{"total":110,"rows":[{"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":"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":"2403.04279","title":"Controllable Generation with Text-to-Image Diffusion Models: A Survey","created":"2024-03-07","cited_by_count":83,"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":"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":"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":"2406.10210","title":"Make It Count: Text-to-Image Generation with an Accurate Number of Objects","created":"2024-06-14","cited_by_count":47,"area":"diffusion"},{"id":"2310.16656","title":"A Picture is Worth a Thousand Words: Principled Recaptioning Improves Image Generation","created":"2023-10-25","cited_by_count":41,"area":"diffusion"},{"id":"2403.17064","title":"Continuous, Subject-Specific Attribute Control in T2I Models by Identifying Semantic Directions","created":"2024-03-25","cited_by_count":41,"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":"2310.07972","title":"Interpretable Diffusion via Information Decomposition","created":"2023-10-12","cited_by_count":37,"area":"diffusion"},{"id":"2409.07253","title":"Alignment of Diffusion Models: Fundamentals, Challenges, and Future","created":"2024-09-11","cited_by_count":35,"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":"2407.07197","title":"ColorPeel: Color Prompt Learning with Diffusion Models via Color and Shape Disentanglement","created":"2024-07-09","cited_by_count":28,"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":"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":"2403.17924","title":"AID: Attention Interpolation of Text-to-Image Diffusion","created":"2024-03-26","cited_by_count":25,"area":"diffusion"},{"id":"2312.11396","title":"MAG-Edit: Localized Image Editing in Complex Scenarios via Mask-Based Attention-Adjusted Guidance","created":"2023-12-18","cited_by_count":21,"area":"diffusion"},{"id":"2506.07986","title":"Rethinking Cross-Modal Interaction in Multimodal Diffusion Transformers","created":"2025-06-09","cited_by_count":20,"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":"2406.07844","title":"Improving Compositional Attribute Binding in Text-to-Image Generative Models via Enhanced Text Embeddings","created":"2024-06-12","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":"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":"2411.18936","title":"Self-Cross Diffusion Guidance for Text-to-Image Synthesis of Similar Subjects","created":"2024-11-28","cited_by_count":16,"area":"diffusion"},{"id":"2503.17675","title":"Towards Transformer-Based Aligned Generation with Self-Coherence Guidance","created":"2025-03-22","cited_by_count":14,"area":"diffusion"},{"id":"2501.06751","title":"Padding Tone: A Mechanistic Analysis of Padding Tokens in T2I Models","created":"2025-01-12","cited_by_count":13,"area":"diffusion"},{"id":"2508.15124","title":"Side Effects of Erasing Concepts from Diffusion Models","created":"2025-08-20","cited_by_count":13,"area":"diffusion"},{"id":"2407.06182","title":"Compositional Video Generation as Flow Equalization","created":"2024-06-10","cited_by_count":12,"area":"diffusion"},{"id":"2411.15236","title":"Text Embedding is Not All You Need: Attention Control for Text-to-Image Semantic Alignment with Text Self-Attention Maps","created":"2024-11-21","cited_by_count":12,"area":"diffusion"},{"id":"2411.18301","title":"Enhancing MMDiT-Based Text-to-Image Models for Similar Subject Generation","created":"2024-11-27","cited_by_count":11,"area":"diffusion"},{"id":"2404.13766","title":"Object-Attribute Binding in Text-to-Image Generation: Evaluation and Control","created":"2024-04-21","cited_by_count":10,"area":"diffusion"},{"id":"2503.22168","title":"Spatial Transport Optimization