S3Diff Model Card

Model Details

• Developed by: Aiping Zhang
• Model type: Degradation-Guided One-Step Image Super-Resolution with Diffusion Priors
• Model Description: This is the model used in Paper.
• Resources for more information: GitHub Repository.
• Cite as:

  @article{2024s3diff,
    author    = {Aiping Zhang, Zongsheng Yue, Renjing Pei, Wenqi Ren, Xiaochun Cao},
    title     = {Degradation-Guided One-Step Image Super-Resolution with Diffusion Priors},
    journal   = {arxiv},
    year      = {2024},
  }

Limitations and Bias

Limitations

• S3Diff requires a tiled operation for generating a high-resolution image, which would largely increase the inference time.
• S3Diff sometimes cannot keep 100% fidelity due to its generative nature.
• S3Diff sometimes cannot generate perfect details under complex real-world scenarios.

Bias

Training

• Our model is finetuned on LSDIR + 10K samples from FFHQ datasets.

• Following SD-Turbo, images are encoded through the fixed autoencoder, which turns images into latent representations. The autoencoder uses a relative downsampling factor of 8 and maps images of shape H x W x 3 to latents of shape H/f x W/f x 4.
• The LR images are fed to the degradation estimation network, trained by mm-realsr, to predict degradation scores.
• We only inject LoRA layers into the VAE encoder and UNet.
• The total loss includes an L2 Loss, an LPIPS loss, and a GAN loss.

• s3diff.pkl: S3Diff finetuned on SD-Turbo for 30k iterations.
• de_net.pth: The degradation estimation network, extracted from mm-realsr.

Evaluation Results