Update README.md

README.md

@@ -11,13 +11,10 @@ library_name: diffusers
 <sup>1</sup> Westlake University,
 <sup>2</sup> Institute of Automation, Chinese Academy of Sciences
 
-## Introduction
-We introduce a novel autoregressive image generation framework named **ARPG**. This framework is capable of conducting **BERT-style masked modeling** by employing a **GPT-style causal architecture**. Consequently, it is able to generate images in parallel following a random token order and also provides support for the KV cache. 
-* 💪 **ARPG** achieves an FID of **1.94**
-* 🚀 **ARPG** delivers throughput **26 times faster** than [LlamaGen](https://github.com/FoundationVision/LlamaGen).
-* ♻️ **ARPG** reducing memory consumption by over **75%** compared to [VAR](https://github.com/FoundationVision/VAR).
-* 🔍 **ARPG** supports **zero-shot inference** (e.g., inpainting and outpainting).
-* 🛠️ **ARPG** can be easily extended to **controllable generation**.
+## 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.