Visual Autoregressive Scalable Image Generation Via Next Scale Prediction 2025 Forecast. Paper Review Visual Autoregressive Modeling Scalable Image Generation via NextScale Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction Keyu Tian · Yi Jiang · Zehuan Yuan · BINGYUE PENG · Liwei Wang East Exhibit Hall A-C #3009 [ Abstract This simple, intuitive methodology allows autoregressive
Modeling Archives bobweb.ai from bobweb.ai
We present Visual AutoRegressive modeling (VAR), a new generation paradigm that redefines the autoregressive learning on images as coarse-to-fine "next. Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction Keyu Tian · Yi Jiang · Zehuan Yuan · BINGYUE PENG · Liwei Wang East Exhibit Hall A-C #3009 [ Abstract
Modeling Archives bobweb.ai
4.1 State-of-the-art image generation; 4.2 Power-law scaling laws; 4.3 Zero-shot task generalization; 4.4 Ablation Study; 5 Future Work; 6 Conclusion; A Token. approach begins by encoding an image into multi-scale token maps.The autoregressive process is then started from the 1×1 token map, and progressively expands in resolution: at each step, the transformer predicts the next higher-resolution token map conditioned on all previous ones. An *ultra-simple, user-friendly yet state-of-the-art* codebase for autoregressive image generation! - FoundationVision/VAR
Autoregressive Model Beats Diffusion Llama for Scalable Image Generation AI Research Paper. The VAR framework reconceptualizes the autoregressive modeling on images by shifting from next-token prediction to next-scale prediction approach, a process under which instead of being a single token, the autoregressive unit is an entire token map. We present Visual AutoRegressive modeling (VAR), a new generation paradigm that redefines the autoregressive learning on images as coarse-to-fine "next-scale prediction" or "next-resolution prediction", diverging from the standard raster-scan "next-token prediction".
Generating HighResolution Images Using Deep Autoregressive Models by Reza Fazeli Towards. [NeurIPS 2024 Best Paper][GPT beats diffusion🔥] [scaling laws in visual generation📈] Official impl Results suggest VAR has initially emulated the two important properties of LLMs: Scaling Laws and zero-shot task generalization, and it is empirically verified that VAR outperforms the Diffusion Transformer in multiple dimensions including image quality, inference speed, data efficiency, and scalability