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[CVPR 2024] Upscale-A-Video: Temporal-Consistent Diffusion Model for Real-World Video Super-Resolution
| Date | Stars |
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<div align="center">
<h1>
Upscale-A-Video:<br>
Temporal-Consistent Diffusion Model for Real-World Video Super-Resolution
</h1>
<div>
<a href='https://shangchenzhou.com/' target='_blank'>Shangchen Zhou<sup>∗</sup></a> 
<a href='https://pq-yang.github.io/' target='_blank'>Peiqing Yang<sup>∗</sup></a> 
<a href='https://iceclear.github.io/' target='_blank'>Jianyi Wang</a> 
<a href='https://github.com/Luo-Yihang' target='_blank'>Yihang Luo</a> 
<a href='https://www.mmlab-ntu.com/person/ccloy/' target='_blank'>Chen Change Loy</a>
</div>
<div>
S-Lab, Nanyang Technological University
</div>
<div>
<strong>CVPR 2024 (Highlight)</strong>
</div>
<div>
<h4 align="center">
<a href="https://shangchenzhou.com/projects/upscale-a-video/" target='_blank'>
<img src="https://img.shields.io/badge/🐳-Project%20Page-blue">
</a>
<a href="https://arxiv.org/abs/2312.06640" target='_blank'>
<img src="https://img.shields.io/badge/arXiv-2312.06640-b31b1b.svg">
</a>
<a href="https://www.youtube.com/watch?v=b9J3lqiKnLM" target='_blank'>
<img src="https://img.shields.io/badge/Demo%20Video-%23FF0000.svg?logo=YouTube&logoColor=white">
</a>
<a href="https://replicate.com/sczhou/upscale-a-video" target='_blank'>
<img src="https://replicate.com/sczhou/upscale-a-video/badge">
</a>
<img src="https://api.infinitescript.com/badgen/count?name=sczhou/Upscale-A-Video">
</h4>
</div>
<strong>Upscale-A-Video is a diffusion-based model that upscales videos by taking the low-resolution video and text prompts as inputs.</strong>
<div style="width: 100%; text-align: center; margin:auto;">
<img style="width:100%" src="assets/teaser.png">
</div>
:open_book: For more visual results, go checkout our <a href="##" target="_blank">project page</a>
---
</div>
## 🔥 Update
- [2024.09] Inference code is released.
- [2024.02] YouHQ dataset is made publicly available.
- [2023.12] This repo is created.
## 🎬 Overview
## 🔧 Dependencies and Installation
1. Clone Repo
```bash
git clone https://github.com/sczhou/Upscale-A-Video.git
cd Upscale-A-Video
```
2. Create Conda Environment and Install Dependencies
```bash
# create new conda env
conda create -n UAV python=3.9 -y
conda activate UAV
# install python dependencies
pip install -r requirements.txt
```
3. Download Models
(a) Download pretrained models and configs from [Google Drive](https://drive.google.com/drive/folders/1O8pbeR1hsRlFUU8O4EULe-lOKNGEWZl1?usp=sharing) and put them under the `pretrained_models/upscale_a_video` folder.
The [`pretrained_models`](./pretrained_models) directory structure should be arranged as:
```
├── pretrained_models
│ ├── upscale_a_video
│ │ ├── low_res_scheduler
│ │ ├── ...
│ │ ├── propagator
│ │ ├── ...
│ │ ├── scheduler
│ │ ├── ...
│ │ ├── text_encoder
│ │ ├── ...
│ │ ├── tokenizer
│ │ ├── ...
│ │ ├── unet
│ │ ├── ...
│ │ ├── vae
│ │ ├── ...
```
(a) (Optional) LLaVA can be downloaded automatically when set `--use_llava` to `True`, for users with access to huggingface.
## ☕️ Quick Inference
The `--input_path` can be either the path to a single video or a folder containing multiple videos.
We provide several examples in the [`inputs`](./inputs) folder.
Run the following commands to try it out:
```shell
## AIGC videos
python inference_upscale_a_video.py \
-i ./inputs/aigc_1.mp4 -o ./results -n 150 -g 6 -s 30 -p 24,26,28
python inference_upscale_a_video.py \
-i ./inputs/aigc_2.mp4 -o ./results -n 150 -g 6 -s 30 -p 24,26,28
python inference_upscale_a_video.py \
-i ./inputs/aigc_3.mp4 -o ./results -n 150 -g 6 -s 30 -p 20,22,24
```
```shell
## old videos/movies/animExcerpt of 5,815 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:59026f04221422fd, desc:diffusion model, desc:super resolution
