SimpleAR: Pushing the Frontier of Autoregressive Visual Generation
  through Pretraining, SFT, and RL
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.
67ffa6ab3a1962b50cbaadd9
Junke Wang
67ffa6ab3a1962b50cbaadda
Zhi Tian
67ffa6ab3a1962b50cbaaddb
Xun Wang
67ffa6ab3a1962b50cbaaddc
Xinyu Zhang
67ffa6ab3a1962b50cbaaddd
Weilin Huang
67ffa6ab3a1962b50cbaadde
Zuxuan Wu
67ffa6ab3a1962b50cbaaddf
Yu-Gang Jiang
SimpleAR: Pushing the Frontier of Autoregressive Visual Generation
  through Pretraining, SFT, and RL
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.
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.
autoregressive visual generation supervised fine-tuning Group Relative Policy Optimization vLLM