title: SimpleAR: Pushing the Frontier of Autoregressive Visual Generation
    through Pretraining, SFT, and RL
summary: This work presents SimpleAR, a vanilla autoregressive visual generation
  framework without complex architecure modifications. Through careful
  exploration of training and inference optimization, we demonstrate that: 1)
  with only 0.5B parameters, our model can generate 1024x1024 resolution images
  with high fidelity, and achieve competitive results on challenging
  text-to-image benchmarks, e.g., 0.59 on GenEval and 79.66 on DPG; 2) both
  supervised fine-tuning (SFT) and Group Relative Policy Optimization (GRPO)
  training could lead to significant improvements on generation aesthectics and
  prompt alignment; and 3) when optimized with inference acceleraton techniques
  like vLLM, the time for SimpleAR to generate an 1024x1024 image could be
  reduced to around 14 seconds. By sharing these findings and open-sourcing the
  code, we hope to reveal the potential of autoregressive visual generation and
  encourage more participation in this research field. Code is available at
  https://github.com/wdrink/SimpleAR.
paper_authors._id: 67ffa6ab3a1962b50cbaadd9
paper_authors.name: Junke Wang
paper_authors._id: 67ffa6ab3a1962b50cbaadda
paper_authors.name: Zhi Tian
paper_authors._id: 67ffa6ab3a1962b50cbaaddb
paper_authors.name: Xun Wang
paper_authors._id: 67ffa6ab3a1962b50cbaaddc
paper_authors.name: Xinyu Zhang
paper_authors._id: 67ffa6ab3a1962b50cbaaddd
paper_authors.name: Weilin Huang
paper_authors._id: 67ffa6ab3a1962b50cbaadde
paper_authors.name: Zuxuan Wu
paper_authors._id: 67ffa6ab3a1962b50cbaaddf
paper_authors.name: Yu-Gang Jiang
paper_title: SimpleAR: Pushing the Frontier of Autoregressive Visual Generation
    through Pretraining, SFT, and RL
paper_summary: This work presents SimpleAR, a vanilla autoregressive visual generation
  framework without complex architecure modifications. Through careful
  exploration of training and inference optimization, we demonstrate that: 1)
  with only 0.5B parameters, our model can generate 1024x1024 resolution images
  with high fidelity, and achieve competitive results on challenging
  text-to-image benchmarks, e.g., 0.59 on GenEval and 79.66 on DPG; 2) both
  supervised fine-tuning (SFT) and Group Relative Policy Optimization (GRPO)
  training could lead to significant improvements on generation aesthectics and
  prompt alignment; and 3) when optimized with inference acceleraton techniques
  like vLLM, the time for SimpleAR to generate an 1024x1024 image could be
  reduced to around 14 seconds. By sharing these findings and open-sourcing the
  code, we hope to reveal the potential of autoregressive visual generation and
  encourage more participation in this research field. Code is available at
  https://github.com/wdrink/SimpleAR.
paper_ai_summary: A vanilla autoregressive visual generation framework with 0.5B parameters achieves high-fidelity image generation, competitive results on text-to-image benchmarks, and improved aesthetics and prompt alignment through optimized training and inference techniques.
paper_ai_keywords: autoregressive visual generation supervised fine-tuning Group Relative Policy Optimization vLLM