by Repositioning Attention Map for Training-Free Text-to-Image Synthesis","created":"2025-03-28","cited_by_count":10,"area":"diffusion"},{"id":"2505.21488","title":"Be Decisive: Noise-Induced Layouts for Multi-Subject Generation","created":"2025-05-27","cited_by_count":10,"area":"diffusion"},{"id":"2402.17910","title":"Box It to Bind It: Unified Layout Control and Attribute Binding in T2I Diffusion Models","created":"2024-02-27","cited_by_count":9,"area":"diffusion"},{"id":"2403.13352","title":"AGFSync: Leveraging AI-Generated Feedback for Preference Optimization in Text-to-Image Generation","created":"2024-03-20","cited_by_count":9,"area":"diffusion"},{"id":"2410.20972","title":"Attention Overlap Is Responsible for The Entity Missing Problem in Text-to-image Diffusion Models!","created":"2024-10-28","cited_by_count":9,"area":"diffusion"},{"id":"2412.10122","title":"The Art of Deception: Color Visual Illusions and Diffusion Models","created":"2024-12-13","cited_by_count":9,"area":"diffusion"},{"id":"2506.01929","title":"Image Generation from Contextually-Contradictory Prompts","created":"2025-06-02","cited_by_count":9,"area":"diffusion"},{"id":"2405.20759","title":"Information Theoretic Text-to-Image Alignment","created":"2024-05-31","cited_by_count":8,"area":"diffusion"},{"id":"2411.05473","title":"Improving image synthesis with diffusion-negative sampling","created":"2024-11-08","cited_by_count":8,"area":"diffusion"},{"id":"2501.02355","title":"CorrFill: Enhancing Faithfulness in Reference-based Inpainting with Correspondence Guidance in Diffusion Models","created":"2025-01-04","cited_by_count":8,"area":"diffusion"},{"id":"2510.23974","title":"Diffusion Adaptive Text Embedding for Text-to-Image Diffusion Models","created":"2025-10-28","cited_by_count":8,"area":"diffusion"},{"id":"2412.04715","title":"Addressing Text Embedding Leakage in Diffusion-based Image Editing","created":"2024-12-06","cited_by_count":7,"area":"diffusion"},{"id":"2505.01104","title":"VSC: Visual Search Compositional Text-to-Image Diffusion Model","created":"2025-05-02","cited_by_count":7,"area":"diffusion"},{"id":"2403.16954","title":"Isolated Diffusion: Optimizing Multi-Concept Text-to-Image Generation Training-Freely with Isolated Diffusion Guidance","created":"2024-03-25","cited_by_count":6,"area":"diffusion"},{"id":"2404.14768","title":"Enhancing Prompt Following with Visual Control Through Training-Free Mask-Guided Diffusion","created":"2024-04-23","cited_by_count":6,"area":"diffusion"},{"id":"2408.11706","title":"FRAP: Faithful and Realistic Text-to-Image Generation with Adaptive Prompt Weighting","created":"2024-08-21","cited_by_count":5,"area":"diffusion"},{"id":"2410.22775","title":"Diffusion Beats Autoregressive: An Evaluation of Compositional Generation in Text-to-Image Models","created":"2024-10-30","cited_by_count":5,"area":"diffusion"},{"id":"2509.25940","title":"Steer Away From Mode Collisions: Improving Composition In Diffusion Models","created":"2025-09-30","cited_by_count":5,"area":"diffusion"},{"id":"2410.01738","title":"VitaGlyph: Vitalizing Artistic Typography with Flexible Dual-branch Diffusion Models","created":"2024-10-02","cited_by_count":4,"area":"diffusion"},{"id":"2410.21638","title":"Adapting Diffusion Models for Improved Prompt Compliance and Controllable Image Synthesis","created":"2024-10-29","cited_by_count":4,"area":"diffusion"},{"id":"2507.17853","title":"Detail++: Training-Free Detail Enhancer for T2I Diffusion Models","created":"2025-07-23","cited_by_count":4,"area":"diffusion"},{"id":"2406.00505","title":"Improving Text Generation on Images with Synthetic Captions","created":"2024-06-01","cited_by_count":3,"area":"diffusion"},{"id":"2409.10597","title":"Optimizing Resource Consumption in Diffusion Models