Update src/app/about-event.tsx

src/app/about-event.tsx

@@ -13,6 +13,18 @@ import React from 'react';
 //   paper_links :""
 // },
 const EVENT_INFO = [
+
+
+{
+  title: "PeRFlow: Piecewise Rectified Flow as Universal Plug-and-Play Accelerator",
+  description: "PeRFlow trains piecewise-linear rectified flow models for fast sampling. These models can be initialized from pretrained diffusion models, such as Stable Diffusion (SD). The obtained weights of PeRFlow serve as a general accelerator module which is compatible with various fine-tuned stylized SD models as well as SD-based generation/editing pipelines. Specifically, \
+  are computed by the PeRFlow's weights minus the pretrained SD. One can fuse the PeRFlow.\
+  into various SD pipelines for (conditional) image generation/editing to enable high-quality few-step inference.",
+  subTitle: "Finetune LORAs / Diffusion Models / PeRFlow",
+  imageName : "perflow-v1.mp4",
+  paper_links :"https://piecewise-rectified-flow.github.io/"
+},
+
   {
   title: "Deformable One-shot Face Stylization via DINO Semantic Guidance",
   description: "This paper presents a novel approach to one-shot face stylization, focusing on appearance and structure. They use a self-supervised vision transformer, DINO-ViT, and integrate spatial transformers into StyleGAN for deformation-aware stylization. Innovative constraints and style-mixing enhance deformability and efficiency, demonstrating superiority over existing methods through extensive comparisons. Code is available at https://github.com/zichongc/DoesFS. ",