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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/

• FFHQ
• CelebaHQ
• LSUN
• DIV2K
• Flickr2K

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}
}