through Hallucination Early Detection","created":"2024-09-16","cited_by_count":3,"area":"diffusion"},{"id":"2409.15259","title":"StarVid: Enhancing Semantic Alignment in Video Diffusion Models via Spatial and SynTactic Guided Attention Refocusing","created":"2024-09-23","cited_by_count":3,"area":"diffusion"},{"id":"2411.17472","title":"Unlocking the Potential of Text-to-Image Diffusion with PAC-Bayesian Theory","created":"2024-11-25","cited_by_count":3,"area":"diffusion"},{"id":"2509.02295","title":"Data-Driven Loss Functions for Inference-Time Optimization in Text-to-Image","created":"2025-09-02","cited_by_count":3,"area":"diffusion"},{"id":"2510.04504","title":"Asynchronous Denoising Diffusion Models for Aligning Text-to-Image Generation","created":"2025-10-06","cited_by_count":3,"area":"diffusion"},{"id":"2510.05509","title":"Be Tangential to Manifold: Discovering Riemannian Metric for Diffusion Models","created":"2025-10-07","cited_by_count":3,"area":"diffusion"},{"id":"2406.18893","title":"AlignIT: Enhancing Prompt Alignment in Customization of Text-to-Image Models","created":"2024-06-27","cited_by_count":2,"area":"diffusion"},{"id":"2505.03097","title":"Not All Parameters Matter: Masking Diffusion Models for Enhancing Generation Ability","created":"2025-05-06","cited_by_count":2,"area":"diffusion"},{"id":"2509.10058","title":"Color Me Correctly: Bridging Perceptual Color Spaces and Text Embeddings for Improved Diffusion Generation","created":"2025-09-12","cited_by_count":2,"area":"diffusion"},{"id":"2512.09350","title":"TextGuider: Training-Free Guidance for Text Rendering via Attention Alignment","created":"2025-12-10","cited_by_count":2,"area":"diffusion"},{"id":"2603.06533","title":"NEGATE: Constrained Semantic Guidance for Linguistic Negation in Text-to-Video Diffusion","created":"2026-03-06","cited_by_count":2,"area":"diffusion"},{"id":"2605.14270","title":"Diagnosing and Correcting Concept Omission in Multimodal Diffusion Transformers","created":"2026-05-14","cited_by_count":2,"area":"diffusion"},{"id":"2406.08482","title":"Words Worth a Thousand Pictures: Measuring and Understanding Perceptual Variability in Text-to-Image Generation","created":"2024-06-12","cited_by_count":1,"area":"diffusion"},{"id":"2409.01327","title":"SPDiffusion: Semantic Protection Diffusion Models for Multi-concept Text-to-image Generation","created":"2024-09-02","cited_by_count":1,"area":"diffusion"},{"id":"2504.05594","title":"Tuning-Free Image Editing with Fidelity and Editability via Unified Latent Diffusion Model","created":"2025-04-08","cited_by_count":1,"area":"diffusion"},{"id":"2504.07008","title":"Latent Diffusion U-Net Representations Contain Positional Embeddings and Anomalies","created":"2025-04-09","cited_by_count":1,"area":"diffusion"},{"id":"2508.02973","title":"Diffusion Models with Adaptive Negative Sampling Without External Resources","created":"2025-08-05","cited_by_count":1,"area":"diffusion"},{"id":"2508.10710","title":"CountCluster: Training-Free Object Quantity Guidance with Cross-Attention Map Clustering for Text-to-Image Generation","created":"2025-08-14","cited_by_count":1,"area":"diffusion"},{"id":"2512.20666","title":"Dominant vs. Dominated: Concept-Level Generative Collapse in Diffusion Models","created":"2025-12-19","cited_by_count":1,"area":"diffusion"},{"id":"2602.06886","title":"Prompt Reinjection: Alleviating Prompt Forgetting in Multimodal Diffusion Transformers for Text-to-Image Generation","created":"2026-02-06","cited_by_count":1,"area":"diffusion"},{"id":"2602.08749","title":"Shifting the Breaking Point of Flow Matching for Multi-Instance Editing","created":"2026-02-09","cited_by_count":1,"area":"diffusion"},{"id":"2603.20828","title":"EruDiff: Refactoring Knowledge in Diffusion Models for Advanced