Update README.md

README.md

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 in SIGGRAPH Asia 2023 Conference Proceedings <br>
 [**Project Page**](https://www.mmlab-ntu.com/project/rerender/) | [**Paper**](https://arxiv.org/abs/2306.07954) | [**Supplementary Video**](https://youtu.be/cxfxdepKVaM) | [**Input Data and Video Results**](https://drive.google.com/file/d/1HkxG5eiLM_TQbbMZYOwjDbd5gWisOy4m/view?usp=sharing) <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>
+<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> ![visitors](https://visitor-badge.laobi.icu/badge?page_id=williamyang1991/Rerender_A_Video)
 
 > **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.*