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Arbitrary-Scale Image Generation and Upsampling using Latent Diffusion Model and Implicit Neural Decoder (CVPR 2024)
Data preparation
Download the dataset you want to use and put it in the ../datasets/
Please place the txt file to split into training and validation images in the /data
.
The split file for the lsun dataset can be downloaded from here.
Model Training
Training First-Stage Models
CUDA_VISIBLE_DEVICES=<GPU_ID> python main.py --base configs/first-stage/<config_spec>.yaml -t --gpus 0, --scale_lr False
Training LDMs
Creates or modifies the config file in configs/latent-diffusion/
.
Type ckpt_path to load the first_stage_model.
CUDA_VISIBLE_DEVICES=<GPU_ID> python main.py --base configs/latent-diffusion/<config_spec>.yaml -t --gpus 0, --scale_lr False
Test
Super-Resolution
python eval_sr.py --exp logs/<exp_path> --lr_size <input_lr_image_size> --scale_ratio <scale>
Image Generation
python inference.py --log_dir logs/<exp_path> --save_dir <output_path> --size <output_size_1> <output_size_2> ...
Measure the FID or SSIM between the real image and the generated image.
Citation
If you find this work useful, please consider citing our paper.
@inproceedings{kim2024arbitraryscale,
title={Arbitrary-Scale Image Generation and Upsampling using Latent Diffusion Model and Implicit Neural Decoder},
author={Kim, Jinseok and Kim, Tae-Kyun},
booktitle={CVPR},
year={2024}
}