HACK++ is a head-aware KV cache compression framework for VAR models that decouples current-scale attention from historical cache under adaptive per-head budgets to achieve near-lossless generation at 30% attention and 10% cache budgets.
Infinity: Scaling bitwise autoregressive modeling for high-resolution image synthesis
8 Pith papers cite this work. Polarity classification is still indexing.
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ImageAttributionBench is a benchmark dataset demonstrating that state-of-the-art image attribution methods lack robustness to image degradation and fail to generalize to semantically disjoint domains.
VARestorer converts a text-to-image VAR model into a fast one-step real-world image super-resolution model via distribution matching distillation and pyramid image conditioning.
Hierarchical Binary Quantization plus global refinement AR yields 0.56 rFID reconstruction and 1.81 gFID class-conditional generation on ImageNet, with competitive T2I/T2V at 2B scale.
ImgEdit supplies 1.2 million curated edit pairs and a three-part benchmark that let a VLM-based model outperform prior open-source editors on adherence, quality, and detail preservation.
Mogao presents a causal unified model with deep fusion, dual encoders, and interleaved position embeddings that achieves strong performance on multi-modal understanding, text-to-image generation, and coherent interleaved outputs including zero-shot editing.
SMPL-GPTexture uses text-to-image generation to produce dual-view human images, aligns them to SMPL meshes via 2D-to-3D recovery, projects colors to UV space, and applies diffusion inpainting to create full high-resolution textures aligned to user prompts.
citing papers explorer
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HACK++: Towards More Effective Head-Aware Key-Value Compression for Efficient Visual Autoregressive Modeling
HACK++ is a head-aware KV cache compression framework for VAR models that decouples current-scale attention from historical cache under adaptive per-head budgets to achieve near-lossless generation at 30% attention and 10% cache budgets.
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ImageAttributionBench: How Far Are We from Generalizable Attribution?
ImageAttributionBench is a benchmark dataset demonstrating that state-of-the-art image attribution methods lack robustness to image degradation and fail to generalize to semantically disjoint domains.
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VARestorer: One-Step VAR Distillation for Real-World Image Super-Resolution
VARestorer converts a text-to-image VAR model into a fast one-step real-world image super-resolution model via distribution matching distillation and pyramid image conditioning.
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Generative Refinement Networks for Visual Synthesis
Hierarchical Binary Quantization plus global refinement AR yields 0.56 rFID reconstruction and 1.81 gFID class-conditional generation on ImageNet, with competitive T2I/T2V at 2B scale.
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ImgEdit: A Unified Image Editing Dataset and Benchmark
ImgEdit supplies 1.2 million curated edit pairs and a three-part benchmark that let a VLM-based model outperform prior open-source editors on adherence, quality, and detail preservation.
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Mogao: An Omni Foundation Model for Interleaved Multi-Modal Generation
Mogao presents a causal unified model with deep fusion, dual encoders, and interleaved position embeddings that achieves strong performance on multi-modal understanding, text-to-image generation, and coherent interleaved outputs including zero-shot editing.
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SMPL-GPTexture: Dual-View 3D Human Texture Estimation using Text-to-Image Generation Models
SMPL-GPTexture uses text-to-image generation to produce dual-view human images, aligns them to SMPL meshes via 2D-to-3D recovery, projects colors to UV space, and applies diffusion inpainting to create full high-resolution textures aligned to user prompts.
- WISE: A World Knowledge-Informed Semantic Evaluation for Text-to-Image Generation