ARPG

--- license: mit pipeline_tag: unconditional-image-generation library_name: diffusers --- # Autoregressive Image Generation with Randomized Parallel Decoding [Haopeng Li](https://github.com/hp-l33)¹, Jinyue Yang², [Guoqi Li](https://casialiguoqi.github.io)^2,📧, [Huan Wang](https://huanwang.tech)^1,📧 ¹ Westlake University, ² Institute of Automation, Chinese Academy of Sciences ## TL;DR **ARPG** is a novel autoregressive image generation framework capable of performing **BERT-style masked modeling** with a **GPT-style causal architecture**. ``💪 FID 1.94`` ``🚀 Fast Speed`` ``♻️ Low Memory Usage`` ``🎲 Radnom Order`` ``💡 Zero-shot Inference`` ## Usage: You can easily load it through the Hugging Face DiffusionPipeline and optionally customize various parameters such as the model type, number of steps, and class labels. ```python from diffusers import DiffusionPipeline pipeline = DiffusionPipeline.from_pretrained("hp-l33/ARPG", custom_pipeline="hp-l33/ARPG") class_labels = [207, 360, 388, 113, 355, 980, 323, 979] generated_image = pipeline( model_type="ARPG-XL", # choose from 'ARPG-L', 'ARPG-XL', or 'ARPG-XXL' seed=0, # set a seed for reproducibility num_steps=64, # number of autoregressive steps class_labels=class_labels, # provide valid ImageNet class labels cfg_scale=4, # classifier-free guidance scale output_dir="./images", # directory to save generated images cfg_schedule="constant", # choose between 'constant' (suggested) and 'linear' sample_schedule="arccos", # choose between 'arccos' (suggested) and 'cosine' ) generated_image.show() ``` ## Citation If this work is helpful for your research, please give it a star or cite it: ```bibtex @article{li2025autoregressive, title={Autoregressive Image Generation with Randomized Parallel Decoding}, author={Haopeng Li and Jinyue Yang and Guoqi Li and Huan Wang}, journal={arXiv preprint arXiv:2503.10568}, year={2025} } ``` ## Acknowledgement Thanks to [LlamaGen](https://github.com/FoundationVision/LlamaGen) for its open-source codebase. Appreciate [RandAR](https://github.com/ziqipang/RandAR) and [RAR](https://github.com/bytedance/1d-tokenizer/blob/main/README_RAR.md) for inspiring this work, and also thank [ControlAR](https://github.com/hustvl/ControlAR).

Source context: 176 downloads · 4 likes · Pipeline unconditional-image-generation · Library diffusers · Repo hp-l33/ARPG