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README.md

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 license: apache-2.0
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+<h2 align="center">lyraDiff: An Out-of-box Acceleration Engine for Diffusion and DiT Models</h2>
+<div align="center">   
+Sa Xiao<sup>*</sup>, Yibo Lu<sup>*</sup>,  Kangjian Wu<sup>*</sup>, Bin Wu<sup>†</sup>, Haoxiong Su, Mian Peng, Qiwen Mao, Wenjiang Zhou</br>(*co-first author), (†Corresponding Author, benbinwu@tencent.com)</br>
+Lyra Lab, Tencent Music Entertainment</br>     
+<p>[<a href="https://github.com/TMElyralab/lyraDiff">github</a>] [<a href="https://huggingface.co/TMElyralab/lyraDiff">huggingface</a>] </p>
+</div>    
+
+## Introduction
+
+🌈`lyraDiff` is currently the **Fastest Diffusion Acceleration Engine** that **doesn't need recompilation with dynamic input shapes**.
+
+The core features include:
+- 🚀 **State-of-the-art Inference Speed**: `lyraDiff` utilizes multiple techniques to achieve up to 2x speedup of the model inference, including **Quantization**, **Fused GEMM Kernels**, **Flash Attention**, and **NHWC & Fused GroupNorm**.
+- 🔥 **Memory Efficiency**: `lyraDiff` utilizes buffer-based DRAM reuse strategy and multiple types of quantizations (FP8/INT8/INT4) to save **10-40%** of DRAM usage.
+- 🔥 **Extensive Model Support**: `lyraDiff` supports a wide range of Generative/SR models such as **SD1.5, SDXL, FLUX, S3Diff, SUPIR, etc.**, and those most commonly used plugins such as **LoRA, ControlNet and Ip-Adapter**.
+- 🔥 **Zero Compilation Deployment**: Unlike **TensorRT** or **AITemplate**, which takes minutes to compile, `lyraDiff` eliminates runtime recompilation overhead even with model inputs of dynamic shapes.
+- 🔥 **Image Gen Consistency**: The outputs of `lyraDiff` are aligned with the ones of [HF diffusers](https://github.com/huggingface/diffusers) at the pixel level, even under LoRA switch in quantization mode.
+- 🚀 **Fast Plugin Hot-swap**: `lyraDiff` provides **Super Fast Model Hot-swap for ControlNet and LoRA** which can hugely benefit a real-time image gen service.
 
 
 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6461b412846a6c8c8305319d/41_dvtx232Kzu8MkY6qEx.png)
 
-`lyraDiff-Flux.1-dev` converted from the standard [FLUX.1-dev](https://huggingface.co/black-forest-labs/FLUX.1-dev) model weights using this [script](https://github.com/TMElyralab/lyraDiff/blob/main/lyradiff/convert_model_scripts/quantize.py) to be compatiable with [lyraDiff](https://github.com/TMElyralab/lyraDiff)
+`lyraDiff-Flux.1-dev` is converted from the standard [FLUX.1-dev](https://huggingface.co/black-forest-labs/FLUX.1-dev) model weights using this [script](https://github.com/TMElyralab/lyraDiff/blob/main/lyradiff/convert_model_scripts/quantize.py) to be compatiable with [lyraDiff](https://github.com/TMElyralab/lyraDiff)
 
 It contains both `FP8` and `FP16` version of converted Flux.1-dev
 
 ## Usage
 
-We provide a reference implementation of lyraDiff version of Flux.1-dev, as well as sampling code, in a dedicated [github repository](https://github.com/TMElyralab/lyraDiff).
+We provide a reference implementation of lyraDiff version of Flux.1-dev, as well as sampling code, in a dedicated [github repository](https://github.com/TMElyralab/lyraDiff).
+
+## Citation
+``` bibtex
+@Misc{lyraDiff_2025,
+  author =       {Kangjian Wu, Zhengtao Wang, Yibo Lu, Haoxiong Su, Sa Xiao, Qiwen Mao, Mian Peng, Bin Wu, Wenjiang Zhou},
+  title =        {lyraDiff: Accelerating Diffusion Models with best flexibility},
+  howpublished = {\url{https://github.com/TMElyralab/lyraDiff}},
+  year =         {2025}
+}
+```