From 7e6de1b59a66ea619ed9281e97d1e87e6c0e8922 Mon Sep 17 00:00:00 2001
From: Shuai Yang <596836482@qq.com>
Date: Fri, 28 Jul 2023 19:57:38 +0800
Subject: [PATCH] Update README.md

---
 README.md | 12 +++++++++++-
 1 file changed, 11 insertions(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 12fc1b3..ce7e2b0 100644
--- a/README.md
+++ b/README.md
@@ -9,6 +9,8 @@
 [Shuai Yang](https://williamyang1991.github.io/), [Yifan Zhou](https://zhouyifan.net/), [Ziwei Liu](https://liuziwei7.github.io/) and [Chen Change Loy](https://www.mmlab-ntu.com/person/ccloy/)<br>
 [**Project Page**](https://www.mmlab-ntu.com/project/rerender/) | [**Paper**](#) | [**Supplementary Video**](#) <br>
 
+<a href="https://huggingface.co/spaces/Anonymous-sub/Rerender"><img src="https://huggingface.co/datasets/huggingface/badges/raw/main/open-in-hf-spaces-sm-dark.svg" alt="Web Demo"></a>
+
 > **Abstract:** *Large text-to-image diffusion models have exhibited impressive proficiency in generating high-quality images. However, when applying these models to video domain, ensuring temporal consistency across video frames remains a formidable challenge. This paper proposes a novel zero-shot text-guided video-to-video translation framework to adapt image models to videos. The framework includes two parts: key frame translation and full video translation. The first part uses an adapted diffusion model to generate key frames, with hierarchical cross-frame constraints applied to enforce coherence in shapes, textures and colors. The second part propagates the key frames to other frames with temporal-aware patch matching and frame blending. Our framework achieves global style and local texture temporal consistency at a low cost (without re-training or optimization). The adaptation is compatible with existing image diffusion techniques, allowing our framework to take advantage of them, such as customizing a specific subject with LoRA, and introducing extra spatial guidance with ControlNet. Extensive experimental results demonstrate the effectiveness of our proposed framework over existing methods in rendering high-quality and temporally-coherent videos.*
 
 **Features**:<br>
@@ -90,7 +92,15 @@ Upload your video, input the prompt, select the seed, and hit:
 
 ![UI](https://github.com/williamyang1991/Rerender_A_Video/assets/18130694/d4d9160d-0990-4397-bf3d-07edcf56a738)
 
-We provide abundant inference options to play with
+We provide abundant advanced options to play with
+
+<details>
+<summary> <b>Advanced options for the 1st frame translation</b></summary>
+
+1. Frame Resolution
+2. 
+
+</details>
 
 
 ![options](https://github.com/williamyang1991/Rerender_A_Video/assets/18130694/d133e495-01f1-456f-8c41-0ff319721781)