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--- |
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datasets: |
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- nkp37/OpenVid-1M |
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base_model: |
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- ali-vilab/i2vgen-xl |
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- THUDM/CogVideoX-5b |
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tags: |
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- video super-resolution |
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--- |
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# STAR: Spatial-Temporal Augmentation with Text-to-Video Models for Real-World Video Super-Resolution |
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### Code: https://github.com/NJU-PCALab/STAR |
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### Paper: https://arxiv.org/abs/2501.02976 |
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### Project Page: https://nju-pcalab.github.io/projects/STAR |
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### Demo Video: https://youtu.be/hx0zrql-SrU |
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## ⚙️ Dependencies and Installation |
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``` |
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## git clone this repository |
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git clone https://github.com/NJU-PCALab/STAR.git |
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cd STAR |
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## create an environment |
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conda create -n star python=3.10 |
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conda activate star |
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pip install -r requirements.txt |
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sudo apt-get update && apt-get install ffmpeg libsm6 libxext6 -y |
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``` |
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## 🚀 Inference |
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### Model Weight |
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| Base Model | Type | URL | |
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|------------|--------|-----------------------------------------------------------------------------------------------| |
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| I2VGen-XL | Light Degradation | [:link:](https://huggingface.co/SherryX/STAR/resolve/main/I2VGen-XL-based/light_deg.pt?download=true) | |
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| I2VGen-XL | Heavy Degradation | [:link:](https://huggingface.co/SherryX/STAR/resolve/main/I2VGen-XL-based/heavy_deg.pt?download=true) | |
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| CogVideoX-5B | Heavy Degradation | [:link:](https://huggingface.co/SherryX/STAR/tree/main/CogVideoX-5B-based) | |
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### 1. I2VGen-XL-based |
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#### Step 1: Download the pretrained model STAR from [HuggingFace](https://huggingface.co/SherryX/STAR). |
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We provide two verisions for I2VGen-XL-based model, `heavy_deg.pt` for heavy degraded videos and `light_deg.pt` for light degraded videos (e.g., the low-resolution video downloaded from video websites). |
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You can put the weight into `pretrained_weight/`. |
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#### Step 2: Prepare testing data |
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You can put the testing videos in the `input/video/`. |
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As for the prompt, there are three options: 1. No prompt. 2. Automatically generate a prompt [using Pllava](https://github.com/hpcaitech/Open-Sora/tree/main/tools/caption#pllava-captioning). 3. Manually write the prompt. You can put the txt file in the `input/text/`. |
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#### Step 3: Change the path |
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You need to change the paths in `video_super_resolution/scripts/inference_sr.sh` to your local corresponding paths, including `video_folder_path`, `txt_file_path`, `model_path`, and `save_dir`. |
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#### Step 4: Running inference command |
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``` |
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bash video_super_resolution/scripts/inference_sr.sh |
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``` |
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If you encounter an OOM problem, you can set a smaller `frame_length` in `inference_sr.sh`. |
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### 2. CogVideoX-based |
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Refer to these [instructions](https://github.com/NJU-PCALab/STAR/tree/main/cogvideox-based#cogvideox-based-model-inference) for inference with the CogVideX-5B-based model. |
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Please note that the CogVideX-5B-based model supports only 720x480 input. |
