","title":"The Lottery Ticket Hypothesis in Denoising: Towards Semantic-Driven Initialization","created":"2023-12-13","cited_by_count":23,"area":"diffusion"},{"id":"2505.02648","title":"MCCD: Multi-Agent Collaboration-based Compositional Diffusion for Complex Text-to-Image Generation","created":"2025-05-05","cited_by_count":23,"area":"diffusion"},{"id":"2311.12342","title":"LoCo: Locally Constrained Training-Free Layout-to-Image Synthesis","created":"2023-11-21","cited_by_count":21,"area":"diffusion"},{"id":"2404.00234","title":"Grid Diffusion Models for Text-to-Video Generation","created":"2024-03-30","cited_by_count":21,"area":"diffusion"},{"id":"2405.15321","title":"SG-Adapter: Enhancing Text-to-Image Generation with Scene Graph Guidance","created":"2024-05-24","cited_by_count":20,"area":"diffusion"},{"id":"2311.13833","title":"Lego: Learning to Disentangle and Invert Personalized Concepts Beyond Object Appearance in Text-to-Image Diffusion Models","created":"2023-11-23","cited_by_count":19,"area":"diffusion"},{"id":"2312.06712","title":"Separate-and-Enhance: Compositional Finetuning for Text2Image Diffusion Models","created":"2023-12-10","cited_by_count":19,"area":"diffusion"},{"id":"2406.01970","title":"The Crystal Ball Hypothesis in diffusion models: Anticipating object positions from initial noise","created":"2024-06-04","cited_by_count":19,"area":"diffusion"},{"id":"2409.08400","title":"Scores as Actions: a framework of fine-tuning diffusion models by continuous-time reinforcement learning","created":"2024-09-12","cited_by_count":19,"area":"diffusion"},{"id":"2410.00321","title":"A Cat Is A Cat (Not A Dog!): Unraveling Information Mix-ups in Text-to-Image Encoders through Causal Analysis and Embedding Optimization","created":"2024-10-01","cited_by_count":19,"area":"diffusion"},{"id":"2402.12908","title":"RealCompo: Balancing Realism and Compositionality Improves Text-to-Image Diffusion Models","created":"2024-02-20","cited_by_count":18,"area":"diffusion"},{"id":"2404.07389","title":"Object-Conditioned Energy-Based Attention Map Alignment in Text-to-Image Diffusion Models","created":"2024-04-10","cited_by_count":18,"area":"diffusion"},{"id":"2405.17532","title":"ClassDiffusion: More Aligned Personalization Tuning with Explicit Class Guidance","created":"2024-05-27","cited_by_count":18,"area":"diffusion"},{"id":"2409.08240","title":"IFAdapter: Instance Feature Control for Grounded Text-to-Image Generation","created":"2024-09-12","cited_by_count":18,"area":"diffusion"},{"id":"2303.17546","title":"PAIR-Diffusion: A Comprehensive Multimodal Object-Level Image Editor","created":"2023-03-30","cited_by_count":17,"area":"diffusion"},{"id":"2309.04399","title":"MaskDiffusion: Boosting Text-to-Image Consistency with Conditional Mask","created":"2023-09-08","cited_by_count":17,"area":"diffusion"},{"id":"2403.05239","title":"Towards Effective Usage of Human-Centric Priors in Diffusion Models for Text-based Human Image Generation","created":"2024-03-08","cited_by_count":17,"area":"diffusion"},{"id":"2405.15199","title":"ODGEN: Domain-specific Object Detection Data Generation with Diffusion Models","created":"2024-05-24","cited_by_count":17,"area":"diffusion"},{"id":"2311.16117","title":"Predicated Diffusion: Predicate Logic-Based Attention Guidance for Text-to-Image Diffusion Models","created":"2023-10-03","cited_by_count":16,"area":"diffusion"},{"id":"2406.05191","title":"DiffusionPID: Interpreting Diffusion via Partial Information Decomposition","created":"2024-06-07","cited_by_count":16,"area":"diffusion"},{"id":"2305.15296","title":"MultiFusion: Fusing Pre-Trained Models for Multi-Lingual, Multi-Modal Image Generation","created":"2023-05-24","cited_by_count":15,"area":"diffusion"},{"id":"2312.06038","title":"Correcting Diffusion Generation through Resampling","created":"2023-12-10","cited_by_count":15,"area":"diffusion"},{"id":"2405.06914","title":"Non-confusing Generation of Customized Concepts in Diffusion Models","created":"2024-05-11","cited_by_count":15,"area":"diffusion"},{"id":"2502.04725","title":"Can Diffusion Models Learn Hidden Inter-Feature Rules Behind Images?","created":"2025-02-07","cited_by_count":15,"area":"diffusion"},{"id":"2305.10722","title":"Discffusion: Discriminative Diffusion Models as Few-shot Vision and Language Learners","created":"2023-05-18","cited_by_count":14,"area":"diffusion"},{"id":"2305.12716","title":"The CLIP Model is Secretly an Image-to-Prompt Converter","created":"2023-05-22","cited_by_count":14,"area":"diffusion"}],"next_cursor":"100"}