{"total":22,"rows":[{"id":"2511.20645","title":"PixelDiT: Pixel Diffusion Transformers for Image Generation","created":"2025-11-25","cited_by_count":83,"area":"diffusion"},{"id":"2507.23268","title":"PixNerd: Pixel Neural Field Diffusion","created":"2025-07-31","cited_by_count":82,"area":"diffusion"},{"id":"2511.19365","title":"DeCo: Frequency-Decoupled Pixel Diffusion for End-to-End Image Generation","created":"2025-11-24","cited_by_count":60,"area":"diffusion"},{"id":"2509.24335","title":"Hyperspherical Latents Improve Continuous-Token Autoregressive Generation","created":"2025-09-29","cited_by_count":16,"area":"diffusion"},{"id":"2602.02493","title":"PixelGen: Improving Pixel Diffusion with Perceptual Supervision","created":"2026-02-02","cited_by_count":14,"area":"diffusion"},{"id":"2512.17951","title":"SuperFlow: Training Flow Matching Models with RL on the Fly","created":"2025-12-17","cited_by_count":10,"area":"diffusion"},{"id":"2605.15741","title":"HyperDiT: Hyper-Connected Transformers for High-Fidelity Pixel-Space Diffusion","created":"2026-05-15","cited_by_count":9,"area":"diffusion"},{"id":"2511.19664","title":"Demystifying Diffusion Objectives: Reweighted Losses are Better Variational Bounds","created":"2025-11-24","cited_by_count":8,"area":"diffusion"},{"id":"2602.15030","title":"Image Generation with a Sphere Encoder","created":"2026-02-16","cited_by_count":8,"area":"diffusion"},{"id":"2606.27760","title":"PixelU: A U-Shaped Transformer for Efficient End-to-End Pixel Diffusion","created":"2026-06-26","cited_by_count":8,"area":"diffusion"},{"id":"2605.17759","title":"FrequencyBooster: Full-Frequency Modeling for High-Fidelity Pixel Diffusion","created":"2026-05-18","cited_by_count":4,"area":"diffusion"},{"id":"2603.21208","title":"JANUS: A Lightweight Framework for Jailbreaking Text-to-Image Models via Distribution Optimization","created":"2026-03-22","cited_by_count":3,"area":"diffusion"},{"id":"2604.20041","title":"Normalizing Flows with Iterative Denoising","created":"2026-04-21","cited_by_count":3,"area":"diffusion"},{"id":"2605.06421","title":"FREPix: Frequency-Heterogeneous Flow Matching for Pixel-Space Image Generation","created":"2026-05-07","cited_by_count":3,"area":"diffusion"},{"id":"2603.16792","title":"V-Co: A Closer Look at Visual Representation Alignment via Co-Denoising","created":"2026-03-17","cited_by_count":2,"area":"diffusion"},{"id":"2506.20294","title":"Ctrl-Z Sampling: Scaling Diffusion Sampling with Controlled Random Zigzag Explorations","created":"2025-06-25","cited_by_count":1,"area":"diffusion"},{"id":"2512.12083","title":"RePack then Refine: Efficient Diffusion Transformer with Vision Foundation Model","created":"2025-12-12","cited_by_count":1,"area":"diffusion"},{"id":"2607.18510","title":"DuSPiT: Dual-Branch Sub-Patch Pixel Diffusion Transformer","created":"2026-07-20","cited_by_count":1,"area":"diffusion"},{"id":"2603.19122","title":"Revisiting Autoregressive Models for Generative Image Classification","created":"2026-03-19","cited_by_count":0,"area":"diffusion"},{"id":"2604.17492","title":"Coevolving Representations in Joint Image-Feature Diffusion","created":"2026-04-19","cited_by_count":0,"area":"diffusion"},{"id":"2609.24919","title":"PixelDiT2: Representation-Grounded Pixel Diffusion Transformers","created":"2026-09-21","cited_by_count":0,"area":"diffusion"},{"id":"2610.18857","title":"PixelMoE: Scaling Pixel-Space Diffusion with LLM-Style Mixture-of-Experts","created":"2026-10-13","cited_by_count":0,"area":"diffusion"}],"next_cursor":null}