Text-to-Image Synthesis","created":"2026-03-21","cited_by_count":1,"area":"diffusion"},{"id":"2605.06512","title":"DCR: Counterfactual Attractor Guidance for Rare Compositional Generation","created":"2026-05-07","cited_by_count":1,"area":"diffusion"},{"id":"2609.03586","title":"The Attention Triangle in Audio-Video Models","created":"2026-09-03","cited_by_count":1,"area":"diffusion"},{"id":"2406.03582","title":"Understanding the Limitations of Diffusion Concept Algebra Through Food","created":"2024-06-05","cited_by_count":0,"area":"diffusion"},{"id":"2409.11874","title":"ABHINAW: A method for Automatic Evaluation of Typography within AI-Generated Images","created":"2024-09-18","cited_by_count":0,"area":"diffusion"},{"id":"2504.03292","title":"FaR: Enhancing Multi-Concept Text-to-Image Diffusion via Concept Fusion and Localized Refinement","created":"2025-04-04","cited_by_count":0,"area":"diffusion"},{"id":"2505.03203","title":"PiCo: Enhancing Text-Image Alignment with Improved Noise Selection and Precise Mask Control in Diffusion Models","created":"2025-05-06","cited_by_count":0,"area":"diffusion"},{"id":"2505.20808","title":"Score Replacement with Bounded Deviation for Rare Prompt Generation","created":"2025-05-27","cited_by_count":0,"area":"diffusion"},{"id":"2505.20935","title":"ISAC: Training-Free Instance-to-Semantic Attention Control for Multi-Instance Generation","created":"2025-05-27","cited_by_count":0,"area":"diffusion"},{"id":"2506.09740","title":"ELBO-T2IAlign: A Generic ELBO-Based Method for Calibrating Pixel-level Text-Image Alignment in Diffusion Models","created":"2025-06-11","cited_by_count":0,"area":"diffusion"},{"id":"2508.05399","title":"UNCAGE: Contrastive Attention Guidance for Masked Generative Transformers in Text-to-Image Generation","created":"2025-08-07","cited_by_count":0,"area":"diffusion"},{"id":"2509.21938","title":"SemanticControl: A Training-Free Approach for Handling Loosely Aligned Visual Conditions in ControlNet","created":"2025-09-26","cited_by_count":0,"area":"diffusion"},{"id":"2512.23245","title":"ASemConsist: Adaptive Semantic Feature Control for Training-Free Identity-Consistent Generation","created":"2025-12-29","cited_by_count":0,"area":"diffusion"},{"id":"2602.09268","title":"Rethinking Global Text Conditioning in Diffusion Transformers","created":"2026-02-09","cited_by_count":0,"area":"diffusion"},{"id":"2603.10210","title":"Delta-K: Boosting Multi-Instance Generation via Cross-Attention Augmentation","created":"2026-03-10","cited_by_count":0,"area":"diffusion"},{"id":"2603.19157","title":"ADAPT: Attention Driven Adaptive Prompt Scheduling and InTerpolating Orthogonal Complements for Rare Concepts Generation","created":"2026-03-19","cited_by_count":0,"area":"diffusion"},{"id":"2603.19158","title":"Adaptive Auxiliary Prompt Blending for Target-Faithful Diffusion Generation","created":"2026-03-19","cited_by_count":0,"area":"diffusion"},{"id":"2604.20354","title":"Hallucination Early Detection in Diffusion Models","created":"2026-04-22","cited_by_count":0,"area":"diffusion"},{"id":"2605.19532","title":"Boosting Text-to-Image Diffusion Models via Core Token Attention-Based Seed Selection","created":"2026-05-19","cited_by_count":0,"area":"diffusion"},{"id":"2605.25763","title":"AI-T2I: Aggregating-and-Isolating Cross-Attention to Diffusion Models for Text-to-Image Synthesis","created":"2026-05-25","cited_by_count":0,"area":"diffusion"},{"id":"2606.10653","title":"STEDiff: Strengthening Text Embedding for Text-to-Image Alignment in Diffusion Model","created":"2026-06-09","cited_by_count":0,"area":"diffusion"},{"id":"2606.20924","title":"ELDiff: When Evidential Learning Meets Text-to-Image Diffusion","created":"2026-06-18","cited_by_count":0,"area":"diffusion"}],"next_cursor":"100"}